Can AI Startups Compete with Tech Giants?

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access to gigantic amounts of data. He draws an analogy with another industry that requires massive resources and scale: “If you are in the steel industry and you don’t have a large factory, it doesn’t mean that there is nothing you can do. It means that you should do other things,” he says. “You can work on shaping steel that is produced elsewhere. You can work on making the factory more efficient, so that factory owners would buy that service from you. I don’t think it is hopeless at all for startups.”

Last year, Fast Company published a list of the 10 Most Innovative Companies In AI/Machine Learning. And while it’s not surprising that the top three spots are occupied by tech giants—Google, IBM, and Baidu—the list also features Iris AI, an Oslo, Norway-based company with a staff of eight, Descartes Labs in Los Alamos, New Mexico (staff of 25), and an Israeli company, Zebra Medical Vision (staff of 21). These and other small companies made Fast Company’s list because of their unique contributions to AI-enabled technological innovations in a wide variety of fields, from healthcare to agriculture to legal contracts.

We still need crazy ideas and lots of them

“One thing that small companies can often do better than big ones is try a whole lot of crazy ideas and see which ones work. The market lets different small companies try different ideas,” says Malone. He explains, “It’s often harder for a big company to let a whole bunch of different people in the company try to do the same thing in competition with each other. It’s unlikely that they’d all get a fair chance.”

The big companies realize that, of course, and like many of their peers in other fields, they are trying to figure out how to work with startups more effectively. A few weeks ago, Microsoft Ventures, the venture capital arm of Microsoft, announced a global startup competition called Innovate.AI. Google’s Launchpad Studio is a startup incubator focused on accelerating applied machine learning in healthcare and biotech, to start.

May the best minds win

As companies big and small race to capitalize on the AI potential, the billion-dollar question is: who will power all these efforts? Until machines are smart enough to run themselves (and possibly, us), all these companies need people and many of these people will come from academia. The lure of lucrative industry jobs may prove a strong incentive for AI experts to leave the lab. Already there is a growing concern among some academic institutions that industry is going to vacuum up AI engineers and scientists, so no one will be left to teach the next generation of students, let alone advance basic science.

The apparent AI-skills shortage puts engineers and computer science researchers and graduates in these fields in an enviable position to pick and choose where they work. However, it’s important to remember that many bright minds choose academia over industry, driven by the intrinsic motivation for scientific discovery. After all, the hottest applied research—such as that done by the R&D departments at corporations—relies on basic research that may have been conducted in university labs decades ago. (Case in point: MIT’s own Marvin Minsky built the first “learning machine” in 1951.)

Although the “brain drain” issue is real and shouldn’t be dismissed outright, being at MIT gives me confidence in academia as the wellspring of innovation. At MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), leading AI experts are working to apply their findings to solve real-world problems. Just last year, for example, CSAIL researchers teamed up with Massachusetts General Hospital and Harvard Medical School to develop a model for improving breast-cancer detection with the help of machine learning, and designed a virtual reality (VR) system to help manufacturing workers operate machinery remotely.

All this goes to say that, while tech giants do have vast resources and capabilities, important innovations in AI are just as likely to come from a startup or a university lab. At least until human brains are no longer needed, which is a subject I aim to explore more in the near future.

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