We now live in a world where Ozlo and Viv bots can find your ideal coffee and sushi. Where your car wants to drive on its own. Where your thermostat is cooling the house, as it knows you will be home in 15 minutes. And where the world champion at Go, a game with more permutations than the number of atoms in the observable universe, was roundly beaten by a computer program named AlphaGo.
In the history of artificial intelligence, we have seen a lot of hype, immense promise, countless movie plots and many false starts. But there has not been a lot of significant progress in real world applications.
Now, it feels like there is an announcement every day in this space - some incremental in nature, some profound.
The landscape is changing rapidly, particularly in the last six to eight months. We have seen radical sharpening of the intelligence of machines – computers can now use image recognition for diagnosing diseases or developing scientific theories.
What AI is going to do is accelerate the pivot from simple clustering around inventory, to combining intelligence about individuals, behaviors, trends, and context.
Advances in hardware, mathematics and computing have led us to a tipping point. Deep learning can now go broad across billions of data points with thousands of aspects and dozens of layers. There have been game-changing advances in graphics processing (GPUs) and the dedicated hardware (TPU) used for deep learning. Massive math calculations can now be made quickly and cost-effectively. And new algorithms coming online have increased the speed and depth of learning.
Over the next two to three years, I believe we’ll see the pace of development of AI accelerate beyond anything we have previously imagined. In the near term, specialized or vertical industry applications could have more potential for impact. By contrast, horizontal “do everything” use cases may conjure up ethical and social dilemmas.
I believe that commerce, in particular, will be the focus of some of the most immediate and exciting applications of AI.
Unlocking True Buying Intent
The holy grail for commerce has always been deducing the buying intent of consumers -- when they walk into stores, when they browse online, when they order on apps. Regardless of the platform, understanding what they want is the key to delivering a truly personal, contextual shopping experience.
To be clear, personalization isn’t new – it has been a part of the playbook of every commerce company. But so far, it has largely focused on either inventory clustering or user behavior patterns. Crudely translated, this means that we know that people who buy soap generally also buy toothbrushes, and we make recommendations accordingly.
What AI is going to do is accelerate the pivot from simple clustering around inventory, to combining intelligence about individuals, behaviors, trends, and context.
As consumers, you will find it is almost like having a personal shopper on hand - except it will get smarter, more attuned to your needs, every time you use it. Imagine you want to buy a scarf. Using AI, commerce platforms and tools will be able to predict whether you want a $7 scarf or a $700 scarf. They will know what color you want and whether you prefer natural fibers to manmade. And they will be able to anticipate the next thing you’ll want to buy.
As the world’s inventory becomes available online, AI will help consumers create order out of chaos.
Deploying Data for Deeper Commerce
eBay’s approach to AI is informed by a history of research and development and decades of insights and data about consumer behavior.
We already use machine learning algorithms to recognize objects in listings, find similar products, and rank recommendations. And we deploy AI in various areas, from structured data to machine translation to risk and fraud management.
As machines get better at decoding natural language, commerce should become increasingly conversational -- eventually rendering the search box redundant.
We recently acquired Expertmaker – a company that has created an advanced AI platform enabling optimization and automation. We currently expect to apply their technology across our platform, to help improve shipping and delivery times, trust, pricing, and more.
Leaping Ahead to Conversational Commerce
Deep learning has given us all the opportunity to deliver people-centric experiences that scale - deep commerce, as I like to call it.
The possibilities for consumers are intriguing: If you see something you like, imagine being able to snap a picture with your phone, and buy it in seconds. As machines get better at decoding natural language, commerce should become increasingly conversational -- eventually rendering the search box redundant.
We as an industry are on the cusp of a new revolution in commerce; in the next few years, we’ll witness an unprecedented convergence of technology, commerce and consumer expectations. And I believe the outcome will be a smarter, more frictionless, more accessible, more contextual shopping experience -- an experience powered by technology, with consumers at its heart.
This article originally appeared on Devin's LinkedIn page. You can also follow him on Twitter and Facebook.