Making mental connections is our most crucial learning tool, the essence of human intelligence; to forge links; to go beyond the given; to see patterns, relationships, context. —Marilyn Ferguson, author
Understanding human behavior is complicated because humans do not always act rationally or logically. We can improve a machine’s ability to predict human behavior by searching for patterns. These patterns help to discover and define trends. By analyzing these trends and modeling them with algorithms, machines can mimic human responses to certain inputs. Then, when encountering these inputs in real-world contexts, they are able to respond accordingly.
What Is Learning?
If we could simplify human learning, we might say that humans take information from their environment, relate it to something, and then learn or act. These inputs could be something they see, smell, taste, hear, feel, or even their interpretation of a mood or tone. That information is related to prior knowledge a person has about the world, making a connection. From there, a human might act on their new knowledge, explore, or innovate.
Machines are given input in the form of data. The machine interprets that data and learns from it. Then the machine evaluates that data before outputting the information that has been defined as useful to a human to interpret. This is the prediction phase, as shown in.
Where does the data come from? Machines are collecting data through hardware inputs as well as software programs. You can think of the hardware as the body, and software as the mind of a machine. Hardware addresses the area of machine perception, and software addresses the idea of both machine language and human language.
Machines can perceive the environment through sight, feeling, and hearing via sensors. Sensors are a part of a machine’s hardware system. They measure physical events like temperature, pressure, force, acceleration, sound, and light.
In fact, your phone can measure almost all these things. Phones sense through small electronics called microelectromechanical systems (MEMS). The microphone, camera, inertial measurement units (or IMUs, which help track position), and proximity sensors are all examples of MEMs. These sensors can also be found in various Internet of Things (IoT) devices.
In collaboration with these sensors, software systems on a machine can do things like interpret when a phone is upside down or right side up, measure human locomotion, and detect faces or sounds.
Human languages are critical for communication. We use words and phrases, combining them in multiple ways in order to express ideas and emotions. Machines use machine languages to define models and parameters. Human language and machine perception both provide inputs in the form of data for machines to use to learn from.
An important distinction exists between machine languages and human languages. Machine languages are written in code. Originally, this code was only a series of 0s and 1s, or binary. Different combinations of 0s and 1s encode different information to machines. Over time, humans have created programming languages that interface between human language and machine language to make the job of coding easier.
The output of a machine is most useful when it can be interpreted by a human, which makes human language a useful concept for machines to understand.
Topics in Artificial Intelligence
The role of the computer is not to displace human creativity but rather to amplify it — Ray Kurzweil, The Age of Intelligent Machines.
Successfully applying AI today requires understanding which techniques should be used for solving a given problem. There is currently no single algorithm that will provide value in every aspect of the fashion industry. The term AI as an overarching category can be confusing because it often leads people to believe that AI is a mysterious black box that can solve any problem. In reality, it is made up of several application areas, tools, and techniques. Understanding the broader categories and more specific subcategorization of the field gives a picture of how it all fits together.
Application areas discussed in this website include:
- Natural language processing (NLP)
- Computer vision (CV)
- Predictive analytics
Some commonly used tools and techniques include:
- Neural networks
- Generative adversarial networks (GANs)
- Data mining
Not every topic in the field of AI is covered in this website. Because the categories often overlap, this website is written with more information being introduced cumulatively as you read through.