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.
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Automation has not reduced headcount pressure on the plant floor; it has concentrated it. The team is smaller now, the roles are more specialized, and each person who leaves takes institutional knowledge that takes months to rebuild, not weeks. When a skilled technician walks out, the ripple hits coverage schedules, training load, and output simultaneously. […]
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.
Hexagon Robotics and Schaeffler, a motion technology company, has announced the next milestone in their strategic collaboration as AEON enters Schaeffler’s Humanoid Gym in Germany, marking the next step towards the planned deployment of at least 1,000 AEON humanoids in the coming years. The Humanoid Gym provides a Train-Validate-Deploy model in a dedicated industrial environment. Schaeffler […]
The Association for Advancing Automation (A3) and the Mahoning County Career & Technical Center (MCCTC) will offer no-cost robotics and automation training across Ohio through a $499,000 workforce grant that will help the state’s residents build skills for increasingly technology-driven careers. Awarded through Ohio’s Individual Microcredential Assistance Program (IMAP), the grant will support approximately 3,000 […]
LTX has launched LTX-2.5, the latest version of its open-weights world model, introducing new capabilities for video generation, real-time applications and physical AI. The company says the new model delivers improvements in visual quality, prompt understanding, generation speed and efficiency, while allowing developers and enterprises to run and customize the model on their own hardware. […]
Every founder knows the feeling. The company that ran beautifully at thirty people starts to creak at a hundred, and by three hundred it feels like the wheels are coming off. Nowhere is that strain more visible than in people operations. The informal, everyone-knows-everyone approach to skills, roles and development that worked in the early […]
Reimagine Robotics, an AI robotics company founded by former leaders of Google DeepMind’s Applied Robotics team, has emerged from stealth with new technology that allows robots to learn on the job. The company is developing intelligent robots that anyone can train and use. Instead of requiring specialist programmers whenever a task or production process changes, […]
Neura Robotics has partnered with RWTH Aachen University to establish a new Neura Gym in Germany as part of its expanding global network of Physical AI training centres. The new facility, known as Neura Gym RWTH Aachen, is scheduled to open by the end of 2026 at the university’s Hightech Campus Melaten. It will form […]
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.
Unified context across fragmented workflows
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.
Join us on July 22
Just because open source doesn’t have a direct cost doesn’t mean it’s free. That’s why Dotan Horovits and Rekha Thottanof 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.
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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