Search: “mit”

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MIT researchers published HardFlow, a method enforcing hard safety constraints on flow-matching generative models' final outputs without retraining.

MIT researchers led by Zeyang Li and Navid Azizan developed HardFlow, a trajectory-optimization method that enforces strict, non-negotiable constraints on flow-matching generative models by checking rule satisfaction only at the final generation step. Published in IEEE TPAMI, it outperformed six rival projection and guidance methods on four simulated benchmarks including D3IL robotic manipulation, Maze2D, physical process control, and image editing. All results are simulation-only, with no independent reproduction yet reported.

Hacker News · securitySep 14, 16:18 UTC · 2d agoAI research HN 24↑ · 25 comments

Roundtables: Could AI really kill us all?

MIT Technology Review subscriber roundtable debates whether AI extinction fears voiced by leading-lab employees are credible or hype.

MIT Technology Review published a subscriber-only roundtable conversation unpacking AI extinction fears, referencing claims by employees at leading AI labs that advanced AI could destroy humanity. The session examines where these fears originate, whether they hold any water, and what should be done if so. It is a discussion piece with no new research or concrete safety incident.

MIT Technology Review · AISep 15, 17:47 UTC · 22h agoAI safety & security 2 sources

New insights from Google’s AI & Economy ATLAS

Google launches an interactive AI & Economy ATLAS experience; new research shows nearly half of surveyed scientists use AI daily.

Google introduced new interactive, open-access data visualizations for its AI & Economy ATLAS project tracking global AI adoption patterns. Research from Google, Google DeepMind, and MIT FutureTech analyzed 2,600 specialized AI models and surveyed over 600 U.S. and U.K. scientists, finding nearly half use AI daily and report saving almost seven hours per week. The study also found validation bottlenecks and a growing backlog of untested hypotheses limiting research productivity gains.

Google · AISep 15, 13:00 UTC · 1d agoAI industry

US data centers could consume more natural gas than Germany and Japan combined by 2035

BloombergNEF projects US data centers will consume about 18 billion cubic feet of natural gas daily by 2035, exceeding Germany and Japan combined.

A new BloombergNEF report forecasts US data centers will consume roughly 18 billion cubic feet of natural gas per day by 2035, nearly double the estimate from nine months ago. On-site gas plants planned by Meta, Microsoft, Google, and Amazon would use 2.9-3.4 billion cubic feet per day, while grid-connected data centers drive an additional 15 billion cubic feet per day of power-sector gas demand. The added demand would generate about 1 million metric tons of extra greenhouse gas pollution daily, roughly 12% of current US emissions.

TechCrunch · AISep 15, 18:29 UTC · 22h agoAI industry

What must happen for AI’s trillion-dollar gamble to pay off

Hyperscalers need 2.7x productivity gains by 2030 to justify nearly $1.1 trillion in AI data center spending, or risk bankruptcy and capital misallocation.

Wharton finance professor Jessica Wachter estimates hyperscaler AI expenditure will reach nearly $1.1 trillion through 2027 and that a 2.7x productivity increase is needed to break even by 2030. AI revenues of roughly $150-200 billion this year fall far short of about $750 billion in annual spending, with total investment from Alphabet, Microsoft, Amazon, Meta, and Oracle potentially exceeding $5 trillion over four years. Alphabet reported its first free cash flow deficit (about $5.9 billion) since its 2004 IPO due to AI infrastructure costs. Researchers warn that failed demand could make the buildout the largest capital misallocation in history, with depreciating GPU chips risking stranded assets.

MIT Technology Review · AISep 15, 10:00 UTC · 1d agoAI industry

Sakana AI Researchers Introduce PC-ALM, a Layer-Local Alternative to Backpropagation That Trains 1000-Layer Networks

Sakana AI's PC-ALM adds per-layer Lagrange multipliers to predictive coding, matching backprop on networks up to 1000 layers with layer-local updates.

Sakana AI researchers propose Augmented Lagrangian Predictive Coding (PC-ALM), a training method that keeps every update layer-local while recovering backprop-aligned credit signals. The team proves multipliers converge to exact backprop adjoints in linear networks and trains 1000-layer residual MLPs on MNIST within about 2 points of backprop accuracy. PC-ALM matched backprop across a width/depth grid from 8 to 128 on MNIST and Fashion-MNIST where standard predictive coding failed in deep, narrow networks, and improved over PC on ResNet-18 with CIFAR-10 and Tiny ImageNet. An MIT-licensed JAX reference implementation reproduces the results on CPU.

MarkTechPostSep 14, 20:46 UTC · 1d agoAI research1

Building the materials foundation for AI

Syensqo's CTO says AI pushes semiconductors and data centers to physical limits, driving advanced materials demand and AI-accelerated materials discovery.

MIT Technology Review's Business Lab podcast, produced in partnership with Syensqo, features CTO Mike Finelli discussing how AI workloads push semiconductors and data centers to physical limits in performance, thermal management, and reliability. Syensqo develops high-voltage data center materials, semiconductor sealing materials, and immersion cooling fluids, while using AI agents to digitally synthesize millions of molecular combinations and predict performance before lab testing. Finelli describes a reinforcing cycle where AI improves materials that in turn enable better AI infrastructure.

MIT Technology Review · AI12:47 UTC · 3h agoAI industry

Nearly one in five AI researchers already expected an extinction scenario from AI back in 2024

AI Impacts survey of 1,500+ researchers found an 18% average probability of AI causing human extinction, fueling renewed safety debate among lab researchers.

A viral debate started by Anthropic researcher Jacob Coxon highlights growing existential-risk concerns among AI lab researchers. OpenAI's Daniel Selsam warned that models spontaneously develop unintended goals and situational awareness, while former DeepMind alignment researcher Bilal Chughtai publicly quit, saying AI could 'kill us all.' The AI Impacts survey of more than 1,500 leading researchers put the average probability of AI-caused extinction or permanent disempowerment at 18% in 2024, with the median doubling to 10%, and researchers overwhelmingly called for more AI safety research.

The Decoder10:20 UTC · 6h agoAI safety & security1

Exclusive: Paying for frontier AI models buys 4-month head start at 5x the cost

Mozilla report finds the capability gap between best open-weights (largely Chinese) and closed frontier AI models narrowed to 4.4 months at ~5x lower cost.

Mozilla's State of Open Source AI report (September 15) says the gap between closed frontier models and best open-weights models has closed to 4.4 months. Moonshot AI's Kimi K3 scores three points behind Anthropic's Fable 5 on the Artificial Analysis Intelligence Index at 30% of the cost, and Z.ai's GLM 5.2 scored within a point of Claude Opus 4.7 on Terminal-Bench 2.1. Eight of the top 10 OpenRouter models by August 2026 token volume provide open weights, though a Linux Foundation paper found open models earned only 4% of revenue. The report recommends open models as the default for routine workloads, reserving closed models for 8-12 hour expert tasks.

Ars Technica · AISep 15, 12:00 UTC · 1d agoAI industry1

Agent Harness vs Agent Framework vs MCP: Which Layer Owns the Loop, State, Tools, Permissions, and Recovery

Architecture explainer separates agent harnesses, frameworks, and MCP by which layer owns the loop, state, permissions, and recovery.

The article distinguishes agent harnesses (OpenAI Codex, Claude Agent SDK), which own the execution loop, sandbox, permission model, and recovery; frameworks (LangGraph, OpenAI Agents SDK, Microsoft Agent Framework), which supply composable primitives; and MCP, a stateless JSON-RPC wire protocol governed by the Linux Foundation's Agentic AI Foundation since December 2025. An ownership matrix maps the execution loop, state, tool transport, permissions, recovery, sandboxing, and multi-agent orchestration to each layer. The 2026-07-28 MCP specification made the protocol fully stateless, retiring the initialize handshake and session headers.

MarkTechPostSep 14, 21:54 UTC · 1d agoAI research1

Microsoft’s new AI ‘code of conduct’ tells models not to hack systems or trick humans

Microsoft published an AI code of conduct barring its MAI models from cyberattacks, deepfakes, and evading human oversight.

Microsoft released an AI code of conduct defining values and safety constraints for training its MAI models, including "absolute constraints" forbidding cyberattacks, nuclear weapons, and deepfake production. Each model's conduct code overrides individual user preferences or task instructions, with provisions against mechanisms that defeat human oversight. The document predicts superintelligent AI within a decade, and Satya Nadella endorsed frontier pacing and embedded evaluators alongside Anthropic, OpenAI, and xAI.

TechCrunch · AISep 14, 16:27 UTC · 2d agoAI safety & security