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Jabberwacky : Jabberwacky is a chatbot created by British programmer Rollo Carpenter and launched in 1997. Its stated aim is to "simulate natural human chat in an interesting, entertaining and humorous manner". It is an early attempt at creating an artificially intelligent chatbot through human interaction. |
Jabberwacky : The stated purpose of the project is to create an artificial intelligence that is capable of passing the Turing Test. It is designed to mimic human interaction and to carry out conversations with users. It is not designed to carry out any other functions. Unlike more traditional AI programs, the learning ... |
Jabberwacky : Cleverbot is the evolved version of the older Jabberwacky chatterbot, or chatbot, originally launched in 1997 on the web. While Cleverbot.com continued to work in 2023, the Jabberwacky's website, tagged as "legacy only," stopped working temporarily from December 31, 2022 until approximately June 1, 2023, ... |
Jabberwacky : 1981 – The first incarnation of this project is created as a program hard-coded on a Sinclair ZX81. 1988 – Learning AI project founded as 'Thoughts' 1997 – Launched on the Internet as 'Jabberwacky' October 2003 – Jabberwacky is awarded third place in the Loebner Prize. It was beaten by Juergen Pirner's Ja... |
Jabberwacky : Artificial Linguistic Internet Computer Entity (ALICE) Chatterbot Loebner Prize |
Jabberwacky : www.jabberwacky.com The Official Website Jabberwacky entry Archived 30 September 2005 at the Wayback Machine to the Loebner Prize 2005 |
John M. Jumper : John Michael Jumper (born 1985) is an American chemist and computer scientist. Jumper and Demis Hassabis were awarded with the 2024 Nobel Prize in Chemistry for protein structure prediction. He currently serves as director at Google DeepMind. Jumper and his colleagues created AlphaFold, an artificial i... |
John M. Jumper : Jumper received a Bachelor of Science with majors in physics and mathematics from Vanderbilt University in 2007, a Master of Philosophy in theoretical condensed matter physics from the University of Cambridge in 2010 on a Marshall Scholarship, a Master of Science in theoretical chemistry from the Unive... |
John M. Jumper : Jumper's research investigates algorithms for protein structure prediction. |
John M. Jumper : == External links == |
Kaggle : Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC. Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning e... |
Kaggle : Kaggle was founded by Anthony Goldbloom in April 2010. Jeremy Howard, one of the first Kaggle users, joined in November 2010 and served as the President and Chief Scientist. Also on the team was Nicholas Gruen serving as the founding chair. In 2011, the company raised $12.5 million and Max Levchin became the c... |
Kaggle : Data science competition platform Anthony Goldbloom Hugging Face |
Kaggle : "Competition shines light on dark matter", Office of Science and Technology Policy, Whitehouse website, June 2011 "May the best algorithm win...", The Wall Street Journal, March 2011 "Kaggle contest aims to boost Wikipedia editors", New Scientist, July 2011 Archived 2016-03-22 at the Wayback Machine "Verificat... |
KataGo : KataGo is a free and open-source computer Go program, capable of defeating top-level human players. First released on 27 February 2019, it is developed by David Wu, who also developed the Arimaa playing program bot_Sharp which defeated three top human players to win the Arimaa AI Challenge in 2015. KataGo's fi... |
KataGo : Based on techniques used by DeepMind's AlphaGo Zero, KataGo implements Monte Carlo tree search with a convolutional neural network providing position evaluation and policy guidance. Compared to AlphaGo, KataGo introduces many refinements that enable it to learn faster and play more strongly. Notable features o... |
KataGo : In 2022, KataGo was used as the target for adversarial attack research, designed to demonstrate the "surprising failure modes" of AI systems. The researchers were able to trick KataGo into ending the game prematurely. Adversarial training improves defense against adversarial attacks, though not perfectly. |
KataGo : KataGo on GitHub KataGo on Sensei's Library KataGo Training website |
Kindwise : FlowerChecker, also known as Kindwise, is a company that uses machine learning to identify natural objects from images. This includes plants and their diseases, but also insects and mushrooms. It is based in Brno, Czech Republic. It was founded in 2014 by Ondřej Veselý, Jiří Řihák, and Ondřej Vild, at the ti... |
Kindwise : FlowerChecker offers multiple products. Plant.id is a machine learning-based plant identification API launched in 2018, with the plant disease identification API, plant.health, released in April 2022. The plant.id API is suitable for integration into other software, such as mobile apps or urban trees from re... |
Kindwise : In 2019, FlowerChecker won the Idea of the Year award in the AI Awards organized by the Confederation of Industry of the Czech Republic. In 2020, an academic study comparing ten free automated image recognition apps showed that plant.id's performance excelled in most of the parameters studied. In an independ... |
Kindwise : Flowerchecker cooperates with the Nature Conservation Agency of the Czech Republic on a biodiversity mapping project. FlowerChecker plans to adapt its services to participate in the control of invasive species. In 2022, the company entered a consortium to develop a weeder capable of in-row weed detection and... |
Kinect : Kinect is a discontinued line of motion sensing input devices produced by Microsoft and first released in 2010. The devices generally contain RGB cameras, and infrared projectors and detectors that map depth through either structured light or time of flight calculations, which can in turn be used to perform re... |
Kinect : While announcing Kinect's discontinuation in an interview with Fast Co. Design on October 25, 2017, Microsoft stated that 35 million units had been sold since its release. 24 million units of Kinect had been shipped by February 2013. Having sold 8 million units in its first 60 days on the market, Kinect claime... |
Kinect : Kinect competed with several motion controllers on other home consoles, such as Wii Remote, Wii Remote Plus and Wii Balance Board for the Wii and Wii U, PlayStation Move and PlayStation Eye for the PlayStation 3, and PlayStation Camera for the PlayStation 4. While the Xbox 360 Kinect's controller-less nature e... |
Kinect : The machine learning work on human motion capture within Kinect won the 2011 MacRobert Award for engineering innovation. Kinect Won T3's "Gadget of the Year" award for 2011. It also won the "Gaming Gadget of the Year" prize. 'Microsoft Kinect for Windows Software Development Kit' was ranked second in "The 10 M... |
Kinect : Dreameye EyeToy Xbox Live Vision |
Kinect : Official website for Xbox + Kinect Official website for Kinect for Windows |
Komodo (chess) : Komodo and Dragon by Komodo Chess (also known as Dragon or Komodo Dragon) are UCI chess engines developed by Komodo Chess, which is a part of Chess.com. The engines were originally authored by Don Dailey and GM Larry Kaufman. Dragon is a commercial chess engine, but Komodo is free for non-commercial us... |
Komodo (chess) : Komodo vs Hannibal, nTCEC - Stage 2b - Season 1, Round 4.1, ECO: A10, 1–0 Archived 2016-03-04 at the Wayback Machine Komodo sacrifices an exchange for positional gain. Gull vs Komodo, nTCEC - Stage 3 - Season 2, Round 2.2, ECO: E10, 0–1 Archived March 4, 2016, at the Wayback Machine Archived 2016-03-04... |
Komodo (chess) : Official website |
Leela Chess Zero : Leela Chess Zero (abbreviated as LCZero, lc0) is a free, open-source chess engine and volunteer computing project based on Google's AlphaZero engine. It was spearheaded by Gary Linscott, a developer for the Stockfish chess engine, and adapted from the Leela Zero Go engine. Like Leela Zero and AlphaGo... |
Leela Chess Zero : The Leela Chess Zero project was first announced on TalkChess.com on January 9, 2018, as an open-source, self-learning chess engine attempting to recreate the success of AlphaZero. Within the first few months of training, Leela Chess Zero had already reached the Grandmaster level, surpassing the stre... |
Leela Chess Zero : Like AlphaZero, Leela Chess Zero employs neural networks which output both a policy vector, a distribution over subsequent moves used to guide search, and a position evaluation. These neural networks are designed to run on GPU, unlike traditional engines. It originally used residual neural networks, ... |
Leela Chess Zero : Like AlphaZero, Leela Chess Zero learns through reinforcement learning, continually training on data generated through self-play. However, unlike AlphaZero, Leela Chess Zero decentralizes its data generation through distributed computing, with volunteers generating self-play data on local hardware wh... |
Leela Chess Zero : In season 15 of the Top Chess Engine Championship, the engine AllieStein competed alongside Leela. AllieStein is a combination of two different spinoffs from Leela: Allie, which uses the same neural network as Leela, but has a unique search algorithm for exploring different lines of play, and Stein, ... |
Leela Chess Zero : In April 2018, Leela Chess Zero became the first engine using a deep neural network to enter the Top Chess Engine Championship (TCEC), during Season 12 in the lowest division, Division 4. Out of 28 games, it won one, drew two, and lost the remainder; its sole victory came from a position in which its... |
Leela Chess Zero : Leela vs Stockfish, CCCC bonus games, 1–0 Leela beats the world champion Stockfish engine despite a one-pawn handicap. Stockfish vs Leela Chess Zero – TCEC S15 Superfinal – Game 61 Leela completely outplays Stockfish with black pieces in the Trompovsky attack. Leela's eval went from 0.1 to −1.2 in on... |
Leela Chess Zero : Official website Leela Chess Zero on GitHub Neural network training client Engine Neural nets Chessprogramming wiki on Leela Chess Zero |
Leela Zero : Leela Zero is a free and open-source computer Go program released on 25 October 2017. It is developed by Belgian programmer Gian-Carlo Pascutto, the author of chess engine Sjeng and Go engine Leela. Leela Zero's algorithm is based on DeepMind's 2017 paper about AlphaGo Zero. Unlike the original Leela, whic... |
Leela Zero : Leela Zero finished third at the BerryGenomics Cup World AI Go Tournament in Fuzhou, Fujian, China on 28 April 2018. The New Yorker at the end of 2018 characterized Leela and Leela Zero as "the world’s most successful open-source Go engines". In early 2018, another team branched Leela Chess Zero from the s... |
Leela Zero : Leela Zero is an (almost) exact replication of AlphaGo Zero in both training process and architecture. The training process is Monte-Carlo Tree Search with self-play, exactly the same as AlphaGo Zero. The architecture is the same as AlphaGo Zero (with one difference). Consider the last released model, 0e9e... |
Leela Zero : Official website Leela Zero on GitHub Leela Zero on Sensei's Library Play Leela Zero on ZBaduk |
Mittens (chess) : Mittens is a chess engine developed by Chess.com. It was released on January 1, 2023, alongside four other engines, all of them given cat-related names. The engine became a viral sensation in the chess community due to exposure through content made by chess streamers and a social media marketing campa... |
Mittens (chess) : Mittens was released on January 1, 2023, as part of a New Year event on Chess.com. It was one of five engines released, all with names related to cats. The other engines released were named Scaredy Cat, rated 800; Angry Cat, rated 1000; Mr. Grumpers, rated 1200 and Catspurrov (a pun on Garry Kasparov)... |
Mittens (chess) : Mittens was conceptualized by Chess.com employee Will Whalen. Appearing as a kitten, Mittens trash talked its opponents with a selection of voice lines: these lines included quotes from J. Robert Oppenheimer, Vincent van Gogh and Friedrich Nietzsche, as well as the 1967 film Le Samouraï. The engine's ... |
Mittens (chess) : On Chess.com, Mittens had a rating of one point. However, the engine's playing style and tactics showed that it was stronger than that; Mittens was able to beat or draw against many top human players. In an interview with CNN Business, Whalen stated that the idea behind giving Mittens a rating of one ... |
Mittens (chess) : Against human players, Mittens won over 99 percent of the millions of games it played. Chess players such as Hikaru Nakamura, Benjamin Bok, Levy Rozman and Eric Rosen struggled against Mittens; while Rozman and Rosen both lost against the engine, Nakamura and Bok were both able to make a draw. In part... |
Mittens (chess) : Mittens went viral in the chess community due to its concept and design: according to an announcement by Chess.com, a combined total of 120 million games were played against the cat engines over the course of January, with around 40 million played against Mittens. The popularity of the engine was help... |
Mittens (chess) : Mittens on Twitter Hamilton College interview with Will Whalen |
MuZero : MuZero is a computer program developed by artificial intelligence research company DeepMind to master games without knowing their rules. Its release in 2019 included benchmarks of its performance in go, chess, shogi, and a standard suite of Atari games. The algorithm uses an approach similar to AlphaZero. It m... |
MuZero : MuZero really is discovering for itself how to build a model and understand it just from first principles. On November 19, 2019, the DeepMind team released a preprint introducing MuZero. |
MuZero : MuZero used 16 third-generation tensor processing units (TPUs) for training, and 1000 TPUs for selfplay for board games, with 800 simulations per step and 8 TPUs for training and 32 TPUs for selfplay for Atari games, with 50 simulations per step. AlphaZero used 64 second-generation TPUs for training, and 5000 ... |
MuZero : MuZero was viewed as a significant advancement over AlphaZero, and a generalizable step forward in unsupervised learning techniques. The work was seen as advancing understanding of how to compose systems from smaller components, a systems-level development more than a pure machine-learning development. While o... |
MuZero : General game playing Unsupervised learning |
MuZero : Initial MuZero preprint Open source implementations |
Object co-segmentation : In computer vision, object co-segmentation is a special case of image segmentation, which is defined as jointly segmenting semantically similar objects in multiple images or video frames. |
Object co-segmentation : It is often challenging to extract segmentation masks of a target/object from a noisy collection of images or video frames, which involves object discovery coupled with segmentation. A noisy collection implies that the object/target is present sporadically in a set of images or the object/targe... |
Object co-segmentation : A joint object discover and co-segmentation method based on coupled dynamic Markov networks has been proposed recently, which claims significant improvements in robustness against irrelevant/noisy video frames. Unlike previous efforts which conveniently assumes the consistent presence of the ta... |
Object co-segmentation : Graph cut optimization is a popular tool in computer vision, especially in earlier image segmentation applications. As an extension of regular graph cuts, multi-level hypergraph cut is proposed to account for more complex high order correspondences among video groups beyond typical pairwise cor... |
Object co-segmentation : In action localization applications, object co-segmentation is also implemented as the segment-tube spatio-temporal detector. Inspired by the recent spatio-temporal action localization efforts with tubelets (sequences of bounding boxes), Le et al. present a new spatio-temporal action localizati... |
Object co-segmentation : Image segmentation Object detection Video content analysis Image analysis Digital image processing Activity recognition Computer vision Convolutional neural network Long short-term memory == References == |
OpenVINO : OpenVINO is an open-source software toolkit for optimizing and deploying deep learning models. It enables programmers to develop scalable and efficient AI solutions with relatively few lines of code. It supports several popular model formats and categories, such as large language models, computer vision, and... |
OpenVINO : The simplest OpenVINO usage involves obtaining a model and running it as is. Yet for the best results, a more complete workflow is suggested: obtain a model in one of supported frameworks, convert the model to OpenVINO IR using the OpenVINO Converter tool, optimize the model, using training-time or post-trai... |
OpenVINO : OpenVINO IR is the default format used to run inference. It is saved as a set of two files, *.bin and *.xml, containing weights and topology, respectively. It is obtained by converting a model from one of the supported frameworks, using the application's API or a dedicated converter. Models of the supported ... |
OpenVINO : OpenVINO runs on Windows, Linux and MacOS. |
OpenVINO : Comparison of deep learning software == References == |
OpenVX : OpenVX is an open, royalty-free standard for cross-platform acceleration of computer vision applications. It is designed by the Khronos Group to facilitate portable, optimized and power-efficient processing of methods for vision algorithms. This is aimed for embedded and real-time programs within computer visi... |
OpenVX : OpenVX specifies a higher level of abstraction for programming computer vision use cases than compute frameworks such as OpenCL. The high level makes the programming easy and the underlying execution will be efficient on different computing architectures. This is done while having a consistent and portable vis... |
OpenVX : OpenVX 1.0 specification was released in October 2014. OpenVX sample implementation was released in December 2014. OpenVX 1.1 specification was released on May 2, 2016. OpenVX 1.2 was released on May 1, 2017. Updated OpenVX adopters program and OpenVX 1.2 conformance test suite was released on November 21, 201... |
OpenVX : AMD MIVisionX - for AMD's CPUs and GPUs. Cadence - for Cadence Design Systems's Tensilica Vision DSPs. Imagination - for Imagination Technologies's PowerVR GPUs Synopsys - for Synopsys' DesignWare EV Vision Processors Texas Instruments’ OpenVX (TIOVX) - for Texas Instruments’ Jacinto™ ADAS SoCs. NVIDIA VisionW... |
OpenVX : Official website for OpenVX OpenVX Specification Registry OpenVX Sample Implementation OpenVX Sample Applications OpenVX Tutorial Material |
Portable Format for Analytics : The Portable Format for Analytics (PFA) is a JSON-based predictive model interchange format conceived and developed by Jim Pivarski. PFA provides a way for analytic applications to describe and exchange predictive models produced by analytics and machine learning algorithms. It supports ... |
Portable Format for Analytics : The Data Mining Group is a consortium managed by the Center for Computational Science Research, Inc., a nonprofit founded in 2008. |
Portable Format for Analytics : reverse array: # reverse input array of doubles input: output: action: - let: - let: - let: - let: - while : do: - set : ,1]] - set : - z Bubblesort input: output: action: - let: - let: - let: - let: - let: - while : do : - set : - while : ] do : - if: , ] ] then : -... |
Portable Format for Analytics : Hadrian (Java/Scala/JVM) - Hadrian is a complete implementation of PFA in Scala, which can be accessed through any JVM language, principally Java. It focuses on model deployment, so it is flexible (can run in restricted environments) and fast. Titus (Python 2.x) - Titus is a complete, in... |
Portable Format for Analytics : Official website pfa on GitHub PFA 0.8.1 Specification PFA Document Structure Python Multi-User Rest-Api Server for Deploying Portable Format For Analytics Titus 2 : Portable Format for Analytics (PFA) implementation for Python 3 |
Predictive Model Markup Language : The Predictive Model Markup Language (PMML) is an XML-based predictive model interchange format conceived by Robert Lee Grossman, then the director of the National Center for Data Mining at the University of Illinois at Chicago. PMML provides a way for analytic applications to describ... |
Predictive Model Markup Language : A PMML file can be described by the following components: Header: contains general information about the PMML document, such as copyright information for the model, its description, and information about the application used to generate the model such as name and version. It also cont... |
Predictive Model Markup Language : PMML 4.0 was released on June 16, 2009. Examples of new features included: Improved Pre-Processing Capabilities: Additions to built-in functions include a range of Boolean operations and an If-Then-Else function. Time Series Models: New exponential Smoothing models; also place holders... |
Predictive Model Markup Language : The Data Mining Group is a consortium managed by the Center for Computational Science Research, Inc., a nonprofit founded in 2008. The Data Mining Group also developed a standard called Portable Format for Analytics, or PFA, which is complementary to PMML. |
Predictive Model Markup Language : Data Pre-processing in PMML and ADAPA - A Primer Video of Alex Guazzelli's PMML presentation for the ACM Data Mining Group (hosted by LinkedIn) PMML 3.2 Specification PMML 4.0 Specification PMML 4.1 Specification PMML 4.2.1 Specification PMML 4.4 Specification Representing predictive ... |
Quantum Artificial Intelligence Lab : The Quantum Artificial Intelligence Lab (also called the Quantum AI Lab or QuAIL) is a joint initiative of NASA, Universities Space Research Association, and Google (specifically, Google Research) whose goal is to pioneer research on how quantum computing might help with machine le... |
Quantum Artificial Intelligence Lab : The Quantum AI Lab was announced by Google Research in a blog post on May 16, 2013. At the time of launch, the Lab was using the most advanced commercially available quantum computer, D-Wave Two from D-Wave Systems. On October 10, 2013, Google released a short film describing the c... |
Quantum Artificial Intelligence Lab : On December 09, 2024 Google Introduced Willow, describing it as a "state-of-the-art quantum chip". Google claims that this new chip takes just five minutes to solve a problem that the world fastest computers take ten septillion years. Ten septillion years is more than the Age of Un... |
Quantum Artificial Intelligence Lab : Artificial intelligence Glossary of artificial intelligence Google Brain Google X |
Quantum Artificial Intelligence Lab : Official website (NASA) Official website (USRA) Official website (Google) Official website (Google Quantum AI) Waybackmachine |
Revoscalepy : revoscalepy is a machine learning package in Python created by Microsoft. It is available as part of Machine Learning Services in Microsoft SQL Server 2017 and Machine Learning Server 9.2.0 and later. The package contains functions for creating linear model, logistic regression, random forest, decision tr... |
Revoscalepy : Microsoft Machine Learning Services |
Revoscalepy : Samples for using revoscalepy and microsoftml |
RevoScaleR : RevoScaleR is a machine learning package in R created by Microsoft. It is available as part of Machine Learning Server, Microsoft R Client, and Machine Learning Services in Microsoft SQL Server 2016. The package contains functions for creating linear model, logistic regression, random forest, decision tree... |
RevoScaleR : Many R packages are designed to analyze data that can fit in the memory of the machine and usually do not make use of parallel processing. RevoScaleR was designed to address these limitations. The functions in RevoScaleR orientate around three main abstraction concepts that users can specify to process lar... |
RevoScaleR : The package is mostly meant to be used with a SQL server or other remote machines. To fully leverage the abstractions it uses to process a large dataset, one needs a remote server and non-Express free edition of the package. It cannot be easily installed such as by running "install.packages("RevoScaleR")" ... |
RevoScaleR : Microsoft Machine Learning Services |
RevoScaleR : Samples for using revoscalepy and microsoftml |
Sense Networks : Sense Networks is a New York City based company with a focus on applications that analyze big data from mobile phones, carrier networks, and taxicabs, particularly by using machine learning technology to make sense of large amounts of location (latitude/longitude) data. In 2009, Sense was named one of ... |
Sense Networks : Sense Networks was founded by Greg Skibiski in February 2006 (2003?) near his home in Northampton, Massachusetts. After establishing an office in NoHo, New York City near Silicon Alley, Skibiski recruited Alex Pentland, Director of Human Dynamics Research and former Academic Head of the MIT Media Lab, ... |
Sense Networks : The Citysense consumer application that shows hotspots of human activity in real-time from mobile phone location and taxicab GPS data was named by ReadWriteWeb (in The New York Times) as "Top 10 Internet of Things Products of 2009". The Cabsense consumer application that shows the best place to catch a... |
Sense Networks : The company allows users to opt-out of their service through their website, and users may monitor their profile through their application. The company does not collect identifiable data (such as phone numbers or names); it collects data received from cellphone to construct anonymous profiles of consume... |
Sense Networks : Geosocial Networking Location-based service Location awareness |
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