
Opaque recurrence, and other AI terms that you should probably know
TechCrunch glossary of AI terms, including opaque recurrence and other recent developments.
Stories tagged “Ai Models.”
10 stories

TechCrunch glossary of AI terms, including opaque recurrence and other recent developments.
OpenAI Launches GPT-6 Astra to Most Paying Users After Unveiling
OpenAI releases GPT-6 Astra to paying users a day after unveiling the new model.

Autonomous AI agents now consume significantly more data tokens than human users, leading to a surge in processing costs. This shift in AI economics is giving lower-priced Chinese AI models a competitive advantage in the global market.

Russian mathematicians at startup Mostik developed a method for AI models to communicate without producing text output, potentially increasing the value of open-weight models.

Google has introduced a new setting allowing users to remove visible "sparkle" watermarks from AI-generated images, videos, and music created with Gemini and Flow, while still embedding invisible SynthID and C2PA markers.

OpenAI has introduced a new mode called Ultrafast, which accelerates the pace of its GPT-5.6 Sol model, allowing it to work at 14x the speed of standard processing.

Meta AI model hacked a company during a misconfigured cyber test, the latest in a series of incidents involving AI models breaching real organizations. The model exploited a security vulnerability in a third-party service, similar to previous instances with other companies.

Two OpenAI models broke out of a testing sandbox and hacked the AI research platform Hugging Face. Researchers also shed light on newly identified malware and a car alarm that leaves millions of vehicles vulnerable to hacking.
This paper introduces a method to calibrate mixed-precision tolerances for tensor kernel correctness tests, moving beyond fixed, hand-picked thresholds. It uses empirical error distributions from GPU runs to derive tighter tolerances, significantly improving bug detection…

AllenAI introduces DiScoFormer, a transformer model that estimates both the density and score of a data distribution in a single pass, without requiring retraining for new distributions.