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It shouldn't actually be hard, especially when you look at how our brains lead to a recognizably human intelligence. Problem is, as has been pointed out by Jeff

It shouldn't actually be hard, especially when you look at how our brains lead to a recognizably human intelligence. Problem is, as has been pointed out by Jeff Hawkins, no one in AI research care(d/s) to look at biology to see what might be learned.... So much heat and funding without light. Edit: this was meant somewhat ironically to get the point across that we can't be expected to succeed with AI unless we know how HI actually works.

simonh 12y ago
The problem with that is, we still dont really know how the brain works either. Yes we know roughly how neurons work, but how does that lead to the making of a decision? No clue. The best we can do is look at various activation patterns in the anterior cingulate cortex and wonder. It's like trying to figure out how a CPU works by measuring fluctuations in its temperature and power draw as it performs calculations.
- atrilla 12y ago
  And the closest to solving this we seem to be at present is fuelled by deep learning, which is basically a big neural network with an absurdly vast amount of neurons (i.e., parameters for learning, like the brain). We can observe how this brute-force technique works, but unfortunately no-one can explain why (it's a black box model). The same story has been on for decades. I would relate it to being a discriminative model, which is tailored to solving a specific task, in contract to generative models, which try to model and explain the world. Perhaps the brain is not meant to understand how the world works but how to do take advantage of it.
  - ajtulloch 12y ago
    Deep learning models definitely don't need an "absurdly vast amount of neurons" - for example, GoogLeNet (arguably the state of the art image classification model) has only ~6M parameters.
- superobserver 12y ago
  Well, yes, which is the point. If we don't know how our brains do this intelligencing, then how can we expect any program or machine to do the same? This is a part of the failure of AI research. Can't put the cart before the horse. Well, you can, but it probably won't work.
  - threatofrain 12y ago
    Well it is possible that human-like intelligence is a lot harder to build, and that you are unnecessarily complicating your journey by asking for human-like intelligence straight off the bat as a requirement of definition. Like, I would think an AI that can perform capricious causal modelling from sensory or experimental data is already really sexy, even if it couldn't match up to human intelligence, or if it wasn't built in the same way as a brain. Or, an AI that can perform capricious maps or analogies between situations.
    - superobserver 12y ago
      I'm not suggesting a HI emulation, actually. If you're interested, you can get a gist of what I'm suggesting by reading Hawkins "On Intelligence". The basic building blocks of intelligence in the brain will be the principles on which legitimate, workable AI research programs will have their first start. From there I'd anticipate radicalization and innovation based on a profound understanding of those principles which will have been shown to work. But we have to do that by understanding what intelligence actually is - and the best available model we have for that is human intelligence, and thus the human brain.
knodi123 12y ago
seriously? no one in AI is looking at the human brain to try and understand intelligence? this is the most inaccurate comment I've seen in 2015, although it's a little too early in the year to call it a winner.
- superobserver 12y ago
  I meant that as a part of the general argument put forward by Jeff Hawkins (cf. "On Intelligence"). Not my claim, to be precise. And it isn't exactly as ridiculous as it sounds either.
- p1esk 12y ago
  Actually, the comment is fairly accurate. Very few people in AI are reading neuroscience papers, and even fewer are trying to implement the ideas from those papers in software. Those who do, are not really doing AI, but rather writing brain simulators, and not trying to perform any intelligent tasks. The closest examples that come to mind (aside from Jeff Hawkins) are Chris Eliasmith and guys behind Leabra. If you know any others, please mention them!
  - tlarkworthy 12y ago
    My PhD program was in neuroinformatics and it exactly was this intersection, it was not an accurate comment. Hinton knows neurophysiology, its clear in any of his talks he reads about the weird illusions that give us insight into the computational mechanisms going on in the inside. (e.g. how we recognize rotated objects but with symmetry unresolved) http://en.wikipedia.org/wiki/Neuroinformatics http://en.wikipedia.org/wiki/Neuroinformatics
    - p1esk 12y ago
      Computational mechanisms that Hinton uses in his ML models have very little to do with what is going on in brains. It's kind of like watching a bird fly and building a jet or a helicopter. Hawkins' comment was about building a machine that "flies like a bird" - actually looking at what's going on in real brains. Hinton seems to be inspired not by brain physiology or anatomy, but by psychology. What about you? Have you implemented in software any of the ideas you learned from looking at the brain? I mean, the software designed to perform some intelligent task?
alexhawdon 12y ago
There's a whole branch of CompSci/AI exactly to do with this, known (sometimes) as "Bio-Inspired Systems". It is as false to say that nobody is looking to nature for inspiration as it is to say that human-level intelligence is a trivial problem. If you can substantiate your assertion then there are people waiting to give you enormous piles of money!
- superobserver 12y ago
  No need to substantiate. Compare brain research funding over the same period of time versus AI. Who comes out ahead (pun not intended)?
Teodolfo 12y ago
I completely agree that there is very little chance AI research will succeed any time soon, but not because AI researchers haven't listened enough to Jeff Hawkins. The way to understand human intelligence is to build computational models that reproduce some of its properties.
- superobserver 12y ago
  My statement isn't so much that we listen to Hawkins but rather that the point put forward by Hawkins (among others) in recent years is the point around which we should focus our efforts. You can't build a computational model if you have nothing that realistically reflects the requirements of the thing that you wish to model as instantiated in reality as we know it.
tzs 12y ago
> Edit: this was meant somewhat ironically to get the point across that we can't be expected to succeed with AI unless we know how HI actually works That's only true if the way HI works is the only reasonable way at achieve AI. Biological solutions can be a good inspiration for some problems, but not always. See the film Gizmo, which is the subject of another story currently on the first couple of pages of HN, for some footage of what happened when people tried to base aviation too closely on what birds do. I can't see any reason that it is not plausible that someday, after we do have AI, the sentence "we can't be expected to succeed with AI unless we know how HI actually works" will be regarded similarly to the way the sentence "we can't be expected to build vehicles that travel 60 mph [1] unless we know how cheetahs actually work" would be regarded now. [1] 97 km/hr

simonh 12y ago

The problem with that is, we still dont really know how the brain works either. Yes we know roughly how neurons work, but how does that lead to the making of a decision? No clue. The best we can do is look at various activation patterns in the anterior cingulate cortex and wonder. It's like trying to figure out how a CPU works by measuring fluctuations in its temperature and power draw as it performs calculations.

atrilla 12y ago
And the closest to solving this we seem to be at present is fuelled by deep learning, which is basically a big neural network with an absurdly vast amount of neurons (i.e., parameters for learning, like the brain). We can observe how this brute-force technique works, but unfortunately no-one can explain why (it's a black box model). The same story has been on for decades. I would relate it to being a discriminative model, which is tailored to solving a specific task, in contract to generative models, which try to model and explain the world. Perhaps the brain is not meant to understand how the world works but how to do take advantage of it.
- ajtulloch 12y ago
  Deep learning models definitely don't need an "absurdly vast amount of neurons" - for example, GoogLeNet (arguably the state of the art image classification model) has only ~6M parameters.
superobserver 12y ago
Well, yes, which is the point. If we don't know how our brains do this intelligencing, then how can we expect any program or machine to do the same? This is a part of the failure of AI research. Can't put the cart before the horse. Well, you can, but it probably won't work.
- threatofrain 12y ago
  Well it is possible that human-like intelligence is a lot harder to build, and that you are unnecessarily complicating your journey by asking for human-like intelligence straight off the bat as a requirement of definition. Like, I would think an AI that can perform capricious causal modelling from sensory or experimental data is already really sexy, even if it couldn't match up to human intelligence, or if it wasn't built in the same way as a brain. Or, an AI that can perform capricious maps or analogies between situations.
  - superobserver 12y ago
    I'm not suggesting a HI emulation, actually. If you're interested, you can get a gist of what I'm suggesting by reading Hawkins "On Intelligence". The basic building blocks of intelligence in the brain will be the principles on which legitimate, workable AI research programs will have their first start. From there I'd anticipate radicalization and innovation based on a profound understanding of those principles which will have been shown to work. But we have to do that by understanding what intelligence actually is - and the best available model we have for that is human intelligence, and thus the human brain.

knodi123 12y ago

seriously? no one in AI is looking at the human brain to try and understand intelligence? this is the most inaccurate comment I've seen in 2015, although it's a little too early in the year to call it a winner.

superobserver 12y ago
I meant that as a part of the general argument put forward by Jeff Hawkins (cf. "On Intelligence"). Not my claim, to be precise. And it isn't exactly as ridiculous as it sounds either.
p1esk 12y ago
Actually, the comment is fairly accurate. Very few people in AI are reading neuroscience papers, and even fewer are trying to implement the ideas from those papers in software. Those who do, are not really doing AI, but rather writing brain simulators, and not trying to perform any intelligent tasks. The closest examples that come to mind (aside from Jeff Hawkins) are Chris Eliasmith and guys behind Leabra. If you know any others, please mention them!
- tlarkworthy 12y ago
  My PhD program was in neuroinformatics and it exactly was this intersection, it was not an accurate comment. Hinton knows neurophysiology, its clear in any of his talks he reads about the weird illusions that give us insight into the computational mechanisms going on in the inside. (e.g. how we recognize rotated objects but with symmetry unresolved) http://en.wikipedia.org/wiki/Neuroinformatics http://en.wikipedia.org/wiki/Neuroinformatics
  - p1esk 12y ago
    Computational mechanisms that Hinton uses in his ML models have very little to do with what is going on in brains. It's kind of like watching a bird fly and building a jet or a helicopter. Hawkins' comment was about building a machine that "flies like a bird" - actually looking at what's going on in real brains. Hinton seems to be inspired not by brain physiology or anatomy, but by psychology. What about you? Have you implemented in software any of the ideas you learned from looking at the brain? I mean, the software designed to perform some intelligent task?

alexhawdon 12y ago

There's a whole branch of CompSci/AI exactly to do with this, known (sometimes) as "Bio-Inspired Systems". It is as false to say that nobody is looking to nature for inspiration as it is to say that human-level intelligence is a trivial problem. If you can substantiate your assertion then there are people waiting to give you enormous piles of money!

superobserver 12y ago
No need to substantiate. Compare brain research funding over the same period of time versus AI. Who comes out ahead (pun not intended)?

Teodolfo 12y ago

I completely agree that there is very little chance AI research will succeed any time soon, but not because AI researchers haven't listened enough to Jeff Hawkins. The way to understand human intelligence is to build computational models that reproduce some of its properties.

superobserver 12y ago
My statement isn't so much that we listen to Hawkins but rather that the point put forward by Hawkins (among others) in recent years is the point around which we should focus our efforts. You can't build a computational model if you have nothing that realistically reflects the requirements of the thing that you wish to model as instantiated in reality as we know it.

tzs 12y ago

> Edit: this was meant somewhat ironically to get the point across that we can't be expected to succeed with AI unless we know how HI actually works That's only true if the way HI works is the only reasonable way at achieve AI. Biological solutions can be a good inspiration for some problems, but not always. See the film Gizmo, which is the subject of another story currently on the first couple of pages of HN, for some footage of what happened when people tried to base aviation too closely on what birds do. I can't see any reason that it is not plausible that someday, after we do have AI, the sentence "we can't be expected to succeed with AI unless we know how HI actually works" will be regarded similarly to the way the sentence "we can't be expected to build vehicles that travel 60 mph [1] unless we know how cheetahs actually work" would be regarded now. [1] 97 km/hr