Brain implants let paralyzed man make music with his thoughts
A diving accident at age 16 left Buckwalter paralyzed from the chest down. In 2024, he enrolled in a Caltech brain-computer interface study and underwent a craniotomy to have six Blackrock Neurotech chips implanted in his motor cortex. Read Entire Article
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AI uptake across Italian firms remains patchy, study suggests, despite generative AI buzz - Phys.org
<a href="https://news.google.com/rss/articles/CBMic0FVX3lxTE9XZ2I2Vm9CdGcyNll4bEhqX0Etb2Ixa0VlMlFDODBKZFViLW5XWUdWNTdnNy1NcUg2anNkZTFGWVh5QTU1ZmlnT2NRVk4teEw2TDNpSzc1Ny1kNDJNYjRRdS1tS1BYUllnYkROYW94blBQZjQ?oc=5" target="_blank">AI uptake across Italian firms remains patchy, study suggests, despite generative AI buzz</a> <font color="#6f6f6f">Phys.org</font>
SYNTHONY: A Stress-Aware, Intent-Conditioned Agent for Deep Tabular Generative Models Selection
arXiv:2604.00293v1 Announce Type: new Abstract: Deep generative models for tabular data (GANs, diffusion models, and LLM-based generators) exhibit highly non-uniform behavior across datasets; the best-performing synthesizer family depends strongly on distributional stressors such as long-tailed marginals, high-cardinality categorical, Zipfian imbalance, and small-sample regimes. This brittleness makes practical deployment challenging, especially when users must balance competing objectives of fidelity, privacy, and utility. We study {intent-conditioned tabular synthesis selection}: given a dataset and a user intent expressed as a preference over evaluation metrics, the goal is to select a synthesizer that minimizes regret relative to an intent-specific oracle. We propose {stress profiling}
Do LLMs Know What Is Private Internally? Probing and Steering Contextual Privacy Norms in Large Language Model Representations
arXiv:2604.00209v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in high-stakes settings, yet they frequently violate contextual privacy by disclosing private information in situations where humans would exercise discretion. This raises a fundamental question: do LLMs internally encode contextual privacy norms, and if so, why do violations persist? We present the first systematic study of contextual privacy as a structured latent representation in LLMs, grounded in contextual integrity (CI) theory. Probing multiple models, we find that the three norm-determining CI parameters (information type, recipient, and transmission principle) are encoded as linearly separable and functionally independent directions in activation space. Despite this internal stru
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