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I know someone who is a non native speaker based in another country who has struggled using Google translate to accomplish his job. With a specialized prompt he

I know someone who is a non native speaker based in another country who has struggled using Google translate to accomplish his job. With a specialized prompt he is able to translate from his native language into English with alacrity. This has revolutionized his job. The outputs capture the nuances of idiomatic language across both languages. That’s not a toy. That’s a universal translator that’s eluded us for decades. I would also note that the next obvious step for the LLM is to combine its semantic understanding with specialized models and expert systems and other ML to build ensembles like the one discussed here. Yes, LLM isn’t sufficient to do everything. But it’s sufficient to do the things we’ve been unable to do with these specialized systems. Layering in semantic reasoning systems, agent based goal finding and optimization, and other capabilities in a feedback loop with the LLM, will see remarkable capabilities unlock. I’m always surprised how little imagination I see on H.N. at times, and there is a real impetus to call monumental breakthroughs nothingburgers.

obmelvin 4y ago
I most certainly agree with your last point - I do think that many of us are underestimating the impact of LLMs due to certain issues. That said, I think your example does highlight how the LLMs are most useful when the operator knows what they are doing. In this case your friend appears to be competent at their job - they just need help with translation and phrasing. If your friend also needed the LLM to do the problem solving for them, I'd be more skeptical about its use.
- fnordpiglet 4y ago
  That’s why I think problem solving is better done by the 70 years of work on exceptional problem solving engines. Other than language LLMs also have amazing ability to synthesize semantics and generalize concepts, and restructuring into multiple types of output and receiving multiple types of input. LLMs are remarkable at many things, but not everything. It just happens I believe they’re remarkable at precisely the things our remarkable AI inventions of the past are not. The synthesis of the ensemble of reasoning work done over the last near century will yield a lot of the unrealized benefits of that work to now.

obmelvin 4y ago

I most certainly agree with your last point - I do think that many of us are underestimating the impact of LLMs due to certain issues. That said, I think your example does highlight how the LLMs are most useful when the operator knows what they are doing. In this case your friend appears to be competent at their job - they just need help with translation and phrasing. If your friend also needed the LLM to do the problem solving for them, I'd be more skeptical about its use.

fnordpiglet 4y ago
That’s why I think problem solving is better done by the 70 years of work on exceptional problem solving engines. Other than language LLMs also have amazing ability to synthesize semantics and generalize concepts, and restructuring into multiple types of output and receiving multiple types of input. LLMs are remarkable at many things, but not everything. It just happens I believe they’re remarkable at precisely the things our remarkable AI inventions of the past are not. The synthesis of the ensemble of reasoning work done over the last near century will yield a lot of the unrealized benefits of that work to now.