AI in Various fields
Will a robot take your job? How is artificial intelligence likely to change my job in the next ten years? Where are AI technologies being used, and where will they come next?
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How should we define AI?
In our very first section, we’ll become familiar with the concept of AI by looking into its definition and some examples.
As you have probably noticed, AI is currently a “hot topic”: media coverage and public discussion about AI are almost impossible to avoid. However, you may also have noticed that AI means different things to different people. For some, AI is about artificial life forms that can surpass human intelligence, and for others, almost any data processing technology can be called AI.
To set the scene, so to speak, we’ll discuss what AI is, how it can be defined, and what other fields or technologies are closely related. Before we do so, however, we’ll highlight three applications of AI that illustrate different aspects of AI. We’ll return to each of them throughout the course to deepen our understanding.
Application 1. Self-driving cars
Self-driving cars require a combination of AI techniques of many kinds: search and planning to find the most convenient route from A to B, computer vision to identify obstacles, and decision-making under uncertainty to cope with the complex and dynamic environment. Each of these must work with almost flawless precision to avoid accidents.
The same technologies are also used in other autonomous systems such as delivery robots, flying drones, and autonomous ships.
Implications: road safety should eventually improve as the reliability of the systems surpasses the human level. The efficiency of logistics chains when moving goods should improve. Humans move into a supervisory role, keeping an eye on what’s going on while machines take care of the driving. Since transportation is such a crucial element in our daily life, likely, there are also some implications that we haven’t even thought about yet.
Application 2. Content recommendation
A lot of the information that we encounter on a typical day is personalized. Examples include Facebook, Twitter, Instagram, and other social media content; online advertisements; music recommendations on Spotify; movie recommendations on Netflix, HBO, and other streaming services. Many online publishers such as newspapers and broadcasting companies’ websites as well as search engines such as Google also personalize the content they offer.
While the front page of the printed version of the New York Times or China Daily is the same for all readers, the front page of the online version is different for each user. The algorithms that determine the content that you see are based on AI.
Implications: while many companies don’t want to reveal the details of their algorithms, being aware of the basic principles helps you understand the potential implications: these involve so-called filter bubbles, echo chambers, troll factories, fake news, and new forms of importance.
Application 3. Image and video processing
Face recognition is already a commodity used in many customers, businesses, and government applications such as organizing your photos according to people, automatic tagging on social media, and passport control. Similar techniques can be used to recognize other cars and obstacles around an autonomous car or to estimate just to name a few examples.
AI can also be used to generate or alter visual content. Examples already in use today include style transfer, by which you can adapt your photos to look like they were painted by Vincent van Gogh, and computer-generated characters in motion pictures such as Avatar, the Lord of the Rings, and popular Pixar animations where the animated characters replicate gestures made by real human actors.
Implications: When such techniques advance and become more widely available, it will be easy to create natural-looking fake videos of events that are impossible to distinguish from real footage. This challenges the notion that “seeing is believing”.
Related fields
Machine learning can be said to be a subfield of AI, which itself is a subfield of computer science (such categories are often somewhat imprecise and some parts of machine learning could be equally well or better belong to statistics). Machine learning enables AI solutions that are adaptive. A concise definition can be given as follows:
MACHINE AND HUMANS
Deep learning is a subfield of machine learning, which itself is a subfield of AI, which itself is a subfield of computer science. We will meet deep learning in some more detail in Chapter 5, but for now let us just note that the “depth” of deep learning refers to the complexity of a mathematical model, and that the increased computing power of modern computers has allowed researchers to increase this complexity to reach levels that appear not only quantitatively but also qualitatively different from before. As you notice, science often involves a number of progressively more special subfields, subfields of subfields, and so on. This enables researchers to zoom into a particular topic so that it is possible to catch up with the ever increasing amount of knowledge accrued over the years, and produce new knowledge on the topic — or sometimes, correct earlier knowledge to be more accurate.
Data science is a recent umbrella term (term that covers several subdisciplines) that includes machine learning and statistics, certain aspects of computer science including algorithms, data storage, and web application development. Data science is also a practical discipline that requires understanding of the domain in which it is applied in, for example, business or science: its purpose (what "added value" means), basic assumptions, and constraints. Data science solutions often involve at least a pinch of AI (but usually not as much as one would expect from the headlines).
Robotics means building and programming robots so that they can operate in complex, real-world scenarios. In a way, robotics is the ultimate challenge of AI since it requires a combination of virtually all areas of AI. For example:
- Computer vision and speech recognition for sensing the environment
- Natural language processing, information retrieval, and reasoning under uncertainty for processing instructions and predicting consequences of potential actions
- Cognitive modeling and affective computing (systems that respond to expressions of human feelings or that mimic feelings) for interacting and working together with humans
Many of the robotics-related AI problems are best approached by machine learning, which makes machine learning a central branch of AI for robotics.
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