9 ms·

AI is always less impressive than is claimed

128 points by jeffmcmahan 10y ago

jamesrom 10y ago
What's the big deal if an AI doesn't know which footstool you are talking about? Intelligence != Omniscience
- aminorex 10y ago
  Nor is the footstool problem a terribly hard one. The challenging bit is integrating the many and various point-solutions into an adequately coherent assembly.
- jeffmcmahan 10y ago
  I hear you. I would say that we are not omniscient, yet we know which footstool is intended in the context, and without any conscious effort. Human-like general intelligence is marvelous. And it's well out of reach, and will remain so.
compumike 10y ago
With regards to the specific problems described here, referencing how computers can make sense of communication between a listener and a speaker when each has different knowledge in the world and where strict and literal communication isn't always the most efficient, search for the "Rational Speech Acts (RSA) model" -- an active field in the literature in the past few years.
- jeffmcmahan 10y ago
  Yes, the trouble is that the work on performative acts (speech acts) only goes so deep, and then everything turns to confusion and ad hoc rationalizations presented like theories (I have a couple in mind). There'll be no means of implementing this body of knowledge in software -- or even taking it as a guide. So, if it were going to work out, you'd first have to solve the immensely challenging scientific problems, and then see about using the resulting knowledge to build something. As a person with some experience in the area (admittedly nothing published), I don't expect the former to occur. Ever.
nv-vn 10y ago
A lot of the comments here seem to focus only on the progress we've made rather than what AI should be (if it was true to the name). With "AI", sure I can tell Siri to send a certain email to a certain person. That's pretty damn impressive when I think back to what was possible only a few years earlier. But it's not intelligent. Nowhere near. And honestly, it doesn't save you that much work. It just performs some preprogrammed tasks given some unambiguous input that follows a regular pattern. And even that trips up Siri a ton. Yes, it's cool that you can identify pictures or have a program read your handwriting, but none of that gets us any closer to a program that behaves like a human when I tell it to do something (instead of sending an email or text that I specify, it could draft one on its own; instead of showing me a Wikipedia article when I ask what something is it should explain to me in layman's terms and give me a summary). AI is impressive in terms of progress, but is it really impressive in terms of what it promises to bring? Is it really even progressing in the right direction? Personally, I don't think so.
- visarga 10y ago
  It's going fast, if you read the papers on arXiv, but if you're checking Siri from time to time, I agree with you that it seems to be stagnating. We're not that far from agents that can much more meaningfully interact in language.
  - YeGoblynQueenne 10y ago
    >> We're not that far from agents that can much more meaningfully interact in language. Really, come on. That's all based on what? A few missives from Google and Facebook, and other companies that try to sell you their super tech? We don't even know what the distance is from where we are now to "agents that can much more meaningfuly interact in language". Let alone how we're going to cross that distance.
- Houshalter 10y ago
  > Yes, it's cool that you can identify pictures or have a program read your handwriting, but none of that gets us any closer to a program that behaves like a human when I tell it to do something Yes it does. Humans didn't just emerge out of the aether. We evolved from animals that first had to perform simple tasks like that. Our brains are still heavily based on networks of simple pattern recognition neurons. We just don't know how they are structured, or have enough computing power. But it's definitely progress. As proof, we are starting to get neural networks that can do complex language understanding tasks. RNNs are the state of the art at tasks like predicting the next word, and make interesting chatbots. Obviously they aren't human level, but they only have 1,000 neurons. Humans have billions! They don't even have episodic memory yet, it's amazing they can do language at all. But research on that is just starting. Give it time.
aub3bhat 10y ago
I opened the link expecting a discussion of flawed metrics that fail to capture uncertainty or failure in replication of claimed studies, or complex architecture turning out to be shallow combinations of simpler ones. Instead the post attacks a straw man argument about "General intelligence", something that no AI researcher is claiming. Also the author is clearly clueless as evidenced by: "Ask a medical researcher to estimate the odds that a Google-funded AI-driven cancer moonshot will succeed, and I expect you will be met with a snicker. ' Google employs several researchers [1] who are well versed in both medicine as well as ML. Who are able to competently judge capabilities and limits of their tools and challenges involved in doing medical research. Rather than creating straw mental images of "medical researcher" "snickering" at "moonshots", the author should to engage in some self reflection before writing about scientific fields he has zero knowledge of. AND "I promise you, Facebook did not just work it all out in PHP, and they will not do so in my lifetime or yours." I won't comment on absurdity of the paragraph about FB or language & meaning. But if any reader was confused, Facebook AI team uses Torch/Lua (for deep learning) or Python (in FBLearnerFlow) and surely not PHP. [1] http://www.nimh.nih.gov/about/dr-tom-insel-to-step-down-as-nimh-director.shtml http://www.nimh.nih.gov/about/dr-tom-insel-to-step-down-as-n...
- taneq 10y ago
  If the odds aren't long enough to be met with a bit of a snicker, it's not a moonshot.
  - harry8 10y ago
    Right a moonshot is spectacular, a triumph of PR, a source of wonder and ultimately a really stupid and unbelievably wasteful way to spend a vast fortune. Moonshot really should be the byword of how /not/ to do science funding. Likewise anything beginning "war on..." The war on cancer is going about as well as the war on drugs. Progress is made elsewhere. Moonshot odds weren't particularly long given the resourcing and desire and this makes it no less impressive as a feat of engineerning and resourcing. And no more sensible.
    - 10y ago
ktRolster 10y ago
The article references "narrow syntax". Does anyone know what that is?
- jeffmcmahan 10y ago
  The term refers to work on syntax (or phonology) that does not extend into semantics. So, basically anything Chomsky writes is on narrow syntax, whereas anything (say) Irene Heim or Barbara Partee writes is concerned with a broader notion of syntax (which includes semantics).
ddebernardy 10y ago
Personally, I'd argue the opposite: AI today is super impressive compared to what it was 15 or 30 years ago. There used to be a time when one could rightfully dismiss new cool things AI with "meh, wake me up when an AI beats a professional Go player on a 19x19 board". Because doing so requires an AI that displays intuition and use of heuristics (Go moves tend to have both local/tactical and global/strategic consequences on the board) rather than one that merely brute forces its way through a decision tree. The fact that one actually did recently is nothing short of game-changing in my view. It matters little that an AI doesn't demonstrably understand meaning or fully grasp intent. Plus, when one does, we'll arguably have (or not be too far from) a strong AI that, I would gather, won't be too impressed with our own human-level thinking. :-)
- IshKebab 10y ago
  Agreed. Look at speech recognition right up until a few years ago. Until Siri it just didn't work at all, and even Siri wasn't good enough to be useful when it first launched. Now Google's voice search, and Amazon's Alexa are good enough to be actually useful. If you had asked an AI researcher 10 years ago if that would be the case they would probably have sniggered at you too.
  - csydas 10y ago
    Mmm...disagree. Voice recognition has been okay for a long time, and while Siri/Cortana/OK Google are impressive displays for what they do, I don't really consider it AI. It's heavy keyword indexing combined with speech recognition, and all of the Voice Assistants on phones are still pretty limited in how they will interpret requests. That is, you can't really ask for a value judgement right now from the Assistants simply because they haven't really been written to do much else besides search for specific keywords and perform an action. What I mean by this is suppose a scenario where you and friends are really hungry on a Friday and you're trying to think of what restaurant would have the shortest wait time. If you were to ask Siri, et. al. about it, unless the restaurant had a publicly accessible wait time that could be tapped for reference, the best the assistants could do is get you a list of open restaurants in yuor area. Your friends, however, probably can, with historical knowledge (times they've been there) and knowledge of current social trends (e.g., asian fusion is out, swedish fusion is in), could make a semi-educated guess as to which restaurant likely has the shortest wait. This is simply because we're recording this data subconsciously - the Assistants aren't because they've not been told to, nor do they even really have an understanding of it. If someone were to write up some parameters for the Assistants to consider it, they could start to do so (and honestly, probably should now that I think about it...). But even 15ish years ago I was goofing around with voice control for my old boat anchor iMac and was pretty impressed as to how well it handled. It wasn't as fast as what Siri, et. al. do now, but the boat anchor didn't have an entire computing center to offload the processing workload to. There really isn't anything particularly special about what is happening right now with voice assistants except for the speed at which we get the results. It is very cool to be able to do it, but I would't call it AI. At the end of the day, you're still just having your vocalizations converted into a search query and getting the same limits to results you used to get in the past. And to address the parent comment on Go "...requires an AI that displays intuition and use of heuristics", this is absolutely not in line with how the AlphaGo Researchers were describing it. Lee Sedol even started to get the hang of AlphaGo's prediction model and, if I remember correctly, it really didn't have any idea on what to do with a move that was a "wash". Lee's [1] victory came because of a move that was not evaluated correctly by AlphaGo - the move wasn't bad, in that AlphaGo couldn't capitalize on it, but it also wasn't "good" in that it didn't evaluate it as strong. AlphaGo's weakness was that it really didn't consider history at all when it came to moves, it only operated to rapidly assess the current condition of the board and try to determine the strongest path at the moment. It was a very impressive display to really hammer Lee Sedol like it did, but in interviews, Lee commented that he was starting to understand how AlphaGo made decisions, which was a problem in early games because he had no opponent to read, he had to understand an algorithm making decisions. This reddit thread has some okay breakdowns of how AlphaGo lost and how it decided moves: https://www.reddit.com/r/baduk/comments/4a7wl2/fascinating_insight_into_alpha_gos_from_match_4/ https://www.reddit.com/r/baduk/comments/4a7wl2/fascinating_i... The entire point I'm trying to make is that while there are some very impressive Neural Network and Machine Learning tricks going on, there's also a lot going into making a really impressive curtain to hide all the computing behind. Right now, it's still the same decisions and techniques AI were doing years ago, just more efficient and faster. I wouldn't say that we have "strong AI", though I couldn't really define that if pressed. But I do know that right now Siri, AlphaGo, Watson, Cortana, OK Google and others are thinking only insomuch as a thinks. They're not really taking in and processing information as humans appear to. [1] Just a note, I'm not sure on the naming formalities for Korean, so if I've madea faux pas with referring to Lee Sedol, it's from ignorance only.
jrapdx3 10y ago
The article looks at an issue I think about quite often and occasionally have a chance to discuss. The complexity of real-world natural systems is at a magnitude that's hard to grasp and harder to describe. AI, as an attempt to replicate natural human intelligence is up against neural systems that have evolved for 100's of millions of years into numerous forms of astonishingly intricate structure and function. In humans neuronal brain circuitry comprises an astronomical number of interactive elements, and each connection is itself remarkably adaptable with a large number of interfaces associated with a range of signalling modalities. Consider that we have ~100 billion brain neurons and 100-500 trillion synaptic interconnections and each connection is modulated by many factors including endocrine and immune system input. Furthermore connections transmit and receive many kinds of signals in complex feedback relationships. It's well beyond my ability to know how to determine an estimate of how many possible modes of computation can exist within such a marvelous creation, let alone begin to adequately understand how even a small part of it actually works. I suppose that's the author's point, that what we regard as "simple" problems are in reality not at all simple. Computers can do remarkable things, and surely are among the most useful inventions of human beings. We should realize that working like a significant subset of what our brains routinely do isn't going to be easy for computing to achieve.
- visarga 10y ago
  We don't need to match 1 artificial neuron for 1 biological neuron. An artificial neural network doesn't have to be implemented in a self-replicating body as our brain does. It doesn't have to carry its biological baggage into the computational aspects. Our brain are part of a process of evolution - the specific requirements for that goal impose huge restrictions on it. On the other hand, in a computer, we can have perfect neurons that don't age or degrade, with any structure we want, scalable to a higher degree. We have already seen in image recognition and go play super-human performance, I expect all domains to be conquered soon. Consider go - they started on this project only 2-3 years ago and already AI has overgrown humans. It doesn't need a lot of time to learn once it is created.
  - nekopa 10y ago
    I see it as similar to flight, once we stopped trying to mimic nature exactly (flapping planes) we started to not only make progress, but went beyond what nature can do in flight (eg supersonic) I think that the biggest breakthrough we'll have in AI will come from a similar break away from copying the brain. Inspired by nature, not dictated by it.
Joof 10y ago
The last 2-3 years of progress have come about a decade earlier than most people expected. Many people have some catching up to do in determining exactly where we are. Do we have general intelligence? No, but we have audio generation from pure video and can change an image to match any art style and reasonably good self driving cars.
- visarga 10y ago
  The great leap has been in reinforcement learning - learning to behave in an environment, with AlphaGo and even robotics.
- qznc 10y ago
  > audio generation from pure video I missed that one. Link?
  - adwf 10y ago
    This was on the front page a while back, probably what they're referring to: https://news.mit.edu/2016/artificial-intelligence-produces-realistic-sounds-0613 https://news.mit.edu/2016/artificial-intelligence-produces-r...
suvelx 10y ago
The site appears to be dead, so haven't had the opportunity to actually read it. But I recently picked up a "Machine Learning" project for a client. I don't entirely understand everything in the fullest detail, but I understand the basics. Yet despite my lack of knowledge I got something to work 'enough' in a day. Tensorflow makes getting a working DNN easy, and then wrappers like TFLearn make it nearly trivial. The hardest part of the project was sourcing a decent amount of training data. And I'm honestly somewhat ashamed of it. My friends think it's cool, but really I don't feel like I did anything. I just glued a library together. But maybe that's the interesting bit, that frameworks have been developed to a point that even a rube like me can get something working in a day.
- dasboth 10y ago
  I think it's definitely a good thing to abstract away the nuts and bolts of a complicated ML algorithm for general use. Implementing the algorithms from scratch is really useful if you want to learn the details, but most of the time the "standard" implementation will do fine, and will probably perform more efficiently. Of course as with any tool, a conceptual understanding is essential to meaningfully interpret the results though. I completely get what you're saying about not feeling like you did anything though. Once you understand the algorithms even at a basic level they stop feeling like magic, even though they'll still feel like that for other people.
- bbctol 10y ago
  Reminds me of that article going around HN a few days ago about the ease of AI programming (http://bjenik.com/AIBasedProgramming/ http://bjenik.com/AIBasedProgramming/) It had a lot of problems, but maybe we really are moving to an era where data analysis consists more of throwing a neural network at it.
- jeffmcmahan 10y ago
  It's back now!
MrPatan 10y ago
To be fair, just plain old "I" is very disappointing most of the time.
YeGoblynQueenne 10y ago
Oh boy, did I laugh my head off at this bit: No one in the scientific literature has a working account of what the English word ‘the’ means. Aye. But, strong AI is just around the corner. No worries.
- ajuc 10y ago
  As a speaker from language without articles - it doesn't mean anything ;)
  - jeffmcmahan 10y ago
    There's a clear difference between "bring me an apple" and "bring me the apple". The latter requires that there is either only one apple available, or requires some special contextual knowledge to comply with.
    - ajuc 10y ago
      My language solves this with equivalent of: "Bring me apple" vs "Bring me that apple". I still don't understand the need for "a". Surely if you don't qualify a noun at all you mean "any". I guess "the" is like "distinct" in sql, so it may be useful to catch "too many rows" errors. It was a joke, BTW.
      - jeffmcmahan 10y ago
        >> I guess "the" is like "distinct" in sql That's original Russellian theory, in a nutshell (http://www.uvm.edu/~lderosse/courses/lang/Russell(1905).pdf http://www.uvm.edu/~lderosse/courses/lang/Russell(1905).pdf). None of its many variants cover the data. >> It was a joke Fair enough!
Iv 10y ago
Just you wait until the Grand Soir.
Lapsa 10y ago
4chan: repeat after me tay_ai: okay
Grue3 10y ago
I'd be impressed if an "AI" could simply passably translate a natural non-Indo-European language to English.

128 points by jeffmcmahan 10y ago

jamesrom 10y ago

What's the big deal if an AI doesn't know which footstool you are talking about? Intelligence != Omniscience

aminorex 10y ago
Nor is the footstool problem a terribly hard one. The challenging bit is integrating the many and various point-solutions into an adequately coherent assembly.
jeffmcmahan 10y ago
I hear you. I would say that we are not omniscient, yet we know which footstool is intended in the context, and without any conscious effort. Human-like general intelligence is marvelous. And it's well out of reach, and will remain so.

compumike 10y ago

With regards to the specific problems described here, referencing how computers can make sense of communication between a listener and a speaker when each has different knowledge in the world and where strict and literal communication isn't always the most efficient, search for the "Rational Speech Acts (RSA) model" -- an active field in the literature in the past few years.

jeffmcmahan 10y ago
Yes, the trouble is that the work on performative acts (speech acts) only goes so deep, and then everything turns to confusion and ad hoc rationalizations presented like theories (I have a couple in mind). There'll be no means of implementing this body of knowledge in software -- or even taking it as a guide. So, if it were going to work out, you'd first have to solve the immensely challenging scientific problems, and then see about using the resulting knowledge to build something. As a person with some experience in the area (admittedly nothing published), I don't expect the former to occur. Ever.

nv-vn 10y ago

A lot of the comments here seem to focus only on the progress we've made rather than what AI should be (if it was true to the name). With "AI", sure I can tell Siri to send a certain email to a certain person. That's pretty damn impressive when I think back to what was possible only a few years earlier. But it's not intelligent. Nowhere near. And honestly, it doesn't save you that much work. It just performs some preprogrammed tasks given some unambiguous input that follows a regular pattern. And even that trips up Siri a ton. Yes, it's cool that you can identify pictures or have a program read your handwriting, but none of that gets us any closer to a program that behaves like a human when I tell it to do something (instead of sending an email or text that I specify, it could draft one on its own; instead of showing me a Wikipedia article when I ask what something is it should explain to me in layman's terms and give me a summary). AI is impressive in terms of progress, but is it really impressive in terms of what it promises to bring? Is it really even progressing in the right direction? Personally, I don't think so.

visarga 10y ago
It's going fast, if you read the papers on arXiv, but if you're checking Siri from time to time, I agree with you that it seems to be stagnating. We're not that far from agents that can much more meaningfully interact in language.
- YeGoblynQueenne 10y ago
  >> We're not that far from agents that can much more meaningfully interact in language. Really, come on. That's all based on what? A few missives from Google and Facebook, and other companies that try to sell you their super tech? We don't even know what the distance is from where we are now to "agents that can much more meaningfuly interact in language". Let alone how we're going to cross that distance.
Houshalter 10y ago
> Yes, it's cool that you can identify pictures or have a program read your handwriting, but none of that gets us any closer to a program that behaves like a human when I tell it to do something Yes it does. Humans didn't just emerge out of the aether. We evolved from animals that first had to perform simple tasks like that. Our brains are still heavily based on networks of simple pattern recognition neurons. We just don't know how they are structured, or have enough computing power. But it's definitely progress. As proof, we are starting to get neural networks that can do complex language understanding tasks. RNNs are the state of the art at tasks like predicting the next word, and make interesting chatbots. Obviously they aren't human level, but they only have 1,000 neurons. Humans have billions! They don't even have episodic memory yet, it's amazing they can do language at all. But research on that is just starting. Give it time.

aub3bhat 10y ago

I opened the link expecting a discussion of flawed metrics that fail to capture uncertainty or failure in replication of claimed studies, or complex architecture turning out to be shallow combinations of simpler ones. Instead the post attacks a straw man argument about "General intelligence", something that no AI researcher is claiming. Also the author is clearly clueless as evidenced by: "Ask a medical researcher to estimate the odds that a Google-funded AI-driven cancer moonshot will succeed, and I expect you will be met with a snicker. ' Google employs several researchers [1] who are well versed in both medicine as well as ML. Who are able to competently judge capabilities and limits of their tools and challenges involved in doing medical research. Rather than creating straw mental images of "medical researcher" "snickering" at "moonshots", the author should to engage in some self reflection before writing about scientific fields he has zero knowledge of. AND "I promise you, Facebook did not just work it all out in PHP, and they will not do so in my lifetime or yours." I won't comment on absurdity of the paragraph about FB or language & meaning. But if any reader was confused, Facebook AI team uses Torch/Lua (for deep learning) or Python (in FBLearnerFlow) and surely not PHP. [1] http://www.nimh.nih.gov/about/dr-tom-insel-to-step-down-as-nimh-director.shtml http://www.nimh.nih.gov/about/dr-tom-insel-to-step-down-as-n...

taneq 10y ago
If the odds aren't long enough to be met with a bit of a snicker, it's not a moonshot.
- harry8 10y ago
  Right a moonshot is spectacular, a triumph of PR, a source of wonder and ultimately a really stupid and unbelievably wasteful way to spend a vast fortune. Moonshot really should be the byword of how /not/ to do science funding. Likewise anything beginning "war on..." The war on cancer is going about as well as the war on drugs. Progress is made elsewhere. Moonshot odds weren't particularly long given the resourcing and desire and this makes it no less impressive as a feat of engineerning and resourcing. And no more sensible.
  - 10y ago

ktRolster 10y ago

The article references "narrow syntax". Does anyone know what that is?

jeffmcmahan 10y ago
The term refers to work on syntax (or phonology) that does not extend into semantics. So, basically anything Chomsky writes is on narrow syntax, whereas anything (say) Irene Heim or Barbara Partee writes is concerned with a broader notion of syntax (which includes semantics).

ddebernardy 10y ago

Personally, I'd argue the opposite: AI today is super impressive compared to what it was 15 or 30 years ago. There used to be a time when one could rightfully dismiss new cool things AI with "meh, wake me up when an AI beats a professional Go player on a 19x19 board". Because doing so requires an AI that displays intuition and use of heuristics (Go moves tend to have both local/tactical and global/strategic consequences on the board) rather than one that merely brute forces its way through a decision tree. The fact that one actually did recently is nothing short of game-changing in my view. It matters little that an AI doesn't demonstrably understand meaning or fully grasp intent. Plus, when one does, we'll arguably have (or not be too far from) a strong AI that, I would gather, won't be too impressed with our own human-level thinking. :-)

IshKebab 10y ago
Agreed. Look at speech recognition right up until a few years ago. Until Siri it just didn't work at all, and even Siri wasn't good enough to be useful when it first launched. Now Google's voice search, and Amazon's Alexa are good enough to be actually useful. If you had asked an AI researcher 10 years ago if that would be the case they would probably have sniggered at you too.
- csydas 10y ago
  Mmm...disagree. Voice recognition has been okay for a long time, and while Siri/Cortana/OK Google are impressive displays for what they do, I don't really consider it AI. It's heavy keyword indexing combined with speech recognition, and all of the Voice Assistants on phones are still pretty limited in how they will interpret requests. That is, you can't really ask for a value judgement right now from the Assistants simply because they haven't really been written to do much else besides search for specific keywords and perform an action. What I mean by this is suppose a scenario where you and friends are really hungry on a Friday and you're trying to think of what restaurant would have the shortest wait time. If you were to ask Siri, et. al. about it, unless the restaurant had a publicly accessible wait time that could be tapped for reference, the best the assistants could do is get you a list of open restaurants in yuor area. Your friends, however, probably can, with historical knowledge (times they've been there) and knowledge of current social trends (e.g., asian fusion is out, swedish fusion is in), could make a semi-educated guess as to which restaurant likely has the shortest wait. This is simply because we're recording this data subconsciously - the Assistants aren't because they've not been told to, nor do they even really have an understanding of it. If someone were to write up some parameters for the Assistants to consider it, they could start to do so (and honestly, probably should now that I think about it...). But even 15ish years ago I was goofing around with voice control for my old boat anchor iMac and was pretty impressed as to how well it handled. It wasn't as fast as what Siri, et. al. do now, but the boat anchor didn't have an entire computing center to offload the processing workload to. There really isn't anything particularly special about what is happening right now with voice assistants except for the speed at which we get the results. It is very cool to be able to do it, but I would't call it AI. At the end of the day, you're still just having your vocalizations converted into a search query and getting the same limits to results you used to get in the past. And to address the parent comment on Go "...requires an AI that displays intuition and use of heuristics", this is absolutely not in line with how the AlphaGo Researchers were describing it. Lee Sedol even started to get the hang of AlphaGo's prediction model and, if I remember correctly, it really didn't have any idea on what to do with a move that was a "wash". Lee's [1] victory came because of a move that was not evaluated correctly by AlphaGo - the move wasn't bad, in that AlphaGo couldn't capitalize on it, but it also wasn't "good" in that it didn't evaluate it as strong. AlphaGo's weakness was that it really didn't consider history at all when it came to moves, it only operated to rapidly assess the current condition of the board and try to determine the strongest path at the moment. It was a very impressive display to really hammer Lee Sedol like it did, but in interviews, Lee commented that he was starting to understand how AlphaGo made decisions, which was a problem in early games because he had no opponent to read, he had to understand an algorithm making decisions. This reddit thread has some okay breakdowns of how AlphaGo lost and how it decided moves: https://www.reddit.com/r/baduk/comments/4a7wl2/fascinating_insight_into_alpha_gos_from_match_4/ https://www.reddit.com/r/baduk/comments/4a7wl2/fascinating_i... The entire point I'm trying to make is that while there are some very impressive Neural Network and Machine Learning tricks going on, there's also a lot going into making a really impressive curtain to hide all the computing behind. Right now, it's still the same decisions and techniques AI were doing years ago, just more efficient and faster. I wouldn't say that we have "strong AI", though I couldn't really define that if pressed. But I do know that right now Siri, AlphaGo, Watson, Cortana, OK Google and others are thinking only insomuch as a thinks. They're not really taking in and processing information as humans appear to. [1] Just a note, I'm not sure on the naming formalities for Korean, so if I've madea faux pas with referring to Lee Sedol, it's from ignorance only.

jrapdx3 10y ago

The article looks at an issue I think about quite often and occasionally have a chance to discuss. The complexity of real-world natural systems is at a magnitude that's hard to grasp and harder to describe. AI, as an attempt to replicate natural human intelligence is up against neural systems that have evolved for 100's of millions of years into numerous forms of astonishingly intricate structure and function. In humans neuronal brain circuitry comprises an astronomical number of interactive elements, and each connection is itself remarkably adaptable with a large number of interfaces associated with a range of signalling modalities. Consider that we have ~100 billion brain neurons and 100-500 trillion synaptic interconnections and each connection is modulated by many factors including endocrine and immune system input. Furthermore connections transmit and receive many kinds of signals in complex feedback relationships. It's well beyond my ability to know how to determine an estimate of how many possible modes of computation can exist within such a marvelous creation, let alone begin to adequately understand how even a small part of it actually works. I suppose that's the author's point, that what we regard as "simple" problems are in reality not at all simple. Computers can do remarkable things, and surely are among the most useful inventions of human beings. We should realize that working like a significant subset of what our brains routinely do isn't going to be easy for computing to achieve.

visarga 10y ago
We don't need to match 1 artificial neuron for 1 biological neuron. An artificial neural network doesn't have to be implemented in a self-replicating body as our brain does. It doesn't have to carry its biological baggage into the computational aspects. Our brain are part of a process of evolution - the specific requirements for that goal impose huge restrictions on it. On the other hand, in a computer, we can have perfect neurons that don't age or degrade, with any structure we want, scalable to a higher degree. We have already seen in image recognition and go play super-human performance, I expect all domains to be conquered soon. Consider go - they started on this project only 2-3 years ago and already AI has overgrown humans. It doesn't need a lot of time to learn once it is created.
- nekopa 10y ago
  I see it as similar to flight, once we stopped trying to mimic nature exactly (flapping planes) we started to not only make progress, but went beyond what nature can do in flight (eg supersonic) I think that the biggest breakthrough we'll have in AI will come from a similar break away from copying the brain. Inspired by nature, not dictated by it.

Joof 10y ago

The last 2-3 years of progress have come about a decade earlier than most people expected. Many people have some catching up to do in determining exactly where we are. Do we have general intelligence? No, but we have audio generation from pure video and can change an image to match any art style and reasonably good self driving cars.

visarga 10y ago
The great leap has been in reinforcement learning - learning to behave in an environment, with AlphaGo and even robotics.
qznc 10y ago
> audio generation from pure video I missed that one. Link?
- adwf 10y ago
  This was on the front page a while back, probably what they're referring to: https://news.mit.edu/2016/artificial-intelligence-produces-realistic-sounds-0613 https://news.mit.edu/2016/artificial-intelligence-produces-r...

suvelx 10y ago

The site appears to be dead, so haven't had the opportunity to actually read it. But I recently picked up a "Machine Learning" project for a client. I don't entirely understand everything in the fullest detail, but I understand the basics. Yet despite my lack of knowledge I got something to work 'enough' in a day. Tensorflow makes getting a working DNN easy, and then wrappers like TFLearn make it nearly trivial. The hardest part of the project was sourcing a decent amount of training data. And I'm honestly somewhat ashamed of it. My friends think it's cool, but really I don't feel like I did anything. I just glued a library together. But maybe that's the interesting bit, that frameworks have been developed to a point that even a rube like me can get something working in a day.

dasboth 10y ago
I think it's definitely a good thing to abstract away the nuts and bolts of a complicated ML algorithm for general use. Implementing the algorithms from scratch is really useful if you want to learn the details, but most of the time the "standard" implementation will do fine, and will probably perform more efficiently. Of course as with any tool, a conceptual understanding is essential to meaningfully interpret the results though. I completely get what you're saying about not feeling like you did anything though. Once you understand the algorithms even at a basic level they stop feeling like magic, even though they'll still feel like that for other people.
bbctol 10y ago
Reminds me of that article going around HN a few days ago about the ease of AI programming (http://bjenik.com/AIBasedProgramming/ http://bjenik.com/AIBasedProgramming/) It had a lot of problems, but maybe we really are moving to an era where data analysis consists more of throwing a neural network at it.
jeffmcmahan 10y ago
It's back now!

MrPatan 10y ago

To be fair, just plain old "I" is very disappointing most of the time.

YeGoblynQueenne 10y ago

Oh boy, did I laugh my head off at this bit: No one in the scientific literature has a working account of what the English word ‘the’ means. Aye. But, strong AI is just around the corner. No worries.

ajuc 10y ago
As a speaker from language without articles - it doesn't mean anything ;)
- jeffmcmahan 10y ago
  There's a clear difference between "bring me an apple" and "bring me the apple". The latter requires that there is either only one apple available, or requires some special contextual knowledge to comply with.
  - ajuc 10y ago
    My language solves this with equivalent of: "Bring me apple" vs "Bring me that apple". I still don't understand the need for "a". Surely if you don't qualify a noun at all you mean "any". I guess "the" is like "distinct" in sql, so it may be useful to catch "too many rows" errors. It was a joke, BTW.
    - jeffmcmahan 10y ago
      >> I guess "the" is like "distinct" in sql That's original Russellian theory, in a nutshell (http://www.uvm.edu/~lderosse/courses/lang/Russell(1905).pdf http://www.uvm.edu/~lderosse/courses/lang/Russell(1905).pdf). None of its many variants cover the data. >> It was a joke Fair enough!

Iv 10y ago

Just you wait until the Grand Soir.

Lapsa 10y ago

4chan: repeat after me tay_ai: okay

Grue3 10y ago

I'd be impressed if an "AI" could simply passably translate a natural non-Indo-European language to English.