Hi, friends! Welcome to Installer No. 136, your guide to the best and Verge-iest stuff in the world. (If you're new here, welcome, hope your neighborhood isn't as smoky as mine, and also you can read all the old editions at the Installer homepage.) This week, I've been recording the next season of Version History […]
TikTok is starting to test an opt-in tool that scans for AI likenesses and lets creators report them to the company, as spotted by social media consultant Matt Navarra. The tool is initially being tested with "some" US creators, TikTok US spokesperson Zachary Kizer tells The Verge. YouTube has been working on a similar tool […]
Best AI for Adult Content in 2026: NSFW Generators and the Models Behind Them StartupHub.ai
Apple is suing OpenAI. The complaint is readable and intense, as these things often are, though many experts seem to think many of the allegations are just the ways things are done. So what does Apple really want here, and why is it picking such a public fight with OpenAI? On this episode of The […]
World Models, The Complete Guide To LeCun's $1bn LLM Bet Quantum Zeitgeist
Bunkerhill Health has raised $55 million to scale its agentic AI platform, Carebricks. The closing of the company’s Series B round, announced today, folds in continued participation from Sequoia Capital, Felicis, Optum Ventures, and Y Combinator. However, a funding total doesn’t answer the key question any hospital executive wants to know about healthcare AI: does […] The post Bunkerhill raises $55M to scale agentic AI across health systems appeared first on AI News .
Fine-tune video and image models at scale with NVIDIA NeMo Automodel and 🤗 Diffusers
Mozilla: The state of open source AI
The 10 Coolest AI Observability And Governance Tools Of 2026 (So Far) crn.com
Blatant AI slop just won a 25k USD DeepMind Kaggle Grand Prize
a great week for open models continues.
Vision-language models (VLMs) can follow complex textual instructions, yet they struggle to reason from purely visual context. In particular, current models fail to infer shared concepts from sets of example images and apply them to new inputs. We introduce Visual Concept Inference from Sets (VICIS), a task that evaluates this capability.
As concerns around data privacy in machine learning grow, the ability to unlearn—or remove—specific data points from trained models becomes increasingly important. While state-of-the-art unlearning methods have emerged in response, they typically treat all points in the forget set equally. In this work, we challenge this approach by asking: do points that have a negligible impact on the model’s learning need to be removed?
The Little Book of Reinforcement Learning
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.
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.
Google DeepMind and Isomorphic Labs outlined a bioresilience program to curb AI misuse in biology while aiding outbreak response. The two organisations published an update on a joint initiative that began quietly and has now built out more than 15 partnerships with government bodies, biosecurity organisations, and research groups over the past 12 months. The […] The post Examining Google DeepMind’s AI bioresilience push appeared first on AI News .
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.
NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval
Lila is betting that science, not the internet, is the last untapped source of training data. We went to find out what that actually looks like in a room full of robots.
Newer Models, Same Advantage
Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general text generation, its impact on code generation quality and programming conventions remains largely unexplored. We investigate how the language used to describe programming tasks affects the source code generated by GPT-4o mini, DeepSeek, and Claude.
Neko Health has raised $700 million to expand its AI body scans in the United States, starting with a clinic in New York. The company’s preventive screening service combines medical imaging, blood tests, proprietary sensors, and clinician review. The Series C round was led by Lightspeed Venture Partners and co-led by O.G. Venture Partners. Existing […] The post Neko Health raises $700 million to expand AI body scans in the US appeared first on AI News .
Cross-modal learning, i.e., learning to predict one modality from another, is a fundamental mechanism for self-supervision via leveraging multimodality. Many practical applications, e.g., deploying a household robot, involve devices that are equipped with a rich set of sensors that enable multimodal sensing in their test environment. This presents an opportunity to apply cross-modal learning to the multimodal data sensed by these devices to learn representations.
Thinky's first full LLM release is a banger and bonus: it's open weights!
Qubes OS is a revealing case for security measurement because its architecture makes component boundaries security-relevant. We present a protocol-driven longitudinal analysis of 109 public Qubes Security Bulletins (QSBs, 2011--2025), the official Qubes-maintained Xen Security Advisory (XSA) tracker, and a secondary vulnerability-event sensitivity series. The study measures the public advisory record rather than latent vulnerability incidence or realized compromise.
Reinforcement learning for multi-turn search reasoning typically relies on terminal outcome rewards, which cannot distinguish useful, redundant, and harmful intermediate interactions. We propose LAPO, a self-generated process-supervision method based on backward leave-one-turn attribution. For each search turn, LAPO replaces the turn and its retrieval observation with a fixed [DELETE] placeholder and measures the resulting change in the current policy's mean log-likelihood of the gold answer.
Large language models produce chain-of-thought (CoT) reasoning that appears logically sound yet may not genuinely depend on its stated premises. We introduce interventional grounding audits, a black-box, step-level test of premise dependency: we intervene on a single premise by substituting its target predicate with a fresh symbol, re-run the model, and check whether each reasoning step's normalized conclusion (canonical predicate form) changes.
Knowing when to say "I don't know" is fundamental to human judgment, yet AI assistants offer a fluent answer to almost any question. In five experiments (N = 3,132; four preregistered, one direct replication), participants answered difficult questions and could always decline to respond. We engineered the questions so that AI advice was wrong, separating AI use from its accuracy.
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