Despite progress, the future of AI will require human assistance
Part one of this story on the future of AI explained how technology developments have led to a resurgence in a…
field that has progressed in fits and starts since the 1950s. Today’s cheap storage and amped-up and inexpensive compute power, combined with an explosion in data, have revived an interest in deep learning and “neural nets,” which use multiple layers of data processing that proponents sometimes liken to how the brain takes in information.
The field is red hot today, with Google, Facebook and other technology giants racing to apply the technology to consumer products. In the second part of this story, SearchCIO senior news writer Nicole Laskowski reports on where two AI luminaries — Facebook‘s Yann LeCun and Microsoft’s Eric Horvitz — see the trend going.
Like Microsoft, IBM and Google, Facebook Inc. is placing serious bets on deep learning, neural networks and natural language processing. The social media maven recently signaled its commitment to advancing these types of machine learning by hiring Yann LeCun, a well-regarded authority on deep learning and neural nets, to head up its new artificial intelligence (AI) lab. A tangible byproduct of this renewed focus on neural nets is Facebook‘s digital personal assistant, M, which rolled out to select users a few months back.
Today, M’s AI technology is backed by human assistants, who oversee how M is responding to queries (such as placing a take-out order or making a reservation) and can step in when needed. According to a Wired article, the AI-plus-human system is helping Facebook build a model: When human assistants intervene to perform a task, their process is recorded, creating valuable data for the AI system.
Once enough of the “right data” is collected, M will be built on neural nets, which is where LeCun’s team comes in. But even as the AI behind M advances to neural nets, humans will need to be in the loop to continue training the technology, according to the article.
Still work to be done That’s because M, like most contemporary AI systems, is a supervised learning system, which means the system can’t teach itself. Instead, if LeCun wants an algorithm to recognize dogs, he has to feed it examples of what dogs look like — and not just a handful of examples. “You have to do it a million times,” LeCun said at EmTech, an emerging technology conference hosted by the MIT Technology Review. “But, of course, humans don’t learn that way. We learn by observing the world. We figure out that the world is three-dimensional, that objects move independently. … We’d like machines to be able to do this, but we don’t have good techniques for this.” Building machines that have a kind of artificial common sense, according to LeCun, will be the big challenge for the future of AI. “It’s done by solving a problem we don’t really have good solutions for yet, which is unsupervised learning,” he said. One of the ways Facebook (among others) is trying to insert rudimentary reasoning into AI systems is with vector embedding, where unstructured data is mapped to a sequence of numbers that describe the text or object in detail.
