I don't see a hard reason. If it works it works. No hard need for a good, universal, and long lasting solution. At some point you just stack slop on top of slop and it works for your use case - and if it doesn't you'll slop it out yourself.
tbh... it seems to work contrary to all expectation that slop shouldn't work. but, then, just when I think about the incredible corporate slop i've seen here and there, that not only works, but pays. well I guess we have to agree - slop works. and that's fine.
Based on my own experience AI (Fable, Astra) still has major blind spots and is prone to draw obviously wrong conclusions from thin air. Conclusions based on weak evidence that are so obviously blatantly wrong that you question if it can think at all. At the same time it already completely outperforms humans in many cases in many areas. The utility of current AI is in what you need. Can you take the good parts and cheaply compensate for the weakness OR do the mistakes completely kill it for you?
I would therefore not call it AGI (Artificial General Intelligence) because clearly it isn't but ASI (Artificial Special Intelligence) which clearly it is in many areas.
The problem is that ASI may be enough to win wars but not enough to save the economy.
"it already completely outperforms humans in many cases in many areas" and
"is prone to draw obviously wrong conclusions from thin air." seem contradictory imo. If the performance is not reliable, how can you confidently state that? Also, "ASI" is "Artificial Super Intelligence". Let's not do an Altman and start redefining terms, please.
It seems to start understanding how bicycles work. Look at the front fork. There are reasons why it's shaped the way it is in real bicycles. If you do physical simulations in 3D with reinforcement learning you will start understanding that most of the arrangements that the other agents use just won't work.
Yes, the curve of the front fork on the max pelican was what I noticed also. It's impressive (if not an accident), and a remarkably accurate bike overall. The pelican just needs to raise their seat a little.
Apparently that's necessary and sufficient for getting views. In these interesting times when the future is very uncertain we like to hear people speaking with certainty about the future. Especially people who appear to not be paid for it.
Can he predict the future? No. Can he pretend to be able to predict the future? Yes.
The first time I heard of Ed Zitron was 18 or so years ago when he wrote a review of the Darkfall MMORPG. He tore it to pieces, but as someone who played the game I could tell he had never actually played it, which is what the game developers also claimed when they reviewed his logged in session, that he did nothing for 30 minutes then logged out (I might be a bit off on the details, it has been awhile). He’s always been someone who wont let the truth get in the way of his success. I really hope people start valuing accuracy over confidence
Nice saying. If in the prediction "given [intricate analysis] the whole [shebang] goes [bust] at [date]", only the [date] turns out to be wrong, I'd say it is still a valuable prediction however, though it was wrong.
There is plenty of evidence that they have improved in all benchmarks and also in my private experience. But have they improved in the things they still fail at? No, they still fail at them. You need only one example of failure to prove that it still fails. They still fail a lot on many real world tasks.
So, depending on what you ask, they may have not improved even a tiny bit.
I am a very light user, so this is my feeling from reading about other people's experience; I wouldn't say that they plateau'd but up to 4.5/4.8 the gains in the models felt exponential while since then they feel more linear and the big improvements are coming less from the models and more from everything around it (harnesses, agentic development, skills...).
So, while I don't feel like there has not been improvement, it really feels like there is a limit that will be reached sooner than later (and for sure before any AGI).
Since almost everything eventually goes bust, I wouldn't agree that this was particularly valuable, unless the analysis proved out in other ways. It's like the saying that some economists have predicted 10 of the last 4 recessions. The analysis that leads to that prediction may or may not be valuable, but is only valuable if it predicts something, because otherwise how can you know it has any accuracy at all?
True, if you have a codebase that works in practice but has dozens of loose ends and poorly defined edge cases than it can chase off into rabbit holes because "oh wait, what if x is undefined instead of null? How is y defined? This outdated package has long known severe security holes and should not be used anymore, do we actually need it?".
Nobody except corporations who built workflows on top of it and don't care about the price because the developer already moved on and nobody wants to touch it.
It may seem overly simplistic, but 2-port models for capacitors can be configured in series (as shown here) or in shunt (much more common for power distribution network applications). So, as basic as you might think this diagram is, it is entirely expected to be there so that we instantly know if the S-parameters are in series or shunt configuration - it would be more unusual if it were missing.
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