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ml4se: post #680 — TG.ME

ml4se@ml4se · 494 subscribers Open in TelegramOpen

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October 21, 2025 500 1

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TurboQuant: Redefining AI efficiency with extreme compression Google Research presents TurboQuant—a suite of theoretically grounded quantization algorithms that tackle the biggest memory bottlenecks in AI. By combining PolarQuant (which uses polar coordinates to eliminate memory overhead) and QJL (a 1‑bit error‑correction trick), TurboQuant compresses the key‑value cache to just 3 bits per value with zero accuracy loss. In benchmarks, this delivers up to 8x faster attention computation on H100 GPUs and reduces memory footprint by 5–10x compared to standard 16/32‑bit formats—all without retraining. The implications are huge: longer contexts, lower GPU requirements, and faster vector search at scale. A rare win where extreme compression meets provable efficiency. 268·Mar 26 WebMCP – Proposed Specification WebMCP allows web applications to expose JavaScript functions as tools that AI agents can discover and invoke. - Sites register tools via navigator.modelContext.registerTool() with name, description, JSON schema, and execute callback. - Agents call tools to perform actions; tools can request user confirmation. - Currently a draft by the W3C Community Group, not a formal web standard. 263·Mar 24

Can LLMs Be Computers? Researchers at Percepta built a computer inside a transformer by compiling C code to WebAssembly and having the model generate the execution trace step by step. The key breakthrough: using 2D attention heads to turn linear KV cache scans into logarithmic-time geometric lookups. The result: The model runs the Hungarian algorithm on a 10×10 matrix, solves the world’s hardest Sudoku, and streams execution traces at >30k tokens/sec on a CPU—all without ever leaving the transformer. It makes the whole computation differentiable and opens the door to compiling arbitrary programs directly into weights. 313·Mar 20

Towards a Science of AI Agent Reliability AI agents are getting smarter, but are they getting more reliable? Not really. The paper shows a worrying gap: while accuracy on benchmarks keeps rising, reliability lags far behind. The authors propose a safety‑critical engineering lens for agents, breaking reliability into four dimensions: - Consistency – do they give the same result every time? - Robustness – can they handle rephrased prompts or API glitches? - Predictability – do they know when they’re about to fail? - Safety – how bad are the failures when they happen? The takeaway: accuracy alone is not enough. If we want agents that can be trusted to act autonomously, we need to evaluate—and design for—reliability as a separate, measurable property. 225·Mar 20 System 3: Collective Intelligence in the Multiplayer AI Era The author expands on Daniel Kahneman's two systems of thought (fast System 1 and slow System 2), applying them not to individuals, but to organizations. With the rise of "multiplayer AI," a System 3 is emerging—a new mode of collective cognition where synthesis happens not inside a single mind, but within the network of interactions between people and AI agents. Early collective intelligence (like Wikipedia) was possible but slow and fragile because it relied entirely on human effort. Now, AI is becoming an active participant, capable of acting, remembering context, and coordinating with other AIs. What System 3 Is: It's when multiple people interact with a shared layer of AI agents. These agents accumulate "reasoning traces," notice contradictions in team decisions, and connect informal conversations with formal documents. The organization develops a "nervous system" and memory that works like a living organism, not a file cabinet. What System 3 Is Not: The author emphasizes this is not about dissolving individuality into a "hive mind". Instead, it amplifies individual thinking by giving everyone access to shared context and an AI with "infinite patience." 269·Mar 3

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