I think it often goes understated just how much the emergence of massive datasets led to the successes of neural networks.
I once heard Daphne Koller say that before big data, neural networks were always the second best way to do anything.
I once heard Daphne Koller say that before big data, neural networks were always the second best way to do anything.
Which is why I wish there was a copyleft open source data analogue. If you train on everyone’s public data, your model should have to be just as publicly available.
Is this really common enough it's even worth mentioning?
It's not clear whether the model trained on some data is copyrightable at all (and if it isn't, it can't be a derived work and gets no protection nor restrictions from copyright law) as in general facts about a work - including things like word frequency statistics, which was a popular type of trained language models not that long ago (e.g. n-gram models used in statistical MT systems) - are not copyrightable and in that case making/distributing those is not an exclusive right of the author and needs no license or permission, that was settled long ago between publishers and e.g. dictionary makers.
There is also the notion that mechanistic transformations or difficult labor can't result in copyrightable work, there needs to be human creativity involved; in US copyright law (as in Fiest Publications Inc. v. Rural phone service Co, also see https://www.gutenberg.org/help/no_sweat_copyright.html) the fact that making some work required lots of work and cost you lots of money does not imply that it deserves copyright protection, no matter how much work was required; so it's irrelevant that someone spent millions of dollars worth of GPU time, and it could certainly be argued (case law would be useful here!) that the software used to train the model is copyrightable, but the model output by that software is not.
Perhaps case law or some new explicit law will settle otherwise, but currently all the research and industry is proceeding with the assumption that a trained model is not a derived work (in the copyright law sense) from the training data, and as far as I see this assumption is not being challenged in courts.
Before the ImageNet dataset, CNNs weren't worth it.
Too early to say that with a straght face yet.
"Deep learning" produced some amazing generative art, and some very flashy research papers, but it failed to drive business decisions. (And not for the lack of trying, that's for sure.)
You don’t have to fall for the hype and think deep learning will lead us to AGI in the next 5 years, but dismissing it as generative art and flashy papers isn’t any more accurate.
As for Alexa, et al - these things fall into the "generative art" bucket. The search results they give aren't any better than the Eliza-tier expert systems of yore. They just feel much better and more human when you use them.
(Which is also important, but doesn't drive business decisions, except as part of a marketing strategy.)
I personally own a startup that uses deep neural networks as part of its core product functionality and our business decisions would be drastically different if it weren’t for modern machine learning. We could technically ship some similar products, but they would either be far inferior or take orders of magnitude longer to engineer by hand.
Deep learning is a cool feature to differentiate Alexa from competing home electronic gadgets.
But if you want to forecast Alexa sales, or understand market segmentation, or the portrait of a typical Alexa buyer then you need something other than deep learning because neural nets utterly failed in this domain.
The business metrics problem is vastly, vastly more important than the "making cool gadgets" problem, and huge resources were poured into making deep learning a thing in this space. The money was mostly wasted. (A negative result is still a result, but still the misallocation of resources is staggering.)
FYI, deep learning isn’t a differentiating feature for the Alexa, it powers essentially all modern voice applications.
https://ai.googleblog.com/2020/11/improving-on-device-speech...