Dan Roth (Hebrew: דן רוט) is the Eduardo D. Glandt Distinguished Professor of Computer and Information Science at the University of Pennsylvania and the Chief AI Scientist at Oracle. Until June 2024 Roth was a VP and distinguished scientist at AWS AI. In his role at AWS, Roth led over the last three years the scientific effort behind the first-generation Generative AI products from AWS, including Titan Models, Amazon Q efforts, and Bedrock, from inception until they became generally available. Roth got his B.A. summa cum laude in mathematics from the Technion, Israel, and his Ph.D. in computer science from Harvard University in 1995. He taught at the University of Illinois at Urbana-Champaign from 1998 to 2017 before moving to the University of Pennsylvania. == Professional career == Roth is a Fellow of the American Association for the Advancement of Science (AAAS), the Association for Computing Machinery (ACM), the Association for the Advancement of Artificial Intelligence (AAAI), and the Association of Computational Linguistics (ACL). Roth’s research focuses on the computational foundations of intelligent behavior. He develops theories and systems pertaining to intelligent behavior using a unified methodology, at the heart of which is the idea that learning has a central role in intelligence. His work centers around the study of machine learning and inference methods to facilitate natural language understanding. In doing that he has pursued several interrelated lines of work that span multiple aspects of this problem - from fundamental questions in learning and inference and how they interact, to the study of a range of natural language processing (NLP) problems and developing advanced machine learning based tools for natural language applications. Roth has made seminal contribution to the fusion of Learning and Reasoning, Machine Learning with weak, incidental supervision, and to machine learning and inference approaches to natural language understanding. He has written the first paper on zero-shot learning in natural language processing, a 2008 paper by Chang, Ratinov, Roth, and Srikumar that was published at AAAI’08, but the name given to the learning paradigm there was dataless classification. Roth has worked on probabilistic reasoning (including its complexity and probabilistic lifted inference ), Constrained Conditional Models (ILP formulations of NLP problems) and constraints-driven learning, part-based (constellation) methods in object recognition, response based Learning, He has developed NLP and Information extraction tools that are being used broadly by researchers and commercially, including NER, coreference resolution, wikification, SRL, and ESL text correction. Roth is a co-founder of NexLP, Inc., a startup that applies natural language processing and machine learning in the legal and compliance domains. In 2020, NexLP was acquired by Reveal, Inc., an e-discovery software company. He is currently on the scientific advisory board of the Allen Institute for AI.
Mean shift
Mean shift is a non-parametric feature-space mathematical analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm. Application domains include cluster analysis in computer vision and image processing. == History == The mean shift procedure is usually credited to work by Fukunaga and Hostetler in 1975. It is, however, reminiscent of earlier work by Schnell in 1964. == Overview == Mean shift is a procedure for locating the maxima—the modes—of a density function given discrete data sampled from that function. This is an iterative method, and we start with an initial estimate x {\displaystyle x} . Let a kernel function K ( x i − x ) {\displaystyle K(x_{i}-x)} be given. This function determines the weight of nearby points for re-estimation of the mean. Typically a Gaussian kernel on the distance to the current estimate is used, K ( x i − x ) = e − c | | x i − x | | 2 {\displaystyle K(x_{i}-x)=e^{-c||x_{i}-x||^{2}}} . The weighted mean of the density in the window determined by K {\displaystyle K} is m ( x ) = ∑ x i ∈ N ( x ) K ( x i − x ) x i ∑ x i ∈ N ( x ) K ( x i − x ) {\displaystyle m(x)={\frac {\sum _{x_{i}\in N(x)}K(x_{i}-x)x_{i}}{\sum _{x_{i}\in N(x)}K(x_{i}-x)}}} where N ( x ) {\displaystyle N(x)} is the neighborhood of x {\displaystyle x} , a set of points for which K ( x i − x ) ≠ 0 {\displaystyle K(x_{i}-x)\neq 0} . The difference m ( x ) − x {\displaystyle m(x)-x} is called mean shift in Fukunaga and Hostetler. The mean-shift algorithm now sets x ← m ( x ) {\displaystyle x\leftarrow m(x)} , and repeats the estimation until m ( x ) {\displaystyle m(x)} converges. Although the mean shift algorithm has been widely used in many applications, a rigid proof for the convergence of the algorithm using a general kernel in a high dimensional space is still not known. Aliyari Ghassabeh showed the convergence of the mean shift algorithm in one dimension with a differentiable, convex, and strictly decreasing profile function. However, the one-dimensional case has limited real world applications. Also, the convergence of the algorithm in higher dimensions with a finite number of the stationary (or isolated) points has been proved. However, sufficient conditions for a general kernel function to have finite stationary (or isolated) points have not been provided. Gaussian Mean-Shift is an Expectation–maximization algorithm. == Details == Let data be a finite set S {\displaystyle S} embedded in the n {\displaystyle n} -dimensional Euclidean space, X {\displaystyle X} . Let K {\displaystyle K} be a flat kernel that is the characteristic function of the λ {\displaystyle \lambda } -ball in X {\displaystyle X} , In each iteration of the algorithm, s ← m ( s ) {\displaystyle s\leftarrow m(s)} is performed for all s ∈ S {\displaystyle s\in S} simultaneously. The first question, then, is how to estimate the density function given a sparse set of samples. One of the simplest approaches is to just smooth the data, e.g., by convolving it with a fixed kernel of width h {\displaystyle h} , where x i {\displaystyle x_{i}} are the input samples and k ( r ) {\displaystyle k(r)} is the kernel function (or Parzen window). h {\displaystyle h} is the only parameter in the algorithm and is called the bandwidth. This approach is known as kernel density estimation or the Parzen window technique. Once we have computed f ( x ) {\displaystyle f(x)} from the equation above, we can find its local maxima using gradient ascent or some other optimization technique. The problem with this "brute force" approach is that, for higher dimensions, it becomes computationally prohibitive to evaluate f ( x ) {\displaystyle f(x)} over the complete search space. Instead, mean shift uses a variant of what is known in the optimization literature as multiple restart gradient descent. Starting at some guess for a local maximum, y k {\displaystyle y_{k}} , which can be a random input data point x 1 {\displaystyle x_{1}} , mean shift computes the gradient of the density estimate f ( x ) {\displaystyle f(x)} at y k {\displaystyle y_{k}} and takes an uphill step in that direction. == Types of kernels == Kernel definition: Let X {\displaystyle X} be the n {\displaystyle n} -dimensional Euclidean space, R n {\displaystyle \mathbb {R} ^{n}} . The norm of x {\displaystyle x} is a non-negative number, ‖ x ‖ 2 = x ⊤ x ≥ 0 {\displaystyle \|x\|^{2}=x^{\top }x\geq 0} . A function K : X → R {\displaystyle K:X\rightarrow \mathbb {R} } is said to be a kernel if there exists a profile, k : [ 0 , ∞ ] → R {\displaystyle k:[0,\infty ]\rightarrow \mathbb {R} } , such that K ( x ) = k ( ‖ x ‖ 2 ) {\displaystyle K(x)=k(\|x\|^{2})} and k is non-negative. k is non-increasing: k ( a ) ≥ k ( b ) {\displaystyle k(a)\geq k(b)} if a < b {\displaystyle a Color (or colour in Commonwealth English) is the visual perception produced by the activation of the different types of cone cells in the eye caused by light. Though color is not an inherent property of matter, color perception is related to an object's light absorption, emission, reflection and transmission. For most humans, visible wavelengths of light are the ones perceived in the visible light spectrum, with three types of cone cells (trichromacy). Other animals may have a different number of cone cell types or have eyes sensitive to different wavelengths, such as bees that can distinguish ultraviolet, and thus have a different color sensitivity range. Animal perception of color originates from different light wavelength or spectral sensitivity in cone cell types, which is then processed by the brain. Colors have perceived properties such as hue, colorfulness, and lightness. Colors can also be additively mixed (mixing light) or subtractively mixed (mixing pigments). If one color is mixed in the right proportions, because of metamerism, they may look the same as another stimulus with a different reflection or emission spectrum. For convenience, colors can be organized in a color space, which when being abstracted as a mathematical color model can assign each region of color with a corresponding set of numbers. Thus, color spaces are an essential tool for color reproduction in print, photography, computer monitors, and television. Some of the most well-known color models and color spaces are RGB, CMYK, HSL/HSV, CIE Lab, and YCbCr/YUV. Because the perception of color is an important aspect of human life, different colors have been associated with emotions, activity, and nationality. Names of color regions in different cultures can have different, sometimes overlapping areas. In visual arts, color theory is used to govern the use of colors in an aesthetically pleasing and harmonious way. The theory of color includes the color complements; color balance; and classification of primary colors, secondary colors, and tertiary colors. The study of colors in general is called color science. == Physical properties == Electromagnetic radiation is characterized by its wavelength (or frequency) and its intensity. When the wavelength is within the visible spectrum (the range of wavelengths humans can perceive, approximately from 390 nm to 700 nm), it is known as "visible light". Most light sources emit light at many different wavelengths; a source's spectrum is a distribution giving its intensity at each wavelength. Although the spectrum of light arriving at the eye from a given direction determines the color sensation in that direction, there are many more possible spectral combinations than color sensations. In fact, one may formally define a color as a class of spectra that give rise to the same color sensation, although such classes would vary widely among different animal species, and to a lesser extent among individuals within the same species. In each such class, the members are called metamers of the color in question. This effect can be visualized by comparing the light sources' spectral power distributions and the resulting colors. === Spectral colors === The familiar colors of the rainbow in the spectrum—named using the Latin word for appearance or apparition by Isaac Newton in 1671—include all those colors that can be produced by visible light of a single wavelength only, the pure spectral or monochromatic colors. The spectrum above shows approximate wavelengths (in nm) for spectral colors in the visible range. Spectral colors have 100% purity, and are fully saturated. A complex mixture of spectral colors can be used to describe any color, which is the definition of a light power spectrum. The spectral colors form a continuous spectrum, and how it is divided into distinct colors linguistically is a matter of culture and historical contingency. Despite the ubiquitous ROYGBIV mnemonic used to remember the spectral colors in English, the inclusion or exclusion of colors is contentious, with disagreement often focused on indigo and cyan. Even if the subset of color terms is agreed, their wavelength ranges and borders between them may not be. The intensity of a spectral color, relative to the context in which it is viewed, may alter its perception considerably. For example, a low-intensity orange-yellow is brown, and a low-intensity yellow-green is olive green. Additionally, hue shifts towards yellow or blue happen if the intensity of a spectral light is increased; this is called Bezold–Brücke shift. In color models capable of representing spectral colors, such as CIELUV, a spectral color has the maximal saturation. In Helmholtz coordinates, this is described as 100% purity. === Color of objects === The physical color of an object depends on how it absorbs and scatters light. Most objects scatter light to some degree and do not reflect or transmit light specularly like glasses or mirrors. A transparent object allows almost all light to transmit or pass through, thus transparent objects are perceived as colorless. Conversely, an opaque object does not allow light to transmit through and instead absorbs or reflects the light it receives. Like transparent objects, translucent objects allow light to transmit through, but translucent objects are seen colored because they scatter or absorb certain wavelengths of light via internal scattering. The absorbed light is often dissipated as heat. == Color vision == === Development of theories of color vision === Although Aristotle and other ancient scientists had already written on the nature of light and color vision, it was not until Isaac Newton that light was identified as the source of the color sensation. In 1810, Johann Wolfgang von Goethe published his comprehensive Theory of Colors in which he provided a rational description of color experience, which "tells us how it originates, not what it is". In 1801, Thomas Young proposed his trichromatic theory, to explain how a wide spectrum of different wavelengths could be detected by the human eye. It would be unreasonable to suppose that the human eye contained hundreds of different receptors each responding to the presence of a specific wavelength. Instead, he suggested that the human experience of color derives from a complex interaction and mixing from the output three receptors. This theory was later confirmed by James Clerk Maxwell and refined by Hermann von Helmholtz. Maxwell experimentally demonstrated that any color could be matched with a combination of three lights. As Helmholtz puts it, "the principles of Newton's law of mixture were experimentally confirmed by Maxwell in 1856. Young's theory of color sensations, like so much else that this marvelous investigator achieved in advance of his time, remained unnoticed until Maxwell directed attention to it." At the same time as Helmholtz, Ewald Hering developed the opponent process theory of color, noting that color blindness and afterimages typically come in opponent pairs (red-green, blue-orange, yellow-violet, and black-white). Ultimately these two theories were synthesized in 1957 by Hurvich and Jameson, who showed that retinal processing corresponds to the trichromatic theory, while processing at the level of the lateral geniculate nucleus corresponds to the opponent theory. In 1931, the International Commission on Illumination (CIE), an international group of experts, developed a mathematical color model which mapped out the space of observable colors, allowing every individual color able to be specified with a set of three numbers. === Color in the eye === The ability of the human eye to distinguish colors is based upon the varying sensitivity of different cells in the retina to light of different wavelengths. Humans are trichromatic—the retina contains three types of color receptor cells, or cones. One type, relatively distinct from the other two, is most responsive to light that is perceived as blue or blue-violet, with wavelengths around 450 nm; cones of this type are sometimes called short-wavelength cones or S cones (or misleadingly, blue cones). The other two types are closely related genetically and chemically: middle-wavelength cones, M cones, or green cones are most sensitive to light perceived as green, with wavelengths around 540 nm, while the long-wavelength cones, L cones, or red cones, are most sensitive to light that is perceived as greenish yellow, with wavelengths around 570 nm. Light, no matter how complex its composition of wavelengths, is reduced to three color components by the eye. Each cone type adheres to the principle of univariance, which is that each cone's output is determined by the amount of light that falls on it over all wavelengths. For each location in the visual field, the three types of cones yield three signals based on the extent to which each is stimulated. These amounts of stimulation are sometimes called tristimulus values. The response cu Teaspiller was a US-based web application for customers to find accountants and hire them to do their taxes and accounting online. In 2013 the company was acquired by Intuit, Inc and added to its TurboTax product line. The Teaspiller employees and code were all acquired and the product was renamed as "TurboTax CPA select". It enabled accountants to work remotely with clients (share files, send secure messages, schedule appointments), as well as find new clients looking for their specific skills through a complex search algorithm. This was done through extended profiles containing licensing information, professional histories, user ratings, peer endorsements, association memberships, and practice areas. The service had been called an H&R Block killer by Business Insider as it helped customers find accountants to prepare tax returns online. As of 2011 it had 20,000 US accountants listed on the site. The application was built using the Django framework. == History == Teaspiller was built by Vemdara, LLC, a web company based in New York and founded in 2009 by Amit Vemuri (a former VP at Travelocity). The web application was launched in 2010. In 2013 the company was acquired by Intuit as part of their TurboTax product line and renamed as "TurboTax CPA select". A software bot is a type of software agent in the service of software project management and software engineering. A software bot has an identity and potentially personified aspects in order to serve their stakeholders. Software bots often compose software services and provide an alternative user interface, which is sometimes, but not necessarily conversational. Software bots are typically used to execute tasks, suggest actions, engage in dialogue, and promote social and cultural aspects of a software project. The term bot is derived from robot. However, robots act in the physical world and software bots act only in digital spaces. Some software bots are designed and behave as chatbots, but not all chatbots are software bots. Discussions about the past and future of software bots show that software bots have been adopted for many years. == Usage == Software bots are used to support development activities, such as communication among software developers and automation of repetitive tasks. Software bots have been adopted by several communities related to software development, such as open-source communities on GitHub and Stack Overflow. GitHub bots have user accounts and can open, close, or comment on pull requests and issues. GitHub bots have been used to assign reviewers, ask contributors to sign the Contributor License Agreement, report continuous integration failures, review code and pull requests, welcome newcomers, run automated tests, merge pull requests, fix bugs and vulnerabilities, etc. The Slack tool includes an API for developing software bots. There are slack bots for keeping track of todo lists, coordinating standup meetings, and managing support tickets. The ChatBot company products further simplify the process of creating a custom Slack bot. On Wikipedia, Wikipedia bots automate a variety of tasks, such as creating stub articles, consistently updating the format of multiple articles, and so on. Bots like ClueBot NG are capable of recognizing vandalism and automatically remove disruptive content. == Taxonomies and Classification Frameworks == Lebeuf et al. provide a faceted taxonomy to characterize bots based on a literature review. It is composed of 3 main facets: (i) properties of the environment that the bot was created in; (ii) intrinsic properties of the bot itself; and (iii) the bot's interactions within its environment. They further detail the facets into sets of sub-facets under each of the main facets. Paikari and van der Hoek defined a set of dimensions to enable comparison of software bots, applied specifically to chatbots. It resulted in six dimensions: Type: the main purpose of the bot (information, collaboration, or automation) Direction of the "conversation" (input, output, or bi-directional) Guidance (human-mediated, or autonomous) Predictability (deterministic, or evolving) Interaction style (dull, alternate vocabulary, relationship-builder, human-like) Communication channel (text, voice, or both) Erlenhov et al. raised the question of the difference between a bot and simple automation, since much research done in the name of software bots uses the term bot to describe various different tools and sometimes things are "just" plain old development tools. After interviewing and surveying over 100 developers the authors found that not one, but three definitions dominated the community. They created three personas based on these definitions and the difference between what the three personas see as being a bot is mainly the association with a different set of human-like traits. The chat bot persona (Charlie) primarily thinks of bots as tools that communicates with the developer through a natural language interface (typically voice or chat), and caring little about what tasks the bot is used for or how it actually implements these tasks. The autonomous bot persona (Alex) thinks of bots as tools that work on their own (without requiring much input from a developer) on a task that would normally be done by a human. The smart bot persona (Sam) separates bots and plain old development tools through how smart (technically sophisticated) a tool is. Sam cares less about how the tool communicates, but more about if it is unusually good or adaptive at executing a task. The authors recommends that people doing research or writing about bots try to put their work in the context of one of the personas since the personas have different expectations and problems with the tools. == Example of notable bots == Dependabot and Renovatebot update software dependencies and detect vulnerabilities. (https://dependabot.com/) Probot is an organization that create and maintain bots for GitHub. The example bots using Probot are the following. Auto Assign (https://probot.github.io/apps/auto-assign/) license bot (https://probot.github.io/) Sentiment bot (https://probot.github.io/apps/sentiment-bot/) Untrivializer bot (https://probot.github.io/apps/untrivializer/) Refactoring-Bot (Refactoring-Bot): provides refactoring based on static code analysis Looks good to me bot (LGTM) is a Semmle product that inspects pull requests on GitHub for code style and unsafe code practices. == Issues and threats == Software bots may not be well accepted by humans. A study from the University of Antwerp has compared how developers active on Stack Overflow perceive answers generated by software bots. They find that developers perceive the quality of software bot-generated answers to be significantly worse if the identity of the software bot is made apparent. By contrast, answers from software bots with human-like identity were better received. In practice, when software bots are used on platforms like GitHub or Wikipedia, their username makes it clear that they are bots, e.g., DependaBot, RenovateBot, DatBot, SineBot. Bots may be subject to special rules. For instance, the GitHub terms of service does not allow 'bots' but accepts 'machine account', where a 'machine account' has two properties: 1) a human takes full responsibility of the bot's actions 2) it cannot create other accounts. The Loebner Prize was an annual competition in artificial intelligence that awarded prizes to the computer programs considered by the judges to be the most human-like. The format of the competition was that of a standard Turing test. In each round, a human judge simultaneously held textual conversations with a computer program and a human being via computer. Based upon the responses, the judge would attempt to determine which was which. The contest was launched in 1990 by Hugh Loebner in conjunction with the Cambridge Center for Behavioral Studies, Massachusetts, United States. In 2004 and 2005, it was held in Loebner's apartment in New York City. Within the field of artificial intelligence, the Loebner Prize is somewhat controversial; the most prominent critic, Marvin Minsky, called it a publicity stunt that does not help the field along. Beginning in 2014, it was organised by the AISB at Bletchley Park. It has also been associated with Flinders University, Dartmouth College, the Science Museum in London, University of Reading and Ulster University, Magee Campus, Derry, UK City of Culture. For the final 2019 competition, the format changed. There was no panel of judges. Instead, the chatbots were judged by the public and there were to be no human competitors. The prize has been reported as defunct as of 2020. == Prizes == Originally, $2,000 was awarded for the most human-seeming program in the competition. The prize was $3,000 in 2005 and $2,250 in 2006. In 2008, $3,000 was awarded. In addition, there were two one-time-only prizes that have never been awarded. $25,000 is offered for the first program that judges cannot distinguish from a real human and which can convince judges that the human is the computer program. $100,000 is the reward for the first program that judges cannot distinguish from a real human in a Turing test that includes deciphering and understanding text, visual, and auditory input. The competition was planned to end after the achievement of this prize. == Competition rules and restrictions == The rules varied over the years and early competitions featured restricted conversation Turing tests but since 1995 the discussion has been unrestricted. For the three entries in 2007, Robert Medeksza, Noah Duncan and Rollo Carpenter, some basic "screening questions" were used by the sponsor to evaluate the state of the technology. These included simple questions about the time, what round of the contest it is, etc.; general knowledge ("What is a hammer for?"); comparisons ("Which is faster, a train or a plane?"); and questions demonstrating memory for preceding parts of the same conversation. "All nouns, adjectives and verbs will come from a dictionary suitable for children or adolescents under the age of 12." Entries did not need to respond "intelligently" to the questions to be accepted. For the first time in 2008 the sponsor allowed introduction of a preliminary phase to the contest opening up the competition to previously disallowed web-based entries judged by a variety of invited interrogators. The available rules do not state how interrogators are selected or instructed. Interrogators (who judge the systems) have limited time: 5 minutes per entity in the 2003 competition, 20+ per pair in 2004–2007 competitions, 5 minutes to conduct simultaneous conversations with a human and the program in 2008–2009, increased to 25 minutes of simultaneous conversation since 2010. == Criticisms == The prize has long been scorned by experts in the field, for a variety of reasons. It is regarded by many as a publicity stunt. Marvin Minsky scathingly offered a "prize" to anyone who could stop the competition. Loebner responded by jokingly observing that Minsky's offering a prize to stop the competition effectively made him a co-sponsor. The rules of the competition have encouraged poorly qualified judges to make rapid judgements. Interactions between judges and competitors was originally very brief, for example effectively 2.5 mins of questioning, which permitted only a few questions. Questioning was initially restricted to a single topic of the contestant's choice, such as "whimsical conversation", a domain suiting standard chatbot tricks. Competition entrants do not aim at understanding or intelligence but resort to basic ELIZA style tricks, and successful entrants find deception and pretense is rewarded. == Contests == See article history for more details of some earlier contests. A very incomplete listing of a few of the contests: === 2003 === In 2003, the contest was organised by Professor Richard H. R. Harper and Dr. Lynne Hamill from the Digital World Research Centre at the University of Surrey. Although no bot passed the Turing test, the winner was Jabberwock, created by Juergen Pirner. Second was Elbot (Fred Roberts, Artificial Solutions). Third was Jabberwacky, (Rollo Carpenter). === 2006 === In 2006, the contest was organised by Tim Child (CEO of Televirtual) and Huma Shah. On August 30, the four finalists were announced: Rollo Carpenter Richard Churchill and Marie-Claire Jenkins Noah Duncan Robert Medeksza The contest was held on 17 September in the VR theatre, Torrington Place campus of University College London. The judges included the University of Reading's cybernetics professor, Kevin Warwick, a professor of artificial intelligence, John Barnden (specialist in metaphor research at the University of Birmingham), a barrister, Victoria Butler-Cole and a journalist, Graham Duncan-Rowe. The latter's experience of the event can be found in an article in Technology Review. The winner was 'Joan', based on Jabberwacky, both created by Rollo Carpenter. === 2007 === The 2007 competition was held on October 21 in New York City. The judges were: computer science professor Russ Abbott, philosophy professor Hartry Field, psychology assistant professor Clayton Curtis and English lecturer Scott Hutchins. No bot passed the Turing test, but the judges ranked the three contestants as follows: 1st: Robert Medeksza, creator of Ultra Hal 2nd: Noah Duncan, a private entry, creator of Cletus 3rd: Rollo Carpenter from Icogno, creator of Jabberwacky The winner received $2,250 and the annual medal. The runners-up received $250 each. === 2008 === The 2008 competition was organised by professor Kevin Warwick, coordinated by Huma Shah and held on October 12 at the University of Reading, UK. After testing by over one hundred judges during the preliminary phase, in June and July 2008, six finalists were selected from thirteen original entrant artificial conversational entities (ACEs). Five of those invited competed in the finals: Brother Jerome, Peter Cole and Benji Adams Elbot, Fred Roberts / Artificial Solutions Eugene Goostman, Vladimir Veselov, Eugene Demchenko and Sergey Ulasen Jabberwacky, Rollo Carpenter Ultra Hal, Robert Medeksza In the finals, each of the judges was given five minutes to conduct simultaneous, split-screen conversations with two hidden entities. Elbot of Artificial Solutions won the 2008 Loebner Prize bronze award, for most human-like artificial conversational entity, through fooling three of the twelve judges who interrogated it (in the human-parallel comparisons) into believing it was human. This is coming very close to the 30% traditionally required to consider that a program has actually passed the Turing test. Eugene Goostman and Ultra Hal both deceived one judge each that it was the human. Will Pavia, a journalist for The Times, has written about his experience; a Loebner finals' judge, he was deceived by Elbot and Eugene. Kevin Warwick and Huma Shah have reported on the parallel-paired Turing tests. === 2009 === The 2009 Loebner Prize Competition was held September 6, 2009, at the Brighton Centre, Brighton UK in conjunction with the Interspeech 2009 conference. The prize amount for 2009 was $3,000. Entrants were David Levy, Rollo Carpenter, and Mohan Embar, who finished in that order. The writer Brian Christian participated in the 2009 Loebner Prize Competition as a human confederate, and described his experiences at the competition in his book The Most Human Human. === 2010 === The 2010 Loebner Prize Competition was held on October 23 at California State University, Los Angeles. The 2010 competition was the 20th running of the contest. The winner was Bruce Wilcox with Suzette. === 2011 === The 2011 Loebner Prize Competition was held on October 19 at the University of Exeter, Devon, United Kingdom. The prize amount for 2011 was $4,000. The four finalists and their chatterbots were Bruce Wilcox (Rosette), Adeena Mignogna (Zoe), Mohan Embar (Chip Vivant) and Ron Lee (Tutor), who finished in that order. That year there was an addition of a panel of junior judges, namely Georgia-Mae Lindfield, William Dunne, Sam Keat and Kirill Jerdev. The results of the junior contest were markedly different from the main contest, with chatterbots Tutor and Zoe tying for first place and Chip Vivant and Rosette coming in third and fourt Stride was a cloud-based team business communication and collaboration tool, launched by Atlassian on 7 September 2017 to replace the cloud-based version of HipChat. Stride software was available to download onto computers running Windows, Mac or Linux, as well as Android, iOS smartphones, and tablets. Stride was bought by Atlassian's competitor Slack Technologies and was discontinued on February 15, 2019. The features of Stride include chat rooms, one-on-one messaging, file sharing, 5 GB of file storage, group voice and video calling, built-in collaboration tools, and up to 25,000 of searchable message history. Premium features include unlimited file storage, users, group chat rooms, file sharing and storage, apps, and history retention. The premium version, priced at $3/user/month, also includes advanced meeting functionality like group screen sharing, remote desktop control, and dial-in/dial-out capabilities. Stride offered integrations with Atlassian's other products as well as other third-party applications listed in the Atlassian Marketplace, such as GitHub, Giphy, Stand-Bot and Google Calendar. Stride offered additional features beyond messaging to improve efficiency and productivity. It aimed to reduce collaboration noise by introducing a "focus" mode, and eliminates the divisions between text chat, voice meetings, and videoconferencing, by simplifying transitioning between these modes in the same channel. On July 26, 2018, Atlassian announced that HipChat and Stride would be discontinued February 15, 2019, and that it had reached a deal to sell their intellectual property to Slack. Slack paid an undisclosed amount over three years to assume the user bases of the services, while Atlassian took a minority investment in Slack. The companies also announced a commitment to work on integration of Slack with Atlassian services.Color
Teaspiller
Software bot
Loebner Prize
Stride (software)