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I model people constantly, but agency and the "PC vs. NPC" distinction don't even come into it. There are classes of models, but they're more like classes of computational automata: less or more complex, roughly scaling with the scope of my interactions with a person. For instance, it's usually fine to model a grocery store cashier as a nondeterministic finite state machine; handing over groceries and paying are simple enough interactions that an NFSM suffices. Of course the cashier has just as much agency and free will as I do -- but there's a point of diminishing returns on how much effort I invest into forming a more comprehensive model, and since my time and willpower are limited, I prefer to spend that effort on people I spend more time with. Agency is always present in every model, but whether it affects the predictions a given model outputs depends on the complexity of the model.
I wouldn't call Google's search personalization "self-training" because the user is responsible for adding new data points to his or her own model; it's the same online algorithm it's always been, just tailored to billions of individual users rather than a set of billions of users. The set of links that a user has clicked on through Google searches is updated every time the user clicks a new link, and the algorithm uses this to tweak the ordering of presented search results, but AFAIK the algorithm has no way to evaluate whether the model update actually brought the ordering closer to the user's preferred ordering unless the user tells it so by clicking on one of the results. It could compare the ordering it did present to the ordering it would have presented if some set of data points wasn't in the model, but then it would have to have some heuristic for which points to drop for cross-validation.
The way I see it, the difference between an online algorithm and a self-training AI is that the latter would not only need such a heuristic -- let's call it "knowledge base evaluation" -- it would also need to be able to evaluate the fitness of novel knowledge base evaluation heuristics. (I'm torn as to whether that goalpost should also include "can generate novel KBE heuristics"; I'll have to think about that a while longer.) Even so, as long as the user dictates which points the algorithm can even consider adding to its KB, the user is acting as a gatekeeper on what knowledge the algorithm can acquire.
The way I see it, the difference between an online algorithm and a self-training AI is that the latter would not only need such a heuristic -- let's call it "knowledge base evaluation" -- it would also need to be able to evaluate the fitness of novel knowledge base evaluation heuristics.
On reflection, I'm now contradicting my original statement; the above is a stab toward an algorithmic notion of "self-training" that is orthogonal to how restricted an algorithm's training input set is, or who is restricting it, or how. Using this half-formed notion, I observe that Google's ranking algorithm is AFAIK not self-training, and is also subject to a severely restricted input set. I apologize for any confusion.
I haven't found one, so I'll try to summarize here:
"Prokaryotic life probably came to Earth from somewhere else. It was successful and made Earth into a finely tuned paradise. (A key point here is the role of life in preserving liquid water, but there are many other points, the author is a scientist and likes to point out improbable coincidences.) Then a tragic accident caused individualistic eukaryotic life to appear, which led to much suffering and death. Evolution is not directionless, its goal is to correct the mistake and invent a non-individualistic way of life for eukaryotes. Multicellularity and human society are intermediate steps to that goal. The ultimate goal is to spread life, but spreading individualistic life would be bad, the mistake has to be corrected first. Humans have a chance to help with that process, but aren't intended to see the outcome."
The details of the text are more interesting than the main idea, though.
Hold on, is he trying to imply that prokaryotes aren't competitive? Not only does all single-celled life compete, it competes at a much faster pace than multicellular life does.
Robin Hanson defines “viewquakes” as "insights which dramatically change my world view."
Are there any particular books that have caused you personally to experience a viewquake?
Or to put the question differently, if you wanted someone to experience a viewquake, can you name any books that you believe have a high probability of provoking a viewquake?
The Feynman Lectures on Computation did this for me by grounding computability theory in physics.
Are self-training narrow AIs even a going concern yet?
Is what Google does for search results based in part on what you do and don't do considered self training?
What I mean is that two people don't see the exact same Google results for some queries if we were both signed into Google, and in some cases even if we both aren't. Article: http://themetaq.com/articles/reasons-your-google-search-results-are-different-than-mine
An entirely separate question is whether or not Google is a narrow AI, but I figured I should check one thing at a time.
I wouldn't call Google's search personalization "self-training" because the user is responsible for adding new data points to his or her own model; it's the same online algorithm it's always been, just tailored to billions of individual users rather than a set of billions of users. The set of links that a user has clicked on through Google searches is updated every time the user clicks a new link, and the algorithm uses this to tweak the ordering of presented search results, but AFAIK the algorithm has no way to evaluate whether the model update actually brought the ordering closer to the user's preferred ordering unless the user tells it so by clicking on one of the results. It could compare the ordering it did present to the ordering it would have presented if some set of data points wasn't in the model, but then it would have to have some heuristic for which points to drop for cross-validation.
The way I see it, the difference between an online algorithm and a self-training AI is that the latter would not only need such a heuristic -- let's call it "knowledge base evaluation" -- it would also need to be able to evaluate the fitness of novel knowledge base evaluation heuristics. (I'm torn as to whether that goalpost should also include "can generate novel KBE heuristics"; I'll have to think about that a while longer.) Even so, as long as the user dictates which points the algorithm can even consider adding to its KB, the user is acting as a gatekeeper on what knowledge the algorithm can acquire.
Are self-training narrow AIs even a going concern yet? DeepQA can update its knowledge base in situ, but must be instructed to do so. Extracting syntactic and semantic information from a corpus is the easy part; figuring out what that corpus should include is still an open problem, requiring significant human curation. I don't think anyone's solved the problem of how an AI should evaluate whether to update its knowledge base with a new piece of information or not. In the Watson case, an iterative process would be something like "add new information -> re-evaluate on gold standard question set -> decide whether to keep new information", but Watson's fitness function is tied to that question set. It's not clear to me how an AI with a domain-specific fitness function would acquire any knowledge unrelated to improving the accuracy of its fitness function -- though that says more about the fitness functions that humans have come up with so far than it does about AGI.
It's certainly the case that an above-human general intelligence could copy the algorithms and models behind a narrow AI, but then, it could just as easily copy the algorithms and models that we use to target missiles. I don't think the question "is targeting software narrow AI" is a useful one; targeting software is a tool, just as (e.g.) pharmaceutical candidate structure generation software is a tool, and an AGI that can recognize the utility of a tool should be expected to use it if its fitness function selects a course of action that includes that tool. Recognition of utility is still the hard part.
Are you using any tools to keep yourself cut off, or do you merely choose not to visit those sites?
In the past I've manually edited my hosts file to point time-wasting sites to 127.0.0.1, which is a small obstacle at most -- I can easily edit it back -- but is enough of a speedbump to remind me "oh, right, I'm trying to avoid that right now." This time I haven't needed to; I managed to become so frustrated with the fact that my queue was saturated with short-term rewards that weren't netting me anything in the long run that choosing not to visit the sites doesn't seem to require much willpower.
This leads me to wonder whether I could eliminate other influences I want to avoid by making myself sufficiently frustrated with them, but that seems like it would be an unpleasant skill to pick up.
Have you read Pierce's Types and Programming Languages? If so, would you say it provides sufficient foundation for this book?
Possible counterexamples (there are probably better ones):
- "It's the economy, stupid"
- http://sub.garrytan.com/its-not-the-morphine-its-the-size-of-the-cage-rat-park-experiment-upturns-conventional-wisdom-about-addiction (http://lesswrong.com/r/discussion/lw/ing/open_thread_september_1622_2013/9rdu)
- http://parenting.blogs.nytimes.com/2013/08/25/its-not-a-problem-its-called-being-a-child/
All of these are dummy subjects. English does not allow a null anaphor in subject position; there are other languages that do. ("There", in that last clause, was also a dummy pronoun.)
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Toby Ord has some elderly C code (see Appendix II) that he used in his societal iterated prisoner's dilemma tournaments. You'd have to modify it for your purposes, but it's a small codebase.