Hacker News new | past | comments | ask | show | jobs | submit login

Well... no, not really. It is kinda hard to discuss in general, because it depends so much on the details: the application and the algorithms in question. But there is nothing inherently conservative about ML algorithms.

I see why you assume that "stationary distribution is the source of the conservativeness", so maybe I should clarify this moment. It is kind of true in the most general sense: sure, when querying the stationary distribution we can only ever ask how things are in a timeless universe. How anything new can be obtained this way? The problem is, that if we are this general, then the word "conservativeness" loses any meaning, since everything in the [deterministic] Universe can be framed as a question of "how things are", everything is conservative, nothing new can be obtained anywhere, ever.

And we don't even need to get this general for the word "conservativeness" to lose practical sense. When you ask another human for an advice, all he ever does is, in essence, pattern recognition and querying his internal database of "how things generally are to the best of his knowledge". Yet you don't call every human advice ever "conservative": only the kind of advice that recommends the safest, most popular thing, thing that everybody likes, pretty much ignoring the nuance of your personal taste. In fact, even then, you call it "conservative" only if you can notice that, which means that the recommendation isn't new for you personally (and by that criteria alone most humans would lose to a very simple, absolutely currently implementable music recommendation algorithm, since they probably know much lesser number of "artists similar to what you like" than Spotify knows: the only thing Spotify has to do to win is not to recommend the most popular choice almost every time).

One more thing. I could probably convey to you that assuming "ML = conservativeness" is wrong much faster by invoking the example of reinforcement learning, since it is sort of intuitive: there is the obvious existence of "time" in that, you can imagine a "dialogue" where it adapts to what user wants using his feedback, etc. It is easy to see how it could behave "not conservatively". I intentionally avoided doing that, since it can lead to the false impression that RL is somehow different and less conservative than other algorithms. On the contrary, the point I'm trying to make is that every algorithm, even the purest form of memorizing the stationary distribution (like Naïve Bayes) is not inherently conservative. It all depends on what exactly you ask (i.e. how you represent inputs and outputs) and how you query the distribution you have (e.g. how much variability you allow, but not only that).

So, when you see the application that uses ML algorithm and behaves "conservatively", it isn't because of the ML algorithm, it is because of the application: it asks the ML algorithm wrong questions.




Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact

Search: