Critically speaking, this article doesn't add anything. I don't know why I read it. Can anyone explain why they upvoted?
It's not that complicated.
The only non-obvious insights Fitt’s law bring are that objects twice as big and twice as far away take the same time to aim for, and that as objects become smaller or distance increases the time only grows logarithmically. Everything else is just squeezed into its definition to make it sound well-founded.
We don't see animals self-improving to become humans (in the "consciously deciding what revisions to make to their mental architecture at each step" sense), so why would you expect electronic animal-level minds to be able to do the same to become electronic human-level minds, or beyond?
Personally, I doubt even electronic human-equivalent minds would be capable of self-improvement. After all, we are not smart enough to build AIs better than us (yet), so why should electronic minds only as smart as we are be capable of that, either?
A possible answer to that, I suppose, would be that the electronic mind would have far more input/training data fed to it per second than biological minds receive, and far more time to "work on" that sense data to derive patterns between each decision-step.
If humans created an AI to solve some kind of open-ended problem, where "more AI" made the solution better, there would be every incentive to spend the money on more or better hardware for it. It's not clear that a shark would gain much by being 100x smarter, particularly if the metabolic cost were high; for a lot of human problems, spending 100x more on hardware/power/etc. for a 10% better solution would be quite desirable.
And maybe, once you have a fully functional brain, that it constantly derives out of bounds. Like sudden overheating, which is litterally the step #1 of a depression in a human. So we might not be able to scale an AI beyond the size of one brain. Apart from clusters obviously, but then you need to sustain civilizations of brains, and civs do collapse every dozen generations.
We might not be materially able to find enough energy to power all of those trials and errors.
So common sense is a perfectly satisfying qualification to post in this particular thread. Concerning the 5 senses, I refer to common wisdom because it speaks to everyone. Those who know better are probably smart enough to translate "5 senses" into an accurate scientific wording.
You're off by 3 orders of magnitude. Try 200 Billion[0].
[1] http://en.wikipedia.org/wiki/Deep_learning#Convolutional_neu...
[2] http://deeplearning.net/reading-list/
[3] http://en.wikipedia.org/wiki/Deep_learning#Results
[4] http://www.wired.com/wiredscience/2012/06/google-x-neural-ne...
Deep Learning generally refers to machine learning algorithms that deal with stacking multiple layers of simpler functions to enable more complicated functions, and optimizing all the parameters to best fit your training set and generalize to new samples (the hard part). Though it usually refers to neural networks, I dont think there's any reason it doesn't also apply to other layered approaches as long as there's a relatively unified learning algorithm applied across the whole system.
There are clearly many different deep learning algorithms, even if you just count the permutations of tricks you can choose from to improve layered NN generalization. Though to be fair I think very good progress is being made towards developing "better" algorithms in the sense that new ones (e.g. RBM pretraining + dropout) usual perform better than than older algorithms, no matter what data you use it on (now network architecture is another matter entirely).
But I do agree with your point.
to be honest, it sounds really great and it could even be the case that there is a very general underlying principle to cortical information processing and pattern recognition. But one should be careful not to mix solid scientific hypotheses with mainstream media hysteria and people who try to grab attention with their simplifications, claiming today that entropy maximization is the underlying principle and changing to sparse coding tomorrow. We are not that far and what we need is solid research instead of over-the-head assumptions and claims "to have solved the riddle" (in that respect, it might not be that far off alchemy :P)
As algorithms can be combined, the existence of any set of algorithms satisfying this goal would automatically imply the existence of a single algorithm incorporating all of them.
If a sufficiently-detailed physical simulation of a human's brain satisfied this goal, then that would be one such algorithm.
[1]: http://www.amazon.com/On-Intelligence-Jeff-Hawkins/dp/080507...
That said, I hope that you will think more critically and clearly before publishing vague, fuzzy, uninformed, and unlogical thoughts (not illogical, but unlogical) like the following:
>The biggest question for me is not about artificial intelligence, but instead about artificial consciousness, or creativity, or desire, or whatever you want to call it. I am quite confident that we’ll be able to make computer programs that perform specific complex tasks very well. But how do we make a computer program that decides what it wants to do? How do we make a computer decide to care on its own about learning to drive a car? Or write a novel?
Consciousness, creativity, and desire are all quite distinct things. It is very important for people who are attempting to approach the coming reality of artificial intelligence to be able to distinguish between different things like that.
There have been computer programs that decide what they want to do for decades. Perhaps you were thinking of a specific human-like type of decision process, but if so, you must say so and reason that way. Otherwise you are just conveying some fuzzy thoughts. And the problem is that you are doing so in the context of real scientific undertakings with results directly applicable to your thoughts.
A computer deciding what to care about or learn or what behavior to engage in "on its own" is related to the previous topic you mention, and in and of itself, does not require artificial general intelligence.
How do we make a computer program write a novel? I think that is a good question and an effective answer to it I believe _might_ be in the category of 'real' artificial general intelligence. However, I think that it will probably soon be possible to create 'narrow' AIs that can generate novels without being generally intelligent. http://www.nytimes.com/2011/09/11/business/computer-generate...
So I reflexively upvote anything that looks even vaguely interesting.
In this case, I did read the article, and would probably have upvoted it anyway. Why? Because it stands to serve as the seed of an interesting discussion.
Personally, I don't give a fuck if the article itself "adds anything" or not. Who cares about that? It's irrelevant. If the topic itself and/or the content of TFA are interesting enough that it gets a bunch of interesting HN readers talking and commenting and linking and sharing stuff, then it's a worthwhile article in my book. Not everything has to be an earth-shattering scientific breakthrough, that's published in a peer-reviewed journal, blah, blah, blah.
http://www.theatlantic.com/magazine/archive/2013/11/the-man-...
Less cynically, after about 40 years of AI winter, any possible sighting of a sprout is news.