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AlphaDev : Understanding DeepMind's AlphaDev Breakthrough in Optimizing Sorting Algorithms Understanding DeepMind's Sorting Algorithm |
AlphaGo : AlphaGo is a computer program that plays the board game Go. It was developed by the London-based DeepMind Technologies, an acquired subsidiary of Google. Subsequent versions of AlphaGo became increasingly powerful, including a version that competed under the name Master. After retiring from competitive play, ... |
AlphaGo : Go is considered much more difficult for computers to win than other games such as chess, because its strategic and aesthetic nature makes it hard to directly construct an evaluation function, and its much larger branching factor makes it prohibitively difficult to use traditional AI methods such as alpha–bet... |
AlphaGo : An early version of AlphaGo was tested on hardware with various numbers of CPUs and GPUs, running in asynchronous or distributed mode. Two seconds of thinking time was given to each move. The resulting Elo ratings are listed below. In the matches with more time per move higher ratings are achieved. In May 201... |
AlphaGo : As of 2016, AlphaGo's algorithm uses a combination of machine learning and tree search techniques, combined with extensive training, both from human and computer play. It uses Monte Carlo tree search, guided by a "value network" and a "policy network", both implemented using deep neural network technology. A ... |
AlphaGo : Toby Manning, the match referee for AlphaGo vs. Fan Hui, has described the program's style as "conservative". AlphaGo's playing style strongly favours greater probability of winning by fewer points over lesser probability of winning by more points. Its strategy of maximising its probability of winning is dist... |
AlphaGo : Facebook has also been working on its own Go-playing system darkforest, also based on combining machine learning and Monte Carlo tree search. Although a strong player against other computer Go programs, as of early 2016, it had not yet defeated a professional human player. Darkforest has lost to CrazyStone an... |
AlphaGo : AlphaGo Master (white) v. Tang Weixing (31 December 2016), AlphaGo won by resignation. White 36 was widely praised. |
AlphaGo : The documentary film AlphaGo raised hopes that Lee Sedol and Fan Hui would have benefitted from their experience of playing AlphaGo, but as of May 2018, their ratings were little changed; Lee Sedol was ranked 11th in the world, and Fan Hui 545th. On 19 November 2019, Lee announced his retirement from professi... |
AlphaGo : Official website AlphaGo wiki at Sensei's Library, including links to AlphaGo games AlphaGo page, with archive and games Estimated 2017 rating of Alpha Go AlphaGo - The Movie on YouTube Media related to AlphaGo at Wikimedia Commons Quotations related to AlphaGo at Wikiquote |
AlphaGo Zero : AlphaGo Zero is a version of DeepMind's Go software AlphaGo. AlphaGo's team published an article in Nature in October 2017 introducing AlphaGo Zero, a version created without using data from human games, and stronger than any previous version. By playing games against itself, AlphaGo Zero: surpassed the ... |
AlphaGo Zero : The network in AlphaGo Zero is a ResNet with two heads.: Appendix: Methods The stem of the network takes as input a 17x19x19 tensor representation of the Go board. 8 channels are the positions of the current player's stones from the last eight time steps. (1 if there is a stone, 0 otherwise. If the time ... |
AlphaGo Zero : AlphaGo Zero's neural network was trained using TensorFlow, with 64 GPU workers and 19 CPU parameter servers. Only four TPUs were used for inference. The neural network initially knew nothing about Go beyond the rules. Unlike earlier versions of AlphaGo, Zero only perceived the board's stones, rather tha... |
AlphaGo Zero : The hardware cost for a single AlphaGo Zero system in 2017, including the four TPUs, has been quoted as around $25 million. |
AlphaGo Zero : According to Hassabis, AlphaGo's algorithms are likely to be of the most benefit to domains that require an intelligent search through an enormous space of possibilities, such as protein folding (see AlphaFold) or accurately simulating chemical reactions. AlphaGo's techniques are probably less useful in ... |
AlphaGo Zero : AlphaGo Zero was widely regarded as a significant advance, even when compared with its groundbreaking predecessor, AlphaGo. Oren Etzioni of the Allen Institute for Artificial Intelligence called AlphaGo Zero "a very impressive technical result" in "both their ability to do it—and their ability to train t... |
AlphaGo Zero : On 5 December 2017, DeepMind team released a preprint on arXiv, introducing AlphaZero, a program using generalized AlphaGo Zero's approach, which achieved within 24 hours a superhuman level of play in chess, shogi, and Go, defeating world-champion programs, Stockfish, Elmo, and 3-day version of AlphaGo Z... |
AlphaGo Zero : "AlphaGo Zero: Starting from scratch". Archived from the original on 3 January 2020. Singh, S.; Okun, A.; Jackson, A. (2017). "AOP". Nature. 550 (7676): 336–337. Bibcode:2017Natur.550..336S. doi:10.1038/550336a. PMID 29052631. S2CID 4447445. Silver, David; Schrittwieser, Julian; Simonyan, Karen; Antonogl... |
AlphaZero : AlphaZero is a computer program developed by artificial intelligence research company DeepMind to master the games of chess, shogi and go. This algorithm uses an approach similar to AlphaGo Zero. On December 5, 2017, the DeepMind team released a preprint paper introducing AlphaZero, which would soon play th... |
AlphaZero : AlphaZero (AZ) is a more generalized variant of the AlphaGo Zero (AGZ) algorithm, and is able to play shogi and chess as well as Go. Differences between AZ and AGZ include: AZ has hard-coded rules for setting search hyperparameters. The neural network is now updated continually. AZ doesn't use symmetries, u... |
AlphaZero : Comparing Monte Carlo tree search searches, AlphaZero searches just 80,000 positions per second in chess and 40,000 in shogi, compared to 70 million for Stockfish and 35 million for Elmo. AlphaZero compensates for the lower number of evaluations by using its deep neural network to focus much more selectivel... |
AlphaZero : AlphaZero was trained by simply playing against itself multiple times, using 5,000 first-generation TPUs to generate the games and 64 second-generation TPUs to train the neural networks. Training took several days, totaling about 41 TPU-years. It cost 3e22 FLOPs. In parallel, the in-training AlphaZero was p... |
AlphaZero : DeepMind addressed many of the criticisms in their final version of the paper, published in December 2018 in Science. They further clarified that AlphaZero was not running on a supercomputer; it was trained using 5,000 tensor processing units (TPUs), but only ran on four TPUs and a 44-core CPU in its matche... |
AlphaZero : Chessprogramming wiki on AlphaZero Chess.com Youtube playlist for AlphaZero vs. Stockfish |
Cortica : Headquartered in Tel Aviv Cortica utilizes unsupervised learning methods to recognize and analyze digital images and video. The technology developed by the Cortica team is based on research of the function of the human brain. |
Cortica : 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 created technology... |
Cortica : 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 in Neural Ensembles as ... |
Cortica : 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 $1.5 milli... |
Cortica : GigaOm listed Cortica as one of the top deep learning startups in a November 2013 article surveying the field, along with AlchemyAPI, Ersatz, and Semantria. Business Insider ranked Cortica as one of the coolest tech companies in Israel. CB Insights has identified Cortica as the top patent holding AI company. ... |
Cortica : Official website |
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 seen in co... |
Darkforest : Competing with top human players in the ancient game of Go has been a long-term goal of artificial intelligence. Go’s high branching factor makes traditional search techniques ineffective, even on cutting-edge hardware, and Go’s evaluation function could change drastically with one stone change. However, b... |
Darkforest : 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 search-based mac... |
Darkforest : 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 Facebook's most advanced ... |
Darkforest : 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 ranked bot. With the added ... |
Darkforest : Go and mathematics |
Darkforest : Source code on Github |
DARPA LAGR Program : The Learning Applied to Ground Vehicles (LAGR) program, which ran from 2004 until 2008, had the goal of accelerating progress in autonomous, perception-based, off-road navigation in robotic unmanned ground vehicles (UGVs). LAGR was funded by DARPA, a research agency of the United States Department ... |
DARPA LAGR Program : 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 understanding of off... |
DARPA LAGR Program : The principal goal of LAGR was to accelerate progress in off navigation of UGVs. Additional, synergistic goals included (1) establishing benchmarking methodology for measuring progress for autonomous robots operating in unstructured environments, (2) advancing machine vision and thus enabling long-... |
DARPA 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 members of competitive teams, freeing... |
DARPA LAGR Program : 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 Alb... |
DARPA LAGR Program : 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 caster ... |
DARPA LAGR Program : 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 measurement of p... |
DARPA LAGR Program : LAGR was administered under the DARPA Information Processing Technology Office. Larry Jackel conceived the program and was the program manager from 2004 to 2007. Eric Krotkov, Michael Perschbacher, and James Pippine contributed to LAGR conception and management. Charles Sullivan played a major role... |
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 for imp... |
Diffbot : Official website Knowledge Graph |
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 for hardw... |
Direct3D : 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 Mod... |
Direct3D : 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 1995, bringing its staf... |
Direct3D : No substantive changes were planned to Direct3D for DirectX 4.0, which was scheduled to ship in late 1996 and then cancelled. |
Direct3D : 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 Direct3D more c... |
Direct3D : 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 for feature... |
Direct3D : 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 represent the fir... |
Direct3D : 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 compiled those... |
Direct3D : Direct3D 9.0 (released in December, 2002) added a new version of the High Level Shader Language support for floating-point texture formats, Multiple Render Targets (MRT), Multiple-Element Textures, texture lookups in the vertex shader and stencil buffer techniques. |
Direct3D : 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 stages as ... |
Direct3D : 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 hardware a... |
Direct3D : 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 features such ... |
Direct3D : 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 numerous features t... |
Direct3D : 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 vertex data... |
Direct3D : 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 determined using "capa... |
Direct3D : 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, and the OS had l... |
Direct3D : Direct3D Mobile is derived from Direct3D but has a smaller memory footprint. Windows CE provides Direct3D Mobile support. |
Direct3D : 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 implementation can also be... |
Direct3D : List of 3D rendering APIs List of 3D graphics libraries High-Level Shader Language Shader DirectX – collection of APIs in which Direct3D is implemented DirectDraw 3D computer graphics |
Direct3D : DirectX website MSDN: DirectX Graphics and Gaming DirectX 10: The Future of PC Gaming, technical article discussing the new features of DirectX 10 and their impact on computer games |
Dr.Fill : Dr.Fill is a computer program that solves American-style crossword puzzles. It was developed by Matt Ginsberg and described by Ginsberg in an article in the Journal of Artificial Intelligence Research. Ginsberg claims in that article that Dr.Fill is among the top fifty crossword solvers in the world. |
Dr.Fill : 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 Gizmodo.... |
Dr.Fill : 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 techniques base... |
Dr.Fill : Berkeley Crossword Solver on GitHub Dr.Fill source code (except training data) on GitHub |
DREAM Challenges : DREAM Challenges (Dialogue for Reverse Engineering Assessment and Methods) is a non-profit initiative for advancing biomedical and systems biology research via crowd-sourced competitions. Started in 2006, DREAM challenges collaborate with Sage Bionetworks to provide a platform for competitions run on... |
DREAM Challenges : 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... |
DREAM Challenges : DREAM challenge comprises a core DREAM/Sage Bionetworks organization group as well as an extended scientific expert group, who may have contributed to creation and conception of the challenge or by providing key data. Additionally, new DREAM challenges may be proposed by the wider research community.... |
DREAM Challenges : Timelines for key stages (such as introduction webinars, model submission deadlines, and final deadline for participation) are provided in advance. After the winners are announced, organizers start collaborating with the top performing participants to conduct post hoc analyses for a publication descr... |
DREAM Challenges : 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 leaderboards a... |
DREAM Challenges : List of crowdsourcing projects Critical Assessment of Function Annotation (CAFA) Critical Assessment of Genome Interpretation (CAGI) Critical Assessment of Prediction of Interactions (CAPRI) Critical Assessment of protein Structure Prediction (CASP) Kaggle == References == |
Google Brain : Google Brain was a deep learning artificial intelligence research team that served as the sole AI branch of Google before being incorporated under the newer umbrella of Google AI, a research division at Google dedicated to artificial intelligence. Formed in 2011, it combined open-ended machine learning r... |
Google Brain : 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 the e... |
Google Brain : 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 Corrado, Georg... |
Google Brain : Google Brain has received coverage in Wired, NPR, and Big Think. These articles have contained interviews with key team members Ray Kurzweil and Andrew Ng, and focus on explanations of the project's goals and applications. |
Google Brain : Artificial intelligence art Glossary of artificial intelligence List of artificial intelligence projects Noosphere Quantum Artificial Intelligence Lab – run by Google in collaboration with NASA and Universities Space Research Association == References == |
Google Nest : Google Nest is a line of smart home products including smart speakers, smart displays, streaming devices, thermostats, smoke detectors, routers and security systems including smart doorbells, cameras and smart locks. The Nest brand name was originally owned by Nest Labs, co-founded by former Apple enginee... |
Google Nest : Works with Nest was a program that allowed third party devices to communicate with Nest products, such as virtual assistants, along with many third-party home automation platforms. Additionally, many smart device manufacturers have direct integration with the Nest platform, including Whirlpool, GE Applian... |
Google Nest : In February 2012, Honeywell filed a lawsuit claiming that some of its patents had been infringed by Nest. In April 2012, Nest stated they believe that none of the allegedly infringed patents were actually violated. Honeywell claimed that Nest infringed on patents pertaining to remotely controlling a therm... |
Google Nest : Per the terms of service, Google will provide law enforcement with Nest data "If we reasonably believe that we can prevent someone from dying or from suffering serious physical harm. For example, in the case of bomb threats, school shootings, kidnappings, suicide prevention, and missing person cases." In ... |
Google Nest : Internet of things Machine learning Android Things X10 ecobee |
Google Nest : Huitt, Robert; Eubanks, Gordon; Rolander, Thomas "Tom" Alan; Laws, David; Michel, Howard E.; Halla, Brian; Wharton, John Harrison; Berg, Brian; Su, Weilian; Kildall, Scott; Kampe, Bill (April 25, 2014). Laws, David (ed.). "Legacy of Gary Kildall: The CP/M IEEE Milestone Dedication" (PDF) (video transscrip... |
Google Nest : Media related to Google Nest at Wikimedia Commons Official website |
Intel RealSense : Intel RealSense Technology, formerly known as Intel Perceptual Computing, is a product range of depth and tracking technologies designed to give machines and devices depth perception capabilities. The technologies, owned by Intel are used in autonomous drones, robots, AR/VR, smart home devices amongst... |
Intel RealSense : Intel began producing hardware and software that utilized depth tracking, gestures, facial recognition, eye tracking, and other technologies under the branding Perceptual Computing in 2013. According to Intel, much of their research into the technologies is focused around "sensory inputs that make [co... |
Intel RealSense : Previous generations of Intel RealSense depth cameras (F200, R200 and SR300) were implemented in multiple laptop and tablet computers by Asus, HP, Dell, Lenovo, and Acer. Additionally, Razer and Creative offered consumer ready standalone webcams with the Intel RealSense camera built into the design.: ... |
Intel RealSense : In an early preview article in 2015, PC World's Mark Hachman concluded that RealSense is an enabling technology that will be largely defined by the software that will take advantage of its features. He noted that as of the time the article was written, the technology was new and there was no such soft... |
Intel RealSense : Camera 3D uses Intel RealSense (Serie D400) and Microsoft Kinect sensors to create holographic memories, 3D models and Facebook 3D photos |
Intel RealSense : Specifications: Intel RealSense Depth Camera D415, D435 and D455 Specifications: Intel RealSense Vision Processor D4 Series (Not available separately as these are just the bare PCB Vision Processor boards, only used as basis for the RealSense Depth Camera series) Specifications: Intel Stereo DepthModu... |
Intel RealSense : Creative Labs Kinect OpenCV Project Tango |
Intel RealSense : Official website Intel RealSense SDK developer documentation Intel RealSense Product Family D400 Series Datasheet (revision 009, June 2020) |
IRCF360 : Infrared Control Freak 360 (IRCF360) is a 360-degree proximity sensor and a motion sensing devices, developed by ROBOTmaker. The sensor is in BETA developers release as a low cost (software configurable) sensor for use within research, technical and hobby projects. |
IRCF360 : The 360-degree sensor was originally designed as a short range micro robot proximity sensor and mainly intended for Swarm robotics, Ant robotics, Swarm intelligence, autonomous Qaudcopter, Drone, UAV, multi-robot simulations e.g. Jasmine Project where 360 proximity sensing is required to avoid collision with ... |
IRCF360 : Official Websites Dean Camera development of USB interface for Arduino Details of the Sensorium and 360 degree sensor development |
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