The History of Artificial Intelligence: Complete AI Timeline

In a short period, computers evolved so quickly and became such an integral part of our daily lives that it is easy to forget how recent this technology is. The first digital computers were only invented about eight decades ago, as the timeline shows. Models such as GPT-3 released by OpenAI in 2020, and Gato released by DeepMind in 2022, have been described as important achievements of machine learning. The developments amount to a face-plant by Humane, which had positioned itself as a top contender among a wave of A.I. Humane spent five years building a device to disrupt the smartphone — only to flounder.

Some argue that AI-generated art is not truly creative because it lacks the intentionality and emotional resonance of human-made art. Others argue that AI art has its own value and can be used to explore new forms of creativity. Natural language processing (NLP) and computer vision were two areas of AI that saw significant progress in the 1990s, but they were still limited by the amount of data that was available.

Large language models such as GPT-4 have also been used in the field of creative writing, with some authors using them to generate new text or as a tool for inspiration. Velocity refers to the speed at which the data is generated and needs to be processed. For example, data from social media or IoT devices can be generated in real-time and needs to be processed quickly. The concept of big data has been around for decades, but its rise to prominence in the context of artificial intelligence (AI) can be traced back to the early 2000s. Before we dive into how it relates to AI, let’s briefly discuss the term Big Data.

Hinton’s work on neural networks and deep learning—the process by which an AI system learns to process a vast amount of data and make accurate predictions—has been foundational to AI processes such as natural language processing and speech recognition. He eventually resigned in 2023 so that he could speak more freely about the dangers of creating Chat GPT artificial general intelligence. The development of deep learning has led to significant breakthroughs in fields such as computer vision, speech recognition, and natural language processing. For example, deep learning algorithms are now able to accurately classify images, recognise speech, and even generate realistic human-like language.

When you get to the airport, it is an AI system that monitors what you do at the airport. And once you are on the plane, an AI system assists the pilot in flying you to your destination. Just as striking as the advances of image-generating AIs is the rapid development of systems that parse and respond to human language. None of the people in these images exist; all were generated by an AI system.

Money returns: Fifth Generation project

This conference is considered a seminal moment in the history of AI, as it marked the birth of the field along with the moment the name “Artificial Intelligence” was coined. In this article I hope to provide a comprehensive history of Artificial Intelligence right from its lesser-known days (when it wasn’t even called AI) to the current age of Generative AI. Humans have always been interested in making machines that display intelligence. At the same time as massive mainframes were changing the way AI was done, new technology meant smaller computers could also pack a bigger punch. Built to serve as a robotic pack animal in terrain too rough for conventional vehicles, it has never actually seen active service. Their bomb disposal robot, PackBot, marries user control with intelligent capabilities such as explosives sniffing.

Their most advanced programs were only able to handle simplistic problems, and were described as toys by the unimpressed. AI researchers had been overly optimistic in establishing their goals (a recurring theme), and had made naive assumptions about the difficulties they would encounter. After the results they promised never materialized, it should come as no surprise their funding was cut. For example, a deep learning network might learn to recognise the shapes of individual letters, then the structure of words, and finally the meaning of sentences.

It showed that machines could learn from experience and improve their performance over time, much like humans do. Many have called the Logic Theorist the first AI program, though that description was debated then—and still is today. The Logic Theorist was designed to mimic human skills, but there’s disagreement about whether the invention actually mirrored the human mind and whether a machine really can replicate the insightfulness of our intelligence.

This course is best if you already have some experience coding in Python and understand the basics of machine learning. The AI surge in recent years has largely come about thanks to developments in generative AI——or the ability for AI to generate text, images, and videos in response to text prompts. Unlike past systems that were coded to respond to a set inquiry, generative AI continues to learn from materials (documents, photos, and more) from across the internet. In 1974, the applied mathematician Sir James Lighthill published a critical report on academic AI research, claiming that researchers had essentially over-promised and under-delivered when it came to the potential intelligence of machines.

We will always indicate the original source of the data in our documentation, so you should always check the license of any such third-party data before use and redistribution. In the last few years, AI systems have helped to make progress on some of the hardest problems in science. AI systems also increasingly determine whether you get a loan, are eligible for welfare, or get hired for a particular job.

Norbert Wiener’s cybernetics described control and stability in electrical networks. Claude Shannon’s information theory described digital signals (i.e., all-or-nothing signals). Alan Turing’s theory of computation showed that any form of computation could be described digitally. The close relationship between these ideas suggested that it might be possible to construct an “electronic brain”. Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 1980s the investors became disillusioned and withdrew funding again. About a week after the reviews came out, Humane started talking to HP, the computer and printer company, about selling itself for more than $1 billion, three people with knowledge of the conversations said.

• While some argue that AI-generated text lacks the depth and nuance of human writing, others see it as a tool that can enhance human creativity by providing new ideas and perspectives.
• The wide range of listed applications makes clear that this is a very general technology that can be used by people for some extremely good goals — and some extraordinarily bad ones, too.
• The next timeline shows some of the notable artificial intelligence (AI) systems and describes what they were capable of.
• In the future, we will see whether the recent developments will slow down — or even end — or whether we will one day read a bestselling novel written by an AI.
• In 1950, a man named Alan Turing wrote a paper suggesting how to test a “thinking” machine.

Computers could perform functions but were not yet able to remember what they had done. The First AI Winter ended with the promising introduction of “Expert Systems,” which were developed and quickly adopted by large competitive corporations all around the world. The primary focus of AI research was now on the theme of accumulating knowledge from various experts, and sharing that knowledge with its users. For example, early NLP systems were based on hand-crafted rules, which were limited in their ability to handle the complexity and variability of natural language.

It also brilliantly captured some of the public’s fears, that artificial intelligences could turn nasty. Google researchers developed the concept of transformers in the seminal paper “Attention Is All You Need,” inspiring subsequent research into tools that could automatically parse unlabeled text into large language models (LLMs). University of Montreal researchers published “A Neural Probabilistic Language Model,” which suggested a method to model language using feedforward neural networks.

The ancient game of Go is considered straightforward to learn but incredibly difficult—bordering on impossible—for any computer system to play given the vast number of potential positions. Despite that, AlphaGO, an artificial intelligence program created by the AI research lab Google DeepMind, went on to beat Lee Sedol, one of the best players in the worldl, in 2016. Since the early days of this history, some computer scientists have strived to make machines as intelligent as humans. The next timeline shows some of the notable artificial intelligence (AI) systems and describes what they were capable of. The field of AI, now more than a half a century old, finally achieved some of its oldest goals. It began to be used successfully throughout the technology industry, although somewhat behind the scenes.

Artificial Intelligence is Everywhere

The strategic significance of big data technology is not to master huge data information, but to specialize in these meaningful data. In other words, if big data is likened to an industry, the key to realizing profitability in this industry is to increase the “process capability” of the data and realize the “value added” of the data through “processing”. During the late 1970s and throughout the 1980s, a variety of logics and extensions of first-order logic were developed both for negation as failure in logic programming and for default reasoning more generally. Days before gadget reviewers weighed in on the Humane Ai Pin, a futuristic wearable device powered by artificial intelligence, the founders of the company gathered their employees and encouraged them to brace themselves.

Little might be as important for how the future of our world — and the future of our lives — will play out. The wide range of listed applications makes clear that this is a very general technology that can be used by people for some extremely good goals — and some extraordinarily bad ones, too. For such “dual-use technologies”, it is important that all of us develop an understanding of what is happening and how we want the technology to be used. The series begins with an image from 2014 in the top left, a primitive image of a pixelated face in black and white. As the first image in the second row shows, just three years later, AI systems were already able to generate images that were hard to differentiate from a photograph. He showed how such an assumption corresponds to the common sense assumption made in reasoning with frames.

Many years after IBM’s Deep Blue program successfully beat the world chess champion, the company created another competitive computer system in 2011 that would go on to play the hit US quiz show Jeopardy. In the lead-up to its debut, Watson DeepQA was fed data from encyclopedias and across the internet. “I think people are often afraid that technology is making us less human,” Breazeal told MIT News in 2001. “Kismet is a counterpoint to that—it really celebrates our humanity. This is a robot that thrives on social interactions” [6]. AI technologies now work at a far faster pace than human output and have the ability to generate once unthinkable creative responses, such as text, images, and videos, to name just a few of the developments that have taken place.

Expert systems, launched by Edward Feigenbaum, could begin to replicate decision-making processes by humans. Still, there were many obstacles to overcome before this goal could be reached. Computer scientists discovered that natural language processing, self-recognition, abstract thinking, and other human-specific skills were difficult to replicate with machines. And a lack of computational power with computers as they existed at that time was still a significant barrier.

Formal reasoning

They were introduced in a paper by Vaswani et al. in 2017 and have since been used in various tasks, including natural language processing, image recognition, and speech synthesis. In the 1960s, the obvious flaws of the perceptron were discovered and so researchers began to explore other AI approaches beyond the Perceptron. They focused on areas such as symbolic reasoning, natural language processing, and machine learning. I can’t remember the last time I called a company and directly spoke with a human. One could imagine interacting with an expert system in a fluid conversation, or having a conversation in two different languages being translated in real time. We can also expect to see driverless cars on the road in the next twenty years (and that is conservative).

Shopper, written by Anthony Oettinger at the University of Cambridge, ran on the EDSAC computer. When instructed to purchase an item, Shopper would search for it, visiting shops at random until the item was found. While searching, Shopper would memorize a few of the items stocked in each shop visited (just as a human shopper might). The next time Shopper was sent out for the same item, or for some other item that it had already located, it would go to the right shop straight away. This simple form of learning, as is pointed out in the introductory section What is intelligence?

More challenging is the problem of implementing what is called generalization. Generalization involves applying past experience to analogous new situations. You can foun additiona information about ai customer service and artificial intelligence and NLP. During the late 1980s, Natural language processing experienced a leap in evolution, as a result of both a steady increase in computational power, and the use of new machine learning algorithms. These new algorithms focused primarily on statistical models – as opposed to models like decision trees. Brooks was inspired by advances in neuroscience, which had started to explain the mysteries of human cognition.

So, companies like Palantir, UiPath, SAS, Microsoft and others, they allow other companies to be AI-first companies, but they’re not AI-first companies themselves, they’re not fundamentally building these learning loops themselves. So, it starts with strategy, but I think then it very quickly moves into tactics, it moves into experimentation. It moves into building AI into your culture, and just providing very basic tools for people to very quickly start experimenting with models and see if they can very quickly build some sort of effective predictive model.

During Turing’s lifetime, technological limitations significantly impeded potential advances in AI. Computers were rare, extremely expensive (costing up to $200,000 per month in the 1950s), and rudimentary compared to modern hardware. A key problem for Turing’s generation is that computers at the time could only execute commands, not store them.

Fake beauty queens charm judges at the Miss AI pageant

But for others, this simply showed brute force at work on a highly specialised problem with clear rules. See Isaac Asimov explain his Three Laws of Robotics to prevent the first ai intelligent machines from turning evil. World War Two brought together scientists from many disciplines, including the emerging fields of neuroscience and computing.

In 1950, I Robot was published – a collection of short stories by science fiction writer Isaac Asimov. Artificial intelligence provides a number of tools that are useful to bad actors, such as authoritarian governments, terrorists, criminals or rogue states. OpenAI introduced the Dall-E multimodal AI system that can generate images from text prompts. Nvidia announced the beta version of its Omniverse platform to create 3D models in the physical world.

Natural language processing was sparked initially by efforts to use computers as translators for the Russian and English languages, in the early 1960s. These efforts led to thoughts of computers that could understand a human language. Efforts to turn those thoughts into a reality were generally unsuccessful, and by 1966, “many” had given up on the idea, completely. Expert Systems were an approach in artificial intelligence research that became popular throughout the 1970s.

“Because they are all beautiful, I want somebody that I would be proud to say is an AI ambassador and role model giving out brilliant and inspiring messages, rather than just saying, ‘hello, I’m really hot!’ ” said Fawcett. Sign up for free online courses covering the most important core topics in the crypto universe and earn your on-chain certificate – demonstrating your new knowledge of major Web3 topics. But with applications like ChatGPT, Dalle.E, and others, we have only just scratched the surface of the possible applications of AI.

The first true AI programs had to await the arrival of stored-program electronic digital computers. Large AIs called recommender systems determine what you see on social media, which products are shown to you in online shops, and what gets recommended to you on YouTube. Increasingly they are not just recommending the media we consume, but based on their capacity to generate images and texts, they are also creating the media we consume. Foundation models, which are large language models trained on vast quantities of unlabeled data that can be adapted to a wide range of downstream tasks, began to be developed in 2018. The development of metal–oxide–semiconductor (MOS) very-large-scale integration (VLSI), in the form of complementary MOS (CMOS) technology, enabled the development of practical artificial neural network technology in the 1980s. Intelligence is the ability to learn fast, and so, therefore, artificial intelligence is something that is not running on our own wetware, it’s running on a computer.

“Scruffies” expect that it necessarily requires solving a large number of unrelated problems. Neats defend their programs with theoretical rigor, scruffies rely mainly on incremental testing to see if they work. This issue was actively discussed in the 1970s and 1980s,[318] but eventually was seen as irrelevant. Today’s tangible developments — some incremental, some disruptive — are advancing AI’s ultimate goal of achieving artificial general intelligence. Along these lines, neuromorphic processing shows promise in mimicking human brain cells, enabling computer programs to work simultaneously instead of sequentially. Amid these and other mind-boggling advancements, issues of trust, privacy, transparency, accountability, ethics and humanity have emerged and will continue to clash and seek levels of acceptability among business and society.

But, in the last 25 years, new approaches to AI, coupled with advances in technology, mean that we may now be on the brink of realising those pioneers’ dreams. It has been argued AI will become so powerful that humanity may irreversibly lose control of it. In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield. AI has been used to predict the ripening time for crops such as tomatoes, monitor soil moisture, operate agricultural robots, conduct predictive analytics, classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and save water. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Kismet, a robot with a human-like face and which could recognize and simulate emotions, launched in 2000. In 2009, Google developed a driverless car prototype, although news of this advancement did not emerge until later. One of the reasons for AI’s success during this period was strong financial support from the Defense Advanced Research Projects Agency (DARPA) and leading academic institutions. This support and the speed of developments in AI technology led scientists like Marvin Minsky to predict in 1970 that a machine with the “general intelligence of an average human being” was only three to eight years away. They are driving cars, taking the form of robots to provide physical help, and performing research to help with making business decisions.

Artificial intelligence is no longer a technology of the future; AI is here, and much of what is reality now would have looked like sci-fi just recently. It is a technology that already impacts all of us, and the list above includes just a few of its many applications. When you book a flight, it is often an artificial intelligence, no longer a human, that decides what you pay.

Similarly, in the field of Computer Vision, the emergence of Convolutional Neural Networks (CNNs) allowed for more accurate object recognition and image classification. Today, the Perceptron is seen as an important milestone in the history of AI and continues to be studied and used in research and development of new AI technologies. This concept was discussed at the conference and became a central idea in the field of AI research. The Turing test remains an important benchmark for measuring the progress of AI research today.

Shakey was the first general-purpose mobile robot able to make decisions about its own actions by reasoning about its surroundings. A moving object in its field of view could easily bewilder it, sometimes stopping it in its tracks for an hour while it planned its next move. Another definition has been adopted by Google,[309] a major practitioner in the field of AI. This definition stipulates the ability of systems to synthesize information as the manifestation of intelligence, similar to the way it is defined in biological intelligence. Groove X unveiled a home mini-robot called Lovot that could sense and affect mood changes in humans.

In the first half of the 20th century, science fiction familiarized the world with the concept of artificially intelligent robots. It began with the “heartless” Tin man from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis. By the 1950s, we had a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. One such person was Alan Turing, a young British polymath who explored the mathematical possibility of artificial intelligence. Turing suggested that humans use available information as well as reason in order to solve problems and make decisions, so why can’t machines do the same thing? This was the logical framework of his 1950 paper, Computing Machinery and Intelligence in which he discussed how to build intelligent machines and how to test their intelligence.

The AI research community was becoming increasingly disillusioned with the lack of progress in the field. This led to funding cuts, and many AI researchers were forced to abandon their projects and leave the field altogether. Another example is the ELIZA program, created by Joseph Weizenbaum, which was a natural language processing program that simulated a psychotherapist. The participants set out a vision for AI, which included the creation of intelligent machines that could reason, learn, and communicate like human beings. It could also be used for activities in space such as space exploration, including analysis of data from space missions, real-time science decisions of spacecraft, space debris avoidance, and more autonomous operation. Early work, based on Noam Chomsky’s generative grammar and semantic networks, had difficulty with word-sense disambiguation[f] unless restricted to small domains called “micro-worlds” (due to the common sense knowledge problem[32]).

In 1950, a man named Alan Turing wrote a paper suggesting how to test a “thinking” machine. He believed if a machine could carry on a conversation by way of a teleprinter, imitating a human with no noticeable differences, the machine could be described as thinking. His paper was followed in 1952 by the Hodgkin-Huxley model of the brain as neurons forming an electrical network, with individual neurons firing in all-or-nothing (on/off) pulses. These combined events, discussed at a conference sponsored by Dartmouth College in 1956, helped to spark the concept of artificial intelligence. By training deep learning models on large datasets of artwork, generative AI can create new and unique pieces of art.

The period between the late 1970s and early 1990s signaled an “AI winter”—a term first used in 1984—that referred to the gap between AI expectations and the technology’s shortcomings. While Shakey’s abilities were rather crude compared to today’s developments, the robot helped advance elements in AI, including “visual analysis, route finding, and object manipulation” [4]. The early excitement that came out of the Dartmouth Conference grew over the next two decades, with early signs of progress coming in the form of a realistic chatbot and other inventions. Artificial intelligence has already changed what we see, what we know, and what we do. The AI systems that we just considered are the result of decades of steady advances in AI technology.

The instrumental figures behind that work needed opportunities to share information, ideas, and discoveries. To that end, the International Joint Conference on AI was held in 1977 and again in 1979, but a more cohesive society had yet to arise. The speed at which AI continues to expand is unprecedented, and to appreciate how we got to this present moment, it’s worthwhile to understand how it first began. AI has a long history stretching back to the 1950s, with significant milestones at nearly every decade. In this article, we’ll review some of the major events that occurred along the AI timeline. The data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors.

Expert systems were used to automate decision-making processes in various domains, from diagnosing medical conditions to predicting stock prices. The AI boom of the 1960s culminated in the development of several landmark AI systems. One example is the General Problem Solver (GPS), which was created by Herbert Simon, J.C. Shaw, and Allen Newell. GPS was an early AI system that could solve problems by searching through a space of possible solutions. Following the conference, John McCarthy and his colleagues went on to develop the first AI programming language, LISP. Rodney Brook’s spin-off company, iRobot, created the first commercially successful robot for the home – an autonomous vacuum cleaner called Roomba.

It was other developments in 2014 that really showed how far AI had come in 70 years. From Google’s billion dollar investment in driverless cars, to Skype’s launch of real-time voice translation, intelligent machines were now becoming an everyday reality that would change all of our lives. Instead of trying to create a general intelligence, these ‘expert systems’ focused on much narrower tasks. That meant they only needed to be programmed with the rules of a very particular problem.

Machine learning is the method to train a computer to learn from its inputs but without explicit programming for every circumstance. In my humble opinion, digital virtual assistants and chatbots have passed Alan Turing’s test, and achieved true artificial intelligence. Current artificial intelligence, with its ability to make decisions, can be described as capable of thinking. If these entities were communicating with a user by way of a teletype, a person might very well assume there was a human at the other end. That these entities can communicate verbally, and recognize faces and other images, far surpasses Turing’s expectations. Chatbots (sometimes called “conversational agents”) can talk to real people, and are often used for marketing, sales, and customer service.

The AI research company OpenAI built a generative pre-trained transformer (GPT) that became the architectural foundation for its early language models GPT-1 and GPT-2, which were trained on billions of inputs. Even with that amount of learning, their ability to generate distinctive text responses was limited. Cotra’s work is particularly relevant in this context as she based her forecast on the kind of historical long-run trend of training computation that we just studied. But it is worth noting that other forecasters who rely on different considerations arrive at broadly similar conclusions. As I show in my article on AI timelines, many AI experts believe that there is a real chance that human-level artificial intelligence will be developed within the next decades, and some believe that it will exist much sooner. The business community’s fascination with AI rose and fell in the 1980s in the classic pattern of an economic bubble.

This means that the network can automatically learn to recognise patterns and features at different levels of abstraction. To address this limitation, researchers began to develop techniques for processing natural language and visual information. As we spoke about https://chat.openai.com/ earlier, the 1950s was a momentous decade for the AI community due to the creation and popularisation of the Perceptron artificial neural network. The Perceptron was seen as a breakthrough in AI research and sparked a great deal of interest in the field.