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I found the article self-contradictory. In case of Google, he actually makes a pretty strong case for the algorithm, not "data". The data (the links) were alw
by lucifer 17y ago
I found the article self-contradictory.
In case of Google, he actually makes a pretty strong case for the algorithm, not "data". The data (the links) were always there. It was precisely the algorithm that generated information from that data.
And a Bayesian Net itself is clearly the by product of applying an algorithm to a data space.
The NetFlix case is anecdotal, but consider the following (equally anecdotal) counter example: The (massive) increase in the available data to humans since the advent of the networks have not contributed to any significant increase in the general intelligence of the population.
Unless by AI he is referring to the highly narrow case of machine creativity given little to no input (of data), then (obviously) algorithms do require data sources.
- physcab 17y agoI think you're right that the post is slightly contradictory, but his premise I believe is correct. In all that I have studied on machine learning in both academia and start-up land, I have observed that you consistently pick algorithms to glean out the information you need from a particular dataset. In many cases you can do what my graduate advisor recommends "keep it simple stupid" meaning that perhaps all that is needed is a k-nn approach and euclidean distance. But sometimes the data is highly overlapped and complex, so you have to go with a more rigorous means of classification or whatnot. Finally it should be noted that machine learning techniques are relatively new. Neural networks have been around for quite some time and have well documented advantages. ML by contrast is still a lot of black magic (tweaking of various magic parameters and such) so the benefits of various algorithms are somewhat subjective.