Research history Models

IBM's chess computer that in May 1997 became the first computer system to defeat a reigning world chess champion in a match under standard tournament time controls.

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!

AlphaGo is a Go-playing program from DeepMind that combined deep neural networks with Monte Carlo tree search. In 2016 it beat top professional Lee Sedol in a widely watched match, a landmark for learned systems.

BERT is a language model from Google that reads text in both directions and is pre-trained by filling in masked words. Fine-tuned per task, it set new records on question answering and language-understanding benchmarks and later went into Google Search.

GPT-3 is an autoregressive language model with 175 billion parameters, described in a May 2020 paper. It could perform new tasks from a few examples in the prompt, without any gradient updates, and it was first offered through OpenAI's API.

RoseTTAFold is a three-track neural network for protein structure prediction, published by the Baker lab in 2021; its accuracy approached AlphaFold2's at CASP14, it models protein–protein complexes from sequence alone, and it is distributed as open code with a public web server.

InstructGPT is a family of GPT-3 models trained with reinforcement learning from human feedback so that they follow user instructions. OpenAI announced them in January 2022 and made them the default models in its API.

RT-1 is a transformer-based model for real-world robot control, trained on over 130,000 episodes collected with 13 robots and reported to perform more than 700 language instructions at a 97% success rate.

AlexNet is the eight-layer convolutional network that won the ILSVRC2012 image classification task by a wide margin, showing that a large network trained on a large labelled dataset with GPUs could beat hand-designed features.

A DeepMind reinforcement-learning agent that combined Q-learning with a convolutional neural network to play Atari 2600 games straight from raw pixels, reaching human-level performance on many titles without being told the rules.

A DeepMind generative model that predicts raw audio one sample at a time, bringing neural text-to-speech close to human quality and later powering Google Assistant and Cloud Text-to-Speech voices.

A DeepMind Go program that learned the game from scratch by playing against itself, using no human game records, and beat earlier versions of AlphaGo within days.

AlphaZero is a general game-playing system from DeepMind, described in a preprint in December 2017, that reached a superhuman level in chess, shogi and Go within 24 hours starting from random play, using only the rules of each game.

OpenAI Five was a team of five reinforcement-learning bots that played the video game Dota 2; in April 2019 it beat OG, the reigning world champions, in a best-of-three series.

GPT-1 was OpenAI's first generative pre-trained transformer, published in June 2018. It showed that one language model, pre-trained on unlabelled text and then fine-tuned, could improve results on many language tasks.

DeepMind's first protein-structure prediction system, which placed first overall in the 2018 CASP13 blind assessment by estimating probability distributions of distances between residues.

AlphaStar is a DeepMind system that plays StarCraft II. In 2019 it reached Grandmaster level with all three races, ranking above 99.8% of active players on Battle.net.

Baidu's first ERNIE pre-trained language model, released in March 2019, added entity- and phrase-level masking to BERT-style pre-training and reported state-of-the-art results on five Chinese NLP tasks.

AlphaFold2 is DeepMind's protein structure prediction system, announced in 2020. It reached accuracy competitive with experimental methods in many cases, and work in this area received half of the 2024 Nobel Prize in Chemistry.

LaMDA is a family of conversational language models that Google announced in 2021 and described in a 2022 paper, notable for its work on safety and factual grounding in dialogue and for later powering Bard.

GPT-J-6B is a six-billion-parameter open language model released by EleutherAI in June 2021 under the Apache 2.0 license. Its published scores sit close to GPT-3 Curie, making it a widely used free baseline for large-model research.

ERNIE 3.0 is a 10-billion-parameter pre-trained language model from 2021 that trains on plain text plus a large-scale knowledge graph and fuses auto-encoding and auto-regressive networks, so one model handles both language understanding and generation.

OpenAI Codex is a 2021 GPT language model fine-tuned on public code from GitHub. It translates natural language into code, and was introduced alongside the HumanEval benchmark for testing code correctness.

GPT-NeoX-20B is a 20-billion-parameter open autoregressive language model released by EleutherAI in February 2022, with weights under Apache 2.0 and its training code, described by its makers as the largest publicly accessible general-purpose model of its kind at the time.

PaLM is a 540-billion-parameter dense Transformer language model announced by Google Research in April 2022, trained with the Pathways system across 6,144 TPU v4 chips; it set strong few-shot results on many benchmarks.

Imagen is a text-to-image diffusion model announced by Google Research in May 2022, known for pairing a frozen large language model with diffusion and for the DrawBench benchmark; Google did not release it publicly.

BLOOM is a 176-billion-parameter open multilingual language model released on 12 July 2022 by the BigScience project, with weights available under a responsible-use license.

GLM-130B is a 130-billion-parameter open bilingual Chinese–English dense language model from Tsinghua University's KEG group and Zhipu AI, built to give researchers an open model that matches GPT-3 at the hundred-billion scale.

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.

EvoLLM-JP is a Japanese model from Sakana AI built by evolutionarily searching merges of three open-source models instead of training from scratch; the 7B result has mathematics ability none of its sources had alone.

Jamba, released by AI21 Labs on 28 March 2024, is an open-weights model that combines Mamba state-space layers with Transformer attention. It has 52 billion total parameters and a 256k-token context window.

ERNIE 2.0 is Baidu's pre-trained language model framework, published in 2019, which builds pre-training tasks incrementally through continual multi-task learning and reported results above BERT and XLNet on English and Chinese benchmarks.

MuZero is a DeepMind reinforcement-learning algorithm that pairs tree-based search with a learned model. It reached superhuman play in Atari, Go, chess and shogi without being given the environment's rules.

Meena is a 2.6-billion-parameter open-domain chatbot from Google, trained end-to-end on filtered social media conversations. Its 2020 paper introduced the SSA human-evaluation metric and linked next-token perplexity to human-judged chat quality. The work was later renamed LaMDA.

GPT-Neo is a family of open text-generation models released by EleutherAI in March 2021 at 125M, 1.3B and 2.7B parameters, built to replicate the GPT-3 architecture and distributed with freely downloadable weights under an MIT license.

Gopher is DeepMind's 280-billion-parameter language model, announced in December 2021 alongside a study of what scale does to language models and a companion taxonomy of their risks.

ERNIE 3.0 Titan is a Chinese pre-trained language model with over 260 billion parameters, described by its authors as the largest dense Chinese model of its time and reported to beat state-of-the-art systems on 68 NLP datasets.

AlphaCode is a DeepMind code-generation system for competitive programming, announced in February 2022. On Codeforces contests held after its training data, it reached an estimated rank around the median participant. A Gemini-based AlphaCode 2 followed in December 2023.

Flamingo is a DeepMind visual language model announced in April 2022. It takes prompts that mix images, videos and text and replies in language, learning new tasks from only a few examples.

OPT-175B is a 175-billion-parameter language model from Meta AI, announced in May 2022. Meta said it was the first model of its size released with both pretrained weights and the code to train and use them.

Gato is a generalist agent from DeepMind, announced on 12 May 2022. A single network with one set of weights handles text, images, Atari games and robot control, aiming at multi-task, multi-embodiment learning.

Galactica is a large language model for science, unveiled by Meta AI and Papers with Code in November 2022. Its public demo was taken down about three days later, after users found it produced fake papers.

Pythia is a suite of 16 open language models from EleutherAI, ranging from 70 million to 12 billion parameters, all trained on the same data in the same order, with 154 public checkpoints from each training run.

A Google DeepMind generative world model with 11 billion parameters that turns text, images or sketches into controllable virtual worlds, described as the first generative interactive environment trained on unlabelled internet videos without action labels.

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.