The Download: how people really use AI, and Flock’s design choices

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. We still don’t know how people are really using AI AI companies like Anthropic and OpenAI regularly publish reports on how…

Source: MIT Technology Review

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We still don’t know how people are really using AI

AI companies like Anthropic and OpenAI regularly publish reports on how people are using products like Claude and ChatGPT, but they only release the data they want us to see, AI researchers say.  “There is no independent source to corroborate it,” says Anka Reuel, a…

Source: MIT Technology Review

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AI’s recursive self-improvement might not come so quickly after all

The AI industry’s boldest promise right now is that AI will soon improve itself, with almost no need for human oversight. LLMs can already write code, generate synthetic data for training, and optimize the computer chips they run on. Forecasts of explosive AI progress predict…

Source: MIT Technology Review

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Would this change your answer? Evaluating Explanations of LLM Behavior In The Wild with Counterfactual Experiments

arXiv:2608.16747v1 Announce Type: cross Abstract: Many areas of AI research, such as language model interpretability and chain of thought faithfulness, seek to explain model behaviors. But what constitutes a "good" explanation? In this work, we evaluate explanations through the lens of counterfactual simulatability-whether the explanation…

Source: cs.AI updates on arXiv.org

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BrickAnything: Geometry-Conditioned Buildable Brick Generation with Structure-Aware Tokenization

arXiv:2605.26182v2 Announce Type: replace Abstract: Generating physically buildable brick structures from 3D shapes requires more than geometric reconstruction: the output must also satisfy discrete part constraints and structural stability. Existing brick generation methods either rely on heuristic optimization, which can break down when the target…

Source: cs.AI updates on arXiv.org

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Digital Twin Degradation: Detecting Cyber Physical Attacks via Temporal Inconsistencies

arXiv:2608.16159v1 Announce Type: cross Abstract: Digital Twins (DTs) are increasingly used to monitor and analyze Cyber Physical Systems (CPS). However, in adversarial environments, the fidelity of a DT cannot be assumed. Communication delays, data manipulation, sensor degradation, or partial information loss may cause the DT…

Source: cs.AI updates on arXiv.org

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