Artificial intelligence is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.

 How Does Artificial Intelligence Work?

 What Is AI?

Less than a decade after helping the Allied Forces win World War II by breaking the Nazi encryption machine Enigma, mathematician Alan Turing changed history a second time with a simple question: “Can machines think?” 

Turing’s 1950 paper “Computing Machinery and Intelligence” and its subsequent Turing Test established the fundamental goal and vision of AI.   

At its core, AI is the branch of computer science that aims to answer Turing’s question in the affirmative. It is the endeavor to replicate or simulate human intelligence in machines. The expansive goal of AI has given rise to many questions and debates. So much so that no singular definition of the field is universally accepted.

 Defining AI

The major limitation in defining AI as simply “building machines that are intelligent” is that it doesn't actually explain what AI is and what makes a machine intelligent. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.

In their book Artificial Intelligence: A Modern Approach, authors Stuart Russell and Peter Norvig approach the concept of AI by unifying their work around the theme of intelligent agents in machines. With this in mind, AI is “the study of agents that receive precepts from the environment and perform actions.”

Norvig and Russell go on to explore four different approaches that have historically defined the field of AI:

The first two ideas concern thought processes and reasoning, while the others deal with behavior. Norvig and Russell focus particularly on rational agents that act to achieve the best outcome, noting “all the skills needed for the Turing Test also allow an agent to act rationally.”

Former MIT professor of AI and computer science Patrick Winston defined AI as “algorithms enabled by constraints, exposed by representations that support models targeted at loops that tie thinking, perception and action together.”

While these definitions may seem abstract to the average person, they help focus the field as an area of computer science and provide a blueprint for infusing machines and programs with ML and other subsets of AI.

 The Four Types of Artificial Intelligence

AI can be divided into four categories, based on the type and complexity of the tasks a system is able to perform. For example, automated spam filtering falls into the most basic class of AI, while the far-off potential for machines that can perceive people’s thoughts and emotions is part of an entirely different AI subset.

 What Are the Four Types of Artificial Intelligence?

•  Reactive Machines: able to perceive and react to the world in front of it as it performs limited tasks
•  Limited Memory: able to store past data and predictions to inform predictions of what may come next
•  Theory of Mind: able to make decisions based on its perceptions of how others feel and make decisions
•  Self-Awareness: able to operate with human-level consciousness and understand its own existence.

 Reactive Machines

A reactive machine follows the most basic of AI principles and, as its name implies, is capable of only using its intelligence to perceive and react to the world in front of it. A reactive machine cannot store a memory and, as a result, cannot rely on experiences to inform decision-making in real-time.

Perceiving the world directly means that reactive machines are designed to complete only a limited number of specialized duties. Intentionally narrowing a reactive machine’s worldview is not any sort of cost-cutting measure, however, and instead means that this type of AI will be more trustworthy and reliable — it will react the same way to the same stimuli every time.

 Limited Memory

Limited memory, AI has the ability to store previous data and predictions when gathering information and weighing potential decisions — essentially looking into the past for clues on what may come next. Limited memory AI is more complex and presents greater possibilities than reactive machines.

Limited memory AI is created when a team continuously trains a model in how to analyze and utilize new data, or an AI environment is built, so models can be automatically trained and renewed. 

When utilizing limited memory AI in ML, six steps must be followed: Training data must be created, the ML model must be created, the model must be able to make predictions, the model must be able to receive human or environmental feedback, that feedback must be stored as data, and these steps must be reiterated as a cycle.

There are three major ML models that utilize limited memory AI:

•  Reinforcement learning, which learns to make better predictions through repeated trial-and-error.
•  Long short term memory (LSTM), which utilizes past data to help predict the next item in a sequence. Long short term memory view more recent information as most important when making predictions and discounts data from further in the past, though still utilizing it to form conclusions
•  Evolutionary generative adversarial networks , which evolve over time, growing to explore slightly modified paths based off of previous experiences with every new decision. This model is constantly in pursuit of a better path and utilizes simulations and statistics, or chance, to predict outcomes throughout its evolutionary mutation cycle.

 Theory of Mind

Theory of Mind is just that — theoretical. We have not yet achieved the technological and scientific capabilities necessary to reach this next level of AI.

The concept is based on the psychological premise of understanding that other living things have thoughts and emotions that affect the behavior of one’s self. In terms of AI machines, this would mean that AI could comprehend how humans, animals and other machines feel and make decisions through self-reflection and determination, and then will utilize that information to make decisions of their own. Essentially, machines would have to be able to grasp and process the concept of “mind,” the fluctuations of emotions in decision-making and a litany of other psychological concepts in real time, creating a two-way relationship between people and AI.

 Self-awareness

Once Theory of Mind can be established, sometime well into the future of AI, the final step will be for AI to become self-aware. This kind of AI possesses human-level consciousness and understands its own existence in the world, as well as the presence and emotional state of others. It would be able to understand what others may need based on not just what they communicate to them, but how they communicate it. 

Self-awareness in AI relies both on human researchers understanding the premise of consciousness and then learning how to replicate that, so it can be built into machines.