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Unfortunately not only are there plenty of examples in the real world where this isn’t true and people are already being replaced, there’s a larger issue, and t

Unfortunately not only are there plenty of examples in the real world where this isn’t true and people are already being replaced, there’s a larger issue, and that’s the issue of the pace of improvement. People think AI technologies improve in a linear fashion. But there is nothing restricting this area of technology from non-linear progress. Consider the failing prophesies of AGI as a precient example of what’s happening: - It was 2015, and I’m reading articles about AGI being here in 2050 at least. - It’s 2018 and everyone is talking about a few new research papers but secure in their predictions but maybe feeling like it is skewing towards the bottom end of that range (except for a few inspired nerds — my people — who boldly claim 2035). - It’s 2022 and suddenly an AI is blowing people’s minds and we have the fastest growing technical product in the history of the world. Predictions are now 2030 for an AGI. - It’s 2024 and people are debating if AGI has already happened and debating about the definition and many people are calling for an “advanced level” AGI in the next 2-3 years. My point is that predictions of this technology have been terrible. Just like the worst. People have been off on every prediction by orders of magnitude. So now every major tech company is blowing all their money towards AI, realigning their business towards hardware and software solutions and we’re in the middle of an arms race the size of Jupiter towards AI technology, and it’s happening across the world but definitely in both China and the USA where the stock market is going crazy and 25% of the entire markets growth is just nvidia’s massive growth (and those gains are basically powered by the leading ai training solutions). So, the idea that somehow the technology isn’t going to replace people is asinine. This is the biggest, fastest tech wave I have ever seen, it’s growing geometrically, it’s funded by insane amounts of money and has most of the western and eastern world’s technology research focused on it, and has been wildly ahead of predictions from its inception. Let’s get real about this. This is a bomb going off in slow motion and is set to interrupt employment and radically reshape society in a time scale that almost no humans can physically comprehend. But more importantly the trend is accelerating and like most parabolic markets that don’t have physical limitations holding them back (like input materials for a gold rush) it could accelerate to literally crazy levels. There’s no restriction but breakthroughs here. I’m aware of the current reality of the software systems and I know what I’m saying is futurist, but from a trend perspective we are way, way ahead of where we thought we were going to be and the trends point to railgun speed acceleration from here.

Workaccount2 3y ago
The core reason is that nobody understands how these models work, and hence what they are/will be capable of. It's a little unnerving to think about, since the approach is pretty much "Lets build the framework, plant the seeds, and then see what comes out the other side". If AGI is achieved, it will almost certainly be a surprise rather than be intentional.
ben_w 3y ago
Predictions have been wrong in both directions at the same time; myself, I confidently predicted in 2009 that normal people would be able to buy cars which didn't have steering wheels in… 2019. While I'm really impressed with ChatGPT, and am one of the people who regards it as meeting my prior definition of AGI[0], I can still see its current flaws, and do wonder if this is a similar case, where the first 90% needed a major breakthrough but once that was invented anyone could do it and many wanted in on the economic opportunity… but the second 90% turned out to be just as hard, and so was the third, … and you need at least six nines[1] to really replace humans in these roles. [0] All three letters mean different things to different people. To me: it's artificial, it's much too general to count as a narrow AI, and I count it as intelligent because the things it can do were the things I grew up thinking were signs of intelligence, like speak Latin, do algebra, and answer trivia questions, and also things I added later like 'write code' and 'pass medical and law exams'; even though it gets the answers wrong sometimes, I don't think my standard was ever "must be perfect" because nobody ever scored 100% on exams at school either. At its best (and it's weird that it even has a best and a worst), the free version of ChatGPT has given me better code than one specific real human I've had to work with, more if you also add in the students. (And at its worst, it gives me stuff that doesn't compile and wouldn't do what I asked even if I fixed the compiler errors). [1] Assuming a driver is making 1 decision per second that has a serious wrong answer, it would take eight nines to have just under one serious accident in a lifetime of 1-hour-each-way commutes, 5 days a week, 50 weeks a year for 40 years. I suspect the actual time between opportunities for serious mistakes is less than that, but probably at least once per minute even on an empty road. For an LLM, I don't know exactly how good they'll really need to be, all I can guess at is that they're not going to have more than one opportunity to seriously mess up per token.

Workaccount2 3y ago

The core reason is that nobody understands how these models work, and hence what they are/will be capable of. It's a little unnerving to think about, since the approach is pretty much "Lets build the framework, plant the seeds, and then see what comes out the other side". If AGI is achieved, it will almost certainly be a surprise rather than be intentional.

ben_w 3y ago

Predictions have been wrong in both directions at the same time; myself, I confidently predicted in 2009 that normal people would be able to buy cars which didn't have steering wheels in… 2019. While I'm really impressed with ChatGPT, and am one of the people who regards it as meeting my prior definition of AGI[0], I can still see its current flaws, and do wonder if this is a similar case, where the first 90% needed a major breakthrough but once that was invented anyone could do it and many wanted in on the economic opportunity… but the second 90% turned out to be just as hard, and so was the third, … and you need at least six nines[1] to really replace humans in these roles. [0] All three letters mean different things to different people. To me: it's artificial, it's much too general to count as a narrow AI, and I count it as intelligent because the things it can do were the things I grew up thinking were signs of intelligence, like speak Latin, do algebra, and answer trivia questions, and also things I added later like 'write code' and 'pass medical and law exams'; even though it gets the answers wrong sometimes, I don't think my standard was ever "must be perfect" because nobody ever scored 100% on exams at school either. At its best (and it's weird that it even has a best and a worst), the free version of ChatGPT has given me better code than one specific real human I've had to work with, more if you also add in the students. (And at its worst, it gives me stuff that doesn't compile and wouldn't do what I asked even if I fixed the compiler errors). [1] Assuming a driver is making 1 decision per second that has a serious wrong answer, it would take eight nines to have just under one serious accident in a lifetime of 1-hour-each-way commutes, 5 days a week, 50 weeks a year for 40 years. I suspect the actual time between opportunities for serious mistakes is less than that, but probably at least once per minute even on an empty road. For an LLM, I don't know exactly how good they'll really need to be, all I can guess at is that they're not going to have more than one opportunity to seriously mess up per token.