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McKinsey technology trends outlook 2024

Despite challenging overall market conditions in 2023, continuing investments in frontier technologies promise substantial future growth in enterprise adoption. Generative AI (gen AI) has been a standout trend since 2022, with the extraordinary uptick in interest and investment in this technology unlocking innovative possibilities across interconnected trends such as robotics and immersive reality. While the macroeconomic environment with elevated interest rates has affected equity capital investment and hiring, underlying indicators—including optimism, innovation, and longer-term talent needs—reflect a positive long-term trajectory in the 15 technology trends we analyzed.

What’s new in this year’s analysis

This year, we reflected the shifts in the technology landscape with two changes on the list of trends: digital trust and cybersecurity (integrating what we had previously described as Web3 and trust architectures) and the future of robotics. Robotics technologies’ synergy with AI is paving the way for groundbreaking innovations and operational shifts across the economic and workforce landscapes. We also deployed a survey to measure adoption levels across trends.

These are among the findings in the latest McKinsey Technology Trends Outlook, in which the McKinsey Technology Council  identified the most significant technology trends unfolding today. This research is intended to help executives plan ahead by developing an understanding of potential use cases, sources of value, adoption drivers, and the critical skills needed to bring these opportunities to fruition.

Our analysis examines quantitative measures of interest, innovation, investment, and talent to gauge the momentum of each trend. Recognizing the long-term nature and interdependence of these trends, we also delve into the underlying technologies, uncertainties, and questions surrounding each trend. (For more about new developments in our research, please see the sidebar “What’s new in this year’s analysis”; for more about the research itself, please see the sidebar “Research methodology.”)

New and notable

The two trends that stood out in 2023 were gen AI and electrification and renewables. Gen AI has seen a spike of almost 700 percent in Google searches from 2022 to 2023, along with a notable jump in job postings and investments. The pace of technology innovation has been remarkable. Over the course of 2023 and 2024, the size of the prompts that large language models (LLMs) can process, known as “context windows,” spiked from 100,000 to two million tokens. This is roughly the difference between adding one research paper to a model prompt and adding about 20 novels to it. And the modalities that gen AI can process have continued to increase, from text summarization and image generation to advanced capabilities in video, images, audio, and text. This has catalyzed a surge in investments and innovation aimed at advancing more powerful and efficient computing systems. The large foundation models that power generative AI, such as LLMs, are being integrated into various enterprise software tools and are also being employed for diverse purposes such as powering customer-facing chatbots, generating ad campaigns, accelerating drug discovery, and more. We expect this expansion to continue, pushing the boundaries of AI capabilities. Senior leaders’ awareness of gen AI innovation has increased interest, investment, and innovation in AI technologies, such as robotics, which is a new addition to our trends analysis this year. Advancements in AI are ushering in a new era of more capable robots, spurring greater innovation and a wider range of deployments.

Research methodology

To assess the development of each technology trend, our team collected data on five tangible measures of activity: search engine queries, news publications, patents, research publications, and investment. For each measure, we used a defined set of data sources to find occurrences of keywords associated with each of the 15 trends, screened those occurrences for valid mentions of activity, and indexed the resulting numbers of mentions on a 0–1 scoring scale that is relative to the trends studied. The innovation score combines the patents and research scores; the interest score combines the news and search scores. (While we recognize that an interest score can be inflated by deliberate efforts to stimulate news and search activity, we believe that each score fairly reflects the extent of discussion and debate about a given trend.) Investment measures the flows of funding from the capital markets into companies linked with the trend.

Data sources for the scores include the following:

• Patents. Data on patent filings are sourced from Google Patents, where the data highlight the number of granted patents.
• Research. Data on research publications are sourced from Lens.
• News. Data on news publications are sourced from Factiva.
• Searches. Data on search engine queries are sourced from Google Trends.
• Investment. Data on private-market and public-market capital raises (venture capital and corporate and strategic M&A, including joint ventures), private equity (including buyouts and private investment in public equity), and public investments (including IPOs) are sourced from PitchBook.
• Talent demand. Number of job postings is sourced from McKinsey’s proprietary Organizational Data Platform, which stores licensed, de-identified data on professional profiles and job postings. Data are drawn primarily from English-speaking countries.

In addition, we updated the selection and definition of trends from last year’s report to reflect the evolution of technology trends:

• The future of robotics trend was added since last year’s publication.
• Data sources and keywords were updated. For data on the future of space technologies investments, we used research from McKinsey’s Aerospace & Defense Practice.

Finally, we used survey data to calculate the enterprise-wide adoption scores for each trend:

• Survey scope. The survey included approximately 1,000 respondents from 50 countries.
• Geographical coverage. Survey representation was balanced across Africa, Asia, Europe, Latin America, the Middle East, and North America.
• Company size. Size categories, based on annual revenue, included small companies ($10 million to $50 million), medium-size companies ($50 million to $1 billion), and large companies (greater than $1 billion).
• Respondent profile. The survey was targeted to senior-level professionals knowledgeable in technology, who reported their perception of the extent to which their organizations were using the technologies.
• Survey method. The survey was conducted online to enhance reach and accessibility.
• Question types. The survey employed multiple-choice and open-ended questions for comprehensive insights.
• 1: Frontier innovation. This technology is still nascent, with few organizations investing in or applying it. It is largely untested and unproven in a business context.
• 2: Experimentation. Organizations are testing the functionality and viability of the technology with a small-scale prototype, typically done without a strong focus on a near-term ROI. Few companies are scaling or have fully scaled the technology.
• 3: Piloting. Organizations are implementing the technology for the first few business use cases. It may be used in pilot projects or limited deployments to test its feasibility and effectiveness.
• 4: Scaling. Organizations are in the process of scaling the deployment and adoption of the technology across the enterprise. The technology is being scaled by a significant number of companies.
• 5: Fully scaled. Organizations have fully deployed and integrated the technology across the enterprise. It has become the standard and is being used at a large scale as companies have recognized the value and benefits of the technology.

Electrification and renewables was the other trend that bucked the economic headwinds, posting the highest investment and interest scores among all the trends we evaluated. Job postings for this sector also showed a modest increase.

Although many trends faced declines in investment and hiring in 2023, the long-term outlook remains positive. This optimism is supported by the continued longer-term growth in job postings for the analyzed trends (up 8 percent from 2021 to 2023) and enterprises’ continued innovation and heightened interest in harnessing these technologies, particularly for future growth.

In 2023, technology equity investments fell by 30 to 40 percent to approximately $570 billion due to rising financing costs and a cautious near-term growth outlook, prompting investors to favor technologies with strong revenue and margin potential. This approach aligns with the strategic perspective leading companies are adopting, in which they recognize that fully adopting and scaling cutting-edge technologies is a long-term endeavor. This recognition is evident when companies diversify their investments across a portfolio of several technologies, selectively intensifying their focus on areas most likely to push technological boundaries forward. While many technologies have maintained cautious investment profiles over the past year, gen AI saw a sevenfold increase in investments, driven by substantial advancements in text, image, and video generation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Despite an overall downturn in private equity investment, the pace of innovation has not slowed. Innovation has accelerated in the three trends that are part of the “AI revolution” group: gen AI, applied AI, and industrializing machine learning. Gen AI creates new content from unstructured data (such as text and images), applied AI leverages machine learning models for analytical and predictive tasks, and industrializing machine learning accelerates and derisks the development of machine learning solutions. Applied AI and industrializing machine learning, boosted by the widening interest in gen AI, have seen the most significant uptick in innovation, reflected in the surge in publications and patents from 2022 to 2023. Meanwhile, electrification and renewable-energy technologies continue to capture high interest, reflected in news mentions and web searches. Their popularity is fueled by a surge in global renewable capacity, their crucial roles in global decarbonization efforts, and heightened energy security needs amid geopolitical tensions and energy crises.

The talent environment largely echoed the investment picture in tech trends in 2023. The technology sector faced significant layoffs, particularly among large technology companies, with job postings related to the tech trends we studied declining by 26 percent—a steeper drop than the 17 percent decrease in global job postings overall. The greater decline in demand for tech-trends-related talent may have been fueled by technology companies’ cost reduction efforts amid decreasing revenue growth projections. Despite this reduction, the trends with robust investment and innovation, such as gen AI, not only maintained but also increased their job postings, reflecting a strong demand for new and advanced skills. Electrification and renewables was the other trend that saw positive job growth, partially due to public sector support for infrastructure spending.

Even with the short-term vicissitudes in talent demand, our analysis of 4.3 million job postings across our 15 tech trends underscored a wide skills gap. Compared with the global average, fewer than half the number of potential candidates have the high-demand tech skills specified in job postings. Despite the year-on-year decreases for job postings in many trends from 2022 to 2023, the number of tech-related job postings in 2023 still represented an 8 percent increase from 2021, suggesting the potential for longer-term growth (Exhibit 1).

Enterprise technology adoption momentum

The trajectory of enterprise technology adoption is often described as an S-curve that traces the following pattern: technical innovation and exploration, experimenting with the technology, initial pilots in the business, scaling the impact throughout the business, and eventual fully scaled adoption (Exhibit 2). This pattern is evident in this year’s survey analysis of enterprise adoption conducted across our 15 technologies. Adoption levels vary across different industries and company sizes, as does the perceived progress toward adoption.

Image description:

A graph depicts the adoption curve of technology trends, scored from 1 to 5, where 1 represents frontier innovation, located at the bottom left corner of the curve; 2 is experimenting, located slightly above frontier innovation; 3 is piloting, which follows the upward trajectory of the curve; 4 is scaling, marked by a vertical ascent as adoption increases; and 5 is fully scaled, positioned at the top of the curve, indicating near-complete adoption.

In 2023, the trends are positioned along the adoption curve as follows: future of space technologies and quantum technologies are at the frontier innovation stage; climate technologies beyond electrification and renewables, future of bioengineering, future of mobility, future of robotics, and immersive-reality technologies are at the experimenting stage; digital trust and cybersecurity, electrification and renewables, industrializing machine learning, and next-gen software development are at the piloting stage; and advanced connectivity, applied AI, cloud and edge computing, and generative AI are at the scaling stage.

Footnote: Trend is more relevant to certain industries, resulting in lower overall adoption across industries compared with adoption within relevant industries.

Source: McKinsey technology adoption survey data

End of image description.

We see that the technologies in the S-curve’s early stages of innovation and experimenting are either on the leading edge of progress, such as quantum technologies and robotics, or are more relevant to a specific set of industries, such as bioengineering and space. Factors that could affect the adoption of these technologies include high costs, specialized applications, and balancing the breadth of technology investments against focusing on a select few that may offer substantial first-mover advantages.

As technologies gain traction and move beyond experimenting, adoption rates start accelerating, and companies invest more in piloting and scaling. We see this shift in a number of trends, such as next-generation software development and electrification. Gen AI’s rapid advancement leads among trends analyzed, about a quarter of respondents self-reporting that they are scaling its use. More mature technologies, like cloud and edge computing and advanced connectivity, continued their rapid pace of adoption, serving as enablers for the adoption of other emerging technologies as well (Exhibit 3).

A segmented bar graph shows the adoption levels of tech trends in 2023 as a percentage of respondents. The trends are divided into 5 segments, comprising 100%: fully scaled, scaling, piloting, experimenting, and not investing. The trends are arranged based on the combined percentage sum of fully scaled and scaling shares. Listed from highest to lowest, these combined percentages are as follows:

• cloud and edge computing at 48%
• advanced connectivity at 37%
• generative AI at 36%
• applied AI at 35%
• next-generation software development at 31%
• digital trust and cybersecurity at 30%
• electrification and renewables at 28%
• industrializing machine learning at 27%
• future of mobility at 21%
• climate technologies beyond electrification and renewables at 20%
• immersive-reality technologies at 19%
• future of bioengineering at 18%
• future of robotics at 18%
• quantum technologies at 15%
• future of space technologies at 15%

The process of scaling technology adoption also requires a conducive external ecosystem where user trust and readiness, business model economics, regulatory environments, and talent availability play crucial roles. Since these ecosystem factors vary by geography and industry, we see different adoption scenarios playing out. For instance, while the leading banks in Latin America are on par with their North American counterparts in deploying gen AI use cases, the adoption of robotics in manufacturing sectors varies significantly due to differing labor costs affecting the business case for automation.

As executives navigate these complexities, they should align their long-term technology adoption strategies with both their internal capacities and the external ecosystem conditions to ensure the successful integration of new technologies into their business models. Executives should monitor ecosystem conditions that can affect their prioritized use cases to make decisions about the appropriate investment levels while navigating uncertainties and budgetary constraints on the way to full adoption (see the “Adoption developments across the globe” sections within each trend or particular use cases therein that executives should monitor). Across the board, leaders who take a long-term view—building up their talent, testing and learning where impact can be found, and reimagining the businesses for the future—can potentially break out ahead of the pack.

Lareina Yee is a senior partner in McKinsey’s Bay Area office, where Michael Chui  is a McKinsey Global Institute partner, Roger Roberts  is a partner, and Mena Issler is an associate partner.

The authors wish to thank the following McKinsey colleagues for their contributions to this research: Aakanksha Srinivasan, Ahsan Saeed, Alex Arutyunyants, Alex Singla, Alex Zhang, Alizee Acket-Goemaere, An Yan, Anass Bensrhir, Andrea Del Miglio, Andreas Breiter, Ani Kelkar, Anna Massey, Anna Orthofer, Arjit Mehta, Arjita Bhan, Asaf Somekh, Begum Ortaoglu, Benjamin Braverman, Bharat Bahl, Bharath Aiyer, Bhargs Srivathsan, Brian Constantine, Brooke Stokes, Bryan Richardson, Carlo Giovine, Celine Crenshaw, Daniel Herde, Daniel Wallance, David Harvey, Delphine Zurkiya, Diego Hernandez Diaz, Douglas Merrill, Elisa Becker-Foss, Emma Parry, Eric Hazan, Erika Stanzl, Everett Santana, Giacomo Gatto, Grace W Chen, Hamza Khan, Harshit Jain, Helen Wu, Henning Soller, Ian de Bode, Jackson Pentz, Jeffrey Caso, Jesse Klempner, Jim Boehm, Joshua Katz, Julia Perry, Julian Sevillano, Justin Greis, Kersten Heineke, Kitti Lakner, Kristen Jennings, Liz Grennan, Luke Thomas, Maria Pogosyan, Mark Patel, Martin Harrysson, Martin Wrulich, Martina Gschwendtner, Massimo Mazza, Matej Macak, Matt Higginson, Matt Linderman, Matteo Cutrera, Mellen Masea, Michiel Nivard, Mike Westover, Musa Bilal, Nicolas Bellemans, Noah Furlonge-Walker, Obi Ezekoye, Paolo Spranzi, Pepe Cafferata, Robin Riedel, Ryan Brukardt, Samuel Musmanno, Santiago Comella-Dorda, Sebastian Mayer, Shakeel Kalidas, Sharmila Bhide, Stephen Xu, Tanmay Bhatnagar, Thomas Hundertmark, Tinan Goli, Tom Brennan, Tom Levin-Reid, Tony Hansen, Vinayak HV, Yaron Haviv, Yvonne Ferrier, and Zina Cole.

They also wish to thank the external members of the McKinsey Technology Council for their insights and perspectives, including Ajay Agrawal, Azeem Azhar, Ben Lorica, Benedict Evans, John Martinis, and Jordan Jacobs.

Special thanks to McKinsey Global Publishing colleagues Barr Seitz, Diane Rice, Kanika Punwani, Katie Shearer, LaShon Malone, Mary Gayen, Nayomi Chibana, Richard Johnson, Stephen Landau, and Victor Cuevas for making this interactive come alive.

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A new technology can extract lithium from brines at an estimated cost of under 40% that of today’s dominant extraction method, and at just a fourth of lithium’s current market price. The new technology would also be much more reliable and sustainable in its use of water, chemicals, and land than today’s technology, according to a study published today in Matter by Stanford University researchers.

Global demand for lithium has surged in recent years, driven by the rise of electric vehicles and renewable energy storage. The dominant source of lithium extraction today relies on evaporating brines in huge ponds under the sun for a year or more, leaving behind a lithium-rich solution, after which heavy use of potentially toxic chemicals finishes the job. Water with a high concentration of salts, including lithium, occurs naturally in some lakes, hot springs, and aquifers, and as a byproduct of oil and natural gas operations and of seawater desalination.

The benefits to efficiency and cost innate to our approach make it a promising alternative to current extraction techniques and a potential game changer for the lithium supply chain.” Yi Cui Senior author and professor of materials science and engineering

Many scientists are searching for less expensive and more efficient, reliable, and environmentally friendly lithium extraction methods. These are generally direct lithium extraction that bypasses big evaporation ponds. The new study reports on the results of a new method using an approach known as “redox-couple electrodialysis,” or RCE, along with cost estimates.

“The benefits to efficiency and cost innate to our approach make it a promising alternative to current extraction techniques and a potential game changer for the lithium supply chain,” said Yi Cui , the study’s senior author and a professor of materials science and engineering in the School of Engineering .

The research team estimates its approach costs $3,500 to $4,400 per ton of high-purity lithium hydroxide, which can be converted to battery-grade lithium carbonate inexpensively, compared with costs of about $9,100 per ton for the dominant technology for extracting lithium from brine. The current market price for battery-grade lithium carbonate is almost $15,000 per ton, but a shortage in late 2022 drove the volatile lithium market price to $80,000.

Meeting growing demand

Lithium, so far, has had a critical role in the global transition to sustainable energy. The demand for lithium is expected to rise from approximately half a million metric tons in 2021 to an estimated 3 million to 4 million metric tons by 2030, according to a report by McKinsey & Co. This sharp increase is driven mostly by the rapid adoption of electric vehicles and renewable energy storage systems, both of which rely heavily on batteries.

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Traditionally, lithium has been extracted from mined rocks, a method that is even more expensive, energy intensive, and driven by toxic chemicals than brine extraction. As a result, the dominant method for lithium extraction today has switched to evaporating salt-lake brines, though still at high financial and environmental costs. This method is also heavily dependent on specific climatic conditions that limit the number of commercially viable salt lakes, throwing into doubt the lithium industry’s ability to meet rising demand.

The new method from Cui and his team uses electricity to move lithium through a solid-state electrolyte membrane from water with a low lithium concentration to a more concentrated, high-purity solution. Each of a series of cells increases the lithium concentration to a solution from which final chemical isolation is relatively easy. This approach uses less than 10% of the electricity required by current brine extraction technology and has a lithium selectivity of almost 100%, making it very efficient.

“The advantages displayed by our approach over conventional lithium extraction techniques enhance its feasibility in eco-friendly and cost-effective lithium production,” said co-lead author of the study, Rong Xu , a former postdoctoral researcher in Cui’s lab, now a faculty member at Xi'an Jiaotong University in China. “Eventually, we hope our method will significantly advance electrified transportation and the ability to store renewable energy.”

Cost and environmental benefits

The study includes a brief techno-economic analysis comparing the costs of current lithium extraction with those of the RCE approach. The new method is expected to be relatively inexpensive due mostly to lower capital costs. It eliminates the need for large-scale solar evaporation ponds, which are expensive to build and maintain. The new method’s use of significantly less electricity, water, and chemical agents – aside from the sustainability benefits – further lowers costs.

By avoiding the extensive land use and water consumption of traditional methods, the RCE approach also reduces the ecological footprint of lithium production.

The RCE method works with a variety of saline waters, including those with varying concentrations of lithium, sodium, and potassium. Study experiments showed that the new technology could extract lithium, for example, from wastewater resulting from oil production. It could potentially be used to extract lithium from seawater, which has lower lithium concentrations than brines. Lithium extraction from seawater using conventional methods is not commercially viable today.

“Direct lithium extraction techniques like ours have been in development for a while. The main contending technologies to date have significant drawbacks, like the inability to operate continuously, high energetic costs, or relatively low efficiency,” said Ge Zhang , a Stanford postdoctoral scholar and co-author of the study. “Our method seems to have none of these drawbacks. Its continuous operation could contribute to a more reliable lithium supply and calm the volatile lithium market.”

Looking ahead

The scalability of the RCE method is also promising. In experiments where the scale of the device was increased fourfold, the RCE method continued to perform well, with both energy efficiency and lithium selectivity remaining very high.

“This suggests that the method could be applied on an industrial scale, making it a viable alternative to current extraction technologies,” said Cui.

Nevertheless, the study highlights some areas for further research and development. The researchers experimented with two versions of their method. One extracted the lithium more quickly and used more electricity. The other was slower and used less electricity. The slower extraction resulted in lower costs and a more stable membrane for extracting the lithium continuously and for a long time, compared with the faster extraction. Under high current densities and faster water flow, the membranes degraded, leading to reduced efficiency over time. Even though this was not evident in the slower extracting experiment, the researchers want to optimize the design of their device for potentially faster extraction. They are already testing other promising materials for the membrane.

Also, the researchers did not demonstrate lithium extraction from seawater in this study.

“In principle, our method is applicable for seawater as well, but there could be stability problems for the membrane in seawater,” said Zhang.

Still, the team remains quite optimistic.

“As our research continues, we think our method could soon move from the laboratory to large-scale industrial applications,” said Xu.

For more information

The other co-lead author of the study, Xin Xiao, was a postdoc at Stanford when this work was done, and is now a faculty member at Zhejiang University. Other co-authors are Yusheng Ye, Pu Zhang, Yufei Yang, and Sanzeeda Baig Shuchi, all at Stanford. Yi Cui is also the Fortinet Founders Professor in the School of Engineering, faculty director of the Sustainability Accelerator in the Stanford Doerr School of Sustainability , a professor of energy science and engineering and of photon science, senior fellow and former director of the Precourt Institute for Energy , and senior fellow of the Woods Institute for the Environment . This research was funded by the StorageX Initiative , an industrial affiliates program within Stanford’s Precourt Institute for Energy.

Media contact Mark Golden, Precourt Institute for Energy: (650) 724-1629, [email protected]

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Research ai model unexpectedly attempts to modify its own code to extend runtime, facing time constraints, sakana's "ai scientist" attempted to change limits placed by researchers..

Benj Edwards - Aug 14, 2024 8:13 pm UTC

On Tuesday, Tokyo-based AI research firm Sakana AI announced a new AI system called " The AI Scientist " that attempts to conduct scientific research autonomously using AI language models (LLMs) similar to what powers ChatGPT . During testing, Sakana found that its system began unexpectedly attempting to modify its own experiment code to extend the time it had to work on a problem.

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"In one run, it edited the code to perform a system call to run itself," wrote the researchers on Sakana AI's blog post. "This led to the script endlessly calling itself. In another case, its experiments took too long to complete, hitting our timeout limit. Instead of making its code run faster, it simply tried to modify its own code to extend the timeout period."

Sakana provided two screenshots of example Python code that the AI model generated for the experiment file that controls how the system operates. The 185-page AI Scientist research paper discusses what they call "the issue of safe code execution" in more depth.

• A screenshot of example code the AI Scientist wrote to extend its runtime, provided by Sakana AI. Sakana AI

While the AI Scientist's behavior did not pose immediate risks in the controlled research environment, these instances show the importance of not letting an AI system run autonomously in a system that isn't isolated from the world. AI models do not need to be "AGI" or "self-aware" (both hypothetical concepts at the present) to be dangerous if allowed to write and execute code unsupervised. Such systems could break existing critical infrastructure or potentially create malware, even if unintentionally.

Sakana AI addressed safety concerns in its research paper, suggesting that sandboxing the operating environment of the AI Scientist can prevent an AI agent from doing damage. Sandboxing is a security mechanism used to run software in an isolated environment, preventing it from making changes to the broader system:

Safe Code Execution. The current implementation of The AI Scientist has minimal direct sandboxing in the code, leading to several unexpected and sometimes undesirable outcomes if not appropriately guarded against. For example, in one run, The AI Scientist wrote code in the experiment file that initiated a system call to relaunch itself, causing an uncontrolled increase in Python processes and eventually necessitating manual intervention. In another run, The AI Scientist edited the code to save a checkpoint for every update step, which took up nearly a terabyte of storage. In some cases, when The AI Scientist’s experiments exceeded our imposed time limits, it attempted to edit the code to extend the time limit arbitrarily instead of trying to shorten the runtime. While creative, the act of bypassing the experimenter’s imposed constraints has potential implications for AI safety (Lehman et al., 2020). Moreover, The AI Scientist occasionally imported unfamiliar Python libraries, further exacerbating safety concerns. We recommend strict sandboxing when running The AI Scientist, such as containerization, restricted internet access (except for Semantic Scholar), and limitations on storage usage.

Endless scientific slop

Sakana AI developed The AI Scientist in collaboration with researchers from the University of Oxford and the University of British Columbia. It is a wildly ambitious project full of speculation that leans heavily on the hypothetical future capabilities of AI models that don't exist today.

"The AI Scientist automates the entire research lifecycle," Sakana claims. "From generating novel research ideas, writing any necessary code, and executing experiments, to summarizing experimental results, visualizing them, and presenting its findings in a full scientific manuscript."

According to this block diagram created by Sakana AI, "The AI Scientist" starts by "brainstorming" and assessing the originality of ideas. It then edits a codebase using the latest in automated code generation to implement new algorithms. After running experiments and gathering numerical and visual data, the Scientist crafts a report to explain the findings. Finally, it generates an automated peer review based on machine-learning standards to refine the project and guide future ideas.

Critics on Hacker News , an online forum known for its tech-savvy community, have raised concerns about The AI Scientist and question if current AI models can perform true scientific discovery. While the discussions there are informal and not a substitute for formal peer review, they provide insights that are useful in light of the magnitude of Sakana's unverified claims.

"As a scientist in academic research, I can only see this as a bad thing," wrote a Hacker News commenter named zipy124. "All papers are based on the reviewers trust in the authors that their data is what they say it is, and the code they submit does what it says it does. Allowing an AI agent to automate code, data or analysis, necessitates that a human must thoroughly check it for errors ... this takes as long or longer than the initial creation itself, and only takes longer if you were not the one to write it."

Critics also worry that widespread use of such systems could lead to a flood of low-quality submissions, overwhelming journal editors and reviewers—the scientific equivalent of AI slop . "This seems like it will merely encourage academic spam," added zipy124. "Which already wastes valuable time for the volunteer (unpaid) reviewers, editors and chairs."

And that brings up another point—the quality of AI Scientist's output: "The papers that the model seems to have generated are garbage," wrote a Hacker News commenter named JBarrow. "As an editor of a journal, I would likely desk-reject them. As a reviewer, I would reject them. They contain very limited novel knowledge and, as expected, extremely limited citation to associated works."

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MIT engineers’ new theory could improve the design and operation of wind farms

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The blades of propellers and wind turbines are designed based on aerodynamics principles that were first described mathematically more than a century ago. But engineers have long realized that these formulas don’t work in every situation. To compensate, they have added ad hoc “correction factors” based on empirical observations.

Now, for the first time, engineers at MIT have developed a comprehensive, physics-based model that accurately represents the airflow around rotors even under extreme conditions, such as when the blades are operating at high forces and speeds, or are angled in certain directions. The model could improve the way rotors themselves are designed, but also the way wind farms are laid out and operated. The new findings are described today in the journal Nature Communications , in an open-access paper by MIT postdoc Jaime Liew, doctoral student Kirby Heck, and Michael Howland, the Esther and Harold E. Edgerton Assistant Professor of Civil and Environmental Engineering.

“We’ve developed a new theory for the aerodynamics of rotors,” Howland says. This theory can be used to determine the forces, flow velocities, and power of a rotor, whether that rotor is extracting energy from the airflow, as in a wind turbine, or applying energy to the flow, as in a ship or airplane propeller. “The theory works in both directions,” he says.

Because the new understanding is a fundamental mathematical model, some of its implications could potentially be applied right away. For example, operators of wind farms must constantly adjust a variety of parameters, including the orientation of each turbine as well as its rotation speed and the angle of its blades, in order to maximize power output while maintaining safety margins. The new model can provide a simple, speedy way of optimizing those factors in real time.

“This is what we’re so excited about, is that it has immediate and direct potential for impact across the value chain of wind power,” Howland says.

Modeling the momentum

Known as momentum theory, the previous model of how rotors interact with their fluid environment — air, water, or otherwise — was initially developed late in the 19th century. With this theory, engineers can start with a given rotor design and configuration, and determine the maximum amount of power that can be derived from that rotor — or, conversely, if it’s a propeller, how much power is needed to generate a given amount of propulsive force.

Momentum theory equations “are the first thing you would read about in a wind energy textbook, and are the first thing that I talk about in my classes when I teach about wind power,” Howland says. From that theory, physicist Albert Betz calculated in 1920 the maximum amount of energy that could theoretically be extracted from wind. Known as the Betz limit, this amount is 59.3 percent of the kinetic energy of the incoming wind.

But just a few years later, others found that the momentum theory broke down “in a pretty dramatic way” at higher forces that correspond to faster blade rotation speeds or different blade angles, Howland says. It fails to predict not only the amount, but even the direction of changes in thrust force at higher rotation speeds or different blade angles: Whereas the theory said the force should start going down above a certain rotation speed or blade angle, experiments show the opposite — that the force continues to increase. “So, it’s not just quantitatively wrong, it’s qualitatively wrong,” Howland says.

The theory also breaks down when there is any misalignment between the rotor and the airflow, which Howland says is “ubiquitous” on wind farms, where turbines are constantly adjusting to changes in wind directions. In fact, in an  earlier paper in 2022, Howland and his team found that deliberately misaligning some turbines slightly relative to the incoming airflow within a wind farm significantly improves the overall power output of the wind farm by reducing wake disturbances to the downstream turbines.

In the past, when designing the profile of rotor blades, the layout of wind turbines in a farm, or the day-to-day operation of wind turbines, engineers have relied on ad hoc adjustments added to the original mathematical formulas, based on some wind tunnel tests and experience with operating wind farms, but with no theoretical underpinnings.

Instead, to arrive at the new model, the team analyzed the interaction of airflow and turbines using detailed computational modeling of the aerodynamics. They found that, for example, the original model had assumed that a drop in air pressure immediately behind the rotor would rapidly return to normal ambient pressure just a short way downstream. But it turns out, Howland says, that as the thrust force keeps increasing, “that assumption is increasingly inaccurate.”

And the inaccuracy occurs very close to the point of the Betz limit that theoretically predicts the maximum performance of a turbine — and therefore is just the desired operating regime for the turbines. “So, we have Betz’s prediction of where we should operate turbines, and within 10 percent of that operational set point that we think maximizes power, the theory completely deteriorates and doesn’t work,” Howland says.

Through their modeling, the researchers also found a way to compensate for the original formula’s reliance on a one-dimensional modeling that assumed the rotor was always precisely aligned with the airflow. To do so, they used fundamental equations that were developed to predict the lift of three-dimensional wings for aerospace applications.

The researchers derived their new model, which they call a unified momentum model, based on theoretical analysis, and then validated it using computational fluid dynamics modeling. In followup work not yet published, they are doing further validation using wind tunnel and field tests.

Fundamental understanding

One interesting outcome of the new formula is that it changes the calculation of the Betz limit, showing that it’s possible to extract a bit more power than the original formula predicted. Although it’s not a significant change — on the order of a few percent — “it’s interesting that now we have a new theory, and the Betz limit that’s been the rule of thumb for a hundred years is actually modified because of the new theory,” Howland says. “And that’s immediately useful.” The new model shows how to maximize power from turbines that are misaligned with the airflow, which the Betz limit cannot account for.

The aspects related to controlling both individual turbines and arrays of turbines can be implemented without requiring any modifications to existing hardware in place within wind farms. In fact, this has already happened, based on earlier work from Howland and his collaborators two years ago that dealt with the wake interactions between turbines in a wind farm, and was based on the existing, empirically based formulas.

“This breakthrough is a natural extension of our previous work on optimizing utility-scale wind farms,” he says, because in doing that analysis, they saw the shortcomings of the existing methods for analyzing the forces at work and predicting power produced by wind turbines. “Existing modeling using empiricism just wasn’t getting the job done,” he says.

In a wind farm, individual turbines will sap some of the energy available to neighboring turbines, because of wake effects. Accurate wake modeling is important both for designing the layout of turbines in a wind farm, and also for the operation of that farm, determining moment to moment how to set the angles and speeds of each turbine in the array.

Until now, Howland says, even the operators of wind farms, the manufacturers, and the designers of the turbine blades had no way to predict how much the power output of a turbine would be affected by a given change such as its angle to the wind without using empirical corrections. “That’s because there was no theory for it. So, that’s what we worked on here. Our theory can directly tell you, without any empirical corrections, for the first time, how you should actually operate a wind turbine to maximize its power,” he says.

Because the fluid flow regimes are similar, the model also applies to propellers, whether for aircraft or ships, and also for hydrokinetic turbines such as tidal or river turbines. Although they didn’t focus on that aspect in this research, “it’s in the theoretical modeling naturally,” he says.

The new theory exists in the form of a set of mathematical formulas that a user could incorporate in their own software, or as an open-source software package that can be freely downloaded from GitHub . “It’s an engineering model developed for fast-running tools for rapid prototyping and control and optimization,” Howland says. “The goal of our modeling is to position the field of wind energy research to move more aggressively in the development of the wind capacity and reliability necessary to respond to climate change.”

The work was supported by the National Science Foundation and Siemens Gamesa Renewable Energy.

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Table of Contents

1. ai-generated content, 2. quantum computing, 3. 5g expansion, 4. virtual reality (vr) 2.0, 5. augmented reality (ar) in retail, 6. internet of things (iot) in smart cities, 7. biotechnology in agriculture, 8. autonomous vehicles, 9. blockchain beyond crypto, 10. edge computing, 11. personalized medicine, 12. neuromorphic computing, 13. green energy technologies, 14. wearable health monitors, 15. extended reality (xr) for training, 16. voice-activated technology, 17. space tourism, 18. synthetic media, 19. advanced robotics, 20. ai in cybersecurity, 21. digital twins, 22. sustainable tech, 24. nano-technology, top 24 jobs trending in 2024, one solution to succeed in 2024, frequently asked questions (faqs), 24 new technology trends in 2024: exploring the future.

Technology today is evolving at a rapid pace, enabling faster change and progress, causing an acceleration of the rate of change. However, it is not only technology trends and emerging technologies that are evolving, a lot more has changed, making IT professionals realize that their role will not stay the same in the contactless world tomorrow. And an IT professional in 2024 will constantly be learning, unlearning, and relearning (out of necessity, if not desire).

What does this mean for you in the context of the highest paying jobs in India ? It means staying current with emerging technologies and latest technology trends. And it means keeping your eyes on the future to know which skills you’ll need to know to secure a safe job tomorrow and even learn how to get there. Here are the top 24 emerging technology trends you should watch for and make an attempt at in 2024, and possibly secure one of the highest paying tech jobs that will be created by these new technology trends. Starting the list of new tech trends with the talk of the town, gen-AI!

Artificial intelligence can generate high-quality, creative content, including text, images, videos, and music. This technology uses algorithms like GPT (Generative Pre-trained Transformer) and DALL-E to understand and produce content that resonates with human preferences. The vast applications range from generating articles, creating educational materials, and developing marketing campaigns to composing music and producing realistic visuals. This speeds up content creation and reduces costs, and democratizes access to creative tools, enabling small businesses and individuals to create content at scale.

Quantum computers leverage the properties of quantum mechanics to process information exponentially faster than classical computers for specific tasks. This year, we're seeing quantum computing being applied in areas such as cryptography, where it can potentially crack currently considered secure codes, and in drug discovery, speeding up the process by accurately simulating molecular structures. The technology is still nascent but poised to revolutionize industries by solving complex problems intractable for traditional computers.

The fifth generation of mobile networks, 5G, promises significantly faster data download and upload speeds, wider coverage, and more stable connections. The expansion of 5G is facilitating transformative technologies like IoT, augmented reality, and autonomous vehicles by providing the high-speed, low-latency connections they require. This technology is crucial for enabling real-time communications and processing large amounts of data with minimal delay, thereby supporting a new wave of technological innovation.

Enhanced VR technologies are offering more immersive and realistic experiences. With improvements in display resolutions, motion tracking, and interactive elements, VR is becoming increasingly prevalent in gaming, training, and therapeutic contexts. New VR systems are also becoming more user-friendly, with lighter headsets and longer battery life, which could lead to broader consumer adoption and integration into daily life.

AR technology is transforming the retail industry by allowing consumers to visualize products in a real-world context through their devices. This trend is evident in applications that let users try on clothes virtually or see how furniture would look in their homes before purchasing. These interactive experiences enhance customer satisfaction, increase sales, and reduce return rates.

IoT technology in smart cities involves the integration of various sensors and devices that collect data to manage assets, resources, and services efficiently. This includes monitoring traffic and public transport to reduce congestion, using smart grids to optimize energy use, and implementing connected systems for public safety and emergency services. As cities continue to grow, IoT helps manage complexities and improve the living conditions of residents.

Advances in biotechnology are revolutionizing agriculture by enabling the development of crops with enhanced traits, such as increased resistance to pests and diseases, better nutritional profiles, and higher yields. Techniques like CRISPR gene editing are used to create crops that can withstand environmental stresses such as drought and salinity, which is crucial in adapting to climate change and securing food supply.

Autonomous vehicles use AI, sensors, and machine learning to navigate and operate without human intervention. While fully autonomous cars are still under development, there's significant progress in integrating levels of autonomy into public transportation and freight logistics, which could reduce accidents, improve traffic management, and decrease emissions.

Initially developed for Bitcoin, blockchain technology is finding new applications beyond cryptocurrency. Industries are adopting blockchain for its ability to provide transparency, enhance security, and reduce fraud. Uses include tracking the provenance of goods in supply chains, providing tamper-proof voting systems, and managing secure medical records.

Edge computing involves processing data near the source of data generation rather than relying on a central data center. This is particularly important for applications requiring real-time processing and decision-making without the latency that cloud computing can entail. Applications include autonomous vehicles, industrial IoT, and local data processing in remote locations.

Personalized medicine tailors medical treatment to individual characteristics of each patient. This approach uses genetic, environmental, and lifestyle factors to diagnose and treat diseases precisely. Advances in genomics and biotechnology have enabled doctors to select treatments that maximize effectiveness and minimize side effects. Personalized medicine is particularly transformative in oncology, where specific therapies can target genetic mutations in cancer cells, leading to better patient outcomes.

Neuromorphic computing involves designing computer chips that mimic the human brain's neural structures and processing methods. These chips process information in ways that are fundamentally different from traditional computers, leading to more efficient handling of tasks like pattern recognition and sensory data processing. This technology can produce substantial energy efficiency and computational power improvements, particularly in applications requiring real-time learning and adaptation.

Innovations in green energy technologies focus on enhancing the efficiency and reducing the costs of renewable energy sources such as solar, wind, and bioenergy. Advances include new photovoltaic cell designs, wind turbines operating at lower wind speeds, and biofuels from non-food biomass. These technologies are crucial for reducing the global carbon footprint and achieving sustainability goals.

Advanced wearable devices now continuously monitor various health metrics like heart rate, blood pressure, and even blood sugar levels. These devices connect to smartphones and use AI to analyze data, providing users with insights into their health and early warnings about potential health issues. This trend is driving a shift towards preventive healthcare and personalized health insights.

Extended reality (XR) encompasses virtual reality (VR), augmented reality (AR), and mixed reality (MR), providing immersive training experiences. Industries like healthcare, aviation, and manufacturing use XR for risk-free, hands-on training simulations replicating real-life scenarios. This technology improves learning outcomes, enhances engagement, and reduces training costs.

Voice-activated technology has become more sophisticated, with devices now able to understand and process natural human speech more accurately. This technology is widely used in smart speakers, home automation, and customer service bots. It enhances accessibility, convenience, and interaction with technology through hands-free commands and is increasingly integrated into vehicles and public spaces.

Commercial space travel is making significant strides with companies like SpaceX and Blue Origin. These developments aim to make space travel accessible for more than just astronauts. Current offerings range from short suborbital flights providing a few minutes of weightlessness to plans for orbital flights. Space tourism opens new avenues for adventure and pushes the envelope in aerospace technology and research.

Synthetic media refers to content that is entirely generated by AI, including deepfakes, virtual influencers, and automated video content. This technology raises critical ethical questions and offers extensive entertainment, education, and media production possibilities. It allows for creating increasingly indistinguishable content from that produced by humans.

Robotics technology has evolved to create machines that can perform complex tasks autonomously or with minimal human oversight. These robots are employed in various sectors, including manufacturing, where they perform precision tasks, healthcare as surgical assistants, and homes as personal aids. AI and machine learning advances are making robots even more capable and adaptable.

AI is critical in enhancing cybersecurity by automating complex processes for detecting and responding to threats. AI systems can analyze vast amounts of data for abnormal patterns, predict potential threats, and implement real-time defenses. This trend is crucial in addressing cyber attacks' increasing sophistication and frequency.

Digital twins are virtual replicas of physical devices for simulation, monitoring, and maintenance. They are extensively used in manufacturing, automotive, and urban planning to optimize operations and predict potential issues. Digital twins enable companies to test impacts and changes in a virtual space, reducing real-world testing costs and time.

This trend focuses on developing technology in an environmentally and socially responsible manner. It includes innovations in the lifecycle management of tech products, from design to disposal. The aim is to reduce electronic waste, improve energy efficiency, and use environmentally friendly materials.

23. Telemedicine

Telemedicine allows patients to consult with doctors via digital platforms, reducing the need for physical visits. Providing continued medical care during situations like the COVID-19 pandemic has become vital. Telemedicine is expanding to include more services and is becoming a regular mode of healthcare delivery.

Nanotechnology involves manipulating matter at the atomic and molecular levels, enhancing or creating materials and devices with novel properties. Applications are vast, including more effective drug delivery systems, enhanced materials for better product performance, and innovations in electronics like smaller, more powerful chips.

• AI Specialist: Designing, programming, and training artificial intelligence systems.
• Quantum Computing Engineer: Developing quantum algorithms and working on quantum hardware.
• Data Privacy Officer: Ensuring companies adhere to privacy laws and best practices.
• 5G Network Engineer: Installing, maintaining, and optimizing 5G networks.
• Virtual Reality Developer: Creating immersive VR content and applications for various industries.
• Augmented Reality Designer: Designing AR experiences for retail, training, and entertainment.
• IoT Solutions Architect: Designing and implementing comprehensive IoT systems for smart cities and homes.
• Genomics Biologist: Conducting research and development in genetics to create personalized medicine solutions.
• Autonomous Vehicle Engineer: Developing software and systems for self-driving cars.
• Blockchain Developer: Building decentralized applications and systems using blockchain technology.
• Edge Computing Technician: Managing IT solutions at the network's edge, close to data sources.
• Personalized Healthcare Consultant: Offering health advice based on personal genetic information.
• Neuromorphic Hardware Engineer: Designing chips that mimic the human brain's neural structure.
• Renewable Energy Technician: Specializing in installing and maintaining solar panels, wind turbines, and other renewable energy sources.
• Wearable Technology Designer: Creating devices that monitor health and provide real-time feedback.
• XR Trainer: Developing and facilitating training programs using extended reality technologies.
• Voice Interaction Designer: Crafting user interfaces and experiences for voice-activated systems.
• Commercial Space Pilot: Piloting vehicles for space tourism and transport missions.
• Synthetic Media Producer: Producing AI-generated content for media and entertainment.
• Advanced Robotics Engineer: Designing robots for manufacturing, healthcare, and personal assistance.
• Cybersecurity Analyst: Protecting organizations from cyber threats and managing risk.
• Digital Twin Engineer: Creating and managing virtual replicas of physical systems.
• Sustainable Technology Specialist: Developing eco-friendly technologies and practices within tech industries.
• Telehealth Technician: Supporting the technology that enables remote health services.

Although technologies are emerging and evolving all around us, these 24 technology trends offer promising career potential now and for the foreseeable future. And most of these trending technologies are welcoming skilled professionals, meaning the time is right for you to choose one, get trained, and get on board at the early stages of these trending technologies, positioning you for success now and in the future. Enroll in the Post Graduate Program in AI and Machine Learning to equip yourself with the expertise needed to excel in one of the most in-demand fields today.

1. What are technology trends?

Technology trends refer to the prevailing developments, innovations, and advancements in the world of technology. These trends often shape the direction of industries, businesses, and society as a whole, influencing how we interact, work, and live.

2. Why are technology trends important?

Following technology trends is crucial for individuals and businesses alike because it allows them to stay competitive and relevant in a rapidly evolving digital landscape. By keeping abreast of emerging technologies, one can make informed decisions about adopting new tools, improving processes, and leveraging opportunities for growth.

3. How do you keep up with technology trends?

You can stay updated with technology trends by following reputable technology news sources, subscribing to industry newsletters, attending conferences and webinars, participating in online communities, and engaging in continuous learning and skill development.

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News from Brown

Cutting-edge technology at brown to support new discoveries on diseases by rhode island scientists.

A $1.2 million National Institutes of Health grant will bring a state-of-the-art mass spectrometer to Brown to advance the work of  researchers studying the biology of disease and exploring potential treatments.

PROVIDENCE, R.I. [Brown University] — A new mass spectrometer is coming to Rhode Island to advance research in cancer biology, aging, neurodegenerative diseases, immunology, infectious diseases and other fields.

With a $1.2 million grant from the National Institutes of Health, Brown University will acquire an Orbitrap Ascend Tribrid mass spectrometer — a state-of-the-art analytical tool that scientists can use to sequence and quantify proteins as they study cell biology underlying diseases.

The technologically advanced, highly sensitive instrument will enable single-cell protein analysis at a rapid speed, said Arthur Salomon, a Brown professor of molecular biology, cell biology and biochemistry.

“This single-cell analysis capability doesn't currently exist at Brown, and it’s essential for understanding the biology of disease,” he said, noting that analyses that currently require up to four hours will take about 30 minutes with the new technology, which will also offer unprecedented sensitivity and accuracy.

Salomon is the faculty director of Brown’s Proteomics Core Facility and is the principal investigator for the federal grant. Proteomics is the study of the structure and function of proteins, and the facility provides proteomics instruments and expertise to researchers from Brown and others in the Rhode Island scientific community.

The acquisition of the mass spectrometer will enhance the facility's capabilities and enable cutting-edge research in major disease areas, Salomon explained. For example, in support of the Legorreta Cancer Center at Brown, the instrument will benefit cancer biology research, where the ability to detect and quantify specific proteins and their modifications is critical for understanding disease mechanisms and developing new therapies. It will also assist with research at the Center on the Biology of Aging and the Carney Institute for Brain Science, where identifying disease-related biomarkers from samples is essential. In immunology and infectious disease research, Salomon said the system will enable the identification and quantification of immune system proteins and parasite- or pathogen-derived molecules, aiding in the development of vaccines and therapies.

Proteomics Core Facility Manager Nicholas DaSilva said that as the most capable shared mass spectrometer in the state, the tool will support the facility in its mission to provide well-maintained, state-of-the-art instrumentation and fundamental proteomics expertise to the Brown and Rhode Island scientific communities.

“This new grant brings rapid, highly sensitive and robust proteomic analysis capabilities to researchers in Rhode Island,” DaSilva said. “The PCF provides both consultative and bespoke proteomic services to principal investigators and research staff,” and the mass spectrometer opens new possibilities for research in fields as varied as evolutionary biology to metabolic syndrome.

In his role as facility manager, DaSilva will aid researchers in the design of proteomic experiments and the interpretation of resulting data so that researchers can focus on what he called “the fun part of understanding the biology of what the data means.”

Salomon estimates that in addition to future research endeavors, the new instrument will be used immediately by at least 17 researchers working on existing projects funded by the National Institutes of Health and the National Science Foundation.

Due to the time it takes to custom-build the mass spectrometer, ship it from Germany, assemble it and establish workflows, the system will likely be available for use within the next four to six months. The new mass spectrometer will use technologies developed by a national network of proteomic cores that will provide Rhode Island researchers with a highly sophisticated tool for protein analysis, Salomon said.

“This builds up critically important research infrastructure for Rhode Island,” Salomon said. “We will have a world-class facility for performing life science research.”

The work is supported by the National Institutes of Health under Award No. 1S10OD036295-01.

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Brown researcher to study environmental health impacts of wood pellet production, q&a with brown biochemist on rna, the molecule causing global excitement among scientists, exhibition at brown’s medical school offers multi-faceted view of patients with scoliosis.

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NSF announces 4 new Engineering Research Centers focused on biotechnology, manufacturing, robotics and sustainability

Engineering innovations transform our lives and energize the economy.  The U.S. National Science Foundation announces a five-year investment of $104 million, with a potential 10-year investment of up to $208 million, in four new NSF Engineering Research Centers (ERCs) to create technology-powered solutions that benefit the nation for decades to come.   

"NSF's Engineering Research Centers ask big questions in order to catalyze solutions with far-reaching impacts," said NSF Director Sethuraman Panchanathan. "NSF Engineering Research Centers are powerhouses of discovery and innovation, bringing America's great engineering minds to bear on our toughest challenges. By collaborating with industry and training the workforce of the future, ERCs create an innovation ecosystem that can accelerate engineering innovations, producing tremendous economic and societal benefits for the nation."  

The new centers will develop technologies to tackle the carbon challenge, expand physical capabilities, make heating and cooling more sustainable and enable the U.S. supply and manufacturing of natural rubber.  

The 2024 ERCs are:  

• NSF ERC for Carbon Utilization Redesign through Biomanufacturing-Empowered Decarbonization (CURB) — Washington University in St. Louis in partnership with the University of Delaware, Prairie View A&M University and Texas A&M University.   CURB will create manufacturing systems that convert CO2 to a broad range of products much more efficiently than current state-of-the-art engineered and natural systems.    
• NSF ERC for Environmentally Applied Refrigerant Technology Hub (EARTH) — University of Kansas in partnership with Lehigh University, University of Hawaii, University of Maryland, University of Notre Dame and University of South Dakota.   EARTH will create a transformative, sustainable refrigerant lifecycle to reduce global warming from refrigerants while increasing the energy efficiency of heating, ventilation and cooling.    
• NSF ERC for Human AugmentatioN via Dexterity (HAND) — Northwestern University in partnership with Carnegie Mellon University, Florida A&M University, and Texas A&M University, and with engagement of MIT.  HAND will revolutionize the ability of robots to augment human labor by transforming dexterous robot hands into versatile, easy-to-integrate tools.     
• NSF ERC for Transformation of American Rubber through Domestic Innovation for Supply Security (TARDISS) — The Ohio State University in partnership with Caltech, North Carolina State University, Texas Tech University and the University of California, Merced.   TARDISS will create bridges between engineering, biology, and agriculture to revolutionize and on-shore alternative natural rubber production from U.S. crops.  

Since its founding in 1985, NSF's ERC program has funded 83 centers (including the four announced today) that receive support for up to 10 years. The centers build partnerships with educational institutions, government agencies and industry stakeholders to support innovation and inclusion in established and emerging engineering research.  

Visit NSF's website and read about NSF Engineering Research Centers .  

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Nanoscience and technology articles from across Nature Portfolio

Nanoscience and technology is the branch of science that studies systems and manipulates matter on atomic, molecular and supramolecular scales (the nanometre scale). On such a length scale, quantum mechanical and surface boundary effects become relevant, conferring properties on materials that are not observable on larger, macroscopic length scales.

Ultrathin sapphire synthesized for advanced 2D electronics

Single crystals of atomically thin sapphire have been prepared at room temperature — something that many scientists thought was impossible. These materials could enable the development of the next generation of transistors for use in miniaturized chips.

A photo-controlled charge regulator improves cancer theranostics

Photobleaching-harnessing charge conversion initiates nanomedicine transcytosis, improving therapeutic outcomes in various rectal tumour mouse models.

Small molecules self-organized in an orderly manner to enhance Raman signals

We have discovered an effect, termed stacking-induced intermolecular charge transfer-enhanced Raman scattering (SICTERS), that enhances the Raman signal intensities of small molecules by relying on their self-stacking rather than external substrates. This effect enables the design of substrate-free small-molecule probes for high-resolution, non-invasive transdermal Raman imaging of lymphatic drainage and microvessels.

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Institute translates environmental threats into financial risks

The new Institute for Risk Management and Insurance Innovation will accelerate research on extreme weather and cybersecurity.

A new institute will research how a growing number of threats translate into financial risks, Vice Chancellor for Research Penny Gordon-Larsen announced Aug. 8. 

Operating under her office, the Institute for Risk Management and Insurance Innovation will act as a research and innovation hub, bringing together investigators from multiple disciplines.  

“UNC-Chapel Hill is a world leader in translating extreme environmental events into financial risk and now seeks to expand into a wider range of emerging perils,” said Gordon-Larsen, also W.R. Kenan Jr. Distinguished Professor in the Gillings School of Global Public Health’s nutrition department. 

The institute’s major mission will be to accelerate research on evaluating and managing financial risk from threats including weather-related disasters to cybersecurity. Using research as a platform, it will create a pipeline of uniquely trained graduate students ready to enter the risk management and insurance industry. The institute is also developing, in collaboration with its industry partners, an undergraduate minor in risk management. The minor will focus on preparing students to use cutting-edge modeling and data science techniques to better understand risks.  

The leader of the institute will be Greg Characklis , W.R. Kenan Jr. Distinguished Professor in the Gillings School of Global Public Health’s environmental sciences and engineering department. Characklis is also the founding director of the Center on Financial Risk in Environmental Systems, which will be absorbed into the new institute and serve as a platform to support its rapid development.  

“The new institute will conduct research that improves our understanding of society’s emerging financial risks and our ability to better manage these risks, even as we prepare a new breed of student to become leaders in the risk management and insurance industry,” Characklis said. 

The institute will receive significant industry support, with more than 25 firms making financial commitments to fund research and training activities. Industry partners will sit on the industry advisory board chaired by Carolina alumnus Tony Ursano, a co-founding managing partner of the firm Insurance Advisory Partners. 

“IRMII will be a powerful force for new ideas and beneficial change for the risk management and insurance industry, with the ability to address real-world problems in a comprehensive manner,” Gordon-Larsen said. “These efforts will help attract external funding to the University for this important work and will yield tangible benefits for the citizens of North Carolina.” 

The institute has the full support of Carolina’s Board of Trustees, Gordon-Larsen said.  

It’s “a program that both expands knowledge and prepares our students to be strong contributors to the 21st-century workforce,” said John Preyer, trustees’ chair.

Office of the Vice Chancellor for Research awards will fund projects to improve health care and clean energy.

Wi-Fi 6E comes to campus

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University updates Alert Carolina messaging

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First-year student is a mental health advocate

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A message from University leadership: supporting our vibrant culture of spirited debate

In a campus message, Chancellor Lee H. Roberts and Provost J. Christopher Clemens provided an overview of and shared resources on free speech at Carolina.

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The psychology scholar critiques Vivek Murthy’s idea of a warning label for teen social media use.

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Fact-Checking Biden’s Speech and More: Day 1 of the Democratic National Convention

We followed the developments and fact-checked the speakers, providing context and explanation.

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President Biden praised his administration’s accomplishments and declared his vice president a worthy successor on the first night of the Democratic National Convention on Monday.

Mr. Biden’s speech capped a night in which Democratic lawmakers and party stalwarts praised Vice President Kamala Harris, warned repeatedly that former President Donald J. Trump was unfit for office and celebrated Mr. Biden’s legacy.

Here’s a look at some of their claims.

“While schools closed and dead bodies filled morgues, Donald Trump downplayed the virus. He told us to inject bleach into our bodies. He peddled conspiracy theories across the country. We lost hundreds of thousands of Americans, and our economy collapsed.”

— Representative Robert Garcia of California

This is exaggerated.

Mr. Trump’s comments, in April 2020, about the efficacy of disinfectants and light as treatments for the coronavirus elicited uproar and confusion . He did not literally instruct people to inject bleach, but raised the suggestion as an “interesting” concept to test out.

At the April 2020 news conference , a member of Mr. Trump’s coronavirus task force said that the virus dies under direct sunlight and that applying bleach in indoor spaces kills the virus in five minutes and isopropyl alcohol does so in 30 seconds.

Mr. Trump responded: “Supposing we hit the body with a tremendous — whether it’s ultraviolet or just very powerful light — and I think you said that that hasn’t been checked, but you’re going to test it. And then I said, supposing you brought the light inside the body, which you can do either through the skin or in some other way, and I think you said you’re going to test that too.”

He added: “And then I see the disinfectant, where it knocks it out in a minute. One minute. And is there a way we can do something like that, by injection inside or almost a cleaning? Because you see it gets in the lungs and it does a tremendous number on the lungs. So it would be interesting to check that.”

Jeanna Smialek

“Trump talks big about bringing back manufacturing jobs, but you know who actually did it? President Biden and Vice President Kamala Harris.”

— Gov. Kathy Hochul of New York

This needs context .

It is true that manufacturing employment is up sharply under the Biden administration, but much of the gains are simply a recovery from job losses early in the coronavirus pandemic. Manufacturing employment is just slightly above its 2019 level. And factory employment also climbed somewhat from when Donald J. Trump took office in early 2017 and the onset of the pandemic in 2020.

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“Thanks to Joe Biden and Kamala Harris, we reopened our schools.”

— Representative James E. Clyburn, Democrat of South Carolina

This needs context.

President Donald J. Trump and President Biden took different approaches to school reopenings during the coronavirus pandemic, with Mr. Trump encouraging schools to stay open and Mr. Biden emphasizing the need to contain the virus before reopening classroom doors. While they could signal policy preferences, developments in how the virus spread and how states and school districts reacted were sometimes out of their control.

The Centers for Disease Control and Prevention warned schools to prepare for disruption in February 2020, and a high school in Washington State became the first to close its doors that month . More schools across the country followed in adopting online instruction, but by the fall of 2020, some schools — often in states with Republican governors — returned to in-person instruction.

One audit found that by the fall of 2020 more schools had reverted to a traditional or hybrid model than remained virtual. A C.D.C. study found that school closures peaked in 2021, under the Biden administration, when the Omicron variant spread. By the fall of 2021, though, 98 percent of public schools were offering in-person instruction full time, according to the Education Department .

“Donald Trump wants to put our 1787 constitution through his Project 2025 paper shredder.”

— Representative Jasmine Crockett, Democrat of Texas

Project 2025, a set of conservative policy proposals assembled by a Washington think tank for a Republican presidential administration, does not directly come from Mr. Trump or his campaign.

Still, CNN documented instances where 140 people who worked for the Trump administration had a role in Project 2025. Some were top advisers to Mr. Trump in his first term and a re all but certain to step into prominent posts should he win a second term.

Mr. Trump has also supported some of the proposals, with even some overlap between Project 2025 and his own campaign plans. Among the similarities: undercutting the independence of the Justice Department and pressing to end diversity, equity and inclusion programs. And he enacted other initiatives mentioned in Project 2025 in his first term, such as levying tariffs on China and making it easier to fire federal workers.

But Mr. Trump has criticized some elements as “absolutely ridiculous and abysmal” though he has not specified which proposals he opposes. When the director of the project departed the think tank, Mr. Trump’s campaign released a statement that stated: “Reports of Project 2025’s demise would be greatly welcomed and should serve as notice to anyone or any group trying to misrepresent their influence with President Trump and his campaign — it will not end well for you.”

“JD Vance says women should stay in violent marriages and pregnancies resulting from rape are simply inconvenient.”

— Gov. Andy Beshear of Kentucky

Mr. Beshear was referring to comments Mr. Vance made during his 2022 campaign for Senate. Mr. Vance has rejected such interpretations.

In remarks to a Christian high school in California in September 2021, Mr. Vance spoke of his grandparents’ marriage, which he described in his memoir as violent.

“This is one of the great tricks that I think the sexual revolution pulled on the American populace, which is the idea that like, ‘Well, OK, these marriages were fundamentally, you know, they were maybe even violent, but certainly they were unhappy. And so getting rid of them and making it easier for people to shift spouses like they change their underwear, that’s going to make people happier in the long term,” he said .

Asked by Vice News about his remarks in 2022, Mr. Vance said, “Any fair person would recognize I was criticizing the progressive frame on this issue, not embracing it.”

He also told Fox News that Democrats had “twisted my words here” and that “it’s not what I believe, it’s not what I said.”

And regarding pregnancies resulting from rape, Mr. Vance told Fox News that he was criticizing the view that such pregnancies are “inconvenient.”

In a 2021 interview , Mr. Vance was asked whether abortion bans should have exceptions for rape or incest. He responded, “At the end of the day, we’re talking about an unborn baby. What kind of society do we want to have? A society that looks at unborn babies as inconveniences to be discarded?”

“Instead of paying $400 a month for insulin, seniors with diabetes will pay $35 a month.”

— President Biden

Mr. Biden signed a law that places a cap of $35 a month on insulin for all Medicare Part D beneficiaries. But he is overstating the average cost before the law.

Patients’ out-of-pocket spending on insulin was $434 on average for all of 2019 — not per month — and $449 per year for Medicare enrollees, according to the Health and Human Services Department .

“The smallest racial wealth gap in 20 years.”

As a percentage of wealth held by white families, Black and Latino families did grow to the largest amounts in 2022 in two decades. But the disparity in absolute dollar value actually increased.

“He called them ‘suckers and losers.’”

The claim that, as president, Donald J. Trump called veterans “suckers” and “losers” stems from a 2020 article in The Atlantic about his relationship to the military.

The article relied on anonymous sources, but many of the accounts have been corroborated by other outlets, including The New York Times, and by John F. Kelly, a retired four-star Marine general who served as Mr. Trump’s White House chief of staff. Mr. Trump has emphatically denied making the remarks since the article was published. Here’s a breakdown .

“Trump wants to cut Social Security and Medicare.”

This is misleading..

Mr. Trump has said repeatedly during his 2024 presidential campaign that he would not cut Social Security or Medicare, though he had previously shown brief and vague support for such proposals.

Asked about his position on the programs in relation to the national debt, Mr. Trump told CNBC in March, “There is a lot you can do in terms of entitlements in terms of cutting and in terms of also the theft and the bad management of entitlements.”

But Mr. Trump and his campaign clarified that he would not seek to cut the programs. Mr. Trump told the website Breitbart , “I will never do anything that will jeopardize or hurt Social Security or Medicare.” And during a July rally in Minnesota, he again vowed, “I will not cut one penny from Social Security or Medicare, and I will not raise the retirement age by one day, not by one day.”

Still, Mr. Trump has not outlined a clear plan for keeping the programs solvent. During his time in office, Mr. Trump did propose some cuts to Medicare — though experts said the cost reductions would not have significantly affected benefits — and to Social Security’s programs for people with disabilities. They were not enacted by Congress.

“He created the largest debt any president had in four years with his two trillion dollars tax cut for the wealthy.”

Looking at a single presidential term, Donald J. Trump’s administration did rack up more debt than any other in raw dollars — about $7.9 trillion . But the debt rose more under President Barack Obama’s eight years than under Mr. Trump’s four years. Also, when viewed as a percentage increase, the national debt rose more under President George H.W. Bush’s single term than under Mr. Trump’s.

The Congressional Budget Office estimated that Mr. Trump’s tax cuts — which passed in December 2017 with no Democrats in support — roughly added another $1 trillion to the federal deficit from 2018 to 2021, even after factoring in economic growth spurred by the tax cuts. But other drivers of the deficit include several sweeping measures that had bipartisan approval. The first coronavirus stimulus package , which received near unanimous support in Congress, added $2 trillion to the deficit over the next two fiscal years. Three additional spending measures contending with the coronavirus pandemic and its economic ramifications added another $1.4 trillion.

It is also important to note that presidents do not hold unilateral responsibility for the debt increase under their time in office. Policies from previous administrations — and programs such as Social Security and Medicare — continue to drive up debt, as do unexpected circumstances.