Babbage conceives the Analytical Engine
In 1834, Charles Babbage conceived a programmable general-purpose computing engine, later called the Analytical Engine.
AI timeline
How artificial intelligence evolved: from scientific foundations to modern capabilities.
Last published:
Events
Found: 141
Before 1900
The Origins of Artificial Intelligence
For centuries, philosophers, mathematicians, and inventors explored the possibility of creating thinking machines. Advances in formal logic, mechanical computing devices, and early programming concepts laid the foundation for artificial intelligence.
1834
1843
Publication of Ada Lovelace’s notes on the Analytical Engine
In 1843, Ada Lovelace published notes describing a sequence of operations for computing Bernoulli numbers on the proposed Analytical Engine. She also considered operations on other objects whose relationships could be expressed in a suitable formal representation.
1847
Boole publishes The Mathematical Analysis of Logic
George Boole presented an algebraic approach to logic, allowing propositions and arguments to be analyzed through equations.
1879
Frege publishes Begriffsschrift
Gottlob Frege presented a formal language of relations and quantification with an axiomatic calculus. It allowed reasoning with nested quantifiers to be expressed explicitly.
1900–1942
The Mathematical Foundations of Intelligence
Advances in mathematical logic and computation theory established the scientific foundations of AI. Researchers began exploring the capabilities of algorithms, the principles of computation, and the limits of what machines could solve.
1901
Pearson formulates principal component analysis
Karl Pearson formulated principal component analysis (PCA) as approximating data through projections that minimize the sum of squared errors.
1911
The law of effect in Animal Intelligence
In Animal Intelligence: Experimental Studies, Thorndike presented the law of effect: satisfaction or discomfort accompanying or closely following a response changes its likelihood of recurring in the same situation.
1920
Witness functions in Skolem’s proof
In his 1920 paper, Skolem uses functions that select witnesses for each tuple of universally quantified variables in a proof of satisfiability over a finite or countably infinite domain.
1924
Schönfinkel’s paper on eliminating variables is published
In September 1924, Mathematische Annalen published Schönfinkel’s paper on eliminating bound variables using combinators.
1930
Herbrand’s theorem
Herbrand showed that an entailment from a first-order knowledge base can be proved using a finite subset of its ground instances.
Gödel publishes the completeness theorem for first-order logic
Gödel published a proof of completeness for first-order logic: every logically valid formula has a formal proof.
1932
Church published a logical system using lambda notation
Church’s paper “A Set of Postulates for the Foundation of Logic” appeared in Annals of Mathematics. It described a logical system using lambda notation to represent sets.
1935
Gentzen’s calculi and cut elimination
Gerhard Gentzen developed natural deduction and sequent calculus and proved the cut-elimination theorem.
1936
Turing described a universal computing machine
Turing described a machine capable of reading encoded descriptions of other machines and reproducing their computations.
Fisher publishes a seminal paper on discriminant analysis
Fisher used iris data in his work on discriminant analysis and introduced the term discriminant function.
1940
Church formulates simple type theory
In 1940, Alonzo Church presented a formulation of simple type theory with functions as primitive objects and lambda notation as its sole variable-binding mechanism.
1943–1955
The First Models of Artificial Intelligence
Mathematical models of artificial neurons emerge alongside early research into machine learning. Advances in cybernetics and computing technology open new possibilities for creating systems capable of simulating aspects of human thought.
1943
McCulloch and Pitts’s logical model of neural networks
In December 1943, Warren McCulloch and Walter Pitts published a logical account of neural networks. They showed how to construct networks that realize logical expressions under specified conditions.
1949
Hebb’s learning principle in The Organization of Behavior
In The Organization of Behavior (1949), Donald Hebb proposed that a neuron’s repeated or persistent participation in firing another neuron increases its effectiveness in doing so.
Metropolis and Ulam describe the Monte Carlo method
Metropolis and Ulam presented a statistical approach to difficult computations: examining a random sample of trials or trajectories instead of calculating every possibility.
1950
Turing proposes the imitation game
In Computing Machinery and Intelligence, Turing proposed replacing the question “Can machines think?” with a game in which an interrogator judges a machine's written answers.
Shannon describes search and position evaluation for computer chess
Shannon published a chess-program design combining position evaluation with minimax move selection. He also described selective search as a way to reduce enumeration.
1953
Metropolis algorithm published
Metropolis and colleagues described sampling configurations from the Boltzmann distribution through random particle displacements with acceptance or rejection of proposed moves.
1954
Davis implements Presburger's decision procedure
Martin Davis implemented Presburger's decision procedure in a program that performed an automated proof.
The Georgetown–IBM machine translation demonstration
On January 7, 1954, IBM and Georgetown University publicly demonstrated automatic translation of Russian sentences into English on an IBM 701.
Farley and Clark describe a digital simulation of a learning network
In September 1954, B. G. Farley and W. A. Clark published a study of a digitally simulated self-organizing network. Starting from a random initial state, it learned to distinguish two fixed input patterns by changing connection weights.
1955
Prior publishes Diodoran Modalities
Arthur Prior published the first study of his tense logic, introducing future-time operators into propositional calculus.
The Dartmouth proposal on artificial intelligence
The proposal dated August 31, 1955 outlined a shared research program in artificial intelligence. It initiated the 1956 Dartmouth summer research project.
Successful hand simulation of the Logic Theorist method
According to Newell and Simon, the first successful hand simulation of a theorem proof using methods later incorporated into Logic Theorist took place on December 15, 1955.
1956–1973
The Birth of AI as a Scientific Field
Artificial intelligence emerges as a distinct scientific discipline. Early programs capable of logical reasoning, problem-solving, and natural language interaction inspire a wave of optimism about the future of intelligent machines.
1956
Logic Theorist proves theorems on a computer
In 1956, Logic Theorist became a working program for proving theorems in symbolic logic.
Dartmouth Summer Research Project on Artificial Intelligence
In 1956, a small group of scientists gathered for the Dartmouth Summer Research Project on Artificial Intelligence. The meeting was a seminal event in the emergence of AI as a research field.
1958
The NSS chess program and game-tree pruning
Newell, Shaw, and Simon studied a chess program that used a game-tree pruning method.
Metropolis and Ulam describe the Monte Carlo method
Los Alamos LaboratoryWhat happened
The paper discusses estimating the volume of a high-dimensional region and simulating chains of particle interactions. The authors explain how computing machines can carry out these procedures.
Why it matters
Random-sample estimates make it possible to approximate sums and integrals that cannot be computed exactly. The deep learning textbook documents widespread use of this approach in machine learning, including computations for training probabilistic models.
Sources
- The Monte Carlo Method
Supporting excerpt
Sat Nov 17 07:03:49 2007 JOURNAL O F THE A M E R I C A N STATISTICAL ASSOCIATION Number 167 S E P T E M B E R 1949 Volume 44 T H E MONTE CARL0 METHOD METROPOLIS NICHOLAS AND S. ULAM Los Alamos Laboratory We shall present here the motivation and a general descrip- tion of a method dealing with a class of problems in mathe- matical physics. The method is, essentially, a statistical approach t o the study of differential equations, or more generally, of integro-differential equations that occur in
- The Monte Carlo method — PubMed
Supporting excerpt
J Am Stat Assoc Actions Search in PubMed Search in NLM Catalog Add to Search . 1949 Sep;44(247):335-41. doi: 10.1080/01621459.1949.10483310. The Monte Carlo method N METROPOLIS, S ULAM PMID: 18139350 DOI: 10.1080/01621459.1949.10483310 Item in Clipboard The Monte Carlo method N METROPOLIS et al. J Am Stat Assoc. 1949 Sep. Show details Display options Display options Format Abstract PubMed PMID J Am Stat Assoc Actions Search in PubMed Search in NLM Catalog Add to Search . 1949 Sep;44(247):335-41.
- Deep Learning — Chapter 17: Monte Carlo Methods
Supporting excerpt
deterministic approximate algorithms or Monte Carlo approximations. Both approaches are ubiquitous in machine learning. In this chapter, we focus on Monte Carlo methods. 17.1 Sampling and Monte Carlo Methods Many important technologies used to accomplish machine learning goals are based on drawing samples from some probability distribution and using these samples to form a Monte Carlo estimate of some desired quantity. 17.1.1 Why Sampling? We may wish to draw samples from a probability distribut
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