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Analytics Insight reports that Nvidia CEO Jensen Huang opposed Bill Gates’ proposal to tax robots and AI systems, arguing that AI-driven productivity and industrial investment could create jobs. The report adds new details to the continuing debate by quoting Huang’s response and outlining Gates’ proposed rationale.
AWS says SageMaker Python SDK v3 unifies custom model training and deployment through ModelTrainer, ModelBuilder and runtime code injection, with examples spanning scikit-learn and Stable Diffusion.
Perceptron’s website presents a new promptable vision model, an Egocentric API and the Perceptron Mk1 for machines operating in real-world settings. The source makes broad capability and deployment claims but provides no benchmarks, customer deployments, pricing or dated launch details.
Runable has raised $21 million in a Series A round, according to Inc42, which reports that the startup plans to expand its AI agent from software creation into customer acquisition, marketing and routine business operations. Runable’s claimed user and revenue figures have not been independently confirmed.
TechCrunch reports that robotics companies are improving physical capabilities faster than they are developing reliable, commercially useful robot intelligence. Developers are focusing on better data, simulation, reinforcement learning and task-specific deployments while searching for a breakthrough comparable to…
The South China Morning Post reports that distributor inventories of multilayer ceramic capacitors have fallen to a record low as AI infrastructure demand rises, citing UBS data.
Philips says it was selected for an ARPA-H program focused on advancing AI-enabled robotic stroke care within image-guided therapy.
Goodfire’s new Silico platform is designed to inspect hidden model representations, diagnose behavior and guide targeted interventions across life-sciences, robotics, vision and language models.
A new arXiv preprint introduces CoDrift, a one-step generative policy framework that combines behavioral compatibility and value-seeking objectives for offline reinforcement learning. The authors report the best average rank across 73 OGBench and D4RL tasks in both offline and offline-to-online settings.
A new preprint studies agents that can transfer control to a stronger language model during reasoning, combining a Bayesian stopping rule with theoretical guarantees and a limited Qwen2.5-Coder validation.
IBM’s Granite 4.2 family includes 3B, 8B and 30B open-weight models with a native 128,000-token context window. Ars Technica reports that the two larger versions received specialized training for tool use and agentic tasks.
A KDD 2026 workshop paper describes a deployed vision-language-model serving agent that handles unresolved content-quality cases, reporting higher review productivity, lower normalized operating cost and faster signal delivery.
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