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What is Artificial Intelligence?

The term “Artificial Intelligence” often evokes images of personas like HAL, Terminator, or Ex Machina. And while AI has aspired to create artificial persons, AI systems are “mostly used for infrastructure” that are both less flashy and more ubiquitous than what Hollywood has imagined. Moreover, far from the general intelligence many are dreaming of, most AI systems are narrowly focused on very specific and tractable tasks, like identifying cats or human emotions.

Defining AI​

There is no agreed-upon definition of what AI is. Moreover, there are multiple ways one might try to define AI: (a) what it’s intended to do, (b) what it does or how it works, or (c) what it is. We might also do well to define what it is not.

By Intention​

What AI is intended to do:

  • Imitate (but not simulate or replicate) human behavior, both physically and cognitively
  • "Learn" things from collections of data sources
  • Make decisions
  • Solve problems

By Function​

What AI does and how it works:

  • Identifies patterns based on statistics, what we might call "learning"
  • "Learns" from thousands or millions of examples to identify patterns
  • Uses electricity to perform statistical calculations
  • Finds correlations between identified patterns and new data sources
  • Modifies existing patterns based on new data sources

By Identity​

What AI is and is not:

  • AI is not conscious or self-aware
  • AI is not learning or thinking in the way humans do
  • AI is made of silicon and electricity (this is its "embodied" form)
  • Composed of an algorithm, a model, and training data

Core Concepts​

The term “Artificial Intelligence” was coined in 1956 in a workshop at Dartmouth College in the US. Defined most broadly, Artificial Intelligence (AI) is a pattern-matching system that decides what labels to apply (or with LLMs, what the next work should be). So, if you have a collection of shirts that are stripes, polka dots, and plaid, you can ask the AI to sort them into each group. At its core, if you give an AI system a piece of data, it will respond with a label for that data. If you give it a picture of a plaid shirt, a well-trained AI system will return the label “plaid.”

Humans have designed fabric patterns of seemingly infinite variety, and human-level cognition distinguishes among them in various ways—not always systematic. However, for an AI system, all of these capacities would apply methods based around pattern-matching.

Pattern Recognition​

The unique quality that AI systems provide is their method of pattern recognition. Rather than having humans prescribe patterns to the computer, AI systems find patterns on their own. These patterns—called "parameters"—are undefined by humans but become defined by the AI's own analysis of the data set.

Currently, the most expansive AI systems have trillions of parameters, meaning they can track and identify trillions of patterns within a collection of data. So whereas humans might discern a thousand (or a million) patterns, current AI systems can track and identify trillions more.

Decision Making​

Based on the patterns determined by the AI system, it can take new data and suggest (or decide) what labels fit best. For example:

  • A recommender system can determine "people who liked this book also liked this other book"
  • Image recognition systems can label images for search purposes
  • Audio processing systems can transform sound into text for a given language

Types of AI Systems​

AI systems can be categorized in multiple ways:

By Capability​

  • Narrow AI
  • Strong AI
  • General AI
  • Super AI

By Product​

  • Generative AI
  • Discriminative AI

By Application​

  • Machine Learning (ML)
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Expert Systems
  • Robotics and Autonomous Systems
  • Affective Computing
  • Machine Translation
  • Facial Recognition
  • Recommender Systems

By Method​

Various technical approaches and architectures

Applications of AI​

Among the many applications of AI, you’ll hear numerous names thrown out: Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Computer Vision, Expert Systems, Robotics and Autonomous Systems, Generative AI, Affective Computing, Machine Translation, Facial Recognition, Recommender Systems, and more. Many of these could be reports unto themselves.

Recommenders. Similarly, when you get a book recommendation on Amazon, it’s based on this kind of pattern matching: “people who like this book, also liked…”. This decision-making and pairing is then used in increasingly complex ways. While developers intend for AI systems to imitate human thinking and choices, these systems often use very different methods than a human mind does. The degree to which humans can understand these methods is a common AI problem known as “interpretability.”

Interdisciplinary Nature​

AI systems and theories draw on many advanced fields, including:

  • Psychology
  • Neuroscience
  • Linguistics
  • Philosophy
  • Economics
  • Probability
  • Logic

While these fields contribute to AI development, all knowledge must be in a format readable by AI systems. Otherwise, that knowledge is omitted, limiting the reach of AI's "knowledge." Notably, while philosophy is represented, religious traditions are not well-represented, though many Christian computer scientists are working to bridge this gap.