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Trendy Technologies: Everything you wanted to know about AI and machine learning

… but were afraid to ask.

Jerry W. ThomasBy Jerry W. Thomas

The business news is cluttered with stories about new artificial intelligence (AI) and machine learning (ML) startups. Major corporations are rushing to set up internal teams and divisions to exploit AI and ML. Graduate schools are turning out data scientists and business analysts with training in AI and ML.

The big technology companies are creating AI software and systems. Marketing executives are rushing to apply AI and ML to optimize marketing and advertising processes and programs. It’s as though the gods have descended from the heavens to share ultimate truth with the poor human race.

But what is “artificial intelligence” and how does it relate to “machine learning?” What do these terms mean?

“Intelligence,” whatever it is, is presumed to reside inside of biological creatures (cells, bacteria, plants, animals, insects, humans). We don’t think of intelligence as something possessed by a rock, or a mineral, or other nonliving substances. All biological “creatures” can make decisions, or choices, that increase their chances of survival. Let’s define “intelligence,” then, as an ability to make a decision, to choose among alternative paths or possibilities in order to achieve some objective.

“Artificial,” in this context, means nonliving or nonbiological. So, AI is an ability of some nonbiological entity (machine, computer, software, system, algorithm) to make choices, or trigger actions, that help solve a problem or achieve an objective. We’ll come to “machine learning” later.

It is possible that AI devices have been around for a very long time. The spring-loaded “trigger” snare might be considered one of the very earliest AI devices. A tree limb is bent to create stored energy, a trigger device is activated by the movement of an animal, and a wire or rope jerks and entraps the animal. Spring snares date back at least 10,000 years (and more likely 25,000–50,000 years), but their origins are lost in the mist of time. This might be stretching the concept of AI a bit, but I do think these types of devices meet the above definition of AI or come pretty close.

The first definite mechanical AI devices could be credited to Christiaan Huygens, the famous Dutch scientist and mathematician. He perfected the pendulum as a device to improve the accuracy of clocks in 1656, and he invented the centrifugal governor for use in windmills around the same time.

James Watt applied Huygens’ centrifugal-governor concept to his revolutionary steam engine in 1788 to control its speed. Both of Huygens’ inventions (pendulum clock and centrifugal governor) meet the stated definition of AI. They are nonbiological. They make decisions, or trigger actions, that regulate the measurement of time or the run speed of a windmill or engine. These decisions are made automatically – without human or other biological intervention.

The beginning of modern artificial intelligence, as it is now commonly thought of, traces its origins to the development of computers during and following World War II and the possibilities spawned by those machines. The arrival of these powerful machines gave rise to much thinking about what intelligence is and whether machines might be able to think “like humans think.”

Virtually all computer languages and programs, with their ability to compare variables and values and change the flow of logic to achieve some objective, or trigger certain outputs, meet the above definition of AI. These programs are nonbiological and make “decisions” to achieve objectives.

The definition of AI, however, continues to evolve and expand. The current definition and understanding tends to mean machines (broadly defined) that simulate or mirror human thinking. The simulation of human thinking is a much higher standard for what AI is, or could be. The recognition and translation of human speech into text is a good example of AI-derived models that closely simulate a human’s mental capability. Image and pattern recognition are, likewise, human feats that AI can increasingly derive models to mimic. Voice-to-text translation and image recognition, however, are only the tips of the iceberg.

AI is, or will be, applied to developing and improving models to perform medical diagnoses, conduct legal research, do data mining and predictive analytics, analyze business processes, detect fraud, predict market trends, forecast sales and so on. The possibilities are endless.

Machine Learning: The Latest Iteration of AI

Machine learning is closely related to AI, and the two terms are often used as synonyms. With machine learning, computers can “teach themselves” how to simulate processes and decisions. It is the arrival and development of machine learning that offers such great promise and hope.

If computers can “program” themselves or create solutions, then AI can be applied to an array of problems and processes very economically and very quickly.

Machine learning is the linchpin technology that could open up many new applications and allow AI to spread rapidly. So, what is machine learning and how does it work?

Machine learning requires some goal or objective; that is, a dependent variable (or multiple dependent variables). The more narrow and specific the dependent variable(s), the greater the chance(s) that machine learning can derive a formula, equation or mathematical algorithm that helps optimize (or maximize) the dependent variable(s).

The dependent variable could be something as simple as the response rate to a direct mail promotional offer, or the dependent variable could be classifying photos into two groups: those containing an image of a chair and those without an image of a chair. Regardless of the type of dependent variable(s), we must have some way to determine if the dependent-variable prediction is better (or correct) during each iteration of the model derived by machine learning.

The simulation of human thinking is a much higher standard for what AI is, or could be. Source: ThinkStock

The simulation of human thinking is a much higher standard for what AI is, or could be.
Source: ThinkStock

Machine learning also requires a substantial and relevant database of independent variables that might predict or explain the dependent variable(s). The better the independent database in terms of completeness, relevance and accuracy, the greater the chances that machine learning will be able to build a good mathematical model.

In the response-rate prediction example, the independent variables could include things such as household demographics, weather data, mail-delivery days and times, economic data, marketing research data, and historical details about the various mail pieces (claims, type of graphics, colors, length of copy, type font, etc.) used in the past and the resulting response rates.

The bulk of the work and most of the costs related to machine learning revolve around creating a high-quality database of independent variables with data for each variable over a substantial number of cases or over a substantial period of time. In some instances this is weeks or months, but in other cases years or decades. Once this database is assembled and cleaned, we have a dependent variable (response rate), an objective (maximize response rate), and a database of potential explanatory (independent) variables with extensive and clean historical data for each variable including past results. But, we are not yet ready to push the “go” button on the computer.

Machine learning also requires a computational strategy. Computers are dumb and thoughtless. Without a strategy, computers could easily grind away on a dataset for thousands of years without achieving anything. There are many possible models that might be employed as the machine-learning algorithm, including: regression analyses, decision trees, support vector networks, ensemble models, gradient boosting methods, neural networks, Bayesian networks and deep learning.

The computational algorithm could be one of these statistical techniques, or it could be combinations of these techniques (i.e., hybrid models), or it could be completely different techniques, but the human mind has to give the machine-learning system some type of strategy. There are easily more than 100 existing statistical routines or techniques that might form this computational strategy – and no doubt thousands more to be created in the future.

Now it’s time to put the machine learning to work. The computer begins to run calculations following the assigned strategy or strategies. In each iteration the model estimates the response rate (or other outcomes), compares the predicted outcomes to actual outcomes, and tweaks the model to improve its predictive accuracy. This iterative process of model improvement is continuous so that the predictive model becomes better and better over time. Figure 1 shows a simplified diagram of a machine-learning system.

Figure 1: A simplified diagram of a machine-learning system.

Figure 1: A simplified diagram of a machine-learning system.

One other point: During the early stages of model development, how can the accuracy of the new machine learning model be tested? Back when the original database of dependent and independent variables was created, you were very clever and randomly chose a “holdout” data set (also called a “validation” data set), a random subset of the original database. The holdout data set allows you to test your new machine-learning model to see how well it works.

The actual response rate for this holdout data set is known, so you have a benchmark, a measuring stick, to determine how good your new machine-learning model is at predicting the actual response rate. In the other example – classifying photos into those with a picture and those without – the same process would be followed. The original set of photos would be divided into a training data set and a holdout or validation data set, and a huge database of possible predictive or explanatory variables would be created.

Human judgment would determine which photos contained chairs in the training data set, and human judgment would determine which photos in the validation data set pictured chairs. The machine-learning model(s) would ultimately be judged by how well each model identified photos with images of chairs.

There you have it. Now you can pretend to be an expert on AI and machine learning. As is evident, artificial intelligence and machine learning offer promise for the future, but they are not magical solutions to all the world’s questions and problems. We must wait a while longer for the gods to reveal ultimate truth to the long-suffering human race.

Jerry W. Thomas is president and chief executive of Dallas/Fort Worth-based Decision Analyst, Inc. (www.decisionanalyst.com), one of the nation’s oldest and largest marketing research and analytics firms. Decision Analyst serves the Fortune 500 and similarly significant companies, governments and organizations around the world. Thomas welcomes comments and suggestions. He may be reached by email at jthomas@decisionanalyst.com or by phone at 1-817-640-6166.

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