The Basics of Artificial Intelligence

To understand the concept of artificial intelligence, we must first start by defining intelligence. According to the dictionary definition, intelligence is a "capacity for learning, reasoning, understanding, and similar forms of mental activity; aptitude for grasping truths, relationships, facts, meanings, etc." This definition is broad enough to cover both human and computer (artificial) intelligence. Both people and computers can learn, reason, understand relationships, distinguish facts from falsehoods and so forth.

However, some definitions of intelligence raise the bar to include consciousness or self-awareness, wisdom, emotion, sympathy, intuition and creativity. In some definitions, intelligence also involves spirituality. These definitions separate natural, human intelligence from artificial intelligence, at least in the current, real world. In science fiction, in futuristic worlds, artificially intelligent computers and robots often make the leap to self-consciousness and self-determination, which leads to conflict with their human creators. In The Terminator, artificial intelligence leads to all-out war between humans and the intelligent machines they created.

Other Challenges in Defining Artificial General Intelligence

A further challenge to our ability to define “intelligence” is the fact that human intelligence comes in many forms and often includes the element of creativity. While computers can be proficient at math, repetitive tasks, playing certain games (such as chess), and anything else a human being can program them to do (or to learn to do), people excel in a variety of fields, including math, science, art, music, politics, business, medicine, law, linguistics and so on.

Another challenge to defining intelligence is that we have no definitive standard for measuring it. We do have intelligent quotient (IQ) tests, but a typical IQ test evaluates only short-term memory, analytical thinking, mathematical ability and spatial recognition. In high school, we take ACTs and SATs to gauge our mastery of what we should have learned in school, but the results from those tests don't always reflect a person's true intelligence. In addition, while some people excel in academics, others are skilled in trades or have a higher level of emotional competence or spirituality. There are also people who fail in school but still manage to excel in business, politics, or their chosen careers.

Without a reliable standard for measuring human intelligence, it’s very difficult to point to a computer and say that it's behaving intelligently. Computers are certainly very good at performing certain tasks and may do so much better and faster than humans, but does that make them intelligent?

For example, computers have been able to beat humans in chess for decades. IBM Watson beat some of the best champions in the game show Jeopardy. Google's DeepMind has beaten the best players in the 2,500-year-old Chinese game called “Go” — a game so complex that there are thought to be more possible configurations of the board than there are atoms in the universe. Yet none of these computers understands the purpose of a game or has a desire to play.

Expertise in Pattern-Matching: Using Artificial Intelligence

As impressive as these accomplishments are, they are still just a product of a computer’s special talent for pattern-matching. Pattern-matching is what happens when a computer extracts information from its database and uses that information to answer a question or perform a task. This seems to be intelligent behavior only because a computer is excellent at that particular task. However, excellence at performing a specific task is not necessarily a reflection of intelligence in a human sense.

Just because a computer can beat a chess master does not mean that the computer is more intelligent. We generally don't measure a machine's capability in human terms—for example, we don't describe a boat as swimming faster than a human or a hydraulic jack as being stronger than a weightlifter—so it makes little sense to describe a computer as being smarter or more intelligent just because it is better at performing a specific task.

A computer's proficiency at pattern-matching can make it appear to be intelligent in a human sense. For example, computers often beat humans at games traditionally associated with intelligence. But games are the perfect environments for computers to mimic human intelligence through pattern-matching. Every game has specific rules with a certain number of possibilities that can be stored in a database. When IBM's Watson played Jeopardy, all it needed to do was use natural language processing (NLP) to understand the question, buzz in faster than the other contestants, and apply pattern-matching to find the correct answer in its database.

Good Old-Fashioned Artificial Intelligence (GOFAI)

Early AI developers knew that computers had the potential to excel in a world of fixed rules and possibilities. Only a few years after the first AI conference, developers had their first version of a chess program. The program could match an opponent’s move with thousands of possible counter moves and play out thousands of games to determine the potential ramifications of making a move before deciding which piece to move and where to move it, and it could do so in a matter of seconds.

Artificial intelligence is always more impressive when computers are on their home turf — when the rules are clear and the possibilities limited. Organizations that benefit most from AI are those that work within a well-defined space with set rules, so it’s no surprise that organizations like Google fully embrace AI. Google’s entire business involves pattern-matching — matching users’ questions with a massive database of answers. AI experts often refer to this as good old-fashioned artificial intelligence (GOFAI).

If you're thinking about incorporating AI in your business, consider what computers are really good at — pattern-matching. Do you have a lot of pattern-matching in your organization? Does a lot of your work have set rules and possibilities? It will be this work that is first to benefit from AI.

Frequently Asked Questions

What is the definition of AI?

Artificial Intelligence, commonly referred to as AI, is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using that information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.

What are the different types of AI?

There are 4 types of AI: Reactive Machines, Limited Memory, Theory of Mind, and Self-aware AI. These types represent a progression in the capability and complexity of AI systems, from basic task automation to advanced forms of machine intelligence that can simulate human intelligence and problem-solving.

How do AI systems work?

AI systems work by using various techniques, including machine learning algorithms, neural networks, and deep learning. These techniques enable machines to simulate human intelligence by processing large amounts of data, recognizing patterns, and making decisions with minimal human intervention.

Machine learning is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make predictions based on data. It involves the use of statistical techniques to enable machines to improve their performance on tasks through experience.

What is generative AI and what are some examples of it?

Generative AI refers to AI models that can generate new content, such as text, images, or music, based on the data they have been trained on. Notable examples include ChatGPT 4o for text generation and DeepArt for creating artistic images.

What are some common applications of AI?

AI has a wide range of applications across various industries. Some common uses include natural language processing in virtual assistants, image and speech recognition, self-driving cars, predictive analytics in finance, and artificial intelligence in healthcare for diagnostic purposes.

What is the difference between weak AI and strong AI?

Weak AI, also known as Narrow AI, is designed to perform a specific task, such as voice recognition or chess playing, without possessing full cognitive abilities. Strong AI, or General AI, refers to hypothetical AI systems that possess human-level intelligence and can perform any intellectual task that a human being can.

What is AI governance and why is it important?

AI governance involves the frameworks, policies, and regulations that guide the ethical development and deployment of AI technologies. It is important because it ensures the responsible use of AI, addressing concerns related to bias, fairness, transparency, and accountability.

What is the history of AI?

The history of AI dates back to the mid-20th century, with the term "artificial intelligence" introduced by John McCarthy in 1956. Early AI research focused on solving complex problems and simulating human reasoning. Over the decades, advancements in computing power and data access have propelled the development of AI, leading to the sophisticated AI applications we see today.

How is AI used in healthcare?

AI in healthcare is used for a variety of applications, including medical imaging analysis, predictive analytics for patient diagnosis, personalized medicine, drug discovery, and the automation of administrative tasks. AI technologies help in improving the accuracy and efficiency of medical care delivery.

This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or LLMs. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and data ethics.

This newsletter is 100% human written 💪 (* aside from a quick run through grammar and spell check).