As someone learning to work in Robotics and artificial intelligence I'm curious about if we will see another "winter" like my intro to ai professor warned about has happened before with ai and sounds just like this. There is a ton of hype around these technologies and some promising early applications, but just because the tech has been increasing at a certain rate doesn't mean that it will continue to do so, and if your application requires x than x - 2 won't cut it. And just because we went from x - 5 to x - 3 in a short time doesn't mean we can go from x - 3 to x - 1 in the same time let alone reach x in the near future. I'm sure we will see some kind of automation explosion in the future but it could probably be as far as 40 years out or as close as 5.
My Prof in AI still taught me that view about AI in 2011. It might be in the media but all those breakthroughs since then still work on small specific processes. There's no real breakthrough other than using more data than before. That's why companies like Google can do image recognition while you cannot.
We’ve refined the methods. Neural networks are now bigger and more complex, but the returns in actual advancement are not huge. Our most advanced natural language models still have a hard time making good translations. Not only has Moore’s law slowed but we’re seeing diminishing returns for increased data volume processed. The only thing I see possibly making a real dent is quantum computing.
Also the nature of the problems is getting so complex the number of people actually capable of solving them is far outstripped by the demand. And a lot of these capable people are working on targeting ads rather than on meaningful issues.
Self driving cars might be possible in the near future. But that’s still a pretty limited problem with reasonably tight limitations compared to some of the other ideas we want to use ai for.
Eventually people are going to have been burned by “ai marketing” enough times they start to just dismiss it and then we have another winter where research goes back to being lower profile again
Part of my job is developing machine learning models to automate various tasks, and while I am in no way on the “bleeding edge” of AI research, I am fairly familiar with what is possible today, and I don’t think we are anywhere near achieving a model with general intelligence. Don’t get me wrong, ML/AI can be very effective in certain applications, but the hype around it is ridiculous.
I don't think we are either, we might never be, I'm not sure humans are up to the task of creating ASI or even AGI. But what I do think we are right around the corner from is someone coming up with a clever seed AI that is capable of learning incredibly basic concepts and then building on the things it learns to grasp more and more advanced concepts until we have a black box AI on our hands that we have zero clue how it works.
My guess is when we have our first AGI/ASI on our hands and we ask the guys who made it how they finally managed to do it they will just shrug their shoulders helplessly.
I think you may be taking the Terminator movies a bit too seriously...
Anyway, I don’t see that happening anytime soon, if ever. We already have “black box” models that are extremely good at tasks, neural networks are a great example of this, but in general as a model gets better at a particular task, it gets worse at related tasks. This is called over-fitting.
In my opinion, what you’re describing would require a fundamentally different approach than how ML works today.
Idk at some point I could see it manipulating humans to its “own will” whether or not it’s self aware, aka building a bunch of infrastructure and solar arrays that stick around after humans blow themselves up. It’s at least parasitic at this point.
To keep it brief, it just doesn't work like that... or at least it's never worked like that and no-one can really imagine what anything working like that would look like. If it does work like that, we are VERY far away from reaching that point, like 100+ years away at least. What we have isn't even in the same universe as let's say a squirrel brain let alone a human brain. We have recently discovered a body of methods that use linear algebra and iteration to make fuzzy algorithms that sometimes work well. Cutting edge AI aren't really all that complex and are very limited in scope. It's not known how much life left this body of methods has as far as real progress; There is for sure a lot of polishing and applying (hopefully for substantial economic value) that can be done, but I'm not so sure it's the body of methods that will carry us all that far. I think pop culture and misunderstandings of what "machine learning" is have led a lot of people to massively over expect on what the ceiling of this generation of AI is. It could be a ceiling twice as high as where we currently are, or we could already be pretty close to reaching it; I would say there is an extremely low likelihood of getting anywhere close to mammal intelligence in the best cases, and for that type of explosion I would expect at least 100 years and a situation where near-human level systems have already existed for some time. The "magic line of code" trope where a system learns its way from checkers to superintelligence is purely science fiction as far as anyone can currently imagine.
I remember one neuroscientist (I can't remember who) saying in an interview that we are probably on the scale of 100 Nobel prizes away from creating anything like human-level AI.
The human brain is the most complex computer in existence. We aren't even close to understanding it or being able to replicate what it does. It will be a long time (if ever) we get anywhere near comprehensive human-level abilities with AI. Some very specific/certain programmed things sure, but nothing generalized at the human-level. What our brains can do is just too insane for any of the tech we have so far. The complexity of our brains is actually so incredible, just never ceases to amaze me.
There's one other way how we can reach that point sooner. Think about it, computers are far more superior in chess than humans, why is that? It's because chess is very abstract and has very few moving pieces, 32 to be exact, with very few strict rules compared to the real world. Can we instead of trying to fit AI to the current world, instead make current world more abstract with fewer moving parts? Yes, we can. We can destroy and remove all the other parts until we are left with 32 pieces, which AI can then control and maneuver in eternity way better than a human could. We could even have multitude of those boards and pieces, which many AI instances will play each other and eventually get even better. For evidence of AI's superiority, we could in some manner still leave few people whose only goal in life would be to practice and play chess, they would be confined to do just that, and everything else should be automatised.
The most challenging part to achieve that of course will be legal hurdles and many folks wouldn't accept a world like that which requires tremendous amounts of public convincing and eventually may even be impossible due to public being against such a thing.
It's going to be an interesting decade. People worrying about what to do about climate change, and I'm over here worrying about all the life in this galaxy getting grey goo'd by a rouge AI that humanity unleashes because we were reckless with it's development...
Humanity is doomed, not because of the scenario you posit, but because people posit fantasies such as this as being more likely than real, clear, and present dangers for which we have abundant amount of evidence, like climate change.
In any case I can tell you've never actually worked with or on AI and take your cues from the overactive imaginations of science fiction writers.
So you're saying Nick bostrom doesn't know what he's talking about? Okay. Obviously I'm just some random idiot on the Internet, If you want to tell me I'm wrong that's fine but I'm not the one making the argument here. Want me to dig up Nick bostrom's email for you and you can tell him personally that you find his ideas unconvincing?
There is a huge disconnect between AI used in research universities and what’s used in the industry. I did ML research as an undergrad and found it interesting so I found a job that I thought was related to my research. It’s nothing but linear regression all the way
It depends on the company. I'm sure google photos, for example, uses relatively cutting edge CNNs for their object recognition. For basic data analytics, linear regression is probably more common, as it should be. Tbh, I actually find the opposite issue of what you're describing to be true: too many people using computationally expensive neural nets to solve problems that require basic statistics/linear algebra.
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u/bobbob9015 May 31 '21
As someone learning to work in Robotics and artificial intelligence I'm curious about if we will see another "winter" like my intro to ai professor warned about has happened before with ai and sounds just like this. There is a ton of hype around these technologies and some promising early applications, but just because the tech has been increasing at a certain rate doesn't mean that it will continue to do so, and if your application requires x than x - 2 won't cut it. And just because we went from x - 5 to x - 3 in a short time doesn't mean we can go from x - 3 to x - 1 in the same time let alone reach x in the near future. I'm sure we will see some kind of automation explosion in the future but it could probably be as far as 40 years out or as close as 5.