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In this podcast, Eric Jang, an AI researcher and author of 'AI is Good for You', discusses the problem space of humanoid robots, definitions and timelines of AGI, rediscovering old ideas in AI and robotics, the role of human in the loop learning, levels of abstraction for modeling decision-making, embodiment in AI systems, and the potential benefits and drawbacks of AGI.
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Quick takeaways
- Language serves as a powerful tool for scaling up AI systems by providing a compressed representation of the world.
- Human-in-the-loop learning is crucial for developing AGI, helping solve exploration problems and improving model performance.
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The Role of Language in AGI
Language is considered a crucial substrate for generalization in AGI development. By casting prediction problems into the space of language tokens, models can benefit from the structure and knowledge embedded in language. Language provides a compressed representation of the world that humans use to communicate with themselves and each other. It serves as a valuable latent space for reasoning and prediction, making it a powerful tool for scaling up AI systems. While language captures a lot about the world, it is still a lossy compressor and cannot describe everything. Nevertheless, leveraging language as a base representation can greatly improve the efficiency and robustness of AI models.
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highlights
Listen to the best highlights from the podcasts you love and dive into the full episode