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I think this is a fantastic use case for AI, by the by. I recently was working on a complicated (Bane voice: "for you") Excel project and was in uncharted waters. My options were, basically
For opsec reasons I wasn't actually willing to upload the spreadsheet and have Fable one-shot it, but even if I had been, I vastly preferred what I ended up doing: the entire thing, manually, bit by bit. And I think I learned more than if I had just handed it off and had AI (or a coworker) do it.
People are extremely enamored of the generative capabilities of AI, but in many ways I actually think its contextual understanding skills are much more interesting and (I would like to say) useful.
I think "opsec reasons" are ultimately one of the big limiting factors for OpenAI, Anthropic, et al: lots of situations will really prefer something in-house, or at least an ironclad contract about confidentiality.
In the past I've wondered about the long-term market for server-side AI: I'm sure it's non-zero, but I suspect any organization of sufficient size will find themselves rolling out internal hardware and models in the medium term unless the big players keep sufficiently ahead of the commodity models and hardware prices stay high. I've heard of it being done with open weight models already.
Even without seeing the content the AI models see, I've been curious how much intelligence Google (or governments, presumably) could glean from search queries on an aggregate basis. Hypothetically, "Wow, internal Microsoft searches about WINE and Linux are up 100x in the last month, I wonder what they're working on?" gives away potential insider information. Querying the local AI server doesn't give that away.
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This option is good in theory, but in practice requires a good amount of self discipline. It is just so easy to prompt the AI in a way that straight up gives you the answer, then convince yourself that you were the one to think about it following the LLM's guidance. If you are mindful of the pitfalls it can work, but I am not sure I would trust the average person to do it properly.
I've found LLMs the most useful in identifying bugs I caused by embarrassing typos (or cooy paste errors where I didn't change the value of something after pasting). Just 10 minutes wgo it solved a bug by pointing out that I had transposed two letters in a variable name. Though in fairness if I was using a real language instead of a toy language like Python the IDE could have caught it for me.
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It works very well for walking you through how to use a particular tool, app, or device. (e.g. Excel). Because if AI is doing a walkthrough, you still need to press the buttons and key in the inputs to complete the task.
Walking you through solving a math problem - that's extremely dubious.
I've mentioned this before but just a couple of months ago I wanted to solve a simple first year university level math problem (a system of two first order differential equations). I got three different solutions depending on how I wrote the problem (eg. using abstract variables or ones based on the actual problem). Every explanation was very confident, detailed and of course wrong in a way that was apparent if you understood the domain or verified the solution by hand. And this is pretty much as simple as real world university level math can get.
Then I googled the proper syntax for how to input the problem into Matlab and got the correct result in much less time than it took to ask AI and verify it, even had AI given the correct answer.
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