2019-07 Building Better Deep Learning Requires New Approaches Not Just Bigger Data

In its rush to solve all the world’s problems through deep learning, Silicon Valley is increasingly embracing the idea of AI as a universal solver that can be rapidly adapted to any problem in any domain simply by taking a stock algorithm and feeding it relevant training data. The problem with this assumption is that today’s deep learning systems are little more than correlative pattern extractors that search large datasets for basic patterns and encode them into software. While impressive compared to the standards of previous eras, these systems are still extraordinarily limited, capable only of identifying simplistic correlations rather than actually semantically understanding their problem domain. In turn, the hand-coded era’s focus on domain expertise, ethnographic codification and deeply understanding a problem domain has given way to parachute programming in which deep learning specialists take an off-the-shelf algorithm, shove in a pile of training data, dump out the resulting model and move on to the next problem. Truly advancing the state of deep learning and way in which companies make use of it will require a return to the previous era’s focus on understanding problems rather than merely churning canned models off assembly lines.

https://www.forbes.com/sites/kalevleetaru/2019/07/07/building-better-deep-learning-requires-new-approaches-not-just-bigger-data/amp/