How AI Systems Learn

There are several ways that machine learning systems can be trained. Innovation in this area—finding better ways to teach AI systems—will be critical to future progress in the field.

SUPERVISED LEARNING involves providing carefully structured training data that has been categorized or labeled to a learning algorithm. For example, you could teach a deep learning system to recognize a dog in photographs by feeding it many thousands (or even millions) of images containing a dog. Each of these would be labeled “Dog.” You would also need to provide a huge number of images without a dog, labeled “No Dog.” Once the system has been trained, you can then input entirely new photographs, and the system will tell you either “Dog” or “No Dog”—and it might well be able to do this with a proficiency that exceeds that of a typical human being.

Supervised learning is by far the most common technique used in current AI systems, accounting for perhaps 95 percent of practical applications. Supervised learning powers language translation (trained with millions of documents pre-translated into two different languages) and AI radiology systems (trained with millions of medical images labeled either “Cancer” or “No Cancer”). One problem with supervised learning is that it requires massive amounts of labeled data. This explains why companies that control huge amounts of data, like Google, Amazon, and Facebook, have such a dominant position in deep learning technology.

REINFORCEMENT LEARNING essentially means learning through practice or trial and error. Rather than training an algorithm by providing the correct, labeled outcome, the learning system is set loose to find a solution for itself, and if it succeeds it is given a “reward.” Imagine training your dog to sit, and if he succeeds, giving him a treat. Reinforcement learning has been an especially powerful way to build AI systems that play games. As you will learn from the interview with Demis Hassabis in this book, DeepMind is a strong proponent of reinforcement learning and relied on it to create the AlphaGo system.

The problem with reinforcement learning is that it requires a huge number of practice runs before the algorithm can succeed. For this reason, it is primarily used for games or for tasks that can be simulated on a computer at high speed. Reinforcement learning can be used in the development of self-driving cars—but not by having actual cars practice on real roads. Instead virtual cars are trained in simulated environments. Once the software has been trained it can be moved to real-world cars.

UNSUPERVISED LEARNING means teaching machines to learn directly from unstructured data coming from their environments. This is how human beings learn. Young children, for example, learn languages primarily by listening to their parents. Supervised learning and reinforcement learning also play a role, but the human brain has an astonishing ability to learn simply by observation and unsupervised interaction with the environment.

Unsupervised learning represents one of the most promising avenues for progress in AI. We can imagine systems that can learn by themselves without the need for huge volumes of labeled training data. However, it is also one of the most difficult challenges facing the field. A breakthrough that allowed machines to efficiently learn in a truly unsupervised way would likely be considered one of the biggest events in AI so far, and an important waypoint on the road to human-level AI.

ARTIFICIAL GENERAL INTELLIGENCE (AGI) refers to a true thinking machine. AGI is typically considered to be more or less synonymous with the terms HUMAN-LEVEL AI or STRONG AI. You’ve likely seen several examples of AGI—but they have all been in the realm of science fiction. HAL from 2001 A Space Odyssey, the Enterprise’s main computer (or Mr. Data) from Star Trek, C3PO from Star Wars and Agent Smith from The Matrix are all examples of AGI. Each of these fictional systems would be capable of passing the TURING TEST—in other words, these AI systems could carry out a conversation so that they would be indistinguishable from a human being. Alan Turing proposed this test in his 1950 paper, Computing Machinery and Intelligence, which arguably established artificial intelligence as a modern field of study. In other words, AGI has been the goal from the very beginning.

It seems likely that if we someday succeed in achieving AGI, that smart system will soon become even smarter. In other words, we will see the advent of SUPERINTELLIGENCE, or a machine that exceeds the general intellectual capability of any human being. This might happen simply as a result of more powerful hardware, but it could be greatly accelerated if an intelligent machine turns its energies toward designing even smarter versions of itself. This might lead to what has been called a “recursive improvement cycle” or a “fast intelligence take off.” This is the scenario that has led to concern about the “control” or “alignment” problem—where a superintelligent system might act in ways that are not in the best interest of the human race.

I have judged the path to AGI and the prospect for superintelligence to be topics of such high interest that I have discussed these issues with everyone interviewed in this book.

MARTIN FORD is a futurist and the author of two books: The New York Times Bestselling Rise of the Robots: Technology and the Threat of a Jobless Future (winner of the 2015 Financial Times/McKinsey Business Book of the Year Award and translated into more than 20 languages) and The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future, as well as the founder of a Silicon Valley-based software development firm. His TED Talk on the impact of AI and robotics on the economy and society, given on the main stage at the 2017 TED Conference, has been viewed more than 2 million times.

Martin is also the consulting artificial intelligence expert for the new “Rise of the Robots Index” from Societe Generale, underlying the Lyxor Robotics & AI ETF, which is focused specifically on investing in companies that will be significant participants in the AI and robotics revolution. He holds a computer engineering degree from the University of Michigan, Ann Arbor and a graduate business degree from the University of California, Los Angeles.

He has written about future technology and its implications for publications including The New York Times, Fortune, Forbes, The Atlantic, The Washington Post, Harvard Business Review, The Guardian, and The Financial Times. He has also appeared on numerous radio and television shows, including NPR, CNBC, CNN, MSNBC and PBS. Martin is a frequent keynote speaker on the subject of accelerating progress in robotics and artificial intelligence—and what these advances mean for the economy, job market and society of the future.

Martin continues to focus on entrepreneurship and is actively engaged as a board member and investor at Genesis Systems, a startup company that has developed a revolutionary atmospheric water generation (AWG) technology. Genesis will soon deploy automated, self-powered systems that will generate water directly from the air at industrial scale in the world’s most arid regions.