AI Code Tester

AI Code Tester — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • CatDV

    CatDV

    CatDV is a media asset manager program for handling multimedia production workflows developed by Square Box Systems. Quantum Corporation acquired Square Box Systems in 2020. == Versions == The full family of CatDV Products is as follows: CatDV Standalone Products CatDV Professional Edition CatDV Pegasus CatDV Networked Products CatDV Essential - entry level server product CatDV Enterprise Server - for MySQL databases and most common server platforms including Linux, Windows and Mac OS X CatDV Pegasus Server - adds features such as high performance full-text indexing, access control lists, and more CatDV Worker Node - automated workflow and transcoding engine CatDV Web Client - provides access to the CatDV database via a web browser. There is no need to install special software on the desktop, making it easy to deploy to a large number of users. CatDV Professional Edition & Pegasus Clients - designed to support the multi-user capabilities of the CatDV Enterprise and Workgroup Servers from the desktop Using plugins and scripting, which often require additional professional services support to set up, complex integrations with a wide variety of third party systems (including archive, cloud storage, and artificial intelligence) are possible. == Awards == CatDV won two awards in 2010, a blue ribbon from Creative COW Magazine and a "Best of Show Vidy Award" from Videography. In April 2012 Square Box won a Queen's Award for Enterprise for CatDV.

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  • Top 10 AI Pair Programmers Compared (2026)

    Top 10 AI Pair Programmers Compared (2026)

    Looking for the best AI pair programmer? An AI pair programmer is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI pair programmer slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

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  • Brendan Frey

    Brendan Frey

    Brendan John Frey FRSC (born 29 August 1968) is a Canadian computer scientist, entrepreneur, and engineer. He is Founder and CEO of Deep Genomics, Cofounder of the Vector Institute for Artificial Intelligence and Professor of Engineering and Medicine at the University of Toronto. Frey is a pioneer in the development of machine learning and artificial intelligence methods, their use in accurately determining the consequences of genetic mutations, and in designing medications that can slow, stop or reverse the progression of disease. As far back as 1995, Frey co-invented one of the first deep learning methods, called the wake-sleep algorithm, the affinity propagation algorithm for clustering and data summarization, and the factor graph notation for probability models. In the late 1990s, Frey was a leading researcher in the areas of computer vision, speech recognition, and digital communications. == Education == Frey studied computer engineering and physics at the University of Calgary (BSc 1990) and the University of Manitoba (MSc 1993), and then studied neural networks and graphical models as a doctoral candidate at the University of Toronto under the supervision of Geoffrey Hinton (PhD 1997). He was an invited participant of the Machine Learning program at the Isaac Newton Institute for Mathematical Sciences in Cambridge, UK (1997) and was a Beckman Fellow at the University of Illinois at Urbana Champaign (1999). == Career == Following his undergraduate studies, Frey worked as a junior research scientist at Bell-Northern Research from 1990 to 1991. After completing his postdoctoral studies at the University of Illinois at Urbana-Champaign, Frey was an assistant professor in the Department of Computer Science at the University of Waterloo, from 1999 to 2001. In 2001, Frey joined the Department of Electrical and Computer Engineering at the University of Toronto and was cross-appointed to the Department of Computer Science, the Banting and Best Department of Medical Research and the Terrence Donnelly Centre for Cellular and Biomolecular Research. From 2008 to 2009, he was a visiting researcher at Microsoft Research (Cambridge, UK) and a visiting professor in the Cavendish Laboratories and Darwin College at Cambridge University. Between 2001 and 2014, Frey consulted for several groups at Microsoft Research and acted as a member of its Technical Advisory Board. In 2002, a personal crisis led Frey to face the fact that there was a tragic gap between our ability to measure a patient's mutations and our ability to understand and treat the consequences. Recognizing that biology is too complex for humans to understand, that in the decades to come there would be an exponential growth in biology data, and that machine learning is the best technology we have for discovering relationships in large datasets, Frey set out to build machine learning systems that could accurately predict genome and cell biology. Frey’s group pioneered much of the early work in the field and over the next 15 years published more papers in leading-edge journals than any other academic or industrial research lab. In 2015, Frey founded Deep Genomics, with the goal of building a company that can produce effective and safe genetic medicines more rapidly and with a higher rate of success than was previously possible. The company has received 240 million dollars in funding to date from leading Bay Area investors, including the backers of SpaceX and Tesla.

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  • Ω-automaton

    Ω-automaton

    In automata theory, a branch of theoretical computer science, an ω-automaton (or stream automaton) is a variation of a finite automaton that runs on infinite, rather than finite, strings as input. Since ω-automata do not stop, they have a variety of acceptance conditions rather than simply a set of accepting states. ω-automata are useful for specifying behavior of systems that are not expected to terminate, such as hardware, operating systems and control systems. For such systems, one may want to specify a property such as "for every request, an acknowledge eventually follows", or its negation "there is a request that is not followed by an acknowledge". The former is a property of infinite words: one cannot say of a finite sequence that it satisfies this property. Classes of ω-automata include the Büchi automata, Rabin automata, Streett automata, parity automata and Muller automata, each deterministic or non-deterministic. These classes of ω-automata differ only in terms of acceptance condition. They all recognize precisely the regular ω-languages except for the deterministic Büchi automata, which is strictly weaker than all the others. Although all these types of automata recognize the same set of ω-languages, they nonetheless differ in succinctness of representation for a given ω-language. == Deterministic ω-automata == Formally, a deterministic ω-automaton is a tuple A = ( Q , Σ , δ , q 0 , A a c c ) {\textstyle A=(Q,\Sigma ,\delta ,q_{0},A_{acc})} , that consists of the following components: Q {\textstyle Q} , is a finite set. The elements of Q {\textstyle Q} are called the states of A {\textstyle A} . Σ {\textstyle \Sigma } , is a finite set called the alphabet of A {\textstyle A} . δ : Q × Σ → Q {\textstyle \delta \colon Q\times \Sigma \rightarrow Q} is a function, called the transition function of A {\textstyle A} . Q 0 {\textstyle Q_{0}} is an element of Q {\textstyle Q} , called the initial state. A a c c {\textstyle A_{acc}} is a set of accepting states of A {\textstyle A} , formally a subset of Q ω {\textstyle Q^{\omega }} . An input for A {\textstyle A} is an infinite string over the alphabet Σ {\textstyle \Sigma } , i.e. it is an infinite sequence α = ( a 1 , a 2 , a 3 , … ) {\textstyle \alpha =(a_{1},a_{2},a_{3},\ldots )} . The run of A {\textstyle A} on such an input is an infinite sequence ρ = ( r 0 , r 1 , r 2 , … ) {\textstyle \rho =(r_{0},r_{1},r_{2},\ldots )} of states, defined as follows: r 0 = q 0 {\textstyle r_{0}=q_{0}} . r 1 = δ ( r 0 , a 1 ) {\textstyle r_{1}=\delta (r_{0},a_{1})} . r 2 = δ ( r 1 , a 2 ) {\textstyle r_{2}=\delta (r_{1},a_{2})} . ... that is, for every i {\textstyle i} : r i = δ ( r i − 1 , a i ) {\textstyle r_{i}=\delta (r_{i-1},a_{i})} . The main purpose of an ω-automaton is to define a subset of the set of all inputs: The set of accepted inputs. Whereas in the case of an ordinary finite automaton every run ends with a state r n {\textstyle r_{n}} and the input is accepted if and only if r n {\textstyle r_{n}} is an accepting state, the definition of the set of accepted inputs is more complicated for ω-automata. Here we must look at the entire run ρ {\textstyle \rho } . The input is accepted if the corresponding run is in Acc {\textstyle {\text{Acc}}} . The set of accepted input ω-words is called the recognized ω-language by the automaton, which is denoted as L ( A ) {\textstyle L(A)} . The definition of Acc {\textstyle {\text{Acc}}} as a subset of Q ω {\textstyle Q^{\omega }} is purely formal and not suitable for practice because normally such sets are infinite. The difference between various types of ω-automata (Büchi, Rabin etc.) consists in how they encode certain subsets Acc {\textstyle {\text{Acc}}} of Q ω {\textstyle Q^{\omega }} as finite sets, and therefore in which such subsets they can encode. == Nondeterministic ω-automata == Formally, a nondeterministic ω-automaton is a tuple A = ( Q , Σ , Δ , Q 0 , Acc ) {\textstyle A=(Q,\Sigma ,\Delta ,Q_{0},{\text{Acc}})} that consists of the following components: Q {\textstyle Q} is a finite set. The elements of Q {\textstyle Q} are called the states of A {\textstyle A} . Σ {\textstyle \Sigma } is a finite set called the alphabet of A {\textstyle A} . Δ {\textstyle \Delta } is a subset of Q × Σ × Q {\textstyle Q\times \Sigma \times Q} and is called the transition relation of A {\textstyle A} . Q 0 {\textstyle Q_{0}} is a subset of Q {\textstyle Q} , called the initial set of states. Acc {\textstyle {\text{Acc}}} is the acceptance condition, a subset of Q ω {\textstyle Q^{\omega }} . Unlike a deterministic ω-automaton, which has a transition function δ {\textstyle \delta } , the non-deterministic version has a transition relation Δ {\textstyle \Delta } . Note that Δ {\textstyle \Delta } can be regarded as a function Q × Σ → P ( Q ) {\textstyle Q\times \Sigma \rightarrow {\mathcal {P}}(Q)} from Q × Σ {\textstyle Q\times \Sigma } to the power set P ( Q ) {\textstyle {\mathcal {P}}(Q)} . Thus, given a state q n {\textstyle q_{n}} and a symbol a n {\textstyle a_{n}} , the next state q n + 1 {\textstyle q_{n+1}} is not necessarily determined uniquely, rather there is a set of possible next states. A run of A {\textstyle A} on the input α = ( a 1 , a 2 , a 3 , … ) {\textstyle \alpha =(a_{1},a_{2},a_{3},\ldots )} is any infinite sequence ρ = ( r 0 , r 1 , r 2 , … ) {\textstyle \rho =(r_{0},r_{1},r_{2},\ldots )} of states that satisfies the following conditions: r 0 {\textstyle r_{0}} is an element of Q 0 {\textstyle Q_{0}} . r 1 {\textstyle r_{1}} is an element of Δ ( r 0 , a 1 ) {\textstyle \Delta (r_{0},a_{1})} . r 2 {\textstyle r_{2}} is an element of Δ ( r 1 , a 2 ) {\textstyle \Delta (r_{1},a_{2})} . ... that is, for every i {\textstyle i} : r i {\textstyle r_{i}} is an element of Δ ( r i − 1 , a i ) {\textstyle \Delta (r_{i-1},a_{i})} . A nondeterministic ω-automaton may admit many different runs on any given input, or none at all. The input is accepted if at least one of the possible runs is accepting. Whether a run is accepting depends only on Acc {\textstyle {\text{Acc}}} , as for deterministic ω-automata. Every deterministic ω-automaton can be regarded as a nondeterministic ω-automaton by taking Δ {\textstyle \Delta } to be the graph of δ {\textstyle \delta } . The definitions of runs and acceptance for deterministic ω-automata are then special cases of the nondeterministic cases. == Acceptance conditions == Acceptance conditions may be infinite sets of ω-words. However, people mostly study acceptance conditions that are finitely representable. The following lists a variety of popular acceptance conditions. Before discussing the list, let's make the following observation. In the case of infinitely running systems, one is often interested in whether certain behavior is repeated infinitely often. For example, if a network card receives infinitely many ping requests, then it may fail to respond to some of the requests but should respond to an infinite subset of received ping requests. This motivates the following definition: For any run ρ {\textstyle \rho } , let Inf ( ρ ) {\textstyle {\text{Inf}}(\rho )} be the set of states that occur infinitely often in ρ {\textstyle \rho } . This notion of certain states being visited infinitely often will be helpful in defining the following acceptance conditions. A Büchi automaton is an ω-automaton A {\textstyle A} that uses the following acceptance condition, for some subset F {\textstyle F} of Q {\textstyle Q} : Büchi condition A {\textstyle A} accepts exactly those runs ρ {\textstyle \rho } for which Inf ( ρ ) ∩ F ≠ ∅ {\textstyle {\text{Inf}}(\rho )\cap F\neq \emptyset } , i.e. there is an accepting state that occurs infinitely often in ρ {\textstyle \rho } . A Rabin automaton is an ω-automaton A {\textstyle A} that uses the following acceptance condition, for some set Ω {\textstyle \Omega } of pairs ( B i , G i ) {\textstyle (B_{i},G_{i})} of sets of states: Rabin condition A {\textstyle A} accepts exactly those runs ρ {\textstyle \rho } for which there exists a pair ( B i , G i ) {\textstyle (B_{i},G_{i})} in Ω {\textstyle \Omega } such that B i ∩ Inf ( ρ ) = ∅ {\textstyle B_{i}\cap {\text{Inf}}(\rho )=\emptyset } and G i ∩ Inf ( ρ ) ≠ ∅ {\textstyle G_{i}\cap {\text{Inf}}(\rho )\neq \emptyset } . A Streett automaton is an ω-automaton A {\textstyle A} that uses the following acceptance condition, for some set Ω {\textstyle \Omega } of pairs ( B i , G i ) {\textstyle (B_{i},G_{i})} of sets of states: Streett condition A {\textstyle A} accepts exactly those runs ρ {\textstyle \rho } such that for all pairs ( B i , G i ) {\textstyle (B_{i},G_{i})} in Ω {\textstyle \Omega } , B i ∩ Inf ( ρ ) ≠ ∅ {\textstyle B_{i}\cap {\text{Inf}}(\rho )\neq \emptyset } or G i ∩ Inf ( ρ ) = ∅ {\textstyle G_{i}\cap {\text{Inf}}(\rho )=\emptyset } . A parity automaton is an automaton A {\textstyle A} whose set of states is Q = { 0 , 1 , 2 , … , k } {\textstyle Q=\{0,1,2,\ldots ,k\}} for some natural number k {\textst

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  • Echo Lake (software)

    Echo Lake (software)

    Echo Lake (AKA Family Album Creator) was the most notable multimedia software product produced by Delrina, which debuted in June 1995. It was touted internally as a "cross [of] Quark Xpress and Myst". It featured an immersive 3D environment where a user could go to a virtual desktop in a virtual office and assemble video and audio clips along with images, and then print them out as either a virtual book other users of the program could use, or for print. It was a highly innovative product for its time, and ultimately was hampered by the inability of many users able to input their own multimedia content easily into a computer from that period. Creative Wonders bought the rights to the Echo Lake multimedia product, which was re-shaped as an introductory program on multimedia and re-released as Family Album Creator in 1996.

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  • Ross Quinlan

    Ross Quinlan

    John Ross Quinlan is a computer science researcher in data mining and decision theory. He has contributed extensively to the development of decision tree algorithms, including inventing the canonical C4.5 and ID3 algorithms. He also contributed to early ILP literature with First Order Inductive Learner (FOIL). He is currently running the company RuleQuest Research which he founded in 1997. == Education == He received his BSc degree in Physics and Computing from the University of Sydney in 1965 and his computer science doctorate at the University of Washington in 1968. He has held positions at the University of New South Wales, University of Sydney, University of Technology Sydney, and RAND Corporation. == Artificial intelligence == Quinlan is a specialist in artificial intelligence, particularly in the aspect involving machine learning and its application to data mining. He is a Founding Fellow of the Association for the Advancement of Artificial Intelligence. === ID3 === Ross Quinlan invented the Iterative Dichotomiser 3 (ID3) algorithm which is used to generate decision trees. ID3 follows the principle of Occam's razor in attempting to create the smallest decision tree possible. === C4.5 === He then expanded upon the principles used in ID3 to create C4.5. C4.5 improved: discrete and continuous attributes, missing attribute values, attributes with differing costs, pruning trees (replacing irrelevant branches with leaf nodes). === C5.0 === C5.0, which Quinlan is commercially selling (single-threaded version is distributed under the terms of the GNU General Public License), is an improvement on C4.5. The advantages are speed (several orders of magnitude faster), memory efficiency, smaller decision trees, boosting (more accuracy), ability to weight different attributes, and winnowing (reducing noise). == Selected works == === Books === 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers. ISBN 1-55860-238-0. === Articles === Quinlan, J. R. (1982) Semi-autonomous acquisition of pattern-based knowledge, In Machine intelligence 10 (eds J. E. Hayes, D. Michie, and Y.-H. Pao). Ellis Norwood,Chichester. Quinlan, J.R. (1985). Decision trees and multi-valued attributes, In J.E. Hayes & D. Michie (Eds.), Machine intelligence 11. Oxford University Press. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1):81-106 2008. (with Qiang Yang, Philip S. Yu, Zhou Zhihua, and David Hand et al). Top 10 algorithms in data mining. Knowledge and Information Systems 14.1: 1-37 Quinlan, J. R. (1990). Learning logical definitions from relations. Machine Learning, 5:239-266.

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  • Top 10 AI Logo Makers Compared (2026)

    Top 10 AI Logo Makers Compared (2026)

    In search of the best AI logo maker? An AI logo maker is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI logo maker slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.

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  • Stefano Soatto

    Stefano Soatto

    Stefano Soatto is professor of computer science at the University of California, Los Angeles (UCLA), in Los Angeles, CA, where he is also professor of electrical engineering and founding director of the UCLA Vision Lab. He is also Vice President of applied science for Amazon Web Services' (AWS) AI division. == Academic biography == Soatto obtained his D. Eng. in electrical engineering, cum laude, from the University of Padua in 1992, was an EAP Fellow at the University of California, Berkeley in 1990–1991, and received his Ph.D. in control and dynamical systems from the California Institute of Technology in 1996 with dissertation "A Geometric Approach to Dynamic Vision". In 1996–97 he was a postdoctoral scholar at Harvard University, and subsequently held positions as assistant and associate professor of electrical engineering and biomedical engineering at Washington University in St. Louis, and of mathematics and computer science at the University of Udine, Italy. He has been at UCLA since 2000. He is also Vice President of applied science for Amazon Web Services' (AWS) AI division. == Research == Soatto's research focuses on computer vision, machine learning and robotics. He co-developed optimal algorithms for structure from motion (SFM, or visual SLAM, simultaneous localization and mapping, in robotics; Best Paper Award at CVPR 1998), characterized its ambiguities (David Marr Prize at ICCV 1999), also characterized the identifiability and observability of visual-inertial sensor fusion (Best Paper Award at ICRA 2015). His research focus is the development of representations, that are functions of the data that capture their informative content and discard irrelevant variability in the data (a generalized form of 'noise' or 'clutter'). Soatto's lab first to demonstrate real-time SFM and augmented reality (AR) on commodity hardware in live demos at CVPR 2000, ICCV 2001, and ECCV 2002. He also co-led the UCLA-Golem Team in the second DARPA Grand Challenge for autonomous vehicles, with Emilio Frazzoli (co-founder of NuTonomy), and Amnon Shashua (co-founder of Mobileye). == Recognition == Soatto was named Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2013 for contributions to dynamic visual processes. He received the David Marr Prize in Computer Vision in 1999. He was named to the 2022 class of ACM Fellows, "for contributions to the foundations and applications of visual geometry and visual representations learning".

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  • Procreate (software)

    Procreate (software)

    Procreate is a raster graphics editor app for digital painting developed and published by the Australian company Savage Interactive for iOS and iPadOS. It was launched on the App Store in 2011. == Versions == === Procreate === Procreate for iPad was first released in 2011 by the Tasmanian software company Savage Interactive. In June 2013, Savage launched Procreate 2 in conjunction with iOS 7, adding new features such as higher resolution capabilities and more brush options. In 2016, Procreate became one of the top ten best-selling iPad apps on the App Store. In 2018, Procreate became the overall best selling iPad app. With iOS 26, Procreate adapted Liquid Glass into its software. As of March 2026, the most recent version of Procreate for the iPad is 5.4.9. === Procreate Pocket === Procreate Pocket was released to the App Store in December 2014. In 2018, Savage launched Procreate Pocket 2.0 to the App Store. In December 2018, Procreate Pocket received Apple's "App of the Year" award. As of September 2025, the most recent version of Procreate Pocket (for the iPhone) is 4.0.15. === Procreate Dreams === Procreate Dreams, their more recent app focused on 2D animation, was released on the App Store on November 22, 2023. While the application is commended for its intuitive interface and accessibility, some reviewers have noted that it may lack some key animations features, such as reference layers. In June 2024, Procreate Dreams received the 2024 Apple Design Award for Innovation. In December 2025, Savage Interactive released Procreate Dreams 2, a long awaited update and redesign to Procreate Dreams. == Features == The current versions of Procreate use Valkyrie, a proprietary graphics engine to allow customisable brush options and importing brushes from Adobe Photoshop. Procreate offers known features like layers, masks, and blending mode. Its biggest standout compared to other professional drawing software is its simple UI and comparatively easy learning curve. The app also allows for animation. Savage expanded upon Procreate's animation features with a companion app dedicated to 2D animation called Procreate Dreams, released in November 2023. On August 2024, Procreate announced that it would not be incorporating generative artificial intelligence into its software. Savage offers a free internet forum called Procreate Discussions in which users can ask for help, suggest ideas, and share user-generated content on the marketplace or the resources board. == Notable users == Concept artist Doug Chiang creates robot, vehicle, and creature designs for Star Wars in Procreate. Professional artists have also used Procreate to create the posters for Stranger Things, Logan, and Blade Runner 2049, as well as several covers for The New Yorker. It has also been professionally adopted at Marvel Comics, DC Comics, Disney Animation, and Pixar.

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  • AI Marketing Tools Reviews: What Actually Works in 2026

    AI Marketing Tools Reviews: What Actually Works in 2026

    In search of the best AI marketing tool? An AI marketing tool is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI marketing tool slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.

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  • Separating words problem

    Separating words problem

    In theoretical computer science, the separating words problem is the problem of finding the smallest deterministic finite automaton that behaves differently on two given strings, meaning that it accepts one of the two strings and rejects the other string. It is an open problem how large such an automaton must be, in the worst case, as a function of the length of the input strings. == Example == The two strings 0010 and 1000 may be distinguished from each other by a three-state automaton in which the transitions from the start state go to two different states, both of which are terminal in the sense that subsequent transitions from these two states always return to the same state. The state of this automaton records the first symbol of the input string. If one of the two terminal states is accepting and the other is rejecting, then the automaton will accept only one of the strings 0010 and 1000. However, these two strings cannot be distinguished by any automaton with fewer than three states. == Simplifying assumptions == For proving bounds on this problem, it may be assumed without loss of generality that the inputs are strings over a two-letter alphabet. For, if two strings over a larger alphabet differ then there exists a string homomorphism that maps them to binary strings of the same length that also differ. Any automaton that distinguishes the binary strings can be translated into an automaton that distinguishes the original strings, without any increase in the number of states. It may also be assumed that the two strings have equal length. For strings of unequal length, there always exists a prime number p whose value is logarithmic in the smaller of the two input lengths, such that the two lengths are different modulo p. An automaton that counts the length of its input modulo p can be used to distinguish the two strings from each other in this case. Therefore, strings of unequal lengths can always be distinguished from each other by automata with few states. == History and bounds == The problem of bounding the size of an automaton that distinguishes two given strings was first formulated by Goralčík & Koubek (1986), who showed that the automaton size is always sublinear. Later, Robson (1989) proved the upper bound O(n2/5(log n)3/5) on the automaton size that may be required. This was improved by Chase (2020) to O(n1/3(log n)7). There exist pairs of inputs that are both binary strings of length n for which any automaton that distinguishes the inputs must have size Ω(log n). Closing the gap between this lower bound and Chase's upper bound remains an open problem. Jeffrey Shallit has offered a prize of 100 British pounds for any improvement to Robson's upper bound. == Special cases == Several special cases of the separating words problem are known to be solvable using few states: If two binary words have differing numbers of zeros or ones, then they can be distinguished from each other by counting their Hamming weights modulo a prime of logarithmic size, using a logarithmic number of states. More generally, if a pattern of length k appears a different number of times in the two words, they can be distinguished from each other using O(k log n) states. If two binary words differ from each other within their first or last k positions, they can be distinguished from each other using k + O(1) states. This implies that almost all pairs of binary words can be distinguished from each other with a logarithmic number of states, because only a polynomially small fraction of pairs have no difference in their initial O(log n) positions. If two binary words have Hamming distance d, then there exists a prime p with p = O(d log n) and a position i at which the two strings differ, such that i is not equal modulo p to the position of any other difference. By computing the parity of the input symbols at positions congruent to i modulo p, it is possible to distinguish the words using an automaton with O(d log n) states.

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  • Bernard Vauquois

    Bernard Vauquois

    Bernard Vauquois ((1929-06-14)June 14, 1929 — (1985-09-30)September 30, 1985) was a French mathematician and computer scientist. He was a pioneer of computer science and machine translation (MT) in France. An astronomer-turned-computer scientist, he is known for his work on the programming language ALGOL 60, and later for extensive work on the theoretical and practical problems of MT, of which the eponymous Vauquois triangle is one of the most widely-known contributions. He was a professor at what would become the Grenoble Alpes University. == Biography == Bernard Vauquois was initially a researcher at French National Centre for Scientific Research (CNRS) from 1952 to 1958 at the Astrophysics Institute of the Meudon Observatory, after completing studies in mathematics, physics, and astronomy. Since 1957, his research program has also focused on methods applied to physics from the perspective of electronic computers, and he has taught programming to physicists. This double interest in astrophysics and electronic computers is reflected in the subject of his thesis and that of the complementary thesis in physical sciences that he defended in 1958. In 1960, at 31 years old, he was appointed professor of computer science at Grenoble University, where, alongside professors Jean Kuntzmann and Noël Gastinel, he began work in the field. At that time, he was also contributing to the definition of the language ALGOL 60. Also in 1960, he founded the Centre d'Étude pour la Traduction Automatique (CETA), later renamed as Groupe d'Étude pour la Traduction Automatique (GETA) and currently known as GETALP, a team at the Laboratoire d'informatique de Grenoble, and soon showed his gift for rapid understanding, synthesis, and innovation, and his taste for personal communication across linguistic borders and barriers. After visiting a number of centers, mainly in the United States, where machine translation research was conducted, he analyzed the shortcomings of the "first-generation" approach and evaluated the potential of a new generation based on grammar and formal language theory, and proposed a new approach based on a representational "pivot" and the use of (declarative) rule systems that transform a sequential sentence from one level of representation to another. He led the GETA in constructing the first large second-generation system, applied to Russian–French, from 1962 to 1971. At the end of this period, the accumulated experience led him to correct some defects of the "pure" declarative and interlingual approach, and to use heuristic programming methods, implemented with procedural grammars written in LSPLs ("specialized languages for linguistic programming", langages spécialisés pour la programmation linguistique) that were developed under his direction, and integrated into the ARIANE-78 machine translation system. In 1974, when he cofounded the Leibniz laboratory, he proposed "multilevel structure descriptors" (descripteurs de structures multiniveaux) for units larger than sentence translation. This idea, premonitory of later theoretical work (Ray Jackendoff, Gerald Gazdar) is still the cornerstone of all machine translation software built by GETA and the French national TA project. Bernard Vauquois' last contribution was "static grammar" (grammaire statique) in 1982–83, during the ESOPE project, the preparatory phase of the French national MT project. He was a key figure in the field of computational linguistics in France. At CNRS, he was a member of section 22 of the National Committee in 1963: "General Linguistics, Modern Languages and Comparative Literature", and then, in 1969, of section 28: "General Linguistics, Foreign Languages and Literature". Since 1965, he has been vice-president of the Association for Natural Language Processing (ATALA). He was its president from 1966 to 1971. He was also one of the founders, in 1965, of the ICCL (International Committee on Computational Linguistics), which organizes COLING conferences. He was its president from 1969 to 1984. From France, he often collaborated with other countries (notably Canada, the United States, the USSR, Czechoslovakia, Japan, China, Brazil, Malaysia, and Thailand), working on the specification and implementation of grammars and dictionaries. He began cooperating with Malaysia, for example, in 1979, which led to the creation of the Automatic Terjemaan Project, with a first prototype of an English-Malay MT system demonstrated in 1980. == Vauquois triangle == The Vauquois triangle is a conceptual model and diagram illustrating possible approaches to the design of machine translation systems, first proposed in 1968. == Legacy == Bernard Vauquois is regarded as a pioneer of machine translation in France. He played a key role in developing the first large-scale second-generation machine translation system, and his work influenced the field of machine translation for many years. He supervised some twenty doctoral theses, most of them concerning formal aspects of natural and artificial languages, with an emphasis on machine translation. The Center for Studies on Automatic Translation, which Vauquois founded in 1960, later became the Group for the Study of Machine Translation and Automated Processing of Languages and Speech (GETALP). It is still a research institution in natural language processing. Vauquois was a prolific writer and speaker, disseminating knowledge about machine translation and related topics. His papers and presentations were instrumental in establishing the field of machine translation in France and beyond. == Publications == Vauquois, Bernard (1973). Traduction automatique (in French). Paris: Gauthier-Villars. Vauquois, Bernard (1967). Introduction à la traduction automatique (in French). Paris: Gauthier-Villars.

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  • Electronic sell-through

    Electronic sell-through

    Electronic sell-through (EST) is a method of media distribution whereby consumers pay a one-time fee to download a media file for storage on a hard drive. Although EST is often described as a transaction that grants content "ownership" to the consumer, the content may become unusable after a certain period and may not be viewable using competing platforms. EST is used by a wide array of digital media products, including movies, television, music, games, and mobile applications. The term is sometimes used interchangeably with download to own (DTO). == Film and television == The film and television industry's $18.8 billion home entertainment market consists of rental and sell-through segments, the latter of which includes the electronic sell-through of digital content. In 2010, EST generated $683 million of total home entertainment revenues, putting it behind the more lucrative revenue streams of cable video-on-demand (VOD) and internet video-on-demand (iVOD), which brought in a combined $1.8 billion in the same period. In 2010, Apple's iTunes Store accounted for three quarters of the U.S. EST business. The rest of the EST market was captured by Microsoft (via its Zune Video platform), Sony, Amazon VOD (now Amazon Video), and Walmart (via its VUDU service). A number of industry trends indicate the future expansion of EST's share of digital distribution revenues. David Bishop, worldwide president of Sony Pictures Home Entertainment, describes the following outlook: "With the launch of UltraViolet (the cloud-based digital copy locker system) establishing a common digital distribution platform later this year, prices potentially coming down on digital sales, more marketing devoted to digital sellthrough, and studios adding more value to the sellthrough product by making HD available and building in smarter extra features, we see the balance tilting even more toward owning and collecting digital movies."

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  • Collocation

    Collocation

    In corpus linguistics, a collocation is a series of words or terms that co-occur more often than would be expected by chance. In phraseology, a collocation is a type of compositional phraseme, meaning that it can be understood from the words that make it up. This contrasts with an idiom, where the meaning of the whole cannot be inferred from its parts, and may be completely unrelated. There are about seven main types of collocations: adjective + noun, noun + noun (such as collective nouns), noun + verb, verb + noun, adverb + adjective, verbs + prepositional phrase (phrasal verbs), and verb + adverb. Collocation extraction is a computational technique that finds collocations in a document or corpus, using various computational linguistics elements resembling data mining. == Expanded definition == Collocations are partly or fully fixed expressions that become established through repeated context-dependent use. Such terms as crystal clear, middle management, nuclear family, and cosmetic surgery are examples of collocated pairs of words. Collocations can be in a syntactic relation (such as verb–object: make and decision), lexical relation (such as antonymy), or they can be in no linguistically defined relation. Knowledge of collocations is vital for the competent use of a language: a grammatically correct sentence will stand out as awkward if collocational preferences are violated. This makes collocation a common focus for language teaching. Corpus linguists specify a key word in context (KWIC) and identify the words immediately surrounding them, to illustrate the way words are used in practice. The processing of collocations involves a number of parameters, the most important of which is the measure of association, which evaluates whether the co-occurrence is purely by chance or statistically significant. Due to the non-random nature of language, most collocations are classed as significant, and the association scores are simply used to rank the results. Commonly used measures of association include mutual information, t scores, and log-likelihood. Rather than select a single definition, Gledhill proposes that collocation involves at least three different perspectives: co-occurrence, a statistical view, which sees collocation as the recurrent appearance in a text of a node and its collocates; construction, which sees collocation either as a correlation between a lexeme and a lexical-grammatical pattern, or as a relation between a base and its collocative partners; and expression, a pragmatic view of collocation as a conventional unit of expression, regardless of form. These different perspectives contrast with the usual way of presenting collocation in phraseological studies. Traditionally speaking, collocation is explained in terms of all three perspectives at once, in a continuum: == In dictionaries == In 1933, Harold Palmer's Second Interim Report on English Collocations highlighted the importance of collocation as a key to producing natural-sounding language, for anyone learning a foreign language. Thus from the 1940s onwards, information about recurrent word combinations became a standard feature of monolingual learner's dictionaries. As these dictionaries became "less word-centred and more phrase-centred", more attention was paid to collocation. This trend was supported, from the beginning of the 21st century, by the availability of large text corpora and intelligent corpus-querying software, making it possible to provide a more systematic account of collocation in dictionaries. Using these tools, dictionaries such as the Macmillan English Dictionary and the Longman Dictionary of Contemporary English included boxes or panels with lists of frequent collocations. There are also a number of specialized dictionaries devoted to describing the frequent collocations in a language. These include (for Spanish) Redes: Diccionario combinatorio del español contemporaneo (2004), (for French) Le Robert: Dictionnaire des combinaisons de mots (2007), and (for English) the LTP Dictionary of Selected Collocations (1997) and the Macmillan Collocations Dictionary (2010). == Statistically significant collocation == Student's t-test can be used to determine whether the occurrence of a collocation in a corpus is statistically significant. For a bigram w 1 w 2 {\displaystyle w_{1}w_{2}} , let P ( w 1 ) = # w 1 N {\displaystyle P(w_{1})={\frac {\#w_{1}}{N}}} be the unconditional probability of occurrence of w 1 {\displaystyle w_{1}} in a corpus with size N {\displaystyle N} , and let P ( w 2 ) = # w 2 N {\displaystyle P(w_{2})={\frac {\#w_{2}}{N}}} be the unconditional probability of occurrence of w 2 {\displaystyle w_{2}} in the corpus. The t-score for the bigram w 1 w 2 {\displaystyle w_{1}w_{2}} is calculated as: where x ¯ = # w i w j N {\displaystyle {\bar {x}}={\frac {\#w_{i}w_{j}}{N}}} is the sample mean of the occurrence of w 1 w 2 {\displaystyle w_{1}w_{2}} , # w 1 w 2 {\displaystyle \#w_{1}w_{2}} is the number of occurrences of w 1 w 2 {\displaystyle w_{1}w_{2}} , μ = P ( w i ) P ( w j ) {\displaystyle \mu =P(w_{i})P(w_{j})} is the probability of w 1 w 2 {\displaystyle w_{1}w_{2}} under the null-hypothesis that w 1 {\displaystyle w_{1}} and w 2 {\displaystyle w_{2}} appear independently in the text, and s 2 = x ¯ ( 1 − x ¯ ) ≈ x ¯ {\displaystyle s^{2}={\bar {x}}(1-{\bar {x}})\approx {\bar {x}}} is the sample variance. With a large N {\displaystyle N} , the t-test is equivalent to a Z-test.

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  • Sasha Luccioni

    Sasha Luccioni

    Alexandra Sasha Luccioni (née Vorobyova; born 1990) is a computer scientist specializing in the intersection of artificial intelligence (AI) and climate change. Her work focuses on quantifying the environmental impact of AI technologies and promoting sustainable practices in machine learning development. == Early life and education == Alexandra Sasha Vorobyova was born in the Ukrainian Soviet Socialist Republic in 1990. When she was four years old, her family relocated to Ontario, Canada. Her interest in science is influenced by her family's history; her mother, grandmother, and great-grandmother all pursued careers in scientific fields. Luccioni earned a B.A. in language science from University of Paris III: Sorbonne Nouvelle in 2010. Subsequently, she completed a M.S. in cognitive science, with a minor in natural language processing, at École normale supérieure in Paris in 2012. Luccioni obtained her PhD in cognitive computing from Université du Québec à Montréal (UQAM) in 2018. == Career == Luccioni began her professional career at Nuance Communications in 2017, where she focused on natural language processing (NLP) and machine learning (ML) techniques to enhance conversational agents. She then joined Morgan Stanley’s AI/ML Center of Excellence in 2018, working on explainable artificial intelligence (AI) and decision-making systems. In 2019, she became a postdoctoral researcher at Université de Montréal and Mila, collaborating with computer scientist Yoshua Bengio on a project titled This Climate Does Not Exist. This initiative used generative adversarial networks to visualize the effects of climate change. During this time, she also contributed to integrating fairness and accountability into machine learning education at Mila. Luccioni briefly worked with the United Nations Global Pulse in 2021, developing tools to monitor COVID-19 misinformation. Later that year, she joined Hugging Face as a research scientist. Her role includes quantifying the carbon footprint of AI systems, co-chairing the carbon working group in the Big Science project, and advancing responsible machine learning practices. She helped create "CodeCarbon," an open-source software tool that estimates the carbon emissions produced during the training and operation of machine learning models. In addition to her research, she has developed tools to measure the environmental impact of AI models, communicated findings through media engagements, and presented at international conferences, including a TED Talk. In 2024, she was listed on BBC 100 Women and Time 100 AI.

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