What Are the Limitations of Current LLMs in AI?

TL;DR

Current autoaggressive large language models (LLMs) like GPT-4 struggle with understanding the world due to their inability to reason, plan, or represent physical realities. In contrast, joint embedding representations offer a more promising approach, allowing for advanced common sense reasoning through self-supervised learning, which could lead to a deeper comprehension of the world.

Transcript

I see the danger of this concentration of power to to proprietary AI systems as a much bigger danger than everything else what works against this is people who think that for reasons of security we should keep AI systems under lock and key because it's too dangerous to put it in the hands of of everybody that would lead to a very bad future in whic... Read More

Key Insights

• 🌍 Autoaggressive LLMS have limitations in understanding the world due to their focus on predicting words rather than capturing the complexities of the world.
• 🥺 Joint embedding representation, based on self-supervised learning, has shown promise in capturing the internal structure of inputs, leading to improvements in reasoning and planning tasks.
• 🌍 LLMS and joint embedding representation can complement each other, with LLMS providing language fluency and joint embedding representation enabling a deeper understanding of the world.

Questions & Answers

Q: Why are autoaggressive LLMS limited in their ability to understand the world?

Autoaggressive LLMS lack characteristics of intelligent behavior, such as understanding the physical world, reasoning, and planning. They are designed to predict words based on previous words, leading to limitations in capturing the complexities of the world.

Q: Can joint embedding representation replace autoaggressive LLMS in language tasks?

Joint embedding representation complements autoaggressive LLMS by capturing high-level common sense reasoning. While LLMS excel in language-related tasks, joint embedding representation provides a deeper understanding of the world and can enhance reasoning abilities.

Q: How can joint embedding representation be used for complex planning?

Joint embedding representation, combined with a world model, allows for hierarchical planning in complex scenarios. By predicting the outcome of a sequence of actions based on an internal world model, the system can plan actions to achieve specific objectives.

Q: Can LLMS and joint embedding representation be combined to improve AI capabilities?

Combining LLMS and joint embedding representation is possible but may require careful integration. LLMS can provide fluency in manipulating language, while joint embedding representation captures the internal structure of inputs, allowing for a deeper understanding of the world. Further research is needed to explore the potential synergy between these approaches.

Summary & Key Takeaways

• Autoaggressive LLMS, such as GPT-4 and Llama 2, are limited in their ability to understand the world due to their lack of characteristics of intelligent behavior, such as understanding the physical world, reasoning, and planning.

• Joint embedding representation, based on self-supervised learning, has shown success in capturing the internal structure of inputs, such as text, images, and video, and has the potential to be used for high-level common sense reasoning tasks.

• LLMS may excel in language-related tasks, but they lack the comprehensive understanding of the world that is necessary for complex planning and reasoning.