Understanding permutation symmetry in deep learning, and what it means for weight averaging and model merging
The post The Symmetry That Breaks Neural Network Averaging appeared first on Towards Data Science.
Sony Music, Warner Music, and other publishers are suing Anthropic and CEO Dario Amodei personally for allegedly using tens of thousands of copyrighted musical compositions to train Claude without permission. The plaintiffs call it "one of the largest and most blatant ongoing thefts of intellectual property in history." Just months after paying $1.5 billion to settle with book authors, Anthropic faces another major copyright fight.
The article Sony and Warner sue Anthropic over "one of the largest and most blatant ongoing thefts of intellectual property in history" appeared first on The Decoder.
LAION's Big Video Dataset (BVD) is one of the largest open video datasets for AI research, with 80 million videos, 10 million hours of runtime, and 55 million auto-described clips. Models trained on BVD beat the previous benchmark, InternVid, by up to 2.1 percentage points. Legally, LAION can likely point to a 2024 Hamburg court ruling that allows collecting copyrighted content for non-commercial research.
The article LAION drops massive open video dataset with 10 million hours of footage for AI research appeared first on The Decoder.
Turing Award winner Richard Sutton calls synthetic data a "big mistake" for scaling large language models. The world is infinitely complex, and any simulation of it is "microscopic," with human expertise acting as a bottleneck that blocks real scaling. Sutton's alternative is agents that learn continually from their own experience instead of relying on frozen models.
The article KI-Pioneer Sutton calls synthetic data a "big mistake" in the face of an infinitely complex world appeared first on The Decoder.
Amazon buys large quantities of printed books, scans them as AI training data, and destroys them in the process.
The article AirTag reveals how Amazon destroys rare books for AI training appeared first on The Decoder.
AI performance comes down to three dimensions: Accuracy: How well the model reasons and produces outputs Throughput: How many tokens per second a...AI performance comes down to three dimensions: Deployments must balance all three: High accuracy is wasted if responses are slow, and raw throughput means little if each user’s experience is laggy. Practical systems therefore optimize accuracy, throughput, and interactivity together. This post focuses on throughput and interactivity, and how model-design choices shape both without…
Your organization constantly needs more information about system performance, usage, and data while in production — or better yet, before it heads to prod. The challenge of telemetry increases with the complexity of your stack and agentic sprawl. Because “it works in the testing environment” becomes moot in the face of non-deterministic agents.
After all, AI agents span multiple environments, and that leaves traditional log-metric-trace models insufficient to handle the volume of the agentic AI era. The situation can lead companies to think that the best option is to throw everything into the locked box of proprietary tooling, but that creates another problem: Information is siloed within each layer, fragmenting data and taking you further from realizing real AI ROI.
The OpenTelemetry framework and the OpenSearch distributed search and analytics engine make for a powerful, open-source pairing that gives organizations of all sizes unified context across their fragmented workflows. In fact, OTel has crossed the 95% adoption threshold for new cloud-native instrumentation projects and has already become the default choice for Greenfield projects.
OpenSearch, sponsored by Amazon Web Services, is gaining traction with AI engineers, as it recognizes that observability and AI must be united. This year’s OpenSearch roadmap specifically focuses on making it the primary retrieval interface for AI agents and an essential piece of any retrieval-augmented generation and agentic AI stack.
Just because open source doesn’t have a direct cost doesn’t mean it’s free. That’s why Dotan Horovits and Rekha Thottan of AWS are going to perform a live troubleshooting simulation using correlated logs, metrics, and traces, followed by a demo of how agentic traces flow through Otel pipelines. Also learn how the open-source evaluation framework Agent Health can provide a structured pre-production benchmark to flag unpredictable agentic behavior before release.
Join us live on July 22 to learn along and ask questions to learn how your organization can adopt these open-source standards in the second half of this year — across agentic workloads and traditional infrastructure, at scale.
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The post Watch AWS engineers troubleshoot agentic AI with OpenTelemetry and OpenSearch appeared first on The New Stack.
NVIDIA delivered a clean sweep in MLPerf Training v6.0, the latest edition of industry-standard AI training benchmarks developed by the MLCommons consortium....NVIDIA delivered a clean sweep in MLPerf Training v6.0, the latest edition of industry-standard AI training benchmarks developed by the MLCommons consortium. NVIDIA achieved the fastest time to train at scale, and also delivered the highest performance when normalized on a per-accelerator basis on every benchmark. It was also the only platform to submit on every test.
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