Artificial Intelligence (AI) is a field of computer science focused on developing machines that can think and act like humans. AI is used in a variety of applications, including robotics, natural language processing, computer vision, and expert systems. AI can be used to make intelligent decisions, solve complex problems, and even enable autonomous systems.

History

The history of Artificial Intelligence (AI) can be traced back to ancient Greece, where the philosopher Aristotle proposed the idea of an artificial being that could think and reason. In the 1940s, Alan Turing proposed the concept of a “Turing Test” to measure the intelligence of a machine.

The official idea and definition of AI was first coined by John McCarthy in 1955 at the Dartmouth Conference. ^[1]

"Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find out how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves." - J. McCarthy proposed.

The 1955 proposal defines seven original areas of AI:

1. Simulating higher functions of the human brain
2. Programming a computer to use general language
3. Arranging hypothetical neurons in a manner enabling them to form concepts
4. A way to determine and measure problem complexity
5. Self-improvement
6. Abstraction, defined as the quality of dealing with ideas rather than events
7. Randomness and creativity.

In the 1960s, AI began to be used to solve more complex problems. At this time, AI systems began to be used in medical diagnosis, natural language processing, and robotics. This led to the development of expert systems and the first attempts at autonomous machines.

By the 1980s, AI had grown to encompass a variety of fields, including machine learning, computer vision, and natural language processing. In the 1990s, AI was used to create self-driving cars and systems that could play board and card games. ^[1][2]

Types of AI

Purely Reactive

These are the oldest forms of AI systems that have extremely limited capacity. They emulate the human mind’s ability to respond to different kinds of stimuli. These machines do not have memory-based functionality. This means such machines cannot use previously gained experiences to inform their present actions, i.e., these machines do not have the ability to “learn.” These machines could only be used for automatically responding to a limited set or combination of inputs. They cannot be used to rely on memory to improve their operations based on the same. A popular example of a reactive AI machine is IBM’s Deep Blue, a machine that beat chess Grandmaster Garry Kasparov in 1997. ^[4][9]

Limited Memory

Limited memory machines are machines that have the capabilities of both reactive and learning AI systems using historical data to make decisions. They are used to create applications such as those using deep learning, where they store large volumes of training data to form a reference model for making decisions. This reference model is used to classify new images by comparing them to the training data, resulting in increased accuracy. Almost all present-day AI applications, from chatbots and virtual assistants to self-driving vehicles, are all driven by limited-memory AI. ^[9]

Theory of Mind

Research has found that the previous two types of AI are in abundance, while the next two types of AI exist either as a concept or a work in progress. Theory of mind AI is the next level of AI systems that researchers are currently innovating, which would be able to understand the entities it is interacting with by discerning their needs, emotions, beliefs, and thought processes. Artificial emotional intelligence is already a budding industry and an area of interest for leading AI researchers. Achieving Theory of mind level of AI will require development in other branches of AI, as it would need to perceive humans as individuals whose minds can be shaped by multiple factors. ^[8]

Self-Aware

Research into self-aware AI is currently hypothetical, and its potential effects on humanity are a subject of debate. While it could lead to significant advances, it could also cause catastrophe. Therefore, caution is warranted in the development of this technology. ^[8]

The alternate system of classification that is more generally used in tech parlance is the classification of the technology into Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI).

Artificial Narrow Intelligence (ANI)

This classification system identifies AI systems that are capable of performing a specific task autonomously using human-like capabilities. These systems are referred to as artificial narrow intelligence and are limited in the range of competencies they possess. Examples of these systems include reactive and limited-memory AI systems. The most complex AI that uses machine learning and deep learning to teach itself falls under ANI. ^[8]

Artificial General Intelligence (AGI)

AI agents with Artificial General Intelligence have the potential to learn, perceive, understand, and function like humans, building multiple competencies and forming connections and generalizations across domains. This could drastically reduce the amount of time needed for training, potentially making AI systems as capable as humans. ^[8]

Artificial Superintelligence (ASI)

The development of AGI and ASI has the potential to lead to the singularity, a scenario in which machines possess multi-faceted intelligence comparable to that of humans, along with greater memory, faster data processing and analysis, and decision-making capabilities. While this could offer many potential benefits, it also poses potential risks to our existence or way of life. ^[8]

Applications

AI is used in a variety of applications, including robotics, natural language processing, computer vision, and expert systems. AI-enabled robots are used in a variety of industries, including manufacturing and healthcare. AI can be used to make intelligent decisions, solve complex problems, and even enable autonomous systems. AI is also used in natural language processing and computer vision applications, such as facial recognition and image analysis.

AI is used in search engines (such as Google Search), targeting online advertisements, recommendation systems (offered by Netflix, YouTube, or Amazon), driving internet traffic, targeted advertising (AdSense, Facebook), virtual assistants such as Siri or Alexa), autonomous vehicles (including drones, ADAS and self-driving cars), automatic language translation (Microsoft Translator, Google Translate), facial recognition (Apple's Face ID), image labeling (used by Facebook, TikTok, etc.), spam filtering, chatbots (such as Chat GPT), and text-to-image generation (such as DALL-E).

AI-generated images

Game Playing

Game playing has been a test of AI's strength since the 1950s. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997. In 2011, in a Jeopardy! quiz show exhibition match, IBM's question-answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. ^[4][5]

In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps. Other programs handle imperfect-information games; such as for poker
at a superhuman level, Pluribus and Cepheus. DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari games on its own. ^[6][7]

Smart traffic lights

Smart traffic lights or Intelligent traffic lights are a vehicle traffic control system that combines traditional traffic lights with an array of sensors and artificial intelligence to intelligently route vehicle and pedestrian traffic. They can form part of a bigger intelligent transport system. ^[3]