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> The biggest, however, is that supervised machine learning doesn’t live up to the hype. This is the key point. The new DNN approaches can outperform the class
by d_burfoot 7y ago
> The biggest, however, is that supervised machine learning doesn’t live up to the hype.
This is the key point. The new DNN approaches can outperform the classical techniques, but only when they can exploit vast amounts of training data. The dramatic successes of Deep Learning all depend on either unsupervised learning against enormous raw datasets (BERT, GPT-2, word2vec, etc) or games, where you can generate unlimited quantities of labelled data by playing the game against your own agent (AlphaGo, AlphaStar, OpenAI Five, etc).
- cosmodisk 7y agoAs someone already mentioned here,I think it's probably not a goood approach. The way we store info in dstabases is very limited compated to what we can do in our heads. For instance we know a concept of a table.It can be made of almost any material,can have whatever shape,size, and colour we want and yet we can instantly recognise it without having some concrete data points on what it should look like. I can make a glass cube,put it in a middle of the room and people would know it's a table.How the hell we operate this way,I have no idea.
- wolco 7y agoOn a basic level we all: - Know what a room looks like. - Expect certain objects to exist at certain places and not expect others - The brain indexes these - Seeing one or two datapoints allows us to guess. So seeing a glass cube in that context gives us few logical chpices (it must be a table) similar to someone typing a letter in a textbox and having it filter a pre-defined list.