Simple Explanations of Key Artificial Intelligence (AI) Terminology

Just as science fiction authors have always predicted, (AI) is increasingly becoming an everyday part of our lives. From personal assistants such as Apple’s Siri or Microsoft’s Cortana to cutting-edge applications in healthcare and across various industries, self-learning machines have arrived and are busy helping us find new ways to solve problems.

As the predictions and dreams of yesterday’s futurologists have solidified into today’s tools and technology, a sometimes confusing lexicon has sprung up around the subject. As these new words and phrases are often attempts to outline the fundamental thinking behind this ongoing robotic revolution, understanding them is key to coming to grips with what AI devices actually are, where they’ve come from, what they want, and most importantly, how you can put them to work yourself!

So here’s my brief alphabetical guide to some of the most common and some of the latest terminology being used when discussing cutting-edge AI.  

AlphaGo is an AI development that became the first computer program to beat a professional player at the board game Go. Game playing has often been a field in which computer scientists have sought to prove that machines can outperform humans. However, earlier applications such as chess computers are not considered “true” AI today because they don’t really learn – they simply rely on brute force to consider every permutation of a structured dataset (all the moves possible in a game of chess.) AlphaGo uses deep learning to refine its algorithms based on recalling the results of historical games and running simulated games against itself. This means it can be considered to be learning and comes closer to what we consider “true” (human-like) intelligence. 

This is the original catch-all term for machines that can think, first conceived by philosophers and storytellers in ancient times. Technological advancement has brought them closer to reality and also redefined what we consider “intelligence” when it comes to machines. Rather than walking, talking automatons, today’s AI devices are more likely to take the form of discrete computer code dedicated to handing a particular task in an intelligent way.

Big Data is the “fuel” of AI. Knowledge unlocks understanding and wisdom. AI platforms leverage the huge volume, variety and velocity of information available in today’s digitized world to learn faster and make increasingly well-informed recommendations and decisions.

Cognitive Computing

Cognitive computing is the process by which computers think and learn. This term also is used to refer to the development of these processes. These give rise to AI, machine learning (ML), deep learning and all technologies that involve simulating human thought and decision-making. In practice, the term is often used synonymously with modern application-focused AI.  

This subfield of ML (see below) uses many layers of artificial neural networks to handle processing of data in increasingly complex ways. This means that classification (sorting into sets) can be done more precisely and that pattern recognition is more sophisticated. These are two of the most useful fundamental tasks that AI carries out today. Thus, deep learning is a cutting-edge, very active field of research. Layers of neural networks stacked on top of each other to be used in deep learning are known as deep neural networks.  

Generalized AI is a concept – widely thought to still be some way off – of a machine that can carry out any job it is told to do. An android such as those seen in Star Trek or Blade Runner – one that could be given a mop and told to clean a floor or be given a weapon and told to defend against attacking Klingons – would be an archetypal example. While advances such as ML and deep neural networks point toward it being something that we will achieve in the future, currently the majority of AI research focuses on creating applied or specialized AI devices (see below).  

Teaching machines to recognize and classify objects visually – by inputting visual data – is an important foundation of AI because visual information is so valuable to humans, and AI seeks to emulate human thought processes. Using cameras or raw image data such as picture or video files, computers are being taught to classify images according to what they depict by using pattern recognition to identify key features. Advances in ML have greatly improved the ability of computers to do this task, as they have become able to teach themselves from vast image databases, increasing their probability of outputting accurate results. 

This term is often used synonymously with AI these days, but there is an important distinction. While AI applies to the entire concept of “thinking” machines from sci-fi robots to self-learning computer code being developed by business and academia today, ML is the practical implementation that is generating the biggest breakthroughs in the real world. At its most basic it is technology designed around the principle that rather than have to teach machines to carry out every task, we should just be able to feed them data and allow them to work out the rules by themselves. This is done through a process of simulated trial-and-error scenarios in which machines crunch datasets through algorithms that can adapt based on what they learn from the data to more efficiently process subsequent data. 

Natural language processing (NLP) technology is concerned with building machines that can understand human speech patterns. Because spoken communication comes far more naturally to us than writing computer code, it makes sense that machines, with their superior processing powers, learn to adapt to us by understanding and speaking our language, rather than having us adapt to them. Due to the huge variance in human languages and the way they are used, ML is employed to pick out patterns, tonal variances and colloquial or nonliteral use of language and interpret what we are trying to express. ML-derived NLP can be seen or heard in action in virtual assistants such as Apple’s Siri, Microsoft’s Cortana and Amazon’s Alexa. 

Neural Networks

Neural networks are algorithmic models structured as hierarchical networks of nodes that pass information (data) between themselves, extrapolating more and more precise meaning and value from it as it passes along the chain. Their complex, interconnected nature allows data to be processed far more comprehensively than traditional linear algorithms allow, enabling them more insightful output from big, messy unstructured datasets.

The more precise and correct term, artificial neural networks (ANNs), is often simply shortened to neural network, the term for the system of biological neurons in the animal brain that ML attempts to emulate. 

The form of AI becoming commonplace in business, scientific research and our everyday lives – usually in the form of applications designed to carry out one specific task in an increasingly efficient way. This could be anything from giving you tips on improving your fitness by monitoring exercise patterns to predicting when machinery will break down on a production line to spotting genetic indicators of illness in a human gene sequence.  

Supervised learning is a term used for ML processes where the output of the algorithm is checked and the results fed back to the computer to enable it to know how accurate they are. It can then use this knowledge to increase the probability that it will return with an acceptably accurate result next time around. As a simple example, imagine an AI fraud detection algorithm designed to flag suspicious transactions by a bank. In unsupervised learning, data is matched against previous outcomes to look for patterns in transactions (financial transactions, for example) such as their point of origin, size or time of day they take place. These patterns may indicate that some transactions are suspicious. As new suspicious transactions are identified, the algorithm adapts to “learn” that other features of the newly identified suspicious transactions may also be an indicator of fraud. In this way, a supervised learning system can learn to identify fraud from characteristics that were not highlighted in its initial training data as indicators of fraud.  

Unsupervised learning is the flip side of supervised learning. It involves increasing computers’ abilities to accurately recognize and classify data without needing a human, or initial training data, to check this data and determine if it is right or wrong. In unsupervised learning the algorithm only sees the input data, and it classifies it according to patterns that it recognizes from other input data that it has previously processed. This is generally done through a statistical process known as clustering where objects (financial transactions — to carry on my example from above) are grouped according to qualities and attributes that they share. This approach to the problem of data classification has tremendous potential for developing machines that more closely emulate our own thought and decision-making processes, but it also requires huge amounts of processing power compared with the processing needed for supervised learning.