What’s Next in LLM Reasoning? with Roland Memisevic - #646

Today we’re joined by Roland Memisevic, a Senior Director at Qualcomm AI Research. In our conversation with Roland, we discuss the significance of language in humanlike AI systems and the advantages and limitations of autoregressive models like Transformers in building them. We cover the current and future role of recurrence in LLM reasoning and the significance of improving grounding in AI—including the potential of developing a sense of self in agents. Along the way, we discuss Fitness Ally, a fitness coach trained on a visually grounded large language model, which has served as a platform for Roland’s research into neural reasoning, as well as recent research that explores topics like visual grounding for large language models, state-augmented architectures for AI agents, and using deductive reasoning to verify the results of Chain-of-Thought prompting strategies with ChatGPT.

The complete show notes for this episode can be found at twimlai.com/go/646.