Machine learning is a term coined around 1960, composed of two words—machine corresponds to a computer, robot, or other device, and learning refers to an activity intended to acquire or discover event patterns, which we humans are good at.
So, why do we need machine learning and why do we want a machine to learn as a human? First and foremost, of course, computers and robots can work 24/7 and don't get tired, need breaks, call in sick, or go on strike. Their maintenance is much lower than a human's and costs a lot less in the long run. Also, for sophisticated problems that involve a variety of huge datasets or complex calculations, for instance, it's much more justifiable, not to mention intelligent, to let computers do all of the work. Machines driven by algorithms designed by humans are able to learn latent rules and inherent patterns and to fulfill tasks desired by humans. Learning machines are better suited than humans for tasks that are routine, repetitive, or tedious. Beyond that, automation by machine learning can mitigate risks caused by fatigue or inattention. Self-driving cars, as shown in the following photograph, are a great example: a vehicle capable of navigating by sensing its environment and making its decision without human input. Another example is the use of robotic arms in production lines, capable of causing a significant reduction in injuries and costs:
Assume humans don't fatigue or we have resources to hire enough shift workers, would machine learning still have a place? Of course it would; there are many cases, reported and unreported, where machines perform comparably or even better than domain experts. As algorithms are designed to learn from the ground truth, and the best-thought decisions made by human experts, machines can perform just as well as experts. In reality, even the best expert makes mistakes. Machines can minimize the chance of making wrong decisions by utilizing collective intelligence from individual experts. A major study that found machines are better than doctors at diagnosing some types of cancer proves this philosophy, for instance. AlphaGo is probably the best known example of machines beating human masters. Also, it's much more scalable to deploy learning machines than to train individuals to become experts, economically and socially. We can distribute thousands of diagnostic devices across the globe within a week but it's almost impossible to recruit and assign the same number of qualified doctors.
Now you may argue: what if we have sufficient resources and capacity to hire the best domain experts and later aggregate their opinions—would machine learning still have a place? Probably not—learning machines might not perform better than the joint efforts of the most intelligent humans. However, individuals equipped with learning machines can outperform the best group of experts. This is actually an emerging concept called AI-based Assistance or AI Plus Human Intelligence, which advocates combining the efforts of machine learners and humans. We can summarize the previous statement in the following inequality:
human + machine learning → most intelligent tireless human 
machine learning > human
A medical operation involving robots is one example of the best human and machine learning synergy. The following photograph presents robotic arms in an operation room alongside the surgery doctor:
So, does machine learning simply equate to automation that involves the programming and execution of human-crafted or human-curated rule sets? A popular myth says that the majority of code in the world has to do with simple rules possibly programmed in Common Business Oriented Language (COBOL), which covers the bulk of all of the possible scenarios of client interactions. So, if the answer to that question is yes, why can't we just hire many software programmers and continue programming new rules or extending old rules?
One reason is that defining, maintaining, and updating rules becomes more and more expensive over time. The number of possible patterns for an activity or event could be enormous and, therefore, exhausting all enumeration isn't practically feasible. It gets even more challenging when it comes to events that are dynamic, ever-changing, or evolving in real time. It's much easier and more efficient to develop learning algorithms that command computers to learn and extract patterns and to figure things out themselves from abundant data.
The difference between machine learning and traditional programming can be described using the following diagram:
Another reason is that the volume of data is exponentially growing. Nowadays, the floods of textual, audio, image, and video data are hard to fathom. The Internet of Things (IoT) is a recent development of a new kind of internet, which interconnects everyday devices. The IoT will bring data from household appliances and autonomous cars to the forefront. The average company these days has mostly human clients but, for instance, social media companies tend to have many bot accounts. This trend is likely to continue and we'll have more machines talking to each other. Besides the quantity, the quality of data available has kept increasing in the past years due to cheaper storage. This has empowered the evolution of machine learning algorithms and data-driven solutions.
Jack Ma, co-founder of the e-commerce company Alibaba, explained in a speech that IT was the focus of the past 20 years but, for the next 30 years, we'll be in the age of Data Technology (DT). During the age of IT, companies grew larger and stronger thanks to computer software and infrastructure. Now that businesses in most industries have already gathered enormous amounts of data, it's presently the right time to exploit DT to unlock insights, derive patterns, and boost new business growth. Broadly speaking, machine learning technologies enable businesses to better understand customer behavior, engage with customers, and optimize operations management. As for us individuals, machine learning technologies are already making our lives better every day.
An application of machine learning with which we're all familiar is spam email filtering. Another is online advertising, where ads are served automatically based on information advertisers have collected about us. Stay tuned for the next chapters, where we'll learn how to develop algorithms in solving these two problems and more. A search engine is an application of machine learning we can't imagine living without. It involves information retrieval, which parses what we look for, queries related to records, and applies contextual ranking and personalized ranking, which sorts pages by topical relevance and user preference. E-commerce and media companies have been at the forefront of employing recommendation systems, which help customers to find products, services, and articles faster. The application of machine learning is boundless and we just keep hearing new examples everyday: credit card fraud detection, disease diagnosis, presidential election prediction, instant speech translation, and robot advisors—you name it!
In the 1983 War Games movie, a computer made life-and-death decisions that could have resulted in Word War III. As far as we know, technology wasn't able to pull off such feats at the time. However, in 1997, the Deep Blue supercomputer did manage to beat a world chess champion. In 2005, a Stanford self-driving car drove by itself for more than 130 kilometers in a desert. In 2007, the car of another team drove through regular traffic for more than 50 kilometers. In 2011, the Watson computer won a quiz against human opponents. In 2016, the AlphaGo program beat one of the best Go players in the world. If we assume that computer hardware is the limiting factor, then we can try to extrapolate into the future. Ray Kurzweil did just that and, according to him, we can expect human level intelligence around 2029. What's next?