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Join Allen and Mark on Two Voice Devs as they dive into the world of Large Action Models (LAMs) and explore their potential to revolutionize how we build chatbots and voice assistants.

Inspired by Braden Ream's article "How Large Action Models Work and Change the Way We Build Chatbots and Agents," the discussion dissects the core functions of conversational AI - understand, decide, and respond - and examines how LAMs might fit into this framework.

Allen and Mark also compare and contrast LAMs with Large Language Models (LLMs) and Natural Language Understanding (NLU), highlighting the strengths and limitations of each approach.

Tune in to hear their insights on:

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• The evolution of Voiceflow and its shift towards LLMs (03:20)
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• Understanding the core functions of conversational AI (05:40)
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• Clippy as an example of a deterministic agent (06:15)
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• The differences between deterministic and probabilistic models (07:50)
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• NLU vs. LLMs for understanding user input (09:20)
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• How LAMs might fit into the "decide" stage of conversational AI (18:50)
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• The challenges of training LAMs and avoiding hallucinations (20:00)
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• The potential of LAMs to improve response generation (29:30)
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• Cost considerations of using LLMs vs. NLUs (37:00)
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Whether you're a seasoned developer or just curious about the future of conversational AI, this episode offers a thought-provoking discussion on the potential of LAMs and the challenges that lie ahead.

Be sure to share your thoughts in the comments below!

Additional Info:

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• https://www.voiceflow.com/blog/large-action-models-change-the-way-we-build-chatbots-again
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