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“If you’re not doing AI today, don’t expect to be around in a few years,” says Japjit Tulsi, VP of engineering at eBay, “It really is that important for companies to invest in — especially commerce companies.”
Tulsi will speak next week at MB 2017, July 11 and 12 in SF, MobileBeat’s flagship event where this year we’ve gathered more than 30 brands to talk about how AI is being applied in businesses today.
eBay is working to stay ahead of the curve, now that machine learning and AI is growing in importance. It has focused on the potential of AI for the past ten years. The company’s approach to AI has been built on a platform of research and development, Tulsi says, plus decades of insights and data about consumer behavior, making even the simplest applications incredibly valuable.
As an example, Tulsi points to the merchandizing strip at the bottom of every item page, which shows similar items that a shopper might be intrigued by, and often leads them down a positive rabbit hole of shopping and buying.
“It’s machine learning and AI at the very simplest level, and we’ve seen a tremendous amount of return on investment on that.” Tulsi says.
However, evolving that into more sophisticated personalization has proven difficult, say Tulsi, because of the limitations on computing power in the past 10 years. Then in 2015 or so, processors hit the event horizon, with game-changing advances in GPUs and the dedicated hardware used for deep learning.
Massive calculations can now be made swiftly and cost-effectively. New algorithms are increasing the speed and depth of learning. And deep learning can now go broad across billions of data points with thousands of aspects and dozens of layers.
eBay has no shortage of data. The company manages about 1 billion live listings and 164 million active buyers daily, and receives 10 million new listings via mobile every week.
So another big bet was born: Investment in AI technologies like natural language understanding, computer vision, and semantic search, to drive growth and, Tulsi says, “reinvent the future of commerce.”
The future looks pretty much like their engineering team building descriptive and predictive models from the enormous volume of behavioral and description data generated by eBay’s many buyers, sellers, and products. It requires the complex fusion of massive amounts of behavior log, text, and image data, all with a particular emphasis on on developing data-driven models to improve user experience.
“The question now is, can we provide you with even further personalized, relevant information over the course of the next ten years?” he says. “We’re very focused on how AI will impact commerce.”
Specifically, how it will impact the primary goal of commerce: understanding consumer buying intent wherever they are, from bricks and mortar to online browsing. Of course, cross-platform understanding of what a shopper wants is the key to delivering a truly personal, contextual shopping experience.
“You want an exact item that you’re looking for — whether you want it, you need it, or you just like it — at the price point you care about,” Tulsi says. “With AI, our aim is to achieve that kind of perfection underneath the hood so you don’t have to spend a lot of time finding that ideal match for you.”
He points at one of their beta projects, launched last year on Facebook Messenger: the eBay ShopBot. It’s essentially a multimodal search engine, or a personalized shopping assistant, powered by contextual understanding, predictive modeling, and machine learning.
Keywords are not enough any more, and don’t offer the most optimized shopping experience. With ShopBot, consumers can text, talk, or snap a picture, and then the assistant asks questions to better understand your intent and dig up hyper-personalized recommendations. And it gets smarter about what you want, every time you use it.
These consumer interactions also yield a tremendous amount of intent data, which can be poured right back into the algorithm.
“Across the three spectrums of multimodal AI that it represents, we’re starting to get much much better at understanding you and whichever way that you want to interact with us,” Tulsi says.
And as they’re able to improve their ability to simulate human cognitive capabilities like perception, language processing. and visual processing, the company expects that commerce will become increasingly conversational — even to the point where the search box becomes redundant.
“What I think is really exciting going forward is the machine will actually do the thinking for you,” Tulsi laughs. “You will just talk naturally to it as if you’re talking to a friend and spitballing — and the machine should be able to understand your intent.”
And just as importantly, commerce will will become present wherever and whenever the user is engaged on their social messaging platforms.
It’s an approach that digital assistant-focused companies should sit up and take notice of, Tulsi adds. They need to start investing in commerce capabilities or partnering with commerce companies to really make their assistant pan out from a financial model perspective.
“From our perspective, every company should be heavily investing in AI, and it shouldn’t just be about using cognitive services but actually developing your own models that keep you on the cutting edge of technology,” Tulsi says. “And that will hold you in good stead over the course of the next many years to come.”