Outline on how AI works — A video series by FAIR.

FAIR — Advancing the field of machine intelligence

Facebook Artificial Intelligence Researchers (FAIR) seek to understand and develop systems with human level intelligence by advancing the longer-term academic problems surrounding AI.

FAIR research covers the full spectrum of topics related to AI, and to deriving knowledge from data: theory, algorithms, applications, software infrastructure and hardware infrastructure.


The idea of intelligent machines has fascinated people for centuries. And while it may seem like something out of science fiction, people today use artificial intelligence every day in their smartphones, houses, cars and more. It’s becoming more and more prevalent in our world.

At Facebook, AI is used for translation of text between languages, to describe images for visually impaired people, and more.

In the below video, Facebook’s AI research head, Yann LeCun, explains some of the key concepts that make all of this possible and why you should care.

Machine Learning

Can you tell the difference between a car and a dog? That’s probably easy for you, but it’s very complex to teach computers how. The process is called machine learning, and with the right algorithms we can train a computer to recognize objects in images. Check out this video to learn how it works — right down to the pixels.

Gradient Descent

Artificial intelligence is all about math. For example: Gradient descent is a mathematical technique used while training a computer. By calculating how every adjustment in the training will affect the accuracy of the final result, we can teach computers to distinguish between objects in images (like a car and a dog) with lower levels of error. This video explains how.

Deep Learning

When you look at a dog, you immediately know it’s a dog. But computers work differently. To understand an image, intelligent machines need to complete several steps, or “layers” of processing to identify all the features that make up the whole. This is deep learning. Watch this video for a window into how computers “think.”

Back Propagation

With deep learning, a computer performs several layers of processing in order to identify objects in an image. As this happens, it’s important to know how each layer of processing specifically affects the end result. An algorithm called back propagation makes this possible.

Convolutional Neural Networks

In an image, the same object can appear in many different positions — For example, a dog can be lying down or standing up, jumping or facing backward. This makes it very difficult for a computer to recognize a dog in all its possible forms. To do so, we use convolutional neural networks — a way of structuring a deep learning system inspired by the architecture of the human brain. With multiple detectors that each recognize a specific part of the dog, computers can better recognize that object. This also works for text recognition, speech understanding, and is part of many self-driving cars.

What is learnable?

AI also addresses one of the central questions that we as humans grapple with: What is intelligence? Philosophers and scientists have struggled with this question for ages. The answer is elusive and mysterious, yet this central attribute makes us uniquely human.

Concurrently, AI also prompts the large philosophical and theoretical question: What is learnable? And since mathematical theorems tell us that a single learning machine cannot learn all possible tasks efficiently, we also get a sense of what cannot possibly be learned regardless of how much resources you throw at it.

In this way, AI machines are very much like us. We don’t always excel at being general learning machines. Our human brains are incredibly specialized, despite their apparent adaptability. Still current AI systems are very far from having the seemingly general intelligence that humans possess.

In AI, we generally think about three types of learning:

  • Reinforcement learning — This is focused on the problem of how an agent ought to act in order to maximize its rewards, and it’s inspired by behaviorist psychology. In a particular situation, the machine picks an action or a sequence of actions, and gets a reward. This is frequently used when teaching machines to play and win games, like chess, backgammon, go, or simple video games. One issue is that in its purest form, reinforcement learning requires an extremely large number of trials to learn even simple tasks.
  • Supervised learning — Essentially, we tell the machine what the correct answer is for a particular input: here is the image of a car, the correct answer is “car.” It is called supervised learning because the process of an algorithm learning from the labeled training dataset is similar to showing a picture book to a young child. The adult knows the correct answer and the child makes predictions based on previous examples. This is the most common technique for training neural networks and other machine learning architectures. An example might be: Given the descriptions of a large number of houses in your town together with their prices, try to predict the selling price of your own home.
  • Unsupervised learning / predictive learning — Much of what humans and animals learn, they learn it in the first hours, days, months, and years of their lives in an unsupervised manner: we learn how the world works by observing it and seeing the result of our actions. No one is here to tell us the name and function of every object we perceive. We learn very basic concepts, like the fact that the world is three-dimensional, that objects don’t disappear spontaneously, that objects that are not supported fall. We do not know how to do this with machines at the moment, at least not at the level that humans and animals can. Our lack of AI technique for unsupervised or predictive learning is one of the factors that limits the progress of AI at the moment.

These approaches are often used in AI, but there are many problems that are inherently difficult for any computing device. This is why even if we build machines with super-human intelligence, they will still have limited abilities. They may beat us at chess, but not be smart enough to get in out of the rain.

Jobs of the future

As AI, machine learning, and intelligent robots become more pervasive, there will be new jobs in manufacturing, training, sales, maintenance, and fleet management of these robots. AI and robots will enable the creation of new services that are difficult to imagine today. But it’s clear that health care and transportation will be among the first industries to be completely transformed by it.

For young people, just sorting out their career goals, AI offers a wealth of opportunities. So how do we prepare for jobs that don’t yet exist?

If you’re a student:

  • Math and physics classes are where one learns the basic methods for AI, machine learning, data science, and many of the jobs of the future. Take all the math class you can possibly take, including Calc I, Calc II, Calc III, Linear Algebra, Probability, and Statistics. Computer science, too, is essential; you’ll need to learn how to program. Engineering, economics, and neuroscience are also helpful. You may also want to consider some areas of philosophy, such as epistemology, which is the study of what is knowledge, what is a scientific theory, and what does it mean to learn.
  • The goal in these classes is not simple rote memorization. Students must learn how to turn data into knowledge. This includes basic statistics, but also how to collect and analyze data, be aware of possible biases, and to be alert to techniques to prevent self-delusion through biased data manipulation.
  • Find a professor in your school who can help you make your ideas concrete. If their time is limited, you can also look toward senior PhD students or postdocs to work with.
  • Apply to PhD programs. Forget about the “ranking” of the school for now. Find a reputable professor who works on topics that you are interested in, or pick a person whose papers you like or admire. Apply to several PhD programs in the schools of these professors and mention in your letter that you’d like to work with that professor, but would be open to work with others.
  • Engage with an AI-related problem you are passionate about. Start reading the literature on the problem and try to think about it differently than what was done before. Before you graduate, try to write a paper about your research or release a piece of open source code.
  • Apply for industry-focused internships to get hands-on experience on how AI works in practice.

You can get a broad idea of what deep learning is about by going through tutorial lectures that are available online. There are plenty of online materials, tutorials, and courses on machine learning, including Udacity or Coursera lectures.

The future

Increasingly, human intellectual activities will be performed in conjunction with intelligent machines. Our intelligence is what makes us human, and AI is an extension of that quality.

On the way to building truly intelligent machines, we are discovering new theories, new principles, new methods, and new algorithms that have applications and will improve our everyday life today, tomorrow, and next year.

FAIR’s long term goal is to understand intelligence and build intelligent machines. That’s not merely a technology challenge, it’s a scientific question. What is intelligence, and how can we reproduce it in machines? Ultimately, that quest is humanity’s quest. The answers to these questions will help us not just build intelligent machines, but develop keener insight into how the mysterious human mind and brain work. Hopefully, it’ll also help us all better understand what it means to be human.


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