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Artificial Intelligence: The Revolution Hasn’t Happened Yet (2018)
- Ilverin 4y agoI think this essay includes a specific prediction, that human level ai is far away, that might be disproved this decade. If human level ai is close, focusing on some other kind of ai is more likely to be a waste of time.
- goatlover 4y agoThere's no reason to think it's close this time, just like there's little reason to think this time automation is going to put everyone out of work.
- naasking 4y agoActually, there are many good reasons to think it's close: https://www.lesswrong.com/posts/K4urTDkBbtNuLivJx/why-i-think-strong-general-ai-is-coming-soon https://www.lesswrong.com/posts/K4urTDkBbtNuLivJx/why-i-thin...
- blitzar 4y agoMicrosoft already got to human level ai. Twitter taught Microsoft’s AI chatbot to be a racist asshole in less than a day. https://www.theverge.com/2016/3/24/11297050/tay-microsoft-chatbot-racist https://www.theverge.com/2016/3/24/11297050/tay-microsoft-ch...
- vt85 4y ago
- evrydayhustling 4y agoThis is one of my favorites. So much of industrial AI is about replacing labor (usually cheaper but lower quality). In a way, AGI is only slightly more ambitious. We should be setting higher goals for AI, including helping individuals be superhuman, and helping organizations coordinate betteele.
- lob_it 4y agoDoes it resemble how CGI incremented to VR and AR to replace analog experiences (nowhere near as good)? Even tcp/ip has devolved into a "failed social experiement" with petabytes of low quality/low aptitude vocabulary. AI is just ambiguous phrasing to color gibberish.
- twelvedogs 4y agothe way people are learning to interact with stable diffusion is incredibly interesting to me, almost learning a new language via prompts to get it to produce desired results. i feel like that may be the key to the next step in ai, realising that human directed ai fills huge gaps in talent at both ends
- abudabi123 4y agomaybe the A.I. affords every human to be an affluent retiree/philosopherking and have free-will
- slfnflctd 4y agoIf so, it seems it would be undesirable for such AI to possess what we call sentience.
- guestbest 4y agoWe already have artificial intelligence. It’s called children
- Animats 4y agoThis is a common sentiment, and pundits have been making similar remarks for decades. This author writes "Sixty years later, however, high-level reasoning and thought remain elusive." That's the wrong problem with AI. The trouble with AI is that it still sucks at manipulation in unstructured situations and at "common sense". Common sense can usefully be defined as getting through the next 30 seconds of life without a major screwup. At, at least, the competence level of the average squirrel. This is why robots are so limited. If we could build a decent squirrel brain, something "higher level" could give it tasks to do. That would be enough to handle many basic jobs in unstructured spaces, such as store stocking, janitorial, and such. It's not the "high level reasoning" that's the problem. It's the low-level stuff. A squirrel has around 10 million neurons. Even if neurons are complicated [1], somebody ought to be able to build something with 10 million of them. Current hardware is easily up to the task. The AI field is fundamentally missing something. I don't know what it is. I took a few shots at this problem back in the 1990s and got nowhere. Others have beaten their head against the wall on this. The Rethink Robotics failure is a notable example. The real surprise to me is how much progress has been made on vision without manipulation improving much. I'd expected that real-world object recognition would lead to much better manipulation, but it didn't. Even Amazon warehouse bin-picking isn't fully automated yet. Nor is phone manufacturing. Google had a big collection of robots trying to machine-learn basic manual tasks, and they failed at that. That's the real problem. [1] https://www.sciencedirect.com/science/article/pii/S0896627321005018 https://www.sciencedirect.com/science/article/pii/S089662732...
- weavejester 4y agoBiological brains have had a few billion years to optimize. Over the past decade or two, it's been increasingly apparent that the structure and algorithms that govern a particular neural net's behaviour are extremely important to its efficacy. We likely have a very warped view of what intelligence is, because the most prominent examples of it have been aggressively honed over an extremely long period of time to be good at tasks crucial to their survival, such as effectively navigating a 3D environment. We consider art to be a difficult and complex task, and making a sandwich to be a simple one, but that's because our particular brand of intelligence is optimized toward the latter.
- Barrin92 4y ago>"However, the current focus on doing AI research via the gathering of data, the deployment of “deep learning” infrastructure, and the demonstration of systems that mimic certain narrowly-defined human skills — with little in the way of emerging explanatory principles — tends to deflect attention from major open problems in classical AI. These problems include the need to bring meaning and reasoning into systems" I'd go as far as saying that ML is now at a point where it's basically a mirror image of GOFAI with the exact same issues. The old stumbling block was that symbolic solutions worked well until you ran into an edge case, everyone recognized that having to program every edge case in makes no sense. The modern ML problem is that reasoning based on data works fine, unless you run into an edge case, then the solution is to provide a training example to fix that edge case. Unlike with GOFAI apparently though people haven't noticed yet that this is the same old issue with one more level of indirection. When you get attacked in the forest by a guy in a clown costume with an axe you don't need to add that as a training input first before you make a run for it. There's no agency, liveliness, autonomy or learning in a dynamic real-time way to any of the systems we have, they're for the most part just static, 'flat', machines. Honestly rather than thinking of the current systems as intelligent agents they're more like databases who happen to have natural language as a way to query them.
- ml_basics 4y agoGOFAI = "Good old fashioned AI" for those not familiar with the acronym
- jhoechtl 4y agoIf GOFAI is Weizenbaum's Eliza - yes. If GOFAI includes semantic reasoning in real world concepts modeled eg. with theasauri and concept maps - I think AI research was on the right track but went astray as there was not enough resounding business success to warrant further funding.
- weavejester 4y ago"When you get attacked in the forest by a guy in a clown costume with an axe you don't need to add that as a training input first before you make a run for it." Sure, because it's already a training input. We'd run because we recognize the axe, the signs of aggression, the horror movie trope of an evil clown, and so forth. We have to teach "stranger danger" to children. "There's no agency, liveliness, autonomy or learning in a dynamic real-time way to any of the systems we have, they're for the most part just static, 'flat', machines." Well, that's at least in part because we design them that way. It's more convenient to separate out the "learning" and "doing" parts so we have control over how the network is trained.
- LarsDu88 4y agoThe hardware is now here but the algorithms are not. A crow knows not to land on sharp nails without ever having any experience stepping on one. Current architectures lack this basic intuition. Something is missing. Probably an internal world model or simulation
- cowtools 4y agoor perhaps just increased computing power
- goatlover 4y agoHow does increasing computing power help with intuition/common sense about the world? Computing power isn't magic. It has to have a way to understand the world the way animals do.
- CuriouslyC 4y agoIntuition and common sense are the result of latent learning via experience. A model with the right architecture could learn the same things given the training data.
- mach1ne 4y agoThe crow has probably stuck its foot somewhere before and can associate the nail with that past experience. That being said, birds seem to have surprisingly complex innate behavior and even pattern recognition encoded within their brains.
- jeffhwang 4y agoI like how the author emphasizes IA — Intelligence Augmentation as a counterpoint to GOFAI. I’m less inspired by his vision of II (Intelligent Infrastructure); probably bc I’m concerned with the degree of surveillance we already have to live with.
- sn41 4y agoAs a theory person who usually explains O notation using concrete numbers, the degree of the neural network in our brain is approx 7000. Taking approx 86 billion ~ 100 billion, this itself is a graph with approx 6x10^(14) edges - does AGI proponents really hope to be able to do this? I am genuinely curious to know : is there some simplifying assumption which makes things faster?
- zone411 4y agoThis comparison is not perfect for various reasons. For example, the average firing rate of neurons is pretty low. Most attempts at comparison are done using FLOPS, e.g. https://www.openphilanthropy.org/research/how-much-computational-power-does-it-take-to-match-the-human-brain/ https://www.openphilanthropy.org/research/how-much-computati.... You may prefer this comparison using Traversed Edges Per Second: https://aiimpacts.org/brain-performance-in-teps/ https://aiimpacts.org/brain-performance-in-teps/.
- zamfi 4y agoThat’s only 3 orders of magnitude off from today’s largest models like PaLM (5x10^11 parameters), a gap that’s narrowed by 3 orders of magnitude just since 2019. How far away do you think we are, exactly?
- sn41 4y agoThank you for this information. I did not know this. But my view (I may be wrong), is that AGI is too resource-intensive to be within the reach of normal computing of the ordinary user for at least 2 decades.
- naasking 4y ago> is that AGI is too resource-intensive to be within the reach of normal computing of the ordinary user for at least 2 decades. Hardware is still accelerating exponentially in density, albeit a bit slower. What you're not considering is that algorithmic improvements in machine learning are outpacing hardware improvements. For instance, NVidia recently revealed how to switch from 32-bit floats to 16-bit floats with no perceptible loss in effectiveness, and they're working on 8-bit floats next. That's a full doubling in number of parameters in your model in only a single step. Other improvements are refinements to language models themselves to reduce overfitting and boost effectiveness with fewer parameters. Arguably a machine learning model will achieve parity with human neuron density, in terms of number parameters, within the next decade. What that actually means is unclear.
- machina_ex_deus 4y agoI suspect biological brains have a pretty groundbreaking hack to solve the long-term short term learning problem. Maybe involving sleep. What I mean by that is that AIs, the way they are currently built, need to learn very slowly on short term inputs or they overfit. Whereas humans can learn something just by explanation short term and don't have overfitting problems. I suspect this is solved by sleep, and I haven't seen AI with a similar mechanism.
- bagels 4y agoHow is sleep involved? There are a lot more differences than sleep.
- DebtDeflation 4y agoThat's an interesting take. I'll have to sleep on it and get back to you.
- abudabi123 4y agoMemory in the brain has a tree-like structure, kind of like an abstract syntax tree. When Starship returns to the launchpad and sticks the landing on the launch tower, I guess A.I. will have progressed a little bit more.
- ryemigie 4y agoFrom reading these comments, I will say that people should try out GitHub Copilot. A.I. is a bit further one than people might think.
- oldandtired 4y agoThe question to ask is whether or not any algorithmic system is capable of exceeding the programming on which it is based. This question applies to every kind of system we have developed over the years. The other point to make is that we already build systems that can exceed their programming and they are called children.
- Galaxeblaffer 4y agoRandomly watched this yesterday https://www.youtube.com/watch?v=hXgqik6HXc0&ab_channel=LexFridman https://www.youtube.com/watch?v=hXgqik6HXc0&ab_channel=LexFr... where Roger Penrose argues that we're missing something fundamental about consciousness and his best bet is a structure called the microtubules. This talk reminded me of my own research into "AI" back in the 00's and that it's almost impossible to talk about AI since everybody has a different idea as to whay AI is, yes i know there's a pretty good classification ANI, AGI, ASI but most people don't know about this and think of AI as a machine that thinks like conscious human. I'd argue that we've solved or at least partly solved the part of AI that has to do with neural nets. We're still some way off utilizing the full potential of neural nets since our hardware hasn't quite reached the capability of emulating even the simplest of complex animals. The thing is that Neural nets are probably only part of intelligence and creating bigger and more complex neural nets probably wont result in what most people consider AI but i guess there's still a chance it might. We might have to wait several years to find out since moors law is plateauing and neural chips are still in it's infancy. My best guess is that we'll solve "Intelligence" long before we solve consciousness and i think we're actually quite far along here. The best theory of intelligence i've read so far is Jeff Hawkins 1000 Brain Theory and i'm really looking forward to see how far it can go. The problem with this theory is that it's still missing the most critical component which is the illusive mechanism that binds all the "Intelligent" stuff together and i guess that might be hidden in the quantum nature of the microtubules but to solve that we kind of need a new component to our theory of Quantum Mechanics and Quantum Effects. Sorry if i went a bit off topic, but just needed to get my thoughts since yesterday out my head.
- jw1224 4y agoRoger Penrose & Stuart Hammeroff’s “Orchestrated Objective Reduction” theory [1] is fascinating and really captured my imagination when I came across it. But like almost all scientific theories tackling The Hard Problem, it’s built on the assumption that matter gives rise to consciousness. As time goes by and my own understanding deepens, I’m becoming more and more convinced that this assumption is wrong. Instead we should start considering that consciousness is fundamental, and matter is a product of universal conscious experience. Idealism is still compatible with the material world, but it seems futile to search for “the experiencer” within the experience itself. [1] https://en.m.wikipedia.org/wiki/Orchestrated_objective_reduction https://en.m.wikipedia.org/wiki/Orchestrated_objective_reduc...
- rbanffy 4y agoRemember growth is exponential - we won't recognize the next revolution because we'll still be dealing with the fallout of the previous one. Or previous dozen.
- CuriouslyC 4y agoIncorrect. Any growth in a system of finite resources is sigmoidal, with an exponential portion early in the curve before diminishing returns kicks in.
- rbanffy 4y agoWe've been promised this since the 90's, and, yet, we've been pushing back the wall Moore's law was supposed to hit for more than 30 years. And we haven't even properly started to play with non-transistor logic and analog neural devices, so I am cautiously imagining we'll remain exponential for the time being.
- sharemywin 4y agoIt all depends on where we are in the curve.
- rbanffy 4y agoWe are at the stage we are starting to explore the possibilities of using analog components, so my guess would be there is a lot of road to cover. Plus, there are a couple different lines of research for new materials as well, so any one of those can yield something interesting.
- grantcas 4y ago[dead]