Arguments About AI Consciousness Remain Highly Motivated and At Best Overconfident

Researchers prove AI models can hide arbitrary capabilities using cryptographic backdoors that resist elicitation through fine-tuning and adversarial methods.

Empirical comparison of NUTS, emcee, and nested sampling reveals that while NUTS is fastest for smooth problems, it fails on multimodal distributions where dynesty performs reliably but with optimistic error estimates.

An analysis reveals that AI coding agents often produce unnecessary work, with simulated runs showing many extra edits and test executions beyond what's needed to solve programming tasks.

Anthropic research shows small AI models may soon self-replicate on consumer IoT devices, potentially creating unstoppable malware that adapts across different hardware.

OpenAI claims a faster Fourier transform algorithm, but analysis suggests it's more exploit than breakthrough with limited practical benefits.