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 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.
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.