Artificial intelligence is a theory and development of computer systems that can perform tasks that normally require human intelligence. Speech recognition, decision-making, visual perception, for example, are features of human intelligence that artificial intelligence may possess. In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states.
Artificial intelligence or AI refers to software technologies that make a robot or computer act and think like a human. Some software engineers say that it is only artificial intelligence if it performs as well or better than a human. In this context, when we talk about performance, we mean human computational accuracy, speed, and capacity.
Artificial intelligence is a theory and development of computer systems that can perform tasks that normally require human intelligence. Speech recognition, decision-making, visual perception, for example, are features of human intelligence that artificial intelligence may possess. Translation between languages is another feature.
WHAT ARE THE FOUR TYPES OF ARTIFICIAL INTELLIGENCE?
Reactive Machines
Limited Memory
Theory of Mind
Self-Awareness
The Four Types of Artificial Intelligence
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 past 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.
A famous example of a reactive machine is Deep Blue, which was designed by IBM in the 1990’s as a chess-playing supercomputer and defeated international grandmaster Gary Kasparov in a game. Deep Blue was only capable of identifying the pieces on a chess board and knowing how each moves based on the rules of chess, acknowledging each piece’s present position, and determining what the most logical move would be at that moment. The computer was not pursuing future potential moves by its opponent or trying to put its own pieces in better position. Every turn was viewed as its own reality, separate from any other movement that was made beforehand.
Another example of a game-playing reactive machine is Google’s AlphaGo. AlphaGo is also incapable of evaluating future moves but relies on its own neural network to evaluate developments of the present game, giving it an edge over Deep Blue in a more complex game. AlphaGo also bested world-class competitors of the game, defeating champion Go player Lee Sedol in 2016.
Though limited in scope and not easily altered, reactive machine artificial intelligence can attain a level of complexity, and offers reliability when created to fulfill repeatable tasks.
Limited Memory
Limited memory artificial intelligence 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 artificial intelligence 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 machine learning, six steps must be followed: Training data must be created, the machine learning 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 these steps must be reiterated as a cycle.
There are three major machine learning models that utilize limited memory artificial intelligence:
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.
LTSMs 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 (E-GAN), which evolves 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 artificial intelligence.
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 artificial intelligence.
Self-awareness
Once Theory of Mind can be established in artificial intelligence, sometime well into the future, the final step will be for AI to become self-aware. This kind of artificial intelligence 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 artificial intelligence relies both on human researchers understanding the premise of consciousness and then learning how to replicate that so it can be built into machines.
WHAT ARE EXAMPLES OF ARTIFICIAL INTELLIGENCE?
Siri, Alexa and other smart assistants
Self-driving cars
Robo-advisors
Conversational bots
Email spam filters
Netflix's recommendations
What is AI Really Doing?
AI systems work by combining large sets of data with intelligent, iterative processing algorithms to learn from patterns and features in the data that they analyze.
Each time an AI system runs a round of data processing, it tests and measures its own performance and develops additional expertise.
Because AI never needs a break, it can run through hundreds, thousands, or even millions of tasks extremely quickly, learning a great deal in very little time, and becoming extremely capable at whatever it’s being trained to accomplish.
But the trick to understanding how AI truly works is understanding the idea that AI isn’t just a single computer program or application, but an entire discipline, or a science.
The goal of AI science is to build a computer system that is capable of modeling human behavior so that it can use human-like thinking processes to solve complex problems.
To accomplish this objective, AI systems utilize a whole series of techniques and processes, as well as a vast array of different technologies.
By looking at these techniques and technologies, we can begin to really understand what AI actually does, and thus, how it works, so let’s take a look at those next.
What Technology Does AI Require?
AI isn’t new, but its widespread application and utility have skyrocketed in recent years thanks to considerable improvements in technology.
In fact, the explosive growth of AI’s scale and value is closely related to recent technological improvements, including:
Larger, More Accessible Data Sets – AI thrives on data, and has grown in importance alongside the rapid increase of data, along with better access to data. Without developments like “The Internet of Things”, which produces a huge amount of data from connected devices, AI would have far fewer potential applications.
Graphical Processing Units – GPUs are one of the key enablers of AI’s rising value, as they are critical to providing AI systems with the power to perform millions of calculations needed for interactive processing. GPUs provide the computing power needed for AI to rapidly process and interpret big data.
Intelligent Data Processing – New and more advanced algorithms allow AI systems to analyze data faster and at multiple levels simultaneously, helping those systems analyze data sets far faster so they can better and more quickly understand complex systems and predict rare events.
Application Programming Interfaces – APIs allow AI functions to be added to traditional computer programs and software applications, essentially making those systems and programs smarter by enhancing their ability to identify and understand patterns in data.
Why Should You Consider Studying AI?
AI technologies are being developed and applied to virtually every industry, helping improve results, automate processes, and enhance organizational performance.
The AI industry itself is growing rapidly, with the International Data Corporation (IDC) reporting that the AI market, “including software, hardware, and services, is forecast to grow 16.4% year over year in 2021 to $327.5 billion.”
Top jobs in the field also tend to come with great salaries, with U.S. Census Bureau data reporting that the average salary for AI professionals is $102,521.
If you’re interested in pushing the boundaries of computer technology and you want to launch a career in a field that’s growing, and pays well, then AI may be the perfect opportunity.
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