Moving beyond the hype

As any technology becomes bigger, so does the hype, and AI certainly has a lot of hype surrounding it. For one thing, some people have decided to engage in fear mongering rather than science. Killer robots, such as those found in the film The Terminator, really aren’t going to be the next big thing. Your first real experience with an android AI is more likely to be in the form a health-care assistant (www.good.is/articles/robots-elder-care-pepper-exoskeletons-japan) or possibly as a coworker (www.computerworld.com/article/2990849/robotics/meet-the-virtual-woman-who-may-take-your-job.html). The reality is that you already interact with AI and machine learning in far more mundane ways. Part of the reason you need to read this chapter is to get past the hype and discover what AI can do for you today.

You may also have heard machine learning and AI used interchangeably. AI includes machine learning, but machine learning doesn’t fully define AI. This chapter helps you understand the relationship between machine learning and AI.

Machine learning and AI both have strong engineering components. That is, you can quantify both technologies precisely based on theory (substantiated and tested explanations) rather than simply hypothesis (a suggested explanation for a phenomenon). In addition, both have strong science components, through which people test concepts and create new ideas of how expressing the thought process might be possible. Finally, machine learning also has an artistic component, and this is where a talented scientist can excel. In some cases, AI and machine learning both seemingly defy logic, and only the true artist can make them work as expected.

Dreaming of electric sheep

Androids (a specialized kind of robot that looks and acts like a human, such as Data in Star Trek) and some types of humanoid robots (a kind of robot that has human characteristics but is easily distinguished from a human, such as C-3PO in Star Wars) have become the poster children for AI. They present computers in a form that people can anthropomorphize (for example, make human). In fact, it’s entirely possible that one day you won’t be able to distinguish between human and artificial life with ease. Science fiction authors, such as Philip K. Dick, have long predicted such an occurrence, and it seems all too possible today. The story Do Androids Dream of Electric Sheep? discusses the whole concept of more real than real. The idea appears as part of the plot in the movie Blade Runner (www.warnerbros.com/blade-runner). The sections that follow help you understand how close technology currently gets to the ideals presented by science fiction authors and the movies.

The current state of the art is lifelike, but you can easily tell that you’re talking to an android. Viewing videos online can help you understand that androids that are indistinguishable from humans are nowhere near any sort of reality today. Check out the Japanese robots at www.youtube.com/watch?v=MaTfzYDZG8c and www.nbcnews.com/tech/innovation/humanoid-robot-starts-work-japanese-department-store-n345526. One of the more lifelike examples is Amelia (https://vimeo.com/166359613). Her story appears in Computerworld at www.computerworld.com/article/2990849/robotics/meet-the-virtual-woman-who-may-take-your-job.html. The point is, technology is just starting to get to the point where people may eventually be able to create lifelike robots and androids, but they don’t exist today.

Overcoming AI fantasies

As with many other technologies, AI and machine learning both have their fantasy or fad uses. For example, some people are using machine learning to create Picasso-like art from photos. You can read all about it at www.washingtonpost.com/news/innovations/wp/2015/08/31/this-algorithm-can-create-a-new-van-gogh-or-picasso-in-just-an-hour. As the article points out, the computer can copy only an existing style at this stage — not create an entirely new style of its own. The following sections discuss AI and machine learning fantasies of various sorts.

Considering the relationship between AI and machine learning

Machine learning is only part of what a system requires to become an AI. The machine learning portion of the picture enables an AI to perform these tasks:

• Adapt to new circumstances that the original developer didn’t envision.
• Detect patterns in all sorts of data sources.
• Create new behaviors based on the recognized patterns.
• Make decisions based on the success or failure of these behaviors.

The use of algorithms to manipulate data is the centerpiece of machine learning. To prove successful, a machine learning session must use an appropriate algorithm to achieve a desired result. In addition, the data must lend itself to analysis using the desired algorithm, or it requires a careful preparation by scientists.

AI encompasses many other disciplines to simulate the thought process successfully. In addition to machine learning, AI normally includes

• Natural language processing: The act of allowing language input and putting it into a form that a computer can use
• Natural language understanding: The act of deciphering the language in order to act upon the meaning it provides
• Knowledge representation: The ability to store information in a form that makes fast access possible
• Planning (in the form of goal seeking): The ability to use stored information to draw conclusions in near real time (almost at the moment it happens, but with a slight delay, sometimes so short that a human won’t notice, but the computer can)
• Robotics: The ability to act upon requests from a user in some physical form

In fact, you might be surprised to find that the number of disciplines required to create an AI is huge. Consequently, this book exposes you to only a portion of what an AI contains. However, even the machine learning portion of the picture can become complex because understanding the world through the data inputs that a computer receives is a complex task. Just think about all the decisions that you constantly make without thinking about them. For example, just the concept of seeing something and knowing whether you can interact successfully with it can become a complex task.

Considering AI and machine learning specifications

As scientists continue to work with a technology and turn hypotheses into theories, the technology becomes related more to engineering (where theories are implemented) than science (where theories are created). As the rules governing a technology become clearer, groups of experts work together to define these rules in written form. The result is specifications (a group of rules that everyone agrees upon).

Eventually, implementations of the specifications become standards that a governing body, such as the IEEE (Institute of Electrical and Electronics Engineers) or a combination of the ISO/IEC (International Organization for Standardization/International Electrotechnical Commission), manages. AI and machine learning have both been around long enough to create specifications, but you currently won’t find any standards for either technology.

The basis for machine learning is math. Algorithms determine how to interpret big data in specific ways. The math basics for machine learning appear in Book 8, Chapter 2. You discover that algorithms process input data in specific ways and create predictable outputs based on the data patterns. What isn’t predictable is the data itself. The reason you need AI and machine learning is to decipher the data in such a manner to be able to see the patterns in it and make sense of them.

You see the specifications detailed in Book 8, Chapter 4 in the form of algorithms used to perform specific tasks. When you get to Book 9, you begin to see the reason that everyone agrees to specific sets of rules governing the use of algorithms to perform tasks. The point is to use an algorithm that will best suit the data you have in hand to achieve the specific goals you’ve created. Professionals implement algorithms using languages that work best for the task. Machine learning relies on Python and R, and to some extent MATLAB, Java, Julia, and C++. (See the discussion at www.quora.com/What-is-the-best-language-to-use-while-learning-machine-learning-for-the-first-time for details.)

Defining the divide between art and engineering

The reason that AI and machine learning are both sciences and not engineering disciplines is that both require some level of art to achieve good results. The artistic element of machine learning takes many forms. For example, you must consider how the data is used. Some data acts as a baseline that trains an algorithm to achieve specific results. The remaining data provides the output used to understand the underlying patterns. No specific rules governing the balancing of data exist; the scientists working with the data must discover whether a specific balance produces optimal output.

Cleaning the data also lends a certain amount of artistic quality to the result. The manner in which a scientist prepares the data for use is important. Some tasks, such as removing duplicate records, occur regularly. However, a scientist may also choose to filter the data in some ways or look at only a subset of the data. As a result, the cleaned data set used by one scientist for machine learning tasks may not precisely match the cleaned data set used by another.

You can also tune the algorithms in certain ways or refine how the algorithm works. Again, the idea is to create output that truly exposes the desired patterns so that you can make sense of the data. For example, when viewing a picture, a robot may have to determine which elements of the picture it can interact with and which elements it can’t. The answer to that question is important if the robot must avoid some elements to keep on track or to achieve specific goals.

When working in a machine learning environment, you also have the problem of input data to consider. For example, the microphone found in one smartphone won’t produce precisely the same input data that a microphone in another smartphone will. The characteristics of the microphones differ, yet the result of interpreting the vocal commands provided by the user must remain the same. Likewise, environmental noise changes the input quality of the vocal command, and the smartphone can experience certain forms of electromagnetic interference. Clearly, the variables that a designer faces when creating a machine learning environment are both large and complex.

The art behind the engineering is an essential part of machine learning. The experience that a scientist gains in working through data problems is essential because it provides the means for the scientist to add values that make the algorithm work better. A finely tuned algorithm can make the difference between a robot successfully threading a path through obstacles and hitting every one of them.

Defining big data

Big data is substantially different from being just a large database. Yes, big data implies lots of data, but it also includes the idea of complexity and depth. A big data source describes something in enough detail that you can begin working with that data to solve problems for which general programming proves inadequate. For example, consider Google’s self-driving cars. The car must consider not only the mechanics of the car’s hardware and position with space but also the effects of human decisions, road conditions, environmental conditions, and other vehicles on the road. The data source contains many variables — all of which affect the vehicle in some way. Traditional programming might be able to crunch all the numbers, but not in real time. You don’t want the car to crash into a wall and have the computer finally decide five minutes later that the car is going to crash into a wall. The processing must prove timely so that the car can avoid the wall.

The acquisition of big data can also prove daunting. The sheer bulk of the data set isn’t the only problem to consider — also essential is to consider how the data set is stored and transferred so that the system can process it. In most cases, developers try to store the data set in memory to allow fast processing. Using a hard drive to store the data would prove too costly, time-wise.

When thinking about big data, you also consider anonymity. Big data presents privacy concerns. However, because of the way machine learning works, knowing specifics about individuals isn’t particularly helpful anyway. Machine learning is all about determining patterns — analyzing training data in such a manner that the trained algorithm can perform tasks that the developer didn’t originally program it to do. Personal data has no place in such an environment.

Finally, big data is so large that humans can’t reasonably visualize it without help. Part of what defined big data as big is the fact that a human can learn something from it, but the sheer magnitude of the data set makes recognition of the patterns impossible (or would take a really long time to accomplish). Machine learning helps humans make sense and use of big data.

Considering the sources of big data

Before you can use big data for a machine learning application, you need a source for big data. Of course, the first thing that most developers think about is the huge, corporate-owned database, which could contain interesting information, but it’s just one source. The fact of the matter is that your corporate databases might not even contain particularly useful data for a specific need. The following sections describe locations you can use to obtain additional big data.

Specifying the role of statistics in machine learning

Some sites online would have you believe that statistics and machine learning are two completely different technologies. For example, when you read Statistics vs. Machine Learning, fight! (http://brenocon.com/blog/2008/12/statistics-vs-machine-learning-fight/), you get the idea that the two technologies are not only different, but downright hostile toward each other. The fact is that statistics and machine learning have a lot in common and that statistics represents one of the five tribes (schools of thought) that make machine learning feasible. The five tribes are

• Symbolists: The origin of this tribe is in logic and philosophy. This group relies on inverse deduction to solve problems.
• Connectionists: The origin of this tribe is in neuroscience. This group relies on backpropagation to solve problems.
• Evolutionaries: The origin of this tribe is in evolutionary biology. This group relies on genetic programming to solve problems.
• Bayesians: This origin of this tribe is in statistics. This group relies on probabilistic inference to solve problems.
• Analogizers: The origin of this tribe is in psychology. This group relies on kernel machines to solve problems.

The ultimate goal of machine learning is to combine the technologies and strategies embraced by the five tribes to create a single algorithm (the master algorithm) that can learn anything. Of course, achieving that goal is a long way off. Even so, scientists such as Pedro Domingos (homes.cs.washington.edu/~pedrod/) are currently working toward that goal.

Book 9 follows the Bayesian tribe strategy, for the most part, in that you solve most problems using some form of statistical analysis. You do see strategies embraced by other tribes described, but the main reason you begin with statistics is that the technology is already well established and understood. In fact, many elements of statistics qualify more as engineering (in which theories are implemented) than science (in which theories are created). The next section of the chapter delves deeper into the five tribes by viewing the kinds of algorithms each tribe uses. Understanding the role of algorithms in machine learning is essential to defining how machine learning works.

Understanding the role of algorithms

Everything in machine learning revolves around algorithms. An algorithm is a procedure or formula used to solve a problem. The problem domain affects the kind of algorithm needed, but the basic premise is always the same — to solve some sort of problem, such as driving a car or playing dominoes. In the first case, the problems are complex and many, but the ultimate problem is one of getting a passenger from one place to another without crashing the car. Likewise, the goal of playing dominoes is to win. The following sections discuss algorithms in more detail.

Defining what training means

Many people are somewhat used to the idea that applications start with a function, accept data as input, and then provide a result. For example, a programmer might create a function called Add() that accepts two values as input, such as 1 and 2. The result of Add() is 3. The output of this process is a value. In the past, writing a program meant understanding the function used to manipulate data to create a given result with certain inputs.

Machine learning turns this process around. In this case, you know that you have inputs, such as 1 and 2. You also know that the desired result is 3. However, you don’t know what function to apply to create the desired result. Training provides a learner algorithm with all sorts of examples of the desired inputs and results expected from those inputs. The learner then uses this input to create a function. In other words, training is the process whereby the learner algorithm maps a flexible function to the data. The output is typically the probability of a certain class or a numeric value.

A single learner algorithm can learn many different things, but not every algorithm is suited for certain tasks. Some algorithms are general enough that they can play chess, recognize faces on Facebook, and diagnose cancer in patients. An algorithm reduces the data inputs and the expected results of those inputs to a function in every case, but the function is specific to the kind of task you want the algorithm to perform.

The secret to machine learning is generalization. The goal is to generalize the output function so that it works on data beyond the training set. For example, consider a spam filter. Your dictionary contains 100,000 words (actually a small dictionary). A limited training data set of 4,000 or 5,000 word combinations must create a generalized function that can then find spam in the 2^100,000 combinations that the function will see when working with actual data.

When viewed from this perspective, training might seem impossible and learning even worse. However, to create this generalized function, the learner algorithm relies on just three components:

• Representation: The learner algorithm creates a model, which is a function that will produce a given result for specific inputs. The representation is a set of models that a learner algorithm can learn. In other words, the learner algorithm must create a model that will produce the desired results from the input data. If the learner algorithm can’t perform this task, it can’t learn from the data, and the data is outside the hypothesis space of the learner algorithm. Part of the representation is to discover which features (data elements within the data source) to use for the learning process.
• Evaluation: The learner can create more than one model. However, it doesn’t know the difference between good and bad models. An evaluation function determines which of the models works best in creating a desired result from a set of inputs. The evaluation function scores the models because more than one model could provide the required results.
• Optimization: At some point, the training process produces a set of models that can generally output the right result for a given set of inputs. At this point, the training process searches through these models to determine which one works best. The best model is then output as the result of the training process.

Much of Book 8 and Book 9 focuses on representation. For example, in Book 9, Chapter 2 you discover how to work with the k-Nearest Neighbor (KNN) algorithm. However, the training process is more involved than simply choosing a representation. All three steps come into play when performing the training process. Fortunately, you can start by focusing on representation and allow the various libraries discussed in Book 9 to do the rest of the work for you.