📢 New paper: Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts AI creates many risks, from discrimination, privacy loss, and fraud to more emerging concerns such as overreliance, dangerous capabilities being misused in weapons or cyberattacks, and AI systems pursuing unintended goals. But which risks are most severe? Who is most vulnerable? And who is most responsible for addressing them? To answer these questions, we conducted a three-round expert consultation with 272 AI experts. 💡 Four insights from our findings: 1️⃣ If things continue as they are over the next 5 years, experts assigned ≥10% probability of catastrophic outcomes (e.g., >1 million deaths or >$100 billion in losses) to 18 of 24 risks. Top concerns: cyberattacks and weapons, dangerous AI capabilities, competitive dynamics, power centralization, and disinformation and influence at scale. 2️⃣ Even assuming pragmatic mitigations, 5 risks remained above the 10% catastrophic threshold: dangerous AI capabilities, cyberattacks and weapons, environmental harm, inequality, and power centralization. 3️⃣ Vulnerability is broadly distributed, but responsibility is concentrated. Experts assigned the highest vulnerability to AI users and the general public, while assigning primary responsibility for mitigation to frontier AI developers, governments, regulators, and standards bodies. 4️⃣ Information, finance, and national security were rated the sectors most vulnerable to AI risks. 🔗How can you engage? See our (fancy) new webpage for our interactive summaries of the findings and preprint, and please share with anyone working on AI risk, governance, or policy. airisk.mit.edu/priorities This research is part of the MIT AI Risk Initiative (@MITAIRisk), which aims to help society understand, prioritize, and manage risks from AI. The initiative includes the MIT AI Risk Repository, a living database of more than 1,700 AI risks, the AI Incident Tracker, a collaboration with the Responsible AI Collaborative, which connects risks to over 1,400 incidents, and the MIT AI Governance Map, which analyzes risk coverage across more than 1,000 laws, standards, policies, and other governance documents curated by the Center for Security and Emerging Technology (CSET). #AI #AIrisk #AISafety #AIGovernance #ResponsibleAI #RiskManagement

How can we ensure AI's economic benefits are widely shared? At the AI for the Economy Forum, @Google and @MITFutureTech are convening policymakers & experts to discuss the partnerships needed to support workers in the AI transition. Learn more: goo.gle/4dGy7cN

Crashing Waves vs. Rising Tides: Preliminary Findings on AI Automation from Thousands of Worker Evaluations of Labor Market Tasks abs: 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… #AI #Science #AIResearch #FutureOfScience

Can Academic Science Keep Up with the AI Frontier? Our newest substack tracks the meteoric rise of AI foundation models in science and the constraints on future breakthroughs. Read here: mitfuturetech.substack.com/p… #AIResearch #AIinScience #FoundationModels

For example, @MITFutureTech found that shifting from LSTMs (green) to Modern Transformers (purple) has an efficiency gain that depends on the compute scale: - At 1e15 FLOP, the gain is 6.3× - At 3e16 FLOP, the gain is 26× Naively extrapolating to 1e23 FLOP, the gain is 20,000×!

What 2 #MIT experts are thinking about #AI and work buff.ly/eGkdoKj @MITSloan @MIT_CSAIL @MIT_IDE @MITFutureTech @DavidAutor #business #leadership #management #tech #automation #CIO #CTO #CDO #CEO #digital #innovation #disruption #digitaltransformation #jobs #skills #talent #talentmanagement #upskilling #futureofwork

Great to see the MIT AI incident tracker, led by Simon Mylius, featured in @TIME: time.com/7346091/ai-harm-ris… The AI Incident Tracker maps incidents in the AI Incident Database according to the MIT AI Risk Repository’s causal and domain taxonomies, and assigns each incident a harm-severity score. Using an LLM, it processes raw incident reports, providing a scalable methodology that can be applied cost-effectively across much larger datasets as numbers of reported incidents grow. In the dashboard you can explore trends such as: - distribution of incident classifications by year - distribution of incident sub-domains by year - incidents with high direct harm severity scores by year - incidents causing severe harm in more than one harm category - distribution of harm severity scores by year Our last update added new evaluation fields for each incident, including: - 5 categories of NatSec impact: Physical Security & Critical Infrastructure / Information Warfare & Intelligence Security / Sovereignty & Government Functions / Economic & Technological Security / Societal Stability & Human Rights - A Fishbone/Ishikawa diagram that presents a number of potential causes for each incident - The primary goal of the AI system involved Visit our website to explore the data. Congratulations also to Daniel Atherton and the AI Incident Database (@IncidentsDB) for the coverage. We are lucky to be able to build on their critical work. Thanks to Harry Booth for the write-up (@HarryBooth59643)

The world’s top AI researchers are paid millions for their expertise—but how much do individual breakthroughs really drive AI progress, compared to simply building bigger datacenters? Our new paper, On the Origins of Algorithmic Progress in AI, suggests the literature overestimates algorithms and underestimates compute. Deep dive via our substack: substack.com/home/post/p-181… #AIResearch #ComputeScaling #AlgorithmicProgress

I'm excited to share an article I wrote for the @WSJ on AI-related jobs of the future. e.g., the “AI explainer,” an expert who understands AI deeply and can translate it into plain language for managers and others wsj.com/tech/ai/new-jobs-ai-… via @WSJ

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/opportuni… #EconRA #AIEconomics #ResearchJobs #AIResearch

Are you looking to gain direct experience in technical and governance AI Safety research? Sharing this fantastic opportunity for a full-time, paid, in-person Spring Research Fellowship with Cambridge Boston Alignment Initiative. MIT FutureTech’s @aksaeri and @PeterSlattery1  are among the mentors supporting fellows in developing impactful, rigorous AI safety research: cbai.ai

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/1taG… #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