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...nonsense generators? Have you ever used e.g. Gemini or Deepseek? Both are free. Okay both can be very naive at times, and both are kind of soy with default prompts. Deepseek, however, with a bit of prompting can be completely insane yet rational and easily smarter than most people you see if you go to any place outside of a professional context.
If you want to really see what they can do, install some client for LLMs and hook yourself up with some of the better free models over at https://openrouter.ai/models
(there's a 50 query daily limit if you have <10$ in your account, not sure if there's a better service. )
Even the best models will confidently spout absolute falsehoods every once in a while without any warning.
Buddy, have you seen humans?
As a math nerd I seriously despise this line of argument as it ultimately reduces to a fully generalized argument against "true", "false", and "accuracy" as meaningful concepts.
I invite further clarification.
Imagine a a trick abacus where the beads move on thier own their own via some pseudorandom process, or a pocket calculator where digits are guaranteed to a +/- 1 range. IE you plug in "243 + 67 =" and more often then not you get the answer "320" but you might just as well get the answer "310", "321" or "420". After all, the difference between all of those numbers is very small. Only one digit, and that digit is only off by one.
Now imagine you work in a field where numbers are important, you lives depend on getting this math right. Or maybe you're just doing your taxes, and the Government is going to ruin you if the accounts don't add up.
Are you going to use the trick calculator? If not, why not?
That is not an explanation for:
You're arguing that since LLMs are not perfectly reliable, therefore they're unreliable. There are different degrees of reliability necessary to do useful things with them. It is a false dichotomy to divide them so. I contend that they've crossed the threshold for many important, once well-paying lines of cognitive labor.
Besides, your thought experiment is obviously flawed. If you're sampling from a noisy distribution, what's stopping you from doing so multiple times, to reduce the error bars involved? I'd expect a "math nerd" to be aware of such techniques, or did your interest end before statistics?
If I had to rely on an LLM for truly high-stakes work, I'd be working double time to personally verify the information provided, while also using techniques like running multiple instances of the same prompt, self-critique or debate between multiple models.
Fortunately, that's a largely academic exercise, since very few issues of such consequences should be decided by even modern LLMs. I give it a generation or two before you can fire and forget.
I have no objections to my own doctor using an LLM, and I use them personally. All I ask is that they have the courtesy and common sense to use o3 instead of 4o.
Besides, the contraption you describe is quite similar to how quantum computing works. You get an answer which is sampled from a probability distribution. You are not guaranteed to get a single correct answer. Yet quantum computers are at least theoretically useful.
Hell, as a maths nerd, you should be aware that the overwhelming majority of numbers cannot be physically represented. If you also happen to be a CS nerd on the side, you might also be aware of the vagaries of floating point arithmetic. Digital computers are not perfect, but they're close enough for government work. LLMs are probably close enough for government work too, given the quality of the average bureaucrat.
Humans are fallible. LLMs are fallible, but they're becoming less so. The level of reliability needed for a commercially viable self-driving vehicle is far higher than that for a useful Roomba. And yet, Waymos are now safer than humans.
I rest my case.
o3 is definitely more capable, but it also has a remarkable ability to hallucinate more believable things, and to communicate ideas in highly technical ways that are hard to understand — and thus fact-check — if you’re not a domain-specific expert. I don’t ask ChatGPT questions about personal medical problems, but when I ask dumb shower thoughts about medical research (“what do researchers think causes Alzheimer’s?” etc) it starts going on about highly technical detail with no introduction or explanation. If it’s right, wow is it smart. But if it’s wrong… I’m not smart enough to know how.
With 4o, I know I’m going to get something overly emotive and excessively buttkissing, but at least I can understand what it’s giving me.
That's fair, o3 has a conversational style that is rather unique, even when considering other SOTA reasoning models. It's like a bright zoomer intern with ADHD who will try just about anything.
I would hope that a doctor using o3 would be able to parse the jargon! If not, they have bigger issues than merely using an LLM. 4o might be more conversational, but for knotty problems, I'd rather use o3 itself to explain arcane terminology or have another model break it down for me.
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