I tend to disagree with the idea that we don't have the computational power equivalent to the brain. The reasoning is that, yes, all the things you say are true about having billions of slow neurons, but that makes the assumption that their only fault is that they are slow. I don't necessarily agree with that assumption. The evolutionary process doesn't find true maxima, it finds local maxima. The key is that the neuron is good enough for the task of intelligence and planning and pattern detection. However, the architecture is most likely highly inefficient. The reason why they are so good comparatively is because we just kept adding neurons, either by increasing the size of the frontal cortex and by the folding, so as to jam more in there.
That is not an architectural design per se, and I highly doubt that it is optimal. Rather, it is good enough. Now, the Von Neumann computer architecture is of course lacking. Parallel computing as we know it works, but it's a total nightmare, and hardly competes with the incredible parallelism in nature. But I do think that there is sufficient power now to really feasibly compete with the brain, intelligence wise.
All this with a grain of salt. I have been reading stuff in the weird corners of AI research. I think this article is fundamentally a straw man. (I believe that...) Deep learning and probabilistic machine learning are fundamentally flawed for strong AI. Jeff Hawkins is a well respected AI researcher that I think seems to agree. Another problem is of course that these are all trained in a supervised fashion. Google does well because they have huge tagged data sets. However, the brain doesn't work by training on tagged data sets, it can learn on it's own, unsupervised. So long as we keep pointing mainstream AI down the supervised statistical machine learning path, we will always be far away from strong AI.
I don't agree that brains can learn on their own, unsupervised.
Firstly, the brain has a basic feedback mechanism, which can be thought of as basic supervision: it receives quick feedback on certain kinds of hardware issues its actions caused. Most computers don't even get a similar level of feedback (eg, that they tied up the cluster on junk computations).
Secondarily, modern humans are booted through this mechanism by already running humans, in what is direct supervised learning: from the time we're infants -- and for the next two decades -- our actions are supervised, commented on, corrected and our exposure to materials is metered and selected to create a (theoretically) optimum consumption plan.
That we get any results out of computers with a couple months of training when comparing them to humans with a couple decades of training is testament to the fact that computer learning is orders of magnitude more effective than human learning.
I find it very strange that people leave out the 2 decades of hardware tuning and supervised knowledge building that humans get when discussing about how awful it is we having to train our machines if we want them to be smart.
Brains can definitely learn without supervision. Observe any child playing; they explore and learn from stimulus in ways far more sophisticated than our current machine learning models.
An interesting aspect of brains vs machines is how brains can learn from other brains indirectly. When someone tags a database for a machine to learn from, that is a form of direct communication (the kind machines are good at). A crow can watch another crow use a stick as a tool, and in turn learns that a stick can be used as a tool. This requires a complex understanding of the situation.
If you feed one AI a tagged data set to train it, I think you would be able to easily hook it up to an untrained AI to train the second AI. As in, pass the first AI an un-tagged input, get it's result, and pass the second AI the same input tagged with the first AI's result. That would be essentially the same thing as a crow learning from watching another crow, aside from the fact that the crows are self-directed.
> I don't agree that brains can learn on their own, unsupervised.
Facts don't require your agreement. They're still facts.
You're suggesting that the "2 decades of hardware tuning" is equivalent to programming, and that just isn't the case. While parents definitely provide instruction, each child's brain is fully on its own to program itself. It teaches itself how to make sense of the visual signals it's receiving from the retina... no one teaches a child up from down, how shading and colors differ, how to use both eyes in tandem to provide depth perception. It's all automatic and 'magical' in a way, because literally NOTHING you can do as a parent can speed that process up in any meaningful way. The same is true for hearing. You can't just upload your current understanding of the world... the closest you can come is by trying to help them along mechanically, by holding them up as they 'walk' their legs, for instance, but their brain is still figuring it out entirely independently. Showing the child how to move their legs is NOT the equivalent of showing them how to detect orientation using their inner ear, or teaching them how to send impulses to their muscles in the right order and at the right time, or teaching them to predict momentum so that they don't just fall on their face.
> computer learning is orders of magnitude more effective than human learning
The only reason one could even ATTEMPT to make that claim is that a computer's learning is something that can be copied digitally. A single machine can't even approach the learning abilities of the brain. Computers are so fundamentally different that it's not even a fair comparison to the computer... the brain will win every time.
> I find it very strange that people leave out the 2 decades of hardware tuning and supervised knowledge building that humans get
It's because it just isn't relevant. Take the most advanced AI available today, slap it in a robot and spend 2 decades raising it. I guarantee it won't end up anywhere close to a human in abilities or intelligence. If you can't see that, you're being intentionally obtuse.
Modern robotics has been around for more than 20 years and there is no autonomy whatsoever.
It might surprise many people who claim that AIG is 20-30 away from now that in robotics, something as simple as cup stacking is a major problem. If you can figure out how to do that you will get slightly famous.
The brain and a computer work so fundamentally differently that its impossible to provide a single framework to compare them. This is why I used the metric of computational complexity but even that is not at all accurate.
I think due to the tremendous amount of automation we have seen in the past 10-20 years, many programmer have difficulty appreciating the awesomeness of biological intelligence. Rather than view the success of automation as a failure of how human education works.
By fully appreciating how complexity of the biological intelligence can we start to learn from it and append it.
Totally agree! I think we have deviated too far from biological models in order to achieve marketable results. Google is satisfied with probabilistic machine learning because it will still make them plenty of money being able to tag images. This is why I admire the approach Jeff Hawkins is taking by going back to the biological model for more inspiration.
Of course, speaking from physics - yes. Its a local maxima.
Its one of the shortcoming of natural selection and genetic programming.
We have better tools to do sequential computation than biology can match. And soon we will be able to harness the power of quantum mechanics to do our bidding.
But the point I am trying to make is whatever approach we use, sequential or parallel, Just in terms of computational complexity. The brain's capacity is greater than the sum of the computers humans have ever produced. Just using that metric we are still far away from having the tools to simulate brains, how long will it take for Intel to compress the complexity of ALL computers ever produced into a single chip ?
Surely its not tomorrow ? maybe 20-30 years ? I do not know.
What I do know is in 2015 it seems that there is a lot of work that needs to be done just from a hardware perspective.
I would agree that the computational complexity of every single quark in every single atomic particle in every atom in every synapse is far far greater than the computational power we poses at this day. I just think it's a red herring to chase down that level of computational completixy (for now. True physical simulations truly amaze me, and I can't wait to see them be more granular and accurate). When it comes to intelligence, I think we are mostly missing the algorithm, not the computational power. I think once somebody finds the algorithm (which may be very soon in my own opinion), we will have more than enough computational power to blow the mind away by orders of magnitude.
There's a lot of structure in how the brain is wired that is is baked in when you're born. The visual cortex is in the back, the hippocampus is in the middle, the frontal cortex is in the front. You do have unsupervised learning, but you start out with highly tuned hardware. Is that not some form of basic programming?
yeah, absolutely. But that's the power of millenia of evolutionary tuning, which we have to intelligence to design ourselves, removing the inefficiencies. That then drops the computational power required for equivalent behavior.
That is not an architectural design per se, and I highly doubt that it is optimal. Rather, it is good enough. Now, the Von Neumann computer architecture is of course lacking. Parallel computing as we know it works, but it's a total nightmare, and hardly competes with the incredible parallelism in nature. But I do think that there is sufficient power now to really feasibly compete with the brain, intelligence wise.
All this with a grain of salt. I have been reading stuff in the weird corners of AI research. I think this article is fundamentally a straw man. (I believe that...) Deep learning and probabilistic machine learning are fundamentally flawed for strong AI. Jeff Hawkins is a well respected AI researcher that I think seems to agree. Another problem is of course that these are all trained in a supervised fashion. Google does well because they have huge tagged data sets. However, the brain doesn't work by training on tagged data sets, it can learn on it's own, unsupervised. So long as we keep pointing mainstream AI down the supervised statistical machine learning path, we will always be far away from strong AI.