How Does AI Actually Work?

Recently, we explained why AI is so important, and we discussed how to launch a career in artificial intelligence, and here we’re going to explore how AI actually works.

As part of this discussion, we’ll cover what artificial intelligence is, What is AI Really Doing, The Four Types of AI, and Machine Learning Vs. Deep Learning and more.

What Actually Is Artificial Intelligence?

Before we can explain how AI works, let’s first define what AI is:

Artificial Intelligence is a technology that allows machines and computer applications to mimic human intelligence, learning from experience via iterative processing and algorithmic training.

You can think of AI as being a form of intelligence that is used to solve problems, come up with solutions, answer questions, make predictions, or offer strategic suggestions.

Because AI can do all these things, it’s become incredibly important to modern businesses and other types of organizations.

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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.

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What Disciplines Make Up the Field of AI?

There are many different components to an AI system, which you can think of as sub-fields of the overarching science of artificial intelligence. 

Each of the following fields is commonly utilized by AI technology:

• Machine Learning - A specific application of AI that lets computer systems, programs, or applications learn automatically and develop better results based on experience, all without being programmed to do so. Machine Learning allows AI to find patterns in data, uncover insights, and improve the results of whatever task the system has been set out to achieve.
• Deep Learning - A specific type of machine learning that allows AI to learn and improve by processing data. Deep Learning uses artificial neural networks which mimic biological neural networks in the human brain to process information, find connections between the data, and come up with inferences, or results based on positive and negative reinforcement.
• Neural Networks - A process that analyzes data sets over and over again to find associations and interpret meaning from undefined data. Neural Networks operate like networks of neurons in the human brain, allowing AI systems to take in large data sets, uncover patterns amongst the data, and answer questions about it.
• Cognitive Computing - Another important component of AI systems designed to imitate the interactions between humans and machines, allowing computer models to mimic the way that a human brain works when performing a complex task, like analyzing text, speech, or images.
• Natural Language Processing - A critical piece of the AI process since it allows computers to recognize, analyze, interpret, and truly understand human language, either written or spoken. Natural Language Processing is critical for any AI-driven system that interacts with humans in some way, either via text or spoken inputs.
• Computer Vision - One of the prolific uses of AI technologies is the ability to review and interpret the content of an image via pattern recognition and deep learning. Computer Vision lets AI systems identify components of visual data, like the captchas you’ll find all over the web which learn by asking humans to help them identify cars, crosswalks, bicycles, mountains, etc.

Machine Learning Vs. Deep Learning

Although the terms “machine learning” and “deep learning” come up frequently in conversations about AI, they should not be used interchangeably. 

Deep learning is a form of machine learning, and machine learning is a subfield of artificial intelligence.

Machine Learning

A machine learning algorithm is fed data by a computer and uses statistical techniques to help it “learn” how to get progressively better at a task, without necessarily having been specifically programmed for that task. 

Instead, ML algorithms use historical data as input to predict new output values. 

To that end, ML consists of both supervised learning (where the expected output for the input is known thanks to labeled data sets) and unsupervised learning (where the expected outputs are unknown due to the use of unlabeled data sets).

Deep Learning

Deep learning is a type of machine learning that runs inputs through a biologically inspired neural network architecture. 

The neural networks contain a number of hidden layers through which the data is processed, allowing the machine to go “deep” in its learning, making connections and weighting input for the best results.

The Four Types of AI

AI can be divided into four categories, based on the type and complexity of the tasks a system is able to perform. They are:

1. Reactive machines
2. Limited memory
3. Theory of mind
4. Self awareness

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 has its benefits, however: This type of AI will be more trustworthy and reliable, and it will react the same way to the same stimuli every time. 

Reactive Machine Examples

• Deep Blue was designed by IBM in the 1990s as a chess-playing supercomputer and defeated international grandmaster Gary Kasparov in a game. 
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• 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. 
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• 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.
   
• Google’s 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.

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: 

1. Establish training data
2. Create the machine learning model
3. Ensure the model can make predictions
4. Ensure the model can receive human or environmental feedback
5. Store human and environmental feedback as data
6. Reiterate the steps above as a 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 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.