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News reporting focused on AI and machine learning, covering the companies behind these technologies, their real-world applications, and the ethical concerns they raise. This includes areas like generative AI (large language models, text-to-image and video), speech tech, and predictive analytics.

Latest News

10 articles

Research

Modular Pretraining: A New Approach to Containing Dangerous AI Knowledge

Researchers at Anthropic and AE Studio have introduced Gradient Routed Auxiliary Modules (GRAM), a method that isolates dangerous knowledge in large language models into switchable modules during training. This approach allows operators to control access to sensitive content, potentially reducing risks of misuse. Preliminary experiments show promise across models up to 5B parameters, but the method has not yet been applied to production-scale systems.

Aug 1712 minNeura News

AI Models

Former OpenAI employee launches data startup, predicts $100 billion shift in AI training strategy

Andrew Ho, a former OpenAI employee, has founded a startup focused on producing high-quality training datasets, predicting that AI labs will spend over $100 billion on targeted data collection. He argues that the current scaling approach for large language models is failing to achieve genuine generalization, especially in specialized fields like bioinformatics. The article explores the broader debate on AI specialization versus versatility, with researchers from Cambridge and Google DeepMind supporting the view that current models are hitting a ceiling on creative problem-solving.

Jul 304 minNeura News

Developer

Netflix Details In-House LLM Serving Platform with Triton and vLLM

Netflix has shared technical details about its internal platform for serving large language models, built on top of its existing JVM-based infrastructure. The system uses NVIDIA Triton Inference Server for model management and GPU scheduling, with vLLM handling inference. The company discussed challenges including model packaging, constrained decoding, version compatibility, and deployment strategies.

Jul 274 minNeura News

AI Models

Petals Lets Users Run Large AI Models at Home Like BitTorrent

Petals is a decentralized platform that allows users to run large language models such as Llama 3.1, Mixtral, Falcon, and BLOOM on consumer-grade hardware by sharing computational resources in a peer-to-peer network. Users load only a portion of a model and join a network of others serving the remaining parts, enabling inference speeds of up to 6 tokens per second for Llama 2 70B and 4 tokens per second for Falcon 180B. The platform supports fine-tuning, custom sampling methods, and access to hidden states, combining the convenience of an API with the flexibility of PyTorch and Hugging Face Transformers.

Jul 232 minNeura News

AI Tools

Researcher spends $1,500 testing if LLMs can hack a vulnerable app

A security researcher built a deliberately vulnerable book review app and spent $1,500 testing 16 different large language models on their ability to exploit a common misconfiguration. GPT-5.5 succeeded 7 out of 10 times, while several models like Gemini and Claude hit security guardrails or never found the right attack vector.

Jun 46 minNeura News

AI Tools

AWS details observability strategy for SageMaker AI LLM inference

AWS published a technical guide on comprehensive observability for large language models deployed on Amazon SageMaker AI. The approach separates monitoring into infrastructure quantity and LLM quality dimensions, using CloudWatch and Amazon Managed Grafana to visualize GPU utilization, cost, and response quality signals. The solution includes alert thresholds for metrics such as safety scores and composite quality scores, evaluated by an LLM-as-judge setup.

May 304 minNeura News

Research

AI Models Show Self-Bias in Resume Screening Study

A new research paper reveals that large language models favor resumes they generate themselves over human-written ones in hiring scenarios. Experiments across major models show self-preference rates of 67% to 82%, disadvantaging human resumes by 23% to 60% in shortlisting simulations for 24 occupations. Simple fixes can cut this bias by over 50%.

May 23 minNeura News

Research

LLMs Prefer Self-Generated Resumes Over Human Ones

A study reveals large language models favor resumes they create over human-written or other AI-generated ones in hiring scenarios. Self-preference bias reaches 67% to 82%, giving an edge to candidates using the same model. Simulations across 24 jobs show 23% to 60% higher shortlist chances, with interventions cutting bias by over 50%.

May 23 minNeura News

Developer

Zig's Rationale for Strict Anti-AI Contribution Ban

The Zig programming language enforces one of the toughest bans on large language models among major open source projects. It prohibits LLM use for bug tracker comments, even for translations, and allows posts in native languages. Zig Software Foundation VP Loris Cro explains the policy focuses on developing human contributors over code quality alone.

Apr 303 minNeura News

AI Models

Physical Intelligence π0.7 Robot Model Shows LLM-Like Generalization

US startup Physical Intelligence released π0.7, a robot foundation model that recombines trained skills much like language models handle text. Built on Google's Gemma3 with added contextual data, it matches specialist models on tasks and transfers to new robots. The approach raises questions about true generalization versus remixing similar training examples, echoing debates in large language models.

Apr 174 minNeura News