Of all the buzzwords in business, there are two letters in particular that seem to be everywhere, all the time, even in places they don’t belong: AI. While too many businesses make unjustified claims to artificial intelligence, make no bones about it—AI in general and machine learning in particular will be a major driver in technology this year. The ability of an unsupervised algorithm to learn from data is big business, and it’s disrupting industries across the board.
This extraordinary AI boom is being spurred by tech giants like Google that seem to be making headlines daily with their record-breaking AI research studies. But what does this mean for the larger tech ecosystem, in particular the smaller start-ups that we count on to occasionally upend the behemoths?
“We keep hearing that for AI to be effective, machines need vast amounts of data on which they can train to become better at whatever task they are intended to perform,” writes Peter Hirst, our Associate Dean of MIT Sloan Executive Education, in a recent Xconomy Boston article. “This parameter is hard to get around if your company doesn’t have control over massive datasets (like Google or Facebook, for example) or doesn’t have the resources to purchase datasets from companies who amass those for sale.”
In a recent Wired article, “AI And ‘Enormous Data’ Could Make Tech Giants Harder To Topple,“ author Tim Simonite writes, “… when competition in tech depends on machine learning systems powered by huge stockpiles of data, slaying a tech giant may be harder than ever.”
So, what’s a startup in the AI space to do?
At the recent Synergy Global Forum in New York, Hirst sat down with inventor, futurist, and AI luminary Ray Kurzweil and asked him whether it is possible to compete with the likes of Google (Kurzwel took a position with the company in 2012). Do you have to be a Google (or a Microsoft or an IBM), with the kind of resources they have available, to do this? Kurzweil’s answer was, to some extent, “yes.”
Let’s look at the odds. A recent study by McKinsey Global Institute reports that “Tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions.”
And then there is data hoarding, already well established as a defensive strategy among AI-centric companies. According to Wired, tech giants have open-sourced lots of software—and even hardware designs—but not necessarily the kind of data that offer much of value to potential competitors.
The answer for small start-ups may be best summarized in two words: collaboration and creativity. During his conversation with Hirst, Kurzweil acknowledged that there are ways for much smaller companies to collaborate and get some of the benefits of scale that the larger companies enjoy, citing the Open Source Initiative as an example. And the recent Wired article offered up the model seen in insurance where smaller companies carefully and collaboratively pool data to make their risk predictions competitive with larger competitors.
Thomas Malone, Professor of Information Technology and Director of the MIT Center for Collective Intelligence, points out that there are plenty of opportunities for innovation and discovery in the field of AI that don’t require access to gigantic amounts of data. And, when it comes to experimentation, smaller companies may have an advantage.
“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,” 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,” he says. (Malone teaches in the live, online program Artificial Intelligence: Implications for Business Strategy.)
Are enormous amounts of data always necessary to be competitive? Possibly not. Right now, there are companies making progress on less-data hungry forms of machine learning. There are also opportunities for start-ups to apply machine learning in places where data is rarely collected, if at all, such as agriculture/smart farming.
Even more promising, perhaps, is Fast Company’s 2017 list of the 10 Most Innovative Companies In AI/Machine Learning. While the top three spots are occupied by Google, IBM, and Baidu, respectively, the list also features Iris AI (Norway), Descartes Labs (New Mexico), and Zebra Medical Systems (Israel), none with more than 25 employees.
“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,” says Hirst.
In other words, there is still room for AI savvy start-ups to change the world.