By Jacqueline Koch
Artificial intelligence (AI) is expanding into an array of unexpected applications, looming ever larger on the horizon of our tech-powered future. It’s increasingly dominating media headlines as well. Take the recent news of AlphaGo. This is the AI “mind” that defeated one of the best players of Go, considered perhaps one of the most complex games developed by humans.
In China, more than 280 million people watched the game live, marvelling at a machine’s strategic mastery over man. However, not all AI developments are winners. On the flip side of AlphaGo’s triumph, was Tay, the chatbot released by Microsoft that was trained to respond like a millennial. In short time, its vulnerabilities were exposed and Tay, mimicking users, transformed into a racist, xenophobic, misogynistic chatterbox.
Whoops.
Nonetheless, AI is rapidly on the rise with mind-boggling potential, from fighting cancer and creating original art to seeing for the blind. And AI is advancing in tandem with a precipitous shift on the web, which is moving more and more from text to images.
Each day, two billion photos are shared online, keeping pace with the surge toward visually driven apps such as Snapchat and Instagram. Bringing AI together with a glut of photos has driven image recognition technology into high gear, yielding great gains for retailers, advertisers and consumers, but most importantly, publishers.
The ABCs of AI
Images. Images. Images. They are the foundational building blocks buttressing AI development of ever more powerful image recognition technology, and it’s near relative, face recognition technology. A recent issue of Wired offered the 30,000-foot view by taking a deep dive into machine learning, the foundation of AI. This approach, which relies on “training” a computer rather than coding, is now far more powerful thanks to “deep neural networks.”
These are massively distributed computational systems that mimic the multilayered connections of neurons in the brain. As a simple example, to train a computer to recognise a photo of an elephant, developers “show” it thousands of photos, including many with elephants. The more images, the better the computer learns. Take this process to its next logical steps, and billions of images down the road, behold the birth of image recognition technology.
Image recognition technology has moved rapidly ahead. Today, it can correctly analyse objects, faces, places, colours, logos and more. This also means that any company with access to large collections of photos wields a new knowledge gathering superpower: data mining. It’s a competitive edge, leapfrogging previous information gathering and marketing research tactics, and with the capacity to drill down to a granular level of user insights, image recognition technology is predicted to be a $30 billion market by 2020.
AI-empowered fashionistas
In the retail space, Macy’s set the pace in 2014 with the launch of an iOS app allowing shoppers to upload a photo, find an equivalent product on Macys.com and purchase it immediately. The result: instant gratification for shoppers and a doubling of mobile sales in fiscal 2015 for Macy’s.
Google, not surprisingly, moved in quickly with the Google Photos app, which hit 100 million users in its first five months. It allows users to store, organise, catalogue and search their images. However, just as there’s no such thing as a free lunch, there is a lucrative strategy behind Google’s free cloud-based app. Google has effectively deployed armies of users, who are rebelling and reorganising their images, to provide a vast dataset. This allows Google to hone and re-hone algorithms and thereby dramatically improve visual searches and expand services.
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