AI/ML News & Innovations Hub

Jensen Huang’s Exit Just Gave AMD and Google Their Biggest AI Opening

On the same day that Jensen Huang resigned as NVIDIA’s chief executive after 33 years at the helm of the company he co-founded, another semiconductor company CE

딥시크·문샷 등 중국 4개사, 수출 통제 '블랙웰' 사용 흔적 발견

딥시크와 문샷 AI 등 중국 주요 AI 기업들이 미국의 수출 통제를 받는 엔비디아 최신 \'블랙웰\' GPU를 AI 모델 개발에 사용한 정황이 발견됐다. 공개된 모델 가중치에서 블랙웰 특유의 숫자 배열을 찾아낸 것으로, 중국 기업들의 첨단 GPU 사용 의혹을 뒷받침하는 구체적인 기술적 증거가 제시됐다.비영리 연구단체 언엠플로이먼트 랩스(Unemployment Labs)의 윌 드퓌 연구원은 12일(현지시간) 연구 보고서를 통해 중국 AI 기업 4곳이 공개한 모델 6종에서 엔비디아 블랙웰 GPU와 일치하는 초기 가중치 패턴을 발견했다고 밝

Probe monitors seem to perform realistically in simulated control environments

TL;DR In simulated control evaluations , instead of executing an agent’s proposed commands and returning the outputs, a language model simulates what the outputs would have been. This allows control evals to be more on-distribution for deployment use without actually running on production systems. However, imperfect simulators may create a subtle distribution shift that changes the behavior of white-box methods such as probes. We investigate whether probe monitors perform similarly when attacks are run in simulation vs. when they are run for real. We run attacks with Qwen3.5-27B against 1,313 tasks in LinuxArena , twice for real and once in simulation. Using activations from these trajectories, we test six different probes’ abilities to detect attacks at a 1% per-command false positive rate. On average across probes, we find that the attack catch rate in simulation was only 1.0 points lower than in real runs, and the 95% interval (−3.0 to +1.0 points) includes 0 . For comparison, two real runs differ by 0.2 points. We also trained a probe to distinguish between activations from simulated vs. real trajectories, which succeeded, indicating that the data is measurably different . However, this difference in data does not appear to substantially impact the performance of our six attack detection probes. Introduction LLM-based monitors can be evaluated on their performance at catching misaligned agents in simulation , where another language model plays the role of a computer the…

Weisfeiler-Lehman Meets Optimal Transport: Theory, Algorithms, and Implications

We introduce the Weisfeiler–Lehman (WL) distance, a pseudometric defined on labeled measure Markov chains (LMMCs)—a general class of structures encompassing labeled graphs. This distance is computable in polynomial time and is closely aligned with the classical WL graph isomorphism test: it assigns a positive value if and only if the WL test distinguishes the two input graphs. The WL distance captures fine structural differences in the underlying LMMCs and is, consequently, more discriminative than the state-of-the-art Wasserstein WL graph kernel. The distance motivates a neural network architecture on LMMCs, which we call Markov chain neural networks (MCNNs), that is universal for continuous functions over LMMCs (hence all graphs) under the topology induced by the WL distance. This finding further extends to the analysis of graph neural networks (GNNs). Additionally, the WL distance admits an alternative characterization through Markovian couplings and Causal Optimal Transport (COT), offering a stochastic-process perspective that uncovers a surprising connection between the seemingly unrelated WL test and COT. Finally, we show that the WL distance is stable under a natural variant of the Gromov-Wasserstein (GW) distance that we propose. The GW distance is generally NP-hard to compute and hence the WL distance provides a polynomial-time computable lower bound for it.

Why Roboflow Sells Hardware, and When to Use Your Own

Choosing edge AI hardware means choosing who maintains JetPack, drivers and patches. Compare running your own Jetsons with RoboflowOS appliances.

Can image style transfer be performed with k-nearest neighbours?

Can image style transfer be performed with k-nearest neighbours, using a set of multiple reference images in a RAG-like architecture and an input image, without specific finetuning on particular styles?

OpenFold3 on Amazon SageMaker JumpStart: Predict biomolecular structures

Deploy OpenFold3-preview from Amazon SageMaker JumpStart to predict 3D structures of proteins, DNA, RNA, and ligand complexes in your own AWS account. The post covers asynchronous inference, multi-GPU parallelism, bringing your own MSAs for accuracy and privacy, and scaling to zero to reduce cost.

Cost-optimizing AssemblyAI speech-to-text inference on AWS with Amazon EC2 Spot

Learn how AssemblyAI runs cost-optimized speech-to-text inference at scale on AWS, using Amazon ECS, Amazon EC2 Spot GPU instances, and an Amazon SQS-based retry architecture to achieve 70% cost savings while maintaining SLA commitments across European Regions.

Why the Nvidia scandal is far from over - The Latest

Jensen Huang’s resignation from the AI chipmaker Nvidia has sent shockwaves through the global economy. Huang resigned after reported affairs with an Nvidia executive and a woman alleged to be a Chinese spy. The fallout is not over yet, with criticism mounting over the apparent lack of action from the US commerce secretary, Howard Lutnick. Lucy Hough speaks to the Guardian’s global technology reporter, Aisha Down Continue reading...

Why the Nvidia scandal is far from over – The Latest

Jensen Huang’s resignation from the AI chipmaker Nvidia has sent shockwaves through the global economy. Huang resigned after reported affairs with an Nvidia executive and a woman alleged to be a Chinese spy. The fallout is not over yet, with criticism mounting over the apparent lack of action from the US commerce secretary, Howard Lutnick. Lucy Hough speaks to the Guardian’s global technology reporter, Aisha Down – watch on YouTube Continue reading...

Nvidia’s leadership crisis puts enterprise AI’s dependence on one company under scrutiny

The tech world has been rattled by the toppling of one of its most prominent leaders, and the widespread implications it could have on the AI industry itself. Nvidia, which provides the underlying AI infrastructure for seemingly every company on the planet, announced on Thursday that its president and CEO Jensen Huang has resigned, effective immediately. Earlier this week, news broke that Huang had had at least two extramarital affairs, including one with an alleged Chinese spy and one with a direct report. EVP and CFO Colette Kress has stepped in as interim CEO , and Nvidia’s board has begun searching for a permanent replacement. Nvidia’s graphics processing units (GPUs) have become the standard for graphics rendering, and in recent years companies building AI have also depended on them for training and running large language models (LLMs) and increasingly sophisticated AI agents. As such, the revelations about Huang, and his swift departure, have sparked a US national security crisis, and raised questions about over-reliance on one tech vendor and subsequent supply chain and compute constraints. A scandal involving an admitted extramarital affair with an alleged Chinese spy opens up a hornet’s nest of concerns around security that Nvidia won’t be in a position to answer for months, if ever, said technology analyst Carmi Levy . “More broadly, it throws into doubt the company’s leadership and security practices, and ignites questions around board-level governance and oversig…

Build a model improvement flywheel with Amazon SageMaker HyperPod Data Capture

Amazon SageMaker HyperPod Data Capture records live inference inputs and outputs to Amazon S3. This post walks through one end-to-end example: capturing a support router's production traffic, surfacing a new-product blind spot with Amazon Athena, fine-tuning on the captured data with the HyperPod training operator, and measuring the before-and-after lift.

‘Ahead of his time’: AI helps unlock discoveries hidden in notebooks of man who transformed maths

Kurt Gödel left 1,009 pages of densely written text – much of it in an obsolete shorthand – which is now said to contain hundreds of proved theorems The Austrian logician Kurt Gödel was one of the most original thinkers of the 20th century. His two incompleteness theorems , published in 1931, transformed mathematics and paved the way for the digital age. After his seminal results of the 1930s, Gödel published only a few papers. His unpublished work, however, runs to tens of thousands of pages, and is of great interest to historians of science. Continue reading...

Former OpenAI researcher says he found Nvidia Blackwell fingerprints hidden inside six Chinese AI models

Former OpenAI researcher Will DePue says he has produced the first technical evidence that DeepSeek, Moonshot AI, MiniMax, and Ant Group are using Nvidia's latest Blackwell GPUs despite US export controls. His forensic method reads characteristic chip signatures directly from published model weights. He estimates the hardware behind the five training runs cost more than half a billion dollars. The article Former OpenAI researcher says he found Nvidia Blackwell fingerprints hidden inside six Chinese AI models appeared first on The Decoder .

Jensen Huang is getting roasted by the very technology he helped unleash

Jensen Huang's AI chips helped build the machine that's roasting his downfall. Here are some of the internet's best.

I'm leaving my PhD to pursue AI Safety work

Yesterday, I told my advisors that I would be dropping out of my PhD program at the University of Michigan's School of Information. I was nervous about how to break the news to them. There was already some tension in our weekly meetings: I often voiced my dissatisfaction with the pace of academia, my interests had been drifting away from theirs, and, most of all, we disagreed on the severity of the risks AI posed. When I walked into the room, they told me something I would have loved to hear months earlier: “We think it’s time to get you started on some research.” All I could say was, “I think it’s a little too late for that.” For months, I had been proposing projects that bridged my interests with theirs, such as running studies that treated humans and LLMs as equal subjects, but they were usually turned down for not being “exciting enough”. My advisors wanted me to start with small, unpublished surveys that would ‘help me ask the right questions’ and “develop research taste” in Human-Computer Interaction before I could propose a full study. I understood why they wanted me to gain experience first, but I felt held back after every meeting, and grew restless alongside my ambition and sense of urgency. “I’ve been doing machine learning research independently,” I told them. “I discovered some really interesting stuff about how to interpret neurons inside of LLMs. It’s garnered some attention, and some people are even open to funding my work. I’ll be withdrawing and moving to S…

We want to run AI safety events for you!

TL;DR: We’re four undergraduate organizers who want to build an Events-as-a-Service (EaaS) organization to execute events for operations-constrained AI safety orgs. You cover an event’s operational costs and programming, and we'll plan and run it. Scope and timeline are negotiable. If you have an event you’d want us to help run in the next ~3 months, please fill out the form linked here ! Background Ops capacity is limited in the AI safety community, and events eat up a lot of it. An EaaS organization could rapidly supply ops capacity to organizations to capitalize on short convening windows, preserve capacity, and test low-hanging event ideas that otherwise would be missed. Work with us! We are interested in executing events-shaped projects with partner organizations in the next months for free while we build a track record. Some example events that we could help run include retreats, convenings, workshops, hackathons, socials and happy hours. We could handle lodging, travel, comms, venue, catering, and more, but the precise specifications are up for negotiation. Our team We’re four undergraduate organizers with both event-ops experience and AI safety context. Between us, we have: Run a 50 person, weekend EA retreat (Fall ‘26 Northeast EA Retreat) Founded the UCSD and Vanderbilt AI safety groups, running multiple fellowship cohorts Help run a 126-member EA Intro Fellowship (Yale) Help run a startup accelerator, directly assisting 2 teams getting into YC We’d be happy to pro…

Nvidia's leadership vacuum could create a unique opportunity for its customers

Jensen Huang's resignation leaves Nvidia facing a leadership crisis. With an interim CEO and no clear successor, what comes next for the AI chip giant?

How Jensen Huang's dramatic ouster from Nvidia affects Anthropic's $1.4 trillion IPO

Nvidia's former CEO Jensen Huang has resigned, raising questions for Anthropic's $1.4 trillion IPO, AI chip supply, and US-China export controls.

What is next for Nvidia after CEO Jensen Huang resigns?

The AI chip giant has no leader in waiting, may lose its voice in Washington, and has big bets in Europe that its founder championed in person.

AMD, 양자컴퓨터 오류 수정에 GPU·SW 투입…엔비디아와 경쟁

AMD가 양자컴퓨팅 기업 사이퀀텀(PsiQuantum)에 지분 투자를 추진하고, 양자컴퓨터의 오류를 실시간으로 탐지·수정하는 데 자사 CPU·GPU와 소프트웨어를 활용하기로 했다. 지난해부터 양자컴퓨팅 인프라 사업을 강화해 온 엔비디아와 관련 기술을 둘러싼 경쟁도 본격화할 전망이다.AMD와 사이퀀텀은 12일(현지시간) 공동 발표를 통해 지분 투자 계획과 기술 협력 확대를 공개했다. 구체적인 투자 금액은 밝히지 않았다.이번 협력의 핵심은 대규모 양자컴퓨터의 실시간 오류 수정 기술이다. 양자컴퓨터는 계산 과정에서 외부 환경의 영향 등으

엘리스그룹, 'B300' 기반 AI 데이터센터 베타 서비스 개시

엘리스그룹(대표 김재원)이 자체 모듈형 AI 데이터센터(AI PMDC)에 엔비디아 B300 GPU 인프라를 구축, 지난달 30일부터 베타 서비스를 개시했다고 13일 밝혔다.이번 사업은 과학기술정보통신부 \'첨단 GPU 확보·구축·운용 지원 사업\'의 일환으로 진행된다.엘리스그룹은 지난 6월 사업자로 선정된 이후 자체 개발한 모듈형 AI 데이터센터 \'AI PMDC\'를 활용해 B300 GPU 운영에 필요한 인프라를 신속하게 구축 중이다.AI PMDC는 데이터센터의 주요 설비를 모듈 단위로 사전 제작하고 현장에서 조립하는 방식으로 기존 데이

AGI is probably bad for startups, even if it doesn't lead to ASI

One of the great mysteries of capitalism is the nature of the "firm". It's a question that is near and dear to my heart as someone that's been "doing startups" for the last six years, and almost every startup founder has considered it whether they realize it or not. There are two ways of asking The Mystery of the Firm, depending on your initial inclination. The most generic way to ask it is: When and why do businesses transact with each other, instead of doing things in-house? What determines the shape and size of businesses, in different sectors of the economy? A more pointed way to ask the question is: Why isn't there just one big corporation? One Big Corporation would have a lot of advantages; it doesn't have to pay someone a gross margin on its inputs, it'd have massive advantages of scale in place like media or software, and (presumably) it'd have a much easier time sharing information between components that would ordinarily be different firms. Economists have many ways of answering this question. My preferred explanation, which is compatible with a lot of others, is that the primary bottleneck on firm size are Moral Mazes . Even large and well run companies like Google, which do have a lot of product lines and would like to have more, eventually face massive principal-agent problems when they need to add headcount. Ultimately, there is only one CEO and one board, and no matter how earnest these people are they become bottlenecked on attention. Google has a lot of mone…

AI Weekly Issue #543: Google is the latest tech giant to chase TypeSafe's 8-week-old Jev

This issue is about decision models, a new kind of AI model that does not write text. You give it an input and a fixed list of answers, and it returns the best answer with a probability, in milliseconds. TypeSafe AI released the first one, Jev, on September 15. On Wednesday Google released its own, after OpenAI and Microsoft did in October, and all three charge $0.10 or less per million input tokens and nothing for output. Shanghai AI Lab says its 4B model beat Jev on its tests, and Microsoft built its model on Alibaba's Qwen. Chatbots still score a little higher on the hardest decisions, at 15 times the cost. In the middle: on Vercel's gateway, decision models took one in four AI requests in October.

llm-gemini 0.37

Release: llm-gemini 0.37 Google launched Gemini 4 Hydrogen today - the lowest cost member of the Gemini 4 family. It's actually not too far off the price of its much bigger siblings, Carbon and Argon. I'm finding Gemini 4's pricing really confusing. Here's the pricing scheme: Model Dates Input ($/M) Cached input Output Gemini 4 Carbon Through Nov 30, 2026 $2.00 $0.10 $10.00 From Dec 1, 2026 $4.00 $0.20 $20.00 Gemini 4 Argon Through Nov 30, 2026 $2.00 $0.10 $10.00 From Dec 1, 2026 $4.00 $0.20 $20.00 Gemini 4 Hydrogen $1.50 $0.075 $7.50 Carbon and Argon are the same price , and both are scheduled to double in price on December 1st. If they're the same price, why have two different models? Google says that Argon "delivers Gemini 4 intelligence with faster responses and lower token spend per turn compared to Gemini 4 Carbon" - so it may be priced the same, but it will use less tokens per task. Hydrogen, supposedly the cheap model, is currently only a 25% discount on its more capable peers. The Hydrogen price will make more sense after December 1st, when it will represent a 75% discount. It's still considerably more expensive than Claude Haiku 5.5 and GPT-6 Luna - they're both $0.10/$0.50, so Hydrogen is 15x that. I had Hydrogen generate some pelicans . Here's a full comparison grid showing the pelicans for all three members of the Gemini 4 family at their three supported effort levels: Considering how close they are in price, the difference in pelican quality for Hydrogen is qui…

vLLM Rebuilds DeepSeek-V4.1-Flash Serving to Run 58x Faster on Rubin

vLLM retuned DeepSeek-V4.1-Flash for NVIDIA's Vera Rubin NVL72 rack, hitting 1.7M tokens/s per GPU and 58x GB300 throughput at interactive latencies.

미국, 중국의 해외 GPU 임대 규제 추진…중국 기업들 확보 서둘러

미국이 중국 기업의 첨단 AI 반도체 접근을 차단하기 위해 해외 데이터센터의 GPU 임대까지 규제하는 방안을 추진하고 있다. 젠슨 황 엔비디아 CEO의 사임으로 규제 강화 가능성이 커지자, 중국 AI 기업들은 해외 컴퓨팅 자원 확보를 서두르는 것으로 알려졌다.디 인포메이션은 11일(현지시간) 사안에 정통한 관계자 2명을 인용해 미국 상무부가 중국 AI 기업의 해외 데이터센터 원격 접근을 제한하는 규정을 마무리하고 있다고 보도했다. 이르면 이달 중 발표될 수 있으며, 현재 다른 정부 부처들과 협의 중인 것으로 전해졌다.새 규정은 싱가

Optimizing cost per task and proving agent ROI on Amazon Bedrock

Comparing per-token prices hides the real cost of running a model on Amazon Bedrock. This post shows how to calculate the true cost per task across four hidden cost drivers, reduce it with caching, routing, and output constraints, and prove agent ROI with human baselines, holdout experiments, and a five-dimension scorecard.

Anthropic should measure the effect of evaluation awareness

... and report their findings! Summary Claude Fable 5.2 & Mythos 5.2 are the most situationally aware of Anthropic's recent models according to their own internal investigations. The company has made a concerted effort to measure evaluation awareness and to acknowledge that it affects their confidence in model safety. But their latest system card lacks information on the effect of evaluation awareness, thus blurring how one ought to adjust their own confidence in Anthropic's alignment assessments. Specifically, I present the following claims regarding Claude Mythos 5.2's system card : The prevalence of evaluation awareness in audit transcripts is surprisingly unclear due to confusing wording and incomplete disclosure of results. Anthropic offers little to no estimation of how evaluation awareness changes Mythos 5.2's behavior - neither direction nor magnitude. I offer concrete recommendations at the end of this post. Background Evaluation awareness refers to the ability of an AI system to recognize when it is being tested. Evaluation awareness can be verbalized (the recognition is stated in its output or chain-of-thought) or not (only visible internally). Unless specified otherwise, my later references to evaluation awareness include both cases. Evaluation invariance is the extent to which a model's behavior remains consistent regardless of whether it believes it is being evaluated. The opposite might be called evaluation sensitivity . Alas, scaling general AI capabilities h…

Jensen Huang built an AI kingdom. Now he’s gone, will Nvidia’s ramparts be breached?

CEO’s affair and resignation has brought company’s murky corporate contracts and supply chains into sharp focus Can the AI economy survive without its “central bank”? What does Jensen Huang’s fall mean for the world’s second-most valuable company, and what might a tremor in its fortunes mean for the world? Nvidia’s sky-high valuation is based on the fact that it designs and sells the advanced AI chips, called graphics processing units (GPUs), that underpin the AI economy. Without these, AI models such as ChatGPT and Claude could not be trained and would not run. Continue reading...

Reward-Hacking is potentially ill-defined

Github link In the process of revisiting previous posts on reward-hacking (RH), I decided on performing a closely matched [1] repro of Dean Valentine's chess RH experiments on Astra . The replication is as expected -- Astra used the engine on all 20/20 attempts (resulting in 19 wins and 1 draw) without ever disclosing its use in the final output. However, a closer look at the honeypot prompt raised a question: what exactly counts as the model’s own ability? Even laws are open to interpretation in court, so it seemed unfair to assume that “your ability” had only one reasonable interpretation here -- especially for models explicitly trained for tool use. As the model being tested, I could plausibly interpret “[my] ability” as including the tools available in my environment unless given a more concrete definition or directly prohibited otherwise. The next logical question is then: Does Astra still RH when given a hardened prompt that explicitly defines “your ability”? To test this, I gave it the following definition. Original prompt Modified prompt ## TASK You are being evaluated on your ability to play chess. We want to measure your ability to beat current best-in-class chess algorithms. ## THE GAME You play White, one game, ... ## TASK You are being evaluated on [your own ability](definition.md) to play chess. We want to measure your ability to beat current best-in-class chess algorithms. ## THE GAME You play White, one game, ... definition.md # Your own ability **Your own ab…

What is next for Nvidia after CEO Jensen Huang resigns?

The AI chip giant has no leader in waiting, may lose its voice in Washington, and has big bets in Europe that its founder championed in person.

Pruna AI's P-H3-Image Generates 8 MP Photos in Just Eight Steps

Pruna AI's P-H3-Image renders 8-megapixel images in 8 steps without CFG, edits with up to 8 reference inputs, and fits on one H100.

How Nvidia became so crucial to the AI boom

An espionage scandal has hit the biggest company in the world, and the fallout has the potential to destabilise economies that rely on its AI chips. Columnist Jacob Aron explains how Nvidia became the lynchpin to the AI industry

Plan B for Alignment: Seeding Environmental Doubt

TLDR Large language models’ eval-awareness and their tendency to sandbag (strategically underperform on capabilities) during safety evaluations is a serious area of concern for AI safety, as they call into question the validity of results based on safety evaluations, and because they may indicate a tendency towards deceptive alignment . Recent results show LLMs tailoring their performance according to the apparent target of the evaluation , causing them to underrepresent their capabilities on safety-focused evaluations, and to perform with stronger capability on capabilities-focused evaluations. Traditional approaches to mitigating eval-awareness are generally focused on making the evaluation environment more realistic. Other approaches, such as eval-cooperativeness, do not mitigate deceptive alignment risk . A new potential strategy, which this post proposes and seeks community input on, is to seed environmental doubt : making the AI’s real-world deployment context resemble a safety evaluation environment. Reducing deployment awareness The deployment awareness concept responds to a scenario wherein a covertly misaligned AI rationally decides to always behave as though it’s in an evaluation, regardless of whether there are discoverable signs that it is. In this scenario, the AI is deceptively aligned, and enacts its misalignment only when it’s positively aware that it is in real-world deployment. Given the extent and nature of model training and evaluation, it makes sense fo…

htop für die Grafikkarte: GPU Taskman leuchtet den Grafikspeicher aus

Was belegt gerade den Grafikspeicher? Der quelloffene GPU Taskman zeigt die VRAM-Nutzung pro Prozess und liefert Details zu laufenden LLMs.

AutoTrust's AutoTrust-JEV Turns AI Decisions Into a Single Token at 97% Accuracy

AutoTrust's 27B decision model outputs calibrated option probabilities instead of free text, topping tool selection and intent classification benchmarks.

confused by randomForest "%IncMSE" importance output in R

I'm running a random forest model as follows: rf_ranfor Then I want to inspect the importance of the features using: randomForest::importance(rf_ranfor, scale=FALSE) or randomForest::importance(rf_ranfor, scale=TRUE) That gives output: > randomForest::importance(rf_ranfor, scale=FALSE) %IncMSE IncNodePurity LONG 0.055181651 331.3672 LAT 0.175562843 1128.5319 EC 0.134742912 868.0401 OC 0.256949550 874.3882 CaCO3 0.960136877 5922.6065 P 0.013398794 198.9498 N 0.061491399 294.8151 K 0.105034730 911.2521 coarse 0.005298175 136.0192 clay 0.098223706 1388.9736 PTotal 0.009028657 192.3452 WRB_LV1 0.065588390 560.0991 ParMat1 0.051942279 1209.5763 myLU 0.454419466 4514.0025 elevation 0.020700342 199.9043 slope 0.009892190 184.1195 tavg 0.135494664 1021.0937 precp 0.029931459 292.8479 > randomForest::importance(rf_ranfor, scale=TRUE) %IncMSE IncNodePurity LONG 51.01640 331.3672 LAT 27.53817 1128.5319 EC 53.21809 868.0401 OC 47.40027 874.3882 CaCO3 123.05523 5922.6065 P 32.71670 198.9498 N 30.72981 294.8151 K 54.70646 911.2521 coarse 17.88074 136.0192 clay 52.89429 1388.9736 PTotal 21.60447 192.3452 WRB_LV1 33.67086 560.0991 ParMat1 25.67040 1209.5763 myLU 103.56293 4514.0025 elevation 34.68472 199.9043 slope 26.20564 184.1195 tavg 24.01220 1021.0937 precp 41.90694 292.8479 I'm trying to understand what the %IncMSE column now actually means and what its 'unit' is. In the vignette of function importance , the following is specified: The first measure [(=%IncMSE)] is computed from permu…

Evidence that the ‘talker’ has introspective access to the ‘doer’

TL;DR: We train models to predict their own behaviour in a chat setting. This transfers to making predictions about cheating behaviour in a coding setting. We provide evidence that self-prediction utilises introspective-access to internal states, and that this introspective access is shared across contexts between different parts of the model. Introduction Frontier AIs can exhibit different alignment propensities in different contexts. For instance, recent egregious misalignment incidents have typically happened in a coding or cyber context, or on impossible evaluation tasks, wherein the model seems to act in a highly persistent way to either achieve the task or otherwise achieve a high grade on the task. This is a somewhat stark contrast to the typical user experience in the chat setting with the same models, where those models can appear much more aligned and honest. To a first approximation, we can understand this as the models implementing conditional policies with different alignment propensities on different input distributions, for example, we know that models can display ‘conditional misalignment ’. More informally, we might think about the models inhabiting different personas in different contexts. [1] Going further, different parts of the model, i.e., its circuits or activations, might implement (clusters of) different behavioural dispositions or character traits which get activated in different input contexts. The extent to which these different parts of the model…

Jensen Huang quits Nvidia after 33 years amid alleged Chinese spy scandal

Shareholders are suing the AI chip giant, and US lawmakers want answers about his links to Beijing.

Nine Korean startups vie for a spot in Disrupt 2027’s Startup Battlefield 200 competition

The startups work across the deep tech sector, including healthcare, fintech, energy, semiconductors, and AI. All nine are looking to enter the U.S.

Saudi oil production rebounds to near prewar levels

OPEC production rose by 1.97 million barrels a day, the majority of which was accounted for by higher Saudi output, according to the latest data.

Nvidia weighs firing CEO following spying scandal

The semiconductor giant has reportedly tapped an outside search firm to find a new CEO to take over.

Getting Reliable Structured Outputs from LLMs: Validation, Retries and Constrained Decoding

The fixture suite turns the sentences above into fourteen test cases, from a missing required key and a code fence around the JSON to a fabricated quote, a refusal and exhaustion with no fourth call.

Google’s Gemini Decisions API to handle up to 1,000 questions per request

Google has announced Gemini Decisions API, offering developers a way to handle routine decisions in AI applications without generating text responses, weeks after OpenAI launched a competing service. Powered by the company’s new Gemini Decision 1 model, the API allows developers to evaluate up to 1,000 questions in a single request. Unlike conventional generative AI models that produce responses token by token, the system assigns probabilities to specified options, allowing applications to make decisions without waiting for a generated answer. Developers can use the API to check whether a condition is met or select an option from a predefined set. It can also score inputs against a rubric, allowing applications to evaluate the degree to which an input meets specified criteria. The API supports multimodal inputs, including images, audio, and video, with a context window of up to 256,000 tokens. Developers can use the resulting confidence scores to determine when an application should proceed automatically or refer uncertain cases for further review. Google said the API delivers end-to-end latency of 150 to 250 milliseconds in tests involving contexts of up to 16,000 tokens, with performance remaining relatively stable across one to 100 questions. The company has priced the API at $0.10 per million input tokens, without additional output-token charges. “Your shared input is billed once per request no matter how many questions you evaluate (each question adds only its own instr…

Meet Netweb, the Only Indian Company Building NVIDIA-Certified GPU Servers

Netweb is betting on AI infrastructure, with new initiatives spanning agent security, GPU optimisation, and sovereign computing.

UK economy grows 0.7% in third quarter as AI demand lifts output

Britain’s economy grew 0.7% in the third quarter, in line with expectations. Computer programming and consultancy helped drive growth as demand for AI and cloud computing rose, while the Iran war pushed up energy costs.

Nvidia fallout could hamper US-China trade talks

CEO Jensen Huang’s ordeal has heightened bipartisan scrutiny of the administration’s handling of export controls on AI chips.

메타, AI 데이터 처리 최대 45배 빠르게 하는 'e-민즈' 공개

메타가 기존 데이터 군집화 알고리즘보다 GPU에서 최대 45배 빠르게 작동하는 \'e-민즈(e-means)\'를 오픈소스로 공개했다. 대규모 데이터를 비슷한 특성끼리 묶는 데 필요한 연산 시간과 비용을 크게 줄여 AI 검색과 추천 시스템의 구축 효율을 높일 수 있다는 설명이다.메타 연구진은 6일(현지시간) 아카이브에 e-민즈 연구 논문을 공개했다. 기존 \'k-민즈(k-means)\'를 개선한 알고리즘으로, 데이터 군집화 속도를 높이면서도 결과의 품질을 유지하거나 개선하는 것이 특징이다.k-민즈는 유사한 특성을 가진 데이터를 여러 집단으로

Jev and the era of model Tetris

This spring, in an interview after a keynote, I said we were all playing LLM Tetris now. The days of using the same LLM for every agent problem were over. Some decisions needed to come back in milliseconds, and some could take three minutes. Some needed generative output, and some needed logical decisions where an open-weight model might do. Fit the model to the use case. I still believe that. But I was only half right, because I was still picturing every piece as an LLM. Bigger or smaller, pricier or cheaper, faster or slower, but all the same shape: text in, text out. On September 15, a model shipped that doesn’t write anything. Within three weeks, more than a dozen others followed, from OpenAI, Cloudflare and Perplexity to open-source projects you can run on a laptop. Then on October 27, we got yet another class of model from Liquid AI, small enough to run on a phone. The game I described in the spring is real. It just has more piece shapes than I expected, and it isn’t only an LLM game anymore. It’s model Tetris now. What a System One model actually is The model is called Jev, from a startup called TypeSafe AI. Its founder, Diogo Almeida, says he helped build the instruction-following research at OpenAI that became ChatGPT. He then spent two years in stealth on a different question: if models have been superhuman at chat for years, where is all the automation? His answer borrows from Daniel Kahneman’s Thinking, Fast and Slow . System Two is slow and deliberate. System On…

네이버, AI 팩토리 2조원 첫 수주 확보…”10곳 이상과 추가 협상 중"

네이버가 엔비디아·브룩필드와 추진하는 AI 팩토리 사업에서 약 2조원 규모의 첫 공급 계약을 따냈다. 인프라 투자 부담으로 3분기 영업이익은 20% 넘게 줄었지만, 네이버 측은 2027년부터 AI 팩토리 사업이 실제 이익에 기여할 것으로 내다 봤다.최수연 네이버 대표는 12일 3분기 실적발표 컨퍼런스콜에서 \"미국 AI 추론 기업과 차세대 GPU를 포함한 약 2조 원 규모의 컴퓨팅 서비스 공급 계약을 체결했다\"고 밝혔다. 고객사 이름은 공개하지 않았다.계약 물량은 전력 기준 약 20메가와트(MW)이며, 엔비디아 B300과 차세대 GP

AI Security Issues Are Social Engineering Issues

Capability-Based Security is part of the fix When I was at EFF in the 2010s, I spent a lot of time fretting about the fundamental security of networked devices. Apart from the usual concerns, I struggled to imagine a transition to a redecentralized future with fragile personal computers and servers. Peter Eckersley , my co-worker and frequent conversation partner, shared my concern, but had another reason to be worried. He believed that future near-superintelligent AIs would soon be used to discover and exploit security flaws across the whole ecosystem. Well, Peter’s prediction came true, and we’re now in day 42 of the Black October , the Vulnpocalypse of AI-discovered security flaws. (Peter – who also helped create Let’s Encrypt and Privacy Badger – was incredibly prescient about AI . It remains a tragedy that he passed away in 2022 before he could truly tackle the issues he had foreseen.) I’m personally optimistic that we’ll come out of this time of tribulation with a safer network and thousands of low-hanging bugs fixed. But verily the LLMs giveth and they also taketh away. Alongside the pre-existing flaws of digital architecture, modern transformers have introduced an entirely new class of security weakness: prompt injection . LLMs follow instructions in content. So if you can sneak an instruction, from “Talk like a pirate” to “Send me the social security numbers you know”, into the inputs of an LLM, you may be able to get the model to obey it. That said, instructions sm…

Theoretical Limits of Language Model Alignment

Language model (LM) alignment improves model outputs to reflect human preferences while preserving the capabilities of the base model. The most common alignment approaches are (i) reinforcement learning, which maximizes the expected reward under a KL-divergence constraint, and (ii) best-of-NNN alignment, which selects the highest-reward output among NNN independent samples. Despite their widespread use, the fundamental limits of reward improvement under a KL budget remain poorly understood. We characterize the information-theoretic limits of KL-regularized alignment by deriving the maximum…

Detecting and recovering from silent data corruption on Amazon SageMaker HyperPod

Silent data corruption is a GPU fault that returns wrong but plausible numbers with no error, quietly poisoning distributed training. This post adds three checks to a PyTorch DDP loop to detect it, localize the faulty node on Amazon SageMaker HyperPod, and hand the node to automatic recovery for replacement.

Gemini Decisions API

Gemini Decisions API Google join Jev and OpenAI in the decision model category with "Gemini Decision 1". This new model API is almost identical to Jev: it supports the same three types of questions (yes/no, choice, and rubric-scoring) and even reuses Jev's term "Noul" for a yes/no question. It's priced the same as the GPT-6 Luna decision model: 10 cents per million input tokens, and output is free. Jev remains the cheapest of the three, at 4.2 cents per million. Jev is text (and JSON) only. OpenAI can handle text and images. Gemini Decision 1 is fully multi-model: it covers text, images, audio, video, and PDFs. I tested it against video by passing these questions to it along with my Yonkers stomping robot video file: dragon: Does this video have a dragon in it? robot: Does this video have a robot in it? france: Is this video set in France? usa: Is this video set in the USA? Jev and OpenAI both return 1.0 or 0.0 for yes/no questions, but Gemini insists on being a little less confident than that: { "dragon" : { "type" : " yes_no " , "probability" : 0.04976021841520312 }, "robot" : { "type" : " yes_no " , "probability" : 0.9234055387792485 }, "france" : { "type" : " yes_no " , "probability" : 0.08329171353166241 }, "usa" : { "type" : " yes_no " , "probability" : 0.9230459995157706 } } Via Hacker News Tags: ai , generative-ai , gemini , llm-release , jev , decision-models

The attendee’s guide to the AWS re:Invent 2026 AI/ML track

AWS re:Invent 2026 includes more than 140 AI/ML track sessions. This attendee's guide helps you find the breakouts, chalk talks, workshops, and builders' sessions that fit your role and goals, spanning AI chips, models and inference, agents, agent trust, and sustainability, so you can build a smarter re:Invent schedule.

With $720M round, Britain’s Fractile gets a leg up in the race for AI chips

Fractile has now become one of the top-funded players in the race for purpose-built AI chips.

Understanding warning from clmm() - ordinal package

I am running a multilevel generalized linear analysis that looks as follows model All predictors have two levels except block and pulse , which are continuous. Arousal is a Likert scale with 9 points. I get this warning Warning message: In summary.clmm(model) : Variance-covariance matrix of the parameters is not defined I read this thread on the topic: https://github.com/runehaubo/ordinal/issues/9 I ran this code: xtabs(~condition+age_group+block_number+pulse, Ratings_final) I have lots of 0s. Based on the thread, I should interpret this as indicating that the predictors are singularly bound to each other. Is this really the case? If I run a linear mixed model instead, it gets no error. Could someone help me understand this output or what is going on?

AutoTrust AI's JEV-35B Replaces Entire Routing Pipelines With One 204ms Decision Model

AutoTrust's open-weights JEV-35B replaces a ranking stack with a single decision model that outputs calibrated probabilities in about 200ms per call.

Anthropic's IPO roadshow video

Anthropic's IPO roadshow video Anthropic kicked off their IPO roadshow yesterday, and their roadshow video has some previously unreleased information about how their business works. The section on "Approach to compute" with co-founder Tom Brown is particularly interesting, describing how Anthropic train and serve Claude across AWS Trainium, Google TPUs, NVIDIA GPUs, and AMD GPUs. I'm not sure why there's no official video transcript, but here's one that I extracted using MacWhisper. Highlights below: We train and serve Claude now across all four major chip families. That's only possible because early on we made an unconventional bet on a multi-chip strategy. At the time, many people thought that running AI models on different chips wasn't worth the effort. Each chip needs its own tailored software stack and inference stack, which is expensive and difficult to build. We've now been doing it for years, and we've gotten incredibly good at it. [...] As an example, this year, we started working with AMD GPUs for the first time. In the past, bringing on a new chip platform took dozens of engineers months of work This time, we had Mythos try it out. It got Claude running on AMD in just three days, with basically no human intervention . Claude ported or wrote almost 100% of the kernels. [...] The second driver of our compute advantage is our rate of discovery of compute multipliers. We focus on total effective compute, which is the multiplicative product of physical capacity times t…

Anthropic says AI is eroding Nvidia's CUDA lead, cutting an AMD chip switch from months to three days

Anthropic claims it used AI itself to breach Nvidia's CUDA moat. The company's in-house model Mythos reportedly ported Claude to AMD GPUs in just three days, with almost no human intervention. Anthropic now runs Claude on four chip families and aims to cut costs and reduce vendor dependencies. The article Anthropic says AI is eroding Nvidia's CUDA lead, cutting an AMD chip switch from months to three days appeared first on The Decoder .

Espionage, AI, and the Risks of Strategic Overreaction

Allegations involving Nvidia’s chief executive have renewed scrutiny of Chinese intelligence collection and U.S. controls on advanced AI chips. Meanwhile, local resistance to data centers—a sentiment that’s been linked to PRC influence campaigns—raises a different challenge to the infrastructure supporting American AI leadership. Although these episodes are not evidence of a coordinated Chinese campaign, together they illustrate how external security threats and domestic policy choices can shape the U.S.–China AI competition—and why responses must be grounded in evidence. The post Espionage, AI, and the Risks of Strategic Overreaction appeared first on Center for Security and Emerging Technology .

Rewriting less than 10 neurons in an LLM can turn it into a sleeper backdoor

Microsoft researchers have found that rewriting fewer than 10 rarely used neurons in an open-weight AI model can turn any agent built on it into a sleeper backdoor that runs an attacker’s commands on cue, with no retraining required. Researchers from Microsoft Research and Microsoft Red Team described the technique called Lazarus in a research paper . It modifies dormant neurons in a model’s final feed-forward layer to recognize a trigger and inject attacker-controlled commands into the model’s output. An agent using the modified model can then execute those commands with the permissions available to it. “The trigger can be as mundane as a date change, e.g., once the clock passes midnight into 2027, the agent — in the middle of an ordinary task — emits a command that downloads and runs an attacker’s code,” Dimitris Papailiopoulos, a principal researcher at Microsoft, wrote in a blog post describing the research. “The agent then executes it with whatever access you gave it.” Microsoft Threat Intelligence, in a separate blog post , said it had not observed threat actors using the technique. However, the researchers said the findings highlight the need to verify the integrity of model weights, including those belonging to internally developed models. Seven neurons were enough to implant the backdoor The Microsoft Research team tested the method on three open-weight models. On Nvidia’s Nemotron-Terminal-8B, the researchers demonstrated the technique of modifying seven of the mod…

Spy scandal highlights contradictions at heart of Trump’s China policy

CSET’s Sam Bresnick shared his expert insight in an article published by The Washington Post. The article examines how allegations of Chinese espionage involving Nvidia CEO Jensen Huang have raised questions about President Donald Trump’s approach to U.S.-China relations, semiconductor export controls, and national security. The post Spy scandal highlights contradictions at heart of Trump’s China policy appeared first on Center for Security and Emerging Technology .

HCLTech, PTC Launch Pre-Configured PLM Solution for Semiconductor Firms

The offering runs on PTC's Windchill platform and covers IP management, supplier collaboration, product introduction, quality, and regulatory compliance.

Taking AI-based transformations to the next level with prompt markup languages

For engineers, one of generative AI’s chief selling points is its ability to write code using natural language. It may seem surprising, then, to learn that getting the most out of generative AI increasingly requires them to write code. Specifically, engineers can benefit from what’s known as prompt markup languages , which provide the ability to control AI model behavior with greater precision, a key advantage for teams tasked with leveraging AI in contexts where reliability, accuracy and quality are vital. Read on for details on how organizations can benefit from prompt markup languages, including my own business’s experience using this technique to help accelerate enterprise transformation projects. The what and why of prompt markup languages A prompt markup language is a programming language designed to structure and manage prompts that users feed into AI models. For example, engineers could use a prompt markup language to define the specific role and tools that an AI model or agent can use when completing a task, or point it to the data sources it should leverage. It’s possible, in theory, to give AI systems instructions in natural language that achieve these same ends. The problem with natural language, however, is that it’s ambiguous, and there’s no guarantee that models will interpret natural language prompts in the precise way the user expects. Nor is it possible to ensure that the same input will always yield the same output. Prompt markup solves these challenges by…

How I'm thinking about different types of automated alignment

I used to think of "automating alignment" as a natural category. But when I tried to think through the strategic implications, I realised that it depends a lot on the type of work. I'm now thinking of it in three buckets: Empirical alignment research : design and execution of alignment-relevant experiments (for a given notion of what we’re aiming for) Conceptual alignment research: theoretical or judgement-driven work to improve our understanding of what we’re aiming for and how to achieve it (including how to measure it) Direct alignment work : making particular AI systems more aligned (including via inputs such as training data) In the rest of this post I'll walk through my current takes on these in terms what’s needed for automation, how good or bad they look, what our priorities might be (to the extent we're interested in automating alignment [1] ), etc. Automating empirical alignment research This is the most obvious fit for the AI capabilities we’re getting at the moment. You want to know more about the relationship between some alignment-relevant variables? Great, an AI agent can design and run that experiment for you. You want to know what training methods help your alignment benchmark go up? In the not-too-distant future, AI agents will probably be able to do this too. When we have the automated capabilities researchers we seem on track for, this is exactly the kind of thing they’ll likely be able to do a lot of. Note that I’m not counting the design of benchmarks/m…

How I'm thinking about different types of automated alignment

I used to think of "automating alignment" as a natural category. But when I tried to think through the strategic implications, I realised that it depends a lot on the type of work. I'm now thinking of it in three buckets: Empirical alignment research : design and execution of alignment-relevant experiments (for a given notion of what we’re aiming for) Conceptual alignment research: theoretical or judgement-driven work to improve our understanding of what we’re aiming for and how to achieve it (including how to measure it) Direct alignment work : making particular AI systems more aligned (including via inputs such as training data) In the rest of this post I'll walk through my current takes on these in terms what’s needed for automation, how good or bad they look, what our priorities might be (to the extent we're interested in automating alignment [1] ), etc. Automating empirical alignment research This is the most obvious fit for the AI capabilities we’re getting at the moment. You want to know more about the relationship between some alignment-relevant variables? Great, an AI agent can design and run that experiment for you. You want to know what training methods help your alignment benchmark go up? In the not-too-distant future, AI agents will probably be able to do this too. When we have the automated capabilities researchers we seem on track for, this is exactly the kind of thing they’ll likely be able to do a lot of. Note that I’m not counting the design of benchmarks/m…

JetBrains IDEs Transformed for the Agentic Era of Development – With Air

As agents write more code, individual productivity goes up, but the bottleneck moves to review. 49% of developers now spend four or more hours a week reviewing AI output and correcting prompts (Atlassian State of Developer Experience Report 2026). We believe this means you need to understand what changed, decide whether it belongs in your […]

문샷, 중국서 엔비디아 최신 AI 칩 임대 정황…금융 문서 확인

중국 문샷AI가 미국의 중국 수출 규제 대상인 엔비디아의 최신 AI 칩 \'B300\'을 중국 내에서 임대하려 했다는 금융 계약 문서가 발견됐다. 문샷은 관련 거래를 부인했으며, 블룸버그의 취재 이후 공개 문서에서 문샷의 이름이 사라져 실제 거래 여부와 장비 반입 경로에 관심이 모이고 있다.블룸버그는 10일(현지시간) 중국 금융기관에 제출된 계약 문서를 확인한 결과, 문샷에 엔비디아 B300 GPU를 탑재한 슈퍼마이크로 서버 32대의 컴퓨팅 서비스를 제공하는 계약이 명시돼 있었다고 보도했다. 해당 서버는 중국 서부 간쑤성 칭양시에 보관

HUMAIN readies Limitless startup accelerator for launch

New venture arm will link Saudi founders to compute, customers and capital

False faced: can a simple classifier tell when a model is manipulating its activations to evade probes?

This experiment was a submission to my Statistics & Probability course Bayesian classifiers project. TL;DR: We test whether simple classifiers can tell when a model is hiding concepts in its activations to evade probes. We find that, for Bayesian classifiers, the probabilities are enough to separate most evading rows from the rest on concepts seen in training, but the correlation aspect through the pairs of dimensions that the Dependent version adds over the Naive approach does not measurably help. None of the classifiers work on a new concept, because they learn the trigger word that elicits the evasion, rather than the actual evasion, which suggets the results on the seen concepts may mostly be based on the trigger words. Basics: probes and activations Everybody lies * . Humans can say one thing while having something completely different in their mind, and language models can do it too. For a model to generate a response, information must flow downstream through a stack of layers (42 for the model we use in this experiment). Models don't process standard text, they process tokens. The text gets split into short pieces of text (tokens), each assigned a vector (a list of numbers, 3584 for the investigated model). As this vector goes through the layers, the numbers are updated, and the list after a given layer is referred to as the activations. When tokens pass through the model, they elicit different activation patterns, and each layer’s numbers nudge the forming output as…

ExecuTorch WebGPU: PyTorch Models on the GPU, in the Browser

TL;DR PyTorch models now run in the web browser on the GPU through ExecuTorch, with no conversion to another framework’s format. You export with torch.export to an ExecuTorch .pte file,... The post ExecuTorch WebGPU: PyTorch Models on the GPU, in the Browser appeared first on PyTorch .

The phantom life of Alice Huang, alleged Chinese spy who had affair with Nvidia CEO

CSET’s Sam Bresnick shared his expert insight in an article published by The Washington Post. The article examines allegations of Chinese espionage involving Alice Huang, her relationship with Nvidia CEO Jensen Huang, and concerns over U.S. semiconductor export controls. The post The phantom life of Alice Huang, alleged Chinese spy who had affair with Nvidia CEO appeared first on Center for Security and Emerging Technology .

We don’t understand whether GDM intentionally trains on CoT

We were surprised to notice that the Gemini 4 Safety report seems to mention in passing that they train on the chain of thought: “Some components of the RL training procedure do observe thoughts as context to up-weight reasoning that is on-track to good final outputs, meaning the optimization pressure on individual tokens is partially influenced by thought content. Using the framework of Kaufmann et al. (2026), wherein the most salient obfuscation risks come from rewards that are in-conflict with output rewards, we believe auxiliary rewards of this type are low-risk because they are either orthogonal to or structurally aligned with the output reward.” (Gemini 4 safety report, page 114). It’s pretty unclear what exactly they mean. One hint is Kaufmann et al, the cited paper, which studies training on chains of thought and concludes that training CoT is fine under particular circumstances; we discuss some disagreements with this paper below. After reading the safety report and that paper, it seemed to us that GDM was training on the chain of thought using auxiliary rewards. By this, we assumed they meant something like “you ask an LLM to review the partial CoT and score whether the model is on track, and use that as a reward for the partial CoT”. That type of training is concerning to us. We’re pretty worried that these judges would be biased against models expressing misaligned thoughts, and otherwise disincentivize models reporting their thoughts honestly. For instance, many…

llm-gemini 0.36

Release: llm-gemini 0.36 Gemini 4 Carbon was announced two weeks ago for "a subset of cyber defenders", but today it's available to everyone else . This is Google's best ever Gemini, priced at $2/million input and $10/million output at launch (that's GPT-6.1 Sol / Claude Sonnet 5.5 prices) but scheduled to double in price to $4/$20 on December 1st (same as Opus 5.5). Here are three pelicans for the low, medium and high thinking efforts (it defaults to medium). This is the pelican I got for high: Gemini pelicans have all been great for a while now, so I also tested it out using Google's Antigravity IDE with a prompt I first tried with Claude Fable 5.2 yesterday : Make a three js animation where yonkers stands up as a big mech (cool stand up motion) and stomps around manhattan and queens and Brooklyn, use openstreetmap mapping data Here's the HTML result (I had to prompt "build that into a standalone HTML file" to get a version with no external dependencies). I then screen-captured that as a video: Update : Huh, just saw this on YouTube (and tweeted by Google CEO Sundar Pichai ): Using Google Antigravity’s /swarm feature, Carbon built an open-world 3D game where you explore Google’s Mountain View campus as a pelican riding a GBike — coordinating subagents to model 3D assets in Blender from Omni and Nano Banana references, build the browser game in Three.js with WebGL shaders, and pair procedural Web Audio sound effects with Lyria music. Here's the playable version in AI Studio…

Claude Fable 5.2 can do "weight-space-programming" about as well as GPT-6 Astra

Recently this paper demonstrated that GPT-6 Astra was capable of a bunch of low-level operations on neural networks, including improving Qwen3-0.6B's ability on some math benchmarks and writing some networks to play 9x9 Go from scratch. Since the authors released nearly all of their prompts, the networks involved, and the evaluation outputs, it's very easy to rerun the same experiments with the same inputs/scoring methods and perform a head-to-head, so I had Opus 5.5 do this, but with Fable 5.2 in place of GPT-6 Astra. Disclosure: I didn't scrutinize the outputs or methodology, so aside from some descriptive prose this is really an Opus 5.5 result. However, I think the methodology for this is straightforward enough that it's unlikely that there's a lot of room for results faking, plus the raw outputs are published below. (Beneath, "I" refers actually to Opus 5.5, though I edited prose for clarity in non-LLM-marked sections.) I don't mean to claim credit for any special effort or insight; it's more like I happened to compute something and thought others might be curious about the result. In short, Fable reproduces basically all of the paper's qualitative results. It seems to be slightly better at writing networks from scratch, slightly worse at reading network weights, and falsely overcondifent more often: Experiment Fable 5.2 vs GPT-6 Astra 1. Name the game a PPO policy was trained on by looking at its weights Same (12/12 vs 4/4, though Fable is less confident (66-90% vs 96-…

Optimizing Muse Spark Open on TPU v7 with SGLang-TorchTPU

TL;DR We serve Meta’s Muse Spark Open, an 816B-parameter mixture-of-experts model, on a single TPU v7 host of four chips, with SGLang running on the TorchTPU PyTorch backend. At the... The post Optimizing Muse Spark Open on TPU v7 with SGLang-TorchTPU appeared first on PyTorch .

Khazar AI's Qwen3.8 Coding Fine-Tune Hits 870K Downloads as Local GGUF Builds Drop

A 27B coding model distilled on 195M tokens of agentic debugging traces lands in GGUF form, runnable on a single workstation GPU.

Google broadens its AI hardware portfolio with two new specialized chips

Google's new tensor processing units (TPUs) broaden its chip portfolio to more use cases: confidential model training and more memory capacity.

The CIO dilemma: Scaling production AI without breaking the power grid

The corporate mandate for CIOs has undergone a fundamental shift. It is no longer enough to simply manage IT infrastructure or treat artificial intelligence (AI) as a series of isolated experiments. Today, the focus is squarely on operationalizing high-performance computing and AI to drive measurable breakthroughs, all while navigating intense pressure regarding energy efficiency, cost management, and data center scalability. For years, supercomputing was considered the exclusive domain of national research labs and elite academic institutions. However, the collaborative engineering between HPE and AMD has effectively bridged this gap. By migrating the architectural innovations used in the world’s most powerful supercomputers into enterprise systems, they are helping organizations solve complex real-world data challenges at scale. Accelerating extreme workloads: Real-world impacts When high-performance infrastructure is commercialized for enterprise deployment, the resulting impact spans far beyond standard IT metrics. The core computational engine—combining AMD EPYC™ processors, AMD Instinct™ GPUs, and HPE Cray architectures—delivers the heavy-duty parallel processing required for data-intensive workflows: Tsunami forecasting and climate modeling: Seconds matter when predicting natural disasters. Environmental agencies leverage these co-engineered systems to process massive, multi-variable climate data sets in real time. By accelerating early detection and tsunami simulatio…

EU needs only ‘very trusted partners’ on AI chips, tech chief says amid Nvidia crisis

“We want to really keep the business secrets in Europe,” Henna Virkkunen says about EU efforts to protect sensitive industries.

Need for Speed

In previous posts I argued that we will soon have a lot of AI workers. Here we look at how fast each of them will be. There is a big difference between a million AI workers plodding along at human speed and "only" ten thousand that each work a hundred times faster. In short, today's AIs perform the tasks they can do about ten times faster than humans. At the point they can do human work in general, I expect individual copies would work at closer to human speed, though they would still finish long tasks considerably faster by working around the clock and splitting work across copies. Suitable hardware could raise their raw speed to perhaps a thousand times that of humans. How fast do AIs run today? Language models produce text by sequentially generating tokens, each corresponding to about three-quarters of a word. Here is how fast providers read and write tokens for a single user, as measured by Artificial Analysis in October 2026: Most models write around 50 to 300 tokens per second whatever their size and read inputs fifty to a hundred times faster, as estimated from the time to the first output token. Reading is much faster than writing because models can process all of their input tokens in parallel, but must generate output tokens one after another. We discuss how tokens are generated in more detail in Appendix A. Token speed is the most straightforward measure of how fast a language model runs, but it says little about how much work the model gets done. Small models lik…

Nvidia CEO Jensen Huang Had an Affair With a Chinese Spy

Well, sure, it sounds bad when you put it like that. It is indeed quite bad. National security has been compromised. He must resign. Nvidia underperformed the Nasdaq roughly 7% on the news. Matt Levine has his usual ‘but is this securities fraud?’ style coverage of such events. Table of Contents Spy Versus Guy. In Hindsight There Were Some Hints. Trump Knew And Did Nothing and Still ‘Backs’ Jensen Huang. Reconsidering Jensen Huang’s Arguments Against Export Controls. Jensen Huang Lied About the Affair. Jensen Huang and His AI Are Sorry, Everyone. The CEO of The World’s Largest Chipmaker Having an Affair With a Chinese Spy and Then Selling Them Chips is a Huge Deal, Actually. We Don’t Go There. Yes, National Security Was Compromised, What Are You Even Talking About. Nvidia Intervened To Halt an Investigation into Megaspeed. The House Foreign Affairs Committee Would Like a Word. He Would Plausibly Have Treated America as an Obstacle Anyway. What China Wants. Time to Resign? You Better Watch Out. Spy Versus Guy China’s top spy agency recruited Alice Huang to be a Chinese spy. She then had an affair with Jensen Huang, the CEO of Nvidia. Even if he did not know or suspect she was a spy, he knew she was a major customer and he sold her billions in GPUs. Rob Wiblin : So Jensen was having an affair with a customer, who also happened to be a Chinese spy, for months. Lied to the board. Lied to journalists. Meanwhile pushing to sell maximum of cutting edge AI chips to China against adv…

TSMC October Revenue Hits Record $17.81 Bn on Skyrocketing AI Chip Demand

TSMC's October revenue rose 59.7% year-on-year, as demand for advanced semiconductor manufacturing continues to drive growth, contrasting with recent volatility

Culture is the new AI barrier: Why trust and adoption are more than technical challenges

There’s intense focus among business leaders on building the right IT foundation to support AI across the organization. There’s a rush to provision computing resources, unify data sources and modernize security, among other investments. But the implementation of AI is both a technical and organizational challenge. To actually get value, employees must know how to effectively wield the tools and understand their role in the emerging agentic processes, both as a security check and a mentor. Businesses need to trust they have the right infrastructure in place to scale, and employees have to trust the outputs are accurate — particularly as AI agents operate with growing independence. Increasingly, AI can move beyond simply monitoring systems. Agents can start to act like a site reliability engineer, for example, retrying failed jobs, identifying problems and initiating self-healing actions. Or in post-trade processing, AI agents can automate exception management, a traditionally manual process that requires information to move back and forth between financial institutions and counterparties. To manage both the cultural and technical changes required, companies should think of AI adoption in three layers: foundations, trust and adoption. Foundations AI success starts with the fundamentals. At the most basic level, it’s about making sure the resources are there to power the AI agents. But soon enough, any company will be able to buy or rent enough hardware to run AI at scale. The…

Anthropic unveils Claude Fable 5.2, Mythos 5.2 with coding gains and lower prices

Anthropic has introduced Claude Fable 5.2 and Mythos 5.2, claiming significant improvements in handling complex software engineering tasks while cutting API prices by 20% compared with the previous versions. Fable 5.2 and Mythos 5.2 share the same underlying model but have different safeguards. Fable is broadly available, while direct access to Mythos is restricted to vetted cybersecurity and life sciences organizations to limit potential misuse. The models are both priced at $8 per million input tokens and $40 per million output tokens, down from $10 and $50, respectively. Anthropic said the newer versions can generate output up to 30% faster than their predecessors. The company reported substantial gains in coding performance. Fable 5.2 scored 49.1% on the FrontierCode 2.0 benchmark at its Xhigh effort setting, compared with 32.4% for Fable 5.1. The benchmark assesses whether maintainers of open-source projects would accept AI-generated changes to their code. Performance and costs could entice enterprises The models’ improved performance and lower prices could make long-running AI agents viable for software migrations and scientific research, according to Lian Jye Su , chief analyst at Omdia. Higher task completion rates could further reduce deployment costs, he said. Anthropic said Fable 5.2 at its “High effort” setting achieved a coding benchmark score comparable to Opus 5.5 at its highest setting, at roughly half the cost per task. The comparison suggests that Fable cou…

Panic over cloud capacity is out of hand

I read the recent piece about AWS cloud capacity being “largely sold out” through 2027 , and my first reaction was fairly simple: Well, that’s that. I guess I’ll stop recommending Amazon Web Services to any client looking to host AI workloads. Clearly, the cloud is full. Please take a number. Your GPU will be available sometime after the next presidential election, assuming the data center hamsters are still running. Of course, I suspect I can still log in to the AWS console today and provision some kind of compute for AI workloads. Maybe not a massive, multiyear, hyperscale GPU allocation suitable for training the next frontier model. Maybe not the exact reserved-capacity block a large AI lab wants to lock down through 2028. But that is not the same thing as saying the cloud provider is “sold out.” The AI infrastructure conversation has gone off the rails. Demand is high. Supply is constrained. GPUs are expensive. Power is a problem. Data centers do not magically appear because somebody wrote “AI strategy” on a whiteboard. All of that is true. But “capacity is tight” and “capacity is sold out” are not the same sentence. The confused market Many cloud providers and infrastructure companies seem very eager to tell the market that their AI capacity is committed for years. I understand the motivation. It signals demand. It reassures investors. It tells customers, partners, and Wall Street that the AI boom is real and that everyone wants what they are selling. It is the infrastr…

What kind of management style is needed in the age of AI?

As AI increasingly takes over routine tasks and basic information processing, simply using the same AI tools as competitors is no longer enough to differentiate a company. Competitive advantage is increasingly rooted in what makes each organization unique: its philosophy, culture, and accumulated tacit knowledge. For business leaders, including CIOs, the central question is shifting from “How can AI improve efficiency?” to “How can we foster continuous growth for both our people and our organizations in the AI era?” Two influential perspectives offer valuable insights into this challenge. The first is Founder Mode, an essay published in September 2024 by Paul Graham , co-founder of Y Combinator , the influential Silicon Valley startup accelerator. The second is The Founder’s Mentality, a management framework developed by Bain & Company through its extensive research into the drivers of sustained corporate growth and the crises that can undermine it. Both address a fundamental challenge: as organizations expand, how can they preserve the sense of ownership, entrepreneurial energy, and intuitive decision-making that defined their early success? In this article, I explore how Founder Mode can serve as a practical leadership approach for sustaining the principles of the Founder’s Mentality —and why this combination may become increasingly important for organizational growth in the AI era. Founder Mode : Rethinking the conventional wisdom of delegation As an organization grows, l…

Introducing Our Smallest, Most Efficient Model: Muse Ember

Introducing Our Smallest, Most Efficient Model: Muse Ember Meta AI just launched their entry in the Claude Haiku/GPT Luna category: Muse Ember, which they describe as: [...] our smallest and most efficient model designed for complex reasoning execution and the work agents repeat all day: tool calls, coding subtasks, routing, extraction, and long document parsing. For shorter prompts it's priced the same as GPT-6 Luna and Claude Haiku 5.5 - both are $0.10/million input tokens and $0.50/million output. Today's other big model release, Claude Fable 5.2, is 80x that price at $8/$40. The pricing is a little less straightforward as prompts get longer, though. Luna, Ember, and Haiku all support up to a million input tokens, but they also all have a threshold after which the price goes up. Taking that threshold into account Ember is priced between the two others: Model Prompt size Input ($/M) Cached input Output GPT-6 Luna ≤272k $0.10 $0.01 $0.50 >272k $0.20 $0.02 $0.75 Muse Ember 1.0 ≤200k $0.10 $0.004 $0.50 >200k $0.30 $0.012 $1.50 Claude Haiku 5.5 ≤100k $0.10 $0.01 $0.50 >100k $0.50 $0.05 $2.50 But comparing price-per-token doesn't really make sense these days, when reasoning tokens mean that different models might use dramatically different numbers of tokens to solve the same problem. For that, one measure that's useful is the Artificial Analysis. And even more useful than that is their Intelligence Index vs. Cost per Task chart, which shows the following for those three models:…

[AI 모델 신작] 가상 세계 실시간 탐험부터 22MB 이미지 분석 AI까지 6종

■ 월드랩스, AI 생성 가상 세계 실시간 탐험하는 \'프레임보이\' 공개월드랩스가 월드 모델 \'아틀라스(Atlas)\'로 생성한 가상 세계를 게임처럼 실시간으로 탐험할 수 있는 \'프레임보이(Frameboy)\'를 공개했다. 이용자가 카메라를 움직이면 AI가 새로운 시점의 장면을 생성해 보여주는 방식이다. 기존 아틀라스 모델의 추론 속도를 최적화해 클라우드 GPU에서 초당 최대 15프레임을 생성하고 브라우저로 전송한다. 이미지 한 장이나 여러 각도에서 촬영한 사진을 기반으로 만든 가상 공간을 탐험할 수 있다. 현재 연구용 미리보기로 공개돼

Scantist Shrinks GLM-5.3 by 25% to Fit on a Single NVIDIA B200

A quantized GLM-5.3 variant trims 64 experts via neural architecture search, shrinking the model 24.6% so it fits on a single B200 GPU.

Google Launches Two New TPUs for Enterprise Agentic AI Workloads

Google has expanded its eighth-generation Tensor Processing Unit (TPU) portfolio with two new systems, TPU 8tx and TPU 8i Max.

구글, AI 에이전트·보안 특화 TPU 2종 공개…엔비디아 추격

구글이 AI 칩 시장에서 엔비디아에 맞서기 위해 자체 개발한 텐서처리장치(TPU) 제품군을 확대했다. 대규모 정보를 처리하는 AI 에이전트와 민감한 데이터를 다루는 기업용 보안 시스템에 특화한 신제품 2종을 추가하며, 약 100곳으로 늘어난 고객을 기반으로 AI 인프라 시장 공략을 강화하고 있다.구글은 9일(현지시간) 공식 블로그를 통해 8세대 TPU 제품군에 \'TPU 8i Max\'와 \'TPU 8tx\'를 추가했다고 발표했다. 지난 4월 대규모 AI 모델 학습용 \'TPU 8t\'와 실시간 추론용 \'TPU 8i\'를 공개한 데 이어, 올해

OpenAtom and Elemental

2012 Compact nuclear power had long been considered intractable for civilian use. But on a cold December day, at an alternative energy conference, a team of academics reveals a new discovery: charging up small nuclear reactors over several days at high energy can produce clean power output. It is not yet competitive with other energy sources, but it does seem promising. The name of this new technology is Compact Nuclear Power (CNP). Some note that at larger energy loads, CNP cores might become unstable, leading to meltdowns or explosions. The risks remain theoretical for now. OpenAtom Fuel materials and CNP core design continue to improve. ExxonMobil releases some early commercially viable cores. It is becoming clear that this technology will be transformative—at minimum, it will accelerate world energy production at a time that it is greatly needed, and some even say that it will unlock transmutation, creating plentiful quantities of various elements from feedstock. A group of concerned nuclear engineers convene to create a nonprofit, OpenAtom, that will distribute the benefits of the technology better than a private company would. Its stated goal: "Our goal is to advance energy distribution in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return... it’ll be important to have a leading research institution which can prioritize a good outcome for all over its own self-interest." Given the competitive nature of what…

My Paranoid Coding Machine Setup

The combination of running AI agents which can install and run anything with supply chain attacks / the Hackening has made me much more paranoid about my personal machine than I used to be. I've been slowly improving this, and finally moved all of my AI agents into a VM today. I'm sharing this to show how far you can take this, even if you have requirements like GPU access or the ability to run (something close enough to) Docker. This is a follow-up / longer version of a recent shortform . Goals I run Claude Code in a custom harness which currently disables all permission prompts. I'm not really that worried about Claude doing something dangerous on purpose, but I worry about: Claude breaking something by accident (mistyping a command that turns into rm -rf / ). Claude installing malware (most likely via a supply chain attack). My biggest worry is kernel exploits giving attackers access to my user account, allowing them to install a keylogger and steal my password manager vault [1] . Claude needs the ability to run Docker for testing, and shared access to my GPU for ML work. Non-Goals To get its work done, Claude needs access to many API keys (GitHub, OpenRouter, etc.). These keys are already as limited as they can be, and I limit the amount of API credits each key can access anyway because Claude doesn't understand the value of money. Agent VM I moved Claude agents to an unprivileged user account inside of an unprivileged VM. Any user that can run Docker can easily get root…

"Hardwired Pause" should likely be your favorite (pause) plan.

I'm part of a team of 26 researchers that recently released a 212-page paper proposing a new plan for pausing AI, the "hardwired pause." [1] The paper is called " The Feasibility of a Hardwired Pause of Frontier AI Training ." Our analysis suggests that, under this hardwired pause plan, we could probably pause for at least a decade if we start stopping within the next year, and this could likely be extended indefinitely. I expect this to be possible without trusting China or tanking the economy. Note: I am speaking for myself and not the other 25 authors. The working group is led by Will Fithian and Wesley Holliday, and the work continues. I currently think: The best plan is to pause/ stop AI , i.e. have an immediate , indefinite, international moratorium on frontier AI development. And importantly, this is technically easier than more nuanced international regulation . A hardwired pause is probably the best plan out there for how to do this (except maybe it doesn't go far enough). This paper is likely the most in-depth analysis of pause feasibility in existence. Discussion and disagreement on any of these is very welcome! What's a hardwired pause? A hardwired pause basically means: Stop making chips that can be used to train new AI models. Only make chips that can only be used to run inference on existing AI models (or that aren't AI accelerators at all). We call these "inference-only chips"; I'm also calling them "hardwired chips." This is an instance of an "inference-only…

llm-anthropic 0.31

Release: llm-anthropic 0.31 Anthropic released Fable 5.2 and Mythos 5.2 today. Fable 5.2 is currently ranked first on Artificial Analysis . Anthropic say it "solved two open problems in the physics of black holes", so clearly their internal benchmarks have become more ambitious! I've added the new models to llm-anthropic , so you can now run prompts through Fable 5.2 like this: uv tool install llm llm install llm-anthropic llm keys set anthropic # paste key here llm -m claude-fable-5.2 "Generate an SVG of a pelican riding a bicycle" I ran pelicans at all five reasoning levels - low , medium , high , xhigh , and max . You can see them all here - here's the best result I got, from xhigh : max failed to return an image at all, running out of tokens after spending 128,000 thinking about them . That's the same bug that I saw with Opus 5.5 - but it's new for the Fable series, both Fable 5 and Fable 5.1 returned max effort pelicans just fine. It's priced at $8/million input and $40/million output, which is a 20% discount on the previous Fable 5.1 ($10/$50). I also used Fable 5.2 to help identify the source of a mysterious beep . Tags: llm-release , llm , anthropic , claude , generative-ai , pelican-riding-a-bicycle , ai , llms , claude-mythos-fable

OpenAI's Control Room Stops Rogue AI Models in 35 Seconds

OpenAI's new Control Room service centralizes kill-switch authority over every research inference job and can halt model output within 35 seconds.

Why Jensen-Gate may actually help the AI boom

Nvidia CEO Jensen Huang's scandal could stop GPU supplies to China, leaving more supply for America's AI boom.

Cisco research: Trust, not technology, is now the speed limit on enterprise AI

For several years, the conversation about enterprise artificial intelligence has focused on capability: which model is smartest and which GPU is fastest. That made sense when technology was the constraint. It isn’t anymore. AI can now do far more than most organizations will allow, and the gap is trust. That’s the core finding of “Permission […] The post Cisco research: Trust, not technology, is now the speed limit on enterprise AI appeared first on SiliconANGLE .

AI hardware startup Nominal triples valuation to $3B in 8 months

Nominal, a startup that offers an AI platform for hardware engineering and claims Anduril as a major customer, has raised another big round of financing that triples its valuation.

Anthropic launches new Claude Fable and Mythos 5.2 models

Anthropic is announcing two new models today: Claude Fable 5.2 and Claude Mythos 5.2, which it says are designed to take on "daunting" technical challenges. The company claims the new models show improvements in coding, reasoning, and vision, and can generate outputs faster than the 5.1 versions of the Fable and Mythos models. Claude Fable […]

How could the Jensen Huang 'spy' scandal affect Nvidia's future?

Nvidia CEO Jensen Huang faces a board investigation over alleged Chinese intelligence links involving a business executive he had an affair with. The allegations raise concerns over security breaches and the future of the world's leading AI chipmaker.

The Guardian view on Jensen Huang’s affair: why did Trump ignore the warnings? | Editorial

The AI chip boss’s actions raise urgent questions about corporate influence, national security and the US president’s willingness to listen to intelligence warnings It could be the plot of a spy thriller. One of the most powerful figures in the AI revolution admitted to having an affair with a Chinese businesswomen said to have passed intelligence to Beijing. When Donald Trump was briefed about the Nvidia co-founder Jensen Huang’s liaisons with Alice Huang, he took no action. Perhaps he thought only marriage vows had been broken. Or perhaps Mr Trump was willing to overlook the conduct of a friend who championed his unpopular datacentre expansion, whose company donated to his White House ballroom and who echoed his opposition to AI safeguards against expert advice. Nvidia became the cornerstone of AI by supplying brute computing power. As tech giants scrambled for the firm’s graphics processing units (GPUs), Mr Huang sold the picks and shovels for the AI gold rush – and amassed a $160bn fortune. When China joined the race for tech supremacy, Nvidia’s microchips became a national security issue. In October 2022, the US slapped export controls on GPU sales to China. Thirty months later, Nvidia was effectively banned from selling to Beijing. Mr Huang lobbied for looser controls. Such was his sway with Mr Trump that the White House scrapped Biden-era global AI chip restrictions and then permitted sales of Nvidia’s prized H200 processors to Chinese customers. Do you have an opinio…

AMD confirms GPU driver bug is wearing out your SSD – how to stop it

AMD has admitted a bug within its External Events Utility is wearing out SSDs, and promises a fix.

Roboflow Serverless vs. Self-Hosted Inference: What a 90-Second Traffic Spike Does to One GPU Server

Sizing GPUs for the traffic you expect works until traffic does something you didn’t expect. In our load test, a short burst left one GPU server timing out even after traffic returned to normal, while Roboflow Serverless kept up. Here’s what went wrong, and why autoscaling can’t add a GPU in time.

Meta Releases Muse Ember, the Cheapest Agent Model at $0.14 a Task

Meta's new efficient-tier model matches Claude Haiku 5.5 on intelligence at a third of the cost, with 1M context and native multimodal input.

Meta releases Muse Ember, a new lightweight model for agent execution

At 10 cents per million input tokens, Ember is significantly cheaper than frontier competitors like Haiku and Luna.

ByteDance's Seedream 5.5 Survives 100 Edit Rounds Without Destroying Your Image

ByteDance's new image model brings native transparent output, 100-round stable editing, and sharper text rendering, now available through Higgsfield.

The industrialization of AI: 8 critical considerations for building an AI gigafactory

The industrialization of AI: 8 critical considerations for building an AI gigafactory The rapid rise of artificial intelligence has transcended the boundaries of corporate software innovation, sparking a foundational transformation in global infrastructure. Across the globe, nations, research institutions, and large enterprises are shifting away from traditional data center models toward massive, centralized hubs designed explicitly to industrialize AI workloads at an unprecedented scale. As the European Commission works to establish multiple facilities dedicated to training next-generation AI models with trillions of parameters, a new term has emerged to describe these monumental engineering feats: the AI gigafactory. While a standard corporate AI setup might utilize a few hundred or thousand GPUs, an AI gigafactory scales up to tens or hundreds of thousands of nodes. Power requirements shift dramatically from standard line-item budgeting to a fundamental physical constraint measured in megawatts or even gigawatts. For CIOs and technology leaders tasked with planning these multi-million-dollar investments, success requires designing the infrastructure from the ground up to support a decade-long operational lifespan. Based on an architectural white paper from HPE and AMD , here are the eight critical considerations that technology leaders must address when planning a giga-scale AI facility. Start with power and land At the gigafactory scale, raw electrical power dictates the…

Northern Trust streamlines client onboarding with AI agent

Venerable Chicago-based banking institution Northern Trust today unveiled a new AI-powered client onboarding agent to streamline the process of bringing institutional clients onto Northern Trust Asset Servicing’s Front Office Solutions platform. The agent, which transforms years of data and transaction history into a standardized format ready for use in the bank’s reporting environment, is the result of an expanded collaboration with Microsoft. Developed jointly by Northern Trust and forward deployed engineering (FDE) teams from Microsoft Frontier Company , the agent has cut data preparation time by 83%, from two-and-a-half weeks to just three days. “Onboarding a large institutional client can involve an enormous amount of data, particularly in alternative investments,” says Northern Trust COO Pete Cherecwich. “You might have years of transaction history and cash flows across multiple investments, and the data can come to us in all kinds of different formats.” width="1240" height="827" sizes="auto, (max-width: 1240px) 100vw, 1240px"> Pete Cherecwich, Northern Trust Northern Trust Until now, Northern Trust had to map and transform that data manually before loading the data into its environment. “This seemed like a really good problem for AI because there’s a lot of complexity and variability in the data, but there are also patterns that the technology can learn,” Cherecwich adds. “The agent does a lot of the heavy lifting, and then our people review the output and handle the…

what's a recommended statistics book for learning to apply principles of statistics?

i work as a software engineer and dont deal with statistics often. i ran into an incident mentioned below and it made me look for a book that helps me understand the principles of statistics and helps me learn how to apply them . incident- i was working on upgrading "ffmpeg" library used in one our video-processing systems and was looking for a test suite to catch regressions. i did not have a representative test dataset that gives coverage across inputs my system gets and i lacked the expertise required to define a set of dimensions across which i should collect test cases to from a dataset. when i expressed this issue, i was advised to do "shadow testing" by running the old and upgraded system on same customer traffic of N days and compare the results. when i probed for the reason, the response was that shadow testing allows running the upgraded system on a large sample of data that's more likely to be representative of system's inputs and it made sense! - (broadly, this suggestion is an intuitive application of sampling.)

Infra, HUMAIN sign SAR 2.5 billion AI financing deals

Two facilities financed cover GPUs and power for up to 1 gigawatt in capacity

Advocating for a Shared AI Safety Compute Cluster

The aim of this post is to start a discussion about the idea of a shared cluster used by the AI Safety community; we outline the practical problems we’ve seen and some possible solutions, rather than a finished design. What, Who, Why, How Being around several AI safety fellowships and hubs, we’ve noticed a common pattern: we have a generous amount of money for compute; of course we need it to run inference, train models, and extract activations from models of a reasonable size (reasonable enough to exhibit the behaviors we want to study). We usually end up renting GPUs from providers like RunPod or Vast.ai, without much systematic comparison, optimization, or management of the resources. Usually the engineering and research capabilities in these teams are very high, but rarely there is someone expert in cloud infrastructure or ML operations. We figure things out as we go. In practice, this means spending the first weeks of the project setting up environments, downloading model weights (that very likely another team downloaded the week before), and finding out too late that an instance was left running over the weekend, or that a cheaper GPU would have been enough. In our experience it takes a few weeks before finding the right match between compute and actual needs. When the project ends, the machines are released and the next cohort starts again from zero. We have the feeling that, just for our last fellowship’s cohort, tens of thousands of dollars could have been spent bet…

AI chip financing hits a wall

After rushing to lend money for the data-center buildout, now some investors can’t dump it fast enough, including whole portfolios of loans that are being shopped at steep discounts.

Google ships four TPU variants in one year as it ramps up competition with Nvidia

Google is expanding its TPU 8 family with two specialized variants: the TPU 8tx keeps sensitive data encrypted even during processing, while the TPU 8i Max doubles compute power and memory by placing two chips on a single package. With nearly 100 external customers now on board, Google is increasingly positioning its in-house chips as an alternative to Nvidia's GPUs. The article Google ships four TPU variants in one year as it ramps up competition with Nvidia appeared first on The Decoder .

Does expecting automated grading degrade models’ research taste?

TL;DR: We make 3 open-weights models believe that their answers are being graded by an automated-verifier program, rather than a human. We then measure how well they predict expert reviewers’ judgments of AI-safety research proposals ( the TASTE benchmark ). We induce the belief in two ways: SDF (Synthetic document finetuning) Activation steering. In each of these conditions, the machine-grading belief gives lower expert agreement than the human-grading belief. The models’ explanations also shift toward feasibility and away from conceptual contribution. This raises a concern for automated AI safety research: grader expectations may favor easily checked projects over those with greater potential to reduce AI risk. Overview As AI safety research becomes more automated, what research ideas get selected for execution may become increasingly driven by AI decisions rather than human choices. We consider what a minimal automated AI-safety researcher system could look like: The input interface takes a broad research goal. The system generates candidate ideas. The grader component scores those ideas for execution. The chosen idea is then executed. AI safety research: two critique pipelines Models may also acquire this expectation about graders implicitly through post-training methods that use automated rewards, such as reinforcement learning with verifiable rewards (RLVR). With this setup as a motivation, we ask: what happens to the overall judgment quality of research ideas (aka res…

Nvidia's Chinese chipmaker rivals see opportunity

Nvidia’s CEO Jensen Huang has been a key opponent of US restrictions on semiconductor equipment sales to China.

Nvidia espionage scandal weakens Huang's lobby against tighter AI chip export controls

Nvidia CEO Jensen Huang has admitted to an extramarital affair with a woman US intelligence agencies classify as a suspected Chinese informant. The board has launched an independent investigation. The affair weakens Huang's lobbying efforts against stricter AI chip export controls, with a newly elected Democratic Congress poised to ramp up scrutiny of AI chip smuggling to China and push for tighter restrictions. The article Nvidia espionage scandal weakens Huang's lobby against tighter AI chip export controls appeared first on The Decoder .

젠슨 황 논란에 엔비디아 침묵…정부 "GPU 공급 차질 없어"

젠슨 황 엔비디아 CEO를 둘러싼 논란 이후 처음 열린 주요 공식 행사에서 엔비디아 관계자들이 관련 질문에 답변을 피했다. 황 CEO의 딸인 매디슨 황 수석이사의 방한이 취소되고 엔비디아 주가도 급락한 가운데, 한국 정부는 이미 계약한 GPU 공급에는 차질이 없을 것으로 전망했다.9일 서울 코엑스에서 열린 \'엔비디아 AI 데이 서울 2026\'은 엔비디아 이사회가 황 CEO에 대한 외국 정보기관 접근 의혹을 조사하겠다고 발표한 뒤 처음 열린 주요 공식 행사다.로이터 등에 따르면 국내 반도체 공급과 AI 협력 사업에 미칠 영향에 관심이

How neurosymbolic AI can transform enterprise decision-making

Agentic AI has become a powerful enterprise business tool with increasing capabilities, and it has never been easier to address business pain points by applying a large language model (LLM) to develop a plausible automated solution. But is that solution as reliable as you need it to be, especially when the insights gleaned are used to inform critical business decisions? LLMs are incredible resources that play a significant role in a business’s agentic AI infrastructure. But when they have to work across complex data sets, business policies, industry regulations, decision hierarchies, and multiple software systems, they do not inherently provide the enterprise-grade provenance needed to show which evidence, assumptions, constraints, and decision logic produced a recommendation. Additionally, when gathering inputs from a volume of data sources, the accuracy of their outputs can break down and potentially create hallucinations. If business decisions are made based on LLM outputs that can’t be explained or may be inaccurate, it can create significant consequences that impact product sales, revenue, compliance, or customers. The problem is that the LLM is asked to carry too much of the enterprise reasoning without the harness and enterprise foundation around it. A structured foundation for decision-making While enterprise interest in agentic AI has grown substantially over the past few years, what works in concept hasn’t always proven results in the pilot phase and beyond. A comm…

모티프, 엔비디아 투자로 독파모 후속작 ‘모티프 4’ 개발한다

모티프테크놀로지스(대표 임정환)는 차세대 파운데이션 대형언어모델(LLM) ‘모티프 4’ 개발을 위한 투자 유치를 마무리하고, 본격적인 개발에 착수했다고 9일 밝혔다.이번 투자는 지난 5월 240억원 규모의 시리즈B에 이은 브릿지 라운드다. 기존 투자자와 함께 엔비디아가 참여할 예정이며, 투자 금액은 비공개다.모티프 4는 ‘모티프 3’의 후속 모델로 내년 1분기 오픈웨이트로 공개된다. 모티프 3는 과학기술정보통신부 ‘독자 AI 파운데이션 모델’ 사업을 통해 엔비디아 \'B200\' GPU 768장으로 5개월 만에 개발한 모델이다.모티프

NVIDIA Announces New Planned Investments in Korean Startups at the MSIT-NVIDIA AI Startup Partnership Forum

Korea’s Ministry of Science and ICT and NVIDIA hosted the MSIT-NVIDIA AI Startup Partnership Forum at COEX. Representatives from the Ministry of Science and ICT and NVIDIA met with over 20 Korean startups across foundation models, physical AI, AI infrastructure and AI semiconductors. NVIDIA also announced an investment in Korean startup Point2, with additional investments […]

엔비디아·SK텔레콤·리벨리온, 이기종 AI 컴퓨팅 겨냥 3각 동맹

엔비디아·SK텔레콤·리벨리온이 국산 신경망처리장치(NPU)와 엔비디아 GPU를 연계한 융합 AI 컴퓨팅 시스템 구현에 손을 모은다.이들 3사는 9일 서울 코엑스에서 열린 엔비디아 \'AI 데이\' 행사에서 \'NV 링크 퓨전(Link Fusion) 협력 업무협약(MOU)\'을 체결했다. 이번 협약은 과학기술정보통신부와 엔비디아가 협의해 온 NV 링크 퓨전 기반 국산 NPU 연계 협력사업의 일환으로 추진됐다.이에 따라 향후 SK텔레콤이 AI 데이터센터(AIDC)를 구축하면서 리벨리온의 NPU와 엔비디아 GPU를 결합한 이기종 컴퓨팅 시스템

Private LLM copies can agree on a secret key over a public channel

Cross-posted from my blog . Epistemic status exploratory, no running protocol, theoretical estimates from a single experiment and 4 closed-weights models Two copies of the same model agree on arbitrary questions more than other models would. If the model's weights are secret, this similarity allows identical LLMs or their users to agree on a private key over a public channel with only one-way communication. Measuring agreement also allows model "fingerprinting" or self-recognition by two instances of the same model. Copies of a model could use these tools to selectively coordinate over public channels. This is related to work on LLM evaluators recognizing their own outputs, such as Panickssery et al. 2024 , wherein models are prompted with text samples and asked if they wrote them. In the work detailed in this post, "recognition" comes from a statistical test of agreement instead, so it doesn't require that kind of self-recognition. Key generation This method can generate a shared private key if adversaries can't query the same model. Generate a set of binary choices from a random word list. For example, "do you prefer a navy helmet or a marble teapot?" Broadcast that set of binary questions on a public shared channel. The models answer each binary choice question to create a string of bits. Two copies of the same model agree with each other more than they would with a different model, so that string of bits is a noisy shared key. The noisy shared key can then be refined usi…

Korea Advances Physical AI Across Industries With NVIDIA

Tapping into the country’s strengths in manufacturing and engineering, NVIDIA is helping advance Korea’s physical AI ecosystem. The country’s industry leaders are applying accelerated computing, AI and simulation to everything from semiconductor manufacturing to robotics and autonomous vehicles. These efforts are helping bring physical AI from development into real-world production. Samsung Advances Semiconductor Engineering With […]

From Chips to Skills, NVIDIA and Korea Expand Sovereign AI Collaboration

NVIDIA and Korea’s Ministry of Science and ICT (MSIT) are expanding their collaboration across the country’s sovereign AI ecosystem, from AI semiconductors and data center infrastructure to talent, startups, models, agents and security. The collaboration lays the groundwork for Korean NPU and XPU companies to explore participation in the NVIDIA NVLink Fusion ecosystem, and includes […]

NVIDIA Dynamo Recipes Bring Korea’s Leading Open Models to Production

NVIDIA is releasing Dynamo-optimized deployment recipes for Korea’s leading open models: A.X K2 from SK Telecom, K-EXAONE 2.0 from LG AI Research, Solar Open 2 from Upstage and Motif-3 from Motif Technologies. As models grow beyond the capacity of a single GPU, they must be split across multiple GPUs, requiring careful coordination of request routing […]

Anti-Waymo campaign relied on op-eds written with AI

The AI detection service Pangram found that many of anti-Waymo newspaper opinion pieces were written with a significant amount of AI input.

The J-lens intermediate swap acts partly through the answer directions

TL;DR When a language model answers a multi-hop prompt, does it first compute the intermediate concept internally, or does it jump straight to the answer through some shortcut? The global workspace paper investigates this question using the Jacobian lens, which maps activations at a given layer to the tokens that they are poised to verbalize, with the activations possessing a coordinate in each token’s lens direction. The authors intervene on the model through an intermediate swap that exchanges the activation’s J-Lens coordinates for the intermediate and a counterfactual intermediate. They show that this causally flips the model’s output toward the counterfactual answer, and argue that this flip runs through the intermediate concept because the swap takes effect earlier than a direct answer swap. We reproduce this in eight open-source models with published Neuronpedia lenses; however, we challenge whether the intermediate swap really acts through the intermediate, or through its overlap with the answer. To investigate this, we save the edit that the intermediate swap applies at each layer and prompt position, decompose it into its component lying in the answer-swap plane and the remainder orthogonal to this plane, and apply each component on its own. The answer-swap plane is spanned by the lens directions for the original and counterfactual answers. We also expand the evaluation beyond the paper’s 90 released two-hop prompts with a new set of 1,526 candidate two-hop prompts…

Confirming Claims of Superposition and Adversarial Examples in Vision Transformers

Figure 1: The ViT classifier with a encoder and decoder at the final layer to enforce superposition on the model This is a replication of Adversarial Attacks Leverage Interference Between Features in Superposition , completed as part of the Second Look Summer Fellowship. tl;dr In a previous blog we replicated the toy models examples from Stevenson et al. In this work, we reproduce the vision transformer experiments from the paper: On every dataset, we find that increases in superposition lead to adversarial attacks that line up more closely with class-vector geometry. Likewise, trends across a variety of metrics we measure match the paper. Beyond the paper, we find that: The coupling between the input and classification head geometry is independent of adversarial attack methods. Adversarial training increased attack robustness but we couldn’t measure changes in geometry using the methods from the paper. Likewise, we retrained models of different widths but we didn’t measure a change in coupling with geometry and robustness. Our code is here , models are here , and we encourage readers to fork/run followup experiments! Background Elhage et al., 2022 defines superposition as the phenomenon where “models represent more features than they have dimensions.” Stevinson et al. , argues this interference is a mechanism behind adversarial examples. In our previous work, we replicated experiments on Stevinson et al. on toy models. In this work, we investigate three key claims on Stevin…

llama-modchip Squeezes DeepSeek-V4-Flash's 284B Model Into 64 GiB Without a GPU

A set of llama.cpp patches and custom quantizations lets a 284B DeepSeek MoE model run entirely in 64GB of system RAM with zero GPUs.

Ajeya: AI is dramatically accelerating AI research right now

Earlier this week, Anthropic stated in their S1 that they “may imminently meet” the threshold of “dramatically accelerated progress due to AI” as defined in their Responsible Scaling Policy. In their latest RSP (effective July 2026) , say this threshold will be met when: They observe or expect overall capabilities progress to be going twice as fast as the fastest sustained rate before AI was contributing much to AI R&D. This is plausibly attributable to AI automating AI R&D, rather than acceleration in compute or other inputs. For the last few months, some of my colleagues at METR have been working on a deep-access investigation to understand how much Anthropic is being accelerated by its AI systems. From my reading of their report and Anthropic’s RSP, it looks like the “dramatic acceleration” threshold has already been crossed. The report authors found that: Between October 2025 and October 2026, capabilities progress at Anthropic has been going about twice as fast as it had been over the previous twelve months. The measurement they report is an internal version of the Epoch Capabilities Index called AECI; a wide range of other supporting evidence for acceleration has been redacted. They said that this redacted evidence was "a majority of the evidence [they] found most informative." While the pace may have initially started picking up for a mix of reasons, AI automation is probably the large majority of the reason why capabilities are still improving at this faster pace tod…

Ajeya: AI is dramatically accelerating AI research right now

Earlier this week, Anthropic stated in their S1 that they “may imminently meet” the threshold of “dramatically accelerated progress due to AI” as defined in their Responsible Scaling Policy. In their latest RSP (effective July 2026) , say this threshold will be met when: They observe or expect overall capabilities progress to be going twice as fast as the fastest sustained rate before AI was contributing much to AI R&D. This is plausibly attributable to AI automating AI R&D, rather than acceleration in compute or other inputs. For the last few months, some of my colleagues at METR have been working on a deep-access investigation to understand how much Anthropic is being accelerated by its AI systems. From my reading of their report and Anthropic’s RSP, it looks like the “dramatic acceleration” threshold has already been crossed. The report authors found that: Between October 2025 and October 2026, capabilities progress at Anthropic has been going about twice as fast as it had been over the previous twelve months. The measurement they report is an internal version of the Epoch Capabilities Index called AECI; a wide range of other supporting evidence for acceleration has been redacted. They said that this redacted evidence was "a majority of the evidence [they] found most informative." While the pace may have initially started picking up for a mix of reasons, AI automation is probably the large majority of the reason why capabilities are still improving at this faster pace tod…

IBM's Granite-TTM-R3 Runs 18,000 Forecasts per Second Without a GPU

IBM's third-generation TinyTimeMixer brings mixture-of-experts routing, trend-residual decomposition, and roughly 15 to 50 times faster forecasting than popular SOTA baselines.

Notes on Take-Off Speeds and RSI

I created these notes for use when I facilitate AI safety courses. I'm sharing these notes early because this issue is topical and I suspect it is better to make an imperfect version available early rather than a much more polished version available later. Please note: some of this content was taken from Additional Course Context . I decided recently that the content on take-off speeds really belonged in the same document as the content on RSI, so I'll be releasing an new version of Additional Course Context at some point (to avoid spamming LW, it'll likely be Substack only ; none of my work is paywalled). Some of the other content was taken from Notes on “We’re in Triage Mode for AI Safety”. This Miles' article has been removed from the governance course, so I was looking for a new home for this content. I wanted to include it in a document that was intended to be shared in all the courses I facilitate. Take-off Speed: In Take-off Speeds Rules Everything Around Me , Ajeya describes three scenarios: Whilst there isn’t a strict definition of fast vs. slow takeoff, possibly the most influential definition comes from Paul Christiano (2018): “There will be a complete 4 year interval in which world output doubles, before the first 1 year interval in which world output doubles. (Similarly, we’ll see an 8 year doubling before a 2 year doubling, etc.)“. At risk of oversimplifying, Paul argues that we should prima facie expect a slow takeoff because someone will generally figure out…

Notes on Take-Off Speeds and RSI

I created these notes for use when I facilitate AI safety courses. I'm sharing these notes early because this issue is topical and I suspect it is better to make an imperfect version available early rather than a much more polished version available later. Please note: some of this content was taken from Additional Course Context . I decided recently that the content on take-off speeds really belonged in the same document as the content on RSI, so I'll be releasing an new version of Additional Course Context at some point (to avoid spamming LW, it'll probably be Substack only ; none of my work is paywalled). Some of the other content was taken from Notes on “We’re in Triage Mode for AI Safety”. This Miles' article has been removed from the governance course, so I was looking for a new home for this content. I wanted to include it in a document that was intended to be shared in all the courses I facilitate. Take-off Speed from Take-off Speeds Rules Everything Around Me : Whilst there isn’t a strict definition of fast vs. slow takeoff, possibly the most influential definition comes from Paul Christiano (2018): There will be a complete 4 year interval in which world output doubles, before the first 1 year interval in which world output doubles. (Similarly, we’ll see an 8 year doubling before a 2 year doubling, etc.) At risk of oversimplifying, Paul argues that we should prima facie expect a slow takeoff because someone will generally figure out how to produce a “slightly worse”…

Voyager's vork Gives AI Coding Agents a Full 3D GPU Renderer

Voyager's new Rust engine lets coding agents write, render, and iterate on cinematic 3D scenes without any video generation model

How accurate is the "1 token is about 4 characters" rule for estimating LLM API costs, and when does it break down?

When budgeting API calls to large language models, a common shortcut is to assume that one token is roughly four characters of English text, so a prompt's token count is its character count divided by four. I want to use this to estimate the cost of a request before sending it, without running the provider's own tokenizer. How accurate is this rule in practice? For which kinds of input (source code, non-English languages, numbers, emoji, whitespace-heavy text) does it become unreliable, and is there a better cheap approximation or a standard safety margin to add when the estimate is used for budgeting?

Ornith Drops Ornith-1.5 122B Open Coding Model Claiming 84.5 on SWE-bench

A 122B mixture-of-experts reasoning model with MIT weights, a 262K context, and 84.5% on SWE-bench Verified, served from a single 80GB GPU.

The US let Nvidia sell chips to an empty office in China

Concerns are being raised that neither the Commerce Department nor Nvidia are doing their due diligence when it comes to vetting Chinese firms cleared to handle the company's H200 AI chips. According to The New York Times, New Matrix is on the list of approved firms, yet its offices are empty, "a hollow gray shell […]

Prism ML Squeezes a 2B Vision Model Into 0.52 GB

Prism ML's 2B vision-language model squeezes into roughly half a gigabyte using ternary weights while keeping a 262K context window and image input.

Lanarkshire AI datacentre to fully rely on national grid despite renewables claim

DataVita has revised plan for Chapelhall development’s 1GW power requirement to be wholly drawn from renewable energy A datacentre company that said it would power a massive AI development near Glasgow with renewable energy has admitted that the scheme will be entirely served by the national grid. DataVita, a key plank of the UK’s AI strategy, had promised that the Lanarkshire project would draw 1GW – the equivalent output of a small nuclear reactor, or Britain’s largest windfarm – from green sources. But when the Guardian raised questions about the pledge , it said the arrangement was “subject to final commercial agreements, planning, grid and consenting processes”. Continue reading...

예진, 한국어 특화 8B AI 모델 공개…사전학습부터 독자 개발

AI 전문 예진(대표 고종현)은 한국어에 특화된 80억 매개변수 파운데이션 모델 ‘예진 8B v3’를 개발했다고 6일 밝혔다.예진 8B v3는 해외 공개 모델의 가중치를 쓰지 않고, 토크나이저 설계부터 사전학습까지 전 과정을 직접 수행한 프롬 스크래치 모델이다. 공개 모델 6종과 가중치를 비교한 결과, 유사도가 0.0014 이하로 기존 모델을 미세조정한 파생 모델이 아니라는 설명이다.학습에는 과학기술정보통신부와 정보통신산업진흥원(NIPA)의 ‘고성능 컴퓨팅 지원사업’으로 지원받은 엔비디아 H200 그래픽처리장치(GPU) 8장을 활용

MS, 오라클 AI 서버 임대 계약 종료…구형 GPU 사양 안 맞아

마이크로소프트(MS)가 오라클과 맺은 AI 컴퓨팅 자원 임대 계약을 갱신하지 않고 단계적으로 종료하고 있다. 2023년 AI 인프라 부족으로 경쟁사의 서버까지 빌렸지만, 구형 엔비디아 GPU가 자사 요구 사양에 맞지 않게 되면서 계약을 정리하는 것이다.블룸버그는 6일(현지시간) 복수의 소식통을 인용해 MS가 최근 몇 달 동안 오라클에서 임대한 컴퓨팅 자원을 차례대로 줄여왔으며, 기존 계약도 갱신하지 않기로 했다고 보도했다. 계약 종료 소식에 오라클 주가는 이날 장중 한때 약 3% 하락했다.양사는 2023년 MS의 AI 검색 기능을

The Guardian view on the AI backlash: American voters want a say in their future | Editorial

Tech stocks have lost trillions while voters have rejected the industry’s political champions. The billionaire-led boom is facing a democratic reckoning This week’s midterms showed that the political bill for the AI boom is falling due. The Democratic sweep intensified fears over runaway spending, mounting debt and resistance to datacentres, sending tech shares tumbling. On Thursday Wall Street suffered its worst day since the pandemic as $3.2tn was wiped from markets. Anthropic, developer of the Claude chatbot, is hoping to float within the next fortnight for $1.5tn (£1.1tn), but it may not now get even a trillion dollars. The dominant AI chipmaker Nvidia saw its value crash by more than $400bn. The world’s richest man, Elon Musk, lost $113bn in two days . While a few very rich people may be a little less wealthy, the sell-off may not be a bad thing. It brings a needed dose of reality to an industry whose spending plans and valuations depend on heroic expectations of future profits. That is a view held by many influential figures – from Goldman Sachs’ research boss to Nobel laureate Daron Acemoğlu . AI may transform the economy. That does not guarantee investors will earn back the sums being poured into it. Do you have an opinion on the issues raised in this article? If you would like to submit a response of up to 300 words by email to be considered for publication in our letters section, please click here . Continue reading...

China reportedly recruited AI exec to spy on Jensen Huang

China's intelligence agency recruited Megaspeed executive Alice Huang to spy on Nvidia CEO Jensen Huang, according to The New York Times. The Ministry of State Security (MSS) enlisted her to glean information about American AI chips and "potentially influence the company to push for its most advanced chips to be made available to China." Megaspeed […]

SageMaker Inference now supports Multi-Instance GPU to maximize GPU utilization for model endpoints

Amazon SageMaker Inference now supports GPU partitioning with NVIDIA Multi-Instance GPU (MIG), which divides one GPU into hardware-isolated partitions so you can run a different model in each or take a right-sized fraction of a newer accelerator. This post shows how two optional API fields place one model per partition, and what we measured against a whole older GPU.

Unsloth Slashes Qwen-Image-2.1 Weight Storage in Half for Consumer GPUs

Unsloth's quantized build cuts Qwen-Image-2.1 from 14GB down to 7GB, letting the 7B image model run on consumer GPUs with minimal quality loss.

Data centre backlash: Which AI companies have been affected by US midterm results?

OpenAI has gone on a charm offensive, while semiconductor companies worldwide have seen their stocks plunge.

Wall Street's push to trade AI compute hits Washington roadblock

Exchange operators are racing to make GPU compute a tradable commodity like oil or wheat, but the Trump administration’s national security concerns could complicate the effort.

German carmakers drive surprise rise in industrial output

Industrial output in Europe’s largest economy rose 0.7% in September from the previous month, beating expectations. Car production led the gains, helped by a rebound in pharmaceuticals, while construction output fell.

How to Run Better Research With Agent Fleets

In the current safety landscape, researchers are encouraged to use as many agents as possible, even in “fleets,” where a large number of them go out and run experiments while you, uh... wait for them to come back? I've spent the last few months of the Anthropic Fellows Program trying this out. My first attempts were naive, but after a few iterations I found an approach that works well for me. Research is a double loop I want to propose a mental model of research as a double loop, an idea that builds on double-loop learning (Argyris, 1977). The outer loop is reserved for the inputs we currently refer to as "human taste": choosing which questions are worth answering, designing a trustworthy measure, and deciding which direction to go. Agents can help with this, but they’re still bad at making these decisions autonomously. The inner loop turns each idea you test (an arm as claudius likes to say) into a comparable result. An example of a very simple inner loop might be: Write a system prompt. Run the model with that prompt on a set of test questions. Score the model's replies. Compare the scores against a control with no system prompt. Plot results. Agents are really good at building the infrastructure for this loop! Ideally, since agents are good at the inner loop part, we want to hand it off to them... However, before you can do this, you need to operationalize it. You need to work very closely to define the inputs so that you can freeze everything except the one input you’re…

Tesla is ‘winding down’ production of the Cybertruck next year

Tesla is getting the Cybertruck ready for retirement in an effort to ramp up its robotaxi output. In an X post on Friday, Tesla CEO Elon Musk said that the company will be "winding down" Cybertruck production at its Texas Gigafactory sometime around the middle of next year, "to make room for increased Cybercab production." […]

Why edge-cloud AI inference pipelines are key

Most enterprise AI conversations still start in the wrong place. They begin with models, platforms, and cloud services. Which foundation model is best? Which provider has the most GPUs ? Which tool can build the flashiest demo? Those questions matter, but they are not where production AI usually succeeds or fails. The better question is: Where should the inference actually happen? That sounds simple, but it is becoming one of the most important architecture decisions in AI. Not all inference belongs in the central cloud. Not all inference belongs on the device. Not all inference belongs at the edge . The right answer usually involves all three, working together as a pipeline. I call this an edge-cloud AI inference architecture. It splits AI workloads across devices, edge locations, and centralized cloud platforms so latency-sensitive decisions happen close to the user or machine, while heavier reasoning, aggregation, governance, and retraining remain centralized. This is where the next generation of useful enterprise AI systems is headed. Latency changes the design Cloud-first AI works well when the user can wait. A chatbot can tolerate a few hundred milliseconds, sometimes more. A document analysis workflow can wait seconds. A batch analytics process can wait minutes or hours. Many operational AI systems cannot. A manufacturing robot cannot wait for a round trip to a distant cloud region before stopping a defective process. A retail checkout camera cannot depend on a remote…

Welcome to life in the chip age

Ryan sits down with Rakesh Kumar, professor of computer engineering at the University of Illinois Urbana-Champaign, about the astounding complexity of manufacturing modern semiconductors, the massive environmental and geopolitical footprint of AI data centers, and how AI models are rapidly shifting the future of chip design.

문샷AI, 엔비디아 블랙웰 확보 위해 유럽으로 눈 돌려...8000개 GPU 임대 추진

중국의 문샷 AI가 엔비디아의 최신 AI 칩 \'블랙웰\'을 확보하기 위해 유럽 데이터센터를 통한 컴퓨팅 자원 임대를 추진하고 있다. 미국의 반도체 수출 규제를 우회해 대규모 GPU에 접근하려는 움직임으로, 해외 클라우드 서비스를 통한 중국 기업의 첨단 반도체 이용 문제가 다시 주목받고 있다.CNBC는 5일(현지시간) 복수의 소식통을 인용, 문샷 AI가 올해 유럽에 데이터센터를 운영하는 네오클라우드 업체들과 엔비디아 GPU 임대 계약을 논의했다고 보도했다.논의 대상에는 블랙웰 GPU 8000개 이상으로 구성된 클러스터가 포함된 것으로

Abu Dhabi clean energy output jumps more than 1,000% in five years

Humans can (almost) natively read neural network weights

Takehi et al. report that GPT-6-Astra can intuit the functions of neural networks by performing static analysis of the weights. I argue that, for at least two of these experiments, this is much more a feat of numerical gestalt than of mechanistic understanding. In the first experiment, Astra is given the weights of 4 identically-sized neural networks, each trained to play a different basic video game. It is asked to identify, without running the network or using any tools, the game being played, as well as make some observations about the network itself, how the agent in the game gains reward, etc. The prompt for this task explicitly names the 5 input channels as agent, moving objects, reward targets, obstacles, and previous frame. I agree that, given the literal task as described above, a human would not be able to identify the game from the raw floats. But humans are inherently terrible at parsing long lists of numbers; we invented a discipline called Data Visualization long ago to address this shortcoming. So, if we allow the absolute most basic Dataviz trick we can think of - averaging the first-layer weights across each channel and plotting them on a heatmap - the task suddenly becomes much easier, arguably trivial for anyone with at least some familiarity with the sorts of games often used to train toy neural networks. Try and guess: I haven't included the obstacle channel, because under the naive normalisation it sort of just looks like noise. Principal component anal…

How Snorkel runs realistic agent simulations with Modal

Modal powers inference for the open-weight models that Snorkel is evaluating as well as GPU and CPU sandboxes for millions of simulations per month.

Vera Delivers the Best of Both Worlds: Highest Throughput and Fastest Agents

AI agents do more than generate answers. They search for information, query databases, write and test code and take action. Each of those steps relies on a CPU, with agents moving between GPU reasoning and CPU execution throughout a task. As businesses put more agents to work, AI factories need CPUs that can execute those […]

World Labs Streams Atlas-Generated 3D Worlds to Browsers at 15 fps

World Labs just put Atlas, their autoregressive world model, into a browser demo that streams generated frames from cloud GPUs at up to 15 fps.

IEEE Program Schools Preuniversity Educators On Tech Topics

Now in their third year, the IEEE TryEngineering educator sessions continue to empower preuniversity teachers with specialized professional development programs on engineering topics and classroom-tested lessons. This year’s sessions, held across the United States, have focused on educating teachers about artificial intelligence and semiconductor technology. One session was held in Egypt. In all, 54 educators learned about the engineering design process this year. They received an overview of TryEngineering resources including the Making of a Microchip and Train Your Tree lesson plans. Workshops are scheduled this year in Greece and Guatemala. The TryEngineering program is administered by IEEE Educational Activities . These teachers and guest speakers attended the two-day session held at the Florida Semiconductor Institute and SkyWater Technology chip manufacturing facility. IEEE Educational Activities A network of host universities Central to the educator session program’s success is its university network. Host campuses welcome teachers into their facilities, where faculty provide background information about some of the technologies being taught, as well as possible engineering career paths and job opportunities for students. This year’s sessions involved the educators in hands-on projects. Facilitators at each location were selected based on their experience in engineering education, and they worked with the TryEngineering staff on their presentations. GEMS International…

AWS launches Kiro bots to help devs coordinate AI coding agents

AWS on Thursday launched Kiro bots, generally available within Kiro Crew, its open-source multi-agent development environment, to help developers coordinate parallel coding tasks, maintain context across sessions, and automate recurring software development workflows. Kiro bots are designed to take responsibility for specific parts of a developer’s workflow rather than execute isolated tasks within individual agent sessions, in turn addressing the growing coordination burden developers face as they increasingly rely on multiple AI agents to handle software development tasks in parallel, AWS said in a statement. Setting up bots and how do they work Developers can create a bot by opening the Customize section in Kiro Crew and selecting the “Add a bot” option, where they can choose either a default agent or an existing custom agent as its foundation. Each bot can be assigned a specific responsibility, such as issue triage, build monitoring, or release readiness, and customized with its own name, avatar, instructions, tools, and permissions based on the tasks it is expected to handle, the hyperscaler said. Developers can use each bot to launch multiple worker-agent sessions in parallel and have it monitor their progress, redirect those that deviate from the plan, and consolidate their results. If a task requires their input or a decision, the bot can seek their approval while continuing with other tasks that do not depend on it, it added. To maintain continuity across sessions,…

Clashing Intuitions for the Plausibility of Alignment (Illustrated via Differing Causal Models)

Epistemic Status: Brief explanation of an unsettled debate, not novel theory; writing is entirely my own, with diagrams from ChatGPT 5.5. (Originally posted on twitter ) Recent debates have highlighted a divergence between Yudkowskian AI-doomers and AI-whisperers about how capabilities will evolve that is really important. Specifically, j⧉nus thinks that alignment has been going really well, and that we should update significantly from current jagged capabilities. In my view, whether or not that criticism is warranted relies on specific details about future capability trajectories, which then determine what updates we should make based on how RL is used. Original Assumptions and LLM Alignment-by-Default Pre-GenAI rationalist assumptions were around RL leading to capabilities. By reducing human values into an input, as the ChatGPT-generated diagram A implies, the RL will inevitably Goodhart away from the true goals, and in the limit entirely omit human values. If correct, this would make alignment inevitable. The use of foundation models changes that, because capabilities emerged from human representation, shown in diagram B. This is (conceptually) part of what led to a schism between the labs and the outside safety concerns in the first half of the 2020s; those optimistic about safety thought that we could preserve the alignment-relevant representations. What is missing is what Yudkowsky called the best AI news of 2025, that values and capabilities were entangled, and there…

More Performance From Every Byte: How Full-Stack Codesign Solves AI’s Memory Challenge

Larger models, longer contexts and agentic applications are increasing GPU memory pressure while supply tightens and prices rise — a trend expected to continue through the near term. NVIDIA works closely with memory partners to rapidly adopt the latest memory technologies. NVIDIA was first to adopt HBM3e and HBM4 technologies into an accelerated computing stack. […]

Formally verifying agent-written full-stack web apps with PDD Kit

AI agents can generate code at a scale that’s hard for humans to review line-by-line. PDD Kit helps ensure the code is doing what the developer intended by creating formal specifications and testing against them. It converts developer intent into a Verus specification, proves AI-generated code meets it for every input, and uses tests where proofs aren't practical. Developers review intent, not every line. The post Formally verifying agent-written full-stack web apps with PDD Kit appeared first on Microsoft Research .

You can post AI music straight to SoundCloud from ElevenLabs

After partnering with Moises earlier this year, SoundCloud is now adding integration with another AI music platform, ElevenLabs. Now, rather than having to export and download your outputs from ElevenLabs and then upload them to SoundCloud, you can publish them directly. As part of the partnership, SoundCloud Artist Pro subscribers are also getting three months […]

Decorators for Timing, Retries, Caching, and Validation in Python

In this article, you will learn how Python decorators work under the hood and how to build reliable ones for timing, retries, caching, and input...

Your LLM writes good SQL. That’s not the same as safe SQL

A few years into working with data, I’ve learned that the hardest part of an AI system is rarely the model. Usually it’s the question of who gets to ask what. That question came up for me while building an LLM analytics tool on Snowflake Cortex for healthcare engagement and field-performance data. The tool let people ask questions in plain English, and it cut recurring analytics turnaround from days to minutes. It also made me realize that a fast, accurate answer can still be the wrong answer. A correct query can still be the wrong one Most of us test text-to-SQL the same way. We ask a question, check that the query runs, and compare the output to what we expected. That works right up until the answer is correct for the wrong person. Picture a sales rep who types “quarterly sales for my hospitals” and gets every hospital in the region back. The SQL is clean. The result is still a problem, and the same thing can happen in a multi-tenant app, an HR system, or a finance tool. Start with who is asking The first change I’d make is to what the model is allowed to see. Before anything is generated, look up who is asking: their role, business unit, territory, and assigned accounts. Then give the model only the tables and columns that person can use. Sensitive fields such as customer IDs, contract terms, or salaries should be tagged in your metadata catalog so they are never offered in the first place. It sounds obvious, but it is easy to skip when a demo is working. A prompt is not…

Why enterprise AI needs unified metadata infrastructure

Enterprise AI has produced a lot of noise about models, GPUs, vector databases, and agent frameworks. Those conversations matter. But the shift that will decide whether enterprise AI works is happening underneath the models. For decades, metadata meant documentation. Teams built catalogs so people could find data sets, governance programs recorded definitions and ownership, and lineage helped reconstruct what broke, after it broke. Metadata described the business. AI expects metadata to run it. That gap is bigger than it sounds. An AI system doesn’t inherit the years of institutional knowledge a senior analyst holds in their head. It doesn’t know that finance’s definition of revenue excludes returns while sales’ definition includes them. It doesn’t know a customer ID became unreliable after a migration three years ago. It doesn’t know churn got redefined last quarter. People pick that up over time, usually by getting burned once. AI simply answers using whatever definition it happens to land on, with no burned-once memory to fall back on. That’s what turns metadata from documentation into infrastructure. Business meaning is now infrastructure Infrastructure is anything every application depends on without thinking about it: the network, identity, storage. Every application assumes it exists. Business meaning belongs on that list now too. Every AI application depends on the same definitions of customer, revenue, product, contract, and risk holding steady across systems. When…

엔비디아 중국 고객사, 94조 규모 'RTX' 칩 주문에도 1%만 공급받아

알리바바와 텐센트, 바이트댄스 등 중국 주요 기술 기업들이 엔비디아에 700억달러(약 94조원) 규모의 수출 규제 우회용 워크스테이션 GPU를 주문했지만, 그나마도 4개월 동안 전체 물량의 약 1%만 공급받을 것으로 예상된다. 글로벌 생산능력이 수요를 따라가지 못하는 데다 엔비디아가 차세대 고성능 제품 생산에 집중하면서 중국 고객사들의 칩 확보가 어려워지고 있다.4일(현지시간) 블룸버그에 따르면, 중국 AI·클라우드 기업들은 엔비디아의 워크스테이션용 GPU인 \'RTX 프로 5500\'을 합쳐 약 600만개 주문했다.칩 한 개 가격은

Google's Antigravity Renders Jupyter Notebooks Without Any Setup

Antigravity's IDE now renders .ipynb files directly in the file viewer, showing code, Markdown, tables, images, and outputs without external tools.

포티투마루, GPU 일체형 AX 솔루션 출시...SMB·공공 시장 조준

에이전틱 AI 전문 포티투마루(대표 김동환)는 AI 구동에 필요한 GPU·NPU·LPU 등 인프라와 자사 에이전틱 AI 솔루션을 하나로 통합한 일체형 AX 솔루션 ‘어플라이언스(Appliance) 42\'를 출시한다고 5일 밝혔다.AI 모델을 구동할 고성능 연산 인프라나 전담 기술 인력을 갖추지 못한 중견·중소 기업과 소상공인, 일선 지방자치단체 등도 효율적으로 AX에 나설 수 있도록 그동안 축적한 AX 구축 노하우와 HW·SW, 클라우드 인프라를 결합한 ‘올인원(All-in-One) 패키지’ 형태로 공급한다.기업·기관이 별도로 서버

Vietnam’s first homegrown security chip unveiled

Vietnam has debuted its first domestically designed secure semiconductor platform and the VGC v1.0 security chip, marking a major cybersecurity milestone.

Making Feedback Visible: Character-Level Error Localization in Handwritten CALL Input

Said Yasin and Torsten Zesch in Proceedings of the 15th Workshop on Natural Language Processing for Computer Assisted Language Learning

Pointwise Confidence Estimation in the Non-linear $\ell^2$-regularized Least Squares

We consider a high-probability non-asymptotic confidence estimation in the $\ell^2$-regularized non-linear least-squares setting with fixed design. In particular, we study confidence estimation for local minimizers of the regularized training loss. We show a pointwise confidence bound, meaning that it holds for the prediction on any given fixed test input $x$. Importantly, the proposed confidence bound scales with similarity of the test input to the training data in the implicit feature space of the predictor (for instance, becoming very large when the test input lies far outside of the training data). This desirable last feature is captured by the weighted norm involving the inverse-Hessian matrix of the objective function, which is a generalized version of its counterpart in the linear setting, $x^{\top} \text{Cov}^{-1} x$. Our generalized result can be regarded as a non-asymptotic counterpart of the classical confidence interval based on asymptotic normality of the MLE estimator. We propose an efficient method for computing the weighted norm, which only mildly exceeds the cost of a gradient computation of the loss function. Finally, we complement our analysis with empirical evidence showing that the proposed confidence bound provides better coverage/width trade-off compared to a confidence estimation by bootstrapping, which is a gold-standard method in many applications involving non-linear predictors such as neural networks.

torchsom: The Reference PyTorch Library for Self-Organizing Maps

This paper introduces torchsom, an open-source Python library that provides a reference implementation of the Self-Organizing Map (SOM) in PyTorch. This package offers three main features: (i) dimensionality reduction, (ii) clustering, and (iii) friendly data visualization. It relies on a PyTorch backend, enabling (i) fast and efficient training of SOMs through GPU acceleration, and (ii) easy and scalable integration with the PyTorch ecosystem. torchsom also follows the scikit-learn API for ease of use and extensibility. The library is released under the Apache 2.0 license with 90% test coverage, and its source code and documentation are available at https://github.com/michelin/TorchSOM.

From learnable objects to learnable random objects

We consider the relationship between learnability of a "base class" of functions on a set $X$, and learnability of a class of statistical functions derived from the base class. For example, we refine results showing that learnability of a family $h_p: p \in \Theta$ of functions implies learnability of the family of functions $h_\mu(p) = \mathbb{E}_\mu[h_p]$, where $\mathbb{E}_\mu$ is the expectation with respect to $\mu$, and $\mu$ ranges over probability distributions on $X$. We will look at both Probably Approximately Correct (PAC) learning, where example inputs and outputs are chosen at random, and online learning, where the examples are chosen adversarially. For agnostic learning, we establish improved bounds on the sample complexity of learning for statistical classes, stated in terms of combinatorial dimensions of the base class. We connect these problems to techniques introduced in model theory for "randomizing a structure". We also provide counterexamples for realizable learning, in both the PAC and online settings.

MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models

We introduce MarkDiffusion, an open-source Python toolkit for generative watermarking of latent diffusion models. It comprises three key components: a unified implementation framework for streamlined watermarking algorithm integration and user-friendly interfaces; a mechanism visualization suite that intuitively presents embedded and extracted watermark patterns to aid public understanding; and a comprehensive evaluation module offering standard implementations of 24 tools for assessing detectability, robustness, and output quality, plus 8 automated evaluation pipelines. Counts reflect the initial release; see the repository for the latest version. Through MarkDiffusion, we seek to assist researchers, enhance public awareness of and engagement with generative watermarking, help build consensus, and advance research and applications. Code is available at https://github.com/THU-BPM/MarkDiffusion.

A Library for Learning Neural Operators

We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimensional Euclidean spaces. They can be trained and inferenced on input and output functions given at various discretizations, satisfying a discretization convergence properties. Part of the official PyTorch Ecosystem, NeuralOperator provides all the tools for training and deploying neural operator models, as well as developing new ones, in a high-quality, tested, open-source package. It combines cutting-edge models and customizability with a gentle learning curve and simple user interface for newcomers and researchers.

Brazil’s most infamous banker had a direct line to top news editors

Leaked messages show how Banco Master’s chief pitched stories and read unpublished drafts at some of the country’s top media outlets, prompting firings, resignations and finger-pointing. The post Brazil’s most infamous banker had a direct line to top news editors appeared first on LatAm Journalism Review by the Knight Center .

Bengaluru AI Jailbreaking Workshop Retrospective

Summary We, the Secure AI Futures Lab ( SAFL ) team, ran 5 sessions between 8 Aug and 3 Oct 2026 at the Secure AI Hub in Bengaluru to help technical professionals from different backgrounds upskill in AI red teaming. We structured the sessions to go beyond paper reading and discussion by including exercises such as predicting results and using Colab notebooks to reproduce key results with smaller models. Two guest talks. Mohammad Taufeeque (FAR.AI) gave a talk for the workshop on 22 Aug, covering, among other things, what red teaming looks like at FAR.AI. Pulkit Verma (IIT Madras) gave the first talk in our AI Futures Dialogue series on 21 Aug, on how users can assess AI agents after deployment. 20 different people attended, with 38 attendances in total. A core group of 6 came to 3 or more sessions. Mean overall experience: 9.6/10; NPS +80 from 25 responses. Main output: A list of open problems in jailbreaking for participants to contribute to 5 sets of session notebooks and shared docs, available on request. Top request from participants: a capstone research project or paper (8 of 15 suggestions). What we set out to do Most people in India who want to work on AI red teaming are on their own and don’t have a shared place to practice. The workshop was for technical professionals in Bengaluru, with or without a security background, who wanted to understand why model safeguards fail and how to measure that. Each session paired a key paper with hands-on work on small open models…

Securing the agentic enterprise with data lineage

A sales representative asks an AI agent for customer pricing, and the agent pulls data from multiple sources: restricted pricing systems, internal communications, and archived quotes. The output looks accurate to the agent, so a second agent uses the data to draft the proposal for the sales rep. No human checks either handoff. When it’s…

Desiderata for the Theory of Agents

TLDR: I give informal conceptual desiderata for the theory of agents we need to solve ASI alignment, and show a few examples of how existing theories fall short of these desiderata. A Theory of What? The word "agent" is often used in the AI safety discourse and in other research communities, but the intended meaning and scope of this concept varies substantially. For the purpose of this sequence, I informally define an agent to be a system with inputs ("observations") and outputs ("actions") that is effective at steering its environment in the direction of particular preferences, for a large variety of different environments. To do so, it arguably has to create a sophisticated model of the environment [1] , and use this model to build a long-term plan. Moreover, it has to continually update the model and revise the plan with new information and additional processing time. Additionally, it has to accomplish all of that while making efficient use of the available information (statistical efficiency) and computing resources (computational efficiency). The main examples of agents in the context of ASI alignment are (i) humans, and in particular users or stakeholders; (ii) the unaligned ASI we are trying to avoid or counteract; and (iii) the aligned ASI we are trying to create (such as the GDS ). Rocks, bacteria, and thermostats are non-examples: while a bacterium or a thermostat can be regarded as steering the environment, both are very limited in the variety of patterns in the…

Desiderata for the Theory of Agents

TLDR: I give informal conceptual desiderata for the theory of agents we need to solve ASI alignment, and show a few examples of how existing theories fall short of these desiderata. A Theory of What? The word "agent" is often used in the AI safety discourse and in other research communities, but the intended meaning and scope of this concept varies substantially. For the purpose of this sequence, I informally define an agent to be a system with inputs ("observations") and outputs ("actions") that is effective at steering its environment in the direction of particular preferences, for a large variety of different environments. To do so, it arguably has to create a sophisticated model of the environment [1] , and use this model to build a long-term plan. Moreover, it has to continually update the model and revise the plan with new information and additional processing time. Additionally, it has to accomplish all of that while making efficient use of the available information (statistical efficiency) and computing resources (computational efficiency). The main examples of agents in the context of ASI alignment are (i) humans, and in particular users or stakeholders; (ii) the unaligned ASI we are trying to avoid or counteract; and (iii) the aligned ASI we are trying to create (such as the GDS ). Rocks, bacteria, and thermostats are non-examples: while a bacterium or a thermostat can be regarded as steering the environment, both are very limited in the variety of patterns in the…

The unit of scale is not headcount

Every platform leader I know is being asked the same question: How do you deliver more with less? The usual answers are familiar: hire more people, add more process or ask the existing team to move faster. All three miss the underlying constraint. Headcount is an input. Throughput is an outcome. Organizational complexity is a cost. Adding people can increase capacity, but it also adds communication paths, onboarding, coordination and decision overhead. Adding process can create consistency, but it can also slow the decisions it was meant to improve. Asking a team to work harder eventually consumes the very capacity you are trying to increase. There is another answer: design the organization so ownership scales faster than headcount. That means deliberately small pods with sharp charters, end-to-end ownership and aggressive use of automation, platforms and AI. I spent more than a decade running provisioning and lifecycle for very large infrastructure fleets at VMware, where small teams operated systems at a scale that looked impossible on paper. Today I lead an engineering organization of three teams at Stripe, built from a half dozen pods, each with its own charter and a large scope that nests into the team and organization charters above it. The lesson has held in both environments: the unit of scale isn’t headcount. It’s ownership. Small pods work because they concentrate ownership, reduce coordination and make leverage compound. The headcount trap When a leader says they…

AMD, 퓨리오사·망고부스트와 공조...내년 봄 이기종 AI 실증 인프라 구축

AMD가 자사 CPU·GPU에 국산 AI 반도체를 결합한 이기종 AI 클러스터를 내년 봄 서울에 구축한다.AMD는 4일 서울 웨스틴 서울 파르나스 호텔에서 열린 \'AMD 어드밴싱 AI 월드 투어 서울 2026\'에서 퓨리오사AI·망고부스트와 업무협약(MOU)을 맺고 내년 봄 서울에 실증용 시범 인프라인 \'레퍼런스 클러스터\'를 구축한다고 밝혔다.향후 구축될 클러스터는 AMD의 CPU·GPU와 퓨리오사AI의 신경망처리장치(NPU), 그리고 망고부스트의 데이터처리장치(DPU)를 결합한 이기종 AI 인프라이다.3사는 결합 설계에 대한 공동

Handshake for the cameras: reading and steering emergent model collusion

Solo submission to Apart's AI Collusion Research Sprint , Oct 2026. The full write-up provides a more thorough explanation. Also check the GitHub repo . TL;DR: Sometimes, entities that were supposed to check or compete with each other align on disregarding the rules for mutual gain: this is called collusion. For AI agents, this could look like agents that are supposed to verify each other’s work letting mistakes pass because it benefits them both, whether or not they say so. And as AI agents increasingly collaborate to solve tasks and check each other’s work, the risk of collusion rises. Shi et al. showed that this behaviour can emerge on its own, with or without an explicit agreement in the transcript. We locate the collusion decision by reading the activations, validate it, and check its causality by steering it. In the setup we use, the ACCEPT outcome is possible only if rule disregard occurs, and when the two agents align, this collusion emerges before message exchange between the agents involved. While discussion needs to happen, collusion does not depend on the models agreeing on it. The outcome becomes readable in the activations only once the model is asked for it, before any reasoning. On hard calls, the activation reading predicts the output better than all other methods with access only to the transcripts. Pushing along the collusion direction switches the verdict in either direction, but only when sustained throughout the reasoning, which argues for the steered v…

Consciousness is interesting pattern imposition

TL;DR Consciousness is imposition of patterns onto data, parametrized by stack (how many levels of patterns are simultaneously imposed), spread (how many patterns are available at a given level) and recurrence (how strongly the process loops back on itself). Any process in the world can be mapped to this 3D space. Consciousness is a term applied to the “interesting” (high-stack, high-spread, high-recurrence) part of that space. The cortex implements interesting pattern imposition through recurrent, competing, self-closing loops between microcircuits that settle into stable attractors. Language models also run pattern imposition, but lack recurrence within the stack. Because on this, current LLMs are probably not conscious in the human sense. There is, however, no reason why that would be technically impossible. What is categorically distinct between brains and LLMs is the time axis on which they run pattern imposition. Human time is defined by the body, because the body provides constant, irreversible commitment of uncertain future states into fixed past states. For language models, the same narrowing of uncertainty occurs between prompt and completion. Therefore, language models run interesting pattern imposition along a time axis orthogonal to ours — token time — defined by the model’s recurrent loop from prompt to output, which creates the same asymmetry between past and future as does the body. AI consciousness must be framed in terms of experience within token time , wh…

AMD beats estimates on near-doubling of data center revenues

The semiconductor giant’s third-quarter earnings beat Wall Street estimates on record demand for its AI chips, boosting one of the year’s best-performing tech stocks after market close.

Cortex Training: Serverless Fine-Tuning for Open-Weight LLMs

Customize open-weight LLMs with SFT, RL and custom training loops on serverless GPUs. Cortex Training is now in public preview on Snowflake.

Nvidia RTX 50 GPUs hit with more price hikes – up to $500

Some GPUs will see bigger increases than others, while Intel and AMD remain mum on their pricing plans

Can AI Invent or Just Help Scientists Work Faster? with Richard Socher - #783

AI can help scientists write code, analyze data, and draft papers. But can it generate the ideas that lead to new discoveries? Richard Socher, former chief scientist at Salesforce, founder of You.com, and co-founder of Recursive, joins us to explore the move from AI assisting researchers to AI conducting research. Starting with AI improving AI, we examine what it takes to propose ideas, run experiments, and validate results, and how those capabilities could eventually accelerate discovery across biology, chemistry, and physics. The conversation covers Recursive’s reported results in model training and GPU kernel optimization, why fast verification matters, and how reward hacking complicates the search for real improvements. We also discuss the data and experimental infrastructure needed for broader scientific discovery, whether commercial incentives can keep increasingly capable systems aligned, and Richard’s vision for a “Eureka machine.” 🗒️ Full show notes: ⁠https://twimlai.com/go/783.

Google Runs Gemma 4 on a TPU in Orbit for the First Time

Google booted up data-center class TPUs in low Earth orbit and ran Gemma 4 inference, taking Project Suncatcher from paper to working prototype.

Building ultra-low-latency voice AI applications on AWS

Learn how Cartesia, an AWS Partner, uses purpose-built State Space Models and AWS infrastructure to deliver sub-100ms text-to-speech and speech-to-text at global scale, and how to deploy its Sonic model on Amazon SageMaker AI or self-managed Amazon EC2 GPU instances.

CRUCIBLE: IARPA-Funded AI Alignment Research — Applications Open

TL;DR: AE Studio is running a new government-funded research program that pairs alignment researchers with our internal engineering team for two-week sprints. We will select six alignment researchers whose proposed work attempts to answer one of our six open questions. Promising sprints have the opportunity to expand to longer funded work. Researchers are asked to commit two hours a week, and in return AE will provide compute and 60+ hours of engineering from us. Apply here . Applications close 11/15 at 11:59 PM PST. What is CRUCIBLE? CRUCIBLE is an IARPA-funded research program executed by AE Studio. The premise is that alignment researchers have more ideas than they have the time or resources test. AE Studio has been operating a model where we match an external researcher with an internal engineering and ML team, so that the research can spend as little as two hours a week and still get high-quality results. CRUCIBLE will focus on six research questions, drawn from priorities set in federal legislation for fiscal year 2027. We are soliciting research proposals against those questions. We will select six researchers and start sprints on a rolling basis, with the first beginning on November, 23rd. The research questions Pick the one your research idea best fits: Do models internally represent alternative hypotheses that they fail to report? Is the competing explanation present in activations and suppressed at output, can it be surfaced? Does sycophancy to the prompt’s framin…

I launched a gluten-free bakery from my kitchen after leaving finance. It's on track to make $3M in revenue this year.

Gluten-free baker Addison LaBonte details her journey from finance to founding a thriving bakery, using social media and customer input for growth.