I believe the basic building blocks (prediction, pattern recognition, attention, etc.) give rise to the higher-level phenomenon that you mentioned. I think we need to first understand those fundamental principles, and we'll be able to infer most of the rest from that point.
Emergent phenomenon can seem complicated and impossible to understand, but the mechanisms that give rise to them are usually simple (for example, evolution creating diverse and intricate life).
"Why would we continue to file patents on work that is going to be open source? The principle reason is to protect the NuPIC community. For example, outside developers could work on similar concepts without becoming part of the open source community. They could seek patents on their own work, making it proprietary and blocking progress of open source NuPIC developers. By keeping our patent portfolio current, we retain the ability to protect the NuPIC community from these threats. In other words, by holding patents on the work, we are able to protect the whole community from others who might seek to wall off their work through patents. In addition to filing select patents going forward, we also will evaluate other measures that would enhance patent protection for the NuPIC community."
Since this is a long-term project, it's more important that Numenta is able to protect the community it is building from patent trolls, and this is one approach to doing that.
Most open source projects do not need a foundation or company patenting things related to the work and acting as a guardian. Why does NuPIC?
The blog post (which is not a legally binding contract in any way) also has this little gem:
"It should be noted that Numenta/Grok holds patents that do not pertain to the algorithms released in NuPIC. We do not view these patents as covered under the GPL, and we reserve the right to use these patents in the normal course of our business."
Assuming this were a legally binding document--which it's not--who would decide which of Numenta's patents are assigned by the GPLv3 and which are not?
I'm happy you are trying to open-source such a cool piece of tech. But this is the patent policy of a company hedging its bets, not a company that's giving something to the world. It leaves Numenta legally in charge of the NuPIC community, instead of letting it evolve, because it's the only entity that can write a GPLv3 on future patents.
At least I can download and play with the GPLv3 version. The old license was so onerous that I didn't want to see the code, lest I open myself to patent liability 10 years down the line for using something kinda sorta like NuPIC.
But I wouldn't build a business on software with this kind of patent policy, and the commercial licenses Numenta sells make me think you'd rather I didn't.
> who would decide which of Numenta's patents are assigned by the GPLv3 and which are not?
The GPL text is rather precise in how to determine which patents are effected. Any patents that would be infringed by some manner of using, making, (...), or modify that specific version of the program is covered by the license.
As far as patent grants goes, it is hard to make something cover beyond that. I guess a license could say "you may not own any patents, and that is the final word", but I do not know any licenses that does that.
Actually, there's a growing amount of evidence that there's a single, general-purpose algorithm in the human brain that gives rise to intelligence. For one, there's the fact that every part of the brain looks and behaves the same. There's also the fact that the brain is very plastic in what it learns – the auditory cortex can learn to "see" if we were to rewire the signals from the eyes from the visual cortex to the auditory cortex. It's very unlikely that our brain is hard-wired to recognize faces, for instance, but rather that it learns to do so using this generic learning algorithm.
Yep I've seen his talk. It's quite fascinating. However, what you're talking about is a learning algorithm, which does not necessarily equate with intelligence. OpenCyc would be the best example that illustrates my point.
Edit: on second thought, you probably meant to say that given such a general purpose learning algorithm, and a suitable environment, the algorithm would in time learn enough to produce intelligence of some kind (of what kind, I'm not sure) that's capable of thinking. In that case, I agree with you, and I'll have to revise my opinion, but I'm still not sure if it qualifies as emergent phenomena from simple rules. An analogy would be Google's search algorithm running on huge amounts of data. Would you call the search results an emerging phenomena from simple rules?
The most concrete version of my point is that I don't think the most powerful AI we'll create will have, for instance, a human-coded algorithm for detecting faces. Instead, it'll have the ability to read electrical signals from a camera and understand the changing patterns in them, including the presence of faces. This ability to understand changing patterns would be due to "simpler" rules than the rules specifically designed to understand faces.
So yes, a general purpose learning algorithm, using the correct paradigm, would learn to think in a way as powerful as we do. And it'll do so in a way that its programmers would never be able to predict.
In the same vein, I would say that Google search results is an emerging phenomena, albeit not quite as interesting as general purpose intelligence. This is because it's intractable to predict what Google will return for certain queries, even if we know all of its rules. Keep in mind that there are degrees of emergence, it's not black and white. (On the other hand, I don't think Google's algorithm is as "simple" as it originally was, but that's for another discussion.)
If we keep writing down common sense datums until 2100, we can make computers as smart as people.
We learn more about brains and making smart computers, but we seem to have run out of major architectural innovations -- better ones won't make a huge difference. The big stumbling block seems to be how much "common sense" a system knows, like that things tend to fall down when you bump them. One group has been writing these down for fifteen years with moderate success; a century more effort may be plenty.
This seems like a really inefficient way to go about it.
Quite a simple yet innovative idea. Now if it has some essential features like tabs for quick switching between different current directories, command history, and a beautiful interface, it could actually replace my default terminal app. Great start though!
Recently, I have been practicing a new time management technique and I am calling it the “Minimum Viable Daily Tasks”. Here is how it works: Choose the minimum amount of work that needs to be done everyday so that you have a big smile on your face when you are heading home in the evening. If you accomplish anything more then it is a bonus.
It's hard to call that bad advice... it most certainly is great, until you have nothing left to do that makes you happy. At that point you might either consider yourself a failure or realize that this just is not realistic.
Jobs have ups and downs, and no situation is perfect for anything, whether it's raising kids or maintaining your own particular sanity. Expecting a prescription for "being happy while doing thing x I have not done" to work for you is setting yourself up for failure.
Normally I'd say this is likely to help many people a bit along the way, but raising kids might be the only thing too personal and individual to warrant any kind of "well hey this worked for entrepreneur X in The Ess Vee so it should work for me."
Emergent phenomenon can seem complicated and impossible to understand, but the mechanisms that give rise to them are usually simple (for example, evolution creating diverse and intricate life).