Data & datasets

Mathematician Norbert Wiener is widely considered the founder of cybernetics, the study of regulatory systems. His 1948 book Cybernetics treated feedback formally and framed control and communication as one problem for animals and machines.

American mathematician and electrical engineer whose 1948 paper "A Mathematical Theory of Communication" gave communication engineering a mathematical foundation.

A framework that treats information as a measurable quantity to be encoded and transmitted, founded in Shannon's 1948 paper; it gave the field the bit, entropy and channel capacity.

C. E. Shannon's 1948 paper set out a mathematical framework for communication, defining information in bits, channel capacity, and entropy, and treating messages statistically while setting aside semantic meaning.

Published in 1992, this paper by Boser, Guyon and Vapnik presents a training algorithm that maximizes the margin between training patterns and the decision boundary, with experiments on optical character recognition.[^1]

A 1998 conference paper by Sergey Brin and Lawrence Page describing PageRank, an algorithm that ranks web pages by the number and quality of links pointing to them, and the search engine built on that algorithm.

A generative probabilistic model that represents each document as a finite mixture over topics, fitted with variational approximate inference and EM, and evaluated on document modelling, text classification and collaborative filtering.

CIFAR-10 is a labelled image dataset of 60000 32×32 colour images in 10 mutually exclusive classes, split into 50000 training and 10000 test images; it was created by Krizhevsky, Nair and Hinton for training and comparing image classifiers.

The 2009 paper introduced ImageNet, a large, cleanly labelled image database organised by the WordNet hierarchy. It later became the basis of an annual benchmark, the ImageNet Large Scale Visual Recognition Challenge.

IBM Watson is a question-answering computer system built by an IBM research team led by David Ferrucci; in February 2011 it defeated the two foremost all-time champions of the televised quiz show Jeopardy!

A 2011 paper by Glorot, Bordes and Bengio reporting that deep networks built from rectifying neurons match or beat tanh networks and reach their best performance without unsupervised pre-training on large labelled supervised tasks.

GloVe is a 2014 method that learns word vectors by factorising global word co-occurrence counts. Released together with pre-trained vectors, it became, alongside word2vec, one of the two standard ways of producing static word embeddings.

Retrieval-augmented generation (RAG) lets a language model fetch relevant documents from an external store and put them into its prompt before answering, so it can use knowledge beyond its training data and cite where an answer came from.

A 2021 paper from OpenAI introduces Codex, a GPT model fine-tuned on public GitHub code, and HumanEval, an evaluation set that judges generated Python programs by whether they work rather than by how closely they match a reference.

A family of foundation language models from Meta AI, released in 7B to 65B parameter sizes and trained on public data; its 13B model was reported to beat GPT-3 on most benchmarks.

A 2023 paper that introduces the Segment Anything project: a promptable segmentation task, a model, and a dataset of over 1 billion masks on 11 million images, all released to advance foundation models for computer vision.

A crowdsourced benchmark in which anonymous pairs of language models battle and users vote, producing an Elo leaderboard; launched in 2023 and relaunched as LMArena in 2025.

A benchmark of 2,294 real software engineering problems taken from GitHub issues and the pull requests that resolved them, in which a model must edit a codebase to fix an issue; on release the best model solved under 2% of them.

Cerebras's third-generation wafer-scale AI chip, announced in March 2024: 4 trillion transistors on one 46,225-square-millimetre piece of silicon, built to keep very large models in a single memory space.

NVIDIA's GB200 NVL72 is a liquid-cooled rack that links 72 Blackwell GPUs and 36 Grace CPUs into a single NVLink domain aimed at trillion-parameter AI. Announced at GTC in March 2024, it is the building block of NVIDIA's Blackwell-generation DGX SuperPOD.

TPU7x (Ironwood) is Google's seventh-generation TPU: 192 GB of high-bandwidth memory per chip, FP8 matrix units, 9,216 chips per pod, and a programming model that exposes two devices per chip.

A rack-scale NVIDIA system announced in March 2025 that links 72 Blackwell Ultra GPUs and 36 Grace CPUs into one unit, rated at 1.5 times the AI performance of GB200 NVL72.

AMD's data-center GPU for AI, introduced in June 2025 on the CDNA 4 architecture, with 288 GB of HBM3E memory and very high low-precision throughput aimed at generative AI inference, positioned against Nvidia's Blackwell generation.

AWS Trainium3 is AWS's first 3-nanometer AI chip, delivering 2.52 petaflops of FP8 compute per chip and scaling to 144 chips in a Trn3 UltraServer, available through Amazon EC2 from December 2025.[^1][^2]

NVIDIA Rubin is NVIDIA’s six-chip AI platform and the successor to Blackwell, announced at CES 2026. It targets lower-cost AI token generation, with partner products and cloud instances scheduled for 2026.

Supervised learning trains a model to map inputs to outputs using paired labelled examples, so that it can predict answers for new, unseen data. Its central measure of success is how well the model generalises beyond the training set.

ImageNet is a large, hierarchical image database built on the WordNet conceptual model and released at Princeton University in 2009. It holds more than 14 million images in 21,841 categories, with bounding-box annotations, and a 1,000-class subset used by the ILSVRC challenge.

NIST is a U.S. federal physical science laboratory founded in 1901 and now part of the Department of Commerce, which works on measurement science, standards and technology — and has recently become the home of the government's AI safety and evaluation work.

Judea Pearl is a computer scientist and statistician who invented Bayesian networks and their main inference algorithms, and later built a mathematical framework for causal inference from data. He received the 2011 ACM Turing Award.

A supervised maximum-margin model for classification and regression that separates classes by the widest possible margin and uses kernel functions to handle non-linear data.

Zhou Zhihua is a Chinese computer scientist at Nanjing University who works on ensemble learning, weakly supervised learning and multi-label learning. He founded the LAMDA institute there, serves as a university vice president, and was elected to the Chinese Academy of Sciences in 2025.

Stanford computer scientist Fei-Fei Li created ImageNet, the large-scale image dataset and challenge that gave data-driven computer vision a common benchmark, and has led AI research in both academia and industry.

Oracle is an American software company founded in 1977 that sells database software, enterprise applications, and cloud infrastructure and hardware. A 2025 disclosure of cloud commitments with OpenAI reshaped its standing among the world's largest companies.

Cyc is a hand-built knowledge base and ontology of common-sense knowledge, begun by Douglas Lenat in 1984 to give software the basic concepts and rules about how the world works.

A 1992 technical note by Watkins and Dayan that set out Q-learning, an incremental and computationally light way for an agent to learn optimal action values, and proved it converges to the optimum with probability 1.

Corinna Cortes and Vladimir Vapnik's 1995 paper presents a learning machine for two-group classification that maps inputs nonlinearly into a high-dimensional feature space, constructs a linear decision surface, and extends earlier work from separable to non-separable data.

AdaBoost, short for Adaptive Boosting, is a statistical classification meta-algorithm introduced by Yoav Freund and Robert Schapire in 1995. It reweights training examples, trains weak classifiers in sequence and combines their outputs into one weighted classifier.

LeCun, Bottou, Bengio and Haffner’s 1998 review argued that learning systems should replace hand-designed features. It showed that convolutional networks such as LeNet-5 outperformed other methods on handwritten digits, and it introduced the MNIST dataset.

Brin and Page's PageRank technical report treats hyperlinks as citations to rank web pages and was used to build Google's search engine; it is one of the heavily cited papers on their early work.

Salesforce is a San Francisco enterprise software company founded in 1999 that sells customer relationship management software by subscription, and has grown into one of the largest business software firms in the world.

Leo Breiman's 2001 paper defines random forests as combinations of tree predictors whose random vectors are sampled independently from the same distribution, analyzes generalization error through tree strength and correlation, and reports error rates competitive with Adaboost and more robust to noise.

Topic models represent documents as mixtures of latent topics, giving a compact statistical description of text collections. The 2003 Latent Dirichlet Allocation paper formalized this idea as a hierarchical Bayesian model.

Jeffrey Dean and Sanjay Ghemawat's 2004 OSDI paper presents MapReduce, a programming model in which users write a map function and a reduce function while the run-time system parallelizes the work across large clusters of commodity machines.

OWL is a W3C Recommendation published on 10 February 2004: a semantic markup language for publishing and sharing ontologies on the Web, giving software formal definitions of terms so it can process the meaning of information, not just display it.

Common Crawl is a nonprofit that has archived the public web since 2007 and gives its petabyte-scale datasets away for free. Its corpus became a core source of training data for large language models.

A 2009 technical report by Alex Krizhevsky that introduces CIFAR-10 and CIFAR-100 and studies multi-layer generative models for learning features from tiny colour images.

Wolfram|Alpha is a web service that answers questions by computing results rather than returning links. Built entirely in Mathematica, it went live in test mode on 15 May 2009 and was declared officially launched three days later.

Bing is Microsoft's consumer search brand, launched on 3 June 2009 and pitched as a "Decision Engine" meant to help people make faster, better-informed decisions in areas such as shopping, travel, health and local business.

An open-source Python library for statistical learning, begun in 2007, first released publicly on 1 February 2010 by a team at Inria, and now one of the most popular machine learning programs on GitHub.

The State Internet Information Office — the Cyberspace Administration of China — is the Chinese body that manages online information content, from news licensing and website enforcement to algorithm rules and AI content labeling.

A Google search feature announced in 2012 that models real-world entities and their relationships, so that search can work with "things, not strings".

Databricks is a San Francisco software company founded in 2013 by seven people behind Apache Spark, which grew out of the University of California, Berkeley AMPLab project [1][2]. Its valuation rose from $38 billion in 2021 to $188 billion in 2026 [4][11].

Nonprofit AI research institute founded by Paul Allen in 2014 and based in Seattle, known for Semantic Scholar and for the openly released OLMo family of language models, whose training data, code and checkpoints are published alongside the weights.

A 2014 paper introducing Microsoft COCO, a large dataset of everyday scenes in which common objects are labeled with per-instance segmentations, built to study object recognition as part of scene understanding.

Hugging Face is an AI platform company founded in 2016. Its Hub shares models, datasets and demo apps, its libraries include Transformers, and in September 2026 Nvidia agreed to acquire it.

A data company founded in 2016 that produces labeled and evaluated training data for AI models, counting OpenAI, Meta and Microsoft among its customers; in 2025 Meta took a 49% non-voting stake for more than $14 billion.

NVIDIA's DGX-1, unveiled in April 2016, packed eight Tesla P100 GPUs into a single chassis and was billed as the world's first deep learning supercomputer, turning accelerator count into one purchasable machine.

SQuAD is a reading-comprehension dataset of more than 100,000 crowdworker questions on Wikipedia articles, each answered by a span of its passage. Released in 2016, it gave machine comprehension a large, freely available benchmark with a measured human ceiling.

A New Jersey cloud company that began as an ethereum miner and became a large-scale GPU computing provider for AI, backed by NVIDIA and listed on Nasdaq in 2025 alongside a major OpenAI deal.

GLUE is a benchmark and analysis platform that evaluates natural language understanding models across a diverse set of existing tasks, with an online comparison platform and a hand-crafted diagnostic suite for linguistic analysis.

A free, open catalogue that links machine learning papers to their code and maintains benchmark leaderboards tracking the state of the art; built by Atlas ML, majority-owned by Facebook from December 2019, and relaunched as paperswithcode.co by Hugging Face in 2026.

SuperGLUE is a benchmark for general-purpose language understanding, released in 2019 as a harder successor to GLUE. It bundles eight tasks, a single-number metric, an analysis toolkit and a public leaderboard.

A 2019 paper that casts every language task as text-to-text, systematically compares transfer-learning choices, releases the C4 corpus and models, and reports state-of-the-art results on summarization, question answering, and classification.

T5 is a family of Google Transformer models that recasts every language task as text in, text out. Its 11-billion-parameter version set state of the art on several benchmarks, and the code, models and C4 dataset were released openly.

The Hugging Face Hub is the platform where people share machine-learning models, datasets and demo apps. By September 2026 it had more than 3 million models and 18 million users.

NVIDIA's third-generation DGX system, unveiled on 14 May 2020: eight A100 GPUs, 320 GB of GPU memory, five petaflops of rated AI performance, and the ability to split into as many as 56 instances.

EleutherAI is a grassroots AI research collective that began as a Discord server in July 2020. It released The Pile and the open GPT-style models GPT-Neo, GPT-J and GPT-NeoX-20B, and in 2023 it became a nonprofit research institute.

The Pile is an 825 GiB English text dataset of 22 combined sources, released by EleutherAI on 31 December 2020 with an arXiv paper. It became the training data for several of the group's open models.

A 2021 arXiv paper by Austin, Odena, Nye and eight other authors that introduced MBPP, a benchmark of 974 short Python programming tasks, and measured how language models from 244M to 137B parameters turn natural-language descriptions into working code.

NVIDIA's fourth-generation DGX system, announced in March 2022, packs eight H100 GPUs linked by NVLink plus BlueField-3 networking, and is rated at 32 petaflops of FP8 AI performance for enterprise AI infrastructure.

A DeepMind study showing that, for a fixed computing budget, language models should be trained on far more data than was then standard; its 70-billion-parameter Chinchilla beat larger contemporaries.

LAION-5B is an openly available dataset of 5.85 billion CLIP-filtered image-text pairs, released so that researchers outside a handful of labs could study and reproduce large vision-language models.

An open-source AI model community launched by Alibaba's DAMO Academy with partners in 2022, hosting hundreds of ready-to-use models and promoting "Model-as-a-Service".

A 2022 paper by Percy Liang, Rishi Bommasani, Tony Lee and many co-authors that ran 30 language models through the same 42 scenarios and seven metrics, turning scattered benchmarks into one standardized evaluation.

Huawei's Ascend 910B is a 2023 volume-production AI chip rated at 320 TFLOPS of FP16, with 32 GB of HBM2e memory, 1.6 TB/s of bandwidth and a 310 W envelope on a 7-nanometer domestic process.

A public Hugging Face leaderboard that ran a fixed set of benchmarks on open language models in one identical setup, from 2023 to 2025, so their scores could be reproduced and compared.

The Segment Anything Model (SAM) is a promptable image segmentation model Meta AI released in April 2023. Given a point, a box or a mask, it outlines the object the prompt refers to.

NVIDIA's H200, announced in November 2023, is a data center GPU that was the first to use HBM3e memory, offering 141 GB at 4.8 TB/s — nearly double the A100's capacity — and roughly double the H100's inference speed on Llama 2 70B.

Azure Maia 100 is Microsoft's first custom AI chip, announced in November 2023 and built to train and run large language models inside its own Azure data centres rather than being sold.

A 2023 Nature paper from researchers at Google DeepMind presenting a scaled-up deep-learning approach to predicting stable crystal structures, screening 2.2 million candidates and reporting a large jump in stability-prediction accuracy.

Google TPU v5p is a 2023 TPU generation with 459 teraflops of peak BF16 and FP8 compute per chip, 95 GiB of HBM, and 8,960 chips per pod.

OLMo is the Allen Institute for AI's open language model, released on 1 February 2024 in 1B and 7B sizes with its Dolma training data, training code, logs and checkpoints, all under Apache 2.0.

A benchmark for code LLMs that keeps collecting fresh contest problems from LeetCode, AtCoder and CodeForces, so that scores reflect ability rather than memorised test data, and that also tests code repair and execution reasoning.

Meta's second-generation in-house AI chip, published on 10 April 2024, built on TSMC's 5-nanometer process and aimed at the ranking and recommendation models Meta runs in its own data centers.

FineWeb is a 15-trillion-token pretraining dataset built from 96 Common Crawl snapshots and released in 2024 with its curation code and ablation models; its educational subset, FineWeb-Edu, improves results on knowledge- and reasoning-heavy benchmarks.

LMSYS (Large Model Systems) is a non-profit that started as a multi-university collaboration in 2023 and became an incorporated organisation in September 2024, building open models, serving systems and evaluation platforms such as Chatbot Arena, SGLang, FastChat and Vicuna.

A data-center GPU announced by AMD on 10 October 2024, built on third-generation CDNA with 256 GB of HBM3E memory at 6 TB/s and aimed at models with a trillion parameters.

Google TPU v6e, also called Trillium, is a Google Cloud AI accelerator whose published specifications cover per-chip BF16 and FP8 compute, high-bandwidth HBM, and pod-scale interconnects for large training jobs.

AWS Trainium2 is Amazon Web Services' second-generation machine-learning chip, sold through 16-chip EC2 Trn2 instances and 64-chip Trn2 UltraServers, and pitched by AWS as offering better price performance than comparable GPU instances.

Phi-4 is Microsoft's 14-billion-parameter dense decoder-only language model, built with a data-quality-first recipe heavy on synthetic data and released with full weights under the MIT licence in January 2025.

Arena Intelligence is the company formed in 2025 by the team behind Chatbot Arena; it runs the LMArena model-comparison platform and states that it has 250 million dollars in venture funding.

AMD Instinct MI350X is a data-center GPU in AMD's MI350 series, built on the CDNA 4 architecture with up to 288 GB of HBM3E memory, up to 8 TB/s of bandwidth and a 1,000 W board power.

Olmo 3 is the third generation of the Allen Institute for AI's fully open language models, announced on 20 November 2025 in 7B and 32B dense Transformer sizes, with a 65,536-token context and all datasets openly available.

The AMD Instinct MI400 is AMD's next-generation data-center GPU for AI, expected in 2026 with 432 GB of HBM4 memory, and designed to be deployed inside AMD's Helios rack-scale system.

Microsoft's Maia 200 is an inference accelerator introduced on 26 January 2026, built on TSMC's 3-nanometer process with 216 GB of HBM3e memory and more than 10 petaflops at FP4, aimed at serving models such as OpenAI's GPT-5.2.

Epoch AI's Benchmarking Hub collects benchmark results for leading AI models, including Epoch-run evaluations, and combines many scores into the Epoch Capabilities Index, a single general capability scale.

Overfitting is when a model fits its training data so closely that it performs poorly on new data. It is one half of the bias–variance trade-off and is usually spotted by comparing training and validation performance.

Regularization is the practice of making a problem's answer simpler, usually by adding a penalty term to the objective, so that ill-posed problems become solvable and models avoid overfitting.

The Semantic Web is a W3C standards effort that uses RDF and OWL to describe data so different applications, enterprises and communities can share and reuse information, moving from research toward practical tools.

Intel is a semiconductor company. Its 2017 purchase of Mobileye, later partly floated, and its decision to keep Falcon Shores as an internal test chip show how it enters and steps back from adjacent markets.

Snowflake is a cloud data platform company that also builds its own open language models; its Arctic model pairs a dense transformer with a sparse mixture of experts and shipped under Apache 2.0.

Synthetic data is training material written by a model rather than collected from people or the web. In the phi-1 experiments a small code model trained partly on model-generated "textbooks" reached competitive benchmark scores, suggesting data quality itself shapes capability.

Benchmark contamination means the test items a model is evaluated on, or close variants of them, were present in its pretraining data, so scores partly measure memory of seen text rather than the ability the benchmark is meant to probe.

A loss function turns the cost of a prediction into a single number, and training a model means making that number as small as possible. Its choice decides which mistakes a model is punished for.

Princeton University, chartered in 1746 as the College of New Jersey, is where Turing, McCarthy and Minsky took their doctorates and where Fei-Fei Li began ImageNet; seventeen Turing Award laureates have been affiliated with it.

k-means clustering splits n observations into k groups by repeatedly sending each point to its nearest centroid and recomputing each centroid as its cluster's mean. It is simple and scales to large data, but k must be fixed in advance.

American politician born in San Francisco in 1967; founder of the PlumpJack Group, former mayor of San Francisco, and California's 40th governor, who has both signed and declined to sign frontier-AI legislation.

A model validation technique that repeatedly splits data into folds, trains on some parts and validates on others, then averages the results to estimate how well a model or statistical result will generalise to unseen data.

The International Energy Agency is a Paris-based intergovernmental organisation founded in 1974 to respond to oil supply disruptions. It publishes the annual World Energy Outlook and, since 2024, has run work on energy and AI.

The UCI Letter Recognition dataset is an archive of 20,000 distorted capital-letter images, each reduced to 16 integer attributes, used to benchmark classification algorithms on a 26-class recognition task.

Informatica is an American software company founded in 1993 that sells enterprise cloud data management and data integration software, from data catalog and governance to master data management. It went public in 1999, was taken private in 2015, relisted in 2021, and was acquired by Salesforce in 2025.

A 1993 journal article by Mitchell P. Marcus, Beatrice Santorini and Mary Ann Marcinkiewicz describing the Penn Treebank: over one million words of Wall Street Journal text annotated with syntactic bracketing, built as a resource for work on English sentence structure.

American entrepreneur born in 1997 who co-founded Scale AI, a data-labelling and model-evaluation company, and became Meta's chief AI officer and leader of Meta Superintelligence Labs in 2025.

Apache Hadoop is an open-source framework for distributed storage and processing of big data, with the Hadoop Distributed File System for storage and MapReduce for processing; it grew out of the Nutch web-search project and was first released in 2006.

Kaggle is a data science competition platform and online community founded by Anthony Goldbloom in April 2010, where most submissions are scored instantly on a live leaderboard. Google acquired it in March 2017.

NVIDIA's Tesla K80 is a dual-GPU data-center accelerator announced in November 2014, putting two GK210 chips, 24 GB of memory and up to 8.74 teraflops of single-precision performance on one 300 W board.

British chip company founded in 2016 in Bristol that designs Intelligence Processing Units for AI workloads; it raised large sums, reported heavy losses, and became a SoftBank Group subsidiary in 2024.

The AI Now Institute is an American research institute, launched in 2017 in partnership with New York University, that studies the social implications of artificial intelligence and the concentration of power in the tech industry.

NVIDIA's DGX-2, announced in March 2018, packed 16 Tesla V100 GPUs and a new NVSwitch interconnect into one server that NVIDIA described as the first to reach two petaflops for deep learning.

Stanford's Institute for Human-Centered Artificial Intelligence, launched in 2019 and co-directed by Fei-Fei Li and John Etchemendy, studies AI's opportunities and risks and houses the Center for Research on Foundation Models.

A 2019 Google paper that frames every language task as text in, text out, using one encoder-decoder Transformer and one training setup. It also introduced the C4 dataset, and the released T5 models became common research starting points.

Epoch AI is a data-first research nonprofit studying the future of artificial intelligence, known for tracking AI training-compute trends, hosting databases on hardware and compute, and running independent model evaluations and its FrontierMath benchmark.

RedPajama is an open-source project that began by reproducing the LLaMA training dataset, releasing a 1.2-trillion-token corpus as a clean-room, fully open implementation.

MTIA v1 is Meta's first-generation in-house AI inference accelerator, built on TSMC's 7 nm process with 128 MB of on-chip memory and designed for deep learning recommendation models.

StarCoder is an open-access code language model of about 15.5 billion parameters, released on 4 May 2023 by the BigCode project led by Hugging Face and ServiceNow.

Phi-1 is a 1.3-billion-parameter Transformer language model for Python from Microsoft Research, which showed that filtered and synthetic "textbook quality" training data can partly substitute for model and data scale.

Phi-1.5 is a 1.3-billion-parameter language model from Microsoft Research, trained on curated synthetic "textbook" text rather than web crawls; its report claims natural-language performance comparable to models five times larger.

GNoME is a Google DeepMind deep-learning tool, announced on 29 November 2023, that predicts new inorganic crystals; it produced 2.2 million candidate structures, of which 380,000 were predicted to be stable.

Phi-2 is a 2.7-billion-parameter base language model from Microsoft Research, announced on 12 December 2023. Microsoft reported that it matched or beat models up to 25 times larger on complex benchmarks, making it a reference point for small-model training.

Dolma is an openly released English corpus of three trillion tokens, built from web text, papers, code, books, social media and Wikipedia, with a paper documenting both the dataset and the pipeline used to curate it.

Aya 101 is a 13-billion-parameter, instruction-tuned language model from Cohere For AI that follows instructions in 101 languages. It was released under Apache 2.0 in February 2024.

StarCoder2 is a family of open code models in 3B, 7B and 15B sizes, released in February 2024 by BigCode (Hugging Face and ServiceNow) with NVIDIA and trained on The Stack v2, published together with its dataset and training code.

OpenELM is a family of small open language models from Apple, published in four sizes from 270M to 3B parameters with its training framework, logs, checkpoints and MLX conversion code.

Nemotron-4 340B is a family of open models from NVIDIA, released on 14 June 2024 as base, instruct and reward models. It was built to generate synthetic data for training other language models, and it fits on a single eight-GPU DGX H100.

SmolLM is a family of small language models with 135M, 360M and 1.7B parameters, released openly by Hugging Face in July 2024 for on-device use and trained on a purpose-built corpus of generated textbooks, code and filtered web text.

SAM 2 is Meta's 2024 successor to the Segment Anything Model. It segments objects in both images and videos in real time, and its release included the SA-V video dataset.

Molmo is a family of open vision-language models released by Ai2 on 25 September 2024 that can point at objects in images, with weights, code, data and evaluations published openly.

SmolLM2 is Hugging Face's second family of small language models, released in November 2024 at 135M, 360M and 1.7B parameters, with open weights and a data-centric training recipe.

OLMo 2 is the second generation of the Allen Institute for AI's fully open language model family, released in 7B and 13B sizes with weights, data, code and training recipes all public.

Falcon Shores is an Intel chip that the company said in January 2025 it would use only as an internal test chip, never bringing it to market, while pointing to Jaguar Shores as its route into rack-scale AI data-centre systems.

The largest model in Ai2's OLMo 2 family: a 32-billion-parameter language model released with its data, code, weights and training details, and reported to outperform GPT-3.5-Turbo and GPT-4o mini on academic benchmarks.

An eight-terabyte collection of public domain and openly licensed text for pretraining language models, released together with two 7B-parameter models trained on it to show the data is enough.

SmolLM3 is a 3-billion-parameter open model released by Hugging Face on 8 July 2025. It supports six languages and up to 128K tokens of context, with switchable reasoning, under Apache 2.0.

DeepSeek-OCR is an open-source model of about 3B parameters that renders long text as 2D images and reads it back from a small number of vision tokens, testing whether optical compression can stand in for very long text contexts.

SAM 3 is Meta's segmentation model from November 2025. It finds, segments and tracks every instance of a concept named by a short text prompt or an example image, and Meta reported it doubles the accuracy of existing systems on this task.

NVIDIA Nemotron 3 is an open family of hybrid mixture-of-experts language models, announced in December 2025 in Nano, Super and Ultra sizes, with up to one million tokens of context.

Magnetar is an investment firm reporting more than $17 billion in assets under management as of mid-2026, working across alternative credit and fixed income, quantitative investing, and ventures.

Thomson Reuters describes itself as an AI and technology company supplying professionals with trusted content and workflow automation. It owns the Westlaw legal research database, and its copyright suit against Ross Intelligence became a closely watched test of fair use in AI training.

The NVIDIA Tesla T4 is a 70-watt, single-slot GPU built on the Turing architecture and aimed at AI inference, pairing Tensor Cores with dedicated video transcoding engines in a card that slots into ordinary servers.

The Xicloud C600 is a MetaX data-center GPU built on the company's own GPGPU architecture, offering very large ECC-protected memory, MetaXLink scaling up to 128-GPU supernodes, a 1,000 W OAM 2.0 module, and air or liquid cooling.

The NVIDIA A10 is a data-center GPU built on the Ampere architecture that combines graphics, video and AI inference in a single-slot PCIe card with 24 GB of memory, aimed at mainstream enterprise servers.

SambaNova SN40L is an AI chip that SambaNova calls a Reconfigurable Dataflow Unit, pairing dataflow-style parallel execution with a three-tier memory system spanning on-chip SRAM, co-packaged HBM and DDR DRAM.

GroqChip is Groq's AI processor, built for fully deterministic execution with no run-to-run variation. It pairs a 14-nanometer die with 230 MB of on-die SRAM, up to 750 INT8 TOPS and RealScale chip-to-chip links, and ships both as a chip and on the PCIe GroqCard.

Cerebras's second-generation wafer-scale chip, presented as the largest AI processor ever made: 2.6 trillion transistors, 850,000 cores and 40 GB of on-wafer memory on a single wafer built on a 7-nanometer process.

OASF is a standardized, extensible schema system from the AGNTCY project for describing AI agent attributes, skills and capabilities in machine-readable records, so that agents can discover and understand each other across protocols.

NVIDIA's DGX Spark is a desktop-sized AI computer built on the GB10 Grace Blackwell Superchip, pairing a 20-core Arm CPU with a Blackwell GPU and up to 128 GB of unified memory for running large models locally.

The FCC is the US agency that administers the Communications Act of 1934. In 2024 it confirmed that restrictions on artificial or prerecorded voices already cover AI-generated voices, and proposed a US$6 million robocall penalty.

Decision tree learning builds a classification or regression tree from data by repeatedly splitting it into more homogeneous groups. It is a widely used supervised method whose rules can be read directly, and whose main weaknesses are instability and a tendency to overfit.

Feature engineering is the preprocessing step that turns raw data into more useful inputs for machine learning and statistical models, shaping, cleaning and trimming the attributes a model learns from.

Cinematch was Netflix's own algorithm for predicting what rating a subscriber would give a film. Its accuracy became the baseline of the Netflix Prize, a $1 million contest to beat it by 10 percent.

An autoencoder is an unsupervised neural network that compresses its input into a low-dimensional code and reconstructs it back, learning a latent representation useful for dimensionality reduction, denoising and anomaly detection.

The NVIDIA Tesla P40 is a data-center GPU accelerator described in a September 2016 NVIDIA datasheet. Built on the Pascal architecture and aimed at deep learning inference, it pairs 24 GB of memory with 47 TOPS of INT8 throughput in a 250 W PCIe card.

Mistral OCR is a document-understanding API released by Mistral AI on 6 March 2025 that reads text, tables, equations and embedded media across thousands of scripts and languages, and was later superseded by newer OCR versions.

Nemotron Nano 2 is NVIDIA's 9B reasoning model, built on a hybrid Mamba-Transformer design and released with open weights and most of its training data; NVIDIA reports much higher throughput than similar-sized models.

Blackhole is Tenstorrent's AI accelerator chip; Galaxy Blackhole is a server holding 32 of them, rated at 23 PFLOPS of block FP8, with Ethernet links wiring chips and clusters together.

A data-center GPU on NVIDIA's Ampere architecture, aimed at AI inference and mainstream enterprise workloads, with 24 GB of HBM2 memory, a 165 W power ceiling and the ability to split into as many as four MIG instances.