Does automation look more like a crashing wave hitting some workers and not others, or a broadly rising tide?

Our latest Substack post from @adamkuzee explains the concepts and data behind a recent MIT FutureTech paper on the shape of AI progress: mitfuturetech.substack.com/p/crashing-wav…
ProfNeilT

#AI #Automation #FutureOfAI #FutureOfWork #AIResearch #MITFutureTech

@porat_ruth leads the discussion on global #AIAdoption with @elerianm and FutureTech Research Scientist @Fleming_Martin at the #google #mitfuturetech #AIfortheEconomy Forum

Read our latest research on the economics of human AI collaboration here: arxiv.org/html/2603.2912…

@ProfNeilT kicks off the #google #MITFuturetech #AIfortheEconomyForum with our findings on AI automation from Thousands of Worker Evaluations of Labor Market Tasks.

Read the full paper:
arxiv.org/abs/2604.01363

Can academic science keep up with the AI frontier?

Our substack tracks the meteoric rise of AI foundation models in science and the constraints on future breakthroughs: mitfuturetech.substack.com/p/can-academic…

#AI #Science #AIResearch #FutureOfScience

MIT FutureTech is hiring a Research Assistant to work with Dr. @DanialLashkari on projects at the intersection of technological progress, innovation, and AI

Join us! futuretech.mit.edu/opportunities/…

#EconRA #AIEconomics #ResearchJobs #AIResearch

Extending our gratitude to Nobel Prize Laureate and Turing Award Winner @geoffreyhinton for speaking at the MIT FutureTech Lab Seminar, we were honored to have him speak.

Read the full presentation: "Living with Alien Beings" drive.google.com/file/d/1taGy8V…
#AISafety #AIResearch

Senior Data Scientist Anna Li points out the limitations in quantum computing as a solution to the AI memory wall. “We would need to reinvent the entire stack, which would take a long time to mature.” #AIMemoryWall #quantumComputing #AIConference #MITFutureTech

That's a wrap on Automation in the Labor Market and Economy: AI Automation with Generative World Models! Thank you @du_yilun #GenerativeAI #AIResearch #MITFutureTech #AIConference

A great reality check from @beena_ammanath - Success in AI deployment in an enterprise setting is driven by adoption - when accurate models are rolled out but not adopted, it's considered a failed project! #AIAdoption #EnterpriseAI #AIDeployment #MITFutureTech

Congratulations to @ProfDavidDeming on his new position: Danoff Dean of Harvard College @Harvard! Fantastic presentation today at #MITFutureTech Conference "The Rapid Adoption of Generative AI", read it here: nber.org/papers/w32966

Does change in expertise (caused by task automation) affect change in wages? @davidautor says YES! Read the full paper on expertise here: nber.org/papers/w33941 📷#TaskAutomation #EconomicsofAutomation #AIEconomics #LabourResearch

#MITFutureTech Conference day 1 drew to a close with an inspirational discussion on Modern Challenges in Automating science led by @ta_broderick #AIResearch #AIAutomation #AutomationinScience

#MITFutureTech Conference day 1 drew to a close with an inspirational discussion on Modern Challenges in Automating science led by @ta_broderick #AIResearch #AIAutomation #AutomationinScience

Patrick Rich from @IBMResearch shares how foundation models accelerate scientific discovery: from experiments to algorithms. “We are at the dawn of a new algorithmic era powered by new representations of information” #foundationmodels #AIResearch #MITFutureTech

AI Adoption in Biology is accelerating new approaches to Drug Discovery via methods such as Cell Painting, as discussed by @shantanuXsingh from The Broad Institute. Advances in technology don't necessarily result in decreased costs in drug discovery however, a phenenom dubbed osite of Moore's law.

Excellent perspective from @KarianneBergen on educating the next generation of researchers in the context of #GenAI

#AIConference #AIResearch #MITFutureTech

@GaryMarcus delivers a bitter lesson on the bitter lesson - scaling works well only for some problems - mainly pattern recognition - but not others #AIExpert #Scaling #MITFutureTech #AIConference

Can the entire scientific process be automated? Research Scientist Yutaro Yamada explores AI Scientists and Self Improving AI #AIScientist #AIResearch #MITFutureTech

Are you a technology optimist or a technology pessimist? Speaker Kristina McElheran says Yes! Read her paper covering AI Adoption in America: nber.org/papers/w31788

#AIAdoption #AIResearch #AIConference #MITCSAIL

Lab Director Dr. Neil Thompson kicks off the conference with key FutureTechs Research, get an overview on our new substack! substack.com/@mitfuturetech #AIResearch #AIEconomics

Welcome to day 1 of the FutureTech Conference: How Fast Will AI Automation Happen? Follow along for live updates! #AIConference

In this @TEDTalks event, Neil Thompson (@ProfNeilT) discusses the quantification of the algorithmic efficiency improvements happening in deep learning and how these have reduced the environmental and energy demands of large language models.

youtu.be/naDR7siJMqE?si…

Our director, Dr. Neil Thompson (@ProfNeilT), recently had his National Bureau of Economic Research working paper with David Autor (@davidautor) featured in the Financial Times (@FinancialTimes).

Access the article here: on.ft.com/4lxAXS4

Read the working paper here: nber.org/papers/w33941

A new paper from David Autor (@davidautor), in collaboration with Neil Thompson (@ProfNeilT), makes an important contribution to explaining how AI is likely to impact labor markets.

Based on a rigorous model, confirmed with an analysis of 40 years of data, they provide a nuanced mation impacts job employment and wages.

Essentially, this depends on the extent to which easy tasks are removed from a role and expert ones are added, and how specialized a role becomes as a result.

When jobs gain inexpert tasks but lose expertise, wages decline, but employment may increase. Think of how taxi driving became less specialized, and well-paid, but more common, due to Uber.

In contrast, when technology automates the easy tasks inside a job, the remaining work becomes more specialized. Employment falls because fewer people now qualify, but the scarcity of expertise drives wages up.

This is what seems to be happening with proofreading, which is now less about spell-checking and more about helping people to write, leading to lower job numbers but higher average wages.

Their model helps us to understand the impacts of AI on labor markets. For instance, why AI tools can raise wages for senior software engineers, but decrease employment, while simultaneously reducing earnings, and increasing employment, for more entry level software engineering roles.

Read: nber.org/papers/w33941

See also this talk from David at Stanford HAI (@StanfordHAI): youtube.com/watch?v=uV3Ltt…

Peter Slattery (@PeterSlattery1) recently coauthored this paper: "We directly compare the persuasion capabilities of a frontier large language model (LLM; Claude Sonnet 3.5) against incentivized human persuaders in an interactive, real-time conversational quiz setting.

In this ale incentivized experiment, participants (quiz takers) completed an online quiz where persuaders (either humans or LLMs) attempted to persuade quiz takers toward correct or incorrect answers.

We find that LLM persuaders achieved significantly higher compliance with their directional persuasion attempts than incentivized human persuaders, demonstrating superior persuasive capabilities in both truthful (toward correct answers) and deceptive (toward incorrect answers) contexts.

We also find that LLM persuaders significantly increased quiz takers’ accuracy, leading to higher earnings, when steering quiz takers toward correct answers, and significantly decreased their accuracy, leading to lower earnings, when steering them toward incorrect answers.

Overall, our findings suggest that AI’s persuasion capabilities already exceed those of humans that have real-money bonuses tied to performance. Our findings of increasingly capable AI persuaders thus underscore the urgency of emerging alignment and governance frameworks. "

"the infrastructure, practices, and norms for reporting flaws in general-purpose AI (GPAI) systems remain seriously underdeveloped, lagging far behind more established fields like software security.

Based on a collaboration between experts from the fields of software security, machine learning, law, social science, and policy, we identify key gaps in the evaluation and reporting of flaws in GPAI systems.

We call for three interventions to advance system safety.

First, we propose using standardized AI flaw reports and rules of engagement for researchers in order to ease the process of submitting, reproducing, and triaging flaws in GPAI systems.

Second, we propose GPAI system providers adopt broadly-scoped flaw disclosure programs, borrowing from bug bounties, with legal safe harbors to protect researchers.

Third, we advocate for the development of improved infrastructure to coordinate distribution of flaw reports across the many stakeholders who may be impacted.

These interventions are increasingly urgent, as evidenced by the prevalence of jailbreaks and other flaws that can transfer across different providers’ GPAI systems."

arxiv.org/pdf/2505.09662

We were cited in this report by the Bipartisan House Task Force on Artificial Intelligence.

republicans-science.house.gov/_cache/files/a…

Peter Slattery (@PeterSlattery1) recently spoke about his work on the MIT AI Risk Repository (@MITAIRisk) on a panel about the future of AI at the U.S. Securities and Exchange Commission (@SECGov) in Washington, DC.

See agenda here: sec.gov/newsroom/press…

Resharing our Quantum Economic Advantage Calculator on #WorldQuantumDay

futuretech.mit.edu/quantum-econom…

Great work from Neil Thompson, Jayson Lynch, and Johannes Galatsanos.

At a recent lab meeting, Ted Xiao from Google DeepMind discussed progress in Robot Foundation Models. This relates to work which Sebastian Sartor and Neil Thompson have been leading (see link in comments).

Jayson Lynch recently collaborated on this article and benchmark with Clinton Wang, Dean Lee, Cristina Menghini, Johannes Mols, Jack Doughty, Adam Khoja, Sean Hendryx, Summer Yue, and Dan Hendrycks.

Christophe Combemale with his final closing remarks, including the importance of widespread inclusion in the decision-making around how AI should affect our collective future.

Martin Fleming (@Fleming_Martin) with some closing remarks, including the importance of ensuring that we don't automate a broken system.

Susan Helper, a Professor from the Weatherhead School of Management at Case Western Reserve University, sharing her reflections and learnings from the workshop.

A panel on 'Data to Support Research on Economics of AI' chaired by Christophe Combemale, and featuring Morgan Frank (@mrfrank5790), Erica Groshen, and Jason Owen-Smith.

Jason Owen-Smith, a Professor from the University of Michigan, discussing a decentralized system for collecting data relevant to AI's employment impacts.

Erica Groshen, a Senior Economic Advisor from Cornell University, discussing systems to curate and integrate data relevant to AI's impact on employment.

Morgan Frank (@mrfrank5790), Assistant Professor in the Department of Economics at the University of Pittsburgh (@PittTweet), discussing his work to develop datasets, and models, that can help us to predict AI unemployment risks.

A panel on 'Industrial Organization and Structural Change' chaired by Morgan Frank (@mrfrank5790), and featuring Avinash (Avi) Collis (@avi_collis) and Youngjin Yoo.

Youngjin Yoo, Associate Dean of Research, Weatherhead School of Management at Case Western Reserve University, discussing AI diffusion.

Avinash (Avi) Collis (@avi_collis) from @HeinzCollege of Information Systems and Public Policy discussing GDP-B, an approach to assess the economic benefits of free goods, including generative AI.

Today is the second day of an event, AI: Who Wins and Who Loses, which we are running in partnership with the Carnegie Mellon University Block Center for Technology and Society (@CMUBlockCenter).

See the agenda attached.

We will be posting updates throughout the day.

A panel on 'Labor Supply and the AI Workforce', chaired by Erica Groshen, featuring Stuart Andreason (@StuartAndreason), and Adam Leonard.

Adam Leonard, a former Director of Information, Innovation & Insight, Texas Workforce Commission, discussing the importance of adapting education.

Stuart Andreason (@StuartAndreason), from The Burning Glass Institute (@TheBGInstitute), discussing the impacts of AI on labor supply, and how regional differences.

Erica Groshen, a Senior Economic Advisor from Cornell University, introduces Session 5, and discusses how we should respond to workforce impacts from AI.

A panel on 'Labor Demand Implications of AI', chaired by Christophe Combemale, featuring Jason Owen-Smith, Susan Helper and Rob Seamans (@robseamans).

Susan Helper, a Professor from Weatherhead School of Management at Case Western Reserve University (@cwru), discussing whether AI could improve job quality.

Jason Owen-Smith, a Professor from the University of Michigan, discussing their approach for collecting data to explain how AI is reshaping employment.

Rob Seamans (@robseamans), a Professor at the NYU Stern School of Business, discussing how AI affects labor supply and demand.

A panel on 'Drivers of AI Deployment, Business Processes and Task Structure', chaired by Christophe Combemale, featuring Daniel Rock (@danielrock), and Marcio Cruz.

Marcio Cruz, Principal Economist, International Finance Corporation, discussing differences in technology adoption across countries.

Daniel Rock (@danielrock), Assistant Professor, Operations, Information and Decisions,
at @Wharton, discussing AI exposure, whether we should focus on things at the task level, and whether automation and augmentation is a false dichotomy.

Christophe Combemale explaining some questions of interest for the next part of the workshop.

A panel on 'Innovation, Adoption and Benefits of AI' chaired by Neil Thompson (@ProfNeilT), and featuring Raul Katz, Bryan Seegmiller, and Todd Lensman (@talensman).

Bryan Seegmiller, Assistant Professor of Finance, @KelloggSchool of Management, explaining how AI may create new employment opportunities, even if it replaces some roles.

Joseph Briggs, explaining @GoldmanSachs assessment of AI impacts on productivity.

A panel discussion on the macroeconomics of AI, chaired by Martin Fleming (@Fleming_Martin), and featuring Ramayya Krishnan from @HeinzCollege, and Joseph Briggs from @GoldmanSachs .

Christophe Combemale, discussing how enterprise characteristics might mediate adoption.

Martin Fleming (@Fleming_Martin), Research Scientist, at @MIT_CSAIL and MIT FutureTech discussing AI adoption and what we should expect.

Christophe Combemale, Assistant Research Professor at the Block Center for Technology and Society, explaining the background to this event.

First up we have a welcome from Ramayya Krishnan, Dean of @HeinzCollege of Information Systems and Public Policy and William W. and Ruth F. Cooper Professor of Management Science and Information Systems at Carnegie Mellon University

Today, in partnership with the Carnegie Mellon University Block Center for Technology and Society (@CMUBlockCenter), we are running an event, AI: Who Wins and Who Loses.

See full agenda here: cmu.edu/block-center/n….

We will be posting updates throughout the day.

Hans and Jayson attended the recent QUARC (QSEC Annual Research Conference) to present work analyzing trends in quantum computing to predict which quantum ML algorithms will be useful and when. The research was conducted in collaboration with Hrovje and Neil.

Danial Lashkari (@DanialLashkari) recently discussed preliminary findings from a project that develops a quantitative task-based theory of AI automation. The model constructs a hierarchical production structure to account for the interactions between downstream producers, AI rs of different skill, and upstream AI-task providers across different industries. We look forward to sharing further results soon!

In a recent lab talk, Emanuele Del Sozzo discussed some of his in progress work to understand trends and progress in NVIDIA GPUs, specifically data center GPUs released from the mid-2000s until today.

As this preliminary image shows, he finds rapid improvements across many key n a selection of prominent GPUs per microarchitecture generation.

Between 2009 and 2024, GPUs have experienced significant advancements in computational performance, particularly in floating-point operations, where 16-bit precision has grown at a compound annual growth rate (CAGR) of 65.3%, and 32-bit precision at 53.5% CAGR. Memory bandwidth has also expanded substantially, increasing at a 24.9% CAGR.

However, these performance gains have been accompanied by rising costs, with release prices growing at a 16.2% CAGR (inflation-adjusted), and increasing energy demands, as reflected in the 5.3% CAGR of thermal design power (TDP).

Alexander Saeri and Peter Slattery presented this poster at the recent International Association for Safe & Ethical AI conference.

@MITAIRisk @IASEAIorg @PeterSlattery1

The MIT AI Risk Repository was one of 50 projects chosen by the Paris Peace Forum to be showcased at the AI Action Summit.

If you are attending, please come to our booth at the Grand Palais on February 10th (we are only presenting on the 10th)

See links in comments for more roject and the other projects selected.

#AIActionSummit #ParisPeaceForum2025 #AIEthics #AIGovernance

At a recent lab meeting, Jason Pruet from Los Alamos National Laboratory (@LosAlamosNatLab) gave a very interesting presentation exploring how advanced AI might impact science and security. See the attached for some of his related prior work (anl.gov/sites/www/file…)

📢 We added 13 new frameworks and nearly 300 new risks to the MIT AI Risk Repository in our December 2024 update.

Authored by experts across the US, UK, Australia, Canada, China, and Germany, the new frameworks include government reports, peer-reviewed articles, and preprints.
:
1️⃣ International Scientific Report on the Safety of Advanced AI – Bengio et al., 2024
2️⃣ AI Risk Categorization Decoded (AIR 2024) – Zeng et al., 2024
3️⃣ A Survey of the Potential Long-Term Impacts of AI – Clarke & Whittlestone, 2022
4️⃣ Harm to Nonhuman Animals from AI – Coghlan & Christine, 2023
5️⃣ AGI Safety Literature Review – Everitt et al., 2018
6️⃣ GenAI Against Humanity – Ferrara, 2024
7️⃣ Future Risks of Frontier AI – UK Government, 2023
8️⃣ Regulating Under Uncertainty: Governance Options for Generative AI – G'SELL, 2024
9️⃣ Ten Hard Problems in AI We Must Get Right – Leech et al., 2024
🔟 Advanced AI Governance: A Literature Review – Maas, 2023
1️⃣1️⃣ Governing General Purpose AI: A Comprehensive Map of Unreliability, Misuse, and Systemic Risks – Maham & Küspert, 2023
1️⃣2️⃣ Artificial Intelligence Risk Management Framework: Generative AI Profile – National Institute of Standards and Technology (NIST), 2024
1️⃣3️⃣ AI Safety Governance Framework – National Technical Committee 260 (China), 2024

Read the attached for more information about the frameworks (including links to the papers) and the process we used.

Visit the website to access the updated database. Please use the feedback form in the comment to suggest new research to consider for future updates.

📢 MIT FutureTech is seeking a Research Assistant to work with Dr. Danial Lashkari (@DanialLashkari) on projects related to economics of technological progress, innovation, and artificial intelligence. This is an exciting opportunity to support a rapidly growing lab who work on nd computing research, and rapidly develop cutting-edge skills and experience.

Danial studies different long-run drivers of economic change, including technological breakthroughs such as IT and AI, the innovation activity of firms, transformations in the consumption patterns of households, and globalization. Since 2023, he has been with the Research and Statistics Department at the Federal Reserve Bank of New York.

Led by Dr. Neil Thompson, MIT FutureTech is an interdisciplinary group of computer scientists, engineers, and economists who study trends in computing and Artificial Intelligence. We aim to identify and understand where these trends will create opportunities or risks, and help leaders in computing, scientific funding bodies, and government to respond appropriately.

Our research therefore helps to answer important questions including: Will AI progress accelerate or decline – and should it? What are the bottlenecks to growth from AI, and how can they be solved? What are the risks from AI, and how can we mitigate them?

To support our research, we run seminars and conferences, to better connect the field of computer scientists, economists and innovation scholars, and build a thriving global research community.

To disseminate it, we advise governments, nonprofits and industry, including via National Academies panels on transformational technologies and scientific reliability, the Council on Competitiveness’ National Commission on Innovation and Competitiveness Frontiers, and the National Science Foundation (NSF)’s National Network for Critical Technology Assessment.

Our work has been funded by Open Philanthropy, the National Science Foundation (NSF), Microsoft, Accenture, IBM, the MIT-Air Force AI accelerator, and the MIT Lincoln Laboratory.

See the attachment and link in comments for more information on the role.

Please share with anyone relevant.

Check out this insightful @TEDxBoston talk by @ProfNeilT on the #AI scaling problem, exploring the quantification of #algorithmic efficiency improvements happening in #deeplearning. Watch now for actionable insights!

#Innovation #MIT #CSAIL #LanguageModels #Economics #Energy Talks

youtu.be/naDR7siJMqE?fe…

📢 MIT FutureTech is hiring a Product and Data Visualization Designer. This is an exciting opportunity to support a rapidly growing lab who work on socially impactful AI and computing research, and rapidly develop cutting-edge skills and experience.

Led by Dr. Neil Thompson reTech is an interdisciplinary group of computer scientists, engineers, and economists who study trends in computing and Artificial Intelligence. We aim to identify and understand where these trends will create opportunities or risks, and help leaders in computing, scientific funding bodies, and government to respond appropriately.

Our research therefore helps to answer important questions including: Will AI progress accelerate or decline – and should it? What are the bottlenecks to growth from AI, and how can they be solved? What are the risks from AI, and how can we mitigate them?

To support our research, we run seminars and conferences, to better connect the field of computer scientists, economists and innovation scholars, and build a thriving global research community.

To disseminate it, we advise governments, nonprofits and industry, including via National Academies panels on transformational technologies and scientific reliability, the Council on Competitiveness’ National Commission on Innovation and Competitiveness Frontiers, and the National Science Foundation (NSF)’s National Network for Critical Technology Assessment.

Our work has been funded by Open Philanthropy, the National Science Foundation (NSF), Microsoft, Accenture, IBM, the MIT-Air Force AI accelerator, and the MIT Lincoln Laboratory.

See the attachment and link in comments for more information on the role.

Please share with anyone relevant.

Our work is featured on page 5 of the 2024 MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) Annual Report. dropbox.com/scl/fi/1s7dbl8…

@MIT_CSAIL

At a recent lab meeting, Ekaterina Prytkova and Simone Vannuccini from the University of Sussex and Université Côte d'Azur presented their paper 'On the basis of brain: neural-network-inspired changes in general-purpose chips".

academic.oup.com/icc/article/31…

@Univ_CotedAzur @svannuccini @SPRU

@MeicenSun finds that China's internet control, the "Great Firewall", has benefited Chinese data-heavy firms (+26% revenue) but hurt Chinese research quality (-10%) in knowledge-driven fields, widening the research gap with the U.S. by 22%.

For more, see the paper: t.co/IxunH2Mpqo

Also, this interview on the technology policy podcast, Two Think Minimum: soundcloud.com/two-think-mini…

@StanfordPACS @StanfordCyber @MITpolisci @MIT_CIS @MITFutureTech @IntOrgjournal @iSchoolUI

At a recent lab meeting, Frank Nagle (@frank_nagle) presented his paper 'Generative AI and the Nature of Work': "We seek to build upon research on #AI and productivity to better understand how #GenAI changes how people do work.

We look at how the release of a GenAI coding tool d how developers allocate their efforts to different types of tasks. We find that GenAI leads workers to spend more time on core work activities and less time on managerial tasks.

We show two mechanisms drive this effect - workers with GenAI allocate more of their work efforts to things they can do by themselves (and less to collaborative work) and also do more exploration (new projects, new languages, etc.) and less exploitation (existing projects). Further we find the effects are greater for workers with lower ability.

Finally, we do a back-of-the-envelope calculation and show that using GenAI allows developers to start coding in languages that have higher wages, leading to a labor market value impact of nearly $500 million (this would likely diminish in the long run).

Though our empirical setting is open source software #OSS, we argue, and find evidence, that the results generalize to private work settings as well."
papers.ssrn.com/sol3/papers.cf…

Eamon Duede (@phil_of_ai), from Purdue University (@LifeAtPurdue) and Argonne National Lab (@argonne), presenting our final talk 'Conceptual and Technological Frictions on AI-Infused Science'

Keyon Vafa (@keyonV), from the Harvard Data Science Initiative (@harvard_data) presenting 'The LLM Conundrum'

Dashun Wang (@dashunwang), from The Center for Science of Science & Innovation (@KelloggCSSI) presenting 'AI and the Science of Science'

Neil Thompson (@ProfNeilT) introducing session 4: Challenges that AI poses to science.

@HaydnBelfield from the University of Cambridge Centre for the Study of Existential Risk (@CSERCambridge), and the Leverhulme Centre for the Future of Intelligence (@LeverhulmeCFI), talking about 'Big Tech Big Science: Challenges and opportunities for frontier AI governance'

Varun Chandola (@ChandolaVarun), from the National Science Foundation (@NSF), presenting 'Strengthening and democratizing the U.S. AI Innovation Ecosystem through the National AI Research Resource Pilot'

Next Rada Mihalcea (@radamihalcea), from the University of Michigan AI Lab (@michigan_AI), presenting 'The uneven access to knowledge'

Jan Eeckhout (@jan_eeckhout) delivering the first talk of the session on 'Market power and technological change'

Today is the second day of our workshop on the role of AI in science! First up, we have Neil Thompson (@ProfNeilT) introducing session 3: De-democratization and concentration of power in AI. See the full agenda here: futuretech.mit.edu/workshop-on-th…

To finish, we have Stephen Wolfram (@stephen_wolfram), the founder of @WolframResearch presenting 'AI, Computation and the Future of Science'

Manuela Veloso, from JPMorgan Chase (@jpmorgan) and Carnegie Mellon University (@CarnegieMellon) presenting 'Humans and AI: The Journey'.

Boris Kozinsky (@bkoz37), from @Harvard University, presenting 'How AI enables novel computations & theory predictions'

Now we have Yuji Roh (@RohYuji), from @Google with her talk on 'The AI Feedback Loop: New Promises and Risks of Large Models'. This is the first talk in session 2, which examines how AI is changing models and data.

Lilach Mollick (@lilach_m), from the Wharton AI & Analytics Initiative (@WhartonAIAI), presenting 'AI and education'.

Our next talk is Ross King from @Cambridge_Uni, presenting 'The automation of science'.

Next up we have Regina Barzilay (@BarzilayRegina), from the MIT Jameel Clinic (@AIHealthMIT), discussing 'How AI contributes to the discovery of new drugs'.

Our first talk is Danial Lashkari (@DanialLashkari), from Princeton University (@Princeton) and MIT FutureTech, kicking off Session 1, which examines the role of AI in advancing scientific research, presenting 'Scientific progress as the driver of economic growth'.

First up we have Director of @MITFutureTech Neil Thompson @ProfNeilT kicking off the workshop.

Today is the first day of the @MITFutureTech workshop on the role of AI in science! We are bringing together some of the most influential voices in AI and scientific research to discuss the profound ways artificial intelligence is reshaping science as we know it.

Topics AI in advancing scientific research
- AI in drug discovery and medical breakthroughs
- The ethical implications and societal impacts of AI
- Challenges and future directions for AI-driven science

See full agenda here: futuretech.mit.edu/workshop-on-th…

We will be posting updates throughout the day.

@MITSloan @MIT_CSAIL @mit_ide @mit

We are pleased to announce that Eamon Duede, from Purdue University, will be presenting at the FutureTech Workshop on The Role of AI in Science, scheduled for November 2nd and 3rd, 2024.

Eamon's talk, titled 'The technological limits of progress in AI', will be delivered in es challenges that AI poses to science.

For more information, see: futuretech.mit.edu/workshop-on-th….

Express interest in attending here: forms.gle/ccxVzx54237sfN…

We are pleased to announce that Manuela Veloso, from JPMorganChase and Carnegie Mellon University, will be presenting at the FutureTech Workshop on The Role of AI in Science, scheduled for November 2nd and 3rd, 2024.

Manuela Veloso's talk, titled 'Humans and AI: The Journey', ssion 4, which examines how AI is changing models and data.

For more information, see: futuretech.mit.edu/workshop-on-th….

Express interest in attending here: forms.gle/ccxVzx54237sfN…