What is the self-driving car of NLP?
Autonomous driving is a landmark problem in computer vision, perhaps the real-world problem, as @geohot from Comma says often:
Self-driving cars are still the coolest applied AI problem today.
I think it’s worth thinking about what such an applied, real-world machine learning problem would be for natural language understanding and text generation. My hypothesis is that a good candidate for this “self-driving car of NLP” is open-domain, abstractive question answering, wherein a human uses an AI system to synthesize a natural language answer to some knowledge-based question, based on a large corpus of diverse documents, only some of which contain information relevant to answering the question.
Natural language web search is the most ambitious form of this problem, but a more tractable target might be to solve a similar problem for organizations with lots of private knowledge — chat histories, emails, planning documents, feedback surveys, paperwork, contacts, and on and on and on — synthesizing answers to questions like "Do I know any recruiters working at a biotech company?" or "Was there any update about our deal with X from last week's board meeting?" or, even higher level, "What are the most common customer complaints we have about Y feature?"
Both autonomous driving and ODQA:
- are challenging and unsolved technical problems, where solving it perfectly requires fully general intelligence, and would advance the state of the art in many related research domains
- meet clear and lucrative market needs with willingness to pay (== willingness for the market to finance R&D necessary to push on the problem)
- are hot problem spaces with many initial players, but probably few real winners in the end, mostly driving by superiority in data, compute ($$), and research capabilities.
- automate real-world activities that most humans perform often in their daily lives, so that solving it would dramatically improve most people’s lives and save humans lots of labor.
In particular, speaking from my personal experience as well as opinions from experts I’ve spoken to, I believe the market is dramatically underestimating the unsolved technical challenges standing in between us today and the long-term solution to this problem.