Summary: This may be the golden age of deep learning but a lot can be learned by looking at where deep neural nets aren’t working yet. This can be a guide to calming the hype. It can also be a roadmap to future opportunities once these barriers are behind us. The full article is accessible here, below is a snapshot..
We are living in the golden age of deep learning. This is quite literally the technology that launched 10,000 startups (to paraphrase Kevin Kelly’s prophetic prediction from 2014 “The business plans of the next 10,000 startups are easy to forecast: Take X and add AI.”) Well that happened.
Kelly was speaking more broadly about AI, but over the last four years we’ve come to understand that it’s about CNNs and RNN/LSTMs that are actually commercially ready and driving this.
Although the last two years have been fairly quiet in terms of new technique and technology breakthroughs for data science, it hasn’t been totally quiet. Like the emergence of Temporal Convolutional Nets (TCNs) to replace RNNs in language translation, research goes on to see how deep learning and specifically CNN architecture can be pushed into new applications.
Roadblocks to Deep Learning
Which brings us to our current topic which is to understand what some of the major roadblocks in research are in trying to expand deep learning into new areas.
In calling our attention to ‘things that aren’t working in deep learning’, we aren’t suggesting that these things will never work, but rather that researchers are currently identifying major stumbling blocks to moving forward.
The value of this is two-fold. First it can help steer us away from projects that might on the surface look like deep learning will work, but in fact may take a year or years to work out. Second, we should keep our eye on these particular issues since once they are resolved they will represent opportunities that others may have decided weren’t possible.
Here are several that we spotted in the research.
Read full article here.
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