train for many years to read x-rays, computer tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) images. After medical school, radiologists do additional training, often involving a four-year residency. Some do additional specialization training after that. Reading images to look for tumors and other ailments uses all of the radiologist's skill, experience, and training. Yet this task is within the reach of a neural network. Given enough training data, an AI can be built with diagnostic abilities similar to those of a human radiologist, a person with about a decade of intense education behind them. As we train the neural network, we are essentially codifying the collective knowledge, and several decades of professional experience, from hundreds of thousands of radiologists. Their experience and diagnostic insight are captured in the model that's generated.
Some radiologists already use AI-based tools to offer a “second opinion” as they read charts. As the accuracy of these tools surpasses that of human radiologists on routine charts, radiologists will be able to focus their attention on more complex, higher-value, and more patient-centered tasks and procedures. The progress made in radiology portends the future for other branches of medicine. Machine learning will be applied to many other fields of medical diagnosis and pathology in the coming decade.
While training an AI requires serious amounts of computing performance to create a model, using that model requires significantly less performance. The process of using a model is known as inference. Often, training occurs on workstations or in the cloud, while inference occurs on devices. Most future computer chips will include inference engines, silicon accelerators optimized to run AI models with relative ease.
Pattern Recognition
Pattern recognition is a core capability of many AI systems, including the radiology example we just discussed. Pattern recognition has many applications and comes in a range of different flavors. It's not important that you remember all these different approaches. They are listed here only to illustrate some of the fundamental capabilities of machine learning. As you read through them, think about how such a capability might be used to solve business problems in your organization.
Classification. AI can classify data into similar types. For example, the radiology AI classifies images as positive or negative. A similar approach might be used to do visual inspection and quality assurance in a manufacturing plant, or to identify spoiled or underripe fruit at a fruit-packing plant.
Clustering. Marketing professionals use clustering algorithms to partition consumers into market segments that share similar characteristics—buying habits, affluence level, and needs or desires. Recommendation engines use clustering, too. Spotify recommends songs that you might enjoy by analyzing historical listening habits. A clustering algorithm finds the complex relationships between songs and listeners. The clustering algorithm might see that I like songs A, B, C, and D, and that you like songs B, C, D, and E. It may conclude that it's probable you will enjoy song A and I might enjoy song E. Clustering is useful to deliver personalized experiences.
Regression analysis finds patterns that describe relationships between pieces of data. For example, regression analysis might observe that if Event A happens, most of the time Event B follows. More complex relationships are found, too, such as “if Datapoint A is below a certain threshold, and Event B and Event C are not happening, then Event D is 46% more likely to occur.” This approach is used to make predictions about the future with predictive analytics tools. Regression analysis is used by Walmart to predict how sales of certain food items are influenced by specific weather conditions.
Sequence labeling is a pattern-recognition approach used in speech recognition, handwriting recognition, and gesture recognition. Sequence labeling is used to break sentences down into constituent words and phrases and to label them in a way that captures their context. For example, sequence labeling identifies which words are nouns, verbs, and proper names. Words are best interpreted in the broader context of a sentence. Sequence-labeling algorithms classify words within a sentence, or cursive letters within a handwritten word, by examining the broader context surrounding them.
Time-series prediction is used in weather forecasting, stock market prediction, and to predict disasters. These algorithms analyze a set of historical data points and use that to project which data points might come next in a sequence.
These pattern-matching algorithms use complex mathematics to work their voodoo. You don't need to understand how pattern matching works, or even recall the names of all the various techniques listed above. What you do need to understand is that AI makes it easier for computers to understand the physical world, to make predictions, and to find complex relationships hidden inside data. These tasks are at the root of solving many business problems.
Beyond Deep Learning: The Future of Artificial Intelligence
Most of the AI breakthroughs in the 2010s were built on deep learning technology and neural networks. Dramatic advances in machine vision, natural language processing, prediction, and content generation resulted. And yet, industry luminaries debate whether AI is about to enter a golden age of rapid technological advancement, or rapidly stagnate.
Stagnation or Golden Age?
The argument for stagnation is that deep learning has severe limitations—training needs too many examples and takes too long, and while these AIs pull off some amazing tricks, they have no true understanding of the world. Deep learning is built on algorithms from the mid-1980s and neural network architectures developed in the 1960s. Once we have perfected the implementation of deep learning technology and solved all the problems that we can with it, there are no viable technologies in the pipeline to keep things rolling. The current era of AI deployment will grind to a halt. So goes the stagnation argument.
On the other side of the debate are those who point to promising research that could take AI in new directions and solve a new set of problems.
Capsule Networks
Capsule networks are the brainchild of Geoff Hinton, the creator of backprop and one of the fathers of deep learning. Capsules overcome some of deep learning's shortcomings. The difference between capsule networks and traditional convolutional neural networks is beyond the scope of this book, but capsules capture some level of understanding about the relationship between features in images, which makes image recognition engines more resilient and better at recognizing objects from many different angles.
Common Sense
AIs are trained to understand something about the world. Typical AIs operate within a bubble. They have no understanding of the way the world works. A lack of common sense limits their abilities. A household robot, on a search to find my reading glasses, should know that my desk and nightstand are good places to look first, and not inside the freezer.
Several organizations are trying to build AIs with common sense. They are building vast databases of the commonsense notions humans use to help them make high-quality decisions. For example, oranges are sweet, but lemons are sour. A tiger won't fit in a shoe box. Water is wet. Oil is viscous. If you overfeed a hamster, it will get fat. We often take this context for granted, but to an AI these notions are not obvious.
Researchers at the Allen Institute crowdsource commonsense insight using Amazon's Mechanical Turk platform. They use machine learning and statistical analysis to extract additional insights and understand the spatial, physical, emotional, and other relationships between things. For example, from a commonsense notion that “A girl ate a cookie,” the system
