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Alex Tolley's avatar

IMO, the best use of LLMs is to create a human conversational interface, rather than used to extract useful information. IOW, think of LLMs as Kahneman's "System 1" thinking - the sort of daily verbal I/O between humans to smooth "social transactions".

We seem to have been led down the path of pattern recognition ML because of the failure of GOFAI to be able to deal with complex data, e.g. images. As a result of the success of the Deep Learning neural network architectures, this approach has been pressed into service for achieving more cognitive roles than is, IMO, warranted. Consider all the old GOFAI goals that depended on logic that are now ignored in favor of pattern matching. Because expert chess and GO players do see patterns in board positions to make decisions on play, this has bolstered the neural net approach to game playing, which is probably perfect for situations that require quick, i.e. systems 1 responses. However, humans, particularly those who have been educated to use other forms of thinking that take more effort, i.e. Kahneman's "System 2", we really need these forms of thinking as the primary method when simple lookup response is not available. We are often told that we need "critical thinking skills" to evaluate information, and there are tools that can be used to support this. LLMs do not have these tools built in. What is needed is to apply these tools to retrieved [mis-, dis-]information to provide the best response. Computers should be able to do this very well as they can vastly exceed human cognitive capabilities in retrieval speed, accuracy, and handling many more pieces of information at the same time. We have to use prostheses like books, writing, etc. to manage complex tasks to reach good [enough] answers and results.

Therefore, I would conclude that we need to marry other ML tools, algorithms, and data retrieval (and even expert systems] to act as the more "reflective" part of cognition, leaving LLMs to parse the output in a way that humans can interact with through a Q&A session. Sophisticated analogy-making algorithms would be a great help in making complex issues understandable based on existing personal knowledge. Then we might even get useful intelligent systems, even embodied in robots.

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Patrick Marren's avatar

Hey, they said it was catchy!

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