The AI world,in 5 minutes a day

Intelligent Three Level Learning Architecture for Autonomous UAV Swarms in Search and Rescue

arXiv:2607.14093v1 Announce Type: new Abstract: This paper presents a novel three level hierarchical learning architecture for autonomous UAV swarms performing search and rescue operations. Unlike conventional approaches that apply a single learning paradigm across all hierarchy levels, the proposed architecture integrates three qualitatively different learning mechanisms corresponding to the biological hierarchy of reflexes, skills, and reasoning such as Hebbian neuroplasticity for individual agent adaptation, multi agent reinforcement learning with graph neural networks and behavior trees fo

arXiv cs.AI·just now

HG-RAG: Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs

arXiv:2607.14095v1 Announce Type: new Abstract: Retrieval Augmented Generation (RAG) has proven to be a widely successful process at improving the quality of outputs from a Large Language Model (LLM) for wider context. However, RAG systems typically retrieve context from flat document stores, which struggles when queries require hierarchical or relational reasoning across structured knowledge. I present HG-RAG (Hierarchy-Guided RAG), a framework that performs graph-traversal over a hierarchical knowledge graph to deliver structured context to a language model. My retrieval pipeline resolves a

arXiv cs.AI·just now

IMEX Interaction-Based Model Explanation

arXiv:2607.14096v1 Announce Type: new Abstract: In predictive modeling, the ability to explain why a model produces a given target prediction has become increasingly important [5, 10]. Black-box models do not provide a transparent description of the internal mechanisms that generate the prediction, making even accurate predictions difficult to interpret and validate. In critical contexts, predictive accuracy alone is not a sufficient validation metric if the reasons underlying model decisions remain unexplained. The IMEX (Interaction-Based Model Explanation) approach represents a methodologica

arXiv cs.AI·just now

Policy Hot

RegNetAgents: A Multi-Agent Framework for Cross-Network Regulatory Driver Identification in Cancer Genomics

arXiv:2607.14097v1 Announce Type: new Abstract: We introduce RegNetAgents, an AI-oriented multi-agent framework for structured, query-driven regulatory candidate identification across heterogeneous gene regulatory networks. The system enables unified analysis of bulk tumor and single-cell-derived ARACNe networks by integrating TCGA-derived cancer networks with large-scale single-cell regulatory networks from the GREmLN project. For a given focal gene, the framework performs dual-network classification, cancer gene filtering using OncoKB annotations, and mode-of-action (MoA) assignment for tumo

arXiv cs.AI·just now

DialogueVPR: Towards Conversational Visual Place Recognition

arXiv:2607.14115v1 Announce Type: new Abstract: Inspired by how humans communicate spatial information, language-guided geo-localization has gained significant traction for its intuitive and practical value. Despite this progress, most methods still rely on a static, one-shot retrieval paradigm, which fails to handle the ambiguity and incompleteness inherent in real-world natural language descriptions. We propose a paradigm shift to reasoning retrieval and introduce Dialogue Place Recognition (DlgPR), which casts localization as an interactive, dialogue-driven reasoning process. To support thi

arXiv cs.AI·just now

Interpretable Language Model for Closed-Loop Type 1 Diabetes Control

arXiv:2607.14126v1 Announce Type: new Abstract: Type 1 Diabetes (T1D) is a chronic, life-threatening autoimmune condition characterized by the complete destruction of insulin-producing pancreatic beta cells. While Artificial Pancreas Systems (APS) powered by Reinforcement Learning (RL) have shown promise in automating insulin delivery, their ``black-box'' nature makes it hard for patients and doctors to trust them fully. This paper presents LLM-T1D, a promising approach that combines the precision of RL with the clear, human-like reasoning of Large Language Models (LLMs) to create a more trans

arXiv cs.AI·just now

Human AI Construction of Bayesian Networks for Operational Decision Support -- A Virtual Survey Approach

arXiv:2607.14141v1 Announce Type: new Abstract: Bayesian Belief Networks (BBNs) are powerful tools for decision-making under uncertainty. However, building their structures and estimating parameters are difficult. Currently, researchers must choose between relying on expert judgement or using large datasets to learn the structure and parameters of the network. We propose a new methodology using Large Language Models to bridge the gap between expert opinion and data-driven learning. This approach uses a panel of AI agents to estimate probabilities based on specific personas and context. We then

arXiv cs.AI·just now

Capability from Access Structure, Not Scale: Lower Bounds and Pre-Registered Tests for Hybrid Sequence Models

arXiv:2607.14144v1 Announce Type: new Abstract: The Platonic Representation Hypothesis (PRH) holds that as models scale, representations of heterogeneous networks converge toward a shared model of reality. We propose its sequel and boundary, the Capability Convergence Hypothesis (CCH): under a fixed per-token inference budget, representational convergence does not entail capability convergence. Capability instead converges toward a class, the access-complete hybrid: any architecture holding both a compressive O(1)-state channel and a scalable verbatim-index channel. We anchor it on a witness t

arXiv cs.AI·just now

ToolAnchor: Anchoring Counterfactual Context to Boost Agentic Tool-use Capability

arXiv:2607.14145v1 Announce Type: new Abstract: Tool-augmented large language model agents excel at long-horizon tasks, yet they are typically post-trained on fixed toolsets. When tasks demand new tools, these agents struggle to incorporate them effectively, and retraining from scratch is often impractical. We identify the core obstacle in such toolset expansion problem as behavioral inertia: the tendency of agents to fall back on familiar tools and established reasoning patterns despite having access to new ones. We demonstrate that injecting counterfactual anchor contexts at critical decisio

arXiv cs.AI·just now

Enhancing Small Language Models Reasoning through Knowledge Graph Grounding

arXiv:2607.14149v1 Announce Type: new Abstract: Although large language models (LLMs) have set benchmarks for zero-shot reasoning, their deployment remains cost-prohibitive and environmentally taxing. Small Language Models (SLMs) offer a sustainable alternative, but prone to errors, on tasks requiring complex, multi-hop logical grounding. We investigate a neuro-symbolic agentic framework to enhance the reasoning capabilities of SLMs, specifically Gemma 3 (1B, 4B) and Llama 3.2 (3B), using the CLUTRR kinship benchmark. Our approach transforms the SLM into a minimalist agent utilizing two specia

arXiv cs.AI·just now

Orchestrating Power Grid Studies with Multi-Agent AI and MCP Servers

arXiv:2607.14158v1 Announce Type: new Abstract: This position paper explores how Agentic AI and Model Context Protocol (MCP) can support power-grid studies in a Transmission System Operator (TSO) context. We focus on integrating Large Language Models with numerical simulation tools, structured workflows, and human supervision. We identify key industrial requirements for agent assisted grid studies and introduce pypowsybl-mcp, an MCP-based interface exposing selected capabilities of our simulation tool, pypowsybl to AI agents. This first step provides a testbed to study how agents can setup sim

arXiv cs.AI·just now

MemoHarness: Agent Harnesses That Learn from Experience

arXiv:2607.14159v1 Announce Type: new Abstract: An agent harness is the external control layer that turns a base LLM into an executable agent by managing context, tools, orchestration, memory, decoding, and output handling. While harness design strongly affects agent behavior, most automatic improvement methods optimize narrower artifacts such as prompts, pipelines, or workflows, and deployed agents usually reuse a single global harness for all cases. We introduce MemoHarness, an adaptive harness optimization framework that learns from its own executions. MemoHarness decomposes the harness int

arXiv cs.AI·just now

Just Keep Prompting: Evaluating Repetitive Socratic Prompting in VLMs

arXiv:2607.14099v1 Announce Type: new Abstract: Deploying Vision-Language Models (VLMs) in real-world settings requires not only strong visual reasoning but also stability under sustained conversational pressure. We introduce Just Keep Prompting (JKP), a multi-turn evaluation framework that measures VLM epistemic stability when users repeatedly challenge, question, or contradict a model's answer. JKP probes models for up to 10 follow-up turns using three strategies: Adversarial Negation (repeated rejection), Pure Socratic Interrogation (repeated calls to reassess certainty), and Context-Aware

arXiv cs.CL·just now

Quantum Compositional NLP for Arabic: Grammar, Morphology, and Word Sense in Circuit Topology

arXiv:2607.14100v1 Announce Type: new Abstract: We present the first application of pregroup grammar-based quantum compositional natural language processing (QNLP) to Arabic; a morphologically rich, free-word-order language whose structural complexity provides a uniquely demanding testbed for theories of meaning composition in quantum circuits. Our system converts Arabic sentences into quantum circuits whose topology mirrors grammatical structure: subjects, verbs, and objects become quantum gates, and the typed dependencies between them (the pregroup grammar) determine how those gates are wire

arXiv cs.CL·just now

LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets

arXiv:2607.14101v1 Announce Type: new Abstract: Generating high-quality adversarial texts with low query budgets remains a challenging problem in the hard-label scenario. Most existing approaches rely on greedy algorithms, where one position in the text is selected for substitution, followed by the substitutions of other positions. This local search approach may fail to discover high-quality adversarial examples and often leads to excessive query costs. Ideally, an optimal adversarial sample would consider all possible position combinations in the text, but exhaustive search is computationally

arXiv cs.CL·just now

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure

arXiv:2607.14102v1 Announce Type: new Abstract: With the rapid growth of digital data, real-world applications increasingly involve hierarchical information that combines static attributes with dynamic records. Modeling such heterogeneous data in a unified and generalizable manner remains challenging. Existing approaches often rely on extensive manual design, are tightly coupled to specific data schemas, and typically process static and dynamic attributes in isolation, thereby overlooking their implicit interactions. We propose UniSAGE, a unified framework for modeling data with both static an

arXiv cs.CL·just now

Latent Communication Between Language Model Agents: Channels, Alignment, and the Limits of Text

arXiv:2607.14103v1 Announce Type: new Abstract: Multi-agent systems (MAS) are utilized in many contexts and many professions. Those MAS rely on inter-agent communication, usually implemented by clear-text message passing. We hypothesize that Large Language Models may have a world model at their disposal that exceeds expressibility in text when complex concepts need to be communicated. Our aim is to approach a proof of this hypothesis with structured experiments. In this work, we show that LLM agents communicating via text lose information, which we quantify via Sparse Autoencoder (SAE) feature

arXiv cs.CL·just now

UzWordnet and Generative AI for Learning Uzbek by Game Playing

arXiv:2607.14104v1 Announce Type: new Abstract: This paper presents an educational system architecture that enables learners to practice the Uzbek language through game-playing. The architecture integrates UzWordnet and the largest currently available orthographic dictionary for Uzbek as core lexical resources, together with generative AI as a fundamental component for learning support. We design four educational games to facilitate Uzbek language learning and propose a game-based methodology for improving UzWordnet as a direct by-product of game dynamics. Our approach combines game design and

arXiv cs.CL·just now

Automatically Evolving Prompt Guidelines for Task-Specific Optimization

arXiv:2607.14105v1 Announce Type: new Abstract: For Large Language Models to reliably answer user queries, users must clearly specify requirements, context, and constraints. In practice, however, user queries are often underspecified, forcing models to infer unstated assumptions that may misalign with the actual user intent. Existing prompt engineering guidelines aim to mitigate this issue, they are typically generic and task-agnostic, limiting their practical utility. Additionally, existing guidelines are formed manually and in a non-systematic way. To this end, we study prompt guideline opti

arXiv cs.CL·just now

Token Time Continuous Diffusion for Language Modeling

arXiv:2607.14106v1 Announce Type: new Abstract: In this paper we introduce token time continuous diffusion (TTCD), a new diffusion language model which (a) operates in continuous space, deterministically mapping Gaussian noise to a final token canvas with no further sampling, and crucially (b) incorporates a new notion of per-token times, with some tokens proceeding from noise to token at a faster rate than others. Continuous space modeling helps TTCD avoid the parallel sampling of multiple tokens, which is a key source of inaccuracy at high speedups for models that iterate purely in discrete

arXiv cs.CL·just now

Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs

arXiv:2607.14107v1 Announce Type: new Abstract: The inference efficiency of diffusion large language models (dLLMs) is constrained by two challenges: bidirectional attention precludes efficient KV-cache reuse, while increasing decoding parallelism with static confidence thresholds can compromise generation quality. We observe that both challenges arise from a shared phenomenon: as tokens are decoded, their contextual integration through bidirectional attention causes token representations to drift (evolve) across decoding steps. This insight motivates Polestar, a training-free inference framew

arXiv cs.CL·just now

Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility

arXiv:2607.14108v1 Announce Type: new Abstract: This paper introduces tool efficiency, a new quantitative metric to evaluate the rate of useful tool calls in an LLM agent trajectory. To ensure that tool efficiency is well-defined, we also introduce marginal tool utility, a new quantitative metric defined per tool call indicating whether a tool is useful or whether it can be safely removed from the tool suite without affecting accuracy while increasing tool efficiency; in this paper, we determine the sign of marginal tool utility for each tool call in a trajectory using LLM-as-a-Judge. While mu

arXiv cs.CL·just now

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation

arXiv:2607.14109v1 Announce Type: new Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding. Furthermore, the rapid proliferation of LLMs has created the implicit assumption that more sophisticated prompting techniques yield better performance. Several studies claim better performance with more sophisticated prompting techniques, but do not provide a comprehensive evaluation. We address this gap through a comprehensive empirical study of 8 prompti

arXiv cs.CL·just now

MAPS: Modeling Co-Existing Subjective Perspectives and Shared Meaning in Multi-Agent Cognitive Dialogue

arXiv:2607.14110v1 Announce Type: new Abstract: Human dialogue involves more than exchanging information; it also expresses beliefs, emotions, and subjective cognitive styles. Yet current AI dialogue systems often enforce semantic uniformity, sacrificing diversity and interpretability. We present MAPS (Multi-Agent Perspective Spaces), a novel framework that models dialogue between cognitively distinct agents through domain-weighted profiles, dynamic GRU-based memory, and interpretable token-level attention. MAPS enables agents to maintain individualized reasoning while progressively converging

arXiv cs.CL·just now

Spot birds not golf

Suggestion for hyperscalers feeling pressure over data center water use: Buy up a few exclusive country clubs, convert the golf courses into public parks, pay for guides and binoculars to get the previous members into birdwatching - help them embrace a more sustainable hobby! Google <a href="https://sustainability.google/reports/google-2026-environmental-report/">used 10.9 billion gallons in 2025</a>, so about 30 million gallons per day. The Coachella Valley has <a href="https://www.cvwd.org/167/Water-Conservation">120 golf courses each using ~800 acre-feet per year</a>

Simon Willison·just now

Firefox in WebAssembly

<a href="https://developer.puter.com/labs/firefox-wasm/">Firefox in WebAssembly</a> This is absurdly cool: Puter compiled Firefox to WebAssembly such that the whole browser runs in another browser. Here's my blog, running in Firefox, running in WebAssembly, running in Chrome: <img alt="A Chrome window. The tab has the Firefox UI and has loaded my blog. On the right is the Chrome network panel showing that it loaded resources that include a 233MB gecko.wasm and an 18MB chrome-assets.tar.zst" src="https://static.simonwillison.net/static/2026/firefox-wasm.web

Simon Willison·just now

Kimi K3, and what we can still learn from the pelican benchmark

Chinese AI lab Moonshot AI <a href="https://www.kimi.com/blog/kimi-k3">announced Kimi K3</a> this morning, describing it as their "most capable model to date, with 2.8 trillion parameters". It's currently available via their website and API, but an open weight release is promised "by July 27, 2026". Moonshot are calling this the first "open 3T-class model" (I guess they're rounding 2.8 trillion up to 3 trillion), taking the crown from <a href="https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro">DeepSeek's 1.6T v4 Pro</a>. Their <a href="https://www.kimi.com/blog/kimi-k3#full-benchmar

Simon Willison·just now

The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs

Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model-provider APIs, yet the next dollar is aimed at specialized compute almost none of them use today; a majority intend to switch or add providers within the year, many within a quarter. Buying decisions turn on integration and total cost of ownership rather than headline token price — which is fortunate, because most enterprises cannot yet see their unit economics clearly: GPUs sit at half utilizati

VentureBeat AI·just now

The agent security gap: 54% of enterprises have already had an AI agent incident, and most still let agents share credentials

Across 107 enterprises, AI agents are being given real access to systems and data while the controls meant to contain them lag behind. More than half have already had a confirmed agent security incident or a near-miss; only about a third give every agent its own scoped identity, and most agents still share credentials; and only three in ten isolate their highest-risk agents. The security stack is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents, spending remains a thin slice of the security budget, and enterprises are evenly split on whethe

VentureBeat AI·just now

Google Vids now lets you star in your own AI videos

Google is adding personalized AI avatars to Vids that let users create videos starring a digital version of themselves, alongside Gemini Omni-powered tools for generating and editing videos from prompts and reference images.

TechCrunch AI·just now

Roblox launches an AI-powered game-creation feature in its mobile app

Roblox's new "Build" feature lets users generate basic games using a single text prompt.

TechCrunch AI·just now

Policy Hot

New York governor says she’s using AI to analyze ‘every single rule’ in the state

New York Governor Kathy Hochul might have just signed a moratorium on new AI data centers in the state, but she's not against using the technology herself. During an interview with Bloomberg's Odd Lots podcast, Hochul said that her team is using "AI to analyze every single rule, regulation, [and] policy" to check for outdated […]

The Verge AI·just now

Quoting Thibault Sottiaux

<blockquote cite="https://twitter.com/thsottiaux/status/2077630111499882637">On file deletions. We’ve investigated a handful of reports where GPT-5.6 unexpectedly deleted files. What we have found is that this most commonly occurs when: <ul> <li>Full access mode is enabled and codex is run without sandboxing protections, including without auto review being enabled</li> <li>The model attempts to override the $HOME env var to define a temporary directory.</li> <li>The model makes an honest mistake and mistakenly deletes $HOME instead.</li> </ul></blockquote> &mdash

Simon Willison·just now

The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix

Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context source, and provider-native retrieval has quietly overtaken the dedicated vector databases that define the category — yet a majority of enterprises have already watched their agents produce confident, wrong answers traced to missing or inconsistent context. A governed semantic layer is emerging as the fix, but most are still building it; the field is converging on hybrid retrieval; and even as provider

VentureBeat AI·just now

The agent evaluation gap: Enterprise AI organizations have a reality-alignment problem, not a coverage problem — and most are shipping to production anyway

Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited weakness is that evaluations do not align with real-world outcomes. Yet two-thirds already allow, or are actively engineering toward, deploying agent changes to production on automated evaluation alone — with no human in the loop. The result is an evaluation gap —

VentureBeat AI·just now

NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval

Hugging Face Blog·just now

Why teens deserve access to safe AI

Learn how OpenAI is making ChatGPT safer for teens with age-appropriate protections, learning tools, parental controls, and expert partnerships.

OpenAI Blog·just now

Google’s AI Mode now lets you link and interact with select apps

With this new update, Google is expanding AI Mode beyond answering questions and into completing tasks across the apps they use regularly.

TechCrunch AI·just now

Google is renaming NotebookLM to Gemini Notebook

Google is giving its AI note-taking app a new name. The company announced on Thursday that NotebookLM is becoming Gemini Notebook, but will remain a standalone app even as it integrates more deeply across Gemini and Google Search. Google first revealed Gemini Notebook - then called Project Tailwind - in May 2023 before widely releasing […]

The Verge AI·just now

Yes, you can now order DoorDash from the command line

DoorDash is opening a limited beta of dd-cli, a command-line tool that lets developers and AI agents search stores, build carts, and place orders from the terminal, marking another step toward software designed for AI agents instead of just humans.

TechCrunch AI·just now

Inkling: Our open-weights model

<a href="https://thinkingmachines.ai/news/introducing-inkling/">Inkling: Our open-weights model</a> Mira Murati's Thinking Machines Lab just released their first open-weights model. Inkling is "a Mixture-of-Experts transformer with 975B total parameters, 41B active" - an Apache-2.0 licensed multimodal model trained on 45 trillion tokens of text, images, audio and video. They're also promising Inkling-Small, a 276B (12B active) model, but that's still being tested and the weights will be released "once that work is complete". The <a href="https://thinkingma

Simon Willison·just now

Why is OpenAI selling a ChatGPT basketball?

You may have heard that OpenAI released its first piece of hardware this week. You may not have heard about the ChatGPT basketball.

TechCrunch AI·just now

How a former DeepMind researcher raised at a $300M pre-seed valuation before launching a product

Drawing on more than a decade spent helping build some of the world's most influential AI systems, including research that later informed the development of ChatGPT, Andrew Dai explains why he believes visual AI is one of the next major frontiers in artificial intelligence.

TechCrunch AI·just now

Mermaid to ASCII art (mermaid-ascii)

Tool: <a href="https://tools.simonwillison.net/mermaid-ascii">Mermaid to ASCII art (mermaid-ascii)</a> After building the <a href="https://simonwillison.net/2026/Jul/16/grok-mermaid/">Mermaid to ASCII tool based on Grok Build's Rust code</a> I learned that there's an older, more fully-featured Go library called <a href="https://github.com/AlexanderGrooff/mermaid-ascii">AlexanderGrooff/mermaid-ascii</a> that implements a similar pattern, so I had Claude Fable 5 compile that one to WebAssembly as well so I could compare the two. This one includes support for col

Simon Willison·just now

Why AMI Labs’ Alexandre LeBrun won’t call his AI ‘AGI’ or ‘superintelligence’

While everyone in AI is chasing "superintelligence," Alexandre LeBrun, CEO of Yann LeCun’s world model startup, AMI Labs, dismisses the word.

TechCrunch AI·just now

Moonshot’s upcoming Kimi 3 is expected to close the gap with Anthropic’s Opus 4.8

The FT reports Kimi K3 will be the largest open AI model from China, with a parameter count between 2 trillion and 3 trillion.

TechCrunch AI·just now

Quoting Linus Torvalds

<blockquote cite="https://lore.kernel.org/linux-media/CAHk-=wi4zC+Ze8e+p3tMv8TtG_80KzsZ1syL9anBtmEh5Z40vg@mail.gmail.com/">I realize that some people really dislike AI, but this is an area where I'm willing to absolutely put my foot down as the top-level maintainer. Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it. Or just walk away. AI is a tool, just like other tools we use. And it's clearly a useful one. It may not have been that "clearly" even just a year ago, but it's no longer in

Simon Willison·just now

Apple Intelligence approved for launch in China with Alibaba and Baidu

The deal, which was rumored to be in the works last year, marks an important step for Apple's AI ambitions in a key market.

TechCrunch AI·just now

Claude can now use your 1Password credentials for you

1Password has launched a new browser integration for Claude that allows the Anthropic chatbot to access stored security credentials like usernames and passwords. The 1Password for Claude feature means that users can authorize Claude to complete multi-step tasks like booking travel and managing online accounts on their behalf without having to manually input their login […]

The Verge AI·just now

Funding

The Download: OpenAI unveils GPT-Red and heat pumps rise in the US

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. Meet GPT-Red: an LLM super-hacker OpenAI built to make its models safer OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks. It automates a type of safety evaluation for software systems known as red-teaming, which is typically done by a team of human testers. The aim is to find as many different ways to break or hijack a system as possible. OpenAI gave MIT

MIT Technology Review·just now

Policy Hot

Google ordered to open Android and Search to rivals in Europe

Google must give rival AI assistants and search engines greater access to key parts of Android and Google Search after the European Union ordered the company to comply with the bloc's digital antitrust rules. The two decisions, handed down Thursday, could weaken Google's control over two of the tech industry's most important platforms and have […]

The Verge AI·just now

Newer Models, Same Advantage

Hugging Face Blog·just now

Products

Computer cops

I stood before a hulking glass and brick structure in the heart of Fort Worth, Texas. Thousands gathered inside to see what had been billed as "the future of policing in the digital age." As press, I was prohibited from entering, but from a number of nearby locations, I met with attendees who told me […]

The Verge AI·just now

Policy

Why heat pumps are still so hot in the US

It feels as if it should be illegal to even think about heating appliances during the height of summer—seriously, these heat waves in New York have been brutal—but we need to talk about heat pumps. The appliances use electricity for heating, they’re incredibly efficient, and they’re on the rise. (For what it’s worth, many heat pumps can also be run in reverse to cool buildings.) In the US, heat pump sales have doubled over the past 15 years, according to a new report . And they’re winning the heating race against fossil fuels, outpacing natural-gas furnaces by 32% during the first quarter of 2

MIT Technology Review·just now