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New AI framework autonomously optimizes training data, architectures and algorithms — outperforming human baselines
AI R&D runs on a cycle of hypothesis, experiment, and analysis — each step demanding substantial manual engineering effort. A new framework from researchers at SII-GAIR aims to close that bottleneck by automating the full optimization loop for training data, model architectures, and learning algorithms. A new framework called ASI-EVOLVE, developed by researchers at the Generative Artificial Intelligence Research Lab (SII-GAIR), aims to solve this bottleneck. Designed as an agentic system for AI-for-AI research, it uses a continuous "learn-design-experiment-analyze" cycle to automate the optimization of the foundational AI stack. In experiments, this self-improvement loop autonomously discovered novel designs that significantly outperformed state-of-the-art human baselines. The system generated novel language model architectures, improved pretraining data pipelines to boost benchmark scores by over 18 points, and designed highly efficient reinforcement learning algorithms. For enterpri
DeepMind researcher raises $1.1B for Ineffable Intelligence
Datavault AI Announces Execution of Binding Term Sheet for $120 Million Cash Contribution From Scilex Holding Company to Fund 100-City GPU Expansion of Quantum-Ready SanQtum Platform
General AI
Alpha Compute AI GPU Launch Update; First Large-Scale NVIDIA Blackwell Cluster
NVIDIA Blackwell Cluster Launch Target of May 8, 2026; Additional Three-Cluster Deployment is Roadmap to $72 million in Annual Recurring RevenueROAD TOWN, British Virgin Islands, April 27, 2026 (GLOBE NEWSWIRE) -- Alpha Compute Corp. (NASDAQ: ALP), a pioneering technology leader in AI GPU-as-a-service (GPUaaS) and AI Confidential Compute, today provided an update on the deployment status of its NVIDIA Blackwell GPU infrastructure. The Company confirms that its first large-scale GPU cluster, a 50
Assessing Accenture (NYSE:ACN) Valuation As New AI Alliances With Google Cloud Microsoft And Piraeus Bank Take Shape
AI alliances put Accenture (ACN) in focus after sector jitters Accenture (ACN) has become a focal point for investors after fresh concerns about consulting weighed on the sector, just as the company stepped up its push into applied AI through a series of new partnerships. See our latest analysis for Accenture. At a share price of US$178.36, Accenture has seen pressure in recent months, with a 90 day share price return of 35.33% decline and a 1 year total shareholder return of 37.59% decline...
Sci-Hub launches Sci-Bot an AI-powered research assistant
Article URL: https://sci-bot.ru/ Comments URL: https://news.ycombinator.com/item?id=47918570 Points: 1 # Comments: 0
The Man Behind AlphaGo Thinks AI Is Taking the Wrong Path
David Silver has a new billion-dollar company that aims to build AI “superlearners.”
Canva apologizes after its AI tool replaces ‘Palestine’ in designs
I suddenly feel so much better about every embarrassing typo I’ve ever made. | Original Illustration (left) by Agathe Singer One of Canva's new AI features has been caught replacing the word "Palestine" in designs. The Magic Layers feature - which is designed to break flat images out into separate editable components - isn't supposed to make visible alterations to user designs, but it was found by X user @ros_ie9 to automatically switch the phrase "cats for Palestine" to "cats for Ukraine." The issue was seemingly limited specifically to the word "Palestine," as @ros_ie9 noted that related words like "Gaza" were unaffected by the feature. Canva says it has now resolved the issue and is taking steps to prevent it from happening again. "We became aware of an issue … Read the full story at The Verge.
Struggling to understand Sanaenomics? A new book helps
Takaichi argues the solution is investment — with Aida proposing a "high-pressure economy" where demand outstrips supply and the government breaks the deadlock.
MemPalace – Local-first AI memory
Article URL: https://github.com/mempalace/mempalace Comments URL: https://news.ycombinator.com/item?id=47918225 Points: 1 # Comments: 0
The next phase of the Microsoft OpenAI partnership
OpenAI and Microsoft announce an amended agreement that simplifies the partnership, adds long-term clarity, and supports continued AI innovation at scale.
The AI-designed car is taking shape
The auto design world is full of advanced 3D visualization tools and VR sculpting platforms, but your average new car still enters the world as a sketch. Those sketches traditionally see endless iteration and refinement from all angles before being turned into 3D models by hand, some dying in the digital world, others sculpted into clay to better visualize lines and profiles. That's just the beginning of a design and development process that often takes a half-decade or more. That means many new cars hitting dealerships this summer were first sketched in 2020 or 2021, initiatives kicked off when alternative fuel incentives were widesprea … Read the full story at The Verge.
OGMA – persistent memory and dual-brain AI, newcomer seeks pro feedback
Article URL: https://github.com/kidshadow79/Ogma Comments URL: https://news.ycombinator.com/item?id=47916377 Points: 1 # Comments: 0
Ask HN: Anyone want to collaborate on a local-first AI-based research assistant
Hi HN Community, I'm Venkatram, a sophomore who's on a mission to build a local alternative to proprietary third-party AI-based research assistants. The idea is to turn documents into researchable assets that contain as much as information as the original information does, but it's more reusable. Well, quite frankly, this is still under a WORK IN PROGRESS, so i'm still figuring on how it can be properly used, and I got to be honest here, i definitely need some help to build this, so if you wish, you are welcome! TlDR: NotebookLM, but Locally with your OWN AI Model Github: https://github.com/venkatram-s/gigabook-lm Comments URL: https://news.ycombinator.com/item?id=47914594 Points: 2 # Comments: 0
Under cover of trade truce with Trump, China expands economic pressure toolkit
Beijing is punishing those who shift supply chains from China, tightening rare earth licensing, banning foreign AI and cybersecurity tech and weighing curbs on its solar gear.
Turbo-OCR Update: Layout Model + Multilingual
Follow-up to my post 18 days ago about the C++/CUDA OCR server. Two additions: What's New: Layout model: Added PP-StructureV3 for layout detection Multilingual: No longer Latin-only. Now supports Chinese, Japanese, Korean, Cyrillic, Arabic, and Latin-script languages. Same stack: C++, TensorRT FP16, multi-stream, gRPC/HTTP, direct pdf endpoint. Benchmarks (Linux / RTX 5090 / CUDA 13.2): Very text-heavy images: 100+ img/s Sparse/Low-text: 1,000+ img/s 270p/s on FUNSD Dataset Source: github.com/aiptimizer/TurboOCR submitted by /u/Civil-Image5411 [link] [comments]
To buy this Bay Area home, you’ll need Anthropic equity
Someone’s offering an unusual deal for a 13-acre property in Mill Valley, just north of South Francisco.
AI news from 200+ sources
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Join the new AI Agents Vibe Coding Course from Google and Kaggle
Google is bringing back its 5-Day AI Agents Intensive Course with Kaggle and registration is open.
AI-Sorted Inboxes Raise the Stakes for Good Email and Newsletter Practices
Google is not just helping users search email. It is teaching the inbox to rank, summarize, and prioritize it. Following updates that began in March 2025, Google introduced AI-powered search that factors in recency, most-clicked emails, and frequent contacts instead of simply returning results in chronological order. In January 2026, the company stated that Gmail's 3 billion users were entering the Gemini era, with AI Overviews designed to turn inbox information into answers. The signal for mark
Skymizer Taiwan Inc. Unveils Breakthrough Architecture Enabling Ultra-Large LLM Inference on a Single Card
Source Article excerpt: With a single PCIe card — powered by six HTX301 chips and 384 GB of memory — enterprises can now run 700B-parameter model inference locally at just ~240W per card. The memory-bandwidth-intensive token generation that dominates real-world inference latency. Existing GPUs handle compute-dense prefill; HTX301 cards handle decode. Each silicon matched to its phase. This is a really interesting approach. It only lets the GPU handle the prefill stage, while everything else, including the model weights and decoding, runs entirely on this card. That way, you can run huge billion parameter models without needing to chase after graphics cards with massive VRAM. As for how the actual product will perform in real life, we'll have to wait until early June at Computex to find out. submitted by /u/lurenjia_3x [link] [comments]
OpenAI could be making a phone with AI agents replacing apps
The phone could go into mass production in 2028, an analyst says.
China blocks Meta’s $2B Manus deal after months-long probe
China has ordered Meta to unwind its multibillion-dollar Manus acquisition, dealing a potential setback to Zuckerberg’s push into AI agents.
RAG precision tuning can quietly cut retrieval accuracy by 40%, putting agentic pipelines at risk
Enterprise teams that fine-tune their RAG embedding models for better precision may be unintentionally degrading the retrieval quality those pipelines depend on, according to new research from Redis. The paper, "Training for Compositional Sensitivity Reduces Dense Retrieval Generalization," tested what happens when teams train embedding models for compositional sensitivity. That is the ability to catch sentences that look nearly identical but mean something different — "the dog bit the man" versus "the man bit the dog," or a negation flip that reverses a statement's meaning entirely. That training consistently broke dense retrieval generalization, how well a model retrieves correctly across broad topics and domains it wasn't specifically trained on. Performance dropped by 8 to 9 percent on smaller models and by 40 percent on a current mid-size embedding model teams are actively using in production. The findings have direct implications for enterprise teams building agentic AI pipeline
Rebuilding the data stack for AI
Artificial intelligence may be dominating boardroom agendas, but many enterprises are discovering that the biggest obstacle to meaningful adoption is the state of their data. While consumer-facing AI tools have dazzled users with speed and ease, enterprise leaders are discovering that deploying AI at scale requires something far less glamorous but far more consequential: data…
Elon Musk and Sam Altman’s court battle over the future of OpenAI
Sam Altman and Elon Musk are set to face off in a high-stakes trial that could alter the future of tech’s leading AI startup, OpenAI. The trial begins with jury selection on April 27th, as Musk pushes forward his 2024 lawsuit that accuses OpenAI of abandoning its founding mission of developing AI to benefit humanity and shifting focus to boosting profits instead. Musk was a cofounder of OpenAI and claims that Altman and co-founder Greg Brockman tricked him into giving the company money, only to turn their backs on their original goal. However, OpenAI says that “This lawsuit has always been a baseless and jealous bid to derail a competitor” in a bid to boost Musk’s own SpaceX / xAI / X companies that have launched Grok as a competitor to ChatGPT. In his lawsuit, Musk is asking for the removal of Altman and Brockman, and for OpenAI to stop operating as a public benefit corporation. Musk has also demanded that OpenAI’s nonprofit receive up to $150 billion in damages he’s asking for if he
The New Linux Kernel AI Bot Uncovering Bugs Is a Local LLM on Framework Desktop
Article URL: https://www.phoronix.com/news/Clanker-T1000-AMD-Ryzen-AI-Max Comments URL: https://news.ycombinator.com/item?id=47914388 Points: 5 # Comments: 0
When Does LLM Self-Correction Help? A Control-Theoretic Markov Diagnostic and Verify-First Intervention
arXiv:2604.22273v1 Announce Type: new Abstract: Iterative self-correction is widely used in agentic LLM systems, but when repeated refinement helps versus hurts remains unclear. We frame self-correction as a cybernetic feedback loop in which the same language model serves as both controller and plant, and use a two-state Markov model over {Correct, Incorrect} to operationalize a simple deployment diagnostic: iterate only when ECR/EIR > Acc/(1 - Acc). In this view, EIR functions as a stability margin and prompting functions as lightweight controller design. Across 7 models and 3 datasets (GSM8K, MATH, StrategyQA), we find a sharp near-zero EIR threshold (<= 0.5%) separating beneficial from harmful self-correction. Only o3-mini (+3.4 pp, EIR = 0%), Claude Opus 4.6 (+0.6 pp, EIR ~ 0.2%), and o4-mini (+/-0 pp) remain non-degrading; GPT-5 degrades by -1.8 pp. A verify-first prompt ablation provides causal evidence that this threshold is actionable through prompting alone: on GPT-4o-mini it
Robotics
Sereact raises $110 million investment to advance its ‘world model’ for robotics and AI
Sereact has raised a $110 million Series B round led by Headline, with participation from Bullhound Capital, Daphni, and Felix Capital. Existing investors Air Street Capital, Creandum (who led the company’s 2025 Series A round), and Point Nine once again invested in Sereact. The round funds two priorities: scaling Cortex 2 and entering the United […]
Squaremind raises $18 million to redefine skin exams with robotics and AI
SquareMind, an AI and robotics company developing solutions for dermatology, has announced $18 million in funding, including previously undisclosed pre-Series A financing, to enable high-quality, consistent skin exams and make them accessible at scale. The round was led by Sonder Capital, a California-based venture fund co-founded by medical robotics pioneer and Intuitive Surgical founder Fred […]
The Biggest Risk of Embodied AI is Governance Lag
arXiv:2604.21938v1 Announce Type: cross Abstract: Embodied AI is widely discussed as a job-displacement problem. The deeper risk, however, is governance lag: the inability of public institutions to keep pace with how fast the technology spreads through the physical economy. As reusable robotic platforms are combined with increasingly general AI models, embodied AI may scale across manufacturing, logistics, care, and infrastructure faster than governance systems can observe, interpret, and respond. We argue that this lag appears in three connected forms: observational, institutional, and distributive. The central policy challenge, therefore, is not automation alone, but whether governance and compliance systems can adapt before disruption becomes entrenched.