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If you wanna have a match that's comparable you have to have Stockfish running on a supercomputer as well." Top US correspondence chess player Wolff Morrow was also unimpressed, claiming that AlphaZero would probably not make the semifinals of a fair competition such as TCEC where all engines play on equal hardware. Mo... | Wikipedia - AlphaZero - Preliminary results > Reaction and criticism | 195 | 846 | null |
Section: Final results > Chess. In the final results, Stockfish 9 dev ran under the same conditions as in the TCEC superfinal: 44 CPU cores, Syzygy endgame tablebases, and a 32 GB hash size. Instead of a fixed time control of one move per minute, both engines were given 3 hours plus 15 seconds per move to finish the ga... | Wikipedia - AlphaZero - Final results > Chess | 164 | 661 | null |
Section: Final results > Reactions and criticisms. Human grandmasters were generally impressed with AlphaZero's games against Stockfish. Former world champion Garry Kasparov said it was a pleasure to watch AlphaZero play, especially since its style was open and dynamic like his own. In the computer chess community, Kom... | Wikipedia - AlphaZero - Final results > Reactions and criticisms | 330 | 1,586 | null |
Section: Company Founding. Cortica was founded in 2007 by Igal Raichelgauz, Karina Odinaev and Yehoshua Zeevi. Together, the founders developed the company’s core technology while at Technion – Israel Institute of Technology. By combining discoveries in neuroscience with developments in computer programming, the team c... | Wikipedia - Cortica - Company Founding | 252 | 1,362 | null |
Section: Research and Technology. In 2006, Founders Raichelgauz, Odinaev, and Zeevi shared their findings with the 28th IEEE EMBS Annual International Conference in New York in a paper titled, “Natural Signal Classification by Neural Cliques and Phase-Locked Attractors”. That same year, the team also published “Cliques... | Wikipedia - Cortica - Research and Technology | 160 | 792 | null |
Section: Funding. Cortica raised $7 million in its Series A funding round, announced in August 2012. Investors included Horizons Ventures (the investment firm of Hong Kong billionaire Li Ka-Shing), and Ynon Kreiz, the former chairman and CEO of the Endemol Group. In May 2013, it was announced that Cortica had raised $1... | Wikipedia - Cortica - Funding | 203 | 915 | null |
Article: COTSBot. COTSBot is a small autonomous underwater vehicle (AUV) 4.5 feet (1.4 m) long, which is designed by Queensland University of Technology (QUT) to kill the very destructive crown-of-thorns starfish (Acanthaster planci) in the Great Barrier Reef off the north-east coast of Australia. It identifies its tar... | Wikipedia - COTSBot - Summary | 253 | 1,083 | null |
Article: Darkforest. Darkforest is a computer go program developed by Meta Platforms, based on deep learning techniques using a convolutional neural network. Its updated version Darkfores2 combines the techniques of its predecessor with Monte Carlo tree search. The MCTS effectively takes tree search methods commonly se... | Wikipedia - Darkforest - Summary | 156 | 764 | null |
Section: Style of play. Darkforest uses a neural network to sort through the 10100 board positions, and find the most powerful next move. However, neural networks alone cannot match the level of good amateur players or the best search-based Go engines, and so Darkfores2 combines the neural network approach with a searc... | Wikipedia - Darkforest - Style of play | 200 | 987 | null |
Section: Program architecture. The family of Darkforest computer go programs is based on convolution neural networks. The most recent advances in Darkfmcts3 combined convolutional neural networks with more traditional Monte Carlo tree search. Darkfmcts3 is the most advanced version of Darkforest, which combines Faceboo... | Wikipedia - Darkforest - Program architecture | 327 | 1,565 | null |
Extended play additionally considers the border (binary plane that is true at the border), position mask (represented as distance from the board center, i.e. x ( − 0.5 ∗ d i s t a n c e 2 ) {\displaystyle x^{(-0.5*distance^{2})}} , where x {\displaystyle x} is a real number at a position), and each player's territory (... | Wikipedia - Darkforest - Program architecture | 329 | 1,435 | null |
Section: Comparison with other systems. Darkfores2 beats Darkforest, its neural network-only predecessor, around 90% of the time, and Pachi, one of the best search-based engines, around 95% of the time. On the Kyu rating system, Darkforest holds a 1-2d level. Darkfores2 achieves a stable 3d level on KGS Go Server as a ... | Wikipedia - Darkforest - Comparison with other systems | 184 | 655 | null |
Section: History and background. While mobile robots had been in existence since the 1960s, (e.g. Shakey), progress in creating robots that could navigate on their own, outdoors, off-road, on irregular, obstacle-rich terrain had been slow. In fact, no clear metrics were in place to measure progress. A baseline understa... | Wikipedia - DARPA LAGR Program - History and background | 274 | 1,399 | null |
Section: Structure and rationale of the LAGR program. The LAGR program was designed to focus on developing new science for robot perception and control rather than on new hardware. Thus, it was decided to create a fleet of identical, relatively simple robots that would be supplied to the LAGR researchers, who were memb... | Wikipedia - DARPA LAGR Program - Structure and rationale of the LAGR program | 320 | 1,706 | null |
Thus, for example, they were able to compare the performance of their own obstacle detection module with that of the Baseline code, while holding everything else fixed. The Baseline code also served as a fixed reference – in any environment and at any time in the program, teams’ code could be compared to the Baseline c... | Wikipedia - DARPA LAGR Program - Structure and rationale of the LAGR program | 157 | 856 | null |
Section: Structure and rationale of the LAGR program > Phase II. To advance to Phase II, each team had to modify the Baseline code so that on the final 3 tests of Phase I of the government tests, robots running the team's code averaged at least 10% faster than a vehicle running the original Baseline code. This rather m... | Wikipedia - DARPA LAGR Program - Structure and rationale of the LAGR program > Phase II | 196 | 938 | null |
Section: The LAGR teams. Eight teams were selected as performers in Phase I, the first 18 months of LAGR. The teams were from Applied Perception (Principal Investigator [PI] Mark Ollis), Georgia Tech (PI Tucker Balch), Jet Propulsion Laboratory (PI Larry Matthies), Net-Scale Technologies (PI Urs Muller), NIST (PI James... | Wikipedia - DARPA LAGR Program - The LAGR teams | 196 | 865 | null |
Section: The LAGR vehicle. The LAGR vehicle, which was about the size of a supermarket shopping cart, was designed to be simple to control. (A companion DARPA program, Learning Locomotion, addressed complex motor control.) It was battery powered and had two independently driven wheelchair motors in the front, and two c... | Wikipedia - DARPA LAGR Program - The LAGR vehicle | 254 | 1,268 | null |
Section: Scientific results. A cornerstone of the program was incorporation of learned behaviors in the robots. In addition, the program used passive optical systems to accomplish long-range scene analysis. The difficulty of testing UGV navigation in unstructured, off-road environments made accurate, objective measurem... | Wikipedia - DARPA LAGR Program - Scientific results | 309 | 1,669 | null |
LAGR also had the goal of expanding the number of performers and removing the need for large system integration so that valuable technology nuggets created by small teams could be recognized and then adopted by the larger community. Some teams developed rapid methods for learning with a human teacher: a human could Rad... | Wikipedia - DARPA LAGR Program - Scientific results | 235 | 1,154 | null |
Article: Diffbot. Diffbot is a developer of machine learning and computer vision algorithms and public APIs for extracting data from web pages / web scraping to create a knowledge base. The company has gained interest from its application of computer vision technology to web pages, wherein it visually parses a web page... | Wikipedia - Diffbot - Summary | 337 | 1,681 | null |
Article: Direct3D. Direct3D is a graphics application programming interface (API) for Microsoft Windows. Part of DirectX, Direct3D is used to render three-dimensional graphics in applications where performance is important, such as games. Direct3D uses hardware acceleration if available on the graphics card, allowing f... | Wikipedia - Direct3D - Summary | 338 | 1,768 | null |
For example, if software programmed using Direct3D requires pixel shaders and the video card on the user's computer does not support that feature, Direct3D will not emulate it, although it will compute and render the polygons and textures of the 3D models, albeit at a usually degraded quality and performance compared t... | Wikipedia - Direct3D - Summary | 290 | 1,316 | null |
Direct3D 6.0 – Multitexturing Direct3D 7.0 – Hardware Transformation, Clipping and Lighting (TCL/T&L), DXVA 1.0 Direct3D 8.0 – Pixel Shader 1.0/1.1 & Vertex Shader 1.0/1.1 Direct3D 8.1 – Pixel Shader 1.2/1.3/1.4 Direct3D 9.0 – Shader Model 2.0 (Pixel Shader 2.0 & Vertex Shader 2.0) Direct3D 9.0a – Shader Model 2.0a (Pi... | Wikipedia - Direct3D - Overview | 350 | 1,025 | null |
Section: Direct3D 2.0 and 3.0. In 1992, Servan Keondjian, Doug Rabson and Kate Seekings started a company named RenderMorphics, which developed a 3D graphics API named Reality Lab, which was used in medical imaging and CAD software. Two versions of this API were released. Microsoft bought RenderMorphics in February 199... | Wikipedia - Direct3D - Direct3D 2.0 and 3.0 | 317 | 1,459 | null |
Execute buffers were intended to be allocated in hardware memory and parsed by the hardware to perform the 3D rendering. They were considered extremely awkward to program at the time, however, hindering adoption of the new API and prompting calls for Microsoft to adopt OpenGL as the official 3D rendering API for games ... | Wikipedia - Direct3D - Direct3D 2.0 and 3.0 | 155 | 746 | null |
Section: Direct3D 5.0. In December 1996, a team in Redmond took over development of the Direct3D Immediate Mode, while the London-based RenderMorphics team continued work on the Retained Mode. The Redmond team added the DrawPrimitive API that eliminated the need for applications to construct execute buffers, making Dir... | Wikipedia - Direct3D - Direct3D 5.0 | 157 | 731 | null |
Section: Direct3D 6.0. DirectX 6.0 (released in August, 1998) introduced numerous features to cover contemporary hardware (such as multitexture and stencil buffers) as well as optimized geometry pipelines for x87, SSE and 3DNow! and optional texture management to simplify programming. Direct3D 6.0 also included support... | Wikipedia - Direct3D - Direct3D 6.0 | 264 | 1,251 | null |
Section: Direct3D 7.0. DirectX 7.0 (released in September, 1999) introduced the .dds texture format and support for transform and lighting hardware acceleration (first available on PC hardware with Nvidia's GeForce 256), as well as the ability to allocate vertex buffers in hardware memory. Hardware vertex buffers repre... | Wikipedia - Direct3D - Direct3D 7.0 | 153 | 734 | null |
Section: Direct3D 8.0. DirectX 8.0 (released in November, 2000) introduced programmability in the form of vertex and pixel shaders, enabling developers to write code without worrying about superfluous hardware state. The complexity of the shader programs depended on the complexity of the task, and the display driver co... | Wikipedia - Direct3D - Direct3D 8.0 | 221 | 1,069 | null |
Section: Direct3D 9 > Direct3D 9Ex. Direct3D 9Ex (previously versioned 9.0L, with "L" standing for Longhorn, the codename for Windows Vista), an extension only available on Windows Vista and newer, allows the use of the advantages offered by Windows Vista's Windows Display Driver Model (WDDM) and is used for Windows Ae... | Wikipedia - Direct3D - Direct3D 9 > Direct3D 9Ex | 159 | 702 | null |
Section: Direct3D 10. Windows Vista includes a major update to the Direct3D API. Originally called WGF 2.0 (Windows Graphics Foundation 2.0), then DirectX 10 and DirectX Next, Direct3D 10 features an updated shader model 4.0 and optional interruptibility for shader programs. In this model shaders still consist of fixed... | Wikipedia - Direct3D - Direct3D 10 | 318 | 1,576 | null |
Instead, it defines a minimum standard of hardware capabilities which must be supported for a display system to be "Direct3D 10 compatible". This is a significant departure, with the goal of streamlining application code by removing capability-checking code and special cases based on the presence or absence of specific... | Wikipedia - Direct3D - Direct3D 10 | 180 | 938 | null |
Section: Direct3D 10 > Direct3D 10.0. Direct3D 10.0 level hardware must support the following features: the ability to process entire primitives in the new geometry-shader stage, the ability to output pipeline-generated vertex data to memory using the stream-output stage, multisampled alpha-to-coverage support, readbac... | Wikipedia - Direct3D - Direct3D 10 > Direct3D 10.0 | 321 | 1,527 | null |
The common shader core provides a full set of IEEE-compliant 32-bit integer and bitwise operations. These operations enable a new class of algorithms in graphics hardware—examples include compression and packing techniques, FFTs, and bitfield program-flow control. Geometry shaders, which work on adjacent triangles whic... | Wikipedia - Direct3D - Direct3D 10 > Direct3D 10.0 | 172 | 860 | null |
Section: Direct3D 10 > Direct3D 10.1. Direct3D 10.1 was announced by Microsoft shortly after the release of Direct3D 10 as a minor update. The specification was finalized with the release of November 2007 DirectX SDK and the runtime was shipped with the Windows Vista SP1, which is available since mid-March 2008. Direct... | Wikipedia - Direct3D - Direct3D 10 > Direct3D 10.1 | 290 | 1,427 | null |
Direct3D 10.1 level hardware must support the following features: Multisampling has been enhanced to generalize coverage based transparency and make multisampling work more effectively with multi-pass rendering, better culling behavior – Zero-area faces are automatically culled; this affects wireframe rendering only, i... | Wikipedia - Direct3D - Direct3D 10 > Direct3D 10.1 | 347 | 1,554 | null |
Blending is also required for UNORM16/SNORM16/SNORM8 formats. Format Conversion while copying between certain 32/64/128 bit prestructured, typed resources and compressed representations of the same bit widths. Mandatory support for 4x MSAA for all render targets except R32G32B32A32 and R32G32B32. Shader model 4.1 Unlik... | Wikipedia - Direct3D - Direct3D 10 > Direct3D 10.1 | 233 | 963 | null |
Section: Direct3D 11. Direct3D 11 was released as part of Windows 7. It was presented at Gamefest 2008 on July 22, 2008 and demonstrated at the Nvision 08 technical conference on August 26, 2008. The Direct3D 11 Technical Preview has been included in November 2008 release of DirectX SDK. AMD previewed working DirectX11... | Wikipedia - Direct3D - Direct3D 11 | 320 | 1,395 | null |
Section: Direct3D 11 > Direct3D 11.0. Direct3D 11.0 features include: Support for Shader Model 5.0, Dynamic shader linking, addressable resources, additional resource types, subroutines, geometry instancing, coverage as pixel shader input, programmable interpolation of inputs, new texture compression formats (1 new LDR... | Wikipedia - Direct3D - Direct3D 11 > Direct3D 11.0 | 326 | 1,371 | null |
Section: Direct3D 11 > Direct3D 11.1. Direct3D 11.1 is an update to the API that ships with Windows 8. The Direct3D runtime in Windows 8 features DXGI 1.2 and requires new WDDM 1.2 device drivers. Preliminary version of the Windows SDK for Windows 8 Developer Preview was released on September 13, 2011. The new API feat... | Wikipedia - Direct3D - Direct3D 11 > Direct3D 11.1 | 308 | 1,347 | null |
The new API features shader tracing and HLSL compiler enhancements, support for minimum precision HLSL scalar data types, UAVs (Unordered Access Views) at every pipeline stage, target-independent rasterization (TIR), option to map SRVs of dynamic buffers with NO_OVERWRITE, shader processing of video resources, option t... | Wikipedia - Direct3D - Direct3D 11 > Direct3D 11.1 | 311 | 1,426 | null |
Section: Direct3D 11 > Direct3D 11.2. Direct3D 11.2 was shipped with Windows 8.1. New hardware features require DXGI 1.3 with WDDM 1.3 drivers and include runtime shader modification and linking, Function linking graph(FLG), inbox HLSL compiler, option to annotate graphics commands. Feature levels 11_0 and 11_1 introdu... | Wikipedia - Direct3D - Direct3D 11 > Direct3D 11.2 | 293 | 1,236 | null |
Section: Direct3D 12. Direct3D 12 allows a lower level of hardware abstraction than earlier versions, enabling future applications to significantly improve multithreaded scaling and decrease CPU utilization. This is achieved by better matching the Direct3D abstraction layer with the underlying hardware, through new fea... | Wikipedia - Direct3D - Direct3D 12 | 342 | 1,654 | null |
Pipeline state objects (PSOs) have evolved from Direct3D 11, and the new concise pipeline states mean that the process has been simplified. DirectX 11 offered flexibility in how its states could be altered, to the detriment of performance. Simplifying the process and unifying the pipelines (e.g. pixel shader states) le... | Wikipedia - Direct3D - Direct3D 12 | 325 | 1,611 | null |
Within DirectX 12 these commands are sent as command lists, containing all the required information within a single package. The GPU is then capable of computing and executing this command in one single process, without having to wait on any additional information from the CPU. Within these command lists are bundles. W... | Wikipedia - Direct3D - Direct3D 12 | 303 | 1,556 | null |
An experimental support of D3D 12 for Windows 7 SP1 has been released by Microsoft in 2019 via a dedicated NuGet package. Direct3D 12 version 1607 – With the Windows 10 anniversary update (version 1607), released on August 2, 2016, the Direct3D 12 runtime has been updated to support constructs for explicit multithreadi... | Wikipedia - Direct3D - Direct3D 12 | 301 | 1,355 | null |
Section: Architecture. Direct3D is a Microsoft DirectX API subsystem component. The aim of Direct3D is to abstract the communication between a graphics application and the graphics hardware drivers. It is presented like a thin abstract layer at a level comparable to GDI (see attached diagram). Direct3D contains numerou... | Wikipedia - Direct3D - Architecture | 328 | 1,684 | null |
Rendering occurs in the back buffer. Moreover, devices contain a collection of resources; specific data used during rendering. Each resource has four attributes: Type: Determines the type of resource: surface, volume, texture, cube texture, volume texture, surface texture, index buffer or vertex buffer. Pool: Describes... | Wikipedia - Direct3D - Architecture | 338 | 1,672 | null |
Section: Pipeline. The Microsoft Direct3D 11 API defines a process to convert a group of vertices, textures, buffers, and state into an image on the screen. This process is described as a rendering pipeline with several distinct stages. The different stages of the Direct3D 11 pipeline are: Input-Assembler: Reads in ver... | Wikipedia - Direct3D - Pipeline | 321 | 1,516 | null |
Section: Feature levels. In Direct3D 5 to 9, when new versions of the API introduced support for new hardware capabilities, most of them were optional – each graphics vendor maintained their own set of supported features in addition to the basic required functionality. Support for individual features had to be determin... | Wikipedia - Direct3D - Feature levels | 313 | 1,553 | null |
This approach allows developers to unify the rendering pipeline and use a single version of the API on both newer and older hardware, taking advantage of performance and usability improvements in the newer runtime. New feature levels are introduced with updated versions of the API and typically encapsulate: major manda... | Wikipedia - Direct3D - Feature levels | 244 | 1,096 | null |
Section: Feature levels > Direct3D 12 levels. Direct3D 12 for Windows 10 requires graphics hardware conforming to feature levels 11_0 and 11_1 which support virtual memory address translations and requires WDDM 2.0 drivers. There are two new feature levels, 12_0 and 12_1, which include some new features exposed by Dire... | Wikipedia - Direct3D - Feature levels > Direct3D 12 levels | 274 | 1,245 | null |
Section: Multithreading. WDDM driver model in Windows Vista and higher supports arbitrarily large number of execution contexts (or threads) in hardware or in software. Windows XP only supported multitasked access to Direct3D, where separate applications could execute in different windows and be hardware accelerated, an... | Wikipedia - Direct3D - Multithreading | 335 | 1,684 | null |
Section: Alternative implementations. The following alternative implementations of Direct3D API exist. They are useful for non-Windows platforms and for hardware without some versions of DX support: WineD3D – The Wine open source project has working implementations of the Direct3D APIs via translation to OpenGL. Wine's... | Wikipedia - Direct3D - Alternative implementations | 332 | 1,379 | null |
Section: Related tools > D3DX. Direct3D comes with D3DX, a library of tools designed to perform common mathematical calculations on vectors, matrices and colors, calculating look-at and projection matrices, spline interpolations, and several more complicated tasks, such as compiling or assembling shaders used for 3D gr... | Wikipedia - Direct3D - Related tools > D3DX | 338 | 1,721 | null |
Section: History. Dr.Fill participated in the 2012 American Crossword Puzzle Tournament, finishing 141st of approximately 650 entrants with a total score of just over 10,000 points. The appearance led to a variety of descriptions of Dr.Fill in the popular press, including The Economist, the San Francisco Chronicle and ... | Wikipedia - Dr.Fill - History | 312 | 1,355 | null |
The 2020 ACPT was cancelled due to COVID-19, and Dr.Fill participated as a non-competitor in the Boswords tournament instead. The program outperformed the humans, scoring 11,218 points (fast solves with a total of one mistake) while the best scoring human scored 10,994 points (slower solves but no mistakes). The 2021 A... | Wikipedia - Dr.Fill - History | 216 | 910 | null |
Section: Algorithm. As described by Ginsberg, Dr.Fill works by converting a crossword to a weighted constraint satisfaction problem and then attempting to maximize the probability that the fill is correct. Probabilities for individual words or phrases in the puzzle are computed using relatively simple statistical techn... | Wikipedia - Dr.Fill - Algorithm | 240 | 1,220 | null |
Section: Overview. DREAM Challenges were founded in 2006 by Gustavo Stolovizky from IBM Research and Andrea Califano from Columbia University. Current chair of the DREAM organization is Paul Boutros from University of California. Further organization spans emeritus chairs Justin Guinney and Gustavo Stolovizky, and mult... | Wikipedia - DREAM Challenges - Overview | 348 | 1,914 | null |
Section: Participation. During DREAM challenges, participants typically build models on provided data, and submit predictions or models that are then validated on held-out data by the organizers. While DREAM challenges avoid leaking validation data to participants, there are typically mid-challenge submission leaderboa... | Wikipedia - DREAM Challenges - Participation | 196 | 1,070 | null |
Section: Technology. Pretrained text-to-image diffusion models, while often capable of offering a diverse range of different image output types, lack the specificity required to generate images of lesser-known subjects, and are limited in their ability to render known subjects in different situations and contexts. The ... | Wikipedia - DreamBooth - Technology | 223 | 1,104 | null |
Section: Usage. DreamBooth can be used to fine-tune models such as Stable Diffusion, where it may alleviate a common shortcoming of Stable Diffusion not being able to adequately generate images of specific individual people. Such a use case is quite VRAM intensive, however, and thus cost-prohibitive for hobbyist users.... | Wikipedia - DreamBooth - Usage | 228 | 1,171 | null |
Section: History. EleutherAI began as a Discord server on July 7, 2020, under the tentative name "LibreAI" before rebranding to "EleutherAI" later that month, in reference to eleutheria, the Greek word for liberty. Its founding members are Connor Leahy, Len Gao, and Sid Black. They co-wrote the code for Eleuther to ser... | Wikipedia - EleutherAI - History | 340 | 1,510 | null |
While EleutherAI initially turned down funding offers, preferring to use Google's TPU Research Cloud Program to source their compute, by early 2021 they had accepted funding from CoreWeave (a small cloud computing company) and SpellML (a cloud infrastructure company) in the form of access to powerful GPU clusters that ... | Wikipedia - EleutherAI - History | 314 | 1,488 | null |
While EleutherAI is still committed to promoting access to AI technologies, they feel that "there is substantially more interest in training and releasing LLMs than there once was," enabling them to focus on other projects. In July 2024, an investigation by Proof news found that EleutherAI's The Pile dataset includes s... | Wikipedia - EleutherAI - History | 150 | 712 | null |
Section: Research > The Pile. The Pile is an 886 GB dataset designed for training large language models. It was originally developed to train EleutherAI's GPT-Neo models but has become widely used to train other models, including Microsoft's Megatron-Turing Natural Language Generation, Meta AI's Open Pre-trained Transf... | Wikipedia - EleutherAI - Research > The Pile | 177 | 799 | null |
Section: Research > GPT models. EleutherAI's most prominent research relates to its work to train open-source large language models inspired by OpenAI's GPT-3. EleutherAI's "GPT-Neo" model series has released 125 million, 1.3 billion, 2.7 billion, 6 billion, and 20 billion parameter models. GPT-Neo (125M, 1.3B, 2.7B): ... | Wikipedia - EleutherAI - Research > GPT models | 307 | 1,217 | null |
Section: Research > VQGAN-CLIP. Following the release of DALL-E by OpenAI in January 2021, EleutherAI started working on text-to-image synthesis models. When OpenAI did not release DALL-E publicly, EleutherAI's Katherine Crowson and digital artist Ryan Murdock developed a technique for using CLIP (another model develop... | Wikipedia - EleutherAI - Research > VQGAN-CLIP | 187 | 858 | null |
Section: Public reception > Praise. EleutherAI's work to democratize GPT-3 won the UNESCO Netexplo Global Innovation Award in 2021, InfoWorld's Best of Open Source Software Award in 2021 and 2022, was nominated for VentureBeat's AI Innovation Award in 2021. Gary Marcus, a cognitive scientist and noted critic of deep le... | Wikipedia - EleutherAI - Public reception > Praise | 192 | 851 | null |
Section: History. The Google Brain project began in 2011 as a part-time research collaboration between Google fellow Jeff Dean and Google Researcher Greg Corrado. Google Brain started as a Google X project and became so successful that it was graduated back to Google: Astro Teller has said that Google Brain paid for th... | Wikipedia - Google Brain - History | 218 | 1,098 | null |
Section: Team and location. Google Brain was initially established by Google Fellow Jeff Dean and visiting Stanford professor Andrew Ng. In 2014, the team included Jeff Dean, Quoc Le, Ilya Sutskever, Alex Krizhevsky, Samy Bengio, and Vincent Vanhoucke. In 2017, team members included Anelia Angelova, Samy Bengio, Greg C... | Wikipedia - Google Brain - Team and location | 306 | 1,183 | null |
Section: Projects > Image enhancement. In February 2017, Google Brain determined a probabilistic method for converting pictures with 8x8 resolution to a resolution of 32x32. The method built upon an already existing probabilistic model called pixelCNN to generate pixel translations. The proposed software utilizes two n... | Wikipedia - Google Brain - Projects > Image enhancement | 192 | 975 | null |
Section: Projects > Google Translate. The Google Brain team contributed to the Google Translate project by employing a new deep learning system that combines artificial neural networks with vast databases of multilingual texts. In September 2016, Google Neural Machine Translation (GNMT) was launched, an end-to-end lear... | Wikipedia - Google Brain - Projects > Google Translate | 348 | 1,851 | null |
This means that it is possible to translate speech in one language directly into text in another language, without first transcribing it to text. According to the Researchers at Google Brain, this intermediate step can be avoided using neural networks. In order for the system to learn this, they exposed it to many hour... | Wikipedia - Google Brain - Projects > Google Translate | 161 | 864 | null |
Section: Projects > Robotics. Aiming to improve traditional robotics control algorithms where new skills of a robot need to be hand-programmed, robotics researchers at Google Brain are developing machine learning techniques to allow robots to learn new skills on their own. They also attempt to develop ways for informat... | Wikipedia - Google Brain - Projects > Robotics | 313 | 1,814 | null |
Section: Reception > Controversies. In December 2020, AI ethicist Timnit Gebru left Google. While the exact nature of her quitting or being fired is disputed, the cause of the departure was her refusal to retract a paper entitled "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" and a related ulti... | Wikipedia - Google Brain - Reception > Controversies | 344 | 1,652 | null |
While Bengio's announcement focused on personal growth as his reason for leaving, anonymous sources indicated to Reuters that the turmoil within the AI ethics team played a role in his considerations. In March 2022, Google fired AI researcher Satrajit Chatterjee after he questioned the findings of a paper published in ... | Wikipedia - Google Brain - Reception > Controversies | 163 | 855 | null |
Article: Google DeepMind. DeepMind Technologies Limited, trading as Google DeepMind or simply DeepMind, is a British–American artificial intelligence research laboratory which serves as a subsidiary of Alphabet Inc. Founded in the UK in 2010, it was acquired by Google in 2014 and merged with Google AI's Google Brain di... | Wikipedia - Google DeepMind - Summary | 330 | 1,748 | null |
Section: History. The start-up was founded by Demis Hassabis, Shane Legg and Mustafa Suleyman in November 2010. Hassabis and Legg first met at the Gatsby Computational Neuroscience Unit at University College London (UCL). Demis Hassabis has said that the start-up began working on artificial intelligence technology by t... | Wikipedia - Google DeepMind - History | 346 | 1,702 | null |
In 2014, DeepMind received the "Company of the Year" award from Cambridge Computer Laboratory. In September 2015, DeepMind and the Royal Free NHS Trust signed their initial information sharing agreement to co-develop a clinical task management app, Streams. After Google's acquisition the company established an artifici... | Wikipedia - Google DeepMind - History | 258 | 1,298 | null |
Section: Products and technologies. Google Research released a paper in 2016 regarding AI safety and avoiding undesirable behaviour during the AI learning process. In 2017 DeepMind released GridWorld, an open-source testbed for evaluating whether an algorithm learns to disable its kill switch or otherwise exhibits cert... | Wikipedia - Google DeepMind - Products and technologies | 150 | 761 | null |
Section: Products and technologies > Games. Unlike earlier AIs, such as IBM's Deep Blue or Watson, which were developed for a pre-defined purpose and only function within that scope, DeepMind's initial algorithms were intended to be general. They used reinforcement learning, an algorithm that learns from experience usi... | Wikipedia - Google DeepMind - Products and technologies > Games | 299 | 1,416 | null |
Section: Products and technologies > Games > AlphaGo and successors. In October 2015, a computer Go program called AlphaGo, developed by DeepMind, beat the European Go champion Fan Hui, a 2 dan (out of 9 dan possible) professional, five to zero. This was the first time an artificial intelligence (AI) defeated a profess... | Wikipedia - Google DeepMind - Products and technologies > Games > AlphaGo and successors | 344 | 1,615 | null |
The number of moves was increased gradually until over 30 million of them were processed. The aim was to have the system mimic the human player, as represented by the input data, and eventually become better. It played against itself and learned from the outcomes; thus, it learned to improve itself over the time and in... | Wikipedia - Google DeepMind - Products and technologies > Games > AlphaGo and successors | 343 | 1,833 | null |
Section: Products and technologies > Games > AlphaStar. In 2016, Hassabis discussed the game StarCraft as a future challenge, since it requires strategic thinking and handling imperfect information. In January 2019, DeepMind introduced AlphaStar, a program playing the real-time strategy game StarCraft II. AlphaStar use... | Wikipedia - Google DeepMind - Products and technologies > Games > AlphaStar | 254 | 1,302 | null |
Section: Products and technologies > Protein folding. In 2016, DeepMind turned its artificial intelligence to protein folding, a long-standing problem in molecular biology. In December 2018, DeepMind's AlphaFold won the 13th Critical Assessment of Techniques for Protein Structure Prediction (CASP) by successfully predi... | Wikipedia - Google DeepMind - Products and technologies > Protein folding | 319 | 1,587 | null |
Section: Products and technologies > Language models. In 2016, DeepMind introduced WaveNet, a text-to-speech system. It was originally too computationally intensive for use in consumer products, but in late 2017 it became ready for use in consumer applications such as Google Assistant. In 2018 Google launched a commerc... | Wikipedia - Google DeepMind - Products and technologies > Language models | 319 | 1,433 | null |
Section: Products and technologies > Language models > Gemini. Gemini is a multimodal large language model which was released on 6 December 2023. It is the successor of Google's LaMDA and PaLM 2 language models and sought to challenge OpenAI's GPT-4. Gemini comes in 3 sizes: Nano, Pro, and Ultra. Gemini is also the nam... | Wikipedia - Google DeepMind - Products and technologies > Language models > Gemini | 209 | 929 | null |
Section: Products and technologies > Language models > Gemma. Gemma is a collection of open-weight large language models. The first ones were released on 21 February 2024 and are available in two distinct sizes: a 7 billion parameter model optimized for GPU and TPU usage, and a 2 billion parameter model designed for CP... | Wikipedia - Google DeepMind - Products and technologies > Language models > Gemma | 302 | 1,382 | null |
Section: Products and technologies > Video generation. In May 2024, a multimodal video generation model called Veo was announced at Google I/O 2024. Google claimed that it could generate 1080p videos beyond a minute long. In December 2024, Google released Veo 2, available via VideoFX. It supports 4K resolution video ge... | Wikipedia - Google DeepMind - Products and technologies > Video generation | 157 | 729 | null |
Section: Products and technologies > Sports. DeepMind researchers have applied machine learning models to the sport of football, often referred to as soccer in North America, modelling the behaviour of football players, including the goalkeeper, defenders, and strikers during different scenarios such as penalty kicks. ... | Wikipedia - Google DeepMind - Products and technologies > Sports | 191 | 1,106 | null |
Section: Products and technologies > Archaeology. Google has unveiled a new archaeology document program, named Ithaca after the Greek island in Homer's Odyssey. This deep neural network helps researchers restore the empty text of damaged Greek documents, and to identify their date and geographical origin. The work bui... | Wikipedia - Google DeepMind - Products and technologies > Archaeology | 185 | 938 | null |
Section: Products and technologies > Mathematics > AlphaTensor. In October 2022, DeepMind released AlphaTensor, which used reinforcement learning techniques similar to those in AlphaGo, to find novel algorithms for matrix multiplication. In the special case of multiplying two 4×4 matrices with integer entries, where on... | Wikipedia - Google DeepMind - Products and technologies > Mathematics > AlphaTensor | 168 | 863 | null |
Section: Products and technologies > Mathematics > AlphaGeometry. AlphaGeometry is a neuro-symbolic AI that was able to solve 25 out of 30 geometry problems of the International Mathematical Olympiad, a performance comparable to that of a gold medalist. Traditional geometry programs are symbolic engines that rely exclu... | Wikipedia - Google DeepMind - Products and technologies > Mathematics > AlphaGeometry | 179 | 948 | null |
Section: Products and technologies > AlphaDev. In June 2023, Deepmind announced that AlphaDev, which searches for improved computer science algorithms using reinforcement learning, discovered a more efficient way of coding a sorting algorithm and a hashing algorithm. The new sorting algorithm was 70% faster for shorter... | Wikipedia - Google DeepMind - Products and technologies > AlphaDev | 155 | 809 | null |
Section: Products and technologies > AlphaEvolve. In May 2025, Google DeepMind unveiled AlphaEvolve, an evolutionary coding agent using LLMs like Gemini to design optimized algorithms. AlphaEvolve begins each optimization process with an initial algorithm and metrics to evaluate the quality of a solution. At each step,... | Wikipedia - Google DeepMind - Products and technologies > AlphaEvolve | 195 | 939 | null |
Section: Products and technologies > Miscellaneous contributions to Google. Google has stated that DeepMind algorithms have greatly increased the efficiency of cooling its data centers by automatically balancing the cost of hardware failures against the cost of cooling. In addition, DeepMind (alongside other Alphabet A... | Wikipedia - Google DeepMind - Products and technologies > Miscellaneous contributions to Google | 161 | 952 | null |
Section: DeepMind Health. In July 2016, a collaboration between DeepMind and Moorfields Eye Hospital was announced to develop AI applications for healthcare. DeepMind would be applied to the analysis of anonymised eye scans, searching for early signs of diseases leading to blindness. In August 2016, a research programm... | Wikipedia - Google DeepMind - DeepMind Health | 342 | 1,855 | null |
Section: DeepMind Health > NHS data-sharing controversy. In April 2016, New Scientist obtained a copy of a data sharing agreement between DeepMind and the Royal Free London NHS Foundation Trust. The latter operates three London hospitals where an estimated 1.6 million patients are treated annually. The agreement shows ... | Wikipedia - Google DeepMind - DeepMind Health > NHS data-sharing controversy | 332 | 1,815 | null |
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