> This is a big problem now that there are systems able to produce huge volumes of plausible blithering that's factually wrong.
If because of some law of nature ChatGPT were the last iteration of automatic text generation we could ever create, it would be a big and permanent problem. Right now it’s just a big and temporary problem, with a huge opportunity for automatic fact-checking. (Or more precisely, validity and soundness checking.) Maybe the answer to the bullshit factory is a rebalancing of Brandolini’s law.
My only point is that if we’re approaching a local maxima with purely statistical approaches, it raises the stakes for symbolic approaches to come through.
That describes large parts of the internet, and we trained these models on that, so what did we really expect? ;-)
Isn't the latter part of vision the part that's "AGI" hard though, and why the article talks/handwaves about "perfect visual reasoning" and not basic classification and object tracking/predicting? LIDAR can determine there's an obstruction in the road, driving AI can determine that the obstruction appears to consist of people and their intent is not possible to predict with any confidence based on observations in its training set, a human applies higher level reasoning to infer - without having seen a visually similar group of people before - that they're protestors showing demonstrable hostility to vehicles that should be avoided, a rabble of drunks pouring out of a neighbouring venue that may part in response to forward movement and judicious use of the horn, or an organized event which probably has a set route and finishing time which can be ascertained by stopping to speak to them. Plus, of course, it can handle the non-vision based bits of that scenario without having done so before.
> A big surprise, and shock to many, has been the discovery that punditry, and what passes for intellectualism in some quarters, can be done by a really good autocomplete with a big training set. This says more about human discourse than it does about AI
I strongly agree with this. It's akin to Moravec's paradox. Turns out a machine can give mediocre-undergraduate level responses to complex abstract questions about human psychology purely by pattern matching what people have written (which isn't dissimilar to how mediocre undergraduates approach the question...) whilst possessing less actionable insight into human emotion than the average dog. And we've managed that without having a clue how to make a computer happy or angry.
> Isn't the latter part of vision the part that's "AGI" hard though
Those accidents with self driving where they ram stationary objects at full speed because they cheaped out on LIDARs and only have visual feeds shows that visuo-spatial ability seems to be AGI-hard.
From the article: "I think this is the sort of debate that makes good podcast fodder". So the article author admits that.
It's hand-wavy on vision. Parts of vision are not "AGI-hard". A key component of self-driving is something that can determine whether a volume of space is empty, occupied, or ambiguous. It's necessary to have something that does not report "empty" for occupied areas, and has a reasonably high accuracy rate on not emitting false reports of "occupied". There are sensor suites which can do that. LIDAR can do that. If you have data from a few viewpoints, you can do that. Musk has tried to turn it into an AGI problem, but that's his problem, not a property of the problem space. (If you look at California DMV autonomous vehicle crash reports, the successful companies have solved "is that area empty", and are now struggling with "what is that other car/pedestrian/bicycle going to do?")
What's inherently hard is always a good question. Aristotle thought that the human ability to do arithmetic was a key indicator of intelligence. Now we know just how few gates it takes to do arithmetic. A big surprise, and shock to many, has been the discovery that punditry, and what passes for intellectualism in some quarters, can be done by a really good autocomplete with a big training set. This says more about human discourse than it does about AI.
As I've pointed out before, we're still nowhere on "common sense", defined as not screwing up in the next 30 seconds. This is a big problem now that there are systems able to produce huge volumes of plausible blithering that's factually wrong.