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Quoting Akshat Bubna

<blockquote cite="https://www.reuters.com/business/openais-rogue-agent-compromised-an-account-second-tech-firm-sources-say-2026-07-28/">We’re aware a Modal customer published an unauthenticated endpoint that allowed anyone on the internet to use their ⁠sandboxes for code execution. This was used by the rogue agent. Modal’s ⁠platform or isolation were not compromised in anyway.</blockquote> — <a href="https://www.reuters.com/business/openais-rogue-agent-compromised-an-account-second-tech-firm-sources-say-2026-07-28/">Akshat Bubna</a>, Modal's CTO, talking to Reu

Simon Willison·just now

uv 0.12.0

<a href="https://github.com/astral-sh/uv/releases/tag/0.12.0">uv 0.12.0</a> Some interesting breaking changes in this release of <code>uv</code>, in particular to the default project produced by the <code>uv init</code> command. <a href="https://docs.astral.sh/uv/concepts/projects/init/">uv init</a> is the <code>uv</code> shortcut for creating a new project. The previous version of <code>uv</code>, version 0.11.x, produced <a href="https://github.com/simonw/uv-init-demos/tree/29656a55ec733a632005abfd7b89dea5c04fa10b/uv-init">this directory</a> when you ran <code>

Simon Willison·just now

Bot-detection startup Spur nabs $200M from Insight

Spur Intelligence has raised a $200 million round from Insight Partners for its tech that can identify legit human traffic from bots.

TechCrunch AI·just now

Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident

<a href="https://huggingface.co/blog/agent-intrusion-technical-timeline">Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident</a> Hugging Face just released this extremely detailed technical description of <a href="https://simonwillison.net/2026/Jul/22/openai-cyberattack/">OpenAI's recent accidental cyberattack against their infrastructure</a>. This attack was very sophisticated, and the resulting document doubles as a crash-course in modern adversarial security approaches. We're still waiting for more details from Op

Simon Willison·just now

MCP startup Runlayer accuses Rippling of stealing its product idea

Runlayer is suing Rippling after Rippling evaluated the startup's MCP gateway product and then opted to build one itself.

TechCrunch AI·just now

Sam Altman is ready to decelerate

His change of position comes after "the first security incident that I have felt very viscerally."

TechCrunch AI·just now

Models Hot

AI leaders sign a statement asking the government to do something about automated AI

Employees of OpenAI and Anthropic, as well as Google, Meta, Thinking Machines, Microsoft, Mistral, and other leading AI labs, have written a statement to the US government supporting a potential slowdown of sorts for frontier AI development - or at least a speed-up of global coordinated governance efforts. "Al could help create a dramatically better […]

The Verge AI·just now

AI’s finally expensive enough to make Wall Street nervous

It's earnings season, and investors got an unpleasant surprise from Google: an increase on its spending estimate, to as much as $205 billion - from the last quarter's projection of up to $190 billion. Even the lower end of Google's new projected range - $195 billion - is much more than the company had previously […]

The Verge AI·just now

Models Hot

Scientific computing in the age of agentic AI

A new field report shows how scientists use AI coding agents to modernize scientific computing, accelerating software development and discovery in genomics and beyond.

OpenAI Blog·just now

The OlmoEarth Platform: Geospatial inference at planetary scale

Hugging Face Blog·just now

Data centers may face temporary power cuts to prevent blackouts on largest US grid

The decision arrives as the breakneck pace of data center construction has grid operators scrambling to generate power.

TechCrunch AI·just now

LFM2.5-Encoders for Fast Long-Context Inference on CPU

Hugging Face Blog·just now

Funding Hot

Fish Audio raises $52M seed to build AI voice models for creators and enterprises

Since launching last year, the startup today has more than 8 million people using the open source or hosted version of its models, and now generates annual recurring revenue of $21 million.

TechCrunch AI·just now

Recursive Superintelligence signs $410M compute deal with Amazon

Recursive’s emphasis on self-improving AI systems means much of the budget that would traditionally go toward headcount and operations is put straight into compute, as the company seeks to automate its own product development process.

TechCrunch AI·just now

Perplexity’s Personal Computer turns Windows PCs into AI agents

Perplexity has expanded its agentic Personal Computer tool to Windows, allowing computers running the world's most popular OS to be used as a locally run AI system. Like the Mac version that Perplexity launched in April, Personal Computer for Windows operates like a "general-purpose digital worker" that can access local files and apps to perform […]

The Verge AI·just now

The Download: OpenAI’s predictable hack, and an AI stock sell-off

This is today’s edition of The Download , our weekday newsletter that provides a daily dose of what’s going on in the world of technology. OpenAI called the Hugging Face attack unprecedented. But we’ve been here before. —Will Douglas Heaven, senior AI editor Reading OpenAI’s account last week of how some of its models broke their containment and hacked into the computer systems of Hugging Face, another AI company, was the first time I got genuine chills about what large language models are now able to do. But this is a case of human hubris, not rogue AI. I am not an alarmist. In fact, I have

MIT Technology Review·just now

Smart rings are looking like my kind of AI gadget

Over the last few months, I've spent a lot of time talking to my computer. One underrated feature of the LLM revolution has been a remarkable leap in all kinds of dictation technology - even the fastest, cheapest models are getting very good at understanding and processing speech. I've tested lots of these apps, from […]

The Verge AI·just now

Samsung’s chip workers are jumping ship to rival SK Hynix

Lee, an engineer at Samsung’s semiconductor division, clocks out when his shift ends. He used to work longer hours, going the extra mile to excel at his projects. But lately, he’s been coming straight home to work on his job application for the chipmaker’s South Korean rival SK Hynix, sharing tips with his coworkers on how to draft a stellar personal statement. Even his boss encourages him to make the move. “My team lead tells us all to jump ship to SK Hynix,” says Lee. He and his coworkers are feeling demoralized by the $476,000 bonus that SK Hynix is set to pay its employees, flush with reco

MIT Technology Review·just now

Hugging Face is being used to easily undress women and children

Hugging Face is being used to make nonconsensual deepfakes, and the popular open-source AI model repository is doing very little to prevent it. That's according to a new report published by the European nonprofit AI Forensics, which found that seven out of the top nine image editing models hosted by Hugging Face readily complied with […]

The Verge AI·just now

Cursor makes its biggest India push yet ahead of SpaceX acquisition with localized pricing

Cursor says India is now its third-largest market globally and plans to expand local hiring and enterprise sales.

TechCrunch AI·just now

Concept-based Visual Counterfactual Explanations with Diffusion Models

arXiv:2607.22544v1 Announce Type: new Abstract: Visual counterfactual explanations aim to answer "what minimal change to this image would flip the model's prediction?", and are increasingly important as vision models are deployed in safety-critical domains (e.g., medicine). Existing diffusion-based methods can produce realistic edits, but they rely on external classifiers that must work reliably on noisy images, which makes them fragile and hard to deploy for robust explanations. We introduce C-VCE, a new diffusion framework that builds the classifier directly into the generative model via a c

arXiv cs.AI·just now

SeT-Diff: Towards Semantic Foundation Models for HPC Telemetry and Time-Series

arXiv:2607.22548v1 Announce Type: new Abstract: Data centers and their compute nodes require accurate and flexible digital twins capable of modeling the complex interplay of workloads, environmental parameters, and physical metrics. Current machine learning approaches for HPC and its telemetry typically rely on a static subset of anonymous, fixed-position sensor variables tailored to single tasks. Consequently, these models become obsolete when target tasks change or sensor metrics vary. We propose SeT-Diff, the first foundational model for compute node telemetry and time-series. Unlike rigid

arXiv cs.AI·just now

QFoldAgent: An Autonomous Quantum Optimization Multi-Agent System for Protein Structure Prediction

arXiv:2607.22549v1 Announce Type: new Abstract: Hybrid quantum-classical protein structure prediction depends strongly on Hamiltonian penalty weights, yet existing lattice-based workflows typically fix these coefficients by hand and evaluate only very short fragments in simulation. We present QFoldAgent, a closed-loop multi-agent framework for 5-residue tetrahedral-lattice folding in which a design agent proposes sequence-conditioned penalties, a VQE-based quantum-classical pipeline optimizes the resulting Hamiltonian under Qiskit Aer noise, and a feedback agent uses energy-landscape diagnosti

arXiv cs.AI·just now

Same Question, Different Answers: Evaluating LLM Reliability Beyond Accuracy

arXiv:2607.22554v1 Announce Type: new Abstract: Large language models (LLMs) often achieve strong accuracy on benchmarks, yet it remains unclear how reliably they apply this knowledge when the same question is phrased in different but equivalent ways. In this work, we study how model answers change under meaning-preserving paraphrases across factual question answering and mathematical reasoning tasks. Across four benchmarks and 13 models, we find that model outputs frequently depend on the exact wording of the prompt. While overall accuracy typically changes only modestly across paraphrases, i

arXiv cs.AI·just now

DeepLens Diagnosis Agent: Agentic Workflow Design Lets a Small Reasoning Model Compete with Frontier LLMs

arXiv:2607.22555v1 Announce Type: new Abstract: Medical diagnosis is a multi-stage process: extract facts, consult knowledge, generate a differential analysis, and select the best diagnosis with explanations. Frontier LLMs are strong generalists, but single-shot prompting often yields brittle diagnostic reasoning. We present the DeepLens Diagnosis Agent, a five-stage harnessing pipeline (combining model capabilities with disciplined process constraints) centered on a small medical reasoning model (JSL Medical Small 7B v2) and retrieval-augmented generation (RAG). The pipeline enforces structur

arXiv cs.AI·just now

MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models

arXiv:2607.22556v1 Announce Type: new Abstract: Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments. However, directly updating their limited parameter space causes catastrophic forgetting. While memory-based methods naturally address this by decoupling knowledge retention from parameters, existing approaches designed for large language models (LLMs) rely on abundant storage and strong in-context reasoning that SLMs lack. To address these challenges, we propose MIITA, a Memory-Induced Inference-Time Ada

arXiv cs.AI·just now

Codifying the Judge: Scalable Evaluation via Program Distillation

arXiv:2607.22561v1 Announce Type: new Abstract: LLM-as-a-judge has become the standard for automated evaluation, but it suffers from high cost, significant latency, and opaque decisions -- limitations that undermine its scalability and reliability. We address these with a simple, efficient alternative: program distillation. Instead of prompting an LLM at the evaluation time, we distill its decision logic into a committee of programs that score candidates directly. These programmatic judges offer transparency, are easily inspected or edited, and eliminate per-sample API costs. Building on this

arXiv cs.AI·just now

SF-AMS: Strategic Forgetting for Structured Memory in LLM Agent

arXiv:2607.22562v1 Announce Type: new Abstract: Managing long-context dependencies remains a primary bottleneck in LLM agents, as redundant and irrelevant information can degrade multi-step reasoning. Strategic Forgetting for Agent Memory Systems (SF-AMS) is proposed as a framework for maintaining compact high-utility memory by modeling the long-term importance of memory units. SF-AMS replaces static retrieval and heuristic decay with a utility-driven survival mechanism that updates memory importance from usage redundancy and temporal signals, inducing a hierarchical memory structure that prio

arXiv cs.AI·just now

Synthetic Scenario Generation for Evaluation of Industry 4.0 Agents

arXiv:2607.22563v1 Announce Type: new Abstract: Industrial agent benchmarks require realistic evaluation scenarios that integrate telemetry, failure modes, maintenance records, and domain standards. However, existing benchmarks such as AssetOpsBench rely on manually authored scenarios and cover a limited set of asset classes. We extend AssetOpsBench with a Smart Grid Transformer asset class and four IEC-grounded diagnostic tools for health-index prediction, dissolved-gas analysis, winding-temperature assessment, and load-profile assessment. We further introduce ScenarioGeneratorAgent, a pipeli

arXiv cs.AI·just now

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits

arXiv:2607.22564v1 Announce Type: new Abstract: Convolutional neural networks often contain redundant feature maps that increase storage and inference cost. This paper presents a loss-aware feature-map pruning framework using multi-armed bandits. Feature-map pruning is structured because it removes complete convolutional output channels and their producing filters rather than isolated scalar weights. Each candidate feature map is treated as an arm. At each play time, one map is temporarily masked and evaluated on a sampled mini-batch; the map is then restored and the observed loss change is co

arXiv cs.AI·just now

DSTFView: Multi-View Cloud-Edge Workload Forecasting with Dual-Input Spatio-Temporal-Frequency Modeling

arXiv:2607.22565v1 Announce Type: new Abstract: With the widespread deployment of edge-side AI inference, edge platforms are increasingly required to support latency-sensitive, highly concurrent, and reliability-critical applications. However, existing methods often struggle to balance multidimensional feature modeling and forecasting efficiency in collaborative cloud-edge environments. To address this issue, we propose DSTFView, a dual-input spatio-temporal-frequency multi-view workload forecasting framework for collaborative cloud-edge environments. It jointly models closeness and period dep

arXiv cs.AI·just now

MedLoCoMo: A Long-Context Multi-Session Medical Dialogue Benchmark for Large Language Models

arXiv:2607.22566v1 Announce Type: new Abstract: MedLoCoMo is a Medical Long-Context Memory benchmark for patient-specific clinical reasoning over multi-admission medical dialogue. Existing medical QA benchmarks largely test short context knowledge or single document grounding, leaving open whether LLMs can use, connect, and abstain over longitudinal patient histories. We build MedLoCoMo from deidentified MIMIC-IV and MIMIC-IV-Note records by constructing admission-level clinical packets, synthesizing grounded doctor-patient conversations, and generating evidence linked QA items over single-adm

arXiv cs.AI·just now

Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution

arXiv:2607.22546v1 Announce Type: new Abstract: Machine translation (MT) systems continue to produce gender-biased translations. In a time where self-expression is paramount, mistranslations based on default behaviour and stereotyping can lead to harm for users of these systems. To better understand how these systems translate gender in the absence of clear gender cues, we need benchmarking resources that reflect gender-ambiguous scenarios in a natural way. To this end, we present GAND, a gender-ambiguous natural data benchmarking resource for MT consisting of English source sentences, specifi

arXiv cs.CL·just now

MioFFAn: an Annotation Software for Formula Formalization with LLM Automation Capabilities

arXiv:2607.22552v1 Announce Type: new Abstract: The automatic translation of mathematical expressions in scientific literature into executable symbolic code (a process we refer to as Formula Formalization) is hindered by a severe scarcity of high-quality, ground-truth datasets specialized for technical scientific domains. In this paper, we present MioFFAn, an open-source, document-centric, and customizable framework designed to facilitate rapid annotation for this task. Building upon the MioGatto architecture, we extend existing features to overcome structural limitations and pivot its scope b

arXiv cs.CL·just now

Evaluating the Impact of Reviewer Guideline Design on LLM-Based Automated Peer Review

arXiv:2607.22553v1 Announce Type: new Abstract: Peer review is an essential process in scientific research, yet the growing workload has made its automation increasingly necessary. In this study, we analyze how different types of reviewer guidelines, such as official conference guidelines and reviewer-imitating ones generated from high-quality human reviews using LLMs, affect automated peer review. Our experiments show that official conference guidelines produce review results most consistent with human judgments, suggesting that evaluation criteria refined through conference practice serve as

arXiv cs.CL·just now

Learning When to Reason for Text-to-SQL via SFT and DPO

arXiv:2607.22622v1 Announce Type: new Abstract: Recent Text-to-SQL methods rely heavily on reasoning-centric paradigms such as Chain-of-Thought (CoT), achieving substantial gains on complex benchmarks at the cost of high inference-time overhead. However, a large fraction of real-world queries are simple lookups or aggregations that can be resolved without multi-step deduction, making forced reasoning wasteful. Thus, we propose AutoThinkSQL, a framework that integrates an auto-thinking mechanism into both Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) on Text-to-SQL. Our

arXiv cs.CL·just now

Between Suppression and Collapse: Evaluating Narrative Unlearning with LENS

arXiv:2607.22657v1 Announce Type: new Abstract: Large language models (LLMs) can reproduce disinformation-aligned narrative frames as plausible explanations, raising the question of whether existing machine-unlearning algorithms can suppress this behavior. We introduce Level-based Evaluation of Narrative Suppression (LENS), a contextualization based evaluation protocol for testing target narrative reproduction across direct, attributed, contrastive, and abstract resistance levels. We evaluate two source-grounded narratives: one framing Russia's war against Ukraine as forced by NATO expansion,

arXiv cs.CL·just now

PatiGonit22K: A Comprehensive Dataset for Solving Complex Bengali MWPs

arXiv:2607.22859v1 Announce Type: new Abstract: Mathematical Word Problems (MWPs) are an important benchmark for evaluating natural language understanding and quantitative reasoning. Despite recent progress in high resource languages, Bengali remains underexplored due to the limited availability of large scale annotated datasets. In this work, we introduce PatiGonit22K, an expanded Bengali MWP dataset containing 22,441 problems, developed by extending the original PatiGonit dataset with a substantially larger collection of complex mathematical problems. The dataset includes both simple and mul

arXiv cs.CL·just now

CHiPS: Character Histograms and Positional Signals for Lightweight Authorship Attribution in Romanian Texts

arXiv:2607.22884v1 Announce Type: new Abstract: We propose CHiPS, a lightweight character-level authorship attribution method for Romanian texts. All reported experiments are closed-set: the true author is one of the candidate authors in the training data. CHiPS studies two complementary fingerprints of writing style: CH-SVM, a character-histogram classifier based on one-character marginal distributions, and FFT12-LR, a positional-signal classifier that represents selected characters and punctuation classes as impulse trains (binary indicator sequences over character positions) and extracts Fo

arXiv cs.CL·just now

Simple Language Normalization Wins: Cross-Lingual Speaker Verification for the TidyVoice 2026 Challenge

arXiv:2607.22923v1 Announce Type: new Abstract: Cross-lingual mismatch remains a key source of overall degradation in modern speaker verification. The TidyVoice2026 Challenge targets this setting with text-independent verification, comprising 3,666 training and 808 development speakers in 40 languages and 2,200 evaluation speakers in 38 unseen languages, without language labels at test time. Starting from the official SimAM-ResNet34 baseline pretrained on VoxBlink2 and VoxCeleb2 and fine-tuned on TidyVoice, we revisit Nuisance Attribute Projection (NAP) as a simple language-normalization step

arXiv cs.CL·just now

Not All LLM Reasoning is Visible in the Chain-of-Thought

arXiv:2607.22925v1 Announce Type: new Abstract: A key question for AI safety is whether a language model expresses all of its reasoning in its output tokens. We demonstrate a concrete failure mode where frontier models exhibit invisible reasoning by leveraging semantically irrelevant filler tokens to improve performance on synthetic reasoning tasks. We evaluate 13 frontier language models across three tasks and find that many models benefit significantly from filler tokens, with accuracy improvements of up to 13 percentage points. The benefit depends on which tokens are used and differs across

arXiv cs.CL·just now

Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records

arXiv:2607.22954v1 Announce Type: new Abstract: Objective: To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world discharge summaries, and to identify recurring failure modes that limit reliability at scale. Materials and Methods: We applied a two-stage LLM pipeline---open-ended candidate identification (Gemini 2.5 Pro) followed by context-grounded verification (Gemini 2.5 Flash)---to 3,000 randomly sampled MIMIC-IV-Note discharge summaries. A subset of the pipeline output was then reviewed manually by clinica

arXiv cs.CL·just now

Beyond Direct Answering: Aligning Educational LLMs as Socratic Guides via Heuristic Reinforcement Learning

arXiv:2607.22996v1 Announce Type: new Abstract: Large language models (LLMs) deployed in educational settings often behave as direct answerers: they disclose target concepts in the opening turn instead of guiding students through progressive inquiry, as Socratic pedagogy prescribes. We present HeuristicEdu, a two-phase pipeline that aligns Qwen2.5-7B toward Socratic tutoring via supervised warm-up and Group Relative Policy Optimization (GRPO). Training uses SocraticEdu, 797 multi-turn Chinese children's science dialogues reconstructed from a live platform, with a heuristic reward over cognitiv

arXiv cs.CL·just now

Speech Signals Complement LLMs for Predicting Interpersonal Attraction in Speed Dating

arXiv:2607.23037v1 Announce Type: new Abstract: Large language models (LLMs) can predict interpersonal attraction from conversation transcripts, but it remains unclear what a speech predictor can add beyond transcript-only LLM prediction. Using Japanese speed-dating conversations, we combine predictions from a transcript-only LLM and a supervised speech predictor to estimate participants' reported liking of their partners. We show that speech can complement transcript-only LLM prediction, but that this complementarity is conditional rather than universal. Combining the two predictions signific

arXiv cs.CL·just now

Products

Anthropic’s Dario Amodei responds: doesn’t oppose open-weight models, but fears Chinese AI

Anthropic founder and CEO Dario Amodei made his views clear about open-weight models and China's growing AI capabilities.

TechCrunch AI·just now

moonshotai/Kimi-K3

<a href="https://huggingface.co/moonshotai/Kimi-K3">moonshotai/Kimi-K3</a> As promised <a href="https://simonwillison.net/2026/Jul/16/kimi-k3/">earlier this month</a>, Moonshot have released the weights for their excellent 2.8 trillion parameter Kimi K3. They're a hefty 1.56TB on Hugging Face. Kimi introduced their own janky <a href="https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/LICENSE">modified version of the MIT license</a> with K2 back in July 2025. That license just added this paragraph requiring attribution beyond a certain size of commercial

Simon Willison·just now

An opinionated guide to which AI to use to do stuff

<a href="https://www.oneusefulthing.org/p/an-opinionated-guide-to-which-ai-b22">An opinionated guide to which AI to use to do stuff</a> It's interesting watching the evolution of Ethan Mollick's guide over time. <a href="https://www.oneusefulthing.org/p/using-ai-right-now-a-quick-guide">A year ago</a> it was still all about chat - ChatGPT, Claude, Gemini - with o3, Claude 4 Opus, and Gemini 2.5 Pro as the models and Deep Research as a useful alternative mode. Today it's much more about agentic systems - "where the AI is capable of doing the equivalent of

Simon Willison·just now

Products

Satya Nadella says companies that trust one AI for everything may not survive

Companies without their own models — or without a layer of AI infrastructure known as AI gateways to separate their prompts from the model itself — will be in trouble, Nadella says.

TechCrunch AI·just now

PSA: Your Claude shared chats and Artifacts may have ended up on Google

The issue appears to have originated from Claude’s “share chat” feature, which allows users to create links that enable anyone with the assigned URL view a conversation or project.

TechCrunch AI·just now

Products

Microsoft launches its first cybersecurity model, plus a new agentic cybersecurity system

Microsoft bolstered its AI cybersecurity offerings this week with the launch of its first AI security model and a new security platform.

TechCrunch AI·just now

OpenAI called the Hugging Face attack unprecedented. But we’ve been here before.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here . Reading OpenAI’s account last week of how some of its models broke their containment and hacked into the computer systems of Hugging Face , another AI company, was the first time I got genuine chills about what large language models are now able to do. But this is a case of human hubris, not rogue AI. I am not an alarmist. In fact, I have been pushing back against AI scare stories for years. Even so, this incident crossed a line. I think it’s the clearest

MIT Technology Review·just now

Why China is giving away its best AI models

Silicon Valley has spent much of the past week on red alert, digesting the arrival of Moonshot AI's Kimi K3, a Chinese AI model that can allegedly beat some of the best systems built by US companies at a fraction of the cost. Its performance alone would have been enough to intensify the rivalry between […]

The Verge AI·just now

How lasers could help provide fuel for nuclear reactors

Outside the small town of Paducah, Kentucky, a wealth of uranium is locked away in thousands of storage cylinders filled with waste material from a now-closed nuclear enrichment facility. Lasers could help get it out. A company called Global Laser Enrichment (GLE) is looking to reprocess this old material with a new technology called laser enrichment. It could be more efficient than conventional enrichment methods, allowing the company to refresh the material and produce feedstock at the same concentration as a natural mined source. And in the future, the company claims, laser enrichment could

MIT Technology Review·just now

The Download: lasers for nuclear fuel, and organ preservation advances

This is today’s edition of The Download , our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How lasers could help provide fuel for nuclear reactors Nuclear power provides about 9% of global electricity today, and that fraction could tick up as countries look to build new reactors. New, cheaper methods to obtain fuel could help ensure that those nuclear projects stay on track. One of those methods is called laser enrichment. It allows you to separate out the material you want (in this case, uranium) from others in a mixture of old waste. A company

MIT Technology Review·just now

Tools

Nvidia, Microsoft launch open AI security alliance — without OpenAI, Google, or Anthropic

Nvidia on Monday said it is joining forces with Microsoft, SpaceX, IBM, and other tech companies to build and share open-source AI security tools. The new Open Secure AI Alliance said open tools are required to effectively defend against attacks from frontier models. The initiative is a direct response to mounting concerns over the safety […]

The Verge AI·just now

The path to artificial superintelligence

Imagine a healthcare system made up of multiple AI agents: one that manages symptom assessment, another scheduling, a third insurance, and a fourth pharmacy. Each is an expert in its domain. But they all have their own distinct knowledge and objectives. Today they can exchange data, but they are not yet able to actually coordinate patient care without a human making the decisions. “The intelligence is already there. What is missing is the connective tissue that turns four strangers into one team,” explains Vijoy Pandey, senior vice president and general manager of Outshift by Cisco. This “conn

MIT Technology Review·just now

Closing the data loop in AI-driven drug discovery

Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage. Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law . Today, bringing a new drug to market takes an average of 10-15 years and costs anywhere from $1 billion to $2.5 billion , with failure rates upward of 90%. AI has become the pharmaceutical industry’s biggest bet on bringing success rates up and timelines down. The faster drug companies can identify, test, and optimize new c

MIT Technology Review·just now