Technology

A manually operated calculating device that represents numbers with movable counters or beads arranged according to numerical values.

AI hallucination is the generation of seemingly credible content that is false, unsupported by evidence, or inconsistent with a system’s input.

AlexNet is a deep convolutional neural network whose victory in the 2012 ImageNet competition helped establish deep learning in computer vision.

AlphaFold is a family of artificial intelligence systems for predicting three-dimensional protein structures and biomolecular complexes.

AlphaGo is DeepMind’s Go-playing artificial intelligence system, combining neural networks, reinforcement learning, and tree search to achieve professional and superhuman performance.

Anomaly detection identifies observations, events, or patterns that depart from an established model of expected behavior.

Arthur Samuel was an American computer scientist whose learning checkers programs helped establish machine learning as a field of research.

Artificial intelligence is the field of computer science that builds systems able to do tasks linked to human intelligence, such as reasoning, learning, perception and language.

A computational model of interconnected units that learns patterns by adjusting numerical parameters, supporting prediction, representation learning, and data generation.

An attention mechanism dynamically weights input representations, enabling neural networks to select and combine information relevant to a particular computation.

An autoencoder is a neural network that learns data representations by encoding inputs and reconstructing them under architectural or training constraints.

Automatic differentiation computes derivatives of numerical programs by propagating local derivative rules through their operations.

Automation is the use of technology to execute, monitor, or control tasks and processes with reduced direct human intervention.

Backpropagation efficiently computes gradients through a computational graph, enabling the training of neural networks and other differentiable models.

A text representation that records word occurrences while disregarding their order, enabling numerical analysis, document retrieval, and machine learning.

Batch normalization standardizes neural-network activations using batch statistics and learned scale and shift parameters to support efficient training.

A battery is an electrochemical device that converts stored chemical energy into electricity, with rechargeable types restoring that energy through an external power supply.

Bell Labs is an industrial research organization established in 1925 whose discoveries helped shape modern telecommunications, electronics, and computing.

BERT is a pretrained Transformer encoder that learns bidirectional text representations and can be adapted to many natural-language processing tasks.

Big data describes datasets whose scale, speed, or complexity requires scalable methods for storage, processing, and analysis.

Carbon capture separates carbon dioxide from industrial gases or ambient air for subsequent use or storage.

Carbon capture and storage separates carbon dioxide from emission sources and stores it durably, usually in deep geological formations.

Charles K. Kao was an electrical engineer whose research established the feasibility of low-loss optical fibers for long-distance telecommunications.

Cloud computing delivers shared computing resources over networks, enabling on-demand access, flexible capacity, and measured use.

CMOS is an integrated-circuit technology that combines n-channel and p-channel MOS transistors to implement low-power logic, memory, analog circuits, and image sensors.

A computational graph represents operations and their dependencies, enabling structured execution, automatic differentiation, and optimization of numerical programs.

A computer is a programmable machine that processes data by carrying out sequences of arithmetic and logical operations automatically.

Computer science studies computation, algorithms, information processing, and the principles underlying software and computer systems.

Computer vision develops computational methods for extracting information about objects, scenes, and activities from images, videos, and other visual measurements.

Concurrent computing organizes overlapping computational activities and coordinates their interactions, whether they execute on one processor or several.

A context window is the bounded amount of tokenized information a language model can use during a generation request.

A representation-learning approach that learns similarities and distinctions by comparing related and unrelated examples.

A neural network architecture that uses shared, locally applied filters to learn patterns in images and other grid-structured data.

Cross-attention lets one set of neural representations selectively retrieve information from another through learned query–key matching.

Cryogenics is the science and engineering of producing, maintaining, and using very low temperatures.

Cybersecurity protects digital systems, networks, and information through technical safeguards, organizational processes, and the management of security risks.

Data augmentation creates additional training examples through transformations or combinations of existing data, helping models learn useful invariances and generalize beyond their training samples.

Data leakage occurs when information unavailable under the intended prediction or evaluation conditions influences model development, making reported performance misleading.

Data mining uses computational and statistical methods to discover patterns, relationships, and predictive models in datasets.

Data privacy concerns the appropriate collection, use, disclosure, and protection of information relating to individuals.

Data science is an interdisciplinary field that combines statistical reasoning, computing, and domain knowledge to extract information from data and support investigation and decision-making.

An organized collection of data maintained for efficient access, modification, and controlled use, usually through a database management system.

A supervised learning method that constructs branching rules from labeled examples to predict categories or numerical values.

Deep Blue was an IBM chess-playing supercomputer that defeated reigning world champion Garry Kasparov in a six-game match in 1997.

Deep learning is a branch of machine learning that learns layered representations of data, typically using neural networks with multiple processing layers.

Density estimation infers the probability density underlying observed data, using parametric models, nonparametric smoothing, or flexible learned distributions.

A generative machine-learning model that learns to produce data by reversing a gradual noise-corruption process.

Direct current is electric current that flows in one direction, used in batteries, electronic equipment, power conversion, and high-voltage transmission.

Distributed computing coordinates networked computers to perform computation, manage shared data, and provide services despite communication delays and component failures.

Distribution shift is a difference between the data distributions used to develop a predictive model and those encountered during evaluation or deployment.

DNA sequencing determines the order of nucleotide bases in DNA, enabling analysis of genes, genomes, biological variation, and disease-associated changes.

Dropout is a neural-network regularization technique that randomly suppresses activations during training to reduce overfitting.

An electric generator converts mechanical energy into electrical energy, usually through electromagnetic induction.

An electric motor converts electrical energy into mechanical motion, usually through electromagnetic interactions, and powers equipment ranging from household appliances to industrial machinery and vehicles.

Electrical engineering is the discipline concerned with designing and analyzing electrical and electronic devices, energy systems, communications, and control systems.

An electrical grid is an interconnected network that delivers electricity from producers to consumers while coordinating power flows, supply, demand, and system reliability.

A transformer transfers electrical energy through electromagnetic induction, typically changing alternating-current voltage and current levels between circuits.

Electricity is the set of physical effects caused by electric charge, and the energy carrier that powers modern society.

A neural-network design in which an encoder represents input data and a decoder uses that representation to produce an output.

Ensemble learning combines multiple predictive models to improve accuracy, stability, or uncertainty estimation through methods such as bagging, boosting, voting, and stacking.