The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

by Pedro Domingos

This book’s first goal is to let you in on the secrets of machine learning. Only engineers and mechanics need to know how a car’s engine works, but every driver needs to know that turning the

steering wheel changes the car’s direction and stepping on the brake brings it to a stop. Few people today know what the corresponding elements of a learner even are, let alone how to use them. The psychologist Don Norman coined the term conceptual model to refer to the rough knowledge of a technology we need to have in order to use it effectively. This book provides you with a conceptual model of machine learning.

This book has me hooked on just the introduction.

Symbolists view learning as the inverse of deduction and take ideas from philosophy, psychology, and logic. Connectionists reverse engineer the brain and are inspired by neuroscience and physics. Evolutionaries simulate evolution on the computer and draw on genetics and evolutionary biology. Bayesians believe learning is a form of probabilistic inference and have their roots in statistics. Analogizers learn by extrapolating from similarity judgments and are influenced by psychology and mathematical optimization.

What?! Can’t wait to see where this goes.

Each of the five tribes of machine learning has its own master algorithm, a general-purpose learner that you can in principle use to discover knowledge from data in any domain. The symbolists’ master algorithm is inverse deduction, the connectionists’ is backpropagation, the evolutionaries’ is genetic programming, the Bayesians’ is Bayesian inference, and the analogizers’ is the support vector machine.

Computers are made of billions of tiny switches called transistors, and algorithms turn those switches on and off billions of times per second.

Believe it or not, every algorithm, no matter how complex, can be reduced to just these three operations: AND, OR, and NOT.

Ta dah! I hope people in general can be more encouraged by Computng.

Over time, computer scientists build on each other’s work and invent algorithms for new things.

Inevitably, however, there is a serpent in this Eden. It’s called the complexity monster. Like the Hydra, the complexity monster has many heads.

In farming, we plant the seeds, make sure they have enough water and nutrients, and reap the grown crops. Why can’t technology be more like this? It can, and that’s the promise of machine learning. Learning algorithms are the seeds, data is the soil, and the learned programs are the grown plants. The machine-learning

I am All about plant references.

learning expert is like a farmer, sowing the seeds, irrigating and fertilizing the soil, and keeping an eye on the health of the crop but otherwise staying out of the way.

Machine learning is sometimes confused with artificial intelligence (or AI for short). Technically, machine learning is a subfield of AI, but it’s grown so large and successful that it now eclipses its proud parent. The goal of AI is to teach computers to do what humans currently do better, and learning is arguably the most important of those things: without it, no computer can keep up with a human for long; with it, the rest follows.

Good point. Buzzwords like these two are better to clarify.

In the information-processing ecosystem, learners are the superpredators. Databases, crawlers, indexers, and so on are the herbivores, patiently munging on endless fields of data. Statistical algorithms, online analytical processing, and so on are the predators. Herbivores are necessary, since without them the others couldn’t exist, but superpredators have a more exciting life. A crawler is like a cow, the web is its worldwide meadow, each page is a blade of grass. When the crawler is done munging, a copy of the web is sitting on its hard disks. An indexer then makes a list of the pages where each word appears, much like the index at the end of a book. Databases, like elephants, are big and heavy and never forget. Among these patient beasts dart statistical and analytical algorithms, compacting and selecting, turning data into information.

Circle of data life.

This difference in thinking is a large part of why Microsoft has had a lot more trouble catching up with Google than it did with Netscape. At the end of the day, a browser is just a standard piece of software, but a search engine requires a different mind-set.

Microsoft the software company vs Google the AI company. From the founding Google had AI in mind. Microsoft has caught up and has some great services on the machine learning front.

The Industrial Revolution automated manual work and the Information Revolution did the same for mental work, but machine learning automates automation itself. Without it, programmers become the bottleneck holding up progress.

This is eye opening.

Learning algorithms are the matchmakers: they find producers and consumers for each other, cutting through the information

overload. If they’re smart enough, you get the best of both worlds: the vast choice and low cost of the large scale, with the personalized touch of the small. Learners are not perfect, and the last step of the decision is usually still for humans to make, but learners intelligently reduce the choices to something a human can manage.

With big data and machine learning, you can understand much more complex phenomena than before. In most fields, scientists have traditionally used only very limited kinds of models, like linear regression, where the curve you fit to the data is always a straight line. Unfortunately, most phenomena in the world are

nonlinear. (Or fortunately, since otherwise life would be very boring—in fact, there would be no life.) Machine learning opens up a vast new world of nonlinear models. It’s like turning on the lights in a room where only a sliver of moonlight filtered before.

As knowledge grows, scientists specialize ever more narrowly, but no one is able to put the pieces together because there are far too many pieces. Scientists collaborate, but language is a very slow medium of communication. Scientists try to keep up with others’ research, but the volume of publications is so high that they fall farther and farther behind. Often, redoing an experiment is easier than finding the paper that reported it. Machine learning comes to the rescue, scouring the literature for relevant information, translating one area’s jargon into another’s, and even making connections that scientists weren’t aware of. Increasingly, machine learning acts as a giant hub, through which modeling techniques invented in one field make their way into others.

Science in a whole new view.

You might think the 2012 election was a fluke: most elections are not close enough for machine learning to be the deciding factor. But machine learning will cause more elections to be close in the future.

There’s a further twist: once a learned program is deployed, the bad guys change their behavior to defeat it. This contrasts with the natural world, which always works the same way. The solution is to marry machine learning with game theory, something I’ve worked on in the past: don’t just learn to defeat what your opponent does now; learn to parry what he might do against your learner. Factoring in the costs and benefits of different actions, as game theory does, can also help strike the right balance between privacy and security.

Sounds concerning. For are humans the pattern driven?

The question then becomes: How weak can the assumptions be and still allow all relevant knowledge to be derived from finite data? Notice the word relevant: we’re only interested in knowledge about our world, not about worlds that don’t exist. So inventing a universal learner boils down to discovering the deepest regularities in our universe, those that all phenomena share, and then figuring out a computationally efficient way to combine them with data.

Evolution is the ultimate example of how much a simple learning algorithm can achieve given enough data. Its input is the experience and fate of all living creatures that ever existed. (Now that’s big data.) On the other hand, it’s been running for over three billion years on the most powerful computer on Earth: Earth itself. A computer version of it had better be faster and less data intensive than the original. Which one is the better model for the Master Algorithm: evolution or the brain? This is machine learning’s version of the nature versus nurture debate. And, just as nature and nurture combine to produce us, perhaps the true Master Algorithm contains elements of both.

experts. Sure, learners can extract some things from data, but nothing you’d confuse with real knowledge. To knowledge engineers, big data is not the new oil; it’s the new snake oil.

Fascinating learning about these knowledge engineers.

It’s not that the Master Algorithm will single-handedly crack AI; there’ll still be great feats of engineering to perform, and Watson is a good preview of them. But the 80/20 rule applies: the Master Algorithm will be 80 percent of the solution and 20 percent of the work, so it’s surely the best place to start.

To use a technology, we don’t need to master its inner workings, but we do need to have a good conceptual model of it. We need to know how to find a station on the radio, or change the volume.

I am with this statement. There is so many ways to improve the flow of one’s daily life with a few lessons in turning knobs.

Algorithm. A microprocessor is not the best hardware for running any particular algorithm. That would be an ASIC (application-specific integrated circuit) designed very precisely for that algorithm. Yet microprocessors are what we use for almost all applications, because their flexibility trumps their relative inefficiency. If we had to build an ASIC for every new application, the Information Revolution would never have happened.

Newton’s principle is the first unwritten rule of machine learning. We induce the most widely applicable rules we can and reduce their scope only when the data forces us to. At first sight this may seem ridiculously overconfident, but it’s been working for science for over three hundred years. It’s certainly possible to imagine a universe so varied and capricious that Newton’s principle would systematically fail, but that’s not our universe.

Symbolist machine learners share this belief in the power of symbol manipulation with many other computer scientists, psychologists, and philosophers. The psychologist David Marr argued that every information processing system should be studied at three distinct levels: the fundamental properties of the problem it’s solving; the algorithms and representations used to solve it; and how they are physically implemented.

As the statistician George Box famously put it: “All models are wrong, but some are useful.”

The first law of ecology, according to biologist Barry Commoner, is that everything is connected to everything else.

For a Bayesian, in fact, there is no such thing as the truth; you have a prior distribution over hypotheses, after seeing the data it becomes the posterior distribution, as given by Bayes’ theorem, and that’s all.

As we go through life, similar episodes gradually become abstracted into rule-based structures, like “eating at a restaurant.” You know that going to a restaurant involves ordering from a menu and leaving a tip, and you follow those “rules of conduct” every time you eat out, but you probably don’t remember the specific restaurants where you first became aware of them.