I love AI, but I have that hope too. That somehow we won't be made irrelevant by our own creations. Makes me think of our autonomous vehicle fun taking over the trucking industry. Millions of people made irrelevant through no fault of their own.
It would make a good punchline for a fictional story of people researching brain disorders and intelligence. It would work like this; The researchers in the story develop the means to 'cure' someone of all known neurological disorders. They try it on their test subject and the result is someone who perfectly happy just to be there, has no ambition or curiosity, and requires no entertainment or outside stimulus. The researchers recognize that the person is acting like an intelligent but non-sentient species, and they realize they have "undone" what ever happened to humans according to the garden of eden story in Genesis.
...or they watch the tiny creatures in universe simulation cages.
If we had autonomous vehicles would millions of people become irrelevant? Their "relevance" is solely constrained to trucking? Or are you projecting that "relevance" of people is constrained to their ability to work as a cog in your society?
I would love a future where so many pursuits are actively being consumed by computing that I can just enjoy a life of pursuing whatever I figured was interesting at the time. Where I wasn't constrained by work not because I was "irrelevant" but because the notion of needing work was.
If this [huge google] network had been fed thousands of images labelled as ‘contains cats’ or ‘doesn’t contain cats’ and trained to work out the difference for itself by iteratively tweeking its 1.7 billion parameters until it had found a classification rule, that would have been impressive enough, given the scale of the task involved in mapping from pixels to low-level image features and then to something as varied and complex as a cat’s face. What Google actually achieved is much more extraordinary, and slightly chilling. The input images weren’t labelled in any way: the network distilled the concept of ‘cat face’ out of the data without any guidance.
You can't ask the "cat face" neural network anything about cats. It has no idea what they actually are in relation to the world. A two-year-old human can usually tell you more about cats than you'd care to listen.
A separate network of connections between "knowledge" bits could maybe be used to associate related knowledge like you talk about in terms of their relation to the world. This could also probably be formed in a similar manner giving the algorithm the ability to distinguish "important" concepts contained in the data. The thing I find most odd and interesting about this is that the network tends to identify different important concepts than humans do.
My eight year old son is enamored with "Ok Google" on my phone. He can ask it questions until we tell him "that is enough, let google rest"... and it is very interesting to see where it takes him.
He has learned to tailor his questions to elicit a voice response in addition to the actual google search. The questions must use keywords to achieve this desired response. It is like a new form of boolean search logic, just used verbally. Not only that, but "Ok, Google" according to him "knows everything"...
We call it searching the internet to learn something, an eight year has decided that "Ok, Google" already knows everything. He just has to ask it to see a video of the Puff adder eating its prey and it will show him. In fact, there are not many things that have stumped "Ok, Google" and my son assumes the problem was with his question, not with the machine.
So to circle back to my original question, I didn't know about the Taipan snake or smallest person in the world, or seen videos of Puff adders eating prey... If all I have to do is ask, isn't the machine already smarter than I?
Put another way, if I can tell you (verbatim) what some person wrote about quantum mechanics, but I can't rephrase it into some other form to help you understand the information, then how smart am I? I can repeat rote information without any analysis (just like a sheet of paper could), but I can't tell you what it means. On the other hand, if I tell you enough information, you're likely to connect it together meaningfully, and come to your own conclusions and explanations about it. That seems like a fundamentally different kind of "smart" than what OK, Google can do.
Kind of... I mean, is it useless trivia or is that information that can be used? I can't use information that I don't know exists. The only way I learn what I don't know is by using energy to search, read, do, to hopefully comprehend.
What if "Ok, Google" had some kind of RNN or other ML technique that learned my sons questions and thought process based on how he use the service. Who is to say they are not already doing that? The ads from Google are downright frightening with respect to the accuracy of what I am currently contemplating/researching.
To take this a bit further into "silly, not so silly" land...
With ML techniques; Deepmind, Watson, and others are showing that computers connected to this "encyclopedia" are besting their human counterparts. Is it a giant stretch to say that everything requires some input or energy to learn? Therefore, the only thing we are really missing is the wiring... that is, and I hate to use the "skynet" aphorism, but one day "it" will just turn on and there will be no looking back.
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>On the other hand, if I tell you enough information, you're likely to connect it together meaningfully, and come to your own conclusions and explanations about it. That seems like a fundamentally different kind of "smart" than what OK, Google can do.
We still have a problem of accuracy. Why should I trust your perception and/or your explanation of the concept? I realize this is a problem with computers as well. However, anything outside of emotional arbitrage should be easy to verify.
My own conclusions are biased as hell. I fully admit that is a problem, but it is a problem shared by humanity. I am not saying Wikipedia isn't biased... but it is 100x better in most cases than one single person's perception simply due to scale.
I am rooting for advanced ML/AI... I also hope that "going analog" is always a viable option.
Upon looking at this wiki: https://en.wikipedia.org/wiki/Hyperthymesia It seems it's some kind of obsessive disorder that causes them to remember it all. That's unfortunate as it could have been a great advantage for those afflicted.
[Edit spelling, grammar]
Of course - then we program this heuristic into the search engine and then what? :-) [edit] I feel this is one of the things about IBM's "Watson" which was truly revolutionary.
That is goddamn chilling.
One of his preliminary results was that his algorithms successfully "discovered" 4 big classical music movements on their own, i.e. without any prior labelling or classification, by using clustering algorithms. He posted about it on his blog with a link to his paper [2].
I always had a hard time explaining to non-computer people how amazing that seems.
[1] http://www.peachnote.com/ [2] http://pablozivic.com.ar/post/51774763596/perceptual-basis-o...
Humans have to be trained to perceive the differences in complex streams of information; by default, they just perceive the low-level features—a "wall of noise."
Machine-learning algorithms, meanwhile, can use general techniques to notice information-theoretic properties of various pieces of data. Effectively, computers can "do statistical aggregation" about as effortlessly as humans "do hierarchical knowledge representation."
And, in an information-theoretic sense, the different "trends" throughout the history of music look different under statistical analysis. They're complex in different ways; they have different "lumps"; different aspects of them can or cannot be compressed together as redundancies.
If you would like to see this for yourself, simply feed a raw melodic note-structure (not embedded in XML or anything) to any modern dumb compression algorithm, and then look at the result in a hex editor, while also having the source still open. You should quickly be able to recognize the "transformational signature" that characterizes something like a chaconne, vs. something like a sonata.
What I mean is, some of the "cat faces" they identify will correspond to things that are "real" but that also violate our assumptions about reality. When this happens the typical reaction is to shut the door and burn the room.
Quite often it's just assumed to be error.
Those errors have traditionally led to great discoveries though, (such as the Mercury wobble https://en.wikipedia.org/wiki/Tests_of_general_relativity#Pe...) so if they are repeatable, then such "ghosts in the data" could turn up great fundamental truths.
Every bit of this is speculation though. I am not so sure that anything so profound is going to turn up in AI datasets.
