Get ready to witness a turning point in mathematical history: in this episode, we dive into the AI breakthroughs that stunned number theorists worldwide. Join us as Professor Yang-Hui He discusses the murmuration conjecture, shows how DeepMind, OpenAI, and EpochAI are rewriting the rules of pure math, and reveals what happens when machines start making research-level discoveries faster than any human could. AI is taking us beyond proof straight into the future of discovery.

Echo Hunter - Official Short Film

So beyond excited to share this with you all. Endless hours of work went into this. This project means the world to me and I hope you enjoy it as much as we all did making it. A SAG-AFTRA approved AI film.

Built with the cutting edge tools inside Arcana Labs.

@ArcanaLabsAI
presents a @PhantomX_ai
production.

Starring
@breckinmeyer
, @_TaylorJSmith
, @DanielleBisutti
, Hanna Balicki, Gedeon Burkhard and Xander Bailey

Executive Producer
Jonathan Yunger

Producer
@mike_j_mitch


Music Composed By @MiguelJmusic


VFX By @FilmsBySav


Written and Directed by Kavan the Kid

For best viewing experience, head to YT and watch it in 4K

#aifilm #filmmaking

This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and Brain-only (no tools). Each completed three sessions under the same condition. In a fourth session, LLM users were reassigned to Brain-only group (LLM-to-Brain), and Brain-only users were reassigned to LLM condition (Brain-to-LLM).
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These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.

Large language models (LLMs) are regularly evaluated using benchmark datasets. But what justifies making inferences about an LLM's capabilities based on its answers to a curated set of questions? This paper first introduces a formal framework to address this question. The key is to note that the benchmarks used to test LLMs -- such as AP exams -- are also those used to test people. However, this raises an implication: these benchmarks are only valid tests if LLMs misunderstand concepts in ways that mirror human misunderstandings. Otherwise, success on benchmarks only demonstrates potemkin understanding: the illusion of understanding driven by answers irreconcilable with how any human would interpret a concept.