# History of AI | Jotform AI

# History of AI

Discover the historical milestones that guided the development of artificial intelligence.

Latest to Oldest Oldest to Latest

Modern AI Machine Learning Early AI Program Meet Alan Turing AI in Mythology

*   ## Modern AI: 2025 - 2025 
*   
### 2025

Jotform AI Agents launched!

Jotform AI Agents are powerful automated customer service tools that provide real-time assistance, answer user queries, and guide customers through processes like form-filling and troubleshooting. By offering personalized, conversational AI interactions and 24-7 availability, they enhance customer satisfaction, streamline support workflows, and reduce response times, ensuring a seamless and efficient customer experience.

Resources

    1.   [Jotform AI Agents](https://www.jotform.com/ai/agents/)

*   ### 2024

Pioneering AI Solves Protein Folding Puzzle, Wins Nobel Prize

AlphaFold, a pioneering AI system that can predict the 3D structure of proteins based on their amino acid sequences, earned Sir Demis Hassabis, the co-founder and CEO of Google DeepMind and Isomorphic Labs, and Dr. John Jumper, the Director of Google DeepMind, the prestigious 2024 Nobel Prize in Chemistry for their groundbreaking contributions to this revolutionary technology.   
*   2024

Apple Unveils Apple Intelligence with ChatGPT Integration

Apple unveiled its cutting-edge "Apple Intelligence" feature, seamlessly integrating ChatGPT's capabilities into the latest iPhones and the digital assistant Siri, offering users an elevated and intelligent experience.   
*   2024

Sora: OpenAI's Text-to-Video Magic

OpenAI unveils Sora, an AI model capable of creating short videos from textual descriptions, on February 15, 2024.   
*   ### 2023

Unleashing the Power of Gemini 1.0 Ultra

Google unveiled an advanced version of its Gemini platform, dubbed Gemini 1.0 Ultra, offering enhanced capabilities and performance upgrades.   
*   2023

Historic AI Executive Order Signed by Biden

President Biden enacted an Executive Order on October 30, 2023, aimed at promoting the responsible and ethical development and utilization of Artificial Intelligence technology, ensuring its safety, security, and trustworthiness.   
*   2023

AI Extinction Risk: Urgent Call from Tech Titans

Prominent AI researchers, tech leaders, and influential figures such as Geoffrey Hinton, Sam Altman, and Bill Gates signed a statement in late May 2023, expressing their concern about AI risk. They emphasized that mitigating the potential existential threat posed by AI should be a global priority, on par with addressing other significant risks like pandemics and nuclear war.   
*   2023

Google Unveils Bard's AI Evolution

Google transitions Bard from LaMDA to the more advanced PaLM2 language model in May 2023.   
*   2023

Tech Titans Demand AI Pause

Tech giants and innovators, including Elon Musk and Steve Wozniak, have endorsed a petition advocating for a temporary pause in the rapid development of advanced AI systems. The petition cites concerns over the potential risks of creating AI models that could surpass human comprehension and control, urging a six-month hiatus to reassess the trajectory of this technology.   
*   2023

Google Unleashes Bard, Its ChatGPT Rival

Google introduced its chatbot Bard, powered by LaMDA and PaLM language models, in a limited capacity as a response to ChatGPT in March 2023.   
*   2023

The AI Revolution Continues: Meet GPT-4

The recently unveiled GPT-4 model by OpenAI represents a significant advancement over its predecessor, GPT-3.5, although it still shares some inherent limitations. Notably, GPT-4 introduces multimodal capabilities, enabling it to process both text and image inputs. This enhanced model has been integrated into ChatGPT as a premium offering. According to OpenAI's internal evaluations, GPT-4 demonstrated exceptional performance, scoring in the 94th percentile on the SAT, 88th percentile on the LSAT, and 90th percentile on the Uniform Bar Exam.   
*   2023

100 Million Users in a Blink: ChatGPT's Meteoric Rise

ChatGPT, the remarkable AI language model, achieved an unprecedented milestone by amassing over 100 million users by January 2023, establishing itself as the most rapidly growing consumer application ever witnessed.   
*   ### 2022

ChatGPT Unveils AI's Brilliance and Blunders

An AI chatbot named ChatGPT, created by OpenAI and based on GPT-3.5, launched in November 2022. While widely praised for its extensive knowledge, reasoning capabilities, and natural language responses, it faced criticism for sometimes providing inaccurate information with high confidence, a phenomenon known as "hallucination." ChatGPT's release sparked widespread public discourse on AI's societal implications.   
*   ### 2020

Revolutionary Language Model: GPT-3 Blurs Lines Between Human and Machine Writing

GPT-3, a groundbreaking language model developed by OpenAI, leverages advanced deep learning techniques to generate human-like text across various domains, including computer code, poetry, and other language tasks. With an unprecedented model capacity ten times larger than its predecessor T-NLG, GPT-3 produces outputs that are strikingly similar and nearly indistinguishable from human-written content. This innovative language model was unveiled in May 2020 and entered beta testing the following month.   
*   2020

DeepMind's AlphaFold 2 Aces Protein Folding Challenge

AlphaFold 2, DeepMind's protein structure prediction model, emerged victorious in the 2020 CASP competition held in November.   
*   2020

Microsoft's 17 Billion Parameter Behemoth

Microsoft unveiled its Turing Natural Language Generation (T-NLG), a massive language model boasting an unprecedented 17 billion parameters, in February 2020, setting a new record for the largest language model ever released.   
*   
### 2018

AI Assistant Puts On Human Voice

Google unveiled Duplex, an AI system capable of engaging in natural conversations to handle tasks like scheduling appointments. The AI's speech mimicked human vocal patterns so convincingly that the Los Angeles Times described it as a "nearly flawless" imitation.

Resources

    1.   [Google Assistant](https://en.wikipedia.org/wiki/Google_Duplex)

*   2018

Alibaba AI Surpasses Human Comprehension on Stanford Test

An AI system developed by Alibaba for language processing surpassed the performance of top human participants in a Stanford University reading and comprehension examination. The AI scored 82.44, slightly outperforming the human score of 82.304, on a test comprising 100,000 questions.   
*   
### 2017

The Birth of Transformer Models and Language Giants

The creation of the transformer architecture paved the way for innovative large language models like Google's BERT, and subsequently, OpenAI pioneered the generative pre-trained transformer model.

Resources

    1.   [Large language model](https://en.wikipedia.org/wiki/Large_language_model)
    2.   [Transformer (deep learning architecture)](https://en.wikipedia.org/wiki/Transformer_(machine_learning_model))
    3.   [BERT (language model)](https://en.wikipedia.org/wiki/BERT_(language_model))
    4.   [Generative pre-trained transformer](https://en.wikipedia.org/wiki/Generative_pre-trained_transformer)

*   
2017

AI Bot Outplays Pro Gamer in Dota 2 Tournament Showdown

An artificial intelligence system developed by OpenAI competed in The International 2017, a prestigious Dota 2 tournament, where it emerged victorious against professional player Dendi in a 1v1 match.

Resources

    1.   [OpenAI](https://en.wikipedia.org/wiki/OpenAI)

*   
2017

First Mathematical Proof by a SAT Solver

A software tool used to solve boolean satisfiability problems in propositional logic has been employed to substantiate a long-standing mathematical conjecture concerning Pythagorean triples within the set of integers. The initial proof, spanning an enormous 200 terabytes, underwent validation by two separate certified automatic proof verification systems.

Resources

    1.   [Propositional calculus](https://en.wikipedia.org/wiki/Propositional_logic)
    2.   [Pythagorean triple](https://en.wikipedia.org/wiki/Pythagorean_triples)
    3.   [Boolean satisfiability problem](https://en.wikipedia.org/wiki/Boolean_satisfiability_problem)

*   2017

AI Conquers Poker's Imperfect Information

The Deepstack116 algorithm outperformed human players in imperfect information games, specifically heads-up no-limit poker, with statistical significance. Subsequently, the Libratus poker AI, developed by a different research group, defeated each of its four highly skilled human opponents, achieving an exceptionally high overall win rate over a statistically significant sample. Unlike Chess and Go, Poker is an imperfect information game, making the achievement more challenging.   
*   
### 2015

AI Defeats Human Champion 5-0

An AI system called AlphaGo, developed by Google DeepMind, decisively defeated Fan Hui, a professional Go player and three-time European champion, with a score of 5 games to 0.

Resources

    1.   [Google DeepMind](https://en.wikipedia.org/wiki/Google_DeepMind)
    2.   [AlphaGo](https://en.wikipedia.org/wiki/AlphaGo)

*   
2015

AI Experts Sound the Alarm on Technological Impact

Renowned figures like Stephen Hawking, Elon Musk, and numerous AI experts advocated for studying the potential societal impacts of artificial intelligence through an open letter in January 2015.

Resources

    1.   [Open letter on artificial intelligence (2015)](https://en.wikipedia.org/wiki/Open_letter_on_artificial_intelligence_(2015))
    2.   [Stephen Hawking](https://en.wikipedia.org/wiki/Stephen_Hawking)
    3.   [Elon Musk](https://en.wikipedia.org/wiki/Elon_Musk)

*   
2015

Breakthrough in training ultra-deep neural networks

Techniques like highway networks and ResNets enabled the training of extremely deep neural networks with over 1000 layers, which was previously challenging to achieve.

Resources

    1.   [Highway network](https://en.wikipedia.org/wiki/Highway_network)
    2.   [Residual neural network](https://en.wikipedia.org/wiki/Residual_neural_network)

*   
### 2013

Endless Visual Learning: NEIL Analyzes Images Continuously

NEIL, a system designed to endlessly learn and analyze visual connections between images, was unveiled at Carnegie Mellon University, allowing it to continually compare and examine relationships across various image data.

Resources

    1.   [Never-Ending Language Learning](https://en.wikipedia.org/wiki/Never-Ending_Language_Learning)

*   ## Machine Learning: 1987 - 2012 
*   
### 2012

AlexNet Breakthrough: Deep Learning Dominates Image Recognition

AlexNet, a pioneering deep learning model for image recognition developed by Alex Krizhevsky, achieved a breakthrough by winning the ImageNet Large Scale Visual Recognition Challenge with significantly fewer errors than the runner-up. This marked a pivotal moment in AI history, leading to the widespread adoption of deep learning techniques for image recognition tasks and the abandonment of numerous alternative approaches. Krizhevsky's innovative use of GPU chips for training the deep learning network contributed to this success.

Resources

    1.   [Deep learning](https://en.wikipedia.org/wiki/Deep_learning)
    2.   [AlexNet](https://en.wikipedia.org/wiki/AlexNet)
    3.   [ImageNet](https://en.wikipedia.org/wiki/ImageNet_Large_Scale_Visual_Recognition_Challenge)

*   ### 2011–2014

Siri, Cortana, Google Now: When Phones Became Speaking Partners

These intelligent virtual assistants leverage natural language processing capabilities to comprehend user queries, provide relevant information, offer suggestions, and execute tasks on smartphones.   
*   
### 2011

Watson's Jeopardy! Triumph: AI Conquers Human Champions

An artificial intelligence system named Watson, developed by IBM, outperformed the champions of the popular TV quiz show Jeopardy!, Rutter and Jennings.

Resources

    1.   [IBM Watson](https://en.wikipedia.org/wiki/IBM_Watson)

*   
2011

AI Meets Sustainability: Pioneering AAAI Workshop

Mary Lou Maher and Doug Fisher spearheaded an inaugural workshop by the Association for the Advancement of Artificial Intelligence, focused on the nexus of AI and environmental sustainability.

Resources

    1.   [Association for the Advancement of Artificial Intelligence](https://en.wikipedia.org/wiki/AAAI)

*   
### 2009

First LSTM RNN Conquers Handwriting Recognition Contests

A recurrent neural network called LSTM, trained using connectionist temporal classification, emerged victorious in pattern recognition competitions, specifically in the realm of connected handwriting recognition, marking a significant milestone as the first of its kind to achieve such success.

Resources

    1.   [Long short-term memory](https://en.wikipedia.org/wiki/LSTM)
    2.   [Connectionist temporal classification](https://en.wikipedia.org/wiki/Connectionist_temporal_classification)
    3.   [Recurrent neural network](https://en.wikipedia.org/wiki/Recurrent_neural_network)
    4.   [Pattern recognition](https://en.wikipedia.org/wiki/Pattern_recognition)

*   
### 2007

Checkers Conquered: Researchers Crack Classic Game

A group of researchers at the University of Alberta successfully determined the outcome of the game of checkers through computational analysis.

Resources

    1.   [Solved game](https://en.wikipedia.org/wiki/Solved_game)
    2.   [Checkers](https://en.wikipedia.org/wiki/Checkers)

*   
2007

AI Meets Biology: Unlocking Nature's Intelligence

The renowned scientific journal Philosophical Transactions of the Royal Society, B – Biology, published a special edition exploring the application of artificial intelligence to comprehend biological intelligence, entitled "Models of Natural Action Selection."

Resources

    1.   [Action Selection](https://en.wikipedia.org/wiki/Action_selection)

*   
### 2006

AI@50: Redefining Artificial Intelligence

The Dartmouth Artificial Intelligence Conference explored the future of AI over the course of the next five decades. The event, titled "AI@50," took place from July 14th to July 16th, 2006.

Resources

    1.   [AI@50](https://en.wikipedia.org/wiki/AI@50)

*   
### 2005

Inside the Blue Brain: Modeling Mind's Molecules

A groundbreaking initiative, Blue Brain, was launched to develop a comprehensive simulation of the brain at the molecular level.

Resources

    1.   [Blue Brain Project](https://en.wikipedia.org/wiki/Blue_Brain)

*   
### 2004

OWL: Catchy The Lingua Franca of Semantic Web

OWL (Web Ontology Language), which is a W3C Recommendation published on February 10, 2004. It is a language used to represent ontologies or knowledge domains on the web.

Resources

    1.   [Web Ontology Language](https://en.wikipedia.org/wiki/Web_Ontology_Language)

*   
### 2000

Smart Toys Come to Life: Interactive Robopets Now Available

Commercially viable interactive robopets, dubbed "smart toys," hit the market, bringing to fruition the aspirations of 18th-century novelty toy creators.

Resources

    1.   [Smart toy](https://en.wikipedia.org/wiki/Smart_toy)

*   
### 1999

Adaptive Network Bridging Mobile and Stationary Computing

Oxygen architecture project, a system designed to integrate mobile and stationary computing devices into an adaptable network environment.

Resources

    1.   [Project Oxygen](https://en.wikipedia.org/wiki/Project_Oxygen)
    2.   [Computer network](https://en.wikipedia.org/wiki/Computer_network)

*   1999

Intelligent Room and Emotional Agents at MIT's AI Lab

An intelligent room and emotional agents developed at MIT's AI Lab.   
*   
1999

AI-based web crawlers drive information extraction on the Web

AI-powered programs like web crawlers play a vital role in extracting and utilizing information from the vast expanse of the World Wide Web.

Resources

    1.   [Web crawler](https://en.wikipedia.org/wiki/Web_crawler)
    2.   [Information extraction](https://en.wikipedia.org/wiki/Information_extraction)
    3.   [World Wide Web](https://en.wikipedia.org/wiki/World_Wide_Web)

*   
### 1998

The Birth of Environmental AI: Pioneering Workshop Merges Nature and Intelligence

Ulises Cortés and Miquel Sànchez-Marrè initiated a pioneering workshop in Europe, titled "Binding Environmental Sciences and Artificial Intelligence," which aimed to bridge the gap between environmental sciences and artificial intelligence during the ECAI conference.

Resources

    1.   [European Conference on Artificial Intelligence](https://en.wikipedia.org/wiki/European_Conference_on_Artificial_Intelligence)

*   
1998

Paving the Way for the Semantic Web

The Semantic Web Roadmap paper, which outlined a vision for a more intelligent and interconnected web, was introduced by Tim Berners-Lee.

Resources

    1.   [Tim Berners-Lee](https://en.wikipedia.org/wiki/Tim_Berners-Lee)
    2.   [Semantic Web](https://en.wikipedia.org/wiki/Semantic_Web)

*   
1998

Pioneering Domestic A.I.: Furby's Arrival

The launch of Tiger Electronics' Furby marked a significant milestone as the pioneering domestic AI product that achieved commercial success.

Resources

    1.   [Tiger Electronics](https://en.wikipedia.org/wiki/Tiger_Electronics)
    2.   [Domestic robot](https://en.wikipedia.org/wiki/Domestic_robot)

*   
### 1997

Introducing Long Short-Term Memory (LSTM)

An artificial neural network called Long Short-Term Memory (LSTM) was introduced by Sepp Hochreiter and Juergen Schmidhuber in their research paper published in the journal Neural Computation.

Resources

    1.   [Long short-term memory](https://en.wikipedia.org/wiki/Long_short-term_memory)
    2.   [Sepp Hochreiter](https://en.wikipedia.org/wiki/Sepp_Hochreiter)
    3.   [Jürgen Schmidhuber](https://en.wikipedia.org/wiki/Juergen_Schmidhuber)

*   
1997

Computer Othello Program Dominates World Champion

An artificial intelligence software for playing Othello, known as Logistello, achieved a decisive 6-0 victory against Takeshi Murakami, the reigning world champion in the game.

Resources

    1.   [Reversi](https://en.wikipedia.org/wiki/Reversi)
    2.   [Logistello](https://en.wikipedia.org/wiki/Logistello)

*   
1997

Machine Conquers Human in Chess Showdown

A powerful chess computer named Deep Blue, developed by IBM, triumphed over the reigning world chess champion Garry Kasparov in a historic match.

Resources

    1.   [Deep Blue (chess computer)](https://en.wikipedia.org/wiki/IBM_Deep_Blue)
    2.   [IBM](https://en.wikipedia.org/wiki/IBM)

*   ### 1995

AI Takes on Environmental Challenges: NASA Pioneers the Way

Cindy Mason, employed by NASA, orchestrates the inaugural International IJCAI Workshop dedicated to exploring Artificial Intelligence's role in environmental matters.   
*   
### 1994

NASA Hosts Pioneering AI and Environment Workshop

Cindy Mason from NASA coordinated the inaugural workshop on Artificial Intelligence and Environmental Issues organized by the Association for the Advancement of Artificial Intelligence (AAAI).

Resources

    1.   [Association for the Advancement of Artificial Intelligence](https://en.wikipedia.org/wiki/AAAI)

*   
1994

Computer Dominance in Draughts: Chinook Conquers World Champion and National Tournament

Computer program Chinook triumphed over the English draughts world champion Tinsley, who resigned their match. Additionally, Chinook defeated Lafferty, the second highest rated player. It also achieved a resounding victory in the USA National Tournament, winning by an unprecedented wide margin.

Resources

    1.   [English draughts](https://en.wikipedia.org/wiki/English_draughts)
    2.   [Chinook (computer program)](https://en.wikipedia.org/wiki/Chinook_(draughts_player))

*   
1994

Zadeh's Soft Computing Revolution: Merging Neural Nets, Fuzzy Logic, and Chaos Theory

Zadeh, a Berkeley professor, developed the concept of "soft computing," which integrates different fields like neural networks, fuzzy logic, evolutionary algorithms, genetic programming, and chaos theory. He established a global research network that merged these disciplines, enabling advancements in computational intelligence and decision-making systems.

Resources

    1.   [Soft computing](https://en.wikipedia.org/wiki/Soft_computing)
    2.   [Fuzzy set](https://en.wikipedia.org/wiki/Fuzzy_set)
    3.   [Fuzzy control system](https://en.wikipedia.org/wiki/Fuzzy_systems)
    4.   [Genetic programming](https://en.wikipedia.org/wiki/Genetic_programming)
    5.   [Chaos theory](https://en.wikipedia.org/wiki/Chaos_theory)

*   
### 1993

DARPA AI Tool Pays Off Decades of Investment

ISX corporation received recognition as the top contractor from DARPA (Defense Advanced Research Projects Agency) for their Dynamic Analysis and Replanning Tool (DART). This AI-powered tool's success was considered highly valuable, surpassing the government's total investment in AI research over several decades.

Resources

    1.   [Dynamic Analysis and Replanning Tool](https://en.wikipedia.org/wiki/Dynamic_Analysis_and_Replanning_Tool)

*   
### 1991

AI Application Proves its Worth in the Gulf War

The DART scheduling application, utilized during the Gulf War, successfully demonstrated the value of DARPA's three decades of research efforts in artificial intelligence, justifying the substantial investment made in this field.

Resources

    1.   [Dynamic Analysis and Replanning Tool](https://en.wikipedia.org/wiki/Dynamic_Analysis_and_Replanning_Tool)
    2.   [DARPA](https://en.wikipedia.org/wiki/DARPA)

*   
### 1990

Suggested Reinforcement Learning Masters Backgammon

TD-Gammon, a backgammon program by Gerry Tesauro, exhibits the efficacy of reinforcement learning in developing a game-playing program capable of competing against world-class players at a championship level.

Resources

    1.   [TD-Gammon](https://en.wikipedia.org/wiki/TD-Gammon)
    2.   [Backgammon](https://en.wikipedia.org/wiki/Backgammon)
    3.   [Reinforcement](https://en.wikipedia.org/wiki/Reinforcement)

*   
1990

Groundbreaking Progress Across the AI Landscape

AI has experienced significant progress across various domains, including machine learning, intelligent tutoring systems, case-based reasoning, multi-agent planning, scheduling algorithms, uncertain reasoning techniques, data mining methods, natural language processing and translation models, computer vision, virtual reality simulations, game development, and other emerging areas.

Resources

    1.   [Scheduling (computing)](https://en.wikipedia.org/wiki/Scheduling_(computing))
    2.   [Data mining](https://en.wikipedia.org/wiki/Data_mining)
    3.   [Virtual reality](https://en.wikipedia.org/wiki/Virtual_reality)

*   
### 1989

Pioneering Neural Network for Autonomous Vehicles

ALVINN (An Autonomous Land Vehicle in a Neural Network), a system developed by Dean Pomerleau at Carnegie Mellon University, was utilized in the Navlab project, enabling autonomous land vehicle navigation through neural network technology.

Resources

    1.   [Navlab](https://en.wikipedia.org/wiki/Navlab)

*   
1989

Breakthrough in Artificial Neural Networks with CMOS Technology

The advancement of complementary metal-oxide-semiconductor (CMOS) technology, which is a type of metal-oxide-semiconductor (MOS) Very-large-scale integration (VLSI), facilitated the practical implementation of artificial neural networks (ANNs) in the 1980s. A seminal work in this field was the 1989 book "Analog VLSI Implementation of Neural Systems" by Carver A. Mead and Mohammed Ismail, which played a significant role in the development of ANN technology.

Resources

    1.   [Neural network (machine learning)](https://en.wikipedia.org/wiki/Neural_network_(machine_learning))
    2.   [Very-large-scale integration](https://en.wikipedia.org/wiki/Very-large-scale_integration)
    3.   [CMOS](https://en.wikipedia.org/wiki/CMOS)

*   ### 1987

Pioneering Expert System Revolutionizes Strategic Advising

A company called Alacritous Inc./Allstar Advice Inc. in Toronto launched the second generation of their commercial strategic and managerial advisory system, Alacrity 2.0. This system utilized a forward-chaining expert system with around 3,000 rules focused on market evolution and competitive strategies. It was co-authored by the firm's founders, Alistair Davidson and Mary Chung, while the underlying engine was developed by Paul Tarvydas. Additionally, Alacrity 2.0 incorporated a small financial expert system capable of interpreting financial statements and models.   
*   
1987

Mind as a Society - Minsky's Groundbreaking Theory

Marvin Minsky's work on a theoretical model of the mind, where he proposed it as a collection of cooperating agents. He had been presenting this idea through lectures years before publishing his book, The Society of Mind.

Resources

    1.   [Society of Mind](https://en.wikipedia.org/wiki/The_Society_of_Mind)
    2.   [What are AI Agents? The Ultimate Guide](https://www.jotform.com/ai/agents/)

*   ## Early AI Program: 1951 - 1986 
*   ### 1986

Pioneering the Computational Study of Discourse

The pioneering researchers Barbara Grosz and Candace Sidner developed the initial computational framework for analyzing discourse, paving the way for a new field of study.   
*   
### 1985

Groundbreaking Autonomous Drawing Program AARON Unveiled

An artificial intelligence program called AARON, developed by Harold Cohen over a decade, demonstrated its autonomous drawing capabilities at the AAAI National Conference, showcasing significant advancements in the field.

Resources

    1.   [AARON](https://en.wikipedia.org/wiki/AARON)

*   
1985

Backpropagation: The Algorithm that Unlocked Neural Networks

The Backpropagation algorithm, also dubbed the reverse mode of automatic differentiation, introduced by Seppo Linnainmaa in 1970 and later applied to neural networks by Paul Werbos, contributed significantly to the widespread adoption of neural networks.

Resources

    1.   [Backpropagation](https://en.wikipedia.org/wiki/Backpropagation)
    2.   [Algorithm](https://en.wikipedia.org/wiki/Algorithm)
    3.   [Automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation)

*   
### 1983

Catchy Pioneering Temporal Logic for Event Formalization

The Interval Calculus, a pioneering formalization of temporal events, was conceived by James F. Allen, marking a significant advancement in the field.

Resources

    1.   [James F. Allen (computer scientist)](https://en.wikipedia.org/wiki/James_F._Allen_(computer_scientist))

*   
1983

Soaring to New Heights: CMU Pioneers Groundbreaking Soar Program

John Laird and Paul Rosenbloom, in collaboration with Allen Newell, finalized their doctoral research on the Soar (program) at Carnegie Mellon University.

Resources

    1.   [Soar (cognitive architecture)](https://en.wikipedia.org/wiki/Soar_(cognitive_architecture))

*   
### 1982

Japan's FGCS Project: Pioneering Massive Parallelism for Next-Gen Computing

The Fifth Generation Computer Systems (FGCS) project, initiated by Japan's Ministry of International Trade and Industry in 1982, aimed to develop a "fifth generation computer" that would leverage massive parallelism to perform extensive computations. The project focused on creating a computing system capable of executing complex calculations through parallel processing.

Resources

    1.   [Fifth Generation Computer Systems](https://en.wikipedia.org/wiki/Fifth_Generation_Computer_Systems_project)

*   
### 1981

Groundbreaking Parallel Computing for AI and Powerful Computations

Danny Hillis engineered a groundbreaking parallel computing system, known as the Connection Machine, which significantly boosted AI and computational capabilities. Subsequently, he founded Thinking Machines Corporation.

Resources

    1.   [Parallel computing](https://en.wikipedia.org/wiki/Parallel_computing)
    2.   [Thinking Machines Corporation](https://en.wikipedia.org/wiki/Thinking_Machines_Corporation)

*   
### 1980

Dawn of AI: AAAI's Pioneering Conference at Stanford

The inaugural conference of the AAAI, an organization dedicated to artificial intelligence, took place at Stanford University.

Resources

    1.   [Association for the Advancement of Artificial Intelligence](https://en.wikipedia.org/wiki/American_Association_for_Artificial_Intelligence)

*   
1980

The Rise of Lisp Machines and Intelligent Systems

In the given period, Lisp machines were designed, manufactured, and commercialized. Additionally, the first expert system shells and commercial applications emerged.

Resources

    1.   [Lisp machine](https://en.wikipedia.org/wiki/Lisp_machine)
    2.   [Expert system](https://en.wikipedia.org/wiki/Expert_system)

*   ### 1979

Pioneering ARPAnet Resource for Scientific Collaboration

Directed by Ed Feigenbaum and Joshua Lederberg, the SUMEX-AIM resource at Stanford showcased how the ARPAnet facilitated scientific cooperation.   
*   
1979

Pioneering Work on Non-Monotonic Logics and Truth Maintenance Systems

The researchers at MIT and Stanford pioneered the study of non-monotonic logics, exploring formal methods to manage and update beliefs or knowledge as new information becomes available.

Resources

    1.   [Non-monotonic logic](https://en.wikipedia.org/wiki/Non-monotonic_logic)

*   
1979

Computer Program Dethrones Backgammon World Champion

A computer program for backgammon, developed by Hans Berliner at CMU, manages to defeat the current world champion, partly due to fortunate circumstances.

Resources

    1.   [Hans Berliner](https://en.wikipedia.org/wiki/Hans_Berliner)

*   
1979

Pioneering Autonomous Vehicle Navigates Through Stanford AI Lab

An autonomous vehicle developed by Hans Moravec at Stanford University, known as the Stanford Cart, achieved a milestone by independently navigating through a room filled with chairs and maneuvering around the Stanford AI Lab.

Resources

    1.   [Autonomous robot](https://en.wikipedia.org/wiki/Autonomous_robot)

*   
1979

Groundbreaking CHI system revolutionizes automatic programming

Researchers at Stanford University, including Cordell Green, David Barstow, and Elaine Kant, introduced the CHI system, which enabled automatic programming.

Resources

    1.   [Automatic programming](https://en.wikipedia.org/wiki/Automatic_programming)

*   
1979

Pioneering Medical AI: INTERNIST Diagnoses with Human Expertise

INTERNIST was a knowledge-based medical diagnosis program created by Jack Myers and Harry Pople, researchers at the University of Pittsburgh. It utilized Myers' clinical expertise to diagnose medical conditions.

Resources

    1.   [University of Pittsburgh](https://en.wikipedia.org/wiki/University_of_Pittsburgh)

*   
1979

Groundbreaking Expert System Shells from Stanford

Bill VanMelle's doctoral research showcased the versatility of knowledge representation and reasoning methods employed in MYCIN, an early expert system, through the development of EMYCIN, a framework that inspired numerous commercial expert system platforms.

Resources

    1.   [Mycin](https://en.wikipedia.org/wiki/MYCIN)

*   
### 1978

Pioneering Gene-Cloning Software with Object-Oriented Programming

The MOLGEN program, developed at Stanford University, showcased the potential of object-oriented programming to represent knowledge and plan gene-cloning experiments.

Resources

    1.   [Mycin](https://en.wikipedia.org/wiki/MYCIN)

*   
1978

"Satisficing" Theory: A Cornerstone of AI Earns Nobel Recognition

Bounded rationality, a groundbreaking concept introduced by Herbert A. Simon, earned him the Nobel Prize in Economics. This theory, which forms a foundational pillar of Artificial Intelligence, posits that individuals make decisions based on limited information and cognitive capacities, resulting in choices that satisfy their needs rather than maximize optimal outcomes.

Resources

    1.   [Bounded rationality](https://en.wikipedia.org/wiki/Bounded_rationality)

*   
1978

Groundbreaking concept formation search space

A researcher named Tom Mitchell from Stanford University proposed the idea of version spaces, which represents the search space for a concept formation algorithm.

Resources

    1.   [Version space learning](https://en.wikipedia.org/wiki/Version_space)

*   ### 1976

Meta-Level Reasoning Breakthrough

Davis's doctoral research at Stanford showcased the effectiveness of meta-level reasoning, where an AI system can reason about its own thought processes.   
*   
1976

Pioneering AI program explored self-guided knowledge discovery

The AM program, developed by Douglas Lenat for his Stanford PhD dissertation, illustrated the discovery model, which involved a loosely guided search for intriguing conjectures.

Resources

    1.   [Automated Mathematician](https://en.wikipedia.org/wiki/Automated_Mathematician)
    2.   [Douglas Lenat](https://en.wikipedia.org/wiki/Douglas_Lenat)

*   
### 1975

Unveiling the "Primal Sketch" of Visual Perception

Visual perception begins with the creation of a preliminary representation, known as the "primal sketch," which captures essential features like edges, boundaries, and surface orientations from the initial visual input. This foundational stage sets the groundwork for subsequent processing and recognition of objects and scenes.

Resources

    1.   [David Marr (neuroscientist)](https://en.wikipedia.org/wiki/David_Marr_(psychologist))
    2.   [Visual perception](https://en.wikipedia.org/wiki/Visual_perception)

*   
1975

Centering: The Key to Discourse Analysis in NLP

The traditional AI methods for modeling discourse were found to have limitations by Barbara Grosz. Later, Grosz, along with Bonnie Webber and Candace Sidner, developed the concept of "centering" which helps determine the focus of discourse and resolve anaphoric references in natural language processing.

Resources

    1.   [Natural language processing](https://en.wikipedia.org/wiki/Natural_language_processing)
    2.   [Barbara J. Grosz](https://en.wikipedia.org/wiki/Barbara_Grosz)
    3.   [Bonnie Webber](https://en.wikipedia.org/wiki/Bonnie_Webber)

*   
1975

AI Makes Scientific Breakthrough in Chemistry

The Meta-Dendral artificial intelligence system made groundbreaking discoveries in the field of chemistry, specifically in the area of mass spectrometry, which were published in a peer-reviewed scientific journal, marking a milestone as the first scientific findings made by a computer.

Resources

    1.   [Mass spectrometry](https://en.wikipedia.org/wiki/Mass_spectrometry)

*   
1975

Minsky's Influential Ideas on Frames and Semantic Links

Marvin Minsky's influential article on Frames introduced a representation of knowledge that combined ideas about schemas and semantic connections.

Resources

    1.   [Frame Problem](https://en.wikipedia.org/wiki/Frame_problem)
    2.   [Link relation](https://en.wikipedia.org/wiki/Semantic_link)

*   
1975

Hierarchical Planning System for Efficient Goal Structuring

Austin Tate created the Nonlin system, which could explore different partial plans as alternative ways to achieve the overall planning goals.

Resources

    1.   [Austin Tate](https://en.wikipedia.org/wiki/Austin_Tate)
    2.   [Partial-order planning](https://en.wikipedia.org/wiki/Partial_plan)

*   
1975

Groundbreaking Partial-Order Planning in NOAH System

Earl Sacerdoti introduced partial-order planning methods with his NOAH system, departing from the previous state space search approach. NOAH was used at SRI International for interactive diagnosis and repair of electromechanical systems.

Resources

    1.   [Partial-order planning](https://en.wikipedia.org/wiki/Partial-order_planning)

*   
### 1974

MYCIN: Pioneering AI for Diagnosis

Ted Shortliffe's doctoral thesis at Stanford introduced MYCIN, a rule-based system for medical diagnosis that could handle uncertainty. Inspired by DENDRAL, MYCIN significantly impacted the development of expert systems, particularly commercial ones.

Resources

    1.   [Mycin](https://en.wikipedia.org/wiki/MYCIN)
    2.   [Edward H. Shortliffe](https://en.wikipedia.org/wiki/Edward_H._Shortliffe)

*   
### 1973

Blow to British AI Research: Government Pulls Funding

The Lighthill report criticized AI research in Britain, leading the government to cut funding for AI studies except at two universities.

Resources

    1.   [Lighthill report](https://en.wikipedia.org/wiki/Lighthill_report)

*   ### 1972

Pioneering the Art of Hierarchical Planning

Earl Sacerdoti created an early hierarchical planning system called ABSTRIPS.   
*   1972

The Birth of Prolog: Unleashing the Power of Logic Programming

The programming language Prolog was created by Alain Colmerauer.   
*   ### 1971

Edinburgh Pioneers Boyer-Moore Theorem Prover

The development of the Boyer-Moore theorem prover began in Edinburgh.   
*   ### 1970

Augmented Transition Networks: A Breakthrough in Natural Language Understanding

Bill Woods introduced Augmented Transition Networks (ATNs) as a way to represent and understand natural language.   
*   1970

Pioneering Semantic Net for Computer-Aided Learning

Jaime Carbonell (Sr.) created SCHOLAR, an interactive software that facilitates computer-aided learning, utilizing semantic networks for knowledge representation.   
*   
1970

Unlocking Neural Networks: Backpropagation Breakthrough

Seppo Linnainmaa introduced the reverse mode of automatic differentiation, a technique later termed backpropagation that is widely employed for training artificial neural networks.

Resources

    1.   [Seppo Linnainmaa](https://en.wikipedia.org/wiki/Seppo_Linnainmaa)
    2.   [Automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation)
    3.   [Neural network (machine learning)](https://en.wikipedia.org/wiki/Artificial_neural_networks)
    4.   [Backpropagation](https://en.wikipedia.org/wiki/Backpropagation)

*   
1970

Pioneering research group focused on Natural Language Processing

At SRI, Jane Robinson and Don Walker founded a pioneering research group focused on Natural Language Processing that had a significant impact on the field.

Resources

    1.   [Natural language processing](https://en.wikipedia.org/wiki/Natural_Language_Processing)

*   
### 1969

Frame problem in AI reasoning

McCarthy and Hayes introduced the frame problem in AI reasoning.

Resources

    1.   [Frame Problem](https://en.wikipedia.org/wiki/Frame_problem)

*   
1969

Perceptrons - Triggering the 1970s AI winter

Minsky and Papert's "Perceptrons" highlighted limitations of simple neural networks, which some view as triggering the 1970s AI winter - though deep learning methods already existed through work by Ivakhnenko, Lapa, and Amari.

Resources

    1.   [Perceptrons (book)](https://en.wikipedia.org/wiki/Perceptrons_(book))
    2.   [AI winter](https://en.wikipedia.org/wiki/AI_winter)

*   
1969

First IJCAI conference

Stanford hosted the first IJCAI (International Joint Conference on Artificial Intelligence) conference.

Resources

    1.   [International Joint Conference on Artificial Intelligence](https://en.wikipedia.org/wiki/International_Joint_Conference_on_Artificial_Intelligence)

*   1969

First semantics-based machine translation system

Yorick Wilks pioneered Preference Semantics at Stanford, creating the first semantics-based machine translation system, which influenced many later researchers.   
*   
1969

Conceptual dependency model for language understanding

Roger Schank developed the conceptual dependency model for language understanding at Stanford, which was later expanded at Yale through PhD work by Wilensky, Lehnert, and Kolodner for story comprehension and memory modeling.

Resources

    1.   [Roger Schank](https://en.wikipedia.org/wiki/Roger_Schank)
    2.   [Natural language understanding](https://en.wikipedia.org/wiki/Natural_language_understanding)

*   
### 1968

Snob - an early clustering algorithm

Wallace and Boulton created Snob, an early clustering algorithm that applied Occam's razor through Bayesian minimum message length principles.

Resources

    1.   [Minimum message length](https://en.wikipedia.org/wiki/Minimum_message_length)
    2.   [Occam's razor](https://en.wikipedia.org/wiki/Occam%27s_razor)

*   
1968

Mac Hack - a groundbreaking chess program

At MIT, Richard Greenblatt developed Mac Hack, a groundbreaking chess program that reached class-C tournament level - making it the first chess AI to compete credibly against humans.

Resources

    1.   [Richard Greenblatt (programmer)](https://en.wikipedia.org/wiki/Richard_Greenblatt_(programmer))
    2.   [Computer chess](https://en.wikipedia.org/wiki/Computer_chess)
    3.   [Mac Hack](https://en.wikipedia.org/wiki/Mac_Hack)

*   
1968

Macsyma

Joel Moses pioneered symbolic mathematics through his MIT doctoral work on Macsyma - the first program to successfully solve calculus integration problems using artificial intelligence and knowledge representation.

Resources

    1.   [Joel Moses](https://en.wikipedia.org/wiki/Joel_Moses)
    2.   [Computer algebra](https://en.wikipedia.org/wiki/Symbolic_mathematics)
    3.   [Macsyma](https://en.wikipedia.org/wiki/Macsyma)

*   
### 1967

First to use stochastic gradient descent for deep learning

Shun'ichi Amari was the first to use stochastic gradient descent for deep learning in multilayer perceptrons. In computer experiments conducted by his student Saito, a five-layer multilayer perceptron with two modifiable layers learned useful internal representations to classify non-linearly separable pattern classes.

Resources

    1.   [Stochastic gradient descent](https://en.wikipedia.org/wiki/Stochastic_gradient_descent)
    2.   [Multilayer perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron)
    3.   [Knowledge representation and reasoning](https://en.wikipedia.org/wiki/Knowledge_representation)

*   
### 1966

1966 was a tough year

- Ross Quillian demonstrated semantic nets in his PhD dissertation at Carnegie Institute of Technology (now CMU).

- The Machine Intelligence 71 workshop at Edinburgh was the first of an influential annual series organized by Donald Michie and others.

- A negative report on machine translation killed much work in natural language processing (NLP) for many years.

- The Dendral program, developed by Edward Feigenbaum, Joshua Lederberg, Bruce Buchanan, and Georgia Sutherland at Stanford University, demonstrated the ability to interpret mass spectra on organic chemical compounds, making it the first successful knowledge-based program for scientific reasoning.

Resources

    1.   [Natural language processing](https://en.wikipedia.org/wiki/Natural_language_processing)
    2.   [Semantic network](https://en.wikipedia.org/wiki/Semantic_network)

*   
### 1965

Dendral: First Expert System

Edward Feigenbaum initiated the Dendral project, a ten-year effort to develop software that could deduce the molecular structure of organic compounds using scientific instrument data. Dendral was the first expert system.

Resources

    1.   [Edward Feigenbaum](https://en.wikipedia.org/wiki/Edward_Feigenbaum)
    2.   [Dendral](https://en.wikipedia.org/wiki/Dendral)
    3.   [Expert system](https://en.wikipedia.org/wiki/Expert_system)

*   
1965

ELIZA

Joseph Weizenbaum, a professor at MIT, built ELIZA, an interactive program that carried on dialogues in English on any topic. ELIZA was a popular toy at AI centers on the ARPANET when a version that simulated the dialogue of a psychotherapist was programmed.

Resources

    1.   [Interactive computing](https://en.wikipedia.org/wiki/Interactive_program)
    2.   [ELIZA](https://en.wikipedia.org/wiki/ELIZA)

*   
1965

Resolution Method

J. Alan Robinson invented the Resolution Method, a mechanical proof procedure that allowed programs to work efficiently with formal logic as a representation language.

Resources

    1.   [Mathematical proof](https://en.wikipedia.org/wiki/Mathematical_proof)

*   
1965

Fuzzy Logic

In 1965, Lotfi A. Zadeh, a professor at the University of California, Berkeley, published his seminal paper "Fuzzy Sets" in the journal Information and Control, introducing the concept of fuzzy logic.

Resources

    1.   [Lotfi A. Zadeh](https://en.wikipedia.org/wiki/Lotfi_A._Zadeh)
    2.   [Fuzzy logic](https://en.wikipedia.org/wiki/Fuzzy_logic)

*   
1965

First deep learning algorithm

Ivakhnenko and Lapa created the first deep learning algorithm for training multilayer perceptrons in the Soviet Union, pioneering techniques that would later become fundamental to modern neural networks.

Resources

    1.   [Multilayer perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron)
    2.   [Alexey Ivakhnenko](https://en.wikipedia.org/wiki/Alexey_Ivakhnenko)
    3.   [Deep learning](https://en.wikipedia.org/wiki/Deep_learning)

*   
### 1964

Project MAC

Bobrow's MIT dissertation demonstrated computers could comprehend natural language sufficiently to solve word problems in algebra, while Raphael's work on SIR showed how logical knowledge representation could enable computer systems to answer questions.

Resources

    1.   [MIT Computer Science and Artificial Intelligence Laboratory](https://en.wikipedia.org/wiki/Project_MAC)

*   
### 1963

First adaptive machine learning programs

Uhr and Vossler created one of the first adaptive machine learning programs that could generate and modify its own features, advancing beyond the limitations of Rosenblatt's perceptrons.

Resources

    1.   [Frank Rosenblatt](https://en.wikipedia.org/wiki/Frank_Rosenblatt)
    2.   [Leonard Uhr](https://en.wikipedia.org/wiki/Leonard_Uhr)

*   
1963

The first collection of articles about artificial intelligence

Feigenbaum and Feldman compiled "Computers and Thought," the first anthology of research papers focused on artificial intelligence.

Resources

    1.   [Edward Feigenbaum](https://en.wikipedia.org/wiki/Edward_Feigenbaum)
    2.   [Julian Feldman](https://en.wikipedia.org/w/index.php?title=Julian_Feldman&action=edit&redlink=1)

*   
### 1960

Solomonoff's Mathematical basis for AI

Solomonoff established a mathematical basis for AI by developing universal Bayesian approaches to making predictions and learning from examples.

Resources

    1.   [Bayesian inference](https://en.wikipedia.org/wiki/Bayesian_method)

*   
1960

Masterman's Semantic Networks

Masterman and her team at Cambridge created semantic networks designed to facilitate machine translation.

Resources

    1.   [Lexical semantics](https://en.wikipedia.org/wiki/Lexical_semantics)
    2.   [Machine translation](https://en.wikipedia.org/wiki/Machine_translation)

*   
### 1959

General Problem Solver, MIT AI Laboratory

Newell, Shaw, and Simon developed the General Problem Solver (GPS) at CMU, while McCarthy and Minsky established the MIT AI Laboratory.

Resources

    1.   [General Problem Solver](https://en.wikipedia.org/wiki/General_Problem_Solver)
    2.   [MIT Computer Science and Artificial Intelligence Laboratory](https://en.wikipedia.org/wiki/MIT_Computer_Science_and_Artificial_Intelligence_Laboratory)

*   
### 1958

Lisp, Advice Taker, Pademonium, Heuristic Programming

McCarthy created Lisp, one of the first programming languages designed for AI. At IBM, Gelernter and Rochester developed a geometry theorem prover that used visual models of typical cases. The Teddington Conference featured several influential papers: McCarthy's proposal for the Advice Taker and "common sense" reasoning, Selfridge's "Pandemonium," and Minsky's work on heuristic programming.

Resources

    1.   [Lisp (programming language)](https://en.wikipedia.org/wiki/Lisp_(programming_language))
    2.   [Proof assistant](https://en.wikipedia.org/wiki/Proof_assistant)
    3.   [Advice taker](https://en.wikipedia.org/wiki/Advice_taker)
    4.   [Heuristic (computer science)](https://en.wikipedia.org/wiki/Heuristic_(computer_science))

*   
### 1956

First AI Program

The Logic Theorist (LT), created by Newell, Shaw, and Simon at Carnegie Tech (now CMU), was arguably the first true AI program. It could perform automated reasoning and successfully proved 38 theorems from Principia Mathematica, sometimes finding better proofs than the original ones. Simon claimed this breakthrough solved the mind-body problem by demonstrating how physical systems could exhibit mental properties.

Resources

    1.   [Logic Theorist](https://en.wikipedia.org/wiki/Logic_Theorist)
    2.   [Principia Mathematica](https://en.wikipedia.org/wiki/Principia_Mathematica)

*   
1956

AI Conference

McCarthy, Minsky, Rochester, and Shannon organized the Dartmouth College summer conference, where McCarthy introduced the term "artificial intelligence" - an event that marked the formal beginning of AI as a field.

Resources

    1.   [Dartmouth workshop](https://en.wikipedia.org/wiki/Dartmouth_workshop)
    2.   [John McCarthy (computer scientist)](https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist))
    3.   [IBM](https://en.wikipedia.org/wiki/International_Business_Machines)

*   
### 1952–1962

First game-playing program

Arthur Samuel developed the first game-playing program that could seriously compete with human players - a checkers program created in 1952. He enhanced it in 1955 to include learning capabilities, allowing it to improve through experience.

Resources

    1.   [Arthur Samuel (computer scientist)](https://en.wikipedia.org/wiki/Arthur_Samuel_(computer_scientist))
    2.   [Machine learning](https://en.wikipedia.org/wiki/Machine_learning)
    3.   [Checkers](https://en.wikipedia.org/wiki/Draughts)

*   
### 1951

First working AI program

Strachey and Prinz created the first functional AI programs in 1951, developing checkers and chess programs respectively for the University of Manchester's Ferranti Mark 1 computer.

Resources

    1.   [Ferranti Mark 1](https://en.wikipedia.org/wiki/Ferranti_Mark_1)
    2.   [Christopher Strachey](https://en.wikipedia.org/wiki/Christopher_Strachey)
    3.   [Dietrich Prinz](https://en.wikipedia.org/wiki/Dietrich_Prinz)

*   ## Meet Alan Turing: 1647 - 1950 
*   
### 1950

Turing Test

Turing published "Computing Machinery and Intelligence," introducing the Turing test as a way to evaluate machine intelligence and systematically addressing major arguments against the possibility of machine thought.

Resources

    1.   [Computing Machinery and Intelligence](https://en.wikipedia.org/wiki/Computing_Machinery_and_Intelligence)
    2.   [Turing test](https://en.wikipedia.org/wiki/Turing_test)

*   
### 1949

Hebbian Theory

Donald Hebb established Hebbian theory, which proposed a mechanism for how neural networks could learn through strengthening connections between neurons that fire together.

Resources

    1.   [Hebbian theory](https://en.wikipedia.org/wiki/Hebbian_theory)
    2.   [Neural network](https://en.wikipedia.org/wiki/Neural_networks)
    3.   [Donald O. Hebb](https://en.wikipedia.org/wiki/Donald_O._Hebb)

*   
### 1948

First AI Manifesto

Turing wrote "Intelligent Machinery," considered the first AI manifesto. The report introduced key concepts that would become fundamental to AI, including logical problem-solving, search-based intelligence, and machine learning through neural connections.

Resources

    1.   [Connectionism](https://en.wikipedia.org/wiki/Connectionism)

*   
### 1945

Prescient vision of the future

Bush published "As We May Think" in The Atlantic Monthly, describing a future where computers would enhance human capabilities across various activities.

Resources

    1.   [Vannevar Bush](https://en.wikipedia.org/wiki/Vannevar_Bush)
    2.   [As We May Think](https://en.wikipedia.org/wiki/As_We_May_Think)

*   
1945

Game Theory

Von Neumann and Morgenstern published "Theory of Games and Economic Behavior," establishing game theory as a mathematical framework that would later become crucial to AI development.

Resources

    1.   [Game theory](https://en.wikipedia.org/wiki/Game_theory)
    2.   [Theory of Games and Economic Behavior](https://en.wikipedia.org/wiki/Theory_of_Games_and_Economic_Behavior)

*   
### 1943

Cybernetics

Rosenblueth, Wiener, and Bigelow introduced the term "cybernetics," which Wiener later popularized in his 1948 book of the same name.

Resources

    1.   [Arturo Rosenblueth](https://en.wikipedia.org/wiki/Arturo_Rosenblueth)
    2.   [Norbert Wiener](https://en.wikipedia.org/wiki/Norbert_Wiener)
    3.   [Cybernetics](https://en.wikipedia.org/wiki/Cybernetics)

*   
1943

First mathematical descriptions of Artificial Neural Networks

McCulloch and Pitts published "A Logical Calculus of the Ideas Immanent in Nervous Activity," which provided the first mathematical model of artificial neural networks, representing how neurons could perform logical operations.

Resources

    1.   [Warren Sturgis McCulloch](https://en.wikipedia.org/wiki/Warren_Sturgis_McCulloch)
    2.   [Walter Pitts](https://en.wikipedia.org/wiki/Walter_Pitts)
    3.   [Neural network (machine learning)](https://en.wikipedia.org/wiki/Artificial_neural_network)

*   
### 1941

First working program-controlled general-purpose computer

Zuse completed construction of the first functional programmable general-purpose computer.

Resources

    1.   [Konrad Zuse](https://en.wikipedia.org/wiki/Konrad_Zuse)

*   
### 1940

Nimatron

Edward Condon created Nimatron - a digital computer that could play the game Nim without making mistakes.

Resources

    1.   [Edward Condon](https://en.wikipedia.org/wiki/Edward_Condon)
    2.   [Nimatron](https://en.wikipedia.org/wiki/Nimatron)
    3.   [Nim](https://en.wikipedia.org/wiki/Nim)

*   
### 1937

Alan Turing was here

Turing's seminal paper "On Computable Numbers" introduced the concept of the Turing machine - a theoretical model that defined computation in physical terms. Using this framework, he proved the halting problem was undecidable, confirming Gödel's earlier work on mathematical incompleteness.

Resources

    1.   [Alan Turing](https://en.wikipedia.org/wiki/Alan_Turing)
    2.   [Turing's proof](https://en.wikipedia.org/wiki/Turing%27s_proof)
    3.   [Theory of computation](https://en.wikipedia.org/wiki/Theory_of_computation)
    4.   [Turing machine](https://en.wikipedia.org/wiki/Turing_machine)
    5.   [Undecidable Problem](https://en.wikipedia.org/wiki/Undecidable_problem)

*   
### 1936

Patent for first programmable computers

Konrad Zuse submitted a patent for one of the first programmable computers.

Resources

    1.   [Konrad Zuse](https://en.wikipedia.org/wiki/Konrad_Zuse)

*   
### 1935

Lambda Calculus

Alonzo Church built on Gödel's work by proving the undecidability of general computational problems and developing lambda calculus, which became essential to computer programming language theory.

Resources

    1.   [Alonzo Church](https://en.wikipedia.org/wiki/Alonzo_Church)
    2.   [Decision problem](https://en.wikipedia.org/wiki/Decision_problem)
    3.   [Lambda calculus](https://en.wikipedia.org/wiki/Lambda_calculus)

*   
### 1931

Kurt Gödel's Impact

Kurt Gödel demonstrated fundamental limitations in computational systems by encoding mathematical statements as numbers and proving that some true statements cannot be proven by any consistent mechanical system - a breakthrough that would shape theoretical computer science and AI.

Resources

    1.   [Kurt Gödel](https://en.wikipedia.org/wiki/Kurt_G%C3%B6del)
    2.   [Theoretical computer science](https://en.wikipedia.org/wiki/Theoretical_computer_science)

*   
### 1920-1925

The Ising model

The Ising model, developed by Lenz and Ising in 1925, was an early form of recurrent neural network using threshold elements. Amari later made this system adaptable in 1972.

Resources

    1.   [Wilhelm Lenz](https://en.wikipedia.org/wiki/Wilhelm_Lenz)
    2.   [Ernst Ising](https://en.wikipedia.org/wiki/Ernst_Ising)
    3.   [Ising model](https://en.wikipedia.org/wiki/Ising_model)
    4.   [Recurrent neural network](https://en.wikipedia.org/wiki/Recurrent_neural_network)

*   
### 1912-1914

Leonardo Torres Quevedo: the 20th century's first AI pioneer

Torres Quevedo created El Ajedrecista, the first machine capable of playing chess endgames, and pioneered concepts like floating-point arithmetic. His Essays on Automatics explored the relationship between thinking and automated machines, earning him recognition as an early AI pioneer.

Resources

    1.   [El Ajedrecista](https://en.wikipedia.org/wiki/El_Ajedrecista)
    2.   [Leonardo Torres Quevedo](https://en.wikipedia.org/wiki/Leonardo_Torres_Quevedo)
    3.   [Floating-point arithmetic](https://en.wikipedia.org/wiki/Floating-point_arithmetic)

*   
### 1910-1913

Principia Mathematica

Russell and Whitehead's Principia Mathematica demonstrated that basic mathematics could be expressed through formal logical reasoning.

Resources

    1.   [Bertrand Russell](https://en.wikipedia.org/wiki/Bertrand_Russell)
    2.   [Alfred North Whitehead](https://en.wikipedia.org/wiki/Alfred_North_Whitehead)
    3.   [Principia Mathematica](https://en.wikipedia.org/wiki/Principia_Mathematica)

*   
### 1863

Butler's Proposal

Samuel Butler proposed that machines might evolve like biological organisms, predicting they could eventually develop consciousness and replace humans.

Resources

    1.   [Evolution](https://en.wikipedia.org/wiki/Evolution)

*   
### 1854

Boolean Algebra

George Boole created Boolean algebra - a mathematical system designed to express and analyze the fundamental operations of human reasoning.

Resources

    1.   [George Boole](https://en.wikipedia.org/wiki/George_Boole)
    2.   [Boolean algebra](https://en.wikipedia.org/wiki/Boolean_algebra_(logic))

*   
### 1837

Bolzano's Semantics

Bernard Bolzano pioneered the modern approach to formalizing how meaning is represented in language and logic (semantics).

Resources

    1.   [Bernard Bolzano](https://en.wikipedia.org/wiki/Bernard_Bolzano)
    2.   [Semantics](https://en.wikipedia.org/wiki/Semantics)

*   
### 1822–1859

Programmable Mechanical Calculating Machines

Babbage and Lovelace collaborated on developing mechanical computers that could be programmed to perform calculations.

Resources

    1.   [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage)
    2.   [Ada Lovelace](https://en.wikipedia.org/wiki/Ada_Lovelace)
    3.   [Difference engine](https://en.wikipedia.org/wiki/Difference_engine)

*   
### 1795-1805

Primitive Artificial Neural Network

Legendre and Gauss developed linear regression (also known as the method of least squares) in the late 18th/early 19th century to predict planetary motion. This mathematical technique would later be recognized as the simplest form of artificial neural network.

Resources

    1.   [Neural network (machine learning)](https://en.wikipedia.org/wiki/Artificial_neural_network)
    2.   [Least squares](https://en.wikipedia.org/wiki/Method_of_least_squares)
    3.   [Linear regression](https://en.wikipedia.org/wiki/Linear_regression)
    4.   [Adrien-Marie Legendre](https://en.wikipedia.org/wiki/Adrien-Marie_Legendre)
    5.   [Carl Friedrich Gauss](https://en.wikipedia.org/wiki/Carl_Friedrich_Gauss)

*   
### 1769

Kempelen's The Turk

Von Kempelen created The Turk, a purported chess-playing machine that toured Europe defeating human opponents. It was later exposed as a hoax concealing a human chess player.

Resources

    1.   [Wolfgang von Kempelen](https://en.wikipedia.org/wiki/Wolfgang_von_Kempelen)
    2.   [Automaton](https://en.wikipedia.org/wiki/Automaton)
    3.   [Mechanical Turk](https://en.wikipedia.org/wiki/Mechanical_Turk)

*   
### 1763

Thomas Bayes' Bayesian networks

Thomas Bayes' posthumously published essay introduced what became known as Bayes' theorem, a mathematical principle that would later become fundamental to AI systems through Bayesian networks.

Resources

    1.   [Thomas Bayes](https://en.wikipedia.org/wiki/Thomas_Bayes)
    2.   [An Essay Towards Solving a Problem in the Doctrine of Chances](https://en.wikipedia.org/wiki/An_Essay_Towards_Solving_a_Problem_in_the_Doctrine_of_Chances)
    3.   [Bayes' theorem](https://en.wikipedia.org/wiki/Bayes%27_theorem)

*   
### 1750

Julien Offray de La Mettrie's foreshadowing

La Mettrie proposed in "L'Homme Machine" (Man Machine) that human thought processes were entirely mechanical in nature.

Resources

    1.   [Julien Offray de La Mettrie](https://en.wikipedia.org/wiki/Julien_Offray_de_La_Mettrie)
    2.   [Man a Machine](https://en.wikipedia.org/wiki/L%27Homme_Machine)

*   
### 1739

David Hume's inductive reasoning

David Hume identified induction - the process of deriving general principles from specific examples - as a fundamental method of learning.

Resources

    1.   [David Hume](https://en.wikipedia.org/wiki/David_Hume)
    2.   [Inductive reasoning](https://en.wikipedia.org/wiki/Inductive_reasoning)

*   
### 1738

Bernoulli's principles

Daniel Bernoulli developed the concept of utility, extending probability theory into a framework for measuring value and decision-making - principles that would later become fundamental to how AI systems model goals and choices.

Resources

    1.   [Intelligent agent](https://en.wikipedia.org/wiki/Intelligent_agent)
    2.   [Decision theory](https://en.wikipedia.org/wiki/Decision_theory)
    3.   [Utility](https://en.wikipedia.org/wiki/Utility_(economics))
    4.   [Daniel Bernoulli](https://en.wikipedia.org/wiki/Daniel_Bernoulli)

*   
### 1726

Gulliver's Travels

Swift's Gulliver's Travels satirized Llull's Ars Magna and Leibniz's mechanical logic through the Engine of Laputa - a fictional machine that claimed to produce scholarly works without requiring intelligence or learning.

Resources

    1.   [Gulliver's Travels](https://en.wikipedia.org/wiki/Gulliver%27s_Travels)

*   
### 1679

Leibniz: The Unstoppable

Leibniz envisioned a universal logical system that would assign numerical values to all objects, aiming to create an algebraic method for solving any problem through mechanical reasoning.

Resources

    1.   [Alphabet of human thought](https://en.wikipedia.org/wiki/Alphabet_of_human_thought)

*   
### 1676

Leibniz discovered the chain rule

Leibniz discovered the chain rule, a mathematical principle that would later become essential for training neural networks through algorithms like backpropagation.

Resources

    1.   [Chain rule](https://en.wikipedia.org/wiki/Chain_rule)
    2.   [Backpropagation](https://en.wikipedia.org/wiki/Backpropagation)

*   
### 1672

Leibniz's Stepped Reckoner

Leibniz advanced mechanical computation by creating the Stepped Reckoner, a calculator capable of multiplication and division operations.

Resources

    1.   [Stepped reckoner](https://en.wikipedia.org/wiki/Stepped_Reckoner)
    2.   [Gottfried Wilhelm Leibniz](https://en.wikipedia.org/wiki/Gottfried_Wilhelm_Leibniz)

*   
### 1654

Blaise Pascal's expected values

The foundations of probability theory emerged in the 1660s through Pascal's work on expected values, followed by Arnauld's maximization formula and Cardano's posthumously published solutions. Bernoulli and Laplace expanded this field in the 1700s. These mathematical principles would later prove crucial to modern AI and machine learning.

Resources

    1.   [Expected value](https://en.wikipedia.org/wiki/Expected_value)

*   
### 1647

Descartes's philosophy

Descartes theorized that animal bodies functioned as sophisticated machines, while maintaining that consciousness and mental processes were fundamentally distinct.

Resources

    1.   [René Descartes](https://en.wikipedia.org/wiki/Ren%C3%A9_Descartes)

*   ## AI in Mythology: BC - 1642 
*   
### 1642

Blaise Pascal created the first digital calculator

Blaise Pascal created the first digital calculator - a mechanical device capable of performing numerical computations.

Resources

    1.   [Pascal's calculator](https://en.wikipedia.org/wiki/Pascal%27s_calculator)

*   
### 1620

Francis Bacon's Novum Organum

Francis Bacon's Novum Organum, named in reference to Aristotle's work, established empirical methodology and inductive reasoning as foundations for acquiring knowledge.

Resources

    1.   [Francis Bacon](https://en.wikipedia.org/wiki/Francis_Bacon)
    2.   [Novum Organum](https://en.wikipedia.org/wiki/Novum_Organum)
    3.   [Organon](https://en.wikipedia.org/wiki/Organon)

*   
### ~1580

Known legend: Golem

According to legend, Rabbi Loew of Prague created the Golem - an artificial being fashioned from clay and magically brought to life.

Resources

    1.   [Golem](https://en.wikipedia.org/wiki/Golem)

*   
### ~1500

Paracelsus manufactured a humanoid being

Paracelsus, the Renaissance physician and alchemist, asserted he had manufactured a humanoid being using a combination of magnetic forces, reproductive material, and alchemical processes.

Resources

    1.   [Paracelsus](https://en.wikipedia.org/wiki/Paracelsus)

*   
### 1275

Ramon Llull created the Ars Magna

In the 13th century, Ramon Llull created the Ars Magna, adapting an Arabic device called the Zairja to mechanically combine concepts. He envisioned these as machines that could generate complex knowledge by combining simple truths. This concept later influenced Leibniz's work in the 1600s.

Resources

    1.   [Ramon Llull](https://en.wikipedia.org/wiki/Ramon_Llull)
    2.   [Gottfried Wilhelm Leibniz](https://en.wikipedia.org/wiki/Gottfried_Wilhelm_Leibniz)

*   
### 1206

Al-Jazari's programmable orchestra of mechanical human beings

Al-Jazari engineered an automated orchestra consisting of mechanical humanoid figures that could be programmed to play music in a coordinated way.

Resources

    1.   [Ismail al-Jazari](https://en.wikipedia.org/wiki/Ismail_al-Jazari)

*   
### 9th Century

Father of the algorithms: al-Khwarizmi

Al-Khwarizmi authored influential mathematical textbooks that provided detailed, systematic procedures for solving arithmetic and algebraic problems. These methods were widely used across Islamic civilization, India, and Europe for several centuries, up until the 1500s. His name is the origin of the word "algorithm," reflecting his contribution to systematic problem-solving methods.

Resources

    1.   [Al-Khwarizmi](https://en.wikipedia.org/wiki/Al-Khwarizmi)
    2.   [Algorithm](https://en.wikipedia.org/wiki/Algorithm)

*   
9th Century

Perhaps the first machine with a stored program

The Banū Mūsā brothers invented a remarkable automated musical instrument - a steam-powered flute whose music was programmed using pins on a rotating cylinder. This design represents what might be considered the first machine with a stored program, as the cylinder's pins essentially contained the "code" that determined which notes would play.

Resources

    1.   [Banū Mūsā brothers](https://en.wikipedia.org/wiki/Ban%C5%AB_M%C5%ABs%C4%81_brothers)
    2.   [Stored-program computer](https://en.wikipedia.org/wiki/Stored_program)

*   
### ~800

Takwin: Artificial creation of in the laboratory

The Arab alchemist Jabir ibn Hayyan developed a theoretical framework called Takwin, which explored the possibility of artificially creating life, including human life, through laboratory processes.

Resources

    1.   [Jabir İbn Hayyan](https://en.wikipedia.org/wiki/Jabir_ibn_Hayyan)
    2.   [Takwin](https://en.wikipedia.org/wiki/Takwin)

*   
### 260

Isagogê aka Semantic Net

Porphyry, a Greek philosopher, wrote the Isagogê - a work that organized knowledge and logic into categories. Notably, it included a visual representation that was an early version of what we now call a semantic network (a way of showing relationships between concepts through a structured diagram).

Resources

    1.   [Porphyry (philosopher)](https://en.wikipedia.org/wiki/Porphyry_(philosopher))
    2.   [Semantic network](https://en.wikipedia.org/wiki/Semantic_net)

*   
### 1st century

World's first practical programmable machine

Hero of Alexandria, an ancient Greek inventor, engineered various automated devices including mechanical humanoid figures. One of his most notable achievements was creating what some consider the first programmable machine in history - an automated theater that could perform scripted movements and scenes.

Resources

    1.   [Automaton](https://en.wikipedia.org/wiki/Automaton)
    2.   [Hero of Alexandria](https://en.wikipedia.org/wiki/Hero_of_Alexandria)

*   
### 3rd century BC

First example of feedback mechanism

Ctesibius, an ancient Greek engineer, created an advanced water clock that included an alarm system. This device was groundbreaking as it represented the first known use of a feedback control mechanism - where the system could monitor and adjust its own operation based on its current state.

Resources

    1.   [Ctesibius](https://en.wikipedia.org/wiki/Ctesibius)

*   
### 384 BC–322 BC

Aristotle and AI

Aristotle made two significant contributions to the foundations of systematic thinking and problem-solving:

1) In his work Organon, he developed the syllogism - a structured method of logical reasoning where conclusions are drawn from two premises. This represented one of the first formalized systems of mechanical, step-by-step logical thinking.

2) In Nicomachean Ethics, he outlined what we now call means-ends analysis - a method of solving problems by repeatedly identifying the gap between your current state and goal state, then taking actions to reduce that gap. Interestingly, this same basic algorithm would be implemented thousands of years later in one of the first AI programs, the General Problem Solver, created by Newell and Simon in 1959.

Resources

    1.   [Syllogism](https://en.wikipedia.org/wiki/Syllogism)
    2.   [Means–ends analysis](https://en.wikipedia.org/wiki/Means%E2%80%93ends_analysis)
    3.   [General Problem Solver](https://en.wikipedia.org/wiki/General_Problem_Solver)

*   
### 10th century BC

AI in China History: Mechanical Men

During China's Zhou Dynasty, an engineer named Yan Shi created and showed King Mu automated figures or "mechanical men" that could move on their own.

Resources

    1.   [King Mu of Zhou](https://en.wikipedia.org/wiki/King_Mu_of_Zhou)

*   
### Ancient Times (BC)

AI in Mythology: Sacred Statues in Egypt and Greece

In ancient Egypt and Greece, people believed that certain sacred statues possessed consciousness and feelings. According to writings attributed to Hermes Trismegistus, these mechanical statues were thought to have both sensory perception (sensus) and life force or breath (spiritus). He claimed that by understanding the divine nature of the gods, humans had learned to recreate these qualities in their constructed forms.

Resources

    1.   [Hermes Trismegistus](https://en.wikipedia.org/wiki/Hermes_Trismegistus)

*   
Ancient Times (BC)

AI in Mythology: Automata in Greece

Ancient Greek mythology explored concepts of artificial life through several stories. In these tales, Hephaestus, the god of craftsmanship, created autonomous machines like the bronze giant Talos. Similarly, the myth of Pygmalion tells of a sculptor who created a statue named Galatea that was brought to life by Aphrodite. The story of Pandora also features an artificial woman, crafted by Hephaestus at Zeus's command.

Resources

    1.   [Automaton](https://en.wikipedia.org/wiki/Automaton)

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