llm/3fd5f01c-dce0-45f5-821d-a9c655fbe87c/topic-2-3f7f7119-efe8-48f4-b6e2-97fb7501994c-input.json
The following is content for you to summarize. Do not respond to the comments—summarize them. <topic> External Tools vs Internal Execution # Discussion of tradeoffs between having models call external tools versus executing computation internally, including security implications, latency concerns, and the overhead of process forking. </topic> <comments_about_topic> 1. I spent the entire time reading it pondering the same thing. 1. The article presents that calling out to a tool like python is "expensive" because of the overhead of forking a process, loading up the python env etc, but why not just eliminate that overhead and embed WebAssembly so this "tool call" is near zero? This feels very similar to the discussion in the 90's around the overhead of threads v.s. processes or kernel space v.s. user space. Could even go further and have a running beam vm so the LLM can write elixir which is ideal for LLM's that stream out code? Elixir programs will be a lot shorter than webassembly. 2. The core argument stated is "A system that cannot compute cannot truly internalize what computation is." The idea being that it could write a program, execute it and by seeing all of the steps maybe even part way through stop and change its mind or when writing new programs write them better, aka be able to debug on the fly? 3. Not mentioned, but there is a 3rd x factor that LLM's will use this new found computation engine to do overall better at "thinking". Computing in very unexpected ways and to unexpected problems. Maybe it would do dramatically better at some benchmark because of this? Unfortunately these are not explored and it is just an execution engine even resulting in the conclusion stating "arbitrary programs can be compiled directly into the transformer weights, bypassing the need to represent them as token sequences at all." which goes to point number 1 of if we are compiling to weights why not just optimize the tool calling? 2. I really liked the article, but food for thought: is a transformer that offloads computation to python really that different from Python code being read and then executed by a compiler? Both examples are of a system we created to abstract most of the hard work. I think a more important concept here is that the term "AI" has a lot of built-in assumptions, one of which being that it is (or will be) super intelligent, and so folks like the author here think (correctly) that it's important for the AI to be actually doing the work itself. </comments_about_topic> Write a concise, engaging paragraph (3-5 sentences) summarizing the key points and perspectives in these comments about the topic. Focus on the most interesting viewpoints. Do not use bullet points—write flowing prose.
External Tools vs Internal Execution # Discussion of tradeoffs between having models call external tools versus executing computation internally, including security implications, latency concerns, and the overhead of process forking.
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