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I guess this shows that the data really are what matter. Most ML methods can get signal that are reasonably approximate to each other. So are we hyper optimiz
by sjg007 7y ago
I guess this shows that the data really are what matter. Most ML methods can get signal that are reasonably approximate to each other. So are we hyper optimizing the methods? So my guess is that the data is the advantage. Having a lot of it and having exclusive access to it. Is it also time to move beyond derivative me too type publications?
- bluejay2387 7y agoThere is plenty of work out now showing that we may have gone as far as we can with the 'throw more data' at it approach. Work out of the Adversarial AI area showing that models are learning shallow representations regardless of the amount of data used, work showing that even with deep learning we are hitting diminishing marginal returns with larger and larger corpus sizes, almost complete lack of progress in dealing with 'context'... I think that is a large part of the sudden resurgence of interest in ontologies, knowledge graphs, etc...
- sjg007 7y agoI agree with the context argument. But essentially with an ontology though we are adding more “data”. In this case it would be relationships between things. The hope is that implicit connections between other things not directly connected might be revealed by the AI.