Baidu Unveils New Model, Chips to Keep Up in China’s AI Race - Bloomberg.com
Baidu Unveils New Model, Chips to Keep Up in China’s AI Race Bloomberg.com
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15 Datasets for Training and Evaluating AI Agents
Datasets for training and evaluating AI agents are the foundation of reliable agentic systems. Agents don’t magically work — they need structured data that teaches action-taking: tool calling, web interaction, and multi-step planning. Just as importantly, they need evaluation datasets that catch regressions before those failures hit production. This is where most teams struggle. A chat model can sound correct while failing at execution, like returning invalid JSON, calling the wrong API, clicking the wrong element, or generating code that doesn’t actually fix the issue. In agentic workflows, those small failures compound across steps, turning minor errors into broken pipelines. That’s why datasets for training and evaluating AI agents should be treated as infrastructure, not a one-time res
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Semantic matching in graph space without matrix computation and hallucinations and no GPU
Hello AI community,For the past few months, I’ve been rethinking how AI should process language and logic. Instead of relying on heavy matrix multiplications (Attention mechanisms) to statistically guess the next word inside an unexplainable black box, I asked a different question: What if concepts existed in a physical, multi-dimensional graph space where logic is visually traceable?I am excited to share our experimental architecture. To be absolutely clear: this is not a GraphRAG system built on top of an existing LLM. This is a standalone Native Graph Cognitive Engine.The Core Philosophy:Zero-Black-Box (Total Explainability): Modern LLMs are black boxes; you never truly know why they chose a specific token. Our engine is a “glass brain.” Every logical leap and every generated sentence i
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