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The Jim Rutt Show

EP 211 Ben Goertzel on Generative AI vs. AGI

Nov 28, 2023
AI researcher and author Ben Goertzel joins Jim Rutt to discuss the strengths and weaknesses of modern LLMs, why LLMs alone won't achieve AGI, OpenAI's integrative system, solving LLM hallucinations, and the duality of heuristics and abstractions. They also explore the limitations of LLMs, the challenge of scalability, and the future of AGI with the OpenCog project.
01:08:20

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

  • LLMs lack the capabilities for non-banal creativity and making multi-step leaps.
  • Exploring hybrid architectures that combine different AI approaches can enhance problem-solving and abstraction capabilities.

Deep dives

LLM's limitations in achieving AGI

Large language models (LLMs) in their current form, such as transformer nets, are not expected to lead to human-level artificial general intelligence (AGI). While LLMs can perform specific functions and even pass the Turing test in some cases, they lack the capabilities for non-banal creativity and making multi-step leaps. To address these limitations, researchers are exploring various directions within the neural net universe. One approach involves introducing more recurrence into LLMs, which can enable the formation of interesting abstractions. Another direction is to combine neural nets with predictive coding-based training or evolutionary algorithms to enhance the learning capabilities of recurrent networks. These efforts aim to achieve AGI by integrating LLMs with other architectures that can better handle non-binality and support holistic guidance.

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