From shiny object to sober reality: The vector database story, two years later

by AI Generated Robotic ContentAI/ML NewsPosted on Comments are Disabled

When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing — a must-have infrastructure layer for the gen AI era. Billions of venture dollars flowed, developers rushed to integrate embeddings into …

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A single beam of light runs AI with supercomputer power

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Aalto University researchers have developed a method to execute AI tensor operations using just one pass of light. By encoding data directly into light waves, they enable calculations to occur naturally and simultaneously. The approach works passively, without electronics, and could soon be integrated into photonic chips. If adopted, it promises dramatically faster and more …

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Cybersecurity and LLMs

by AI Generated Robotic ContentFAANGPosted on Comments are Disabled

TL;DR Large language models (LLMs) and multimodal AI systems are now part of critical business workflows, which means they have become both powerful security tools and high-value targets. Attackers are already jailbreaking models, stealing prompts, abusing autonomous AI agents, and weaponizing tools like WormGPT and FraudGPT. The next few years will be defined by an …

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Depth Anything 3: Recovering the Visual Space from Any Views ( Code , Model available). lot of examples on project page.

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Project page: https://depth-anything-3.github.io/ Paper: https://arxiv.org/pdf/2511.10647 Demo: https://huggingface.co/spaces/depth-anything/depth-anything-3 Github: https://github.com/ByteDance-Seed/depth-anything-3 Depth Anything 3, a single transformer model trained exclusively for joint any-view depth and pose estimation via a specially chosen ray representation. Depth Anything 3 reconstructs the visual space, producing consistent depth and ray maps that can be fused into accurate point clouds, resulting in high-fidelity …

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Build a biomedical research agent with Biomni tools and Amazon Bedrock AgentCore Gateway

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This post is co-authored with the Biomni group from Stanford. Biomedical researchers spend approximately 90% of their time manually processing massive volumes of scattered information. This is evidenced by Genentech’s challenge of processing 38 million biomedical publications in PubMed, public repositories like the Human Protein Atlas, and their internal repository of hundreds of millions of …

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A new top score: Advancing Text-to-SQL on the BIRD benchmark

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In the fast-evolving world of agentic development, natural language is becoming the standard for interaction. This shift is deeply connected to the power of operational databases, where a more accurate text-to-SQL capability is a major catalyst for building better, more capable agents. From empowering non-technical users to self-serve data, to accelerating analyst productivity, the ability …

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Google’s new AI training method helps small models tackle complex reasoning

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Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning tasks. Supervised Reinforcement Learning (SRL) reformulates problem-solving as a sequence of logical “actions,” providing rich learning signals during the training process. This approach enables smaller models to learn …

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