MY biggest disappointment with how AI is being implemented is the inability to incorporate context specific execution if small programs to emulate things like calculators and chess programs. Like why does it doe the hard mode approach to literally everything? When asked to do math why doesn’t it execute something that emulates a calculator?
each tool needs to be hooked up and described extensively.
the naive approach where the LLM generates heaps of text when calling these tools, for example to describe the entire state of the chessboard as JSON or CSV, is unreliable, because text generation is unreliable.
smarter approaches, like having an external program keeping track of the chessboard state and sending it to a chess engine, so that the LLM only has to forward the move that the user described, don’t really make sense to incorporate into a general-purpose language model. You can find chess chatbots on the internet, though.
But all-in-all, it is a path forward where the LLMs could just do the semantics and then call a different tool for each thinky job, serving at least as a user interface.
The hope is for it to also serve as glue between these tools, automatically calling the right tools and passing their output into other tools. I believe, the next step in this direction is “agentic AI”, but I haven’t yet managed to cut through the buzzword soup to figure out what that actually means.
MY biggest disappointment with how AI is being implemented is the inability to incorporate context specific execution if small programs to emulate things like calculators and chess programs. Like why does it doe the hard mode approach to literally everything? When asked to do math why doesn’t it execute something that emulates a calculator?
I’ve been waiting for them to make this improvement since they were first introduced. Any day now…
That’s definitely being done. It’s referred to as “tool calling” or “function calling”: https://python.langchain.com/docs/how_to/tool_calling/
This isn’t as potent as one might think, because:
But all-in-all, it is a path forward where the LLMs could just do the semantics and then call a different tool for each thinky job, serving at least as a user interface.
The hope is for it to also serve as glue between these tools, automatically calling the right tools and passing their output into other tools. I believe, the next step in this direction is “agentic AI”, but I haven’t yet managed to cut through the buzzword soup to figure out what that actually means.