Recurrence in AI
Roland discusses the limitations of LNM models when trained on short sequences, highlighting their struggles with generalization beyond their training data. He emphasizes the need for recurrence in future models to effectively tackle complex problems, suggesting that incorporating recurrent connections could enhance their performance. The evolution of these models is seen as a necessary step forward, building on the insights gained from current architectures.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
What’s Next in LLM Reasoning? with Roland Memisevic - 646
Related Questions
What's next in large language models (LLMs)?
What's next in large language models (LLMs) as discussed in the episode Richard Socher: Re-Imagining Search and the clip Pretraining in Natural Language Processing?
What's next in large language models (LLMs) based on the episode It's Not About Scale, It's About Abstraction - Francois Chollet and the clip Outsider Innovations?