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In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".

As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler's Theorem says "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, intelligent routing in content delivery networks, and military simulations.

Artificial intelligence was founded as an academic discipline in 1955, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into sub-fields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences. Sub-fields have also been based on social factors (particular institutions or the work of particular researchers).

The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field's long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.

The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence. These issues have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI to be a danger to humanity if it progresses unabated. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.

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• Quantum algorithms for supervised and unsupervised machine learning
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• Reinforcement Learning Using Quantum Boltzmann Machines
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• Equivalence of restricted Boltzmann machines and tensor network states
• Quantum Entanglement in Neural Network States
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• Opening the black box of Deep Neural Networks via Information
• Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders
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• Discovering Phases, Phase Transitions and Crossovers through Unsupervised Machine Learning: A critical examination
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• Nonequilibrium Thermodynamics of Restricted Boltzmann Machines
• Reinforcement Learning in Different Phases of Quantum Control
• Decoding Small Surface Codes with Feedforward Neural Networks
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• Machine Learning Z₂ Quantum Spin Liquids with Quasi-particle Statistics
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• Machine learning vortices at the Kosterlitz-Thouless transition
• Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
• Machine learning out-of-equilibrium phases of matter
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• Experimental learning of quantum states
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• Towards reduction of autocorrelation in HMC by machine learning
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• GANs for generating EFT models
• Approaching the adiabatic timescale with machine-learning
• Projective quantum Monte Carlo simulations guided by unrestricted neural network states
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• Identifying Quantum Phase Transitions using Artificial Neural Networks on Experimental Data
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• A quantum autoencoder: the compression of qutrits via machine learning
• Efficient Representation of Topologically Ordered States with Restricted Boltzmann Machines
• Super-resolving the Ising model with convolutional neural networks
• Quantum Convolutional Neural Networks
• Quantum Neural Network and Soft Quantum Computing
• Learning multiple order parameters with interpretable machines
• Nonequilibrium fluctuations of a driven quantum heat engine via machine learning
• Topographic Representation for Quantum Machine Learning
• Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation
• Thermodynamics and Feature Extraction by Machine Learning
• A jamming transition from under- to over-parametrization affects loss landscape and generalization
• Emulating quantum computation with arti cial neural networks
• The Role of Data in Model Building and Prediction: A Survey Through Examples
• Toward an AI Physicist for Unsupervised Learning
• Reconstructing quantum states with generative models
• Quantum data compression by principal component analysis
• Free energies of Boltzmann Machines: self-averaging, annealed and replica symmetric approximations in the thermodynamic limit
• Machine learning density functional theory for the Hubbard model
• Quantum advantage in training binary neural networks
• Quantum-inspired classical algorithms for principal component analysis and supervised clustering
• Deep Learning of Robust and High-Precision Quantum Controls
• Machine learning for molecular dynamics with strongly correlated electrons
• An Artificial Neuron Implemented on an Actual Quantum Processor
• Artificial neural networks for density-functional optimizations in fermionic systems
• Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
• Efficient prediction of 3D electron densities using machine learning
• Neural network state estimation for full quantum state tomography
• Experimental Simultaneous Learning of Multiple Non-Classical Correlations
• Neural Belief-Propagation Decoders for Quantum Error-Correcting Codes
• Advances in Quantum Reinforcement Learning
• Energy Levels of One Dimensional Anharmonic Oscillator via Neural Networks
• Deep Learning and Density Functional Theory
• fPINNs: Fractional Physics-Informed Neural Networks
• Physics-aware Deep Generative Models for Creating Synthetic Microstructures
• Developing a Bubble Chamber Particle Discriminator Using Semi-Supervised Learning
• Estimating of the inertial manifold dimension for a chaotic attractor of complex Ginzburg-Landau equation using a neural network
• Quantum error correction for the toric code using deep reinforcement learning
• Using a Recurrent Neural Network to Reconstruct Quantum Dynamics of a Superconducting Qubit from Physical Observations
• Variational optimization in the AI era: Computational Graph States and Supervised Wave-function Optimization
• Classifying Snapshots of the Doped Hubbard Model with Machine Learning
• Designing neural network based decoders for surface codes
• Quantum topology identi cation with deep neural networks and quantum walks
• Divergence of predictive model output as indication of phase transitions
• Designing quantum experiments with a genetic algorithm
• Phase transition encoded in neural network
• Approximating the solution to wave propagation using deep neural networks
• Boltzmann Generators – Sampling Equilibrium States of Many-Body Systems with Deep Learning
• Enhancing the efficiency of quantum annealing via reinforcement: A path-integral Monte Carlo simulation of the quantum reinforcement algorithm
• Quantum algorithms for feedforward neural networks
• A hybrid machine-learning algorithm for designing quantum experiments
• q-means: A quantum algorithm for unsupervised machine learning
• Adaptive Quantum State Tomography with Neural Networks
• Machine Learning as a universal tool for quantitative investigations of phase transitions
• Machine Learning for Optimal Parameter Prediction in Quantum Key Distribution
• Parameters optimization and real-time calibration of Measurement-Device-Independent Quantum Key Distribution Network based on BackPropagation Artificial Neural Network
• Optimizing Quantum Error Correction Codes with Reinforcement Learning
• Deep ToC: A New Method for Estimating the Solutions of PDEs
• Variational quantum simulation of general processes
• QuCumber: wavefunction reconstruction with neural networks
• Quantum algorithm and quantum circuit for A-Optimal Projection: dimensionality reduction
• Extrapolation of quantum observables with Gaussian processes
• The geometry of quantum learning
• Improved Bounds on Quantum Learning Algorithms