What real world 'knowledge' does Deep Learning retain?
Deep learning is supposed to be knowledge-free, but is in fact one of the most knowledge-intensive subfields of machine learning. More time has gone into designing deep architectures than probably anything else in the last 10 years.
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Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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Not to mention curating the datasets (which in essence via selection is another way of programming these models)
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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So true, it’s as if the architecture search in DL replaced the effort of what used to be expert driven feature engineering in ensembled tree models
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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This is a great line of thinking. My workplace focuses a lot on physics based explainable models, while others solve same problems using kitchensink DL architecture approach, I would be interested to see what aspects of physics are retained in the deep architecture.
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Or better yet, simply combine your physics/boolean/fuzzy models with ML models, in a principled manner, to get to the solution faster and more effectively.
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Is that knowledge or is it experiment
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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Which is one of the reasons why I am increasingly skeptical of it, from the purely intellectual standpoint. There is no doubt about its practical success, but it leaves a lot to be desired when it comes to deeper insights.
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The insights are about the nature of knowledge itself & intelligence as humans understand it—DL raises profound epistemological questions. The idea that any academic would holistically expect ML to be ‘knowledge-free’ entering the strong AI era is a flat-earth view of a 4-D world
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Do you believe that deep learning will continue to evolve to effectively solve harder (AGI) problems?
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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And that’s right and just
. Cc @h1st_aiKiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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