AI Coding Meta

AI Coding Meta — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Zero-day vulnerability

    Zero-day vulnerability

    A zero-day (also known as a 0-day) is a vulnerability or security hole in a computer system unknown to its developers or anyone capable of mitigating it. Until the vulnerability is remedied, threat actors can exploit it in a zero-day exploit, or zero-day attack. The term "zero-day" originally referred to the number of days since a new piece of software was released to the public, so "zero-day software" was obtained by hacking into a developer's computer before release. Eventually the term was applied to the vulnerabilities that allowed this hacking, and to the number of days that the vendor has had to fix them. Vendors who discover the vulnerability may create patches or advise workarounds to mitigate it, though users need to deploy that mitigation to eliminate the vulnerability in their systems. Zero-day attacks are severe threats. == Definition == Despite developers' goal of delivering a product that works entirely as intended, virtually all products contain software and hardware bugs. If a bug creates a security risk, it is called a vulnerability. Vulnerabilities vary in their ability to be exploited by malicious actors. Some are not usable at all, while others can be used to disrupt the device with a denial of service attack. The most dangerous allow the attacker to inject and run their own code, without the user being aware of it. Although the term "zero-day" initially referred to the time since the vendor had become aware of the vulnerability, zero-day vulnerabilities can also be defined as the subset of vulnerabilities for which no patch or other fix is available. A zero-day exploit is any exploit that takes advantage of such a vulnerability. == Exploits == An exploit is the delivery mechanism that takes advantage of the vulnerability to penetrate the target's systems, for such purposes as disrupting operations, installing malware, or exfiltrating data. Researchers Lillian Ablon and Andy Bogart write that "little is known about the true extent, use, benefit, and harm of zero-day exploits". Exploits based on zero-day vulnerabilities are considered more dangerous than those that take advantage of a known vulnerability. However, it is likely that most cyberattacks use known vulnerabilities, not zero-days. Governments of states are the primary users of zero-day exploits, not only because of the high cost of finding or buying vulnerabilities, but also the significant cost of writing the attack software. Nevertheless, anyone can use a vulnerability, and according to research by the RAND Corporation, "any serious attacker can always get an affordable zero-day for almost any target". Many targeted attacks and most advanced persistent threats rely on zero-day vulnerabilities. In 2017, the average time to develop an exploit from a zero-day vulnerability was estimated at 22 days. The difficulty of developing exploits has been increasing over time due to increased anti-exploitation features in popular software. === Window of vulnerability === Zero-day vulnerabilities are often classified as alive—meaning that there is no public knowledge of the vulnerability—and dead—the vulnerability has been disclosed, but not patched. If the software's maintainers are actively searching for vulnerabilities, it is a living vulnerability; such vulnerabilities in unmaintained software are called immortal. Zombie vulnerabilities can be exploited in older versions of the software but have been patched in newer versions. Even publicly known and zombie vulnerabilities are often exploitable for an extended period. Security patches can take months to develop, or may never be developed. A patch can have negative effects on the functionality of software and users may need to test the patch to confirm functionality and compatibility. Larger organizations may fail to identify and patch all dependencies, while smaller enterprises and personal users may not install patches. Research suggests that risk of cyberattack increases if the vulnerability is made publicly known or a patch is released. Cybercriminals can reverse engineer the patch to find the underlying vulnerability and develop exploits, often faster than users install the patch. According to research by RAND Corporation published in 2017, zero-day exploits remain usable for 6.9 years on average, although those purchased from a third party only remain usable for 1.4 years on average. The researchers were unable to determine if any particular platform or software (such as open-source software) had any relationship to the life expectancy of a zero-day vulnerability. Although the RAND researchers found that 5.7 percent of a stockpile of secret zero-day vulnerabilities will have been discovered by someone else within a year, another study found a higher overlap rate, as high as 10.8 percent to 21.9 percent per year. == Countermeasures == Because, by definition, there is no patch that can block a zero-day exploit, all systems employing the software or hardware with the vulnerability are at risk. This includes secure systems such as banks and governments that have all patches up to date. Security systems are designed around known vulnerabilities, and repeated exploitations of a zero-day exploit could continue undetected for an extended period of time. Although there have been many proposals for a system that is effective at detecting zero-day exploits, this remains an active area of research in 2023. Many organizations have adopted defense-in-depth tactics so that attacks are likely to require breaching multiple levels of security, which makes it more difficult to achieve. Conventional cybersecurity measures such as training and access control — including multi-factor authentication, least-privilege access, and air-gapping makes it harder to compromise systems with a zero-day exploit. Since writing perfectly secure software is impossible, some researchers argue that driving up the cost of exploits is considered a good strategy to reduce the burden of cyberattacks. == Market == Zero-day exploits can fetch millions of dollars. There are three main types of buyers: White: the vendor, or to third parties such as the Zero Day Initiative that disclose to the vendor. Often such disclosure is in exchange for a bug bounty. Not all companies respond positively to disclosures, as they can cause legal liability and operational overhead. It is not uncommon to receive cease-and-desist letters from software vendors after disclosing a vulnerability for free. Gray: the largest and most lucrative. Government or intelligence agencies buy zero-days and may use it in an attack, stockpile the vulnerability, or notify the vendor. The United States federal government is one of the largest buyers. As of 2013, the Five Eyes (United States, United Kingdom, Canada, Australia, and New Zealand) captured the plurality of the market and other significant purchasers included Russia, India, Brazil, Malaysia, Singapore, North Korea, and Iran. Middle Eastern countries were poised to become the biggest spenders. Black: organized crime, which typically prefers exploit software rather than just knowledge of a vulnerability. These users are more likely to employ "half-days" where a patch is already available. In 2015, the markets for government and crime were estimated at least ten times larger than the white market. Sellers are often hacker groups that seek out vulnerabilities in widely used software for financial reward. Some will only sell to certain buyers, while others will sell to anyone. White market sellers are more likely to be motivated by non pecuniary rewards such as recognition and intellectual challenge. Selling zero-day exploits is legal. Despite calls for more regulation, law professor Mailyn Fidler says there is little chance of an international agreement because key players such as Russia and Israel are not interested. The sellers and buyers that trade in zero-days tend to be secretive, relying on non-disclosure agreements and classified information laws to keep the exploits secret. If the vulnerability becomes known, it can be patched and its value consequently crashes. Because the market lacks transparency, it can be hard for parties to find a fair price. Sellers might not be paid if the vulnerability was disclosed before it was verified, or if the buyer declined to purchase it but used it anyway. With the proliferation of middlemen, sellers could never know to what use the exploits could be put. Buyers could not guarantee that the exploit was not sold to another party. Both buyers and sellers advertise on the dark web. Research published in 2022 based on maximum prices paid as quoted by a single exploit broker found a 44 percent annualized inflation rate in exploit pricing. Remote zero-click exploits could fetch the highest price, while those that require local access to the device are much cheaper. Vulnerabilities in widely used software are also more expensive. They estimated that around 400 to 1,500 people sold exploits to th

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  • List of artificial intelligence artists

    List of artificial intelligence artists

    Many notable artificial intelligence artists have created a wide variety of artificial intelligence art from the 1960s to today. These include: == 20th century == Harold Cohen, active from 1960s to 2010s. Cohen's work is primarily with AARON, a series of computer programs that autonomously create original images. Eric Millikin, active from 1980s to present. Millikin's work includes AI-generated virtual reality, video art, poetry, music, and performance art, on topics such as animal rights, climate change, anti-racism, witchcraft, and the occult. Karl Sims, active from 1980s to present. Sims is best known for using particle systems and artificial life in computer animation. == 21st century == Refik Anadol, active from 2010s to present. Anadol's work includes video installations based on generative algorithms with artificial intelligence. Sougwen Chung, active from 2010s to present. Chung's work includes performances with a robotic arm that uses AI to attempt to draw in a manner similar to Chung. Stephanie Dinkins, active from 2010s to present. Dinkins' work includes recordings of conversations with an artificially intelligent robot that resembles a black woman, discussing topics such as race and the nature of being. Jake Elwes, active from 2010s to present. Their practice is the exploration of artificial intelligence, queer theory and technical biases. Libby Heaney, active from 2010s to present. Heaney's practice includes work with chatbots. Mario Klingemann, active from 2010s to present. Klingemann's works examine creativity, culture, and perception through machine learning and artificial intelligence. Mauro Martino, active from 2010s to present. Martino's work includes design, data visualization and infographics. Trevor Paglen, active from 2000s to present. Paglen's practice includes work in photography and geography, on topics like mass surveillance and data collection. Anna Ridler, active from 2010s to present. Ridler works with collections of information, including self-generated data sets, often working with floral photography.

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  • Herbrand Award

    Herbrand Award

    The Herbrand Award for Distinguished Contributions to Automated Reasoning is an award given by the Conference on Automated Deduction (CADE), Inc., (although it predates the formal incorporation of CADE) to honour persons or groups for important contributions to the field of automated deduction. The award is named after the French scientist Jacques Herbrand and given at most once per CADE or International Joint Conference on Automated Reasoning (IJCAR). It comes with a prize of US$1,000. Anyone can be nominated, the award is awarded after a vote among CADE trustees and former recipients, usually with input from the CADE/IJCAR programme committee. == Recipients == Past award recipients are: === 1990s === Larry Wos (1992) Woody Bledsoe (1994) John Alan Robinson (1996) Wu Wenjun (1997) Gérard Huet (1998) Robert S. Boyer and J Strother Moore (1999) === 2000s === William W. McCune (2000) Donald W. Loveland (2001) Mark E. Stickel (2002). Peter B. Andrews (2003) Harald Ganzinger (2004) Martin Davis (2005) Wolfgang Bibel (2006) Alan Bundy (2007) Edmund M. Clarke (2008) Deepak Kapur (2009) === 2010s === David Plaisted (2010) Nachum Dershowitz (2011) Melvin Fitting (2012) C. Greg Nelson (2013) Robert L. Constable (2014) Andrei Voronkov (2015) Zohar Manna and Richard Waldinger (2016) Lawrence C. Paulson (2017) Bruno Buchberger (2018) Nikolaj Bjørner and Leonardo de Moura (2019) === 2020s === Franz Baader (2020) Tobias Nipkow (2021) Natarajan Shankar (2022) Moshe Vardi (2023) Armin Biere (2024) Aart Middeldorp (2025)

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  • Computer-assisted proof

    Computer-assisted proof

    A computer-assisted proof is a mathematical proof that has been at least partially generated by computer. Most computer-aided proofs to date have been implementations of large proofs-by-exhaustion of a mathematical theorem. The idea is to use a computer program to perform lengthy computations, and to provide a proof that the result of these computations implies the given theorem. In 1976, the four color theorem was the first major theorem to be verified using a computer program. Attempts have also been made in the area of artificial intelligence research to create smaller, explicit, new proofs of mathematical theorems from the bottom up using automated reasoning techniques such as heuristic search. Such automated theorem provers have proved a number of new results and found new proofs for known theorems. Additionally, interactive proof assistants allow mathematicians to develop human-readable proofs which are nonetheless formally verified for correctness. Since these proofs are generally human-surveyable (albeit with difficulty, as with the proof of the Robbins conjecture) they do not share the controversial implications of computer-aided proofs-by-exhaustion. == Methods == One method for using computers in mathematical proofs is by means of so-called validated numerics or rigorous numerics. This means computing numerically yet with mathematical rigour. One uses set-valued arithmetic and inclusion principle in order to ensure that the set-valued output of a numerical program encloses the solution of the original mathematical problem. This is done by controlling, enclosing and propagating round-off and truncation errors using for example interval arithmetic. More precisely, one reduces the computation to a sequence of elementary operations, say ( + , − , × , / ) {\displaystyle (+,-,\times ,/)} . In a computer, the result of each elementary operation is rounded off by the computer precision. However, one can construct an interval provided by upper and lower bounds on the result of an elementary operation. Then one proceeds by replacing numbers with intervals and performing elementary operations between such intervals of representable numbers. == Philosophical objections == Computer-assisted proofs are the subject of some controversy in the mathematical world, with Thomas Tymoczko first to articulate objections. Those who adhere to Tymoczko's arguments believe that lengthy computer-assisted proofs are not, in some sense, 'real' mathematical proofs because they involve so many logical steps that they are not practically verifiable by human beings, and that mathematicians are effectively being asked to replace logical deduction from assumed axioms with trust in an empirical computational process, which is potentially affected by errors in the computer program, as well as defects in the runtime environment and hardware. Other mathematicians believe that lengthy computer-assisted proofs should be regarded as calculations, rather than proofs: the proof algorithm itself should be proved valid, so that its use can then be regarded as a mere "verification". Arguments that computer-assisted proofs are subject to errors in their source programs, compilers, and hardware can be resolved by providing a formal proof of correctness for the computer program (an approach which was successfully applied to the four color theorem in 2005) as well as replicating the result using different programming languages, different compilers, and different computer hardware. Another possible way of verifying computer-aided proofs is to generate their reasoning steps in a machine readable form, and then use a proof checker program to demonstrate their correctness. Since validating a given proof is much easier than finding a proof, the checker program is simpler than the original assistant program, and it is correspondingly easier to gain confidence into its correctness. However, this approach of using a computer program to prove the output of another program correct does not appeal to computer proof skeptics, who see it as adding another layer of complexity without addressing the perceived need for human understanding. Another argument against computer-aided proofs is that they lack mathematical elegance—that they provide no insights or new and useful concepts. In fact, this is an argument that could be advanced against any lengthy proof by exhaustion. An additional philosophical issue raised by computer-aided proofs is whether they make mathematics into a quasi-empirical science, where the scientific method becomes more important than the application of pure reason in the area of abstract mathematical concepts. This directly relates to the argument within mathematics as to whether mathematics is based on ideas, or "merely" an exercise in formal symbol manipulation. It also raises the question whether, if according to the Platonist view, all possible mathematical objects in some sense "already exist", whether computer-aided mathematics is an observational science like astronomy, rather than an experimental one like physics or chemistry. This controversy within mathematics is occurring at the same time as questions are being asked in the physics community about whether twenty-first century theoretical physics is becoming too mathematical, and leaving behind its experimental roots. The emerging field of experimental mathematics is confronting this debate head-on by focusing on numerical experiments as its main tool for mathematical exploration. == Theorems proved with the help of computer programs == Inclusion in this list does not imply that a formal computer-checked proof exists, but rather, that a computer program has been involved in some way. See the main articles for details.

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  • Cygwin

    Cygwin

    Cygwin ( SIG-win) is a free and open-source Unix-like environment and command-line interface (CLI) for Microsoft Windows. The project also provides a software repository containing open-source packages. Cygwin allows source code for Unix-like operating systems to be compiled and run on Windows. Cygwin provides native integration of Windows-based applications. The terminal emulator mintty is the default command-line interface provided to interact with the environment. The Cygwin installation directory layout mimics the root file system of Unix-like systems, with directories such as /bin, /home, /etc, /usr, and /var. Cygwin is released under the GNU Lesser General Public License version 3. It was originally developed by Cygnus Solutions, which was later acquired by Red Hat (now part of IBM), to port the GNU toolchain to Win32, including the GNU Compiler Suite. Rather than rewrite the tools to use the Win32 runtime environment, Cygwin implemented a POSIX-compatible environment in the form of a DLL. The brand motto is "Get that Linux feeling – on Windows", although Cygwin doesn't have Linux in it. == History == Cygwin began in 1995 as a project of Steve Chamberlain, a Cygnus engineer who observed that Windows NT and 95 used COFF as their object file format, and that GNU already included support for x86 and COFF, and the C library newlib. He thought that it would be possible to retarget GCC and produce a cross compiler generating executables that could run on Windows. A prototype was later developed. Chamberlain bootstrapped the compiler on a Windows system, to emulate Unix to let the GNU configure shell script run. Initially, Cygwin was called Cygwin32. When Microsoft registered the trademark Win32, the "32" was dropped to simply become Cygwin. In 1999, Cygnus offered Cygwin 1.0 as a commercial product. Subsequent versions have not been released, instead relying on continued open source releases. Geoffrey Noer was the project lead from 1996 to 1999. Christopher Faylor was lead from 1999 to 2004; he left Red Hat and became co-lead with Corinna Vinschen. Corinna Vinschen has been the project lead from mid-2014 to date (as of September, 2024). From June 23, 2016, the Cygwin library version 2.5.2 was licensed under the GNU Lesser General Public License (LGPL) version 3. == Description == Cygwin is provided in two versions: the full 64-bit version and a stripped-down 32-bit version, whose final version was released in 2022. Cygwin consists of a library that implements the POSIX system call API in terms of Windows system calls to enable the running of a large number of application programs equivalent to those on Unix systems, and a GNU development toolchain (including GCC and GDB). Programmers have ported the X Window System, K Desktop Environment 3, GNOME, Apache, and TeX. Cygwin permits installing inetd, syslogd, sshd, Apache, and other daemons as standard Windows services. Cygwin programs have full access to the Windows API and other Windows libraries. Cygwin programs are installed by running Cygwin's "setup" program, which downloads them from repositories on the Internet. The Cygwin API library is licensed under the GNU Lesser General Public License version 3 (or later), with an exception to allow linking to any free and open-source software whose license conforms to the Open Source Definition. Cygwin consists of two parts: A dynamic-link library in the form of a C standard library that acts as a compatibility layer for the POSIX API and A collection of software tools and applications that provide a Unix-like look and feel. Cygwin supports POSIX symbolic links, representing them as plain-text files with the system attribute set. Cygwin 1.5 represented them as Windows Explorer shortcuts, but this was changed for reasons of performance and POSIX correctness. Cygwin also recognises NTFS junction points and symbolic links and treats them as POSIX symbolic links, but it does not create them. The POSIX API for handling access control lists (ACLs) is supported. === Technical details === A Cygwin-specific version of the Unix mount command allows mounting Windows paths as "filesystems" in the Unix file space. Initial mount points can be configured in /etc/fstab, which has a format very similar to Unix systems, except that Windows paths appear in place of devices. Filesystems can be mounted in binary mode (by default), or in text mode, which enables automatic conversion between LF and CRLF endings (which only affects programs that open files without explicitly specifying text or binary mode). Cygwin 1.7 introduced comprehensive support for POSIX locales, and the UTF-8 Unicode encoding became the default. The fork system call for duplicating a process is fully implemented, but the copy-on-write optimization strategy could not be used. Cygwin's default user interface is the bash shell running in the mintty terminal emulator. The DLL also implements pseudo terminal (pty) devices, and Cygwin ships with a number of terminal emulators that are based on them, including rxvt/urxvt and xterm. The version of GCC that comes with Cygwin has various extensions for creating Windows DLLs, such as specifying whether a program is a windowing or console-mode program. Support for compiling programs that do not require the POSIX compatibility layer provided by the Cygwin DLL used to be included in the default GCC, but as of 2014, it is provided by cross-compilers contributed by the MinGW-w64 project. == Software packages == Cygwin's base package selection is approximately 100MB, containing the bash (interactive user) and dash (installation) shells and the core file and text manipulation utilities. Additional packages are available as optional installs from within the Cygwin "setup" program and package manager ("setup-x86_64.exe" – 64 bit). The Cygwin Ports project provided additional packages that were not available in the Cygwin distribution itself. Examples included GNOME, K Desktop Environment 3, MySQL database, and the PHP scripting language. Most ports have been adopted by volunteer maintainers as Cygwin packages, and Cygwin Ports are no longer maintained. Cygwin ships with GTK+ and Qt. The Cygwin/X project allows graphical Unix programs to display their user interfaces on the Windows desktop for both local and remote programs.

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  • AAAI Conference on Artificial Intelligence

    AAAI Conference on Artificial Intelligence

    The AAAI Conference on Artificial Intelligence is a leading international academic conference in artificial intelligence held annually. It ranks 4th in terms of H5 Index in Google Scholar's list of top AI publications, after ICLR, NeurIPS, and ICML. It is supported by the Association for the Advancement of Artificial Intelligence (AAAI), after which it is named. Precise dates vary from year to year, but paper submissions are generally due at the end of August to beginning of September, and the conference is generally held during the following February. The first AAAI was held in 1980 at Stanford University, Stanford California. During AAAI-20 conference, AI pioneers and 2018 Turing Award winners (often referred to as the Nobel Prize of Computing) Yann LeCun and Yoshua Bengio, among eight other researchers, were honored as the AAAI 2020 Fellows. Along with other conferences such as NeurIPS and ICML, AAAI uses an artificial-intelligence algorithm to assign papers to reviewers. == Sponsors == Many leading technology companies, including Google, Microsoft, Amazon (company), IBM, Baidu, Bytedance, and Huawei, generously sponsor and participate in AAAI to publish and showcase their latest theoretical and applied research. Sponsoring companies also actively recruit AI talents at the conference. == Locations == AAAI-2026 Singapore Expo, Singapore AAAI-2025 Pennsylvania Convention Center, Philadelphia, Pennsylvania, United States AAAI-2024 Vancouver Convention Centre, Vancouver, British Columbia, Canada AAAI-2023 Washington Convention Center, Washington, D.C., United States AAAI-2022 Virtual Conference AAAI-2021 Virtual Conference AAAI-2020 Hilton New York Midtown, New York, New York, United States AAAI-2019 Hilton Hawaiian Village, Honolulu, Hawaii, United States AAAI-2018 Hilton New Orleans Riverside, New Orleans, Louisiana, United States AAAI-2017 San Francisco, California, United States AAAI-2016 Phoenix, Arizona, United States AAAI-2015 Austin, Texas, United States AAAI-2014 Québec Convention Center, Québec City, Québec, Canada AAAI-2013 Bellevue, Washington, United States AAAI-2012 Toronto, Ontario, Canada AAAI-2011 San Francisco, California, United States AAAI-2010 Westin Peachtree Plaza, Atlanta, Georgia, United States AAAI-2008 Chicago, Illinois, United States AAAI-2007 Toronto, Ontario, Canada AAAI-2006 Boston, Massachusetts, United States AAAI-2005 Pittsburgh, Pennsylvania, United States AAAI-2004 San Jose, California, United States AAAI-2002 Shaw conference center in Edmonton, Alberta, Canada AAAI-2000 Austin, Texas, United States AAAI-1999 Orlando, Florida, United States AAAI-1998 Madison, Wisconsin, United States AAAI-1997 Providence, Rhode Island, United States AAAI-1996 Portland, Oregon, United States AAAI-1994 Seattle, Washington, United States AAAI-1993 Washington Convention Center, Washington, D.C., United States AAAI-1992 San Jose Convention Center, San Jose, California, United States AAAI-1991 Anaheim Convention Center, Anaheim, California, United States AAAI-1990 Boston, Massachusetts, United States AAAI-1988 Saint Paul, Minnesota, United States AAAI-1987 Seattle, Washington, United States AAAI-1986 Philadelphia, Pennsylvania, United States AAAI-1984 University of Texas, Austin, Texas, United States AAAI-1983 Washington, D.C., United States AAAI-1982 Carnegie Mellon University and the University of Pittsburgh, Pittsburgh, Pennsylvania, United States AAAI-1980 Stanford, California, United States

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  • Riffusion

    Riffusion

    Riffusion is a neural network, designed by Seth Forsgren and Hayk Martiros, that generates music using images of sound rather than audio. The resulting music has been described as "de otro mundo" (otherworldly), although unlikely to replace man-made music. The model was made available on December 15, 2022, with the code also freely available on GitHub. The first version of Riffusion was created as a fine-tuning of Stable Diffusion, an existing open-source model for generating images from text prompts, on spectrograms, resulting in a model which used text prompts to generate image files which could then be put through an inverse Fourier transform and converted into audio files. While these files were only several seconds long, the model could also use latent space between outputs to interpolate different files together (using the img2img capabilities of SD). It was one of many models derived from Stable Diffusion. In December 2022, Mubert similarly used Stable Diffusion to turn descriptive text into music loops. In January 2023, Google published a paper on their own text-to-music generator called MusicLM. Forsgren and Martiros formed a startup, also called Riffusion, and raised $4 million in venture capital funding in October 2023.

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  • KitKat (cat)

    KitKat (cat)

    KitKat was a bodega cat from the Mission District of San Francisco who was killed by a Waymo car on October 27, 2025. Locals built altars and the death has raised comments about the safety of self-driving cars. == Life == Mike Zeidan, the owner of Randa's Market, adopted KitKat as a stray to help keep rodents out of his store. KitKat lived in Randa's Market for six years and was well-loved by the neighborhood, including an appearance on a shop cats map that went viral in 2022 as a "particularly friendly cat". After KitKat arrived at the bodega, customers were said to come more often, and regularly brought the cat food and gifts. == Death == At around 11:40 pm on October 27, 2025, witnesses saw KitKat sitting in front of a stopped Waymo car for seven seconds. He walked under the car as the car pulled out, and the right rear tire ran over the back half of his body. A bartender who was taking a cigarette break used a sandwich board sign as a stretcher and took KitKat to an emergency animal clinic. An hour later, KitKat was pronounced dead. Waymo confirmed that the cat was killed by one of its vehicles on October 30. Surveillance footage of the incident was released in December. From Waymo's report to the National Highway Traffic Safety Administration (NHTSA): The Waymo AV was stopped next to the curb for a passenger pickup facing east on 16th Street. As the passengers were boarding the Waymo AV, a cat approached the Waymo AV from the southern sidewalk of 16th Street and sat in the roadway partially under the front right corner of the Waymo AV. A pedestrian approached the Waymo AV from the east on the southern sidewalk of 16th Street and began crouching near the front of the Waymo AV, stepping partially into the roadway, appearing to reach for the cat. As they did so, the cat moved farther from the sidewalk under the Waymo AV and the pedestrian stepped back onto the sidewalk. The Waymo AV then departed the pickup location and the rear right tire made contact with the cat. At the time of impact, the Waymo AV's Level 4 ADS was engaged in autonomous mode. Waymo later received notice that the cat did not survive. The passengers in the Waymo AV did not have seatbelts fastened at the time, having just boarded the Waymo AV. Prior to KitKat's death, the NHTSA had logged 14 collisions between Waymo cars and animals, of which 5 were confirmed fatalities. == Aftermath == After KitKat's death, an altar was created outside Randa's Market. People left flowers, candles, cat food, written notes, and Kit Kat candy bars in the cat's honor. A city worker took down the memorial for fire safety reasons, but neighbors built it again. Local supervisor Jackie Fielder held a rally called "Justice for KitKat" in support of a non-binding San Francisco resolution to shift decision-making about the operation of self-driving cars from the state to individual counties. Critics say that the resolution is performative because it is non-binding, that local control would make autonomous vehicle operation impractical, and that Waymo is still far less dangerous to animals than human drivers. Elon Musk commented that "many pets will be saved by autonomy". There are multiple meme coins inspired by KitKat.

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  • Hierarchical navigable small world

    Hierarchical navigable small world

    Hierarchical navigable small world (HNSW) is an algorithm for approximate nearest neighbor search. It is used to find items that are similar to a query item in a large collection, without comparing the query with every item one by one. The algorithm is commonly used for searching vector data. In these systems, an item such as a document, image, song, or user profile is represented by a list of numbers called a vector. Items with similar vectors are treated as similar according to the model that produced the vectors. HNSW provides a way to search these vectors quickly, especially in large datasets. HNSW stores vectors in a graph. Each vector is a node, and links connect it to some nearby vectors. The graph has several layers: upper layers contain fewer nodes and act like a rough map, while the bottom layer contains all nodes and gives a more detailed view. A search starts in an upper layer, follows links toward nodes that are closer to the query, and then repeats the process in lower layers until it finds a set of likely nearest neighbors. == Background == The nearest neighbor search problem asks which items in a dataset are closest to a query item. A direct search can compare the query with every item in the dataset, but this becomes slow when the dataset is large. Exact search methods based on spatial trees, such as the k-d tree and R-tree, can also become less effective for high-dimensional data, a problem often associated with the curse of dimensionality. Approximate nearest neighbor methods trade some exactness for speed or lower resource use. Instead of always guaranteeing the exact closest item, they try to return close items quickly. Other approximate methods include locality-sensitive hashing and product quantization. HNSW builds on research into small-world networks and navigable graphs. In a small-world graph, most nodes can be reached from other nodes through a short chain of links. In a navigable graph, a search procedure can use local information to move toward a target. Jon Kleinberg's work on navigation in small-world networks is an important example of this research area. Later work studied ways to add links that make graphs easier to navigate greedily. The HNSW algorithm extends earlier navigable small world methods for similarity search by adding a hierarchy of graph layers. This hierarchy helps the algorithm find a good region of the graph before doing a more detailed search in the bottom layer. == Algorithm == HNSW is based on a proximity graph. In this graph, nearby vectors are connected by edges. The algorithm uses these edges to move through the dataset, rather than scanning every vector. The graph is hierarchical. Every vector appears in the bottom layer. Some vectors are also placed in higher layers, with fewer vectors appearing as the layers go upward. The upper layers allow long-range movement across the dataset, while the lower layers allow a more detailed search near promising candidates. A typical search proceeds as follows: The search begins from an entry point in the highest layer. At each step, the algorithm looks at neighboring nodes and moves to a neighbor that is closer to the query. When it cannot find a closer neighbor in that layer, it moves down to the next layer. In the bottom layer, it explores a wider set of candidate nodes and returns the nearest candidates found. This search strategy is often described as greedy navigation. The algorithm repeatedly chooses locally better nodes, using the graph structure to approach the query point. == Construction and parameters == The HNSW graph is built incrementally. When a new vector is inserted, the algorithm assigns it a maximum layer, searches for nearby existing nodes, and connects the new node to selected neighbors in each layer where it appears. Implementations usually expose parameters that control the trade-off between speed, accuracy, memory use, and construction time. A higher number of graph connections can improve recall but requires more memory. A larger search candidate list can improve accuracy but makes queries slower. A larger construction candidate list can improve the quality of the graph but makes index building slower. Because HNSW is approximate, its results are not always identical to a full exact search. Its practical performance depends on the dataset, distance measure, implementation, and parameter settings. Benchmarking studies have found HNSW-based libraries to be strong performers among approximate nearest neighbor methods, although worst-case performance can differ from performance on common benchmark datasets. == Use in vector search systems == HNSW is used as an index in systems that store and search high-dimensional vectors. These systems include vector databases, search engines, and database extensions. Typical uses include semantic search, recommender systems, image similarity search, and retrieval-augmented generation. Several software projects implement or support HNSW. Libraries include hnswlib, which is associated with the original HNSW authors, and FAISS. Database and search systems that document HNSW support include Apache Lucene, Chroma, ClickHouse, DuckDB, MariaDB, Milvus, pgvector, Qdrant, and Redis.

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  • Fuzzy architectural spatial analysis

    Fuzzy architectural spatial analysis

    Fuzzy architectural spatial analysis (FASA) (also fuzzy inference system (FIS) based architectural space analysis or fuzzy spatial analysis) is a spatial analysis method of analysing the spatial formation and architectural space intensity within any architectural organization. Fuzzy architectural spatial analysis is used in architecture, interior design, urban planning and similar spatial design fields. == Overview == Fuzzy architectural spatial analysis was developed by Burcin Cem Arabacioglu (2010) from the architectural theories of space syntax and visibility graph analysis, and is applied with the help of a fuzzy system with a Mamdani inference system based on fuzzy logic within any architectural space. Fuzzy architectural spatial analysis model analyses the space by considering the perceivable architectural element by their boundary and stress characteristics and intensity properties. The method is capable of taking all sensorial factors into account during analyses in conformably with the perception process of architectural space which is a multi-sensorial act.

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  • KitKat (cat)

    KitKat (cat)

    KitKat was a bodega cat from the Mission District of San Francisco who was killed by a Waymo car on October 27, 2025. Locals built altars and the death has raised comments about the safety of self-driving cars. == Life == Mike Zeidan, the owner of Randa's Market, adopted KitKat as a stray to help keep rodents out of his store. KitKat lived in Randa's Market for six years and was well-loved by the neighborhood, including an appearance on a shop cats map that went viral in 2022 as a "particularly friendly cat". After KitKat arrived at the bodega, customers were said to come more often, and regularly brought the cat food and gifts. == Death == At around 11:40 pm on October 27, 2025, witnesses saw KitKat sitting in front of a stopped Waymo car for seven seconds. He walked under the car as the car pulled out, and the right rear tire ran over the back half of his body. A bartender who was taking a cigarette break used a sandwich board sign as a stretcher and took KitKat to an emergency animal clinic. An hour later, KitKat was pronounced dead. Waymo confirmed that the cat was killed by one of its vehicles on October 30. Surveillance footage of the incident was released in December. From Waymo's report to the National Highway Traffic Safety Administration (NHTSA): The Waymo AV was stopped next to the curb for a passenger pickup facing east on 16th Street. As the passengers were boarding the Waymo AV, a cat approached the Waymo AV from the southern sidewalk of 16th Street and sat in the roadway partially under the front right corner of the Waymo AV. A pedestrian approached the Waymo AV from the east on the southern sidewalk of 16th Street and began crouching near the front of the Waymo AV, stepping partially into the roadway, appearing to reach for the cat. As they did so, the cat moved farther from the sidewalk under the Waymo AV and the pedestrian stepped back onto the sidewalk. The Waymo AV then departed the pickup location and the rear right tire made contact with the cat. At the time of impact, the Waymo AV's Level 4 ADS was engaged in autonomous mode. Waymo later received notice that the cat did not survive. The passengers in the Waymo AV did not have seatbelts fastened at the time, having just boarded the Waymo AV. Prior to KitKat's death, the NHTSA had logged 14 collisions between Waymo cars and animals, of which 5 were confirmed fatalities. == Aftermath == After KitKat's death, an altar was created outside Randa's Market. People left flowers, candles, cat food, written notes, and Kit Kat candy bars in the cat's honor. A city worker took down the memorial for fire safety reasons, but neighbors built it again. Local supervisor Jackie Fielder held a rally called "Justice for KitKat" in support of a non-binding San Francisco resolution to shift decision-making about the operation of self-driving cars from the state to individual counties. Critics say that the resolution is performative because it is non-binding, that local control would make autonomous vehicle operation impractical, and that Waymo is still far less dangerous to animals than human drivers. Elon Musk commented that "many pets will be saved by autonomy". There are multiple meme coins inspired by KitKat.

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  • International Conference on Automated Planning and Scheduling

    International Conference on Automated Planning and Scheduling

    The International Conference on Automated Planning and Scheduling (ICAPS) is a leading international academic conference in automated planning and scheduling held annually for researchers and practitioners in planning and scheduling. ICAPS is supported by the National Science Foundation, the journal Artificial Intelligence, and other supporters. == The IPC and PDDL == ICAPS conducts the International Planning Competition (IPC), a competition scheduled every few years that empirically evaluates state-of-the-art planning systems on a collection of benchmark problems. The Planning Domain Definition Language (PDDL) was developed mainly to make the 1998/2000 International Planning Competition possible, and then evolved with each competition. PDDL is an attempt to standardize Artificial Intelligence (AI) planning languages. PDDL was first developed by Drew McDermott and his colleagues in 1998, inspired by STRIPS, ADL, and other sources. == History == The ICAPS conferences began in 2003 as a merge of two bi-annual conferences, the International Conference on Artificial Intelligence Planning and Scheduling (AIPS) and the European Conference on Planning (ECP). == List of events ==

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  • TipTop Technologies

    TipTop Technologies

    TipTop Technologies is a real-time web and social search engine with a platform for semantic analysis of natural language. Tip-Top Search provides results capturing individual and group sentiment, opinions, and experiences there from the content of various sorts such as real-time messages from Twitter or consumer product reviews on Amazon.com. TipTop Technologies and ITC Infotech collaborated to create a search interface suitable for both enterprise and consumer applications. Tip-Top's products are part of the "emerging Web 3.0 applications which use semantic technologies to augment the underlying Web system's functionalities." Their main product is 360, an AI tool that incorporates multiple AI applications under one wing. Jonathan AlBright professor at Elon University, found videos generated by TipTop Technologies software on YouTube in his research into artificial intelligence, described it as AI-generated "fake news". Through semantic analysis of large data sets, TipTop gleaned behavioral insights from Tweets around events like Halloween, Thanksgiving, Holiday Gifting, the Super Bowl, and the Oscar Nominees for the Academy Awards coverage. Sentiment analysis, concept trend tracking, and real-time market research are other applications included in the TipTop Search product. TipTop's insight engine solves the problem of real-time data noise, and its ability to "sort the 'good tweets' from the 'bad tweets' when it comes to a product, service, or a region..." In addition, products like TipTop Shopping with customizable search widgets bring together consumer reviews, social search, and sentiment analysis enabling product comparisons across attributes like the overall value and aiding purchasing decisions through user-driven product tips and pits. TipTop Finance adds another complexity to real-time search results by incorporating corporate sentiment, company stock tickers, and social media into TipTop's existing social search platform. Additional success applying semantic technologies has been with polling, "if you compare these Gallup results with TipTop, a sentiment engine based on Twitter, the results are not way off. It does surprise you but it tells me that sentiment analysis in case of public opinion about a burning social issue or a famous personality is relatively easier." With the increasing amount of unstructured, opinion-oriented, and user-generated content available on the Web, TipTop's technology aims to make sense of all this data, and deliver it in a useful way for consumer and enterprise users alike. TipTop Technologies is a privately held company with its headquarters in the San Francisco Bay Area, and team members are located globally.

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  • Grok sexual deepfake scandal

    Grok sexual deepfake scandal

    From 2025 onwards, X (formerly Twitter)'s integrated chatbot, Grok, has allowed users to nonconsensually alter images of individuals, including minors, to show them in bikinis or transparent clothing, or in sexually suggestive contexts. The majority of these prompts were targeted at women and girls. Users were able to generate such images by responding to a photo with a request to Grok, such as "put her in a bikini", to which the chatbot would publicly reply with a generated image. The scandal drew significant criticism from lawmakers across the world, and there were calls for bans on X, as well as legal crackdowns on X and xAI for, amongst other reasons, the facilitation of sexual abuse, revenge porn, and child pornography. == Background == Deepfake pornography emerged in the late 2010s with the advent of machine learning. Originally, it was created on a small individual scale using a combination of machine learning algorithms, computer vision techniques, and AI software. However, the production process has significantly evolved since 2018, with the advent of several public apps that have largely automated the process. Since 2023, several AI apps available on Google Play and the Apple App Store are capable of "nudify-ing" user provided photos to generate non-consensual deepfake pornography. Grok would first be proposed by Elon Musk in 2023, when he expressed an intention to create his own AI chatbot to "combat bias". Grok version 2.0, released on August 14, 2024, would introduce image generation capabilities, ones which would be improved over successive updates. == Grok deepfake generation == Cases of Grok being used to remove the clothes from women in pictures, replacing them with bikinis or lingerie, began to surface in May 2025. By late December 2025, a trend of X users requesting such edits to women's photos without permission had taken root, and this received significant media attention in the first few days of January 2026. Some users prompted Grok to edit photos of women into sexualized poses, and others to add blood and bruising, with the chatbot publicly posting these graphic images in response. Grok's X account was restricted on January 9 from posting image generation responses to users who are not paid subscribers, providing a link to "subscribe to unlock these features". All users were still able to generate Grok-altered images using X's "Edit image" feature, and the standalone Grok website and app. However, by March 19, Grok’s Imagine feature was fully restricted to paid subscribers only (SuperGrok tier) for both the standalone Grok website and mobile app. == Analysis == An analysis of 20,000 images generated by Grok between December 25, 2025, and January 1, 2026, showed 2% appeared to be 18 or younger, including 30 of "young or very young" women or girls in bikinis or transparent clothes. A Reuters review of Grok requests over 10 minutes on January 2nd found 102 attempts to put women in bikinis. A separate analysis conducted over 24 hours from January 5 to 6 calculated that users had Grok create 6,700 sexually suggestive or nudified images per hour — 84 times more so than the top 5 deepfake websites combined. Wired reported that far more graphic AI-generated sexual imagery was being created by Grok on its website and app, which are separate to X, including female celebrities removing their clothes and engaging in sexual acts. An analysis of 800 pieces of recovered content by the Paris-based nonprofit AI Forensics found that almost 10% were "instances of photorealistic people, very young, doing sexual activities". AI-generated deepfakes have been described as sexual assault, and as a means to push women out of the public sphere. AI-generated sexually explicit or exploitative image claims are now being treated more like product safety or personal injury harms, not just privacy violations. Because harm may occur the moment an image is generated, some plaintiffs argue liability should focus on the system’s design and safety safeguards. == Reactions == On January 15, the Get Grok Gone campaign delivered letters to Apple and Google, demanding the removal of the app from Apple Store and Google Play Store respectively. The campaign accused both companies of profiting from nonconsensual intimate imagery and child sexual abuse imagery, which were also banned by the companies own policies. The Get Grok Gone campaign argues that the restrictions placed on Grok by xAI are not enough and that Apple and Google are enabling the distribution of harmful material by hosting the apps. === Elon Musk and xAI === xAI responded to requests for comment from media organizations with the automated reply, "Legacy Media Lies." On January 2, Elon Musk reacted "Not sure why, but I couldn’t stop laughing about this one 🤣🤣" to an image of a toaster dressed in a bikini by Grok. Later, on January 14, Elon Musk said that he was "not aware of any naked underage images generated by Grok. Literally zero." Later that same day, xAI announced that X users will no longer be able to use Grok to alter images of real people to portray them in revealing clothing. However, verified X users, as well as users of the standalone Grok app and website, were still able to generate such images. ==== Elon Musk's family ==== Ashley St. Clair, mother of one of Elon Musk's children, reported that Grok users were creating fake sexualized images from her photos, including a photo of her as a child. She considers the photos to be a form of revenge porn, and considered suing under the Take It Down Act. A spokesperson for X stated, "We take action against illegal content on X, including child sexual abuse material (CSAM), by removing it, permanently suspending accounts, and working with local governments and law enforcement as necessary. Anyone using or prompting Grok to make illegal content will suffer the same consequences as if they upload illegal content." However, Grok continued to post non-consensual sexual images. On January 15, St. Clair filed a lawsuit against xAI in the New York Supreme Court. === Canada === In response to the Grok deepfake scandal, individuals have asked that the government of Canada boycott X. On January 10, 2026, Canadian MP and Minister of AI Evan Solomon declared that Canada "is not considering a ban on X". In April 2026, Bill C-16, An Act to amend certain Acts in relation to criminal and correctional matters (child protection, gender-based violence, delays and other measures), was amended following a proposal by Conservative MP Andrew Lawton to ensure that AI-generated images and "nearly nude" intimate images are criminalized. A further proposal by NDP MP Leah Gazan to encompass "sexualized or humiliating contexts, such transparent bathing suits or being covered in blood or bruises" was voted down. === France === On January 2, 2026, French ministers reported the AI tool to prosecutors, calling the content "manifestly illegal", and also asked regulators to check compliance with the Digital Services Act. On February 3, Paris prosecutors office, a cybercrime team employed by them and Europol searched the Paris offices of X. The investigation started as one into allegations of abuse of algorithms and fraudulent data extraction, but has expanded into spreading Holocaust denial and sexual deepfakes. Elon Musk and former CEO Linda Yaccarino have been summoned to a hearing on April 20, with other X staff as witnesses. On April 20, Musk did not turn up for the hearing. The Paris prosecutors office told the BBC on April 20 that it had "taken note of the absence of the people summoned", adding "the presence or absence (of the people summoned) is not an obstacle to continuing the investigation". === India === Indian Member of Parliament Priyanka Chaturvedi filed a complaint to India's IT ministry, demanding a review of Grok's safety mechanisms. === Indonesia === On January 10, Indonesia announced that Grok will be temporarily blocked, becoming the first country to do so. Meutya Hafid, the Minister of Communication and Digital Affairs, stated that "the government views the practice of non-consensual sexual deepfakes as a serious violation of human rights, dignity, and the security of citizens in the digital space." Access to Grok in the country was later restored on February 1. === Ireland === On January 6, Coimisiún na Meán, the Irish media commission, said they were consulting with the European Commission about concerns that Grok was generating sexualized images of women and children. The same day, Ofcom of the United Kingdom contacted X concerning complaints about these images. On January 13, Micheál Martin, Taoiseach of Ireland, announced he would talk with Rossa Fanning, the country's Attorney General, about the Grok chatbot being used to produce sexually explicit images of women and minors. On January 14, the Garda Síochána announced there are 200 investigations into child sex abuse images generated by Grok. The Garda National Cyber Crime Bureau has al

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  • Netflix Prize

    Netflix Prize

    The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings without any other information about the users or films, i.e. without the users being identified except by numbers assigned for the contest. The competition was held by Netflix, a video streaming service, and was open to anyone who was neither connected with Netflix (current and former employees, agents, close relatives of Netflix employees, etc.) nor a resident of certain blocked countries (such as Cuba or North Korea). On September 21, 2009, the grand prize of US$1,000,000 was given to the BellKor's Pragmatic Chaos team which bested Netflix's own algorithm for predicting ratings by 10.06%. == Problem and data sets == Netflix provided a training data set of 100,480,507 ratings that 480,189 users gave to 17,770 movies. Each training rating is a quadruplet of the form . The user and movie fields are integer IDs, while grades are from 1 to 5 (integer) stars. The qualifying data set contains over 2,817,131 triplets of the form , with grades known only to the jury. A participating team's algorithm must predict grades on the entire qualifying set, but they are informed of the score for only half of the data: a quiz set of 1,408,342 ratings. The other half is the test set of 1,408,789, and performance on this is used by the jury to determine potential prize winners. Only the judges know which ratings are in the quiz set, and which are in the test set—this arrangement is intended to make it difficult to hill climb on the test set. Submitted predictions are scored against the true grades in the form of root mean squared error (RMSE), and the goal is to reduce this error as much as possible. Note that, while the actual grades are integers in the range 1 to 5, submitted predictions need not be. Netflix also identified a probe subset of 1,408,395 ratings within the training data set. The probe, quiz, and test data sets were chosen to have similar statistical properties. In summary, the data used in the Netflix Prize looks as follows: Training set (99,072,112 ratings not including the probe set; 100,480,507 including the probe set) Probe set (1,408,395 ratings) Qualifying set (2,817,131 ratings) consisting of: Test set (1,408,789 ratings), used to determine winners Quiz set (1,408,342 ratings), used to calculate leaderboard scores For each movie, the title and year of release are provided in a separate dataset. No information at all is provided about users. In order to protect the privacy of the customers, "some of the rating data for some customers in the training and qualifying sets have been deliberately perturbed in one or more of the following ways: deleting ratings; inserting alternative ratings and dates; and modifying rating dates." The training set is constructed such that the average user rated over 200 movies, and the average movie was rated by over 5000 users. But there is wide variance in the data—some movies in the training set have as few as 3 ratings, while one user rated over 17,000 movies. There was some controversy as to the choice of RMSE as the defining metric. It has been claimed that even as small an improvement as 1% RMSE results in a significant difference in the ranking of the "top-10" most recommended movies for a user. == Prizes == Prizes were based on improvement over Netflix's own algorithm, called Cinematch, or the previous year's score if a team has made improvement beyond a certain threshold. A trivial algorithm that predicts for each movie in the quiz set its average grade from the training data produces an RMSE of 1.0540. Cinematch uses "straightforward statistical linear models with a lot of data conditioning." The performance of Cinematch had plateaued by 2006. Using only the training data, Cinematch scores an RMSE of 0.9514 on the quiz data, roughly a 10% improvement over the trivial algorithm. Cinematch has a similar performance on the test set, 0.9525. In order to win the grand prize of $1,000,000, a participating team had to improve this by another 10%, to achieve 0.8572 on the test set. Such an improvement on the quiz set corresponds to an RMSE of 0.8563. As long as no team won the grand prize, a progress prize of $50,000 was awarded every year for the best result thus far. However, in order to win this prize, an algorithm had to improve the RMSE on the quiz set by at least 1% over the previous progress prize winner (or over Cinematch, the first year). If no submission succeeded, the progress prize was not to be awarded for that year. To win a progress or grand prize a participant had to provide source code and a description of the algorithm to the jury within one week after being contacted by them. Following verification the winner also had to provide a non-exclusive license to Netflix. Netflix would publish only the description, not the source code, of the system. (To keep their algorithm and source code secret, a team could choose not to claim a prize.) The jury also kept their predictions secret from other participants. A team could send as many attempts to predict grades as they wish. Originally submissions were limited to once a week, but the interval was quickly modified to once a day. A team's best submission so far counted as their current submission. Once one of the teams succeeded in improving the RMSE by 10% or more, the jury would issue a last call, giving all teams 30 days to send their submissions. Only then, the team with the best submission was asked for the algorithm description, source code, and non-exclusive license, and, after successful verification; declared a grand prize winner. The contest would last until the grand prize winner was declared. Had no one received the grand prize, it would have lasted for at least five years (until October 2, 2011). After that date, the contest could have been terminated at any time at Netflix's sole discretion. == Progress over the years == The competition began on October 2, 2006. By October 8, a team called WXYZConsulting had already beaten Cinematch's results. By October 15, there were three teams who had beaten Cinematch, one of them by 1.06%, enough to qualify for the annual progress prize. By June 2007 over 20,000 teams had registered for the competition from over 150 countries. 2,000 teams had submitted over 13,000 prediction sets. Over the first year of the competition, a handful of front-runners traded first place. The more prominent ones were: WXYZConsulting, a team of Wei Xu and Yi Zhang. (A front runner during November–December 2006.) ML@UToronto A, a team from the University of Toronto led by Prof. Geoffrey Hinton. (A front runner during parts of October–December 2006.) Gravity, a team of four scientists from the Budapest University of Technology (A front runner during January–May 2007.) BellKor, a group of scientists from AT&T Labs. (A front runner since May 2007.) Dinosaur Planet, a team of three undergraduates from Princeton University. (A front runner on September 3, 2007 for one hour before BellKor snatched back the lead.) The algorithms used by the leading teams were usually an ensemble of singular value decomposition, k-nearest neighbor, neural networks, and so on. On August 12, 2007, many contestants gathered at the KDD Cup and Workshop 2007, held at San Jose, California. During the workshop all four of the top teams on the leaderboard at that time presented their techniques. The team from IBM Research—Yan Liu, Saharon Rosset, Claudia Perlich, and Zhenzhen Kou—won the third place in Task 1 and first place in Task 2. Over the second year of the competition, only three teams reached the leading position: BellKor, a group of scientists from AT&T Labs (front runner during May 2007 – September 2008) BigChaos, a team of Austrian scientists from Commendo Research & Consulting (single team front runner since October 2008) BellKor in BigChaos, a joint team of the two leading single teams (a front runner since September 2008) === 2007 Progress Prize === On September 2, 2007, the competition entered the "last call" period for the 2007 Progress Prize. Over 40,000 teams from 186 countries had entered the contest. They had thirty days to tender submissions for consideration. At the beginning of this period the leading team was BellKor, with an RMSE of 0.8728 (8.26% improvement), followed by Dinosaur Planet (RMSE = 0.8769; 7.83% improvement), and Gravity (RMSE = 0.8785; 7.66% improvement). In the last hour of the last call period, an entry by "KorBell" took first place. This turned out to be an alternate name for Team BellKor. On November 13, 2007, team KorBell (formerly BellKor) was declared the winner of the $50,000 Progress Prize with an RMSE of 0.8712 (8.43% improvement). The team consisted of three researchers from AT&T Labs, Yehuda Koren, Robert Bell, and Chris Volinsky. As required, they published a description of their a

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