13 ms·

The Unreasonable Reputation of Neural Networks

269 points by fforflo 11y ago

bsbechtel 11y ago
We will never have human level AI until we can properly understand, define, and model human intelligence. While we are advancing at a very rapid pace on that front, we are still years away from the field being considered mature.
- disantlor 11y ago
  Why? All sorts of behavior emerges out of seemingly simplified rules/systems. Could the convoluted and beautiful patterns that can come from Conway's game of life be predicted in advance? What about the sort of meta "intelligence" on display by ant colonies when taken as a whole? Or how connecting disparate groups of people over the globe changes the way humanity as a whole reacts to situations. I'm no expert and these examples probably betray the shallowness of my understanding as much as they make a point. But if they do make a point it's that we don't need to (and often can't) understand what will result from systems that have a built in feedback mechanism.
  - bsbechtel 11y ago
    >>But if they do make a point it's that we don't need to (and often can't) understand what will result from systems that have a built in feedback mechanism. That may be true, but my point was more to the effect that there are things we fail to understand about human nature that drives decision making, actions, and goal setting within our own society, let alone other cultures. Consider how factors such as your own personal energy levels affect your decision making throughout the day (decision fatigue)...how are we going to model something like that with AI? Or consider that many different cultures around the world have very different societal goals than Americans do pursuing personal freedom and material well-being. How do we consider those different abstract goals, that most citizens don't even realize or understand why they pursue, because it's just how they have been raised. How do environmental factors drive decision making in different climates? Those who live in a very cold climate have to take different precautions and deal with different risks than those in warm climates do. What about locally available resources? Many business (and design) decisions are made based as much upon supply chain availability and risk management as they are customer demand. How does AI consider these factors when AI doesn't even know these things exist? These are the things I was talking about that we still have difficulty understanding about each other, let alone being able to program a robot to imitate.
    - disantlor 11y ago
      Maybe the difference between a human intelligence and a super intelligence comes, in part, from not being affected by day to day mood/chemical processes that are probably related to keeping the body as a whole functioning. The super intelligence has an intelligence that's isolated from these needs. I don't really have an answer to all your points, just a general feeling that AI needed be just like our "I" to be effectively superior.
- j1o1h1n 11y ago
  Similarly, we will never have fusion weapons until we can correctly simulate and model a nuclear reaction on a computer.
- mej10 11y ago
  You state this as fact. Link to credible science saying that it is true. This is like saying we have to completely understand how birds fly before we can make planes, or how fish swim before making submarines. Or how muscles cause movement before inventing the wheel. Or how chemical reactions work before we can make fire. You don't have to fully understand the original system or be able to recreate it to make something that exhibits some or all of the desirable properties. This has been shown time and time again.
hacker_9 11y ago
"Human or superhuman performance in one task is not necessarily a stepping-stone towards near-human performance across most tasks. By the same token, the ability of neural networks to learn interpretable word embeddings, say, does not remotely suggest that they are the right kind of tool for a human-level understanding of the world. It is impressive and surprising that these general-purpose, statistical models can learn meaningful relations from text alone, without any richer perception of the world, but this may speak much more about the unexpected ease of the task itself than it does about the capacity of the models. Just as checkers can be won through tree-search, so too can many semantic relations be learned from text statistics. Both produce impressive intelligent-seeming behaviour, but neither necessarily pave the way towards true machine intelligence." So true, and this is why I don't listen when Elon Musk or Stephen Hawkings spread fear about the impending AI disaster; they think because a neural network can recognize an image like a human can, that it's not a huge leap to say it will be able to soon think and act like a human, but in reality this is just not the case.
- jbhatab 11y ago
  I don't think they are afraid of current technology and neither am I. However, I am afraid of a breakthrough or new model of AI that does approach human intelligence. I don't know how, when, or why but I think it's reasonable in the next 400 years that we approach something like that. 400 years is not very long in terms of humanity but this technology could do things to humanity that we can't grasp right now.
  - pjbrunet 11y ago
    Without getting into our personal, specific definitions of intelligence, I do agree the next 400 years could bring about something horribly disruptive that is not intelligent. Dumb and dangerous, discovered by fools, is the more likely threat to peace in the world.
    - jbhatab 11y ago
      Maybe, but I'm already afraid of dumb and dangerous tools. Nuclear weapons. Maybe we get stronger dumb and dangerous tools, but what scares me much more is a smart drone that has AI and the ability to learn things about me at a rate I can't comprehend.
interdrift 11y ago
We have something that can understand a pattern but we don't have something that can understand how different patterns relate to each other.
- djrjdjcjf 11y ago
  Yes we do. Word2vec.
- TeMPOraL 11y ago
  Relations of patterns are patterns too. A potential for recursive reuse?
  - wolfgke 11y ago
    Rather homotopy type theory ;-) (explanation: homotopy type theory (HoTT) in difference to ITT also considers types of higher order).
    - harveywi 11y ago
      To my knowledge, intuitionistic type theory (ITT) considers type of higher order (called higher-kinded types), so homotopy type theory (HoTT) isn't exactly relevant here - especially since it has, to my knowledge, not cross-pollinated with ML or ANN research. Models of recursion can be found in systems as simple as lambda calculi or cellular spaces, and this was the big idea behind Jeff Hawkins's HTM and other classical models (please correct me if I am wrong). What HoTT brings to the table is the notion that a path between two types can be used to transport a proof from one type to another - currently immensely handy for theorem proving, but applications in ML and beyond remain to be made.
tacos 11y ago
Current top post quotes the most negative observation of the paper. Here's the most positive, and perhaps the most useful to HN readers or investors who are exploring the space: "Deep learning has produced amazing discriminative models, generative models and feature extractors, but common to all of these is the use of a very large training dataset. Its place in the world is as a powerful tool for general-purpose pattern recognition... Very possibly it is the best tool for working in this paradigm. This is a very good fit for one particular class of problems that the brain solves: finding good representations to describe the constant and enormous flood of sensory data it receives."
- boltzmannbrain 11y ago
  I would argue the brain solves every problem by first representing sensory data, and then learns temporal patterns of the encoded information.
- auvrw 11y ago
  the last sentence is also positive as well as insightful Gradient descent in neural networks may well play a big part in helping to build the components of thinking machines, but it is not, itself, the stuff of thought. statistical techniques like these are often used to interpret experimental data. "helping to build the components of thinking machines," (and i think this is the author's intent?) may not mean _being_ the components of thinking machines, just as the 4004 is not a component of the computer that i'm currently using, but it did help build it. idk, though. as cool as the engineering (making) is, i do worry sometimes that the science (understanding) could get overlooked in the process.
skybrian 11y ago
There was a recent paper [1] about learning visual concepts from few examples. I don't know if it generalizes or not, but it seems too early to assume that researchers will hit a dead end. [1] http://science.sciencemag.org/content/350/6266/1332.full http://science.sciencemag.org/content/350/6266/1332.full
- argonaut 11y ago
  This is an algorithm unrelated to neural networks, which incidentally reinforces the author's point.
proc0 11y ago
Another article basically saying something along the lines of "there is no current technology that comes close to producing AGI, therefore let's dismiss all these technologies". Of course we don't know what we don't know, until we do, and then it's not as mysterious. It's not hard to see that the reason NN are becoming the prime candidate for AGI, is because of their inspired architecture based on biological neurons. We are the only known AGI, therefore something similar to the brain will be producing an AGI. NN at least mimic the massively parallel property of biological neurons. And if we're optimistic, the fact that NN is mimicking how vision works in our brain, might mean that we are at some point in the continuum of the evolution of brains, and it's a matter of time until we discover the other ways brains evolved intelligence. What keeps me optimistic is evolution. At some point brains were stupid, and then they definitely evolved AGI. The question is how did this happen and whether or not there is a shortcut, like inventing the wheel for transportation instead of arms and legs.
- argonaut 11y ago
  To paraphrase a comment from another thread: Artificial neural networks have basically nothing to do with actual biological neurons. First, neuroscientists do not have even a decent understanding of biological neurons: you cannot say that neural networks mimic neurons when we don't understand biological neurons. Of the bajillion things we do know neurons do, neural networks do on the order of 1% of those things. On the flip side, neural networks do a ton of things that biological neurons do not (like backpropagation(!)* ). * In fairness, Hinton hypothesizes the brain has a way of doing backprop, but what he talks about only barely resembles actual backprop.
  - proc0 11y ago
    Right, no debate there. They're inspired by biological neurons. But it's still a step in the right direction. The facts are we have billions of neurons and there's some form of information processing going on. Perhaps we could continue the trend, and model NN to include other brain cell features like whatever the glyal cells do for example.
  - mac01021 11y ago
boltzmannbrain 11y ago
I think readers of this post will enjoy "What is Machine Intelligence vs. Deep Learning vs. Artificial Intelligence" by Numenta's Jeff Hawkins: http://numenta.com/blog/machine-intelligence-machine-learning-deep-learning-artificial-intelligence.html http://numenta.com/blog/machine-intelligence-machine-learnin...
- jaytaylor 11y ago
  Thank you for sharing this! I found it a concise and useful overview. Also submitted it to HN: https://news.ycombinator.com/item?id=10921440 https://news.ycombinator.com/item?id=10921440
maciejgryka 11y ago
Nice article - it's good to be realistic about what we can do with current tools. I feel like the gist of what current neural nets can do is "pattern recognition". If that's fair, I also suspect that most people underestimate how many problems can be solved by them (e.g. planning and experiment design can be posed as pattern recognition - the difficulty is obtaining enough training data). It's true that we're most likely a very long way away from general AI - but I'm willing to bet most of us will still be surprised within the next 2 years by just how well some deep-learning based solutions work.
theideasmith 11y ago
Someone once gave the analogy of climbing to the moon. You can report steady progress until you get to the top of the tree/mountain. I think this is applicable here. We'll need a new paradigm, beyond statistical learning, to create AGI
- lightcatcher 11y ago
  > We'll need a new paradigm, beyond statistical learning, to create AGI What makes you say that? Can you suggest a problem that you think is not addressable by statistical learning?
  - jholman 11y ago
    The many facets of human thought include planning towards novel goals, inferring others' goals from their actions, learning structured theories to describe the rules of the world, inventing experiments to test those theories, and learning to recognise new object kinds from just one example.
    - paganel 11y ago
      That and machines making up jokes. And word puns. And jokes again, and machines understanding and enjoying said jokes.
      - upquark 11y ago
        Not being able to imagine how method A may produce result B is no evidence against method A. It may very well be evidence towards lack of imagination :)
  - ZeroFries 11y ago
    The phenomenal binding problem.
- proc0 11y ago
  David Deustch has an article online somewhere and his conclusion is that the leap has to be made by philosophy first. We need to first figure out the theory of intelligence and potentially consciousness first, and then information theory will follow.
DrNuke 11y ago
I think many are missing the point here: AI can just be very stupid and still wipe everything out. It only takes some sort of irreversible minimisation function to let machines destroy all at sight. Drones are the first step, then comes IoT, what else? We fully depend on machine learning just now. So no wonder many are scared even before machines becoming human-intelligent.
andreyk 11y ago
I think this is a good analysis of what Deep Learning is particularly good for and its limitations, but was somewhat annoyed at the lack of any citations of people actually overhyping it. The most there was is this: "This is all well justified, and I have no intention to belittle the current and future impact of deep learning; however, the optimism about the just what these models can achieve in terms of intelligence has been worryingly reminiscent of the 1960s." From what I've read and seen, the leading people in the field (Yann LeCun, Hinton, etc.) seem to be very aware that the current methods are particularly good for problems dealing with perception but not necessarily reasoning. Likewise, I have not seen many popular news sources such as NYT make any crazy claims about the potential of the technology. I hope, at least, that the people who work in AI are too aware of the hype cycles of the past to get caught up in one again, and so there will not be a repeat of the 60's.
- snoman 11y ago
  > I hope, at least, that the people who work in AI are too aware of the hype cycles of the past to get caught up in one again, and so there will not be a repeat of the 60's. Given that AI Winter is a thing, and that it was a reaction to the across-the-board failure of AI to do anything that people expected of it (expectations driven by hype), then I think you'd be right.
Houshalter 11y ago
>Human or superhuman performance in one task is not necessarily a stepping-stone towards near-human performance across most tasks. Here's the important difference about NNs. They are incredibly general. The same algorithms that can do object recognition can also do language tasks, learn to play chess or go, control a robot, etc. With only slightly modifications to the architecture and otherwise no domain information. That's a hugely different thing than brute force game playing programs. Not only could they not learn the rules of the game from no knowledge, they couldn't even play games with large search spaces like Go. They couldn't do anything other than play games with well defined rules. They are not general at all. Current neural networks have limits. But there is no reason to believe that those limits can't be broken as more progress is made. For example, the author references that neural networks overfit. They can't make predictions when they have little data. They need huge amounts of data to do well. But this is a problem that has already been solved to some extent. There has been a great deal of work into bayesian neural networks that avoid overfitting entirely. Including some recent papers on new methods to do them efficiently. There's the invention of dropout, which is believed to approximate bayesian methods, and is very good at avoiding overfitting. There are some tasks that neural network can't do, like episodic memory, and reasoning. And there has been recent work exploring these tasks. We are starting to see neural networks with external memory systems attached to them, or ways of learning to store memories. Neuroscientists have claimed to have made accurate models of the hippocampus. And deepmind said that was their next step. Reasoning is more complicated and no one knows exactly what is meant by it. But we are starting to see RNNs that can learn to do more complicated "thinking" tasks, like attention models, and neural turing machines, and RNNs that are taught to model programming languages and code.
- argonaut 11y ago
  Dropout does very little to solve the lack of data problem, speaking from experience.
sevensor 11y ago
At last, the thing that's unreasonable isn't effectiveness. I've been hoping for a while that someone close to the field would cut through the hype and put ANNs in context.
neom 11y ago
How far are we from general purpose quantum computing?
otakucode 11y ago
I expect that as we improve machine intelligence more and more, aside from the fact that we will simply keep moving the goalposts of what we consider "intelligent" like the irascible scamps we are, we're going to discover that embodiment is absolutely necessary. Not just any embodiment either, but we will need to place the neural networks in bodies very much like our own. Neuroscience continues to find surprising things that link our "general human intelligence" to our bodies. Paralyze a face and a person becomes less able to feel happiness or anger, eventually forgetting what feeling those things even meant, as one example. We shouldn't forget that the mind/body split is a wholly artificial construct that has no basis in reality. The brain is not contained in the head. The nerves running down your spine and out to your toes and all over your body are neurons. Exactly the same neurons, and directly connected to the neurons, that make up what we think of as the separate organ 'the brain'. They're stretched out very long, from head to toe, sure, but they are single cells, with the exact same behavior and DNA, and there is no reason to presume that they must have some especially insignificant role in our overall intelligence. Then there is the fact that it is probably reasonable to presume that a machine which has human-level intelligence will not appear overnight. It would almost necessarily go through long periods of development. During that development, when the machine begins to behave in ways the designers are not able to understand, what will be their reaction? Will they suppose that maybe the machine had intentions they were unaware of, and that it is acting of its own volition? Or will they think the system must be flawed, and seek to eliminate the behavior they didn't expect or understand? I have a hard time imagining that an AI system will be trained on image classification and one day suddenly say "I am alive" to its authors or users. If it instead performs poorly on the image classification because it is pondering the beauty of a flower in one of the images, what are the chances that nascent quasi-consciousness would be protected and developed? I think none. We only have vague ideas about intelligence and consciousness and our ideas about partial intelligence are utterly theoretical. Has there ever been a person who was 1% intelligent? Is mastering checkers, or learning NLP to exclusion of even proprioception 1% of human intelligence? You optimize for what you measure... and we don't know how to measure the things we're looking for.
MichaelMoser123 11y ago
I think that this book is really interesting "Surfaces and essences: Analogy as the fuel and fire of thinking" by Hofstadter and Sander Many people got dissilusioned with classical AI because mathematical logic (inference engines) would not scale to 'strong' AI. Hofstaedter says that most concepts handled by Humans do not fit into clear cut onthologies one to one. Instead each higher order concepts are created by finding analogies between objects or simpler concepts, and by grouping these similar concepts into more complex entities. I have a summary of the book here http://mosermichael.github.io/cstuff/all/blogg/2013/10/15/post-1.html http://mosermichael.github.io/cstuff/all/blogg/2013/10/15/po...
- MichaelMoser123 11y ago
  My personal theory is: semantics of language are neatly/minimally represented by dependency graphs. Maybe analogies can be found by matching colored dependency graphs (where the nodes and links determine the coloring)
tim333 11y ago
>Extrapolating from the last few years’ progress, it is enticing to believe that Deep Artificial General Intelligence is just around the corner and just a few more architectural tricks, bigger data sets and faster computing power are required to take us there. I feel that there are a couple of solid reasons to be much more skeptical. On the other hand there are reasons to be optimistic. Human brains are built from networks of neurons and the artificial neural networks are starting to have quite similar characteristics to components of the brain - things like image recognition (https://news.ycombinator.com/item?id=9584325 https://news.ycombinator.com/item?id=9584325) and Deep Mind playing Atari (http://www.wired.co.uk/news/archive/2015-02/25/google-deepmind-atari http://www.wired.co.uk/news/archive/2015-02/25/google-deepmi...) The next step would may be to wire the things together in a similar structure to the human brain which is kind of what Deep Mind are working on - they are trying to do the hippocampus at the moment. (https://www.youtube.com/watch?v=0X-NdPtFKq0&feature=youtu.be&t=25m24s https://www.youtube.com/watch?v=0X-NdPtFKq0&feature=youtu.be...) Also we are approaching the point where reasonably priced hardware can match the brain, roughly the 2020s (http://www.transhumanist.com/volume1/moravec.htm http://www.transhumanist.com/volume1/moravec.htm) It'll be interesting to see how it goes.
tianlins 11y ago
it is true that most recent success of deep neural network are in the regime where n, d are large. And we surely shouldn't fantasize general AI solved in this way. However, the very appealing aspect of deep neural network is end-to-end training: for image recognition, we can map from raw-pixels to output. This is very different from other ML techniques. In some sense, deep neural networks learn "algorithms", not just "models". This formulation can be richer especially when given lots of data.

269 points by fforflo 11y ago

We will never have human level AI until we can properly understand, define, and model human intelligence. While we are advancing at a very rapid pace on that front, we are still years away from the field being considered mature.

disantlor 11y ago
Why? All sorts of behavior emerges out of seemingly simplified rules/systems. Could the convoluted and beautiful patterns that can come from Conway's game of life be predicted in advance? What about the sort of meta "intelligence" on display by ant colonies when taken as a whole? Or how connecting disparate groups of people over the globe changes the way humanity as a whole reacts to situations. I'm no expert and these examples probably betray the shallowness of my understanding as much as they make a point. But if they do make a point it's that we don't need to (and often can't) understand what will result from systems that have a built in feedback mechanism.
- bsbechtel 11y ago
  >>But if they do make a point it's that we don't need to (and often can't) understand what will result from systems that have a built in feedback mechanism. That may be true, but my point was more to the effect that there are things we fail to understand about human nature that drives decision making, actions, and goal setting within our own society, let alone other cultures. Consider how factors such as your own personal energy levels affect your decision making throughout the day (decision fatigue)...how are we going to model something like that with AI? Or consider that many different cultures around the world have very different societal goals than Americans do pursuing personal freedom and material well-being. How do we consider those different abstract goals, that most citizens don't even realize or understand why they pursue, because it's just how they have been raised. How do environmental factors drive decision making in different climates? Those who live in a very cold climate have to take different precautions and deal with different risks than those in warm climates do. What about locally available resources? Many business (and design) decisions are made based as much upon supply chain availability and risk management as they are customer demand. How does AI consider these factors when AI doesn't even know these things exist? These are the things I was talking about that we still have difficulty understanding about each other, let alone being able to program a robot to imitate.
  - disantlor 11y ago
    Maybe the difference between a human intelligence and a super intelligence comes, in part, from not being affected by day to day mood/chemical processes that are probably related to keeping the body as a whole functioning. The super intelligence has an intelligence that's isolated from these needs. I don't really have an answer to all your points, just a general feeling that AI needed be just like our "I" to be effectively superior.
j1o1h1n 11y ago
Similarly, we will never have fusion weapons until we can correctly simulate and model a nuclear reaction on a computer.
mej10 11y ago
You state this as fact. Link to credible science saying that it is true. This is like saying we have to completely understand how birds fly before we can make planes, or how fish swim before making submarines. Or how muscles cause movement before inventing the wheel. Or how chemical reactions work before we can make fire. You don't have to fully understand the original system or be able to recreate it to make something that exhibits some or all of the desirable properties. This has been shown time and time again.

hacker_9 11y ago

"Human or superhuman performance in one task is not necessarily a stepping-stone towards near-human performance across most tasks. By the same token, the ability of neural networks to learn interpretable word embeddings, say, does not remotely suggest that they are the right kind of tool for a human-level understanding of the world. It is impressive and surprising that these general-purpose, statistical models can learn meaningful relations from text alone, without any richer perception of the world, but this may speak much more about the unexpected ease of the task itself than it does about the capacity of the models. Just as checkers can be won through tree-search, so too can many semantic relations be learned from text statistics. Both produce impressive intelligent-seeming behaviour, but neither necessarily pave the way towards true machine intelligence." So true, and this is why I don't listen when Elon Musk or Stephen Hawkings spread fear about the impending AI disaster; they think because a neural network can recognize an image like a human can, that it's not a huge leap to say it will be able to soon think and act like a human, but in reality this is just not the case.

jbhatab 11y ago
I don't think they are afraid of current technology and neither am I. However, I am afraid of a breakthrough or new model of AI that does approach human intelligence. I don't know how, when, or why but I think it's reasonable in the next 400 years that we approach something like that. 400 years is not very long in terms of humanity but this technology could do things to humanity that we can't grasp right now.
- pjbrunet 11y ago
  Without getting into our personal, specific definitions of intelligence, I do agree the next 400 years could bring about something horribly disruptive that is not intelligent. Dumb and dangerous, discovered by fools, is the more likely threat to peace in the world.
  - jbhatab 11y ago
    Maybe, but I'm already afraid of dumb and dangerous tools. Nuclear weapons. Maybe we get stronger dumb and dangerous tools, but what scares me much more is a smart drone that has AI and the ability to learn things about me at a rate I can't comprehend.

interdrift 11y ago

We have something that can understand a pattern but we don't have something that can understand how different patterns relate to each other.

djrjdjcjf 11y ago
Yes we do. Word2vec.
TeMPOraL 11y ago
Relations of patterns are patterns too. A potential for recursive reuse?
- wolfgke 11y ago
  Rather homotopy type theory ;-) (explanation: homotopy type theory (HoTT) in difference to ITT also considers types of higher order).
  - harveywi 11y ago
    To my knowledge, intuitionistic type theory (ITT) considers type of higher order (called higher-kinded types), so homotopy type theory (HoTT) isn't exactly relevant here - especially since it has, to my knowledge, not cross-pollinated with ML or ANN research. Models of recursion can be found in systems as simple as lambda calculi or cellular spaces, and this was the big idea behind Jeff Hawkins's HTM and other classical models (please correct me if I am wrong). What HoTT brings to the table is the notion that a path between two types can be used to transport a proof from one type to another - currently immensely handy for theorem proving, but applications in ML and beyond remain to be made.

tacos 11y ago

Current top post quotes the most negative observation of the paper. Here's the most positive, and perhaps the most useful to HN readers or investors who are exploring the space: "Deep learning has produced amazing discriminative models, generative models and feature extractors, but common to all of these is the use of a very large training dataset. Its place in the world is as a powerful tool for general-purpose pattern recognition... Very possibly it is the best tool for working in this paradigm. This is a very good fit for one particular class of problems that the brain solves: finding good representations to describe the constant and enormous flood of sensory data it receives."

boltzmannbrain 11y ago
I would argue the brain solves every problem by first representing sensory data, and then learns temporal patterns of the encoded information.
auvrw 11y ago
the last sentence is also positive as well as insightful Gradient descent in neural networks may well play a big part in helping to build the components of thinking machines, but it is not, itself, the stuff of thought. statistical techniques like these are often used to interpret experimental data. "helping to build the components of thinking machines," (and i think this is the author's intent?) may not mean _being_ the components of thinking machines, just as the 4004 is not a component of the computer that i'm currently using, but it did help build it. idk, though. as cool as the engineering (making) is, i do worry sometimes that the science (understanding) could get overlooked in the process.

skybrian 11y ago

There was a recent paper [1] about learning visual concepts from few examples. I don't know if it generalizes or not, but it seems too early to assume that researchers will hit a dead end. [1] http://science.sciencemag.org/content/350/6266/1332.full http://science.sciencemag.org/content/350/6266/1332.full

argonaut 11y ago
This is an algorithm unrelated to neural networks, which incidentally reinforces the author's point.

proc0 11y ago

Another article basically saying something along the lines of "there is no current technology that comes close to producing AGI, therefore let's dismiss all these technologies". Of course we don't know what we don't know, until we do, and then it's not as mysterious. It's not hard to see that the reason NN are becoming the prime candidate for AGI, is because of their inspired architecture based on biological neurons. We are the only known AGI, therefore something similar to the brain will be producing an AGI. NN at least mimic the massively parallel property of biological neurons. And if we're optimistic, the fact that NN is mimicking how vision works in our brain, might mean that we are at some point in the continuum of the evolution of brains, and it's a matter of time until we discover the other ways brains evolved intelligence. What keeps me optimistic is evolution. At some point brains were stupid, and then they definitely evolved AGI. The question is how did this happen and whether or not there is a shortcut, like inventing the wheel for transportation instead of arms and legs.

argonaut 11y ago
To paraphrase a comment from another thread: Artificial neural networks have basically nothing to do with actual biological neurons. First, neuroscientists do not have even a decent understanding of biological neurons: you cannot say that neural networks mimic neurons when we don't understand biological neurons. Of the bajillion things we do know neurons do, neural networks do on the order of 1% of those things. On the flip side, neural networks do a ton of things that biological neurons do not (like backpropagation(!)* ). * In fairness, Hinton hypothesizes the brain has a way of doing backprop, but what he talks about only barely resembles actual backprop.
- proc0 11y ago
  Right, no debate there. They're inspired by biological neurons. But it's still a step in the right direction. The facts are we have billions of neurons and there's some form of information processing going on. Perhaps we could continue the trend, and model NN to include other brain cell features like whatever the glyal cells do for example.
- mac01021 11y ago

boltzmannbrain 11y ago

I think readers of this post will enjoy "What is Machine Intelligence vs. Deep Learning vs. Artificial Intelligence" by Numenta's Jeff Hawkins: http://numenta.com/blog/machine-intelligence-machine-learning-deep-learning-artificial-intelligence.html http://numenta.com/blog/machine-intelligence-machine-learnin...

jaytaylor 11y ago
Thank you for sharing this! I found it a concise and useful overview. Also submitted it to HN: https://news.ycombinator.com/item?id=10921440 https://news.ycombinator.com/item?id=10921440

maciejgryka 11y ago

Nice article - it's good to be realistic about what we can do with current tools. I feel like the gist of what current neural nets can do is "pattern recognition". If that's fair, I also suspect that most people underestimate how many problems can be solved by them (e.g. planning and experiment design can be posed as pattern recognition - the difficulty is obtaining enough training data). It's true that we're most likely a very long way away from general AI - but I'm willing to bet most of us will still be surprised within the next 2 years by just how well some deep-learning based solutions work.

theideasmith 11y ago

Someone once gave the analogy of climbing to the moon. You can report steady progress until you get to the top of the tree/mountain. I think this is applicable here. We'll need a new paradigm, beyond statistical learning, to create AGI

lightcatcher 11y ago
> We'll need a new paradigm, beyond statistical learning, to create AGI What makes you say that? Can you suggest a problem that you think is not addressable by statistical learning?
- jholman 11y ago
  The many facets of human thought include planning towards novel goals, inferring others' goals from their actions, learning structured theories to describe the rules of the world, inventing experiments to test those theories, and learning to recognise new object kinds from just one example.
  - paganel 11y ago
    That and machines making up jokes. And word puns. And jokes again, and machines understanding and enjoying said jokes.
    - upquark 11y ago
      Not being able to imagine how method A may produce result B is no evidence against method A. It may very well be evidence towards lack of imagination :)
- ZeroFries 11y ago
  The phenomenal binding problem.
proc0 11y ago
David Deustch has an article online somewhere and his conclusion is that the leap has to be made by philosophy first. We need to first figure out the theory of intelligence and potentially consciousness first, and then information theory will follow.

DrNuke 11y ago

I think many are missing the point here: AI can just be very stupid and still wipe everything out. It only takes some sort of irreversible minimisation function to let machines destroy all at sight. Drones are the first step, then comes IoT, what else? We fully depend on machine learning just now. So no wonder many are scared even before machines becoming human-intelligent.

andreyk 11y ago

I think this is a good analysis of what Deep Learning is particularly good for and its limitations, but was somewhat annoyed at the lack of any citations of people actually overhyping it. The most there was is this: "This is all well justified, and I have no intention to belittle the current and future impact of deep learning; however, the optimism about the just what these models can achieve in terms of intelligence has been worryingly reminiscent of the 1960s." From what I've read and seen, the leading people in the field (Yann LeCun, Hinton, etc.) seem to be very aware that the current methods are particularly good for problems dealing with perception but not necessarily reasoning. Likewise, I have not seen many popular news sources such as NYT make any crazy claims about the potential of the technology. I hope, at least, that the people who work in AI are too aware of the hype cycles of the past to get caught up in one again, and so there will not be a repeat of the 60's.

snoman 11y ago
> I hope, at least, that the people who work in AI are too aware of the hype cycles of the past to get caught up in one again, and so there will not be a repeat of the 60's. Given that AI Winter is a thing, and that it was a reaction to the across-the-board failure of AI to do anything that people expected of it (expectations driven by hype), then I think you'd be right.

Houshalter 11y ago

>Human or superhuman performance in one task is not necessarily a stepping-stone towards near-human performance across most tasks. Here's the important difference about NNs. They are incredibly general. The same algorithms that can do object recognition can also do language tasks, learn to play chess or go, control a robot, etc. With only slightly modifications to the architecture and otherwise no domain information. That's a hugely different thing than brute force game playing programs. Not only could they not learn the rules of the game from no knowledge, they couldn't even play games with large search spaces like Go. They couldn't do anything other than play games with well defined rules. They are not general at all. Current neural networks have limits. But there is no reason to believe that those limits can't be broken as more progress is made. For example, the author references that neural networks overfit. They can't make predictions when they have little data. They need huge amounts of data to do well. But this is a problem that has already been solved to some extent. There has been a great deal of work into bayesian neural networks that avoid overfitting entirely. Including some recent papers on new methods to do them efficiently. There's the invention of dropout, which is believed to approximate bayesian methods, and is very good at avoiding overfitting. There are some tasks that neural network can't do, like episodic memory, and reasoning. And there has been recent work exploring these tasks. We are starting to see neural networks with external memory systems attached to them, or ways of learning to store memories. Neuroscientists have claimed to have made accurate models of the hippocampus. And deepmind said that was their next step. Reasoning is more complicated and no one knows exactly what is meant by it. But we are starting to see RNNs that can learn to do more complicated "thinking" tasks, like attention models, and neural turing machines, and RNNs that are taught to model programming languages and code.

argonaut 11y ago
Dropout does very little to solve the lack of data problem, speaking from experience.

sevensor 11y ago

At last, the thing that's unreasonable isn't effectiveness. I've been hoping for a while that someone close to the field would cut through the hype and put ANNs in context.

neom 11y ago

How far are we from general purpose quantum computing?

otakucode 11y ago

I expect that as we improve machine intelligence more and more, aside from the fact that we will simply keep moving the goalposts of what we consider "intelligent" like the irascible scamps we are, we're going to discover that embodiment is absolutely necessary. Not just any embodiment either, but we will need to place the neural networks in bodies very much like our own. Neuroscience continues to find surprising things that link our "general human intelligence" to our bodies. Paralyze a face and a person becomes less able to feel happiness or anger, eventually forgetting what feeling those things even meant, as one example. We shouldn't forget that the mind/body split is a wholly artificial construct that has no basis in reality. The brain is not contained in the head. The nerves running down your spine and out to your toes and all over your body are neurons. Exactly the same neurons, and directly connected to the neurons, that make up what we think of as the separate organ 'the brain'. They're stretched out very long, from head to toe, sure, but they are single cells, with the exact same behavior and DNA, and there is no reason to presume that they must have some especially insignificant role in our overall intelligence. Then there is the fact that it is probably reasonable to presume that a machine which has human-level intelligence will not appear overnight. It would almost necessarily go through long periods of development. During that development, when the machine begins to behave in ways the designers are not able to understand, what will be their reaction? Will they suppose that maybe the machine had intentions they were unaware of, and that it is acting of its own volition? Or will they think the system must be flawed, and seek to eliminate the behavior they didn't expect or understand? I have a hard time imagining that an AI system will be trained on image classification and one day suddenly say "I am alive" to its authors or users. If it instead performs poorly on the image classification because it is pondering the beauty of a flower in one of the images, what are the chances that nascent quasi-consciousness would be protected and developed? I think none. We only have vague ideas about intelligence and consciousness and our ideas about partial intelligence are utterly theoretical. Has there ever been a person who was 1% intelligent? Is mastering checkers, or learning NLP to exclusion of even proprioception 1% of human intelligence? You optimize for what you measure... and we don't know how to measure the things we're looking for.

MichaelMoser123 11y ago

I think that this book is really interesting "Surfaces and essences: Analogy as the fuel and fire of thinking" by Hofstadter and Sander Many people got dissilusioned with classical AI because mathematical logic (inference engines) would not scale to 'strong' AI. Hofstaedter says that most concepts handled by Humans do not fit into clear cut onthologies one to one. Instead each higher order concepts are created by finding analogies between objects or simpler concepts, and by grouping these similar concepts into more complex entities. I have a summary of the book here http://mosermichael.github.io/cstuff/all/blogg/2013/10/15/post-1.html http://mosermichael.github.io/cstuff/all/blogg/2013/10/15/po...

MichaelMoser123 11y ago
My personal theory is: semantics of language are neatly/minimally represented by dependency graphs. Maybe analogies can be found by matching colored dependency graphs (where the nodes and links determine the coloring)

tim333 11y ago

>Extrapolating from the last few years’ progress, it is enticing to believe that Deep Artificial General Intelligence is just around the corner and just a few more architectural tricks, bigger data sets and faster computing power are required to take us there. I feel that there are a couple of solid reasons to be much more skeptical. On the other hand there are reasons to be optimistic. Human brains are built from networks of neurons and the artificial neural networks are starting to have quite similar characteristics to components of the brain - things like image recognition (https://news.ycombinator.com/item?id=9584325 https://news.ycombinator.com/item?id=9584325) and Deep Mind playing Atari (http://www.wired.co.uk/news/archive/2015-02/25/google-deepmind-atari http://www.wired.co.uk/news/archive/2015-02/25/google-deepmi...) The next step would may be to wire the things together in a similar structure to the human brain which is kind of what Deep Mind are working on - they are trying to do the hippocampus at the moment. (https://www.youtube.com/watch?v=0X-NdPtFKq0&feature=youtu.be&t=25m24s https://www.youtube.com/watch?v=0X-NdPtFKq0&feature=youtu.be...) Also we are approaching the point where reasonably priced hardware can match the brain, roughly the 2020s (http://www.transhumanist.com/volume1/moravec.htm http://www.transhumanist.com/volume1/moravec.htm) It'll be interesting to see how it goes.

tianlins 11y ago

it is true that most recent success of deep neural network are in the regime where n, d are large. And we surely shouldn't fantasize general AI solved in this way. However, the very appealing aspect of deep neural network is end-to-end training: for image recognition, we can map from raw-pixels to output. This is very different from other ML techniques. In some sense, deep neural networks learn "algorithms", not just "models". This formulation can be richer especially when given lots of data.