Facebook offered an update on its advances in artificial intelligence, touching on object detection, natural language understanding, predictive learning and planning.
Schroepfer said the Facebook AI Research team will present a new paper at AI conference NIPS 2015 next month detailing its state-of-the-art object-detection system, which segments images 30 percent quicker than previous industry benchmarks, using 10 times less training data.
He also shared the image below, writing:
How many zebras do you see in the photo? Hard to tell, right? Imagine how hard this is for a machine, which doesn’t even see the stripes—it sees only pixels. Our researchers have been working to train systems to recognize patterns in the pixels so they can be as good as or better than humans at distinguishing objects in a photo from one another—known in the field as “segmentation”—and then identifying each object.
Natural language understanding
Schroepfer wrote about the combination of the Memory Networks (MemNets) system Facebook introduced last year with image-recognition technology, which the social network refers to as VQA, or visual Q&A. He added:
MemNets add a type of short-term memory to the convolutional neural networks that power our deep-learning systems, allowing those systems to understand language more like a human would. Earlier this year, I showed you this demo of MemNets at work, reading and then answering questions about a short synopsis of The Lord of the Rings. Now we’ve scaled this system from being able to read and answer questions on tens of lines of text to being able to perform the same task on data sets exceeding 100,000 questions, an order of magnitude larger than previous benchmarks.
The Facebook AI Research team developed a system that can “watch” a series of visual tests and predict the outcome, and Schroepfer wrote that the system is now making the correct predictions 90 percent of the time, which is better than the performance of most humans.
Schroepfer described how the Facebook AI Research team is using classic board game Go in its efforts:
Another area of longer-term research is teaching our systems to plan. One of the things we’ve built to help do this is an AI player for the board game Go. Using games to train machines is a pretty common approach in AI research. In the last couple of decades, AI systems have become stronger than humans at games like checkers, chess and even Jeopardy. But despite close to five decades of work on AI Go players, the best humans are still better than the best AI players. This is due in part to the number of different variations in Go. After the first two moves in a chess game, for example, there are 400 possible next moves. In Go, there are close to 130,000.
We’ve been working on our Go player for only a few months, but it’s already on par with the other AI-powered systems that have been published, and it’s already as good as a very strong human player. We’ve achieved this by combining the traditional search-based approach—modeling out each possible move as the game progresses—with a pattern-matching system built by our computer vision team. The best human Go players often take advantage of their ability to recognize patterns on the board as the game evolves, and with this approach our AI player is able to mimic that ability—with very strong early results.
This is a huge technology challenge—it’s so hard that, starting out, M is a human-trained system: Human operators evaluate the AI’s suggested responses, and then they produce responses while the AI observes and learns from them.
We’d ultimately like to scale this service to billions of people around the world, but for that to be possible, the AI will need to be able to handle the majority of requests itself, with no human assistance. And to do that, we need to build all the different capabilities described above—language, vision, prediction and planning—into M so that it can understand the context behind each request and plan ahead at every step of the way. This is a really big challenge, and we’re just getting started. But the early results are promising. For example, we recently deployed our new MemNets system into M, and it has accelerated M’s learning: When someone asks M for help ordering flowers, M now knows that the first two questions to ask are, “What’s your budget?” and “Where are you sending them?”
One last point here: Some of you may look at this and say, “So what? A human could do all of those things.” And you’re right, of course—but most of us don’t have dedicated personal assistants. And that’s the “superpower” offered by a service like M: We could give every one of the billions of people in the world their own digital assistants so they can focus less on day-to-day tasks and more on the things that really matter to them.
Readers: What are your thoughts on Facebook’s AI advancements?