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What is next for deep learning? originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better understand the world.
Answer by Ian Goodfellow, AI Research Scientist at Google Brain, on Quora:
There are a lot of things that are next for deep learning. Instead of thinking of moving forward in one direction, think of expanding outward in many directions:
- Better reinforcement learning/integration of deep learning and reinforcement learning. Reinforcement learning algorithms that can reliably learn how to control robots, etc.
- Better generative models. Algorithms that can reliably learn how to generate images, speech and text that humans can’t tell apart from the real thing.
- Learning to learn and ubiquitous deep learning. Algorithms that redesign their own architecture, tune their own hyperparameters, etc. Right now it still takes a human expert to run the learning-to-learn algorithm, but in the future it will be easier to deploy, and all kinds of businesses that don’t specialize in AI will be able to leverage deep learning.
- Machine learning for security, security for machine learning. More cyberattacks will leverage machine learning to make more autonomous malware, more efficient fuzzing for vulnerabilities, etc. More cyberdefenses will leverage machine learning to respond faster than a human could, detect more subtle intrusions, etc. ML algorithms from opposing camps will fool each other to carry out both attacks and defensive actions.
- Dynamic routing of activity will lead to much larger models that may use even less computation to process a single example than current models use today. But overall, massive amounts of computation will continue to be key for AI; whenever we make one model use less computation, we’ll just want to run thousands of models in parallel to learn-to-learn them.
- Semi-supervised learning and one-shot learning will reduce the amount of data needed to train several kinds of models and make AI use more widespread.
- Research will focus on making extremely robust models that almost never make a mistake, for use in safety-critical applications.
- Deep learning will continue to spread out into general culture and we’ll see artists and meme creators using it to do things that we never would have anticipated. I think Alexei Efros’s lab and projects like CycleGAN are the start of this.
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