Nah even 100% reliability isn't attainable by a human. 100% obviously doesn't imply solving the halting problem.
100% as in 100% as reliable as a human. Even surpassing a human. Sort of in the same way as a computer now beats humans at chess.
>I think applying the sociology of hipster band fans to LLMs is a mistake
Why would it be a mistake? Human psychology is similar across all spectrums. What happens in one area is likely possible in another area. If it happens among hipster bands it can happen among hipster technology fads.
>I think there’s a temptation to apply the analogy of something like the early internet or smartphone to LLMs but those are networked products. LLMs don’t really improve with the various applications built on top of them if the LLM is itself fundamentally broken or faulty to the point that it is unsafe to use in practice.
This is a valid speculation. But it's speculation. Basically your saying that LLMs are fundamentally broken and stuck at 38% forever because of fundamental and permanent flaws. The jury on that one is still out. And your point is highly, highly speculative.
We see quantitative improvements on LLMs constantly AND this is a nascent technology we don't completely understand yet. The most probably and logical conclusion is to follow the technological trendline. That trendline is pointing up.
To speculate on fundamental flaws of the LLM when people don't even fully understand what's going on with the LLM is illogical because you can't derive conclusions from something you don't understand. We can only generalize the trendline and constant improvements we've seen in AI for the past decade. Again that trendline is pointing to further break throughs in the future.
>Obviously, I, like probably everyone else on this website would love LLMs to be reliable to a high degree.
No this is not obvious to me. I disagree. I think some people are like you but other people, for example Geoffrey Hinton are in the apocalyptic camp. Personally I'm in the middle, I think it could go either way. It will definitely harm a segment of our society by taking over work, but whether the benefits of AGI outweighs the harm remains to be seen.
Look. People in reality are basically never this gigabrained. Maybe consider as your first-line theory, that when people say "AI will destroy the world", that they mean to express that they believe that AI will destroy the world?
But to be clear I’m not saying this is a methodical highly coordinated ad campaign amongst multiple companies. I don’t think human beings are that competent. I think it’s way more grassroots and feeds on cultural tropes that have existed since at least the 1950s but probably the early 1900s. I think Anthropic’s leadership probably genuinely believes their own bullshit but I also think they understand that that same bullshit has raised over a billion in funding.
Your point was on whether everybody wants to see AI take over. To that Geoffrey Hinton doesn't want AI to take over, while you do. I for one am not sure.
As for whether Geoffrey Hinton thinks AI is legit or not is a different story. On that topic he's on my side, but that was besides the point I brought him up to point out that your take on "everybody" wanting AI to develop further is incorrect.
> and the doom and existentialism is a marketing ploy to a world imbued in conspiracy theory and institutional distrust in order to compensate for a wildly over promised and under delivered product.
Other way around. With all the money and business interests going into LLMs business interests are promoting LLMs in a future that you want and that is not apocalyptic. The conspiracy theories aren't a thing. It makes no sense as those theories don't align with where the money is being thrown.
>But hey, when you gotta raise money, you gotta raise money, and as you put it, the "trend line is going up" and that's all that matters.
Bro. I am not saying "trendline" as if it's something I have to keep throwing money at to support.
I am talking about a mathematical projection based on data. The pace of technology in the past when graphed points to an ever increasing line on a line graph. When you take the slope of that line and use it to do a quantitative prediction, that line just points up. That's just a fact of reality. The logical outcome of the data we see.
You’re implying 100% reliability is actually attainable. If that were the case, wouldn’t that mean the halting problem would have been solved by AI? I’m not an expert but I’ve heard that’s like one of those fundamental laws of information theory that really can’t be broken.
> It's typical. It's like an indie band is only popular when not many people know about it. Once everybody starts talking about it all the time it loses it's popularity.
I think applying the sociology of hipster band fans to LLMs is a mistake and I’m not sure how the vogueness of the technology correlates with the correctness of the actual models. Sometimes an early technology seems docile or useless at first but eventually reaches ubiquity and in retrospect the utility is obvious. But sometimes (more often than not) standard adoption curves don’t make sense to apply to a new technology because it isn’t useful enough to go through an adoption cycle. I think there’s a temptation to apply the analogy of something like the early internet or smartphone to LLMs but those are networked products. LLMs don’t really improve with the various applications built on top of them if the LLM is itself fundamentally broken or faulty to the point that it is unsafe to use in practice. Furthermore given the massive amount of premature hype due to the AI zeitgeist, you can safely assume enough user hours have been spent messing with LLMs to get a verdict on their utility. Unlike the feeble technology that takes a longtime to reach a scale to know its utility, we don’t have to wait 10 years for 100m people to try LLMs, it happened in a week. My only point being, I think we should be apprehensive about trying to draw analogies of other adoption cycles that structurally are very very different to this one.
Obviously, I, like probably everyone else on this website would love LLMs to be reliable to a high degree. Just this morning I had such a good use for an LLM that I was seriously considering building (and admittedly still am pondering) but the second I started to think through the LLM faultiness, I had to consider the complexity of the safe guards and weigh if it was really better to use GPT or just write a nasty regex script and constrain the problem. I’m leaning heavily towards the latter but I’d much prefer a silver bullet if it really killed vampires. Until then (if that day ever comes), it’s lead bullets for me.