I think the gap is because 1. For coding, Claude is amazing - mainly because of its curated skills and because massive amounts of working code has already been carefully labeled over the last decade or so via GitHub. And because with any Turing complete language, there is only so much one can do.
But 2. For most other things, LLMs are fairly underwhelming. Research is usually mediocre. Try being rigorous and repeat your research prompt many times - then make a confusion matrix to tally up how many false positives and false negatives occur. And for the rest, be honest and ask yourself if the LLM is doing much more than a basic search engine query or trip to Wikipedia would have told you. For “normie” use cases, it’s handy-ish but far from revolutionary
Also because programming is self contained in a computer where the results can be tested and iterated easily. For programming the agent can just run the compiler and tests and keep retrying until it works. If I wanted to for example sew a T shirt, AI is useless.
At the end of the day, the processor can only do Turing operations: assign values to variables (registers, memory locations, storage), loops, bitwise operations, and conditionals. Whether the source code is python, java, or lisp, it has to compile or interpret down to machine code ultimately. Likewise if the running software is a word processor, DOOM, or an LLM, at the end of the day it will be executed by the processor using the three operations. Lots of other fancy hardware and software may accelerate things but ultimately it is those ops that are the running code. The rest is many wonderful conveniences and abstractions.
Basically this. For tax, and law in general, LLMs are prone to giving the wrong answer for anything specific (though they're great at the super general stuff because they're just restating one of the millions of summaries written by humans).
Gemini still isn't sure what details are in the version of OBBA that actually passed, because there was more discussion about various proposals (that didn't make it into the bill) than there was about the final version of the bill itself.
Unfortunately, it's an intractable problem based on the ways that LLMs work. In order to overcome those limitations, you have to provide so much detail to the prompt that you would find the answer faster searching manually.
What other industry regularly acts in such bad faith with respect to claims made? In the securities industry, material misstatement of fact lands people in jail.
"At this point, tech biz leaders are massively over-reaching and trying to influence the rest of us:"
I realize this is what's happening on the headlines, but most of the technology being "deployed" is back-office automation, robotics etc. that no one writes about and none of the tech baddies have monopolistic control over. I refuse to let muxk, thiel, Karp to run the conversation and setup the reaction either. It is exciting and dramatic but not necessarily influential.
Gee, what an optimistic outlook you have. Do you think the truly creative innovators in the fields of tech, science, engineering, and art get out of bed in the morning believing that they are just grimly marching down a path laid out by someone else? While OP’s philosophy may be a bit rosy, it sure leads to better outcomes than dark fatalism.
Forecasts are a thought exercise, not the revelation of something foretold. Best thing to do is think of the outcome you wish for and then try to take whatever actions you can to help make it so. Like with climate change for example.
It can be both. In the field of high-technology (like semiconductor) manufacturing, the techinques of making chips with feature sizes many generations ahead of current SOTA exist in various states of completion, so extrapolation can be made from that.
But what's going on in here is not that - it's reading tea leaves for maximum dramatic effect.
1. so is everyone who is subject to a corporate mandate... but...
2. this may be ok. A good way to learn a piece of software or tool or process is to play with it. We learn lots of general knowledge through play and experimentation. Heck we get better at musical instruments by playing on them.
Mandates are kind of dumb in many ways. But they will force the issue of discovering whether anything useful can come from AI other than coding.
But 2. For most other things, LLMs are fairly underwhelming. Research is usually mediocre. Try being rigorous and repeat your research prompt many times - then make a confusion matrix to tally up how many false positives and false negatives occur. And for the rest, be honest and ask yourself if the LLM is doing much more than a basic search engine query or trip to Wikipedia would have told you. For “normie” use cases, it’s handy-ish but far from revolutionary
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