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Top 30 Artificial Intelligence Projects in 2024 [Source Code]

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AI ha wide range of applications today like marketing, automation, transport, supply chain, and communication, and many more. From cutting-edge research to real-world applications, here we will learn the top artificial intelligence projects. This article will help you in discovering plenty of fascinating ideas and insights to inspire you, whether you are a tech fanatic or want to know about the future of AI. 

Currently, most students and working professionals prefer a Data Science Course to make a smooth transition in the data science field. In this article, we will talk about the top AI project topics. Let us get started!

What are Artificial Intelligence Projects?

Artificial intelligence (AI) projects are software-based initiatives that utilize machine learning, deep learning, natural language processing, computer vision, and other AI technologies to develop intelligent programs capable of performing various tasks with minimal human intervention.

If you're interested in diving into the world of AI, consider exploring an Artificial intelligence course to gain valuable insights and practical knowledge in this exciting field.

List of Top AI Projects with Source Code in 2024

Artificial Intelligence projects with source code are available on various platforms and can be used by beginners to understand the project flow and build their projects. Let us check the top AI project ideas with their technicalities along with their source code.

• Stock Prediction
• Lane line detection while driving
• AI Health Engine
• AI-powered Search engine
• House Security
• Loan Eligibility Prediction
• Resume Parser
• Animal Species Prediction
• Hidden Interfaces for Ambient Computing
• Improved Detection of Elusive Polyps
• Document Extraction using FormNet
• Handwritten Notes recognition
• Consumer Sentiment Analysis
• Real-time Translation Tool
• Spam Email Detector
• Building Chatbot for Customer Service
• Face Detection System
• Object Detection with TensorFlow
• Traffic Sign Recognition
• Image Classification System
• Predictive Maintenance System
• Fake News Detector Project
• Building Teachable Machine
• Building Price Comparison Application
• Ethnicity Detection Model
• GPT-3 Applications
• Reinforcement Learning
• Computer vision system
• NLP application
• Recommendation system

AI Project Ideas for Beginner & Intermediate

Here are some examples of AI project topics for beginners, ranging from simple to complex. When choosing a project, it's important to consider your interests, skills, available resources, and tools. These can be considered ideal AI projects for students in their final year and budding AI engineers.

1. Stock Prediction

• Language: Python
• Data set: CSV file
• Source code : Build Your First stock prediction model

The use of artificial intelligence, such as machine learning and deep learning, to forecast future price movements of stocks and other financial instruments is known as stock prediction. Stock prediction aims to use AI to build models that can analyze historical stock data, spot patterns and trends, and forecast future prices.

Several variables can impact stock prices, including news events, market mood, and economic data. As a result, it's crucial to consider these things while developing an AI based stock prediction model. This can be one of the artificial intelligence topics for the project.

2. Lane line detection while driving

• Data set: mp4 file
• Source code: Lane-lines-detection-using-Python-and-OpenCV

Lane line detection is the simple and AI beginners project. The method of detecting and tracking the lanes on a road while driving using a computer vision system is known as lane line detection while employing machine learning. This is an important use of machine learning in autonomous driving systems since it helps the car stay in its lane and prevent accidents.

Lane line identification faces several difficulties, including shifting lighting, shifting road markers, and collisions with other cars. Therefore, it's critical to create reliable machine-learning models to address these issues and deliver precise lane detection in practical settings.

Overall, machine learning-based lane line identification is a crucial computer vision application in autonomous driving systems that can potentially increase the safety and dependability of self-driving cars.

3. AI Health Engine

• Source code : Patient-Selection-for-Diabetes-Drug-Testing

Artificial intelligence (AI) in healthcare is called the "AI Health Engine." It involves analyzing vast amounts of health-related data, including health records, medical images, and genetic information, using machine learning algorithms, natural language processing, computer vision, and other AI technologies to enhance the health of patients, lower costs, and boost the effectiveness of the delivery of healthcare.

By offering better patient outcomes, personalized treatment options, and more accurate diagnoses, AI Health Engines have the potential to transform the healthcare industry completely. The privacy and security of patient data and ensuring that AI algorithms are accurate, dependable, and impartial must be overcome. Therefore, creating ethical and reliable AI Health Engines that can be applied to healthcare safely and efficiently is crucial.

4. AI-powered Search engine

• Data set: text file
• Source code : ai-powered-search

Source: Towards Data Science

An AI-powered search engine is a search engine that incorporates artificial intelligence (AI) technology, such as machine learning and NLP, to deliver more precise and customized search results. These search engines can process data and employ cutting-edge algorithms to decipher the purpose of a user's query and provide relevant results.

AI-driven search engines may deliver more precise and pertinent search results while providing every user with a more individualized search experience. By removing the need for users to modify their searches or sort through unnecessary outcomes manually, they can also help to increase search efficiency.

5. House Security

• Data set: image file
• Source code: Machine-Learning-Face-Recognition-using-openCV

Using artificial intelligence to monitor and secure a home is known as "house security with AI." AI-powered security systems can detect and analyze various events and activities, including motion, sound, and facial recognition, using a variety of sensors and cameras.

By offering more precise and reliable detection of intrusions and other security breaches, AI-powered security systems have the potential to improve home security. By interacting with other intelligent home systems and gadgets, they can also offer a user experience that is more practical and smoother.

6. Loan Eligibility Prediction

• Source code : Loan_Status_Prediction

Source: GeeksforGeeks

The goal of loan eligibility prediction using AI is to forecast the likelihood of loan approval for new applicants by analyzing historical data on borrowers and their loan applications. This can assist banks and other lenders in setting appropriate terms and conditions for accepted loans, as well as helping them make better decisions about whether to approve or reject loan applications.

The security and privacy of borrower data and preventing unintended outcomes like unintentionally barring specific borrower categories are obstacles to be addressed. Creating moral and open loan eligibility prediction systems that work for both lenders and borrowers is therefore crucial. This is one of the best AI projects.

Artificial Intelligence Project Ideas For Advanced Level

These are a few of the many cutting-edge AI initiatives you might consider. It's crucial to consider your hobbies and areas of skill while selecting advanced AI projects and the initiative's potential influence and worth to the larger community.

1. Resume Parser

• Source cod e: keras-english-resume-parser-and-analyzer

Source: DaXtra Technologies

An AI-powered tool called a resume parser pulls pertinent data from resumes or CVs and turns it into structured data. The structured data can be utilized for various tasks, including applicant tracking, hiring, and talent management. Developing a resume parser might be a challenging but rewarding endeavor that can assist businesses and organizations in automating their hiring and talent management procedures.

2. Animal Species Prediction

• Data set: PNG file
• Source code:  animal_detection

In machine learning and computer vision, predicting animal species includes creating an AI system to recognize an animal's species from an image. To reliably categorize animal species using visual characteristics, including shape, color, and texture, animal species prediction attempts to build a model that can do so.

Because it involves dealing with a vast and diverse range of animals with varying physical characteristics, predicting animal species is difficult. However, recent deep learning and computer vision developments have made significant advancements possible in this field.

3. Hidden Interfaces for Ambient Computing

• Source code:  Hidden Interfaces for Ambient Computing

User interfaces that are smoothly incorporated into the environment allow users to engage with ambient computing devices without requiring explicit actions or inputs. These interfaces are referred to as hidden interfaces for ambient computing. The goal of ambient computing devices is to give consumers a smooth and natural experience without forcing them to engage with the device directly. These devices are embedded into the surroundings.

Voice assistants, smart speakers, and intelligent displays are a few examples of hidden interfaces for ambient computing.

4. Improved Detection of Elusive Polyps

• Source code: Polyp-Segmentation-using-UNET-in-TensorFlow-2.0

Source: Science Direct

Artificial intelligence (AI) and computer vision are two methods for enhancing the detection of evasive polyps. Large datasets of colonoscopy images can be used to train AI systems to identify patterns and traits common to various polyp kinds. Computer vision techniques can also improve photographs' quality and highlight important details that human viewers might overlook.

The development of new imaging methods, such as high-definition colonoscopes, and the use of specialized dyes or markers that can aid in identifying polyps are two more strategies for enhancing the detection of elusive polyps.

5. Document Extraction using FormNet

• Data set: PDF file
• Source code: Representation-Learning-for-Information-Extraction

The information must be extracted from unstructured data, such as text documents, PDFs, or photos, to create structured data that may be used for analysis or processing. A deep learning model called FormNet was explicitly designed for extracting documents from scanned forms.

FormNet extracts fields from structured forms using a convolutional neural network (CNN) architecture. The model can learn the common patterns and features associated with various shapes and areas because it is trained on vast datasets of labeled forms.

Applications for document extraction using FormNet include data entry, processing invoices, and form recognition in sectors like healthcare, banking, and law. FormNet may significantly reduce the time and effort needed for human data entry, improve accuracy, and increase the effectiveness of corporate processes by automating the document extraction process.

6. Handwritten Notes recognition

• Source code:  SimpleHTR

Source: AmyGB.ai

Turning handwritten text or notes into computer-readable digital text is called handwritten note recognition. Optical character recognition (OCR) technology, which recognizes and converts handwritten text into a digital format using computer vision techniques, is often used for this operation.

Various machine learning and deep learning algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and recurrent neural networks (RNNs), can be used to achieve OCR technology for handwritten note recognition. These algorithms can learn the patterns and features of various handwriting styles since they have been trained on enormous datasets of labelled handwritten notes.

7. Consumer Sentiment Analysis

• Source code: Consumer Sentiment Analysis

Consumer sentiment analysis examines consumers' attitudes, feelings, and views toward a specific good, service, or brand. Natural language processing (NLP) and machine learning techniques are usually used in this analysis, giving businesses insightful knowledge on how their customers see them.

The analysis entails extracting and categorizing pertinent data, such as keywords, sentiment, emotions, and themes, to detect patterns and trends in consumer feedback. Businesses can utilize consumer sentiment analysis to raise customer happiness, enhance the quality of their goods and services, and gain a competitive advantage.

8. Real-time Translation Tool

• Source code:  Real-time-voice-recognition-based-language-translation-bot

A software program known as a real-time translation tool enables users to translate speech, writing, or other forms of communication from one language to another in real time. Real-time translation tools rely on machine learning and natural language processing (NLP) approaches to translate languages rapidly and reliably.

Various contexts, including international business meetings, travel, and communication with non-native speakers, can benefit from real-time translation tools. They allow users to connect efficiently with persons who speak different languages since they can translate text or speech in real time. These tools simplify connecting and collaborating worldwide by enhancing communication and lowering language barriers.

List of More Artificial Intelligence Project Ideas

Apart from the above artificial intelligence project, here is the list of some more AI project ideas that you can work on: 

Open Source Artificial Intelligence Project Ideas: Additional Topics

Here are a few open source AI project suggestions that are popular right now on Google.ai and other sites of such nature:

Why Should You Work on AI Based Projects?

Working on Artificial intelligence based projects can be gratifying for several reasons, including:

• High demand: AI is a fast-expanding subject, and skilled individuals are in tall order. Gaining knowledge of AI can lead to various employment choices and job prospects.
• Innovation: AI initiatives frequently involve going beyond what is currently achievable, which results in fresh discoveries and advances in the area.
• Impact: AI can positively impact society, from healthcare and education to finance and transportation. You can make a meaningful contribution by working on AI-based projects.
• Personal growth: Working on AI-based projects can help you acquire new techniques and concepts in programming, data science, and machine learning, improving your personal and professional development.

Best Platforms to Work on AI Projects

To create machine learning models, these platforms offer a vast array of tools and resources, including pre-built algorithms, data visualization tools, and support for distributed computing. They also feature vibrant developer and research communities that exchange knowledge and support ongoing development. Future AI projects are all dependent on this platform.

Here are some of the top platforms to work on AI project Links:

• Scikit-learn
• Microsoft Cognitive Toolkit
• Apache MXNet

Elevate your expertise and stand out with a CBAP certificate . Unlock new career opportunities and succeed in the field of business analysis.

Learn AI the Smart Way!

Learning AI can be a challenging but worthwhile endeavor. Here are some pointers for clever AI learning:

• Begin with the fundamentals: Start by being familiar with the foundational ideas of AI, such as machine learning, deep learning, and neural networks.
• Take online classes: Work with real-world datasets to put your knowledge into practice. Using real-world datasets is an excellent method to put your knowledge into practice. KnowledgeHut Data Science Course provides online courses with thorough AI instruction.
• Create your projects: Creating your own Artificial Intelligence projects is an excellent opportunity to practice what you've learned and put it to the test.
• Emphasise problem-solving: You can develop the skills to manage challenging AI projects by emphasizing problem-solving and critical thinking.

Studying AI generally involves commitment, perseverance, and a readiness to pick things up quickly and adapt. Using these pointers, you can learn AI intelligently and successfully and accomplish your objectives in this fascinating and promptly expanding topic. 

Frequently Asked Questions (FAQs)

• Stock Prediction 

Because they are relatively straightforward but still challenging enough to offer a worthwhile learning experience, these AI projects are great for beginners. They provide a solid foundation for anyone interested in learning AI because they cover many AI ideas and applications. The above can also be used as artificial intelligence research paper topics.

AI project failures can stem from various issues like poor planning, limited funding, subpar data quality, lack of domain knowledge, ineffective communication, unrealistic objectives, unvalidated assumptions, algorithm bias, ethical/legal issues, and changing business needs. Inadequate planning leads to unclear goals and insufficient resources, while poor data affects AI model accuracy. Insufficient expertise can lead to flawed algorithm selection, and poor communication causes misunderstandings and delays.

AI can be categorized into four types:

• Reactive machines: AI systems that respond to specific situations without using past experiences.
• Limited memory: AI that uses past information for decision-making but lacks critical thinking or long-term planning.
• Theory of mind: AI that understands others' emotions, thoughts, and intentions for informed decision-making.
• Self-aware: AI that is conscious of its own feelings and mental states, utilizing this for improved decisions and behavior adjustments.

You can take the following actions to launch your artificial intelligence career:

• Learn the fundamentals of computer science, statistics, and mathematics.
• Acquire knowledge of programming languages like Python, R.
• Learn how to use AI tools.
• Attend machine learning and AI boot camps or online courses from the  KnowledgeHut data science course .
• Take part in Kaggle tournaments to gain experience creating AI models.
• AI projects with source code can be used for learning

Ashish Gulati

Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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Top 20 Artificial Intelligence Projects With Source Code [2023]

Introduction, artificial intelligence projects for beginners, 1. product recommendation systems, 2. plagiarism analyzer, 3. prediction of bird species, 4. dog and cat classification, 5. next word prediction, intermediate artificial intelligence projects, 6. face recognition, 7. mask detection, 8. heart disease prediction, 9. cv analysis, 10. sales predictor, 11. automated attendance system, 12. pneumonia detection, advanced artificial intelligence projects, 13. ai chatbots, 14. ai self-driving cars, 15. image colorization, 16. game of chess, 17. human pose estimation, 18. face aging, 19. image caption generator, 20. voice-based virtual assistant, q.1: how do i start my own ai project, q.2: is google an ai, q.4: can i create my own ai, q.5: can i learn ai without coding, additional resources.

If you think back 30 years, humans could never have dreamed that artificial intelligence would take such a big step forward and have such a positive impact on our lives. Artificial Intelligence has accelerated life’s pace. Artificial intelligence (AI) has given rise to applications that are now having a significant impact on our lives.

The term AI was initially coined in 1956 at a Dartmouth meeting. Artificial intelligence (AI) is the ability of a computer or a computer-controlled robot to accomplish tasks that would normally be performed by intelligent beings. In today’s world, Artificial Intelligence has become highly popular. It is the simulation of human intelligence in computers that have been programmed to learn and mimic human actions. These machines can learn from their mistakes and execute activities that are similar to those performed by humans.

Building an AI system is a painstaking process of reversing our features and talents in a machine and then leveraging its computing strength to outperform our abilities. To comprehend how Artificial Intelligence works, one must go deeply into the many sub-domains of AI and comprehend how those domains can be applied to various industries of the industry. Machine learning, deep learning, neural networks, computer vision, and natural language processing are examples of these fields.

Confused about your next job?

Artificial Intelligence entities are constructed for a variety of goals, which is why they differ. The following are the several types of artificial intelligence:

• Artificial Narrow Intelligence (ANI)
• Artificial General Intelligence (AGI)
• Artificial Super Intelligence (ASI)

Artificial Intelligence’s goal is to augment human capabilities and assist us in making complex decisions with far-reaching repercussions. AI performs regular, high-volume, automated tasks rather than automating manual ones. And it does so consistently and without tiring. Humans still need to set up the system and ask the correct questions, of course.

AI adapts by allowing data to program itself using progressive learning algorithms. In order for algorithms to learn, AI looks for structure and regularities in data. An algorithm can train itself to play chess, just as it can educate itself to recommend a product. Deep neural networks are used by AI to attain remarkable precision. Your interactions with Alexa and Google, for example, are all based on deep learning. And the more you use these things, the more accurate they become. Deep learning and object identification AI techniques can now be utilized in the medical profession to spot cancer on medical photos with greater accuracy.

In this blog, you will come across various such applications of artificial intelligence that can be opted as a project idea for your college assignments or personal development. Let’s dive into this.

Below are a few exciting AI Projects to try. We have divided projects based on beginner, intermediate, and advanced levels.

Recommender systems have become more prevalent in our lives as a result of the emergence of Youtube, Amazon, Netflix, and other similar web services. They’re algorithms that help people find items that are relevant to them. In some businesses, recommender systems are crucial since they can produce a large amount of revenue or serve as a method to differentiate yourself from competitors. It determines the compatibility of the user and the object, as well as the similarities between users and items, in order to make recommendations.

Source Code: Product Recommendation System

On the internet, plagiarism is widespread. The internet is brimming with content, which can be found on millions of different websites. It can be tough to tell which content is plagiarised and which is not at times. Authors of blog postings should check to see if their work has been stolen and put elsewhere. News organizations should investigate whether a content farm has stolen their news pieces and claimed them as their own. The task is demanding. What if you had your own plagiarism detection software? This opportunity is provided by AI.

Source Code: Plagiarism Analyzer

Manual classification of birds can be done by topic experts, but it has become a hard and time-consuming process due to the vast accumulation of data. Artificial intelligence-based categorization becomes critical in this situation. This classification-based AI project can be approached in two ways. If you’re a newbie, you can use a random forest to forecast bird species. To get to an intermediate level, you can utilize a convolution neural network.

Source Code: Bird Species Prediction

Dogs vs. Cats is a simple computer vision project concept that entails categorizing photographs into one of two categories. There were various machine learning algorithms used to handle this use case, however, deep learning convolutional neural networks were the most effective in the recent several years. It can be used to learn and practice building, evaluating, and using convolutional deep-learning neural networks for image categorization from the ground up. You will gain a thorough understanding of how to apply CNN in advanced AI projects as a result of doing so.

Source Code: Dog and Cat Classification

It’s never easy to write rapidly and without making spelling mistakes. It is not difficult to type correctly and quickly while using a keyboard on a desktop computer, but typing on small devices such as mobile phones is a different story, and it can be frustrating for many of us. With the next word prediction project, you can improve your experience of typing on small devices only by predicting the next word in a sentence fragment. You won’t have to type complete sentences because the algorithms will predict the next word for you, and typos will be much reduced.

Source Code: Next Word Prediction

Facial recognition is a technique for recognizing or verifying a person’s identification by looking at their face. This technology can recognize persons in photographs, videos, and in real-time. A type of biometric security is facial recognition. Although there is growing interest in other applications, the technology is mostly employed for security and law enforcement. Typically, face recognition does not need a large database of images to identify an individual’s identification; rather, it merely identifies and recognizes one person as the device’s only owner, while restricting access to others.

Source Code: Face R e cognition

Face mask detection is the process of determining whether or not someone is wearing a mask. We all know that wearing masks is one of the most effective ways to prevent the virus from spreading. Despite this, we notice a lot of people not wearing masks in public locations. Using AI approaches to construct a system that can recognize persons who aren’t wearing masks could be a solution to this problem.

Source Code: Mask Detection

From a medical standpoint, this project is advantageous because it is designed to provide online medical advice and guidance to individuals suffering from cardiac disorders. The application will be taught and fed information about a variety of various cardiac diseases. This clever system uses artificial intelligence (AI) approaches to predict the most accurate disease that might be linked to the information provided by a patient. Users can then seek medical advice from specialists based on the system’s diagnosis.

Source Code: Heart Disease Prediction

One of the more intriguing Artificial Intelligence project concepts is this. Shortlisting deserving individuals from a large pile of CVs is a difficult undertaking. The goal of this project is to develop cutting-edge software that can give a legally sound and equitable CV ranking system. Candidates will be ranked for a specific job profile based on their abilities and expertise. It will also take into account all other important factors, such as soft skills, interests, professional qualifications, and so on. This will exclude all unsuitable candidates for a job role and produce a list of the best contenders for the position.

Source Code: CV Analysis

Any business has an abundance of products, but how they manage to keep track of each product’s sales is beyond our comprehension. That’s where a sales forecaster can help. It allows you to keep track of new product arrivals and out-of-stock items. Sales Predictor is going to be a huge undertaking. You must devise an algorithm to determine how many products are sold on a daily basis and forecast sales for that product on a weekly or monthly basis.

Source Code: Sales Predictor

An automatic attendance system is one that keeps track of individuals’ attendance at a school. Unlike a traditional attendance system, automatic attendance software allows staff to record, store, and monitor students’ attendance history while also efficiently managing the classroom. It does not include the usage of paper or human effort. The technology is beneficial since it generates a detailed report on each class’ attendance. It saves time, money, and institutes resources for the user.

Source Code: Automated Attendance System

Pneumonia is typically identified by doctors using chest X-rays. However, AI is capable of identifying disease in X-ray images of patients. Convolution Neural Networks (CNNs) are used to develop the AI system. By analysing chest X-ray scans, the AI project can automatically determine whether a patient has pneumonia or not. Because people’s lives are on the line, the algorithm has to be highly precise.

Source Code: Pneumonia Detection

Creating a chatbot is one of the top AI-based initiatives. You should begin by developing a basic customer service chatbot. You can get ideas from chatbots that can be found on numerous websites. After you’ve constructed a basic chatbot, you can refine it and create a more complex version. Artificial intelligence enables you to fly and supports you in putting your ideas into reality.

Source Code: AI Chatbot

Artificial intelligence algorithms enable self-driving cars. They allow an automobile to collect data about its surroundings from cameras and other sensors, analyze it, and decide what actions to take. Artificial intelligence breakthroughs have allowed cars to learn to perform these tasks better than humans. It made use of complex math and image recognition techniques. This project is open to those who are AI enthusiasts in college or who have recently graduated from college.

Source Code: AI Self Driving Car

Many of us have a difficult time picturing the colors that the moment captured would have contained when looking at vintage grayscale pictures. To alleviate human suffering, artificial intelligence provides the ideal solution, since it can be used to create a smart image colorization system. The technique of adding colors to a grayscale image in order to make it more visually pleasing and perceptually significant is known as image colorization.

Source Code: Image Colorization

Chess is a popular game, and in order to improve our enjoyment of it, we need to implement a good artificial intelligence system that can compete with humans and make chess a difficult task. Artificial intelligence has changed how top-level chess games are played. The majority of Grandmasters and Super Grandmasters use these latest Artificial Intelligence chess engines to evaluate their own and their opponents’ games.

Source Code: Game of Chess

The art of determining a person’s body alignment by calculating various body joints is known as human pose estimate. It’s a computer vision technique for tracking a person’s or an object’s movements. This is normally accomplished by locating critical spots for the things in question. Snapchat employs position estimation to figure out where the person’s eyes and head are in order to apply a filter. Similarly, we can estimate a human stance in real time and apply filters to the person.

Source Code: Human Pose Estimation

Generative Adversarial Networks (GANs) are a sort of deep neural network design that generates data through unsupervised machine learning. We can now produce high-resolution picture alterations thanks to the recent success of GAN architectures. You may make an application that takes an image of a human as input and returns a picture of that same person in 30 years. It’s a little tricky to put GANs in place.

Source Code: Face Aging

Caption generation is a difficult artificial intelligence challenge in which a textual description for a given photograph must be created. It necessitates both computer vision technologies for comprehending the image’s content and a natural language processing language model for converting the image’s comprehension into words in the correct order. Deep learning approaches have recently reached state-of-the-art results.

Source Code: Image Caption Generator

One of the more intriguing Artificial Intelligence project concepts is this. Voice-activated personal assistants are useful tools for making routine activities easier. You may use virtual voice assistants to do things like search the web for items/services, shop for products for you, compose notes and create reminders, and so much more. Because the assistant has been taught to understand normal human language, it will recognize the command and save it in the database. It will deduce a user’s purpose from the spoken phrase and take appropriate action. It can also convert text to speech.

Source Code: Voice-based Virtual Assistant

Some of the popular Tools and Frameworks that can be used for an AI project are:

• Scikit Learn

Some of the popular languages that can be used to create your AI projects are:

• Python (most popular)

We’ve discussed 20 AI project ideas in this article. We began with some simple projects that you can complete quickly. After you’ve completed these beginner tasks, I recommend going back to understand a few additional principles before moving on to the intermediate projects. After you’ve gained confidence, you can go on to the intermediate tasks. This will boost morale in moving on to more sophisticated tasks. You should get your hands on these Artificial Intelligence project ideas if you want to boost your AI skills. These tasks will assist you in honing your AI skills. Furthermore, these projects will not only put you on the route to becoming an AI specialist, but they will also prepare you for the workforce. This will also improve your chances of getting hired. So don’t stop learning.

Ans: Following are some typical steps to get started with an AI project:

• Pick a topic you are interested in. That can be any problem statement.
• Learn some concepts of AI.
• Find a quick solution to the problem statement chosen.
• Improve your simple solution to make it more optimized.
• Share your solution.
• Repeat the process of improvement.
• Pick up the efficient AI algorithm(s) that could solve your problem.
• Analyze your results.
• Improve your algorithm using AI techniques.

Ans: Google is a company that makes use of Artificial Intelligence to build extraordinary products like Google Photos, Gmail, Self-driving cars, recommendation systems, etc. You can learn more about it at this link .

Ans: Yes, it is possible to build your own AI. You can gain the required skills by practising more on the AI concepts and working on projects from beginner to advanced level. 

Ans: Yes, at some level it is possible to learn AI without coding. There are various tools available that can be helpful in doing such learning. But if you are aiming to be a part of the IT industry, it is recommended to learn to code as well. You can also check out Scaler Topics’ Free Deep Learning course to get started in AI.

• Artificial Intelligence Interview Questions
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• NATURE INDEX
• 09 December 2020

Six researchers who are shaping the future of artificial intelligence

• Gemma Conroy ,
• Hepeng Jia ,
• Benjamin Plackett &

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Andy Tay is a science writer in Singapore.

As artificial intelligence (AI) becomes ubiquitous in fields such as medicine, education and security, there are significant ethical and technical challenges to overcome.

CYNTHIA BREAZEAL: Personal touch

Credit: Taj Francis

While the credits to Star Wars drew to a close in a 1970s cinema, 10-year-old Cynthia Breazeal remained fixated on C-3PO, the anxious robot. “Typically, when you saw robots in science fiction, they were mindless, but in Star Wars they had rich personalities and could form friendships,” says Breazeal, associate director of the Massachusetts Institute of Technology (MIT) Media Lab in Cambridge, Massachusetts. “I assumed these robots would never exist in my lifetime.”

A pioneer of social robotics and human–robot interaction, Breazeal has made a career of conceptualizing and building robots with personality. As a master’s student at MIT’s Humanoid Robotics Group, she created her first robot, an insectile machine named Hannibal that was designed for autonomous planetary exploration and funded by NASA.

Some of the best-known robots Breazeal developed as a young researcher include Kismet, one of the first robots that could demonstrate social and emotional interactions with humans; Cog, a humanoid robot that could track faces and grasp objects; and Leonardo, described by the Institute of Electrical and Electronics Engineers in New Jersey as “one of the most sophisticated social robots ever built”.

Nature Index 2020 Artificial intelligence

In 2014, Breazeal founded Jibo, a Boston-based company that launched her first consumer product, a household robot companion, also called Jibo. The company raised more than US$70 million and sold more than 6,000 units. In May 2020, NTT Disruption, a subsidiary of London-based telecommunications company, NTT, bought the Jibo technology, and plans to explore the robot’s applications in health care and education.

Breazeal returned to academia full time this year as director of the MIT Personal Robots Group. She is investigating whether robots such as Jibo can help to improve students’ mental health and wellbeing by providing companionship. In a preprint published in July, which has yet to be peer-reviewed, Breazeal’s team reports that daily interactions with Jibo significantly improved the mood of university students ( S. Jeong et al . Preprint at https://arxiv.org/abs/2009.03829; 2020 ). “It’s about finding ways to use robots to help support people,” she says.

In April 2020, Breazeal launched AI Education, a free online resource that teaches children how to design and use AI responsibly. “Our hope is to turn the hundreds of students we’ve started with into tens of thousands in a couple of years,” says Breazeal. — by Benjamin Plackett

CHEN HAO: Big picture

Analysing medical images is an intensive and technical task, and there is a shortage of pathologists and radiologists to meet demands. In a 2018 survey by the UK’s Royal College of Pathologists, just 3% of the National Health Service histopathology departments (which study diseases in tissues) said they had enough staff. A June 2020 report published by the Association of American Medical Colleges found that the United States’ shortage of physician specialists could climb to nearly 42,000 by 2033.

AI systems that can automate part of the process of medical imaging analysis could be the key to easing the burden on specialists. They can reduce tasks that usually take hours or days to seconds, says Chen Hao, founder of Imsight, an AI medical imaging start-up based in Shenzhen, China.

Launched in 2017, Imsight’s products include Lung-Sight, which can automatically detect and locate signs of disease in CT scans, and Breast-Sight, which identifies and measures the metastatic area in a tissue sample. “The analysis allows doctors to make a quick decision based on all of the information available,” says Chen.

Since the outbreak of COVID-19, two of Shenzhen’s largest hospitals have been using Imsight’s imaging technology to analyse subtle changes in patients’ lungs caused by treatment, which enables doctors to identify cases with severe side effects.

In 2019, Chen received the Young Scientist Impact Award from the Medical Image Computing and Computer-Assisted Intervention Society, a non-profit organization in Rochester, Minnesota. The award recognized a paper he led that proposed using a neural network to process fetal ultrasound images ( H. Chen et al. in Medical Image Computing and Computer-Assisted Intervention — MICCAI 2015 (eds N. Navab et al. ) 507–514; Springer, 2015 ). The technique, which has since been adopted in clinical practice in China, reduces the workload of the sonographer.

Despite the rapid advancement of AI’s role in health care, Chen rejects the idea that doctors can be easily replaced. “AI will not replace doctors,” he says. “But doctors who are better able to utilize AI will replace doctors who cannot.” — by Hepeng Jia

ANNA SCAIFE: Star sifting

When construction of the Square Kilometre Array (SKA) is complete , it will be the world’s largest radio telescope. With roughly 200 radio dishes in South Africa and 130,000 antennas in Australia expected to be installed by the 2030s, it will produce an enormous amount of raw data, more than current systems can efficiently transmit and process.

Anna Scaife, professor of radio astronomy at the University of Manchester, UK, is building an AI system to automate radio astronomy data processing. Her aim is to reduce manual identification, classification and cataloguing of signals from astronomical objects such as radio galaxies, active galaxies that emit more light at radio wavelengths than at visible wavelengths.

In 2019, Scaife was the recipient of the Jackson-Gwilt Medal, one of the highest honours bestowed by the UK Royal Astronomical Society (RAS). The RAS recognized a study led by Scaife, which outlined data calibration models for Europe’s Low Frequency Array (LOFAR) telescope, the largest radio telescope operating at the lowest frequencies that can be observed from Earth ( A. M. M. Scaife and G. H. Heald Mon. Not. R. Astron. Soc. 423 , L30–L34; 2012 ). The techniques in Scaife’s paper underpin most low-frequency radio observations today.

“It’s a very peculiar feeling to win an RAS medal,” says Scaife. “It’s a mixture of excitement and disbelief, especially because you don’t even know that you were being considered, so you don’t have any opportunity to prepare yourself. Suddenly, your name is on a list that commemorates more than 100 years of astronomy history, and you’ve just got to deal with that.”

Scaife is the academic co-director of Policy@Manchester, the University of Manchester’s policy engagement institute, where she helps researchers to better communicate their findings to policymakers. She also runs a data science training network that involves South African and UK partner universities, with the aim to build a team of researchers to work with the SKA once it comes online. “I hope that the training programmes I have developed can equip young people with skills for the data science sector,” says Scaife. — by Andy Tay

TIMNIT GEBRU: Algorithmic bias

Computer vision is one of the most rapidly developing areas of AI. Algorithms trained to read and interpret images are the foundation of technologies such as self-driving cars, surveillance and augmented reality.

Timnit Gebru, a computer scientist and former co-lead of the Ethical AI Team at Google in Mountain View, California, recognizes the promise of such advances, but is concerned about how they could affect underrepresented communities, particularly people of colour . “My research is about trying to minimize and mitigate the negative impacts of AI,” she says.

In a 2018 study , Gebru and Joy Buolamwini, a computer scientist at the MIT Media Lab, concluded that three commonly used facial analysis algorithms drew overwhelmingly on data obtained from light-skinned people ( J. Buolamwini and T. Gebru. Proc. Mach. Learn. Res. 81 , 77–91; 2018 ). Error rates for dark-skinned females were found to be as high as 34.7% , due to a lack of data, whereas the maximum error rate for light-skinned males was 0.8%. This could result in people with darker skin getting inaccurate medical diagnoses, says Gebru. “If you’re using this technology to detect melanoma from skin photos, for example, then a lot of dark-skinned people could be misdiagnosed.”

Facial recognition used for government surveillance, such as during the Hong Kong protests in 2019, is also highly problematic , says Gebru, because the technology is more likely to misidentify a person with darker skin. “I’m working to have face surveillance banned,” she says. “Even if dark-skinned people were accurately identified, it’s the most marginalized groups that are most subject to surveillance.”

In 2017, as a PhD student at Stanford University in California under the supervision of Li Fei-Fei , Gebru co-founded the non-profit, Black in AI, with Rediet Abebe, a computer scientist at Cornell University in Ithaca, New York. The organization seeks to increase the presence of Black people in AI research by providing mentorship for researchers as they apply to graduate programmes, navigate graduate school, and enter and progress through the postgraduate job market. The organization is also advocating for structural changes within institutions to address bias in hiring and promotion decisions. Its annual workshop calls for papers with at least one Black researcher as the main author or co-author. — by Benjamin Plackett

YUTAKA MATSUO: Internet miner

In 2010, Yutaka Matsuo created an algorithm that could detect the first signs of earthquakes by monitoring Twitter for mentions of tremors. His system not only detected 96% of the earthquakes that were registered by the Japan Meteorological Agency (JMA), it also sent e-mail alerts to registered users much faster than announcements could be broadcast by the JMA.

He applied a similar web-mining technique to the stock market. “We were able to classify news articles about companies as either positive or negative,” says Matsuo. “We combined that data to accurately predict profit growth and performance.”

Matsuo’s ability to extract valuable information from what people are saying online has contributed to his reputation as one of Japan’s leading AI researchers. He is a professor at the University of Tokyo’s Department of Technology Management and president of the Japan Deep Learning Association, a non-profit organization that fosters AI researchers and engineers by offering training and certification exams. In 2019, he was the first AI specialist added to the board of Japanese technology giant Softbank.

Over the past decade, Matsuo and his team have been supporting young entrepreneurs in launching internationally successful AI start-ups. “We want to create an ecosystem like Silicon Valley, which Japan just doesn’t have,” he says.

Among the start-ups supported by Matsuo is Neural Pocket, launched in 2018 by Roi Shigematsu, a University of Tokyo graduate. The company analyses photos and videos to provide insights into consumer behaviour.

Matsuo is also an adviser for ReadyFor, one of Japan’s earliest crowd-funding platforms. The company was launched in 2011 by Haruka Mera, who first collaborated with Matsuo as an undergraduate student at Keio University in Tokyo. The platform is raising funds for people affected by the COVID-19 pandemic, and reports that its total transaction value for donations rose by 4,400% between March and April 2020.

Matsuo encourages young researchers who are interested in launching AI start-ups to seek partnerships with industry. “Japanese society is quite conservative,” he says. “If you’re older, you’re more likely to get a large budget from public funds, but I’m 45, and that’s still considered too young.” — by Benjamin Plackett

DACHENG TAO: Machine visionary

By 2030, an estimated one in ten cars globally will be self-driving. The key to getting these autonomous vehicles on the road is designing computer-vision systems that can identify obstacles to avoid accidents at least as effectively as a human driver .

Neural networks, sets of AI algorithms inspired by neurological processes that fire in the human cerebral cortex, form the ‘brains’ of self-driving cars. Dacheng Tao, a computer scientist at the University of Sydney, Australia, designs neural networks for computer-vision tasks. He is also building models and algorithms that can process videos captured by moving cameras, such as those in self-driving cars.

“Neural networks are very useful for modelling the world,” says Tao, director of the UBTECH Sydney Artificial Intelligence Centre, a partnership between the University of Sydney and global robotics company UBTECH.

In 2017, Tao was awarded an Australian Laureate Fellowship for a five-year project that uses deep-learning techniques to improve moving-camera computer vision in autonomous machines and vehicles. A subset of machine learning, deep learning uses neural networks to build systems that can ‘learn’ through their own data processing.

Since launching in 2018, Tao’s project has resulted in more than 40 journal publications and conference papers. He is among the most prolific researchers in AI research output from 2015 to 2019, as tracked by the Dimensions database, and is one of Australia’s most highly cited computer scientists. Since 2015, Tao’s papers have amassed more than 42,500 citations, as indexed by Google Scholar. In November 2020, he won the Eureka Prize for Excellence in Data Science, awarded by the Australian Museum.

In 2019, Tao and his team trained a neural network to construct 3D environments using a motion-blurred image, such as would be captured by a moving car. Details, including the motion, blurring effect and depth at which it was taken, helped the researchers to recover what they describe as “the 3D world hidden under the blurs”. The findings could help self-driving cars to better process their surroundings. — by Gemma Conroy

Nature 588 , S114-S117 (2020)

doi: https://doi.org/10.1038/d41586-020-03411-0

This article is part of Nature Index 2020 Artificial intelligence , an editorially independent supplement. Advertisers have no influence over the content.

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The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is  increasingly touching people’s lives in settings that range from  movie recommendations  and  voice assistants  to  autonomous driving  and  automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report. 

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.  

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks.  We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.  

In terms of potential, I'm most excited about AIs that might augment and assist people.  They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired.  In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years.  The first report is fairly rosy.  For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges.  The second has a much more mixed view.  I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education!  Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.  I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education.  Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. 

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare? 

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing.  When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI,  it's tricky to nurture both innovation and basic protections.  Perhaps the most important innovation will be in approaches for AI accountability.

Topics: AI / Machine Learning , Computer Science

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Home » Artificial Intelligence » Artificial Intelligence Projects with Source Code

Top 20 Artificial Intelligence Project Ideas [with Source Code]

Artificial intelligence has developed significantly over the past few years and has emerged as one of the most intriguing and promising technological fields. Banking, transportation, and entertainment are just a few of the many sectors of the economy where artificial intelligence is used. The way we live, work, and interact with technology are all being altered by AI’s enormous potential. 

In this blog, we will explore some of the most fascinating artificial intelligence projects with source code . If you are a computer science student or a working professional, this blog will definitely help you.

Table of Contents

What are Artificial Intelligence Project Ideas?

Artificial intelligence projects enable you to get hands-on experience of working with the latest technologies. Further, it fosters skills like problem-solving , analytical thinking, and innovation. You can explore wide-ranging artificial intelligence project ideas to work on and build your portfolio. Some of these popular project ideas include image recognition, chatbot, next-word prediction, and more. 

Artificial Intelligence Projects for Beginners with Source Code

If you have recently started learning artificial intelligence technologies and techniques, you can begin working on simple projects to practice. Here are some popular artificial intelligence projects for beginners: 

1. Image Recognition

One of the most widely used applications of artificial intelligence is image recognition. Machines can analyze photographs and recognize objects, people, and other aspects of the image using image recognition. The use of this technology is widespread, ranging from security systems to driverless cars.

You might investigate a variety of artificial intelligence mini-projects for beginners in the field of picture recognition. For example, you might construct a system that can recognize various animal species or locate things in a congested space. There are numerous chances for invention in the exciting and quickly developing subject of image recognition. 

Source Code: Image Recognition Project

Skills Required: Python

In recent years, chatbots have been employed in various applications. Using text or voice interactions, a chatbot is a computer program that can mimic human communication. Chatbots can serve customers in the same way a human can. 

Natural language processing (NLP) methods are frequently used in chatbot development as they allow conversational understanding. However, the most sophisticated chatbots utilize machine learning strategies to adjust their replies over time in response to user input.

Therefore, creating a chatbot can be a great AI project. For example, you can construct a chatbot that helps users schedule their daily tasks or offers customer assistance for a particular business. For novices who wish to learn more about AI and natural language processing, constructing chatbots is an excellent way to gain knowledge in this field. 

Source Code: Chatbot

Skills: Python

Do you dream of building your own ChatGPT? Enroll in our Generative AI course and take the first step towards making it a reality!

3. Identification of Spam Email

Spam detection is the process of detecting and filtering out unwanted emails or messages sent in bulk or containing malicious content. It uses AI algorithms to identify spam emails and can detect malicious content, phishing scams, and other types of fraudulent activities. 

To create a spam email detector, you should be able to train algorithms to detect the unwanted text content of an email. This training process requires collecting and analyzing data from a large number of emails. Once the algorithms are trained, they can be used to detect spam emails in real time. The user will get notifications of the necessary emails that are important to them.

Source Code: Spam Email Identification Project

4. Next Word Prediction

This project uses artificial intelligence (AI) to predict the next word that a user might type, given a set of words they have already entered. For example, if a person types “I am going to be”, the AI might suggest “late” as the next word. To accomplish this task, the AI must be able to understand natural language processing (NLP) and analyze and process large amounts of data. 

To create this AI-based project, you will need to develop a framework that combines natural language processing (NLP), deep learning , and machine learning algorithms. This framework would need to analyze and process large amounts of data to accurately predict the next word a user might type. 

Once this framework is developed, you will need to train the AI model using a training dataset that contains examples of words and phrases. Once the model is trained, it can be used to predict the next word for a user.

Source Code: Next Word Prediction

Skills: Jupiter Notebook

5. Music Recommendation

Many music streaming services employ music suggestion, a well-liked artificial intelligence tool, to personalize user experiences and boost engagement. Systems for making personalized music suggestions employ machine learning algorithms to examine user behavior, preferences, and the qualities of various songs and artists.

Building a music recommendation system can be done in various ways, including collaborative and content-based filtering. For example, similar tracks are suggested to consumers by content-based filtering algorithms using data on the qualities of songs and artists. On the other hand, collaborative filtering systems employ data on the actions and preferences of comparable users to provide recommendations. 

Creating a music recommendation system is a complex project requiring a thorough grasp of machine learning algorithms and data processing methods. However, you can construct a general-purpose system that can be used on various platforms or develop a music recommendation system for a single music streaming service. Building a music recommendation system will require knowledge of machine learning methods and data sources, making it a wonderful artificial intelligence project for students interested in data science and machine learning.

Source Code: Music Recommendation

Skills: Django & Python 3

6. Keyword Generator for SEO

A keyword generator is a tool that uses algorithms to generate relevant keywords for a given topic or search query. It uses natural language processing to suggest keywords based on user input, analyze search trends, and generate keyword variations. 

To create a keyword generator for SEO using AI technologies, you need to use natural language processing algorithms to generate relevant keywords from user input. You will also need to use machine learning algorithms to identify the most relevant keywords and optimize the content. For this project, you also need to use deep learning algorithms to improve the accuracy of the results.

Source Code: Keyword Generator for SEO Project

Artificial Intelligence Projects for Intermediate Level

Continuous practice is the key to improving your skills. You can work on the following intermediate-level artificial intelligence projects to enhance your skills and knowledge. 

7. Resume Parser

The goal of a resume parser is to extract important information from resumes using machine learning and natural language processing. It helps companies quickly identify potential job applicants and prioritize their applications. It is necessary to have a working knowledge of Python, natural language processing, machine learning, and image recognition technologies to develop a resume parser AI-based project. 

For example, a resume parser driven by AI scans resumes for keywords related to a specific job role and ranks applicants based on their job’s relevance.

Source Code: Resume Parser Project

8. Sentiment Analysis

The emotional tone of text data, such as social media postings, reviews, and news stories, can be analyzed and understood using sentiment analysis, a common artificial intelligence application. Sentiment analysis systems use machine learning techniques to find positive, negative, or neutral attitudes in text data.

Building a sentiment analysis system can be done in various ways, including rule-based and machine-learning-based systems. Rule-based systems analyze text input and find positive and negative attitudes using pre-defined rules and dictionaries. On the other hand, machine learning-based systems employ training data to identify patterns in text data and forecast sentiment.

Source Code: Sentiment Analysis

Skills Required: Machine Learning  

9. Fake Products Detection

This AI-based project detects fake products using high-end blockchain and machine-learning techniques. It determines the accuracy of a product’s features. It could be related to color, texture, size, shape, etc. The test of the product’s accuracy depends on the images and data of the original product. If any of the features are off, the product can be considered defective. 

To create a fake product detection project, you should be familiar with technologies like blockchain , machine learning, and image and data analysis.

Source Code: Fake Products Detection Project

Skills Required: Python, HTML , Blockchain Technology

10. Social Media Suggestions

The use of AI and machine learning is getting more popular in social media networks. For example, X (Twitter) uses AI to personalize the user experience. AI algorithms are used to recommend content and accounts to follow based on the user’s interests, offer personalized trends, and filter out spam and malicious content. Even LinkedIn uses AI to recommend job opportunities based on user’s interests and qualifications. 

With the help of AI, you can create projects that can suggest connections to users, recommend content and products based on their interests, and filter out spam, irrelevant, and malicious content.

Source Code: Social Media Recommendations

Skills: JavaScript, HTML

11. Plagiarism Analyzer and Detector

An AI-powered plagiarism detector and analyzer works on machine learning algorithms to analyze a given text and check for similarities to other texts that already exist. The technology used in this AI-related project includes natural language processing and text analysis algorithms. 

These technologies can identify and compare patterns in texts to detect plagiarism, give the percentage of plagiarized content, and ensure the originality of the text. The project can also employ large databases of existing texts for comparison. 

For example, a plagiarism detector and analyzer is generally used to analyze the percentage of plagiarism in academic papers, research papers, reports, blogs, etc.

Source Code: Plagiarism Analyzer Project

12. Fraud Detection

Another significant area where artificial intelligence is applied is fraud detection. Fraud detection systems use machine learning algorithms to examine data trends and spot possible fraud cases.

AI can detect a wide range of frauds, such as identity theft, insurance fraud, and credit card fraud. For example, machine learning algorithms can analyze large data sets to spot trends that point to fraudulent behavior, such as strange spending habits or several accounts using the same identifying information.

Developing a fraud detection system might be difficult without a thorough grasp of machine learning algorithms and data processing methods. You can construct a fraud detection system specifically for a given business, like insurance firms or credit card companies, or develop a general-purpose system that can be used across various domains. 

Building a fraud detection system can utilize multiple machine learning methods, making it a wonderful artificial intelligence project for students interested in data science and machine learning.

Source Code: Fraud Detection

Skills: Python, Machine Learning, and Data Science

13. Face Recognition System

We have developed many new things in the field of security, advertising, etc. with the help of face recognition. Face recognition software uses machine learning concepts to identify faces. 

You can make a facial recognition system for certain industries, such as schools, colleges, or any workplace. In addition, a facial recognition system is great for computer science and machine learning students, making it a great source for learning machine learning concepts.

Source Code: Face Recognition System

Skills: Python and OpenCV

Artificial Intelligence Projects for Professionals

Experienced professionals can work on more complex projects to develop effective and innovative solutions to real-world problems. Here are some of the best artificial intelligence projects for professionals: 

14. Stock Prediction

Stock prediction is an AI project that uses machine learning algorithms to forecast the future price movement of stocks. This project involves collecting financial data from past stock performances, such as stock prices and volume traded, and then using predictive models to create forecasts of future stock prices. The accuracy of these forecasts can be improved by using more data and more sophisticated models.

For example, a stock prediction AI-based project could involve using financial data from the past five years of a company’s stock performance to create a predictive model. This model could then be used to forecast the trend of the stock’s price over the next six months. 

Source Code: Stock Prediction Project

15. Cleaning Robots

AI-powered cleaning robots are equipped with artificial intelligence and can autonomously clean and maintain a space. These robots use technologies, such as machine learning, computer vision, and natural language processing to detect objects, navigate a space, and respond to commands. 

For example, a cleaning robot might use these technologies to detect dirt and debris on the floor, navigate around the room, and respond to commands to clean the area.

Source Code: Cleaning Robots Project

16. Predictive Maintenance

With advancements in artificial intelligence, humans have increased their dependency on machines. Reading data from the sensors and other sources can help predetermine the maintenance plan beforehand, which eventually leads to savings in equipment breakdown. 

However, making a predictive maintenance system would be difficult without knowing the concepts of machine learning algorithms, artificial intelligence, and data processing techniques. Consequently, it is an excellent project for people interested in data science and machine learning.

Source Code: Predictive Maintenance 

Skills Required: Machine Learning

17. Personalized Medicine

Many therapies are designed for individual patients based on their genetic and environmental makeup. Personalized medicine system uses machine learning algorithms that look over the pattern in the huge data of the patients.

Many applications have been developed in the field of personalized medicine. Frameworks of customized medicine can determine what type of treatment will suit a specific patient. However, as the project is in the field of medications, you need to be very specific in the data you collect. 

Source Code : Personalized Medicines

18. Recommendation Systems

Another well-liked use of artificial intelligence is recommendation systems. These systems make recommendations to consumers for goods, services, or information based on their prior actions or interests. Many e-commerce companies, streaming services, and social media networks employ recommendation algorithms to personalize user experiences and boost engagement.

Content-based recommendation systems provide users with related goods suggestions based on the products’ characteristics. On the other hand, collaborative filtering recommendation systems base their recommendations on data regarding the interests and behavior of comparable users. 

Developing a recommendation system requires a thorough grasp of machine learning algorithms and data processing methods. You can design a recommendation system specifically for a given sector of the economy, like the music or film industries, or you could construct a general-purpose recommendation system that can be used across many other industries. 

Source Code: Book Recommendation System

Skills: Python, Data Science , Machine Learning

19. Autonomous Vehicles

Self-driving cars, normally considered independent vehicles, are one of the most interesting and promising projects of computerized reasoning. By combining sensors, cameras, and machine learning algorithms, autonomous cars can drive themselves and navigate roads without human intervention. Transportation could go through an insurgency because of this innovation, becoming more secure, powerful, and generally accessible.

A good understanding of robotics, computer science, and machine learning is the first requirement for making an autonomous vehicle. You can make a vehicle that can follow a predetermined route or a system that can work with many alterations. 

Source Code: Autonomous Vehicles

Skills Required: Robotics, Computer Science, Machine Learning

20. Voice-Based Virtual Assistant

This is one of the popular artificial intelligence projects for final-year students. Voice-based virtual assistants are AI-driven projects that allow users to interact with machines using voice commands. This technology is used in applications, such as smart speakers, mobile phones, and other devices. The project should include a text-to-speech feature.

To create a voice-based virtual assistant, you should be familiar with natural language processing, voice recognition, and machine learning technologies. Natural language processing enables the machine to understand user commands, while machine learning and speech recognition are used to process and recognize the user’s voice input.

Source Code: Voice-Based Virtual Assistant Project

Things to Remember Before Starting an AI Project

Working on an AI project is exciting but requires preparation and planning. Here are some things to remember before you start with your AI project: 

• Research Your Idea: You need to first understand the problem you wish to solve with your artificial intelligence project and research all aspects of it. Define it well before beginning your work. 
• Data Availability: AI models require large amounts of data to train. Ensure that you have relevant high-quality data needed for the project. 
• Choose Tools and Technologies: Select the right tools and technologies like programming languages, frameworks, and libraries required for your AI project. 
• Understanding of Basic Concepts: Get yourself familiar with basic AI and related concepts like machine learning algorithms and data science. 
• Familiarize Yourself With the Limitations of AI: You should be familiar with the limitations of AI to ensure your results are accurate. These involve the latest technology, ethical considerations, and biases in data. 
• Plan for Testing, Backup, and Documentation: You should have a plan of how you will test your project, backup data in case of system failures, and maintain documentation for future reference. 

Best Platforms for Building AI Projects

Some of the popular platforms where you can build your AI projects include: 

• Google AI platform
• Microsoft Azure AI
• Amazon Web Services (AWS) AI
• Open AI API

Conclusion 

Artificial intelligence is a rapidly growing field changing how people live, work, and use technology. The projects we have covered in this blog post are just an example of how long AI can go to change our world. 

Some AI project ideas for beginners are: a) Image Recognition b) Chatbot c) Predictive Maintenance d) Fraud Detection e) Music Recommendation f) Personalized Medicine g) Face Recognition System

The best AI project idea depends on the individual goals and expertise level. However, some of the popular AI projects include image recognition, chatbot development, predictive maintenance, fraud detection, music recommendation, and personalized medicine. 

A variety of components, including data, algorithms, and infrastructure for training AI models, are needed to build an AI project.

Some examples of AI in everyday life are: a) Use of virtual assistants like Siri and Alexa. b) Personalized content recommendations on streaming platforms. c) Fraud detection systems in banking web applications. d) Navigation apps like Google Maps.

The following are the three types of AI. a) Artificial Narrow Intelligence (ANI) b) Artificial General Intelligence (AGI) c) Artificial Super Intelligence (ASI)

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Praveen Kumar is the Senior Data Scientist of Internshala. He is an analytics savant who has won national analytics competitions. With more than four years of experience in analytics and Data Science, he is a master of many domains including Python, MySQL, Google Analytics, R, PowerBI, Google Data Studio, NLP, and Machine Learning.

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12 Best Artificial Intelligence Topics for Research in 2024

Explore the "12 Best Artificial Intelligence Topics for Research in 2024." Dive into the top AI research areas, including Natural Language Processing, Computer Vision, Reinforcement Learning, Explainable AI (XAI), AI in Healthcare, Autonomous Vehicles, and AI Ethics and Bias. Stay ahead of the curve and make informed choices for your AI research endeavours.

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Table of Contents  

1) Top Artificial Intelligence Topics for Research 

     a) Natural Language Processing 

     b) Computer vision 

     c) Reinforcement Learning 

     d) Explainable AI (XAI) 

     e) Generative Adversarial Networks (GANs) 

     f) Robotics and AI 

     g) AI in healthcare 

     h) AI for social good 

     i) Autonomous vehicles 

     j) AI ethics and bias 

2) Conclusion 

Top Artificial Intelligence Topics for Research   

This section of the blog will expand on some of the best Artificial Intelligence Topics for research.

Natural Language Processing   

Natural Language Processing (NLP) is centred around empowering machines to comprehend, interpret, and even generate human language. Within this domain, three distinctive research avenues beckon: 

1) Sentiment analysis: This entails the study of methodologies to decipher and discern emotions encapsulated within textual content. Understanding sentiments is pivotal in applications ranging from brand perception analysis to social media insights. 

2) Language generation: Generating coherent and contextually apt text is an ongoing pursuit. Investigating mechanisms that allow machines to produce human-like narratives and responses holds immense potential across sectors. 

3) Question answering systems: Constructing systems that can grasp the nuances of natural language questions and provide accurate, coherent responses is a cornerstone of NLP research. This facet has implications for knowledge dissemination, customer support, and more. 

Computer Vision   

Computer Vision, a discipline that bestows machines with the ability to interpret visual data, is replete with intriguing avenues for research: 

1) Object detection and tracking: The development of algorithms capable of identifying and tracking objects within images and videos finds relevance in surveillance, automotive safety, and beyond. 

2) Image captioning: Bridging the gap between visual and textual comprehension, this research area focuses on generating descriptive captions for images, catering to visually impaired individuals and enhancing multimedia indexing. 

3) Facial recognition: Advancements in facial recognition technology hold implications for security, personalisation, and accessibility, necessitating ongoing research into accuracy and ethical considerations. 

Reinforcement Learning   

Reinforcement Learning revolves around training agents to make sequential decisions in order to maximise rewards. Within this realm, three prominent Artificial Intelligence Topics emerge: 

1) Autonomous agents: Crafting AI agents that exhibit decision-making prowess in dynamic environments paves the way for applications like autonomous robotics and adaptive systems. 

2) Deep Q-Networks (DQN): Deep Q-Networks, a class of reinforcement learning algorithms, remain under active research for refining value-based decision-making in complex scenarios. 

3) Policy gradient methods: These methods, aiming to optimise policies directly, play a crucial role in fine-tuning decision-making processes across domains like gaming, finance, and robotics.  

Explainable AI (XAI)   

The pursuit of Explainable AI seeks to demystify the decision-making processes of AI systems. This area comprises Artificial Intelligence Topics such as: 

1) Model interpretability: Unravelling the inner workings of complex models to elucidate the factors influencing their outputs, thus fostering transparency and accountability. 

2) Visualising neural networks: Transforming abstract neural network structures into visual representations aids in comprehending their functionality and behaviour. 

3) Rule-based systems: Augmenting AI decision-making with interpretable, rule-based systems holds promise in domains requiring logical explanations for actions taken. 

Generative Adversarial Networks (GANs)   

The captivating world of Generative Adversarial Networks (GANs) unfolds through the interplay of generator and discriminator networks, birthing remarkable research avenues: 

1) Image generation: Crafting realistic images from random noise showcases the creative potential of GANs, with applications spanning art, design, and data augmentation. 

2) Style transfer: Enabling the transfer of artistic styles between images, merging creativity and technology to yield visually captivating results. 

3) Anomaly detection: GANs find utility in identifying anomalies within datasets, bolstering fraud detection, quality control, and anomaly-sensitive industries. 

Robotics and AI   

The synergy between Robotics and AI is a fertile ground for exploration, with Artificial Intelligence Topics such as: 

1) Human-robot collaboration: Research in this arena strives to establish harmonious collaboration between humans and robots, augmenting industry productivity and efficiency. 

2) Robot learning: By enabling robots to learn and adapt from their experiences, Researchers foster robots' autonomy and the ability to handle diverse tasks. 

3) Ethical considerations: Delving into the ethical implications surrounding AI-powered robots helps establish responsible guidelines for their deployment. 

AI in healthcare   

AI presents a transformative potential within healthcare, spurring research into: 

1) Medical diagnosis: AI aids in accurately diagnosing medical conditions, revolutionising early detection and patient care. 

2) Drug discovery: Leveraging AI for drug discovery expedites the identification of potential candidates, accelerating the development of new treatments. 

3) Personalised treatment: Tailoring medical interventions to individual patient profiles enhances treatment outcomes and patient well-being. 

AI for social good   

Harnessing the prowess of AI for Social Good entails addressing pressing global challenges: 

1) Environmental monitoring: AI-powered solutions facilitate real-time monitoring of ecological changes, supporting conservation and sustainable practices. 

2) Disaster response: Research in this area bolsters disaster response efforts by employing AI to analyse data and optimise resource allocation. 

3) Poverty alleviation: Researchers contribute to humanitarian efforts and socioeconomic equality by devising AI solutions to tackle poverty. 

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Autonomous vehicles   

Autonomous Vehicles represent a realm brimming with potential and complexities, necessitating research in Artificial Intelligence Topics such as: 

1) Sensor fusion: Integrating data from diverse sensors enhances perception accuracy, which is essential for safe autonomous navigation. 

2) Path planning: Developing advanced algorithms for path planning ensures optimal routes while adhering to safety protocols. 

3) Safety and ethics: Ethical considerations, such as programming vehicles to make difficult decisions in potential accident scenarios, require meticulous research and deliberation. 

AI ethics and bias   

Ethical underpinnings in AI drive research efforts in these directions: 

1) Fairness in AI: Ensuring AI systems remain impartial and unbiased across diverse demographic groups. 

2) Bias detection and mitigation: Identifying and rectifying biases present within AI models guarantees equitable outcomes. 

3) Ethical decision-making: Developing frameworks that imbue AI with ethical decision-making capabilities aligns technology with societal values. 

Future of AI  

The vanguard of AI beckons Researchers to explore these horizons: 

1) Artificial General Intelligence (AGI): Speculating on the potential emergence of AI systems capable of emulating human-like intelligence opens dialogues on the implications and challenges. 

2) AI and creativity: Probing the interface between AI and creative domains, such as art and music, unveils the coalescence of human ingenuity and technological prowess. 

3) Ethical and regulatory challenges: Researching the ethical dilemmas and regulatory frameworks underpinning AI's evolution fortifies responsible innovation. 

AI and education   

The intersection of AI and Education opens doors to innovative learning paradigms: 

1) Personalised learning: Developing AI systems that adapt educational content to individual learning styles and paces. 

2) Intelligent tutoring systems: Creating AI-driven tutoring systems that provide targeted support to students. 

3) Educational data mining: Applying AI to analyse educational data for insights into learning patterns and trends. 

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Conclusion  

The domain of AI is ever-expanding, rich with intriguing topics about Artificial Intelligence that beckon Researchers to explore, question, and innovate. Through the pursuit of these twelve diverse Artificial Intelligence Topics, we pave the way for not only technological advancement but also a deeper understanding of the societal impact of AI. By delving into these realms, Researchers stand poised to shape the trajectory of AI, ensuring it remains a force for progress, empowerment, and positive transformation in our world. 

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artificial-intelligence-projects

Here are 52 public repositories matching this topic..., leondavi / nerlnet.

Nerlnet is a framework for research and development of distributed machine learning models on IoT

• Updated Aug 14, 2024

qxresearch / qxresearch-event-1

Python hands on tutorial with 50+ Python Application (10 lines of code) By @xiaowuc2

• Updated Aug 2, 2024

ayusharyan143 / Alexa-Project-

A personal assistant application named Alexa that performs various tasks through voice commands. Features include weather updates, web searches, news headlines, Wikipedia summaries, jokes, and more. Easily customizable and extendable with new functionalities.

• Updated Aug 1, 2024

ashishpatel26 / 500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

• Updated Jul 26, 2024

Ramya-Mahi / DeepFake-Detection-and-Prevention-A-Comprehensive-approach-using-AI

This Deepfake Detection and Prevention project leverages advanced AI techniques to identify manipulated images with 95% accuracy.

• Updated Jul 23, 2024

w00000dy / ai-object-detection

A web AI object detection

• Updated Jul 4, 2024

w00000dy / ai-hand-detection

A web AI hand detection

shreyansh-2003 / CinemAI.Insights-Movie_Script_Analyser

CinemAI is a one stop solution solution created for movie buffs. The fullstack app has a movie scene-wise and character-wise analysis feature. It also has genre classification, movie, movie rating prediction and age restriction prediction based on a movie's script.

• Updated Jun 28, 2024
• Jupyter Notebook

instructor-ai / instructor-rb

Structured outputs for LLMs

• Updated Jun 12, 2024

Anotherafael / DIO_PodcastGenerateByAI

• Updated Jun 4, 2024

mrbid / NEURAL_ANIMATION_TWEENING

Animation Tweening of 3D vertex data using a Feed-Forward Neural Network.

• Updated May 30, 2024

Bassamejlaoui / Awesome-Applied-AI-Bachelor-degree

a structured 3-year Applied AI Bachelor degree path involves covering foundational courses, specialized AI topics, practical applications, and supplementary resources

• Updated May 21, 2024

spear97 / Sentiment-Analysis-Django

A web application built using the Django framework that allows users to analyze the sentiment of text inputs, providing insights into the emotional tone and polarity of the content.

• Updated May 16, 2024

Anotherafael / DIO_ArticleGenerateByAI

• Updated May 13, 2024

nthnn / n2cmu-arduino

This is the official Arduino library for N2CMU (Neural Network Coprocessing Microcontroller Unit) available on Arduino Package Manager and PlatformIO.

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juniorVOPJ / boilerplate-strapi

Boilerplate que utiliza JavaScript, TypeScript e o Framewrok do Strapi CMS para gestão de conteúdo e back-end headless, somando-se ao R2D2, um software de inteligência artificial offline de alta disponibilidade e eficiência.

• Updated May 6, 2024

PialGhosh2233 / Diabetes_prediction_using_ml

• Updated May 5, 2024

FaizanZaheerGit / StudentPerformancePrediction-ML

This is a simple machine learning project using classifiers for predicting factors which affect student grades, using data from CSV file

PialGhosh2233 / Liver_Cirrhosis_Prediction_using_Machine_Learning

• Updated May 3, 2024

Daethyra / Build-RAGAI

Interactive notes (Jupyter Notebooks) for building AI-powered applications

• Updated May 2, 2024

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15 Interesting AI Project Ideas to Brush Up Your Skills [+ 5 Bonus Inspirational Projects]

The world is reeling with the news of tech giants pulling plugs off their ai projects and research divisions. While data science and AI quickly recovered from the Covid-19 crisis, the Ukraine war disrupted the supply chain. Inflation went through the roof, bringing in an economic headwind. As obvious, economic crises always go hand-in-hand with layoffs.

The Silver Lining?

Despite the economic crisis, experts believe there is no slowing down of ai projects and innovation .

Infact, Scott Stephenson, CEO at Deepgram told VentureBeat –

AI will continue to be central to business in 2023, by cutting costs and increasing innovation. Simply put, AI will help us do more with less.[ source ]

AI is so deeply imbibed in our daily life that it is impossible to halt it completely. Experts predict a U-shaped recovery – descent, stagnation, and slow recovery. Also, putting all fears to rest, experts also state that AI will not replace humans entirely.

Vishal Sikka, founder, and CEO of Vian AI , a human-centered AI platform, says –

AI won’t — and shouldn’t — replace humans in the near term.

He strongly believes that AI is nowhere close to human judgment.

More and more systems will be designed to amplify human judgment — to aid people and encourage AI symbiosis, rather than seeking to have AI replace the user.

It is one thing to learn the theory of AI. Looking at how AI is shaping the present and future, the demand for skilled AI professionals will increase – professionals who understand the power of AI and the modern market.

Your chance to show your skills and your understanding of the present market is through your AI project. AI related projects can help you take a modern-day problem head-on and show a solution that is both lean and scalable. Your AI related projects can help you showcase your understanding of an organization’s working or core value proposition.

In this article, we have curated 21 top ai projects ideas for students that take you from simple ai projects to advanced artificial intelligence projects. These artificial intelligence based projects will help you grasp various techniques such as bag-of-words, random forest, LempelZiv (LZ) algorithm, Markov Model (MM), Neural Networks (NNs), Bayesian Networks, Association rules, Word2Vec approach, k-nearest neighbor classifier, Bonferroni, FDR corrections, and much more. 

Table of Contents

Benefits of Doing an AI Project

Industries across the world are demanding AI-based software applications like never before. According to a study by Statista ,  industries will grow to $126 billion by 2025 through AI applications  .

No businesses are ready to ignore this opportunity. Companies that have implemented AI-based chatbots have experienced great growth in their businesses. Through artificial intelligence based projects, you get the upper hand in learning the concepts and applying them practically. In a nutshell, the benefits of doing ai projects include: 

• Self-learning through practical applications
• Understanding market and industries deeply
• Acquire the ability to solve problems with the leanest available solution
• Design AI-based solutions that are scalable
• Create a handsome resume that speaks volumes of your capabilities as a professional

All said and done. Now, let’s get started with ai project ideas.

15 Top AI projects ideas for for Students and Professionals

There are many interesting artificial intelligence based projects. However, selecting the type of project will depend on several factors. These include your interest, time, budget, and trending topics.

You can also shortlist the AI related projects by understanding the challenges in companies from the domain you are interested in. Solving their problems will give you a hands-on experience with the work they are doing. However, certain artificial intelligence projects are typically necessary for you to gain confidence in AI and its associated topics. 

We have put together different AI related project ideas to help you start your journey of learning AI.

Also Read: How to Learn AI and ML Tools by Yourself?

Interesting Artificial Intelligence Projects in Python

1. predicting users’ upcoming location.

Predicting the user’s most probable next location (next summer vacation or holiday destination, next restaurant, etc.) becomes an important requirement to make better decisions for future services. 

This project on artificial intelligence is ideal for services like healthcare applications, network management, travel management, and so on.

Working on this AI project model will help you to understand the LempelZiv (LZ) algorithm, Markov Model (MM), Neural Networks (NNs), Bayesian Networks, and Association rules.

2. Detecting social media scam

The popularity of YouTube, Instagram, and Facebook attract not only genuine users and viewers, but also spammers. As a result, there is an increase in unwanted spam posts, images, videos, and comments. Even bots may be spamming your email, SMS inbox, and the comment section of social media accounts. 

The nature of this spam can range from frivolous promotions of products to more problematic hate-mongering designed to incite people by demeaning their political, social, or religious beliefs.

An AI model can be created by training it on simple spam v/s ham messages.

Distance-based algorithms like Euclidean distance or other similarity-finding algorithms (for example, cosine similarity) can help identify spam messages.

Pretrained models like ALBERT can offer better results. Similar models can also be used to auto-flag offensive or hateful messages.

Here is an example of an AI-based YouTube spam comment detection model . It can be devised as a project on artificial intelligence where you will be focusing on text and words.

In this project, artificial intelligence will help classify internet comments as spam or not. Here’s another sample project to detect Twitter Bullying using AI and ML: 

The spam detection model can be created using bag-of-words and random forest techniques. You can also predict positive and negative reviews with the Word2Vec approach and the k-nearest neighbor classifier in addition to spam detection.

3. Identifying the genre of a song

In this project, artificial intelligence will be used to identify the genre of a song.

Using an artificial neural network , you will detect the song and find its genre to display it in the correct playlist for users. You will use Librosa (python library) to extract features from the song and Mel-frequency cepstral coefficients (MFCC) to detect the music genre .

Also Read: Understanding Perceptron: The Founding Element of Neural Networks

4. Shock front classification

One of the most critical artificial intelligence projects, it detects shock fronts in computational fluid mechanics (CFD) simulations. The presence of shock results in additional complexities in fluid mechanics; hence, it is necessary to detect and handle shock fronts to deal with fluid mechanics problems. 

In the Shock Front Classification AI-based projects , you will be using supervised algorithms for classification, such as classification trees (RPART), linear discriminant analysis (LDA), naive Bayes (NB), support vector machines (SVM), and random forests (RF).

5. Translator app

Another exciting project of AI involving natural language processing can be the creation of a translator app. Such an app will translate a sentence from one language to another. While technically, you can train an AI model from scratch, but that can be difficult, time-consuming, and inefficient. 

Several pre-trained models known as ‘transformers’ can be used to create a translator app that makes it easy to make AI projects.

A pre-trained transformer model will perform feature extraction through tokenization of the input sentences, pass it through the pre-trained model, and deliver the translation in the required language.

You can create a project on artificial intelligence using GluonNLP, a common library available in Python.

Here’s another project idea on the same lines: to create a translator app that can translate sign language.

Simple AI Related Projects for Beginners

6. predicting bird species.

Birds are ecological indicators, and they respond quickly to environmental changes. Hence, it is important to classify birds to understand the problems in ecology.

Domain experts can classify birds manually, but this traditional classification is a tedious and time-consuming. It is also becoming very difficult due to the tremendous increase in amounts of data. 

Here comes the opportunity for those looking for top ai projects ideas for for students. It is among the easy AI mini projects.

The project uses AI-based classification for predicting bird species . It can be approached in two ways. If you are a beginner, you can use a random forest to predict bird species. You can use a convolution neural network if you are looking for an intermediate level.

7. Identifying handwritten mathematical symbols

In this project, artificial intelligence helps comprehend handwriting . It is one of the simplest ai project ideas you can work on as a beginner. You will be using a convolution neural network (CNN) to detect handwritten mathematical symbols. 

The HASYv2 dataset is the input to the neural network; it contains 168,000 images from 369 different classes. Here is a video to help you get started with an AI project on identifying handwritten text:

8. Scotch Whiskey classification

Scotch whiskey is famous for its distinct flavors. In this project on artificial intelligence, you will classify scotch whiskeys based on their flavor characteristics. Here, we will use datasets of scotch whiskeys from several distilleries and cluster them based on their flavors. 

Here is references to the datasets to help you start off – the Whiskey region dataset and the Whiskey varieties dataset .

9. Investigate Enron

Enron is one of the largest energy companies in America that collapsed overnight. Enron investigation is one of the real-life top AI projects for students. In this project, artificial intelligence investigates Enron’s fraud activities with the help of the emails sent by their former senior executives. It has 500 thousand emails from its former employees. 

Check the link for the Enron database- Enron Email Dataset .

10. Fake news detector

In social media, deep fakes, news generators, and fake news have become a menace to society. For example, as per NCRB (National Crime Records Bureau, India), there has been a 214% increase in fake news-related cases.    

Fake news can flare up all types of pre-existing social unrest or create social tensions out of nowhere. Given the sheer number of unregulated, unaccountable news outlets and people increasingly receiving news from social media portals, manually checking the validity of each piece of news can be problematic as by the time the verdict is out, harm can be done.

On top of this, the validity of the fake news reviewer can be questioned because of their perceived political leanings. These complex social problems can be solved through a fake news detector. 

AI can perform social responsibility by cross-checking news contents with official government briefings or prestigious news portals held accountable.

You can create an artificial intelligence project using NLP models like BERT here are helpful and should be explored. A fake news detector model can produce labels such as ‘True’, ‘False’, ‘Mostly False’, or ‘Misleading’ for a news item.

Advanced AI Related Projects

11. automated system to detect fashion trends.

Coolhunter has gained significant importance in the fashion world. They take advantage of social media platforms to understand new trends in fashion. But, due to irrelevant information, it becomes a challenging task to predict fashion trends . 

AI can be used to sort information. This artificial intelligence based projects project filters relevant information from irrelevant data and derives insights for predicting fashion trends.

12. Web pattern navigation profiling

Each time when users search for information on the internet, they leave an invisible blueprint of their preferences. These preferences are recorded based on their browsing behavior in a specific sequence of domains. Here, segments of user groups are created based on their browsing habit or social media opinions.

In this project on artificial intelligence for web pattern navigation profiling , you will learn a new perspective on collecting user preferences. Here, different navigation profiles are extracted based on the consecutive sequence of domain visiting order and the route followed within a certain socio-demographic profile.

You will need to define an algorithm to extract frequent contiguous sequences and also use Bonferroni and FDR corrections to retrieve socio-demographic characteristics.

13. Food attribute classification

One of the most interesting artificial intelligence based projects for food lovers. It classifies the diverse array of food based on cuisine and its flavors. Here, we create a deep learning model based on a multi-scale convolutional network.

The food attribute dataset – Yummly48k – is taken from the website Yummly . In addition to the multi-scale convolutional network, it uses Negative Log-Likelihood (NLL) for the model creation.

14. Resume parser

AI has the advantage of being versatile and can be used in various domains. One such domain is Human Resources (HR), where the concerned people need to understand the human resource requirement and shortlist appropriate candidates for further interviews. This issue can be problematic given the number of applications can often be in the hundreds if not thousand. 

According to a study, an average recruiter spends approximately 7 seconds reviewing a resume. To utilize these ‘7 seconds’, reviewers look for keywords in the resume that can help them know if the resume is relevant to the job profile. However, candidates can deliberately put these keywords alone, causing the resume to get shortlisted.

Also Read: How to Optimize Your Resume for the ATS

This problem can be solved through AI. You can train an AI model with several relevant and irrelevant resumes. Natural Language processing is involved in such a project, and deep learning algorithms like RNN are the ideal choice. The final product can be a resume parser that provides either a yes/no or a score from 1 to 10 in terms of the relevancy of the resume for a given job profile.

15. Object detection system

Google Images can classify images based on their contents, such as ‘Birthday’, ‘Pet’, ‘Car,’ ‘Nature’ etc. On a more complex level, the model can also label the objects in the image. For example, the model can label all the relevant objects in the image if a human looks at this phone sitting beneath a tree. This is done by creating an object detection system that skims through the contents of the image. 

Also Read: Understanding Image Segmentation

Training an AI model on object detection can help companies dealing with autonomous vehicles, smart infrastructure, and security solutions. To work on an artificial intelligence project of this nature, you can use the COCO 2017 dataset available on Kaggle for the output layer and can use an open-source, pre-trained model for this called SSD (Single Shot Detector). 

Get Inspired: AI Projects To Explore

1. healthy diet via diet4you.

Maintaining a healthy lifestyle plays a key role in preventing the cause of chronic diseases. The right amount of nutrition is necessary to maintain a healthy lifestyle, but a major chunk of the population suffers from undernutrition due to a poor diet plan.

Diet4You is an intelligent decision support system (IDSS) that uses different techniques to tailor a personalized menu planner.

It considers the nutritionist’s prescription and various other factors, such as the nutritional guidelines to be followed, the person’s characteristics, health status, habits, food preferences, and allergies. 

This AI project combines advanced techniques such as Knowledge Engineering, Case-Based Reasoning (CBR), and Data Analysis. Diet4You consists of two main modules:

• NPG module – tailoring a nutrition plan for a specific person.
• PMP module – a nutrition plan for a specific period.

2. Phone unlocking using Face ID

It is one artificial intelligence project that uses face biometrics to unlock a phone . Using deep learning, the AI application can extract image features. It mainly uses two types of neural networks: Convolution neural networks and Deep autoencoders network. The ai project comprises a four-step process. They are- face detection, face alignment, face extraction, and face recognition.

Here’s how to build a project that uses AI for high-accuracy facial recognition: 

3. Forecasting earthquake-aftershock locations

Earthquakes cause massive destruction. It initially occurs as the main shock and is followed by a set of aftershocks. The timing and size of aftershocks can be identified using empirical laws, but forecasting the locations remains challenging. 

Google AI project applies deep learning to identify where the aftershock might occur. The project uses information on 118 major earthquakes reported around the world. Here, it uses a neural network to analyze the static stress change of mainshock and aftershock locations.

MEENA is a chatbot that handles various conversational topics and humanizes computer interaction. It can chat about anything and even improve foreign language practice. It is an end-to-end trained neural conversational model with a single Evolved Transformer encoder and 13 Evolved Transformer decoder blocks. These blocks help them to respond sensibly by minimizing the perplexity and uncertainty in prediction.

5. Gmail’s smart reply

Gmail’s Smart Reply uses a machine-learning algorithm to suggest replies to emails. It is based on a novel thinking hierarchy where each hierarchical model can learn, remember, and recognize a sequential pattern. 

While responding, it considers whether it is a positive or negative gesture. It uses long-short-term memory (LSTM) recurrent neural networks and semantics.

Tips to Help You Make AI Related Projects

Making an artificial intelligence project can be an uphill and complex task, and things can fall apart quickly if done in a sporadic and disorganized manner. Therefore having a good roadmap is very important so that when you are working on the project, you must have a clear idea about every stage of the project. 

Here are a few tips to help you improve the outcome of your AI-based project:

1. Update Your Concepts and Foundations

Working on an AI-based project is to use your AI knowledge and demonstrate it to others. Therefore, the logical first step is to ensure you are well-versed in all the important AI concepts.

These include an in-depth understanding of numerous deep learning algorithms, their parameters, data evaluation, and validation techniques, along with having a good command of the language you will build in (e.g., python or R). 

Also Read: Learn the Best ML Programming Languages

2. Understand the Business Problem and its Significance

The next step is to pick a project topic and understand the problem. This includes defining the problem, identifying the key issues that can hinder the model creation, identifying how the solution will benefit the end user, and, most importantly- what role AI plays. You must understand the value added AI provides to the solution.

3. Get Help

Working on AI-based projects can be complex and time-consuming if done individually. So if you need help, then form a team and solve a complex problem.

This will not only help create an advanced AI model but will also be a learning experience for you on how to work in a team.

It will prepare you for the future, as artificial intelligence based projects in companies often involve a team working on various aspects of model development.

4. Layout Deliverables

You must understand that the solution you intend to provide will be used by someone. Therefore, when starting with the project, you or your team must brainstorm the product you intend to provide to the user. 

5. Explore Solutions

One of the most important steps is patiently exploring how the solution will be provided. This includes exploring the type of deep learning algorithm, the model’s architecture, methods of data preparation to be used, types of model evaluation and validation to be deployed, how to implement the model, etc.

6. Create a Roadmap and Design a solution

Creating a roadmap for your AI-based project is crucial to keep it on track. This includes setting up objectives and timelines and assigning responsibilities (if working in a team).

It will help if you also design your solution regarding how your final AI tool will look and what operating procedure the user will be required to follow to use the product.

7. Access Data

The concept of GIGO (garbage-in garage-out) is common in computer science and mathematics, and it is highly relevant to AI. Therefore, identifying or gathering the dataset for training your model is among the most crucial steps.

For your project, you can look at websites like kaggle.com or other repositories like Google dataset and UCI for the dataset.

8. Create a Proof of Concept

Once the model is trained, the next step is to implement it. The implementation can be as simple as running the model on a jupyter notebook or more complex where you create a user interface. UI can be created using libraries like Streamlit, Django, Flask, etc.

9. Perform User Testing

Ideally, before you let others use your AI tool, you or your team must perform user testing to identify if the prototype works appropriately, solves the user’s problem, provides adequate error messages, and has no bugs. If there is any scope for improvement, then it must be perused.

10. Create a Demo and Perform Diligent Documentation

The last step of your AI-based project that may not seem necessary but is essential in practical work life is creating a user demo of the final AI tool. This demo can be video or text with images; you must also document the whole project work process in detail.

While working in an organization, this documentation is vital to demonstrate your work, it is also important when working on an independent project as it can help you explain your project during the interview.

You can adapt to new job trends by pursuing a course in AI. But to excel in your AI-based career, only hands-on experience working on top ai projects ideas can make you efficient. It helps you understand the process end-to-end and derive more value.

You will be better prepared to address challenges in designing and implementing AI projects. You can explore the above ai project ideas to gain skills that companies seek and build a successful career in AI .

1. What are Artificial Intelligence Projects?

Artificial Intelligence projects are intelligent projects that make machines capable of executing tasks requiring human intelligence. These intelligent agents’ goals include learning, reasoning, problem-solving, and perception.

AI includes many theories, methods, and technologies. It consists of many subfields, such as machine learning, neural network, deep learning, cognitive computing, computer vision, and natural language processing.

The additional technologies that support AI are a Graphical processing unit, the Internet of Things, Advanced algorithms, and API.

2. How do I start an AI project?

Gaining skills in AI projects opens a lot of opportunities. Plenty of options are available for those who want to start an AI project. One efficient way is to enroll in an online course. Choose an area of the topic you are interested in and opt for a course that offers real-world projects.

3. What are the 4 types of AI?

We can classify AI into the following 4 types:

• Reactive machine- Reactive machines are AI systems that do not use the experience to perform the current task. They do not form any memory and act based on what it sees. Deep Blue, IBM’s chess-playing supercomputer, is an example.
• Limited memory- Limited memory uses experience to act in present situations. An example of limited memory is autonomous vehicles.
• Theory of mind- Theory of mind is a type of AI system that makes machines capable of decision-making. None of them is extremely capable of decision-making as that humans. But it is showing significant progress.
• Self-aware- Self-aware is an AI system that is aware of itself. These types of systems should be conscious of themselves, be aware of their internal state, and be able to predict others’ feelings.

4. How does AI work?

Data is the new oil. AI combines a large amount of data and intelligent algorithms to help the system learn automatically from data models. AI adds intelligence to your existing application through progressive learning algorithms. This algorithm can be a classifier or a predictor.

Hope this helps you ideate your next AI Project. Happy Learning!

Pritha helps brands streamline content and communication efforts. She has worked with several B2B and B2C brands in SaaS and EdTech domains and helped build a digital footprint for them. She loves writing on social media, user psychology, UI/UX, content marketing guides, and AI-enabled technologies. Currently, she is leading the content, design, and communications team at AnalytixLabs, a premium edtech brand in India.

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It’s highly informative content. I have been looking for AI project ideas. Thanks for sharing Looking to read more content.

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Artificial Intelligence

The U.S. National Science Foundation has invested in foundational artificial intelligence research since the early 1960s, setting the stage for today’s understanding and use of AI technologies.

AI-driven discoveries and technologies are transforming Americans' daily lives — promising practical solutions to global challenges, from food production and climate change to healthcare and education.

The growing adoption of AI also calls for a deeper understanding of its potential risks, like the amplification of bias, displacement of workers, or misuse by malicious actors to cause harm.

As a major federal funder of AI research, NSF advances AI breakthroughs that push the frontiers of knowledge, benefit people, and are aligned to the needs of society.

On this page

What is artificial intelligence?

How does AI affect our daily lives? How does it work in simple terms? Can we trust AI chatbots? In this 10-minute video, Michael Littman, NSF division director for Information and Intelligent Systems, looks at where the field of artificial intelligence has been and where it's going.

Brought to you by NSF

NSF's decades of sustained investments have ensured the continual advancement of AI research. Pioneering work supported by NSF includes:

Reinforcement learning

Which refines chatbots and trains self-driving cars, among other uses.

Neural networks

Which underlie breakthroughs in pattern recognition, image processing and natural language processing.

Large language models

Which power generative AI systems like ChatGPT.

Collaborative filtering

Which fuels content recommendation on the world's largest marketplaces and content platforms, from Amazon to Netflix.

AI-driven learning

Including virtual teachers (both digital and robotic) that incorporate speech, gesture, gaze and facial expression.

What we support

With investments of over $700 million each year, NSF supports:

Innovation in AI methods

We invest in foundational research to understand and develop systems that can sense, learn, reason, communicate and act in the world.

Application of AI techniques and tools

We invest in the application of AI across science and engineering to push the frontiers of knowledge and address pressing societal challenges.

Democratizing AI research resources

We enable access to resources — like computational infrastructure, data, software, testbeds and training — to engage the full breadth of the nation's talent in AI innovation.

Trustworthy and ethical AI

We invest in the development of AI that is safe, secure, fair, transparent and accountable, while ensuring privacy, civil rights and civil liberties.

Education and workforce development

We invest in the creation of educational tools, materials, fellowships and curricula to enhance learning and foster an AI-ready workforce.

Partnerships to accelerate progress

We partner with other federal agencies, industry and nonprofits to leverage expertise; identify use cases; and improve access to data, tools and other resources.

National AI Research Institutes

Launched in 2020, the NSF-led  National Artificial Intelligence Research Institutes  program consists of 25 AI institutes that connect over 500 funded and collaborative institutions across the U.S. and around the world.

The AI institutes focus on different aspects of AI research, including but not limited to:

• Trustworthy and ethical AI.
• Foundations of machine learning.
• Agriculture and food systems.
• AI and advanced cybersecurity.
• Human-AI interaction and collaboration.
• AI-augmented learning.

Learn more by reading the  2020 ,  2021  and  2023  AI Institutes announcements or visiting the AI Institutes Virtual Organization .

National AI Research Institutes: Interactive Map (PDF, 7.96 MB)

AI Institutes Booklet (PDF, 12.58 MB)

Hear from the newest ai research institutes.

• At the Edge of Artificial Intelligence This episode of NSF's Discovery Files podcast features three 2023 AI Research Institutes awardees discussing their work.
• The Frontier of Artificial Intelligence This Discovery Files episode features 2023 AI Research Institutes awardees applying AI to education, agriculture and weather forecasting.

National AI Research Resource Pilot

As part of the "National AI Initiative Act of 2020," the National AI Research Resource (NAIRR) Task Force was charged with creating a roadmap for a shared research infrastructure that would provide U.S.-based researchers, educators and students with significantly expanded access to computational resources, high-quality data, educational tools and user support.

The NSF-led interagency NAIRR Pilot will bring together government-supported, industry and other contributed resources to demonstrate the NAIRR concept and deliver early capabilities to the U.S. research and education community, including the full range of institutions of higher education and federally funded startups and small businesses.

The NAIRR Pilot is aimed to accelerate AI-dependent research such as:

• Societally relevant research on AI safety, reliability, security and privacy.
• Advances in cancer treatment and individual health outcomes.
• Supporting resilience and optimization of agricultural, water and grid infrastructure.
• Improving design, control and quality of advanced manufacturing systems.
• Addressing Earth and environmental challenges via the integration of diverse data and models.

Implementation Plan for a National Artificial Intelligence Research Resource (PDF, 3.02 MB)

Featured funding.

Computer and Information Science and Engineering: Core Programs

Supports foundational and use-inspired research in AI, data science and human-computer interaction — including human language technologies, computer vision, human-AI interaction, and theory of machine learning.

America's Seed Fund (SBIR/STTR)

Supports startups and small businesses to translate research into products and services, including  AI systems and AI-based hardware , for the public good.

Cyber-Physical Systems

Supports research on engineered systems with a seamless integration of cyber and physical components, such as computation, control, networking, learning, autonomy, security, privacy and verification, for a range of application domains.

Engineering Design and Systems Engineering

Supports fundamental research on the design of engineered artifacts — devices, products, processes, platforms, materials, organizations, systems and systems of systems.

Ethical and Responsible Research

Supports research on what promotes responsible and ethical conduct of research in AI and other areas as well as how to encourage researchers, practitioners and educators at all career stages to conduct research with integrity.

Expanding AI Innovation through Capacity Building and Partnerships

Supports capacity-development projects and partnerships within the National AI Research Institutes ecosystem that help broaden participation in artificial intelligence research, education and workforce development.

Experiential Learning for Emerging and Novel Technologies

Supports experiential learning opportunities that provide cohorts of diverse learners with the skills needed to succeed in artificial intelligence and other emerging technology fields.

Responsible Design, Development and Deployment of Technologies  

Supports research, implementation and education projects involving multi-sector teams that focus on the responsible design, development or deployment of technologies.

Research on Innovative Technologies for Enhanced Learning

Supports early-stage research in emerging technologies such as AI, robotics and immersive or augmenting technologies for teaching and learning that respond to pressing needs in real-world educational environments.

Secure and Trustworthy Cyberspace

Supports research addressing cybersecurity and privacy, drawing on expertise in one or more of these areas: computing, communication and information sciences; engineering; economics; education; mathematics; statistics; and social and behavioral sciences.

Smart and Connected Communities

Supports use-inspired research that addresses communities' social, economic and environmental challenges by integrating intelligent technologies with the natural and built environments.

Smart Health and Biomedical Research in the Era of Artificial Intelligence

Supports the development of new methods that intuitively and intelligently collect, sense, connect, analyze and interpret data from individuals, devices and systems.

NSF directorates supporting AI research

Computer and information science and engineering (cise), engineering (eng), technology, innovation and partnerships (tip), mathematical and physical sciences (mps), social, behavioral and economic sciences (sbe), stem education (edu), geosciences (geo), biological sciences (bio), international science and engineering (oise), integrative activities (oia), featured news, nsf invests $2.8m to strengthen technical ai education at two-year institutions.

NSF announces new AI test beds initiative to advance safety and security of AI technologies

NSF announces groundbreaking Leadership-Class Computing Facility project

Additional resources.

• NAIRR Pilot Explore opportunities for researchers, educators and students, including AI-ready datasets, pre-trained models and other NAIRR pilot resources.
• National Artificial Intelligence Initiative A coordinated federal approach to accelerate AI research and the integration of AI systems across all sectors of the economy and society.
• CloudBank Allows the research and education community to access cloud computing platforms.
• Expanding the Frontiers of AI: Fact Sheet Learn how NSF is driving cutting-edge research on AI.
• AI Impacts from Investments: Fact Sheet An overview of NSF’s history of investments in artificial intelligence and how those investments fueled the innovative technologies we use today.
• One Hundred Year Study on Artificial Intelligence A study focused on understanding and anticipating how AI will ripple through every aspect of how people work, live and play.
• "CHIPS and Science Act of 2022" The act authorizes historic investments in use-inspired, solutions-oriented research and innovation in key technology focus areas.

Research projects link neuroscience and AI to advance human health

At the intersection of artificial intelligence (AI) and neuroscience is a mutually beneficial relationship with the potential to transform brain health, counter disease, and develop scientifically grounded AI technologies inspired by the versatility and depth of human intelligence.

For the first time, the Wu Tsai Neurosciences Institute and Institute for Human-Centered Artificial Intelligence (HAI) at Stanford have partnered to award a combined $500,000 to four cross-disciplinary research teams who are reimagining how neuroscience and AI can work together to unlock new insights about the human brain in health and disease.

For example, one of the grantees under this program is working on a new approach to at-home stroke rehabilitation therapy, using robotics, brain-computer interface, virtual reality, and wireless technology. By introducing real-time feedback, the researchers believe the system will be able to engage more neural circuits in the patient’s brain and enhance physical therapy.

Other grantees are exploring applications of AI in restoring speech to people with paralysis and tracking the progression of Parkinson's Disease, while the fourth aims to understand the remarkable energy-efficient computational capacity of the human brain to inform next generation computer chips.

“Neuroscience and artificial intelligence have both seen rapid growth in recent years. Many areas of neuroscience will benefit from the infusion of AI,” said Kang Shen , Vincent V.C. Wu Director of the Wu Tsai Neurosciences Institute, and Frank Lee and Carol Hall Professor of biology and of pathology. “We look forward to seeing these research teams pave the way for ethical advancements in human-inspired AI and its impact on understanding the development and function of the brain in health and disease.”

Proposals were selected based on their probability to make strong advances in both fields. “HAI and Wu Tsai Neuro share a commitment to funding proposals that make a persuasive case for how initial results will catalyze further support from internal and external stakeholders,” said James Landay , Stanford HAI Vice Director and Faculty Director of Research.

Sadly, one awardee, electrical engineering professor Krishna Shenoy , passed away in January. The science will go on, however, said co-PIs Zhenan Bao and Shaul Druckmann . "Krishna's longterm vision was to build brain computer interfaces to restore movement and communication to people with paralysis," said Druckmann. "We hope that by shedding light on how the brain controls the complex musculature underlying speech, our devices can contribute to making his vision a reality."

Learn more about the Wu Tsai Neuro & HAI Partnership Grant recipients:

Funded Projects

At-home stroke rehabilitation system based on augmented reality and brain computer interface paradigm.

This team aims to revolutionize future stroke treatment both in clinics and at home by combining a brain-computer interface and augmented reality (AR) into a single rehabilitation platform.

• Ada Poon , Main PI, School of Engineering, Dept of Electrical Engineering
• Monroe Kennedy III , Co-PI, School of Engineering, Dept of Mechanical Engineering
• Maarten Lansberg , Co-PI, School of Medicine, Dept of Neurology

Silent Speech Decoding Using Flexible Electronics and Artificial Intelligence

This team aims to advance augmentative and alternative communication technology for people with communication disorders and enable new forms of human-computer interaction by combining novel materials science with modern machine learning.

Dr. Krishna Shenoy passed away January 21, 2023. Read his obituary  here . Gifts in Krishna’s honor may be made to the  Pancreatic Cancer Action Network .

• Zhenan Bao , Main PI, School of Engineering, Dept of Chemical Engineering
• Shaul Druckmann , Co-PI, School of Medicine, Dept of Neurobiology
• Krishna Shenoy †, Co-PI, School of Engineering, Dept of Electrical Engineering
• Jaimie Henderson , Co-PI, School of Medicine, Dept of Neurosurgery

The Synaptic Organization of Dendrites

This team aims to mine a microscale reconstruction of a millimeter-cube of brain tissue to uncover how dendrites decode patterns of incoming signals. The project will test hypotheses that could confer the energy efficiency of neural circuits on next generation computer chips.

• Kwabena Boahen , Main PI, School of Engineering, Dept of Bioengineering
• Andreas Tolias *, Co-PI, School of Medicine, Dept of Ophthalmology (Joined Stanford January 2023)

Tracking Parkinson’s Disease with Transformer Models of Everyday Looking Behaviors

This project aims to track cognitive decline in Parkinson’s patients by measuring and modeling how patients explore the world with their eyes. The long-term goal of this project is to set a foundation for minimally-invasive and sensitive measures for diagnosing and tracking neurodegenerative diseases.

• Justin Gardner , Main PI, School of Humanities and Sciences, Dept of Psychology
• Leila Montaser Kouhsari , Co-PI, School of Medicine, Dept of Neurology

Related News

Neuroscience and AI: What artificial intelligence teaches us about the brain (an...

Stanford University launches the Institute for Human-Centered Artificial Intelli...

Stanford helped pioneer artificial intelligence. now the university wants to put....

How artificial intelligence is changing science

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Recent progress in the areas of Artificial Intelligence (AI) and Machine Learning (ML) are tremendous. Almost monthly, we see reports announcing breakthroughs in different technological aspects of AI.

As an organization focussing on research and development, we can look back on an increasing number of research projects .

Publicly funded Research Projects

Ki-dera (2024).

Goal: Development and validation of a radiological AI assistance system to support dementia diagnosis

Duration:  3 years

Partner: DZNE, Institute for Diagnostic and Interventional Radiology, Pediatric- und Neuroradiology, webhub GmbH

CAPTAIN (2023)

Goal: Real-time artificial intelligence annotation of multimodality endoscopy images in pancreatic cancer, allowing tumor cells to be detected during the examination and treated or removed directly

Duration:  3 years

Partner: PolyDiagnost GmbH, University Medical Center Göttingen, Institute for Diagnostic and Interventional Radiology, Faculty Engineering & Health of the University of Allied Science and Arts

Ocean Technology Center - DaTA (2022)

Partner: EvoLogics GmbH, IAV GmbH, Fraunhofer IGD, University of Rostock, IOW

Ocean Technology Center - Genomics (2022)

Partner: Leibnitz Institute for Baltic Sea Research Warnemünde, IOW, LGC Genomics, Hydrobios, Fraunhofer IGD

Intelligent Radiological Assistant (2020)

Partner: University of Rostock

NewsEye (2019)

Partner: University of Rostock, University of La Rochelle, Austrian National Library, University of Helsinki, University of Innsbruck, National Library of France, University of Montpellier, University of Vienna

READ (2016)

Goal: Recognition of historical handwritten texts (European cultural heritage 1500 – 1800)

Partner: University of Rostock, University of Greifswald, National Archives Finland, University of Erlangen-Nuremberg, University of Innsbruck, University of Valencia, University of Edinburgh, National Archives Norway, Swedish National Archives, University of Vienna

Automatic Full-Text Recognition (2014)

Goal: Algorithms for automatic full text recognition in handwritten historical documents

ORGANIC (2009)

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Top 20 Artificial Intelligence project ideas for Beginners

Artificial Intelligence is a technique that enables machines to mimic human behavior. It is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. So based on all these features, we have curated the top 20 Artificial Intelligence Project ideas which are ideal for beginners. If this sounds intriguing, do read the blog till the very end.

• Music Recommendation App
• Stock Prediction
• Social Media Suggestion
• Identify inappropriate language and hate speech
• Lane line detection while driving
• Monitoring crop health
• Medical diagnosis
• AI powered Search engine
• AI powered cleaning robots
• House security
• Handwritten notes recognition
• Loan Eligibility Prediction
• Face filter using facial detection
• E commerce recommendation engine
• Detecting fake products
• Facial Emotion Recognition
• AI Healthengine
• Trying on online clothes and accessories
• Spam email identification

1). Chatbot : A chatbot is an artificial intelligence software that can be used to start a conversation with a user through websites, mobile apps, calls, or messaging applications. Chatbots are increasingly becoming popular. Many of the company’s websites use chatbots to communicate with their customers, it’s used in almost all fields, be it education, medicine, Information Technology, and even banking websites, now having chatbots. For eg, EVA by HDFC bank. Now, if you’re a beginner, then you can program a simple version of a chatbot. There are many chatbots available online. Just learn from them, identify the basic structure and then build your own chatbots using the structure. You can then enhance it using your creativity and make it better.

Join ChatGPT course to understand the AI-powered language model that revolutionizing the way we communicate.

2). Music Recommendation App : Due to AI, music recommendation app which can also be known as music recommendation engines makes it quicker and easier to show music recommendations that are tailored to each user’s interests and preferences.

So how does this work? First, it collects all the data: what are the songs that the users listen to the most, what is the genre of the song, and which language is the song the user listens. Next, it stores all these data and analyses. It then recommended songs from a similar genre and the same language and the songs whose ratings are high. You would have seen this in apps like Spotify or wynk, where they have an entire section on songs recommended for you. So they use AI to make this recommendation engine. You can program this music recommendation app by learning from some online blogs or watching YouTube videos.

3). Stock Prediction : Now, many people invest in stocks, and they need a stock predictor in order for them to know when to buy the stocks. Now, it is not possible to predict what will happen in the future, but we can make estimations and informed forecasts based on the data we have in the present and past regarding the stocks. This is known as Technical Analysis, which is used to predict the stock’s price direction, will the value of the stock increase or decrease after a particular time. So, for your projects, you can create an application that analyses the trends and the stock market and offers data-driven insights. You can start off by keeping your stock prediction cycle small and then go on and try for higher values and insights. Also, if you design a good stock prediction application, there will be a great value & demand for such systems, and will make your career.

4). Social Media Suggestion: AI is being used in most of the popular social media networks that we use on a day-to-day basis. For example, Facebook uses AI and advanced machine learning to serve you all the content based on your preferences and to recognize people’s faces in photos, so you can tag them and also target users for the right advertisement. Also, Instagram which is now owned by Facebook uses AI to identify visuals. Next, LinkedIn uses AI to offer job recommendations based on your qualifications and interest, it suggests people to connect with, this also happens in Facebook. Next, Snapchat uses AI technology, to track your facial features and add filters that move with your face in real-time.

So, these were just some examples of how social media uses AI. So, you can create a project which can do any of the following tasks, like suggest the users to connect with people they might know, suggest to them some content they might like to watch or suggest some products they might be interested in and so on.

5). Identify inappropriate language and hate speech: Now, this is a project which sounds easy, but it is quite hard to identify all the hate speeches and inappropriate language. There are many companies who are trying to create this system such as Facebook, Twitter, and YouTube. So, for this project, you can use detection techniques that identify the character in a context and then compare it to content that’s already been removed as hate speech. Now, usually, this would be used for identifying any hate speech in any post(like Facebook or Twitter posts). So, design an AI system that looks into things like the text in a post, the reactions and comments to the post, and how closely it matches common phrases of hate speech. Also, if it contains at least one inappropriate word, then identify those words and report them.

6). Lane line detection while driving: Now, many of you know that self-driving cars are gaining a lot of popularity. Now, as a beginner, it would be very hard to design this, but you can design a part of it which is lane line detection while driving. This Lane line detection technique is used in many self-driving autonomous vehicles as well as line-following robots.

So you can use computer vision techniques and AI to teach the vehicle to go in a particular lane. You can use computer vision techniques such as colour thresholding to detect the lanes, so usually the lanes are colored in white colour and usually, there are double lanes in the middle of the road which separate the directions the vehicle runs in. Then there is usually one white line at the end of the road after which is the edge of the road. Usually, with all this data, you can design an AI-powered system that detects lane lines.

7). Monitoring crop health : Artificial Intelligence is being increasingly adopted as a part of the agriculture industry’s evolution. Using AI, you can perform predictive analytics to determine: what is the right date for sowing the seeds to obtain maximum yield after the previous harvest,  get insights on the crop health, soil health, the fertilizer recommendations and also the next 7-day weather forecast. You can create a project which uses AI to monitor the health of the crop and check for diseases, by using various images of plants that had the same diseases. So, when a user collects the image of the plants it will be matched with images that are already stored and then diagnosis the particular disease and then maybe even provide a intelligent spraying technique and treatment automatically

8). Medical diagnosis : AI is being used in the medical industries  for layering risk, identifying hotspots in chronic disease, and accounting for the social determinants of health.

For your project, you can use AI to develop a software that can be programmed to accurately spot signs of a certain disease in medical images such as MRIs, x-rays, and CT scans. For example, you can design a system that uses AI for cancer diagnosis by processing photos of skin lesions. This can be very helpful to diagnose patients more accurately and also prescribe the most suitable treatment.

9). AI powered Search engine : Design a search engine which is powered by AI which will scan billions of content available in the web and match the exact search sentences or keywords and will show the relevant information, images, videos, text and other documents. You can also use a ranking algorithm that will rank the content for a particular keyword based on various factors like the engagement rate i.e, for how long did the user spend his/her time on the website, is the content from a reliable website and so many factors. You can refer to some online blogs or watch some videos to get started. Also, for this project, you need to know a little bit about networks and how the data passes on the internet from one place to another.

10). AI powered cleaning robots: Today’s AI-powered robots possess no natural general intelligence, but they are capable of solving problems and thinking in a limited capacity. You can design a robot that uses artificial intelligence to clean a room by scanning the room size, identifying obstacles and remembering the most efficient routes for cleaning. For starters, you can design a robot that does only one of these things, then enhance it until it can effectively clean the room.

11). House security:   So for this project, you can design a system which uses AI to scan and identify the face of the visitor. First the facial structure of the family members or someone who frequently visits the house can be scanned and stored. So, every time a visitor comes near the gate, the system can scan the face and if it matches the existing facial structure that is stored in the database, it can open the door and allow the person to pass, else gate can remain shut and the people living in the house could be notified that a person is waiting outside.

12). Handwritten notes recognition: Handwriting character recognition refers to the computer’s ability to detect and interpret alphabets and numbers. These inputs could be from various sources like paper documents, notes on phone, photos and other sources. Note that handwriting characters remain complex since different individuals have different handwriting styles. So you can develop a system that uses AI to scan the handwritten notes and convert them into digital format. You can use an artificial neural network, which is a field of study in artificial intelligence to design this system.

13). Loan Eligibility Prediction: One of the major problems the banking sectors face is the increasing rate of loan defaults, so the employees find it difficult to decide who they should give loans to and who not to. Even if they do give, what are the chances of the person returning the loan amount?

So to solve this problem, You can use AI to design a program that predicts whether an individual should be given a loan by assessing various attributes like their salary, their previous loans details(did he pay all the installments on time) and many more and then notify whether or not to approve the loan. This can make the process easier of selecting suitable people from a given list of candidates who applied for a loan.

14). Face filter using facial detection: This is a very interesting project. You design a system that scans the face of the users and then add filters. So the system uses AI to recognise a few of the facial features, like eyelids, cheekbones, jawline, nose bridge etc. and then based on these calculations, it then add filters. Now, this project is inspired from Snapchat which also uses AI to identify the user’s faces and then add a filter.

15). E commerce recommendation engine : Have you ever liked any clothing item on any e-commerce website, and then you see the same clothing item in the ads of some website or on social media. AI is responsible for this. In this project, you can build an E commerce recommendation engine using the similarity among the background information of the items or users to propose recommendations to users. So, for example if the user has searched for apple phones, then you can design a recommendation engine that recommends apple phones to the user. Or you can identify trends and patterns in previous and other user-item interactions and advise similar recommendations to a present user based on his existing interactions. So, for example, if the person has bought a formal shirt, then you can design your recommendation engine, to recommend more formal clothing and accessories

16). Detecting fake products: There are many duplications happening for different products. So design a system which uses Artificial Intelligence to analyze the product and determine if it is authentic or not. Unlike humans, machines can analyze minutest of inconsistencies or faults in shape, colour, texture, size and many more. They can calculate all these and analyze if the product is fake or not. This accuracy will be based on numbers of images and data of the original product, it will then compare and detect the fake ones.

17). Facial Emotion Recognition : Now, everything that’s happening in a sci-fi movie, could be our future. There are a variety of fields where Artificial Intelligence is used. One such area of interest is detecting human emotions. There are many top companies investing a lot of money in doing this. So, you can design a facial emotion detection and recognition system that can be used to identify human facial expressions. So for this, first the system would have to analyze the facial expression for some time and then perform facial feature extraction and classify the facial expression. For starters, you can design the system to identify only one expression, maybe just happy or normal. Then you can enhance it and try different emotions.

18). AI Healthengine: Create a project that will use AI to give personalized health guidance to a user. The user must provide all their medical reports and based on that, the AI system will check for any pre-existing conditions, ongoing health concerns, and gaps in general health knowledge. Then the health engine could combine both these personal details and external health data to provide informed advice to the user. It can also help users with prescription support, vaccination advice, recommended doctor visits, and specific condition guidance.

19). Trying on online clothes and accessories: Now, you would have already heard about this feature, if you ever visited the lenskart app, here you can design an AI system that takes the input images and computes the person’s body model, representing their pose and shape. The segments are then selected on which the dresses are going to be displayed on, like for eg, a shirt on the body, gloves for hands, and so on and then when the user selects a particular dress, the system can combine them with the body model and update the image’s shape representation.

Machine Learning, deep learning, and natural language processing algorithms have been implemented in AI tools. To provide correct data, this algorithm will work in several stages of data collection, data preprocessing, model training, and evaluation. To know more, Enroll in our AI tools course today!

20). Spam email identification: Spam detection means detecting unsolicited emails by identifying the text content of the email. So for the project, create an artificial neural network to detect and block spam emails and also ensure that the user only receives notifications regarding the emails that are crucial to them. You can also enhance this by tuning it to user preferences. For example newsletters or updates that one person likes, will be disliked by someone else, so include features that will filter the email based on individual user preferences.

21)  Blindness Detection : A Blindness Detection AI project uses computer vision and deep learning to analyze retinal images and detect signs of eye conditions that may lead to blindness. It aims to facilitate early diagnosis, timely intervention, and preventive measures, potentially reducing preventable blindness. Users can upload images to the AI system, which provides predictions, risk assessments, and referral recommendations for further examination or treatment. Continuous improvement and data privacy are key considerations in the project’s development.

22). Real-time Face Mask Detector: A real-time face mask detector AI project is a computer vision application that uses AI algorithms to detect whether a person is wearing a face mask in real-time video streams or images. It helps enforce mask regulations, enhance public safety, and optimize resource allocation. The system uses a dataset for training, a CNN model for inference, and can be integrated into user-friendly interfaces to provide alerts and notifications when masks are not worn. Privacy and data protection considerations are essential, and continuous model refinement ensures accuracy.

23). Self-Driving Car Behavioral Cloning: The Self-Driving Car Behavioral Cloning AI project aims to teach autonomous vehicles to imitate human drivers by learning from their driving data. It involves collecting extensive datasets of human driving behaviors, training deep learning models using convolutional neural networks, and validating the models in simulation environments before real-world testing. The benefits include faster deployment and human-like driving behavior, but challenges like data bias and ethical considerations must be addressed. Overall, the project contributes to the advancement of safer and more human-like self-driving technology.

24). Building a Telegram Bot: Building a Telegram Bot AI project involves creating an intelligent chatbot on the Telegram platform. It uses natural language processing (NLP) and machine learning models for understanding user inputs and generating relevant responses. The bot can be designed for various purposes, such as customer support, content delivery, or games, and offers improved user experiences, automation, and scalability. Security and proper error handling are essential considerations during development. Once deployed, the bot interacts with users in real-time, providing valuable services and integrating with external services and APIs. Overall, the project enhances user experiences and offers convenient access to information and services within the Telegram messaging app.

25). Keyword Research: Keyword Research using Python is an AI project that automates the process of finding valuable keywords for SEO and content marketing. It scrapes search data, applies NLP and AI algorithms to analyze keywords, and calculates metrics like search volume and competition. The project generates keyword recommendations, aids data-driven decisions, and enhances SEO performance. It offers time efficiency, scalability, and data-driven insights for content creators and SEO professionals.

If you wish to learn AI in detail then I suggest you watch this YouTube video:

Artificial Intelligence Tutorial for Beginners | Edureka

Why do AI Projects fail?

AI projects can fail for various reasons, and understanding these pitfalls is crucial for ensuring successful implementations. Here are some common reasons why AI projects may fail:

• Insufficient Data Quality and Quantity: AI models heavily rely on high-quality and diverse data for training. If the data used is limited, biased, or contains errors, the AI model’s performance can be compromised, leading to inaccurate or unreliable results.
• Lack of Clear Objectives: If the project’s objectives are not well-defined or align poorly with the organization’s goals, the AI project may lack direction, fail to meet expectations, or not deliver meaningful value.
• Inadequate Expertise and Talent: AI projects require skilled professionals, including data scientists, machine learning engineers, and domain experts. A lack of expertise or a shortage of talented individuals can hinder the project’s progress and outcome.
• Overlooking Ethical Considerations : AI systems can have significant societal impacts, and failing to consider ethical concerns like data privacy, bias, and fairness can lead to negative consequences and public backlash.
• Complexity and Overambitious Goals: Complex AI projects with lofty goals can be challenging to execute successfully, especially without a clear step-by-step approach. Overambitious objectives may lead to unrealistic timelines and resource constraints.
• Integration Challenges: Implementing AI solutions into existing systems or workflows can be difficult. Integration issues and resistance to change within the organization can hinder the successful adoption of AI technologies.
• Lack of Continuous Monitoring and Maintenance: AI models require ongoing monitoring and updates to adapt to changing data and business environments. Neglecting this aspect can lead to performance degradation and inefficiencies.
• Cost and Resource Constraints: AI projects can be expensive and resource-intensive. A lack of adequate budget or resources may prevent the project from reaching its full potential or being scaled appropriately.
• Inadequate Testing and Validation: Proper testing and validation are essential to identify and rectify errors or biases in AI models. Skipping this step can lead to unreliable outputs and potential harm.
• Unrealistic Expectations: AI technologies have limitations, and setting unrealistic expectations can lead to disappointment and a perception of project failure, even if progress has been made.

To overcome these challenges and increase the likelihood of success, organizations should invest in proper planning, data preparation, talent acquisition, and ethical considerations. An iterative approach, with continuous monitoring and feedback, allows for course corrections and optimizations during the project’s lifecycle. Transparency and open communication within the team and stakeholders are also crucial for addressing any issues proactively. To create your first AI design project, Enroll in our AI for Designers Course  today!

Edureka has curated an AI in Finance Course to give you cutting-edge Insights, Strategies, and Techniques to Revolutionize Your Financial Analysis and Decision-Making. Enroll in this course and Accelerate Your Career in Finance Today!

How to Launch a Career in AI ?

To launch a career in AI, follow these key steps:

1. Build a solid educational background in computer science or related fields.

2. Develop programming skills, especially in Python, R, or Java.

3. Learn mathematics and statistics for understanding AI algorithms.

4. Enroll in online courses and tutorials to learn AI concepts and tools.

5. Gain practical experience through personal projects, competitions, and open-source contributions.

6. Specialize in a specific AI subfield, such as machine learning or computer vision.

7. Engage with AI communities and attend events to network with professionals.

8. Seek internships or entry-level positions to gain industry experience.

9. Obtain AI certifications to validate your expertise.

10. Stay updated with the latest research and trends in AI.

11. Build a strong portfolio showcasing your AI projects and achievements.

12. Search for AI-related job opportunities in various industries.

Continuously improve your skills, stay persistent, and embrace learning opportunities to succeed in the dynamic field of AI.

Currently, LLM is used in a few industries but in the future LLM will expand its applications in Education, Healthcare, Finance, etc. To know how LLM will be used in the future, Then enroll in our LLM engineering course today!

In this article, you learned about the Top 20 AI Projects Ideas . To learn more concepts on Artificial Intelligence , then check out our Artificial Intelligence Course . This Artificial Intelligence course online will help you learn Python, Predictive Analytics, ML, Deep Learning, Natural Language Processing(NLP), Sequence Learning, etc. This AI certification course provides hands-on experience on 20+ industry projects, and 100+ case studies.

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Also, Elevate your skills and unlock the future of technology with our Prompt Engineering Certification Course ! Dive into the world of creativity, innovation, and intelligence. Harness the power of algorithms to generate unique solutions. Don’t miss out on this opportunity to shape the future – enroll now and become a trailblazer in Generative AI. Your journey to cutting-edge proficiency begins here!

Got a question for us? Please mention it in the comments section and we will get back to you.

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Artificial Intelligence Coined at Dartmouth

The Dartmouth Summer Research Project on Artificial Intelligence was a seminal event for artificial intelligence as a field.

In 1956, a small group of scientists gathered for the Dartmouth Summer Research Project on Artificial Intelligence, which was the birth of this field of research.

To celebrate the anniversary, more than 100 researchers and scholars again met at Dartmouth for AI@50, a conference that not only honored the past and assessed present accomplishments, but also helped seed ideas for future artificial intelligence research.

The initial meeting was organized by John McCarthy, then a mathematics professor at the College. In his proposal, he stated that the conference was “to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

Professor of Philosophy James Moor, the director of AI@50, says that the researchers who came to Hanover 50 years ago thought about ways to make machines more cognizant, and they wanted to lay out a framework to better understand human intelligence.

More Dartmouth Milestones

Artificial Intelligence Projects

Get latest list of artificial intelligence projects for your studies and research at NevonProjects. We provide the widest and most innovative artificial intelligence projects for students. These projects on artificial intelligence have been developed to help engineers, researchers and students in their research and studies in AI based systems. Browse through our list of latest artificial intelligence project ideas and choose the topic that suits you best. These systems have been proposed to help humankind in various walks of life using AI based systems. Go through our artificial intelligence project ideas and topics to find the AI project for your needs.

Need Help Selecting a Topic ?

Get Free Guidance & Support Call/Watsapp: +917777094786 | Skype: Sanjana@Nevonprojects

All AI Projects List

• AI Healthcare Bot System using Python
• Chronic Obstructive Pulmonary Disease Prediction System
• College Placement System Using Python
• Face Recognition Attendance System for Employees using Python
• Liver Cirrhosis Prediction System using Random Forest
• Multiple Disease Prediction System using Machine Learning
• Secure Persona Prediction and Data Leakage Prevention System using Python
• Stroke Prediction System using Linear Regression
• Toxic Comment Classification System using Deep Learning
• Skin Disease Detection System Using CNN
• Signature Verification System Using CNN
• Heart Failure Prediction System
• Python Doctor Appointment Booking System
• Yoga Poses Detection using OpenPose
• Credit Card Fraud Detection System Python
• Automatic Pronunciation Mistake Detector
• Learning Disability Detector and Classifier System
• AI Mental Health Therapist Chatbot
• Ecommerce Fake Product Reviews Monitor and Deletion System
• Smart Time Table Generation Flutter App Using Genetic Algorithm
• Chatbot Assistant System using Python
• Dental Caries Detection System using Python
• Movie Success Prediction System using Python
• Speech Emotion Detection System using Python
• Student Feedback Review System using Python
• Use of Pose Estimation in Elderly People using Python
• Intelligent Video Surveillance Using Deep Learning System
• Leaf Detection System using OpenCV Python
• Music Genres Classification using KNN System
• Traffic Sign Recognition System using CNN
• Auto capture Selfie by Detecting Smile Python
• Face Recognition Attendance System using Python
• Human Detector and Counter using Python
• Pneumonia Detection using Chest X-Ray
• Music Recommendation System by Facial Emotion
• Parkinson’s Detector System using Python
• Cryptocurrency price prediction using Machine Learning Python
• Depression Detection System using Python
• Car Lane Detection Using NumPy OpenCV Python
• Sign Language Recognition Using Python
• Signature verification System using Python
• Driver Drowsiness Detection System Using Python
• Predicting House Price Using Decision Tree
• Blockchain Based Antiques Verification System
• Facial Emotion Detection using Neural Networks
• Cancer Prediction using Naive Bayes
• Voice based Intelligent Virtual Assistance for Windows
• Online Logistic Chatbot System
• Transformer Conversational Chatbot in Python using TensorFlow 2.0
• Lane-Line Detection System in Python using OpenCV
• Facial Emotion Recognition and Detection in Python using Deep Learning
• Artificial Intelligence HealthCare Chatbot System
• Online Assignment Plagiarism Checker Project using Data Mining
• Teachers Automatic Time-Table Software Generation System using PHP
• Read Me My Book App
• Customer Targeted E-Commerce
• Android General Knowledge Chatbot
• Customer Focused Ecommerce Site With AI Bot
• Your Personal Nutritionist Using FatSecret API
• Price Negotiator Ecommerce ChatBot System
• Personality Prediction System Through CV Analysis
• TV Show Popularity Analysis Using Data Mining
• Twitter Trend Analysis Using Latent Dirichlet Allocation
• Online Book Recommendation Using Collaborative Filtering
• Movie Success Prediction Using Data Mining Php
• Fake Product Review Monitoring & Removal For Genuine Ratings Php
• A Commodity Search System For Online Shopping Using Web Mining
• College Enquiry Chat Bot
• Stream Analysis For Career Choice Aptitude Tests
• Product Review Analysis For Genuine Rating
• Android Smart City Traveler
• Artificial Intelligence Dietician
• Heart Disease Prediction Project
• Smart Health Consulting Project
• Banking Bot Project
• Sentiment Based Movie Rating System
• Online AI Shopping With M-Wallet System
• Question paper generator system
• Student Information Chatbot Project
• Website Evaluation Using Opinion Mining
• Android Attendance System
• Intelligent Tourist System Project
• AI Desktop Partner
• Intelligent Chat Bot
• Stock Market Analysis and Prediction
• Automatic Answer Checker

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8 Best Topics for Research and Thesis in Artificial Intelligence

Imagine a future in which intelligence is not restricted to humans!!! A future where machines can think as well as humans and work with them to create an even more exciting universe. While this future is still far away, Artificial Intelligence has still made a lot of advancement in these times. There is a lot of research being conducted in almost all fields of AI like Quantum Computing, Healthcare, Autonomous Vehicles, Internet of Things , Robotics , etc. So much so that there is an increase of 90% in the number of annually published research papers on Artificial Intelligence since 1996.

Keeping this in mind, if you want to research and write a thesis based on Artificial Intelligence, there are many sub-topics that you can focus on. Some of these topics along with a brief introduction are provided in this article. We have also mentioned some published research papers related to each of these topics so that you can better understand the research process.

Table of Content

1. Machine Learning

2. deep learning, 3. reinforcement learning, 4. robotics, 5. natural language processing (nlp), 6. computer vision, 7. recommender systems, 8. internet of things.

So without further ado, let’s see the different Topics for Research and Thesis in Artificial Intelligence!

Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand holding!!!) This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.

However, generally speaking, Machine Learning Algorithms are generally divided into 3 types: Supervised Machine Learning Algorithms , Unsupervised Machine Learning Algorithms , and Reinforcement Machine Learning Algorithms . If you are interested in gaining practical experience and understanding these algorithms in-depth, check out the Data Science Live Course by us.

Deep Learning is a subset of Machine Learning that learns by imitating the inner working of the human brain in order to process data and implement decisions based on that data. Basically, Deep Learning uses artificial neural networks to implement machine learning. These neural networks are connected in a web-like structure like the networks in the human brain (Basically a simplified version of our brain!).

This web-like structure of artificial neural networks means that they are able to process data in a nonlinear approach which is a significant advantage over traditional algorithms that can only process data in a linear approach. An example of a deep neural network is RankBrain which is one of the factors in the Google Search algorithm.

Reinforcement Learning is a part of Artificial Intelligence in which the machine learns something in a way that is similar to how humans learn. As an example, assume that the machine is a student. Here the hypothetical student learns from its own mistakes over time (like we had to!!). So the Reinforcement Machine Learning Algorithms learn optimal actions through trial and error.

This means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future. And like humans, this works for machines as well! For example, Google’s AlphaGo computer program was able to beat the world champion in the game of Go (that’s a human!) in 2017 using Reinforcement Learning.

Robotics is a field that deals with creating humanoid machines that can behave like humans and perform some actions like human beings. Now, robots can act like humans in certain situations but can they think like humans as well? This is where artificial intelligence comes in! AI allows robots to act intelligently in certain situations. These robots may be able to solve problems in a limited sphere or even learn in controlled environments.

An example of this is Kismet , which is a social interaction robot developed at M.I.T’s Artificial Intelligence Lab. It recognizes the human body language and also our voice and interacts with humans accordingly. Another example is Robonaut , which was developed by NASA to work alongside the astronauts in space.

It’s obvious that humans can converse with each other using speech but now machines can too! This is known as Natural Language Processing where machines analyze and understand language and speech as it is spoken (Now if you talk to a machine it may just talk back!). There are many subparts of NLP that deal with language such as speech recognition, natural language generation, natural language translation , etc. NLP is currently extremely popular for customer support applications, particularly the chatbot . These chatbots use ML and NLP to interact with the users in textual form and solve their queries. So you get the human touch in your customer support interactions without ever directly interacting with a human.

Some Research Papers published in the field of Natural Language Processing are provided here. You can study them to get more ideas about research and thesis on this topic.

The internet is full of images! This is the selfie age, where taking an image and sharing it has never been easier. In fact, millions of images are uploaded and viewed every day on the internet. To make the most use of this huge amount of images online, it’s important that computers can see and understand images. And while humans can do this easily without a thought, it’s not so easy for computers! This is where Computer Vision comes in.

Computer Vision uses Artificial Intelligence to extract information from images. This information can be object detection in the image, identification of image content to group various images together, etc. An application of computer vision is navigation for autonomous vehicles by analyzing images of surroundings such as AutoNav used in the Spirit and Opportunity rovers which landed on Mars.

When you are using Netflix, do you get a recommendation of movies and series based on your past choices or genres you like? This is done by Recommender Systems that provide you some guidance on what to choose next among the vast choices available online. A Recommender System can be based on Content-based Recommendation or even Collaborative Filtering.

Content-Based Recommendation is done by analyzing the content of all the items. For example, you can be recommended books you might like based on Natural Language Processing done on the books. On the other hand, Collaborative Filtering is done by analyzing your past reading behavior and then recommending books based on that.

Artificial Intelligence deals with the creation of systems that can learn to emulate human tasks using their prior experience and without any manual intervention. Internet of Things , on the other hand, is a network of various devices that are connected over the internet and they can collect and exchange data with each other.

Now, all these IoT devices generate a lot of data that needs to be collected and mined for actionable results. This is where Artificial Intelligence comes into the picture. Internet of Things is used to collect and handle the huge amount of data that is required by the Artificial Intelligence algorithms. In turn, these algorithms convert the data into useful actionable results that can be implemented by the IoT devices.

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AUKUS Pillar II Milestones Hint at Future Integrated Autonomous, Artificial Intelligence Operations

In March 2024 AUKUS experts successfully deployed autonomous and artificial intelligence (AI)-enabled sensing systems during the Resilient and Autonomous Artificial Intelligence Technology (RAAIT) trials as part of the AUKUS advanced capabilities line of effort, or Pillar II.

The trials took place at multinational Project Convergence exercises hosted by the U.S Army. Military personnel from the three AUKUS nations tested cutting edge autonomous and AI-enabled sensing capabilities in a multi-domain battlespace - land, maritime, air and cyber - that minimize the time between sensing enemy targets, deciding how to respond, and responding to the threat. These trials demonstrated significant progress since the first AUKUS RAAIT trails in the UK in April 2023 and show tangible results of the AUKUS Pillar II commitment to making our warfighter more lethal on the battlefield.

Once matured and integrated into national platforms, these new sensing systems will yield more reliable data that commanders can use to make optimal decisions and service members to act more quickly against kinetic threats – all while enabling seamless joint and combined military operations involving multiple services and nations.

One such system deployed at RAAIT is a plug-in for the Tactical Assault Kit (TAK) – a map-based software application– that helped a UK RedKite Unmanned Aerial Vehicle (UAV) detect opposing force locations using on-the-fly adjustments based on the data collected, while another UAV provided detailed imagery as confirmation. The information was passed to the Tactical Operations Center (TOC) where a uniformed "AI officer" provided human oversight prior to triggering an Australian XT-8 UAV to perform a simulated strike. The TAK is already being put to good use, and the U.S. Air Force Research Laboratory (AFRL) plans to release the plug-in for wider dissemination.

"It used to be that each nation used its own datasets to develop separate models and deploy those models on their own platforms. Under RAAIT, we’ve matured the AI pipeline, focusing on interchangeability and interoperability, which allows for any combinations of datasets, models, algorithms and platforms to be used across all three nations," said Dr. Kimberly Sablon, the Principal Director of Trusted Artificial Intelligence and Autonomy in the Office of the Under Secretary of Defense for Research and Engineering.

Lessons learned at the RAAIT trials will be used for future training events, where the AUKUS Artificial Intelligence and Autonomy (AIA) Working Group hopes to leverage findings to develop an AIA ecosystem that will one day enable the three partner nations to share data for operational success in contested environments.

"Our goal is to get to the point where we have a pipeline that is interchangeable and interoperable but robust," Sablon said. "Being able to collect data, train our AI systems, conduct testing and evaluation and even adapt to unanticipated threats in less than 10-hours at the edge is a huge milestone for our partnership."

A video highlighting this important effort is available here .

U.S. participation in the RAAIT trials at Project Convergence was led by the office of the Under Secretary of Defense for Research and Engineering (OUSD(R&E)). OUSD(R&E) champions research, science, technology, engineering, and innovation to maintain the United States military’s technological superiority. Learn more at www.cto.mil or visit on LinkedIn at OUSD(R&E).

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It looks like Sam Altman is dropping hints about OpenAI's next big thing

• Sam Altman posted a photo of a strawberry bush, and AI nerds went wild.
• The AI community speculated that the post references a secret project code-named "strawberry."
• Strawberry aims to give OpenAI's tech autonomous internet navigation and deep research capabilities, Reuters reported.

The tech industry is reading into a lighthearted post by OpenAI CEO Sam Altman , speculating that the picture might actually have a deeper meaning.

Altman posted a photo of a strawberry bush to X on Wednesday morning, and some say it hints at one of OpenAI's reported ongoing projects.

And, no, the picture wasn't AI-generated.

i love summer in the garden pic.twitter.com/Ter5Z5nFMc — Sam Altman (@sama) August 7, 2024

"i love summer in the garden," Altman captioned the post.

The AI community immediately seized on it as a possible reference to a project code-named "strawberry," which is an apparent top-secret undertaking that Reuters reported about in July of this year.

"STRAWBERRY SUMMER RELEASE CONFIRMED," one user replied on X .

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Not much is known about the apparent project, which was formerly called Q*, because OpenAI employees have been tight-lipped about it, but documents viewed by Reuters indicate that Strawberry will perform research for the company.

Nathaniel Whittemore, founder and CEO of AI skilling company Superintelligent, said the excitement has been brewing for nine months since Altman was ousted and quickly reinstated at OpenAI.

The AI expert told Business Insider that the next big thing at OpenAI will settle the debate between those who believe large language models have plateaued and others who are convinced there's more powerful tech being worked on behind the scenes.

It'd be another step toward OpenAI making AI even more intelligent, and it could help its large language models navigate the internet on their own and perform "deep research," according to Reuters.

"The impact of the model will be completely predicated on whether it actually represents a sea change in capability," Whittemore said.

OpenAI introduced its latest model, GPT-4o, in May, and the demonstrations depicted a virtual assistant with visual and audio capabilities in addition to the text prompts that ChatGPT can handle.

GPT-5 is reportedly in the works , and AI experts have previously said they're waiting to see how much smarter large language models can get.

According to Whittemore, if OpenAI's next big release is significantly more intelligent, "it could reignite the fire around AI."

Watch: What is ChatGPT, and should we be afraid of AI chatbots?

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