24 ms·

As someone primarily interested in interpretation of deep models, I strongly resonate with this warning against anthropomorphization of neural networks. Deep le

As someone primarily interested in interpretation of deep models, I strongly resonate with this warning against anthropomorphization of neural networks. Deep learning isn't special; deep models tend to be more accurate than other methods, but fundamentally they aren't much closer to working like the human brain than e.g. gradient boosting models. I think a lot of the issue stems from layman explanations of neural networks. Pretty much every time DL is covered by media, there has to be some contrived comparison to human brains; these descriptions frequently extend to DL tutorials as well. It's important for that idea to be dispelled when people actually start applying deep models. The model's intuition doesn't work like a human's, and that can often lead to unsatisfying conclusions (e.g. the panda --> gibbon example that Francois presents). Unrelatedly, if people were more cautious about anthropomorphization, we'd probably have to deal a lot less with the irresponsible AI fearmongering that seems to dominate public opinion of the field. (I'm not trying to undermine the danger of AI models here, I just take issue with how most of the populace views the field.)

nerdponx 9y ago
Well said. It's just curve fitting.
- curiousgal 9y ago
  I hope Elon Musk understands that.
  - kinkrtyavimoodh 9y ago
    I am sure he does.
    - urethrafranklin 9y ago
      His public statements would indicate otherwise.
      - nerdponx 9y ago
        Consider that his public statements are made on the advice of his publicist, and that encouraging the AI hype is self-serving.
- pishpash 9y ago
  Maybe everything is "curve fitting." -- Note: I think it's more hierarchical than that but curve fitting is certainly one of the important capabilities of biological systems.
  - kxyvr 9y ago
    I don't think so. There's an incredibly important art and science to model selection that is not encapsulated in curve fitting. For example, say we observe a boy throwing a ball and we want to predict where the ball will land. From basic physics, we know the model is `y = 0.5 a t^2 + v0 t + y0` where `a` is the acceleration due to gravity, `v0` is the initial velocity, and `y0` is the initial height. After observing one or two thrown balls, even with error, we can estimate the parameters `a`, `v0`, and `y0` relatively well. Alternatively, we could apply a generic machine learning model to this problem. Eventually, it will work, but how much more data do we need? How many additional parameters do we need? Do the machine learning parameters have physical meaning like those in the original model? In this case, I contend the original model is superior. Now, certainly, there are cases where we don't have a good or known model and machine learning is an extremely important tool for analyzing these cases. However, the process of making this determination and choosing what model to use is not solved by curve fitting or machine learning. This is a decision made by a person. Perhaps some day that will change, and that will be a major advance in intelligent systems, but we don't have that now and it's not clear to me how extending existing methods will lead us there. Basically, I agree with the sentiment of the grandparent post. Machine learning is largely just curve fitting. How and when to apply a machine learning model vs another model is currently a decision left up to the user.
    - pishpash 9y ago
      You're talking about the complexity of the model. If you take a purely input-output view of the world (which by the way, even classical Physics does), every problem _is_ curve fitting in a sufficiently high dimensional space. There is no _conceptual_ problem here. There is perhaps a complexity problem, but that's why I wrote that "I think it's more hierarchical than that."
- SomeStupidPoint 9y ago
  Is what you do not "just curve fitting"?
- dboreham 9y ago
  Or finding eigenvalues.
ouid 9y ago
Perhaps the problem simply lies in calling them neural networks.
- schoen 9y ago
  This terminology goes back to McCulloch and Pitts in 1943, who said they were making an analogy or model based on the behavior of biological neurons. https://en.wikipedia.org/wiki/Artificial_neuron#History https://en.wikipedia.org/wiki/Artificial_neuron#History There are many things that are inexact about this analogy or model, and many of them were known to be inexact in 1943, but that was the direct inspiration. Apparently there are lots of different mathematical models available about biological neuron behavior: https://en.wikipedia.org/wiki/Biological_neuron_model https://en.wikipedia.org/wiki/Biological_neuron_model
  - TremendousJudge 9y ago
    turns out it's very hard to model a thing that we don't know how it actually works
    - curiousgal 9y ago
      To be fair, we do understand how neurons work, at least on a singular level. Perceptrons model that quite well.
      - philipkglass 9y ago
        Implementing a basic perceptron classifier is an undergrad homework assignment. Biological modeling of neurons is a work of decades: http://www.genesis-sim.org/ http://www.genesis-sim.org/ https://www.neuron.yale.edu/neuron/what_is_neuron https://www.neuron.yale.edu/neuron/what_is_neuron
        robotresearcher 9y ago
        McCulloch's argument was that perhaps the gross behaviour of a NN as layers of simple transfer functions is where the real action is, and the rest of the details are just gravy. The fact we now give this to undergrads as homework suggests that there was some value to this idea.
pishpash 9y ago
It isn't anthropomorphizing. There are undeniable architectural similarities between ANN's and biological neural networks. We don't understand either very well yet but the parts we do understand have led to a lot of cross pollination. I don't think computational intelligence will ever match biological networks detail by detail due to the different substrates and resource usage tradeoffs, and they don't need to match. Intelligence can develop in different ways and we are learning about the universal aspects of it.
- therajiv 9y ago
  This is exactly my point - the danger of "anthropomorphization" lies in taking the brain analogy too far. That is, there shouldn't necessarily be a link between research in neuroscience and advances that make deep learning models more accurate. The tasks are completely different (human learning vs. minimizing a loss function), and it's important for researchers in both fields - neuroscience and AI - to keep that in mind.
  - currymj 9y ago
    However, there definitely are analogies! E.g. early work in convnets was inspired by the architecture of cat brains. I think the fields have useful things to say to each other, but we're getting over a (maybe justified) taboo in talking about machine learning methods being biologically inspired.
    - felippee 9y ago
      The origins of that analogy are very flimsy: 1) Hubel and Wiesel discover simple and complex cells in cat's V1 in the 60's. They came up with an ad hoc explanation that somehow the complex cells "pool" among many simple cells of the same orientation. No one to date knows how such pooling would be accomplished (that selects exactly simple cells of similar orientation and different phase, not vice versa), or whether that pooling is only on V1 or elsewhere in the cortex. 2) Fukushima expanded that ad hoc model into neocognitron in 80's, though there is exactly zero evidence for similar "pooling" in higher cortical areas. In fact, higher cortical areas are essentially impossible to disentangle and characterize even today. 3) Yann Lecun took neocognitron and made a convnet which worked OK for MNIST in the late 80's. Afterward the thing was forgotten for many years. 4) Some few years ago Hinton and some dude who could write good GPU code (Alex Krizhevsky), took the convent and won ImageNet. That is when the current wave of "AI" started. In summary, covnets and very loosely based on an ad hoc explanation to Hubel and Wiesel findings in primary visual cortex, which today in neuroscience are regarded as "incomplete" to say the least (more likely completely wrong). Now this stuff works to a degree, but really all these biological inspirations are very minimal.
deleted 9y ago
[deleted]
deleted 9y ago
[deleted]
kensoh 9y ago
I don't have ML or deep learning background (no Masters or PhD), adding comment from experience with backtesting trading systems. We will collect market data and design algorithms that seem to produce the kind of outcomes we want. Then test on some other data sets which the algorithms have never been applied on. Many iterations later, you can get a decent profitable algorithm. And if the 'holy grail' algo is run in market long enough, eventually there will be severe drawdown and going bust. The quality of the algo and I assume the deep learning model lies in the quality (breadth and depth) of the data, and how honest with himself the person choose to model it. There will be time and again new 'black swan' or edge events happening (remember LTCM), because using machine learning is like using the past to predict the future. I guess as long as the users' expectations are correct it can be useful in some very specific areas. Referencing the AlphaGo game last year, I was a Go player for more than a decade. But yet AlphaGo's weird move inspires new insights that break the conventional structure / thinking-framework of a Go player. From that angle, I do think that even though DL is somewhat a blackbox, humans can pick up new insights because it explores areas which are normally ridiculous to a human with 'common sense' to explore.
- fspeech 9y ago
  The "creative" moves may very well come from the search part of the AlphaGo algorithm, though of course the networks have done their jobs of pruning the search space.
  - kensoh 9y ago
    I see.. That's true. Though credit still goes to the algo for choosing that particular weird move out of the entire search space (it's just 'weird' and something you will think is a move made by a total newbie to the game). I remembered for that whole week during lunchtime I would watch the broadcast live on YouTube. How devastated I was to see Lee Sedol losing match after match. It was a moment I would never forget, in my mind the computer had crossed an imaginary threshold and it won. I know ML/DL experts will say it is only for a very specific area. But what's stopping more mastery of enough 'specific' areas that the mastery will be broad enough to pass Turing tests?
eanzenberg 9y ago
The anthropomorphization was done by academic researchers to gain/increase funding for themselves and the field. You can read the papers and see. This is commonly done for marketing purposes and is important since the pool of research money can be limited.
Florin_Andrei 9y ago
> Pretty much every time DL is covered by media, there has to be some contrived comparison to human brains Well, what we've done so far is emulate maybe 1 mm^3 of brain matter - some isolated, very specialized functional blocks in the greater architecture of the brain. They behave as expected - are experts on very narrow topics, but of course fail to integrate their functioning with a larger body of knowledge, because that body just isn't there (yet). The strength of the human mind is that is has this profusion of little subject matter experts all over the place, covering an enormous array of topics - and then it has an intricate superstructure that integrates the outputs of these narrow expert machines, tweaks their functioning, even subtly alters their inputs, providing coherence to the global output according to the capabilities of the whole system. We're still far from that complex high level architecture.
- civilitty 9y ago
  > Well, what we've done so far is emulate maybe 1 mm^3 of brain matter - some isolated, very specialized functional blocks in the greater architecture of the brain. They behave as expected - are experts on very narrow topics, but of course fail to integrate their functioning with a larger body of knowledge, because that body just isn't there (yet). I think you're falling into the same anthropomorphism trap that the GP is talking about. We haven't even breached the most important topic: neural plasticity - a brain's ability to rewire itself based on a complex feedback loop driven by environmental inputs (which are, at this point in human development, an almost infinitely more complex system of culture built up over tens of thousands of years). From my work in neuroscience, it seems that the computational complexity of the state of the art DL algorithms barely register when compared to a network of a few hundred biological neurons like the nervous system of Caenorhabditis elegans, which is itself far less capable of self reorganization than even the simplest mammalian brain. Hell, even the most basic potentiation that you'd find in decades old research on addiction is far outside the scope of modern machine learning research and we don't yet have any clean mathematical theories that can emulate plasticity like back propagation or gradient descent can with simple learning. The current hype around neural networks is the equivalent of saying that we've analytically solved the n-body problem when all we've done is solve a system of equations with two linear variables. The domains are connected but only in the trivial sense that both have variables named "x" and "y."
L_Rahman 9y ago
I agree with your position. But I want to add a warning against the humanization of the brain. Many parts of it are complex in unknown ways, but some parts are truly mechanical. The parts of your central nervous system that respond to reflexes, that locate the source of sound or parse the color of retinal input are far more similar to deep learning algorithms than they are to what we think of as human consciousness.
- fnl 9y ago
  Because that has nothing to do with consciousness... Every living cell can perceive such inputs, even the simplest of prokaryotes can "sniff" out their food sources.
- taneq 9y ago
  > Many parts of it are complex in unknown ways, but some parts are truly mechanical. I feel like this is a bit of a false dichotomy. We've never encountered any spooky non-mechanical non-physical part of the brain, and we've been looking since Cartesian dualism was in vogue. What we think of as human consciousness is likely just a bunch of feedback loops allowing the brain to analyze some of its own state as if it were an external entity.
  - randcraw 9y ago
    The same oversimplification could have been made of the visual system before we became aware of specialized cortical units and their federated/hierarchical arrangement. In time I suspect we'll yet discover that much of the brain is inhomogeneous in unexpected ways and peculiarly interconnected. If it were not, we'd understand more about how it works by now.
s-macke 9y ago
You might find a slide of my talk interesting: https://ibb.co/fXAn4a https://ibb.co/fXAn4a You have to read it from left to right with an twinking eye of course ;)
- mojomark 9y ago
  In your slide - why is back propogation a further stretch from a true bio-NN than an ANN without back propogation?
  - s-macke 9y ago
    An ANN still resembles major features of an bio-NN. 1. A network 2. Flow of information is mainly unidirectional through a node 3. Multiple inputs, but one output, which is connected to the inputs of other neurons. 4. The connection strength between 2 neurons can be changed. 5. Non-linear behavior. After all, I think, this is not such a bad first approximation. Hence the picture in the middle. But I cannot believe that we learn by comparing thousands or millions of input and output patterns and back propagate the error through the network to perform a gradient descent at the neurons. That is simply not, what our brain does.
    - sqeaky 9y ago
      When there is feedback in neurons, what do you think that conveys? I agree it is not some simple error correction like what is propagated backwards, but it happens often and I presume its something useful or it wouldn't be there.
      - Cybiote 9y ago
        Top down predictions are likely mediated by feedback connections from higher to lower areas. Functions include possibly encoding a generative prior for prediction, speeding up inference. They also play an important role in coding more informative error signals than simple derivatives and are part of how the brain learns even as it predicts.
romaniv 9y ago
I think it would help a lot if we brought random forests and SVMs to the same level of performance as DNNs. Demonstrating that more "mechanical" algorithms can be as efficient would dispel some of the anthropomorphism and allow for better analysis of why certain things work. I also believe that researches have responsibility to outline the limits of their own algorithms in research papers. (For example, presenting examples that aren't recognized or data sets on which the approach doesn't work at all.) That is valuable information and they almost certainly have it at the time of publication.
- raverbashing 9y ago
  Not possible, unfortunately
  - nojvek 9y ago
    I've occasionally found that SVM's work great for one shot learning if you have good features and nicely labelled dataset. CNN's are really good at extracting features. Once you've extracted features that are generic, using an SVM as the last layer to train while keeping the CNN parameters intact yields great accuracy. I think that's where we are really headed. A combination of deep learning, boosted trees, svm, evolutionary algos, knowledge graphs e.t.c all stitched together to build stronger AI systems. Remember our aeroplanes don't flap wings but still carry tonnes of weight and fly half way around the world. Once we discovered fundamentals of aerodynamics a lot of supernatural things were possible. Same with intelligence, once we discover the essentials of intelligence and mathematically formulate it, supernatural intelligence is very possible. This is the thing that really scares people. I have no idea how close we are to it, but I'm sure it will change society the way internet and mobile phones changed the world.
    - taneq 9y ago
      > I'm sure it will change society the way internet and mobile phones changed the world. It will change the entire world the way humans changed the world. And that's scary.
    - 9y ago
ThomPete 9y ago
One can say that the human mind consist of millions of not very special parts. It's the aggregate, the complexity of which they interact that makes it special. Once you start to connect all these seemingly non-special abilities in deep learning the "magic" starts to happen. You get something that is more than the sum of it's parts. Of course it's not DL in itself thats interesting but the potential emergent complex relationships.
- reckoner2 9y ago
  Has anyone been able to do this? Is anyone working on it? I only follow the field as a hobby, but as far as I can tell we are nowhere near getting to this point. I think the ability to combine all these parts in a way that the sum is greater than it's parts is going to require many many breakthroughs still.
  - DiThi 9y ago
    The problem is that we don't really know for sure. We kind of predict things by extrapolating what we know and what we have, but we can never be sure there won't be any sudden breakthroughs.
  - ThomPete 9y ago
    The thing is that it's most likely not something anyone does per se but something that happens with enough complexity. If you happen to believe evolutionary theory is the most convincing then we weren't built either but a byproduct of emergent complexity. It is my belief that humans are pattern recognizing feedback loops and carriers of information. We externalized some of that into books and built libraries to be able to keep even more than humans can remember as individuals and now have technology to save even more information and even manipulate it in ways impossible up until 80 years ago or so. I am fairly certain that a technology is part of nature and that technology based conscience is nothing like our limited conscience but something rather different. The end result will not be like humans just better but nothing like humans at all but much better at the carrying of information part. And so with that (my personal belief) perspective in mind no one is going to be able to do it it will happen as a by-product. Please keep in mind that I saying "we exeternalized" in the same way we say "selfish genes" it's not a conscious effort as such but rather something which happen to be favorized in the game of life. Why that is I have no idea but I am fairly certain humans aren't the last species. But yes it's all very speculative I just haven't been able to find better explanations for now.
pron 9y ago
One of the greatest clear and present dangers of AI is that various existing algorithms are called just that, rather than what they are: statistical analysis algorithms, or, in short, statistics. Statistics used to be what we called the worst kind of lie; now it's becoming associated with intelligence, hinting at the ability to expose some great hidden truth. The problem lies not only with the algorithms, but with the models they learn (which are indirectly shaped by the algorithms' limitations) that are simplistic to begin with. E.g., they are trained to predict behavior based on a snapshot of statistical data, using either a constant model (which assumes behavior doesn't change over time) or some simplistic first-order model of change. They certainly aren't usually trained to take into account long-term changes or how their own recommendations impact behavior. The result is a powerful yet completely unjustified boost to the public image of statistical data with simplistic change models.
- fnl 9y ago
  This. I still cannot forget the disappointment of my parents and some family friends, all retired scientist or MDs, when I explained them how deep learning and natural language processing works a few years ago. They were truly upset that all this was "nothing more than clever accounting and statistics" at the end of the day, and no trace of the "advertised intelligence" - with Hinton's RBMs maybe coming closest, but by the time I was explaining how you use MCMC to train a Boltzmann machine, they again were complaining that even this is just modeling "statistical likelihoods, not true intelligence"... In essence, we are only modeling patterns and their transformations, even if rather complex ones. But even the most basic prokaryote can model patterns, that has nothing to do with intelligence or consciousness per se. (And please don't get me started on swarm intelligence now... :-))
- deleted 9y ago
  [deleted]
sjg007 9y ago
This is only true because we don't know how the brain actually works. But the NN architecture is not unreasonable, it maps structures seen in the brain. Backpropagation is also reasonable to abstract the changes in gene and protein regulation (e.g. how learning could be encoded).
deleted 9y ago
[deleted]
ankurdhama 9y ago
> The model's intuition doesn't work like a human's The model doesn't have intuition, it is just a series of computations.
posterboy 9y ago
I always counter, the intelligence is not in the machine, but the builder. Antropomorphism is in line with that, because it projects the human qualities onto the machine, because, in a broad sense, they are modelled after those. Egoistic as we are, that's the only way to understand anything, to remove the shizm between animate and inanimate objects. Just like a fishing rod is just the extension of an arm.

nerdponx 9y ago

Well said. It's just curve fitting.

curiousgal 9y ago
I hope Elon Musk understands that.
- kinkrtyavimoodh 9y ago
  I am sure he does.
  - urethrafranklin 9y ago
    His public statements would indicate otherwise.
    - nerdponx 9y ago
      Consider that his public statements are made on the advice of his publicist, and that encouraging the AI hype is self-serving.
pishpash 9y ago
Maybe everything is "curve fitting." -- Note: I think it's more hierarchical than that but curve fitting is certainly one of the important capabilities of biological systems.
- kxyvr 9y ago
  I don't think so. There's an incredibly important art and science to model selection that is not encapsulated in curve fitting. For example, say we observe a boy throwing a ball and we want to predict where the ball will land. From basic physics, we know the model is `y = 0.5 a t^2 + v0 t + y0` where `a` is the acceleration due to gravity, `v0` is the initial velocity, and `y0` is the initial height. After observing one or two thrown balls, even with error, we can estimate the parameters `a`, `v0`, and `y0` relatively well. Alternatively, we could apply a generic machine learning model to this problem. Eventually, it will work, but how much more data do we need? How many additional parameters do we need? Do the machine learning parameters have physical meaning like those in the original model? In this case, I contend the original model is superior. Now, certainly, there are cases where we don't have a good or known model and machine learning is an extremely important tool for analyzing these cases. However, the process of making this determination and choosing what model to use is not solved by curve fitting or machine learning. This is a decision made by a person. Perhaps some day that will change, and that will be a major advance in intelligent systems, but we don't have that now and it's not clear to me how extending existing methods will lead us there. Basically, I agree with the sentiment of the grandparent post. Machine learning is largely just curve fitting. How and when to apply a machine learning model vs another model is currently a decision left up to the user.
  - pishpash 9y ago
    You're talking about the complexity of the model. If you take a purely input-output view of the world (which by the way, even classical Physics does), every problem _is_ curve fitting in a sufficiently high dimensional space. There is no _conceptual_ problem here. There is perhaps a complexity problem, but that's why I wrote that "I think it's more hierarchical than that."
SomeStupidPoint 9y ago
Is what you do not "just curve fitting"?
dboreham 9y ago
Or finding eigenvalues.

ouid 9y ago

Perhaps the problem simply lies in calling them neural networks.

schoen 9y ago
This terminology goes back to McCulloch and Pitts in 1943, who said they were making an analogy or model based on the behavior of biological neurons. https://en.wikipedia.org/wiki/Artificial_neuron#History https://en.wikipedia.org/wiki/Artificial_neuron#History There are many things that are inexact about this analogy or model, and many of them were known to be inexact in 1943, but that was the direct inspiration. Apparently there are lots of different mathematical models available about biological neuron behavior: https://en.wikipedia.org/wiki/Biological_neuron_model https://en.wikipedia.org/wiki/Biological_neuron_model
- TremendousJudge 9y ago
  turns out it's very hard to model a thing that we don't know how it actually works
  - curiousgal 9y ago
    To be fair, we do understand how neurons work, at least on a singular level. Perceptrons model that quite well.
    - philipkglass 9y ago
      Implementing a basic perceptron classifier is an undergrad homework assignment. Biological modeling of neurons is a work of decades: http://www.genesis-sim.org/ http://www.genesis-sim.org/ https://www.neuron.yale.edu/neuron/what_is_neuron https://www.neuron.yale.edu/neuron/what_is_neuron
      - robotresearcher 9y ago
        McCulloch's argument was that perhaps the gross behaviour of a NN as layers of simple transfer functions is where the real action is, and the rest of the details are just gravy. The fact we now give this to undergrads as homework suggests that there was some value to this idea.

pishpash 9y ago

It isn't anthropomorphizing. There are undeniable architectural similarities between ANN's and biological neural networks. We don't understand either very well yet but the parts we do understand have led to a lot of cross pollination. I don't think computational intelligence will ever match biological networks detail by detail due to the different substrates and resource usage tradeoffs, and they don't need to match. Intelligence can develop in different ways and we are learning about the universal aspects of it.

therajiv 9y ago
This is exactly my point - the danger of "anthropomorphization" lies in taking the brain analogy too far. That is, there shouldn't necessarily be a link between research in neuroscience and advances that make deep learning models more accurate. The tasks are completely different (human learning vs. minimizing a loss function), and it's important for researchers in both fields - neuroscience and AI - to keep that in mind.
- currymj 9y ago
  However, there definitely are analogies! E.g. early work in convnets was inspired by the architecture of cat brains. I think the fields have useful things to say to each other, but we're getting over a (maybe justified) taboo in talking about machine learning methods being biologically inspired.
  - felippee 9y ago
    The origins of that analogy are very flimsy: 1) Hubel and Wiesel discover simple and complex cells in cat's V1 in the 60's. They came up with an ad hoc explanation that somehow the complex cells "pool" among many simple cells of the same orientation. No one to date knows how such pooling would be accomplished (that selects exactly simple cells of similar orientation and different phase, not vice versa), or whether that pooling is only on V1 or elsewhere in the cortex. 2) Fukushima expanded that ad hoc model into neocognitron in 80's, though there is exactly zero evidence for similar "pooling" in higher cortical areas. In fact, higher cortical areas are essentially impossible to disentangle and characterize even today. 3) Yann Lecun took neocognitron and made a convnet which worked OK for MNIST in the late 80's. Afterward the thing was forgotten for many years. 4) Some few years ago Hinton and some dude who could write good GPU code (Alex Krizhevsky), took the convent and won ImageNet. That is when the current wave of "AI" started. In summary, covnets and very loosely based on an ad hoc explanation to Hubel and Wiesel findings in primary visual cortex, which today in neuroscience are regarded as "incomplete" to say the least (more likely completely wrong). Now this stuff works to a degree, but really all these biological inspirations are very minimal.

deleted 9y ago

[deleted]

deleted 9y ago

[deleted]

kensoh 9y ago

I don't have ML or deep learning background (no Masters or PhD), adding comment from experience with backtesting trading systems. We will collect market data and design algorithms that seem to produce the kind of outcomes we want. Then test on some other data sets which the algorithms have never been applied on. Many iterations later, you can get a decent profitable algorithm. And if the 'holy grail' algo is run in market long enough, eventually there will be severe drawdown and going bust. The quality of the algo and I assume the deep learning model lies in the quality (breadth and depth) of the data, and how honest with himself the person choose to model it. There will be time and again new 'black swan' or edge events happening (remember LTCM), because using machine learning is like using the past to predict the future. I guess as long as the users' expectations are correct it can be useful in some very specific areas. Referencing the AlphaGo game last year, I was a Go player for more than a decade. But yet AlphaGo's weird move inspires new insights that break the conventional structure / thinking-framework of a Go player. From that angle, I do think that even though DL is somewhat a blackbox, humans can pick up new insights because it explores areas which are normally ridiculous to a human with 'common sense' to explore.

fspeech 9y ago
The "creative" moves may very well come from the search part of the AlphaGo algorithm, though of course the networks have done their jobs of pruning the search space.
- kensoh 9y ago
  I see.. That's true. Though credit still goes to the algo for choosing that particular weird move out of the entire search space (it's just 'weird' and something you will think is a move made by a total newbie to the game). I remembered for that whole week during lunchtime I would watch the broadcast live on YouTube. How devastated I was to see Lee Sedol losing match after match. It was a moment I would never forget, in my mind the computer had crossed an imaginary threshold and it won. I know ML/DL experts will say it is only for a very specific area. But what's stopping more mastery of enough 'specific' areas that the mastery will be broad enough to pass Turing tests?

eanzenberg 9y ago

The anthropomorphization was done by academic researchers to gain/increase funding for themselves and the field. You can read the papers and see. This is commonly done for marketing purposes and is important since the pool of research money can be limited.

Florin_Andrei 9y ago

> Pretty much every time DL is covered by media, there has to be some contrived comparison to human brains Well, what we've done so far is emulate maybe 1 mm^3 of brain matter - some isolated, very specialized functional blocks in the greater architecture of the brain. They behave as expected - are experts on very narrow topics, but of course fail to integrate their functioning with a larger body of knowledge, because that body just isn't there (yet). The strength of the human mind is that is has this profusion of little subject matter experts all over the place, covering an enormous array of topics - and then it has an intricate superstructure that integrates the outputs of these narrow expert machines, tweaks their functioning, even subtly alters their inputs, providing coherence to the global output according to the capabilities of the whole system. We're still far from that complex high level architecture.

civilitty 9y ago
> Well, what we've done so far is emulate maybe 1 mm^3 of brain matter - some isolated, very specialized functional blocks in the greater architecture of the brain. They behave as expected - are experts on very narrow topics, but of course fail to integrate their functioning with a larger body of knowledge, because that body just isn't there (yet). I think you're falling into the same anthropomorphism trap that the GP is talking about. We haven't even breached the most important topic: neural plasticity - a brain's ability to rewire itself based on a complex feedback loop driven by environmental inputs (which are, at this point in human development, an almost infinitely more complex system of culture built up over tens of thousands of years). From my work in neuroscience, it seems that the computational complexity of the state of the art DL algorithms barely register when compared to a network of a few hundred biological neurons like the nervous system of Caenorhabditis elegans, which is itself far less capable of self reorganization than even the simplest mammalian brain. Hell, even the most basic potentiation that you'd find in decades old research on addiction is far outside the scope of modern machine learning research and we don't yet have any clean mathematical theories that can emulate plasticity like back propagation or gradient descent can with simple learning. The current hype around neural networks is the equivalent of saying that we've analytically solved the n-body problem when all we've done is solve a system of equations with two linear variables. The domains are connected but only in the trivial sense that both have variables named "x" and "y."

L_Rahman 9y ago

I agree with your position. But I want to add a warning against the humanization of the brain. Many parts of it are complex in unknown ways, but some parts are truly mechanical. The parts of your central nervous system that respond to reflexes, that locate the source of sound or parse the color of retinal input are far more similar to deep learning algorithms than they are to what we think of as human consciousness.

fnl 9y ago
Because that has nothing to do with consciousness... Every living cell can perceive such inputs, even the simplest of prokaryotes can "sniff" out their food sources.
taneq 9y ago
> Many parts of it are complex in unknown ways, but some parts are truly mechanical. I feel like this is a bit of a false dichotomy. We've never encountered any spooky non-mechanical non-physical part of the brain, and we've been looking since Cartesian dualism was in vogue. What we think of as human consciousness is likely just a bunch of feedback loops allowing the brain to analyze some of its own state as if it were an external entity.
- randcraw 9y ago
  The same oversimplification could have been made of the visual system before we became aware of specialized cortical units and their federated/hierarchical arrangement. In time I suspect we'll yet discover that much of the brain is inhomogeneous in unexpected ways and peculiarly interconnected. If it were not, we'd understand more about how it works by now.

s-macke 9y ago

You might find a slide of my talk interesting: https://ibb.co/fXAn4a https://ibb.co/fXAn4a You have to read it from left to right with an twinking eye of course ;)

mojomark 9y ago
In your slide - why is back propogation a further stretch from a true bio-NN than an ANN without back propogation?
- s-macke 9y ago
  An ANN still resembles major features of an bio-NN. 1. A network 2. Flow of information is mainly unidirectional through a node 3. Multiple inputs, but one output, which is connected to the inputs of other neurons. 4. The connection strength between 2 neurons can be changed. 5. Non-linear behavior. After all, I think, this is not such a bad first approximation. Hence the picture in the middle. But I cannot believe that we learn by comparing thousands or millions of input and output patterns and back propagate the error through the network to perform a gradient descent at the neurons. That is simply not, what our brain does.
  - sqeaky 9y ago
    When there is feedback in neurons, what do you think that conveys? I agree it is not some simple error correction like what is propagated backwards, but it happens often and I presume its something useful or it wouldn't be there.
    - Cybiote 9y ago
      Top down predictions are likely mediated by feedback connections from higher to lower areas. Functions include possibly encoding a generative prior for prediction, speeding up inference. They also play an important role in coding more informative error signals than simple derivatives and are part of how the brain learns even as it predicts.

romaniv 9y ago

I think it would help a lot if we brought random forests and SVMs to the same level of performance as DNNs. Demonstrating that more "mechanical" algorithms can be as efficient would dispel some of the anthropomorphism and allow for better analysis of why certain things work. I also believe that researches have responsibility to outline the limits of their own algorithms in research papers. (For example, presenting examples that aren't recognized or data sets on which the approach doesn't work at all.) That is valuable information and they almost certainly have it at the time of publication.

raverbashing 9y ago
Not possible, unfortunately
- nojvek 9y ago
  I've occasionally found that SVM's work great for one shot learning if you have good features and nicely labelled dataset. CNN's are really good at extracting features. Once you've extracted features that are generic, using an SVM as the last layer to train while keeping the CNN parameters intact yields great accuracy. I think that's where we are really headed. A combination of deep learning, boosted trees, svm, evolutionary algos, knowledge graphs e.t.c all stitched together to build stronger AI systems. Remember our aeroplanes don't flap wings but still carry tonnes of weight and fly half way around the world. Once we discovered fundamentals of aerodynamics a lot of supernatural things were possible. Same with intelligence, once we discover the essentials of intelligence and mathematically formulate it, supernatural intelligence is very possible. This is the thing that really scares people. I have no idea how close we are to it, but I'm sure it will change society the way internet and mobile phones changed the world.
  - taneq 9y ago
    > I'm sure it will change society the way internet and mobile phones changed the world. It will change the entire world the way humans changed the world. And that's scary.
  - 9y ago

ThomPete 9y ago

One can say that the human mind consist of millions of not very special parts. It's the aggregate, the complexity of which they interact that makes it special. Once you start to connect all these seemingly non-special abilities in deep learning the "magic" starts to happen. You get something that is more than the sum of it's parts. Of course it's not DL in itself thats interesting but the potential emergent complex relationships.

reckoner2 9y ago
Has anyone been able to do this? Is anyone working on it? I only follow the field as a hobby, but as far as I can tell we are nowhere near getting to this point. I think the ability to combine all these parts in a way that the sum is greater than it's parts is going to require many many breakthroughs still.
- DiThi 9y ago
  The problem is that we don't really know for sure. We kind of predict things by extrapolating what we know and what we have, but we can never be sure there won't be any sudden breakthroughs.
- ThomPete 9y ago
  The thing is that it's most likely not something anyone does per se but something that happens with enough complexity. If you happen to believe evolutionary theory is the most convincing then we weren't built either but a byproduct of emergent complexity. It is my belief that humans are pattern recognizing feedback loops and carriers of information. We externalized some of that into books and built libraries to be able to keep even more than humans can remember as individuals and now have technology to save even more information and even manipulate it in ways impossible up until 80 years ago or so. I am fairly certain that a technology is part of nature and that technology based conscience is nothing like our limited conscience but something rather different. The end result will not be like humans just better but nothing like humans at all but much better at the carrying of information part. And so with that (my personal belief) perspective in mind no one is going to be able to do it it will happen as a by-product. Please keep in mind that I saying "we exeternalized" in the same way we say "selfish genes" it's not a conscious effort as such but rather something which happen to be favorized in the game of life. Why that is I have no idea but I am fairly certain humans aren't the last species. But yes it's all very speculative I just haven't been able to find better explanations for now.

pron 9y ago

One of the greatest clear and present dangers of AI is that various existing algorithms are called just that, rather than what they are: statistical analysis algorithms, or, in short, statistics. Statistics used to be what we called the worst kind of lie; now it's becoming associated with intelligence, hinting at the ability to expose some great hidden truth. The problem lies not only with the algorithms, but with the models they learn (which are indirectly shaped by the algorithms' limitations) that are simplistic to begin with. E.g., they are trained to predict behavior based on a snapshot of statistical data, using either a constant model (which assumes behavior doesn't change over time) or some simplistic first-order model of change. They certainly aren't usually trained to take into account long-term changes or how their own recommendations impact behavior. The result is a powerful yet completely unjustified boost to the public image of statistical data with simplistic change models.

fnl 9y ago
This. I still cannot forget the disappointment of my parents and some family friends, all retired scientist or MDs, when I explained them how deep learning and natural language processing works a few years ago. They were truly upset that all this was "nothing more than clever accounting and statistics" at the end of the day, and no trace of the "advertised intelligence" - with Hinton's RBMs maybe coming closest, but by the time I was explaining how you use MCMC to train a Boltzmann machine, they again were complaining that even this is just modeling "statistical likelihoods, not true intelligence"... In essence, we are only modeling patterns and their transformations, even if rather complex ones. But even the most basic prokaryote can model patterns, that has nothing to do with intelligence or consciousness per se. (And please don't get me started on swarm intelligence now... :-))
deleted 9y ago
[deleted]

sjg007 9y ago

This is only true because we don't know how the brain actually works. But the NN architecture is not unreasonable, it maps structures seen in the brain. Backpropagation is also reasonable to abstract the changes in gene and protein regulation (e.g. how learning could be encoded).

deleted 9y ago

[deleted]

ankurdhama 9y ago

> The model's intuition doesn't work like a human's The model doesn't have intuition, it is just a series of computations.

posterboy 9y ago

I always counter, the intelligence is not in the machine, but the builder. Antropomorphism is in line with that, because it projects the human qualities onto the machine, because, in a broad sense, they are modelled after those. Egoistic as we are, that's the only way to understand anything, to remove the shizm between animate and inanimate objects. Just like a fishing rod is just the extension of an arm.