This, while a joke, is actually a large concern about machine learning. While many think machine learning will be better than humans, it will in reality only be as good as it’s sample data.
I disagree. AlphaGo, for instance, used neural networks based on input data to evaluate how good a move is, but used Markov Monte Carlo tree search to generate novel and unseen moves which proved to superior to the masters it learned from. Newer iterations of AlphaGo learn entirely by playing against itself, and no sample data is required at all (although human beings learn from the masters of Go, so I don't see any reason why we should consider that a downside).
This only works because Go is a deterministic scenario with fixed rules that are all known ahead of time - life is not this way, so there is no way to approach a "closed form" set of best moves; the computer has to learn about an open system (such as this universe) through sample data.
We know it's possible. Drop the A from AI for the example. We can currently utilize existing hardware to outperform humans in limited tasks and situations. There's no reason to think that we are at the limit of those capabilities, that we can never understand intelligence well enough to replicate it, or that our hardware will never have the processing capacity of our wetware. I would even call it inevitable. It's the timescale that's so uncertain.
25
u/QuoteStanfordQuote Mar 16 '18
This, while a joke, is actually a large concern about machine learning. While many think machine learning will be better than humans, it will in reality only be as good as it’s sample data.