Mark Zuckerberg’s FB status updated on 28 Jan:
My personal challenge for 2016 is to build a simple AI — like Jarvis from Iron Man — to help run my home and help me with work.
I’m planning on writing up some thoughts every month on what I’ve built and what I’m learning. I’m still early in coding, so I’ll start this month with a summary of the state of the AI field.
Artificial intelligence may seem like something out of science fiction, but most of us already use tools and services every day that rely on AI. When you do a voice search on your phone, put a check into an ATM, or use a fitness tracker to count your steps, you’re using basic forms of pattern recognition and artificial intelligence. More sophisticated AI systems can already diagnose diseases, drive cars and search the skies for planets better than people. This is why AI is such an exciting field — it opens up so many new possibilities for enhancing humanity’s capabilities.
So what can AI do and what are its limits? What things is AI good at and what is AI bad at?
Simply put, today’s AI is good at recognizing patterns and bad at what we would call “common sense”.
The primary method used to train AI systems is called supervised learning. This is like when you show a picture book to a child and tell them the names of everything they see. If you show an AI thousands of pictures of dogs, you can train it to start recognizing dogs.
You can teach AIs to do a lot of things this way. For example, we can teach an AI to recognize all of your friends’ faces by showing it thousands of photos, and then it can suggest tags for the photos you upload on Facebook. You can teach an AI to recognize speech by having it listen to thousands of hours of speeches throughout history while also showing it transcriptions of what was said. You can teach an AI to diagnose melanoma by showing it thousands of photos of tumors. You can even teach an AI how to drive a car and automatically brake by showing it thousands of examples of people and obstacles it might encounter on the road.
Diagnosing cancer, driving cars, transcribing speech, playing games and tagging photos may sound like very different tasks, but they’re all examples of teaching an AI to recognize patterns by showing them many examples.
Many different problems can be reduced to pattern recognition tasks that sophisticated AIs can then solve. This year, I’ll teach my simple AI to recognize patterns. I’ll train it to recognize my voice so I can control my home through speaking. I’ll train it to recognize my face so it can open the door when I’m approaching, and so on.
But there are lots of limitations of this approach. For one, to teach a person something new, you typically don’t need to tell them about it thousands of times. So the state of the art in AI is still much slower than how we learn.
But more importantly, pattern recognition is very different from common sense — and nobody knows how to teach an AI that yet.
Without common sense, AI systems can’t use knowledge they’ve learned in one area and easily apply it to another situation. This means they can’t effectively react to new problems or situations they haven’t seen before, which is so much of we all do everyday and what we call intelligence.
Our best guess at how to teach an AI common sense is through a method called unsupervised learning. My example of supervised learning above was showing a picture book to a child and telling them the names of everything they see. Unsupervised learning would be giving them a book and letting them figure out what to do with it. They could pick it up and by touching it learn to turn the pages. Or they could let go of it and realize it falls to the ground.
Unsupervised learning is learning how the world works by observing and trying things out rather than being told what to do. This is how most animals learn. It’s key to building systems with human-like common sense because it doesn’t require a person to teach it everything they know. It gives the machine the ability to anticipate what may happen in the future and predict the effect of an action. It could help us build machines that can hold conversations or plan complex sequences of actions — necessary components for any authentic Jarvis.
Unsupervised learning is a long term focus of our AI research team at Facebook, and it remains an important challenge for the whole AI research community.
Since no one understands how general unsupervised learning actually works, we’re quite a ways off from building the general AIs you see in movies. Some people claim this is just a matter of getting more computing power — and that as Moore’s law continues and computing becomes cheaper we’ll naturally have AIs that surpass human intelligence. This is incorrect. We fundamentally do not understand how general learning works. This is an unsolved problem — maybe the most important problem of this century or even millennium. Until we solve this problem, throwing all the machine power in the world at it cannot create an AI that can do everything a person can.
We should not be afraid of AI. Instead, we should hope for the amazing amount of good it will do in the world. It will saves lives by diagnosing diseases and driving us around more safely. It will enable breakthroughs by helping us find new planets and understand Earth’s climate. It will help in areas we haven’t even thought of today.
Jarvis is still a long way off, and we’re not going to solve most of these engineering challenges in the next year. But I’m glad to be joining the effort and doing what I can to push the field of AI forward.