TLDR;
This video discusses why achieving artificial general intelligence (AGI) is proving difficult with current AI models. It highlights three major problems: the purpose-bound nature of current models, the issue of hallucinations, and the unsolvable problem of prompt injection. The video suggests that current AI's inability to perform abstract reasoning and generalize beyond their training data limits their potential, and abstract reasoning networks are needed for AGI.
- Current AI models are purpose-bound and can't generalize.
- Hallucinations can be mitigated but not entirely solved.
- Prompt injection is a fundamental, unsolvable flaw in current large language models.
- Abstract reasoning networks are needed for true AGI.
Intro: The Hurdles to Artificial General Intelligence [0:00]
The video starts by questioning if current AI models can achieve human-level intelligence with more time. The speaker argues that this is unlikely because these models are based on deep neural nets, which are used in large language models and diffusion models for image and video generation. These models are trained to find patterns in specific data types, making them purpose-bound. For AGI, an abstract thinking device usable for any purpose is needed, which these models can't provide.
Hallucinations in Large Language Models [1:20]
The discussion moves to the problem of hallucinations, where AI models give factually incorrect answers. This happens when the correct answer is missing or rare in the training data. Instead of searching for the right answer, these models look for a string of words close to the correct one. While OpenAI suggests rewarding models for acknowledging uncertainty (saying "I don't know"), this isn't a perfect solution. The speaker believes that while hallucinations won't be completely eliminated, reducing their frequency is acceptable.
The Unsolvable Problem of Prompt Injection [3:14]
Prompt injection is presented as a major, potentially unsolvable problem. This involves changing the AI's instructions through input, like telling it to ignore previous instructions and write a poem instead. Large language models can't differentiate between instructions and prompts, making them vulnerable. While some methods exist to mitigate this, the speaker believes these models will remain untrustworthy for many tasks due to this exploit.
Limitations in Generalization and Out-of-Distribution Thinking [4:11]
Current AI models struggle to generalize beyond their training data; they interpolate but don't extrapolate. This is evident in image and video generation, where the AI produces garbage when asked for something outside its training examples. Similarly, large language models are good at summarizing and drafting emails but struggle with novel tasks. This limitation is a significant obstacle to their use in scientific research.
The Need for Abstract Reasoning Networks [5:11]
The speaker concludes that the current generation of generative AI won't go far due to their inability to do abstract reasoning, their vulnerability to prompt injection, and their lack of generalization. Companies relying on these models may face challenges. The solution lies in developing abstract reasoning networks that can process any input, using a logic language without words. Neurosymbolic reasoning is a step in this direction.
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