And Kyndi’s reading and scoring software is fast. A human analyst, Mr. Welsh said, might take two hours on average to read a lengthy scientific document, and perhaps read 1,000 in a year. Kyndi’s technology can read those 1,000 documents in seven hours, he said.
Kyndi serves as a tireless digital assistant, identifying the documents and passages that require human judgment. “The goal is increasing the productivity of the human analysts,” Mr. Welsh said.
Kyndi and others are betting that the time is finally right to take on some of the more daunting challenges in A.I. That echoes the trajectory of deep learning, which made little progress for decades before the recent explosion of digital data and ever-faster computers fueled leaps in performance of its so-called neural networks. Those networks are digital layers loosely analogous to biological neurons. The “deep” refers to many layers.
There are other hopeful signs in the beyond-deep-learning camp. Vicarious, a start-up developing robots that can quickly switch from task to task like humans, published promising research in the journal Science last fall. Its A.I. technology learned from relatively few examples to mimic human visual intelligence, using data 300 times more efficiently than deep learning models. The system also broke through the defenses of captchas, the squiggly letter identification tests on websites meant to foil software intruders.
Vicarious, whose investors include Elon Musk, Jeff Bezos and Mark Zuckerberg, is a prominent example of the entrepreneurial pursuit of new
paths in A.I.
“Deep learning has given us a glimpse of the promised land, but we need to invest in other approaches,” said Dileep George, an A.I. expert and co-founder of Vicarious, which is based in Union City, Calif.
The Pentagon’s research arm, the Defense Advanced Research Projects Agency, has proposed a program to seed university research and provide a noncommercial network for sharing ideas on technology to emulate human common-sense reasoning, where deep learning falls short. If approved, the program, Machine Common Sense, would start this fall and most likely run for five years, with total funding of about $60 million.
“This is a high-risk project, and the problem is bigger than any one company or research group,” said David Gunning, who managed Darpa’s personal assistant program, which ended a decade ago and produced the technology that became Apple’s Siri.
