Could the flood of hype for artificial intelligence lead to a catastrophic collapse in funding?
The fear: AI will fail to deliver on promises inflated by businesses and researchers. Investors will migrate to greener pastures, and AI Winter will descend. Funding will dry up, research will sputter, and progress will stall.
What could go wrong: Enthusiasm surrounding even modest advances in AI is driving an investment bonanza: Venture funds put $9.3 billion into AI startups in 2018, up over 70 percent from the prior year, according to a joint study by PricewaterhouseCoopers and CB Insights. Some critics believe that deep learning has soaked up more than its fair share of investment, draining funds from other approaches that are more likely to lead to fundamental progress. Could funders lose patience?
- If major AI companies were to experience severe shortfalls in earnings, it could cause press coverage to flip from cheery optimism about AI’s potential to relentless criticism. Public sentiment would turn negative.
- Ethical lapses by companies making AI-driven products could further darken the horizon.
- Limits of current technology — for instance, deep learning’s inability to distinguish causation from correlation and autonomous driving’s challenges with image classification and decision making — could become indictments of the entire field.
Behind the worries: AI history is dotted with setbacks brought about by spikes in public skepticism. Two prolonged periods — one lasting for much of the 1970s, the other from the late 80s to early 90s — were dark and cold enough to have earned the name AI Winter.
- Key agencies in the UK cut AI funding in the wake of James Lighthill’s 1973 report on the lack of progress in the field. In the U.S. around the same time, disillusioned officials terminated Darpa’s multi-institute Speech Understanding Research program. The cuts fueled skepticism among commercial ventures and dried up the pipeline of basic research.
- By the early 1980s, AI had rebounded. The technology of the day mostly ran on high-powered computers using the LISP operating system. But the late-’80s personal computer revolution gutted the market for these expensive machines, stalling AI’s commercial growth. Again, AI funding retreated.
How scared should you be: It’s true, AI has received enough hype to make P.T. Barnum blush. Yet the current climate shows little sign of impending winter. Earlier this year, Alphabet reported that DeepMind, its deep learning subsidiary, had cost its parent company $570 million as of 2018. Some observers warned that the expense could portend an industry-wide loss in confidence. Yet technical leaders in the field say they’re well aware of deep learning’s shortcomings, and the march of new research is dedicated to surmounting them. Moreover, AI is generating significant revenue, creating a sustainable economic model for continued investment, while AI research is less reliant than ever on government and institutional funding. Established companies, startups, and research labs all have their eyes open for pitfalls and blind alleys.
What to do: As AI practitioners, we should strive to present our work honestly, criticize one another fairly and openly, and promote projects that demonstrate clear value. Genuine progress in improving peoples’ lives is the best way to ensure that AI enjoys perpetual springtime.