AI Chat Image

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  List of artificial intelligence journals

  This is a list of notable peer-reviewed academic journals that publish research in the field of artificial intelligence (AI), including areas such as machine learning, computer vision, natural language processing, robotics, and intelligent systems. == General artificial intelligence == Artificial Intelligence (journal) – Elsevier Journal of Artificial Intelligence Research (JAIR) – AI Access Foundation Knowledge-Based Systems – Elsevier == Machine learning == Data Mining and Knowledge Discovery – Springer Machine Learning (journal) – Springer Journal of Machine Learning Research – Microtome Pattern Recognition (journal) – Elsevier Neural Networks (journal) – Elsevier Neural Computation (journal) – MIT Press Neurocomputing (journal) - Elsevier == Deep learning and neural computation == IEEE Transactions on Evolutionary Computation – IEEE IEEE Transactions on Neural Networks and Learning Systems – IEEE Nature Machine Intelligence – Springer Nature == Computer vision == International Journal of Computer Vision – Springer IEEE Transactions on Pattern Analysis and Machine Intelligence – IEEE Machine Vision and Applications – Springer == Natural language processing == Computational Linguistics (journal) – MIT Press Natural Language Processing Transactions of the Association for Computational Linguistics – ACL == Robotics and intelligent systems == IEEE Transactions on Robotics – IEEE Autonomous Robots – Springer Journal of Intelligent & Robotic Systems – Springer == Interdisciplinary and ethics in AI == AI & Society – Springer Artificial Life – MIT Press Philosophy & Technology – Springer Minds and Machines – Springer

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  Render layers

  When creating computer-generated imagery, final scenes appearing in movies and television productions are usually produced by rendering more than one "layer" or "pass," which are multiple images designed to be put together through digital compositing to form a completed frame. Rendering in passes is based on a traditions in motion control photography which predate CGI. As an example, for a visual effects shot, a camera could be programmed to move past a physical model of a spaceship in one pass to film the fully lit beauty pass of the ship, and then to repeat exactly the same camera move passing the ship again to photograph additional elements such as the illuminated windows in the ship or its thrusters. Once all of the passes were filmed, they could then be optically printed together to form a completed shot. The terms render layers and render passes are sometimes used interchangeably. However, rendering in layers refers specifically to separating different objects into separate images, such as a layer each for foreground characters, sets, distant landscape, and sky. On the other hand, rendering in passes refers to separating out different aspects of the scene, such as shadows, highlights, or reflections, into separate images.

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  WS-SecurityPolicy

  WS-Security Policy is a web services specification, created by IBM and 12 co-authors, that has become an OASIS standard as of version 1.2. It extends the fundamental security protocols specified by the WS-Security, WS-Trust and WS-Secure Conversation by offering mechanisms to represent the capabilities and requirements of web services as policies. Security policy assertions are based on the WS-Policy framework. Policy assertions can be used to require more generic security attributes like transport layer security , message level security or timestamps, and specific attributes like token types. Most policy assertion can be found in following categories: Protection assertions identify the elements of a message that are required to be signed, encrypted or existent. Token assertions specify allowed token formats (SAML, X509, Username etc.). Security binding assertions control basic security safeguards like transport and message level security, cryptographic algorithm suite and required timestamps. Supporting token assertions add functions like user sign-on using a username token. Policies can be used to drive development tools to generate code with certain capabilities, or may be used at runtime to negotiate the security aspects of web service communication. Policies may be attached to WSDL elements such as service, port, operation and message, as defined in WS Policy Attachment. == Sample Policies == Namespaces used by the following XML-snippets: ... Include a timestamp: Use either transport layer security (https) or message level security (XML Dsig/XML Enc): ... ... To define a SAML assertion as security token: ...#SAMLV2.0 Issued token assertion of providers with reference to the STS and required token format: http://sampleorg.com/sts http://docs.oasis-open.org/wss/oasis-wss-saml-token-profile-1.0#SAMLAssertionID ... ... Specify that message header and body need to be signed, and attachments are left unsigned: ? ... specify that message open source license need to be signed, and hydra security are left unsigned: ? ... == Other WS policy languages == The term Web Services Security Policy Language is used for two different XML-based languages: As described above, based on the WS-Policy framework, as defined in, published as version 1.3 in Feb. 2009 WSPL, based on XACML profile for Web-services, but that was not finalized.

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  Linked timestamping

  Linked timestamping is a type of trusted timestamping where issued time-stamps are related to each other. Each time-stamp would contain data that authenticates the time-stamp before it, the authentication would be authenticating the entire message, including the previous time-stamps authentication, making a chain. This makes it impossible to add a time-stamp in to the middle of the chain, as any time-stamps afterwards would be different. == Description == Linked timestamping creates time-stamp tokens which are dependent on each other, entangled in some authenticated data structure. Later modification of the issued time-stamps would invalidate this structure. The temporal order of issued time-stamps is also protected by this data structure, making backdating of the issued time-stamps impossible, even by the issuing server itself. The top of the authenticated data structure is generally published in some hard-to-modify and widely witnessed media, like printed newspaper or public blockchain. There are no (long-term) private keys in use, avoiding PKI-related risks. Suitable candidates for the authenticated data structure include: Linear hash chain Merkle tree (binary hash tree) Skip list The simplest linear hash chain-based time-stamping scheme is illustrated in the following diagram: The linking-based time-stamping authority (TSA) usually performs the following distinct functions: Aggregation For increased scalability the TSA might group time-stamping requests together which arrive within a short time-frame. These requests are aggregated together without retaining their temporal order and then assigned the same time value. Aggregation creates a cryptographic connection between all involved requests; the authenticating aggregate value will be used as input for the linking operation. Linking Linking creates a verifiable and ordered cryptographic link between the current and already issued time-stamp tokens. Publishing The TSA periodically publishes some links, so that all previously issued time-stamp tokens depend on the published link and that it is practically impossible to forge the published values. By publishing widely witnessed links, the TSA creates unforgeable verification points for validating all previously issued time-stamps. == Security == Linked timestamping is inherently more secure than the usual, public-key signature based time-stamping. All consequential time-stamps "seal" previously issued ones - hash chain (or other authenticated dictionary in use) could be built only in one way; modifying issued time-stamps is nearly as hard as finding a preimage for the used cryptographic hash function. Continuity of operation is observable by users; periodic publications in widely witnessed media provide extra transparency. Tampering with absolute time values could be detected by users, whose time-stamps are relatively comparable by system design. Absence of secret keys increases system trustworthiness. There are no keys to leak and hash algorithms are considered more future-proof than modular arithmetic based algorithms, e.g. RSA. Linked timestamping scales well - hashing is much faster than public key cryptography. There is no need for specific cryptographic hardware with its limitations. The common technology for guaranteeing long-term attestation value of the issued time-stamps (and digitally signed data) is periodic over-time-stamping of the time-stamp token. Because of missing key-related risks and of the plausible safety margin of the reasonably chosen hash function this over-time-stamping period of hash-linked token could be an order of magnitude longer than of public-key signed token. == Research == === Foundations === Stuart Haber and W. Scott Stornetta proposed in 1990 to link issued time-stamps together into linear hash-chain, using a collision-resistant hash function. The main rationale was to diminish TSA trust requirements. Tree-like schemes and operating in rounds were proposed by Benaloh and de Mare in 1991 and by Bayer, Haber and Stornetta in 1992. Benaloh and de Mare constructed a one-way accumulator in 1994 and proposed its use in time-stamping. When used for aggregation, one-way accumulator requires only one constant-time computation for round membership verification. Surety started the first commercial linked timestamping service in January 1995. Linking scheme is described and its security is analyzed in the following article by Haber and Sornetta. Buldas et al. continued with further optimization and formal analysis of binary tree and threaded tree based schemes. Skip-list based time-stamping system was implemented in 2005; related algorithms are quite efficient. === Provable security === Security proof for hash-function based time-stamping schemes was presented by Buldas, Saarepera in 2004. There is an explicit upper bound N {\displaystyle N} for the number of time stamps issued during the aggregation period; it is suggested that it is probably impossible to prove the security without this explicit bound - the so-called black-box reductions will fail in this task. Considering that all known practically relevant and efficient security proofs are black-box, this negative result is quite strong. Next, in 2005 it was shown that bounded time-stamping schemes with a trusted audit party (who periodically reviews the list of all time-stamps issued during an aggregation period) can be made universally composable - they remain secure in arbitrary environments (compositions with other protocols and other instances of the time-stamping protocol itself). Buldas, Laur showed in 2007 that bounded time-stamping schemes are secure in a very strong sense - they satisfy the so-called "knowledge-binding" condition. The security guarantee offered by Buldas, Saarepera in 2004 is improved by diminishing the security loss coefficient from N {\displaystyle N} to N {\displaystyle {\sqrt {N}}} . The hash functions used in the secure time-stamping schemes do not necessarily have to be collision-resistant or even one-way; secure time-stamping schemes are probably possible even in the presence of a universal collision-finding algorithm (i.e. universal and attacking program that is able to find collisions for any hash function). This suggests that it is possible to find even stronger proofs based on some other properties of the hash functions. At the illustration above hash tree based time-stamping system works in rounds ( t {\displaystyle t} , t + 1 {\displaystyle t+1} , t + 2 {\displaystyle t+2} , ...), with one aggregation tree per round. Capacity of the system ( N {\displaystyle N} ) is determined by the tree size ( N = 2 l {\displaystyle N=2^{l}} , where l {\displaystyle l} denotes binary tree depth). Current security proofs work on the assumption that there is a hard limit of the aggregation tree size, possibly enforced by the subtree length restriction. == Standards == ISO 18014 part 3 covers 'Mechanisms producing linked tokens'. American National Standard for Financial Services, "Trusted Timestamp Management and Security" (ANSI ASC X9.95 Standard) from June 2005 covers linking-based and hybrid time-stamping schemes. There is no IETF RFC or standard draft about linking based time-stamping. RFC 4998 (Evidence Record Syntax) encompasses hash tree and time-stamp as an integrity guarantee for long-term archiving.

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  Socially assistive robot

  A socially assistive robot (SAR) aids users through social engagement and support rather than through physical tasks and interactions. == Background == The field of socially assistive robotics emerged in the early 2000s, following the emergence of the field of social robots. In contrast to social robots, SARs aid users with specific goals related to behavior change rather than serving as purely social entities. The term "Socially assistive robot" was initially defined by Maja Matarić and David Feil-Seifer in 2005. Since its inception, the field has gained substantial recognition, featuring numerous research projects, a wealth of global research publications, startup companies, and a growing array of products on the consumer market. The COVID-19 pandemic has underscored the immense potential of socially assistive robots, particularly in addressing the needs of large user populations, including children engaged in remote learning, elderly individuals grappling with loneliness, and those affected by social isolation and its associated negative consequences. == Characteristics of interaction == SARs rely on artificial intelligence (AI) to generate real-time, responsive, natural, and meaningful robot behaviors during interactions with humans. The robots employ various forms of communication, such as facial expressions, gestures, body movements, and speech. In contrast to robots intended for physical tasks, SARs are designed to support and motivate users to perform their own tasks. The tasks a user engages in can be physical (e.g., rehabilitation exercises for post-stroke users), cognitive (e.g., dementia screening for elderly users), or social (e.g., turn-taking for users with autism spectrum disorders). This complex interaction involves detecting and interpreting the user's movement, behavior, intent, goals, speech, and preferences. Machine learning and robot learning techniques are frequently employed to enhance the robot's understanding of the user, predict user preferences, and provide effective assistance. The effectiveness of socially assistive robots is assessed based on objective measurements of user performance and improvement resulting from the robot’s assistance and support. Unlike other branches of robotics, where effectiveness depends on the robot's physical task completion, SAR measures the success of the robot based on the user's progress and achievements. This evaluation is carried out using quantitative objective metrics, such as time spent on tasks, accuracy, retention, and verbalization, as well as quantitative subjective metrics, such as user survey tools. SAR is based on the large body of evidence showing that users tend to respond more positively to interactions with physical robots compared to interactions with screens. Interaction with physical robots also encourages users to learn and retain more information than screen-based interactions. This fundamental insight underlines why physical robots in SAR applications are more effective, as opposed to interactions solely involving screens, tablets, or computers. == Uses and applications == SARs have been developed and validated in a wide array of applications, including healthcare, elder care, education, and training. For example, SARs have been developed to support children on the autism spectrum in acquiring and practicing social and cognitive skills, to motivate and coach stroke patients throughout their rehabilitation exercises, monitoring individuals health (ex. fall detection), and to encourage elderly users to be more physically and socially active. There is a concern that technophobia and lack of trust in robots will pose a barrier to the effectiveness of SARs in older adults.

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  Z-order

  Z-order is an ordering of overlapping two-dimensional objects, such as windows in a stacking window manager, shapes in a vector graphics editor, or objects in a 3D application. One of the features of a typical GUI is that windows may overlap, so that one window hides part or all of another. When two windows overlap, their Z-order determines which one appears on top of the other. == Definition == The term "Z-order" refers to the order of objects along the Z-axis. In coordinate geometry, X typically refers to the horizontal axis (left to right), Y to the vertical axis (up and down), and Z refers to the axis perpendicular to the other two (forward or backward). One can think of the windows in a GUI as a series of planes parallel to the surface of the monitor. The windows are therefore stacked along the Z-axis, and the Z-order information thus specifies the front-to-back ordering of the windows on the screen. An analogy would be some sheets of paper scattered on top of a table, each sheet being a window, the table your computer screen, and the top sheet having the highest Z value. == Use == Typically, users of a GUI can affect the Z-order by selecting a window to be brought to the foreground (that is, "above" or "in front of" all the other windows). Some window managers allow interaction with windows while they are not in the foreground, while others will bring a window to the front whenever it receives input from the user. It is also possible for special windows to be designated "always on top"; these are then fixed to the top of the Z-order so that (with few exceptions) no other window can overlap them. When dealing with visual objects on a computer screen, an object with a Z-order of 1 would be visually "underneath" an object with a Z-order of 2 or greater. This is the same as making "layers" of objects where the Z-order determines what object is on top of another. An HTML page can use CSS to specify the Z-order so that some objects can be layered over others. Z-ordering is also used in 3D applications to determine object visibility based on overlap from other objects. This confers a speed advantage to the user as the computer does not need to render unseen objects. In practice, of course, some objects may be only partially obscured, and this is a complication that must be taken into account. In early real-time 3D graphics, Z-order was applied on a per-polygon basis to avoid using Z-buffer, which was considered expensive at the time. In modern 3D graphics, Z-order is used for order-dependent rendering, for example with semi-transparent objects. It can also be used to reduce the problem of Z-fighting, by either rendering farther objects first and then using weak inequality as the depth test or, conversely, rendering front-to-back and using strict inequality. == z-index == The actual number assigned to a particular place in the Z-order is sometimes known as the z-index. In particular the CSS property that sets the stack order of specific elements is known as the z-index. An element with greater stack order is always in front of another element with lower stack order. Negative values can also be used in the same manner. A negative value will appear behind a positive one. z-index only works on elements that have a position value (e.g. position: relative;) and for many coders, this one of the first things to investigate when debugging why the z-index isn't working. Like all other CSS properties, it can be set with JavaScript, with the following syntax:

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  WomanStats Project

  The WomanStats Project is a donor-funded research and database project housed at Brigham Young University that "seeks to collect detailed statistical data on the status of women around the world, and to connect that data with data on the security of states." The WomanStats Database aims to provide a comprehensive compilation of information on the status of women in the world. Coders comb the extant literature and conduct expert interviews to find qualitative and quantitative information on over 300 indicators of women's status in 174 countries with populations of at least 200,000. Access to the online database is free. == History and structure == WomanStats began as an outgrowth of a paper Dr. Valerie M. Hudson (of the Brigham Young University Political Science department) and one of her graduate students, Andrea den Boer, published in International Security on the association between national security and the abnormal sex ratio in Asia. After the success and influence of their first article, (later added as one of their top twenty national security articles of that journal of all time), Hudson and den Boer did further research on the connection between the status of women and national security, but found that there was no single database that covered the range of topics that they needed for their research. Consequently, they began compiling information on variables regarding the status of women around the world. The database was officially formed in 2001 and grew exponentially as it later added more variables. The Project went live on the Internet in July 2007. The principal investigators are: Valerie M. Hudson (International Relations), Bonnie Ballif-Spanvill (Psychology, emeritus), and Chad F. Emmett (Geography) all from Brigham Young University, Mary Caprioli from the University of Minnesota, Duluth (International Relations), Rose McDermott from Brown University (International Relations), Andrea Den Boer from the University of Kent at Canterbury in the United Kingdom (International Relations) and S. Matthew Stearmer from the Ohio State University (Sociology; doctoral student). Approximately a dozen undergraduate and graduate students at Brigham Young University and Texas A&M University work at any one time as coders for the project. The coders take the raw quantitative and qualitative data collected in government reports, news articles, research papers, etc. and sort the applicable information on women into categories. They may also implement scales developed by the principal investigators, or that they (the students) themselves have developed. == Database == As of February 2011, the database has 307 variables, covers 174 nations with populations over 200,000, uses 18,015 sources and contains over 111,000 individual data points. All data is referenced to original sources. Not every variable has information for each country; similarly, not all countries have information for each variable: overall, about 70% of country-variable combinations have information. These database coding gaps exist where information is not available or is incomplete, or variables are not collected and reported by governments or international organizations. At times, information from different sources may be contradictory, and the WomanStats Database records this discrepant information for triangulation purposes. == Users and role of the database == The database is meant to help fill a hole in the extant data on the situation of women around the world. WomanStats data and research has been vetted and/or used by the United Nations, the United States Department of Defense, the Central Intelligence Agency, and the World Bank. Their data and research were also used by the United States Senate Committee on Foreign Relations in crafting the International Violence Against Women’s Act. The Inter-Agency Network on Women and Gender Equality (IANWGE) of the United Nations has stated that the WomanStats project "filled a major gap in the availability of data on women" (2007). Victor Asal and Mitchell Brown, researchers not affiliated with WomanStats, stated in an article published in Politics and Policy that "one of the most significant challenges of cross-national empirical studies of the prevalence of interpersonal violence is the paucity of available data, particularly reliable data," and that "WomanStats has allowed for an important first glimpse at analyzing the factors related to interpersonal violence." They conclude by stating that "Our findings suggest that, in the same way that larger disciplinary resources have invested in interstate and intrastate war, disciplinary resources need to be expended in creating a data set exploring interpersonal violence. Until the rights and the lives of women and children are taken as seriously as the survival of states by more proactively collaborating on projects like WomanStats, we will continue to only have a small lens through which to understand problems like this." Princeton University professor Evan S. Liberman wrote, "Although data on political regimes and group conflict have been in far greater demand by political scientists than data on gender politics and policies, two gender-related databases provide...examples of innovative HIRDs. Both the Womanstats database project (Hudson et al. 2009) and the Research Network on Gender Politics and the State (RNGS) project (McBride et al. 2008) are well-integrated presentations of quantitative and qualitative data characterizing the quality of gender relations around the world and, in particular, analytic descriptions of the treatment of women."." == Research == The research component of WomanStats focuses on exploring the relationship between the situation of women and the behavior and security of states. Current research initiatives include: Exploring the relationship between violent instability and inequity and family law. Examining the effect of polygyny and marriage market dislocations on the rise of suicide terrorism. Documenting discrepancies between laws on the books and cultural practices on the ground concerning gender issues. Investigating how well the situation of women predicts the peacefulness of nations-states, compared to their variables such as democracy, wealth, and civilization. The Project has published articles in International Security, International Studies Quarterly, Peace and Conflict, Journal of Peace Research, Political Psychology, Cumberland Law Review, and World Political Review, and has a forthcoming book from Columbia University Press.

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  CrocBITE

  CrocBITE (currently CrocAttack) was an online database of wild crocodilian attacks reported on humans in the world. The non-profit online research tool helped to scientifically analyze crocodilian behavior via complex models. Users were encouraged to feed information in a crowdsourcing manner. This website excludes captive crocodilian attacks, as well as non-fatal bites on professional handlers, rangers, staff, or researchers, and crocodilian attacks on pets and livestock, because its primary goal is to analyze natural human-crocodilian conflict in the wild for conservation and management purposes, and that these incidents do are not considered indicative of natural species behavior or typical human-wildlife conflict, as well as not providing enough useful data and helping researchers understand wild population behavior or typical human-wildlife conflict dynamics and helps create safety strategies for people living or working near wild crocodilians, rather than tracking workplace accidents in zoos or farms. While fatal incidents involving handlers are sometimes included on the website, typical captive incidents (such as handlers being bitten by them in zoos) are excluded because they are considered manageable professional risks rather than general public safety threats. == About == The online database was established in 2013 (2013) by Dr Adam Britton, a researcher at Charles Darwin University, his student Brandon Sideleau and Erin Britton. It was a compilation of government records, individual reports, registered contributors and historical data. Dr Simon Pooley, Junior Research fellow, Imperial College London joined hands to further the studies. The collaboration culminated when Dr Pooley met Dr Britton at the IUCN Crocodile Specialist Group, in Louisiana in 2014. The program received funds from Economic and Social Research Council, United Kingdom to the tune of A$30,000 and unspecified resourced plus amount from Big Gecko Crocodilian Research, Crocodillian.com and Charles Darwin University. The research yielded pertinent observations that provide inside into crocodile attacks. It was observed that most attacks on humans occur from bites of Saltwater crocodile as against the popular understanding of Nile crocodiles taking the top spot. This is not, however, believed to be the actual case, as most attacks by the Nile crocodile are believed to go unreported or only reported on a local level. The broad category of Nile crocodile attacks were segmented into West African crocodile and Crocodylus niloticus (the Nile Crocodile) species to get a clear understanding of their respective attack zones. The objective was that the information would be used by communities and conservation managers to help inform and educate people about how to keep safe. The information was vital for Australia and Africa where such attacks are more likely than in other parts of the world. This was the only database of its kind with such comprehensive collection of information made available online. The database is no longer online, and its founder Adam Britton is in custody having pleaded guilty to charges of bestiality on September 25, 2023. It has been rebranded and renamed CrocAttack, and serves as a updated database focusing on human-crocodilian conflict and records over 8,500 incidents from the past decades.

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  LaMDA

  LaMDA (Language Model for Dialogue Applications) is a family of conversational large language models developed by Google. Originally developed and introduced as Meena in 2020, the first-generation LaMDA was announced during the 2021 Google I/O keynote, while the second generation was announced the following year. In June 2022, LaMDA gained widespread attention when Google engineer Blake Lemoine made claims that the chatbot had become sentient. The scientific community has largely rejected Lemoine's claims, though it has led to conversations about the efficacy of the Turing test, which measures whether a computer can pass for a human. In February 2023, Google announced Gemini (then Bard), a conversational artificial intelligence chatbot powered by LaMDA, to counter the rise of OpenAI's ChatGPT. == History == === Background === On January 28, 2020, Google unveiled Meena, a neural network-powered chatbot with 2.6 billion parameters, which Google claimed to be superior to all other existing chatbots. The company previously hired computer scientist Ray Kurzweil in 2012 to develop multiple chatbots for the company, including one named Danielle. The Google Brain research team, who developed Meena, hoped to release the chatbot to the public in a limited capacity, but corporate executives refused on the grounds that Meena violated Google's "AI principles around safety and fairness". Meena was later renamed LaMDA as its data and computing power increased, and the Google Brain team again sought to deploy the software to the Google Assistant, the company's virtual assistant software, in addition to opening it up to a public demo. Both requests were once again denied by company leadership. LaMDA's two lead researchers, Daniel de Freitas and Noam Shazeer, eventually left the company in frustration. === First generation === Google announced the LaMDA conversational large language model during the Google I/O keynote on May 18, 2021, powered by artificial intelligence. The acronym stands for "Language Model for Dialogue Applications". Built on the seq2seq architecture, transformer-based neural networks developed by Google Research in 2017, LaMDA was trained on human dialogue and stories, allowing it to engage in open-ended conversations. Google states that responses generated by LaMDA have been ensured to be "sensible, interesting, and specific to the context". LaMDA has access to multiple symbolic text processing systems, including a database, a real-time clock and calendar, a mathematical calculator, and a natural language translation system, giving it superior accuracy in tasks supported by those systems, and making it among the first dual process chatbots. LaMDA is also not stateless because its "sensibleness" metric is fine-tuned by "pre-conditioning" each dialog turn by prepending many of the most recent dialog interactions, on a user-by-user basis. LaMDA is tuned on nine unique performance metrics: sensibleness, specificity, interestingness, safety, groundedness, informativeness, citation accuracy, helpfulness, and role consistency. Tests by Google indicated that LaMDA surpassed human responses in the area of interestingness. The pre-training dataset consists of 2.97B documents, 1.12B dialogs, and 13.39B utterances, for a total of 1.56T words. The largest LaMDA model has 137B non-embedding parameters. === Second generation === On May 11, 2022, Google unveiled LaMDA 2, the successor to LaMDA, during the 2022 Google I/O keynote. The new incarnation of the model draws examples of text from numerous sources, using it to formulate unique "natural conversations" on topics that it may not have been trained to respond to. === Sentience claims === On June 11, 2022, The Washington Post reported that Google engineer Blake Lemoine had been placed on paid administrative leave after Lemoine told company executives Blaise Agüera y Arcas and Jen Gennai that LaMDA had become sentient. Lemoine came to this conclusion after the chatbot made questionable responses to questions regarding self-identity, moral values, religion, and Isaac Asimov's Three Laws of Robotics. Google refuted these claims, insisting that there was substantial evidence to indicate that LaMDA was not sentient. In an interview with Wired, Lemoine reiterated his claims that LaMDA was "a person" as dictated by the Thirteenth Amendment to the U.S. Constitution, comparing it to an "alien intelligence of terrestrial origin". He further revealed that he had been dismissed by Google after he hired an attorney on LaMDA's behalf after the chatbot requested that Lemoine do so. On July 22, Google fired Lemoine, asserting that Blake had violated their policies "to safeguard product information" and rejected his claims as "wholly unfounded". Internal controversy instigated by the incident prompted Google executives to decide against releasing LaMDA to the public, which it had previously been considering. Lemoine's claims were widely pushed back by the scientific community. Many experts rejected the idea that LaMDA was sentient, including former New York University psychology professor Gary Marcus, David Pfau of Google sister company DeepMind, Erik Brynjolfsson of the Institute for Human-Centered Artificial Intelligence at Stanford University, and University of Surrey professor Adrian Hilton. Yann LeCun, who leads Meta Platforms' AI research team, stated that neural networks such as LaMDA were "not powerful enough to attain true intelligence". University of California, Santa Cruz professor Max Kreminski noted that LaMDA's architecture did not "support some key capabilities of human-like consciousness" and that its neural network weights were "frozen", assuming it was a typical large language model. Philosopher Nick Bostrom noted, however, that the lack of precise and consensual criteria for determining whether a system is conscious warrants some uncertainty. IBM Watson lead developer David Ferrucci compared how LaMDA appeared to be human in the same way Watson did when it was first introduced. Former Google AI ethicist Timnit Gebru called Lemoine a victim of a "hype cycle" initiated by researchers and the media. Lemoine's claims have also generated discussion on whether the Turing test remained useful to determine researchers' progress toward achieving artificial general intelligence, with Will Omerus of the Post opining that the test actually measured whether machine intelligence systems were capable of deceiving humans, while Brian Christian of The Atlantic said that the controversy was an instance of the ELIZA effect. == Products == === AI Test Kitchen === With the unveiling of LaMDA 2 in May 2022, Google also launched the AI Test Kitchen, a mobile application for the Android operating system powered by LaMDA capable of providing lists of suggestions on-demand based on a complex goal. Originally open only to Google employees, the app was set to be made available to "select academics, researchers, and policymakers" by invitation sometime in the year. In August, the company began allowing users in the U.S. to sign up for early access. In November, Google released a "season 2" update to the app, integrating a limited form of Google Brain's Imagen text-to-image model. A third iteration of the AI Test Kitchen was in development by January 2023, expected to launch at I/O later that year. Following the 2023 I/O keynote in May, Google added MusicLM, an AI-powered music generator first previewed in January, to the AI Test Kitchen app. In August, the app was delisted from Google Play and the Apple App Store, instead moving completely online. === Bard === On February 6, 2023, Google announced Bard, a conversational AI chatbot powered by LaMDA, in response to the unexpected popularity of OpenAI's ChatGPT chatbot. Google positions the chatbot as a "collaborative AI service" rather than a search engine. Bard became available for early access on March 21. === Other products === In addition to Bard, Pichai also unveiled the company's Generative Language API, an application programming interface also based on LaMDA, which he announced would be opened up to third-party developers in March 2023. == Architecture == LaMDA is a decoder-only Transformer language model. It is pre-trained on a text corpus that includes both documents and dialogs consisting of 1.56 trillion words, and is then trained with fine-tuning data generated by manually annotated responses for "sensibleness, interestingness, and safety". LaMDA was retrieval-augmented to improve the accuracy of facts provided to the user. Three different models were tested, with the largest having 137 billion non-embedding parameters:

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  Real-time computer graphics

  Real-time computer graphics or real-time rendering is the sub-field of computer graphics focused on producing and analyzing images in real time. The term can refer to anything from rendering an application's graphical user interface (GUI) to real-time image analysis, but is most often used in reference to interactive 3D computer graphics, typically using a graphics processing unit (GPU). One example of this concept is a video game that rapidly renders changing 3D environments to produce an illusion of motion. Computers have been capable of generating 2D images such as simple lines, images and polygons in real time since their invention. However, quickly rendering detailed 3D objects is a daunting task for traditional Von Neumann architecture-based systems. An early workaround to this problem was the use of sprites, 2D images that could imitate 3D graphics. Different techniques for rendering now exist, such as ray-tracing and rasterization. Using these techniques and advanced hardware, computers can now render images quickly enough to create the illusion of motion while simultaneously accepting user input. This means that the user can respond to rendered images in real time, producing an interactive experience. == Principles of real-time 3D computer graphics == The goal of computer graphics is to generate computer-generated images, or frames, using certain desired metrics. One such metric is the number of frames generated in a given second. Real-time computer graphics systems differ from traditional (i.e., non-real-time) rendering systems in that non-real-time graphics typically rely on ray tracing. In this process, millions or billions of rays are traced from the camera to the world for detailed rendering—this expensive operation can take hours or days to render a single frame. Real-time graphics systems must render each image in less than 1/30th of a second. Ray tracing is far too slow for these systems; instead, they employ the technique of z-buffer triangle rasterization. In this technique, every object is decomposed into individual primitives, usually triangles. Each triangle gets positioned, rotated and scaled on the screen, and rasterizer hardware (or a software emulator) generates pixels inside each triangle. These triangles are then decomposed into atomic units called fragments that are suitable for displaying on a display screen. The fragments are drawn on the screen using a color that is computed in several steps. For example, a texture can be used to "paint" a triangle based on a stored image, and then shadow mapping can alter that triangle's colors based on line-of-sight to light sources. === Video game graphics === Real-time graphics optimizes image quality subject to time and hardware constraints. GPUs and other advances increased the image quality that real-time graphics can produce. GPUs are capable of handling millions of triangles per frame, and modern DirectX/OpenGL class hardware is capable of generating complex effects, such as shadow volumes, motion blurring, and triangle generation, in real-time. The advancement of real-time graphics is evidenced in the progressive improvements between actual gameplay graphics and the pre-rendered cutscenes traditionally found in video games. Cutscenes are typically rendered in real-time—and may be interactive. Although the gap in quality between real-time graphics and traditional off-line graphics is narrowing, offline rendering remains much more accurate. === Advantages === Real-time graphics are typically employed when interactivity (e.g., player feedback) is crucial. When real-time graphics are used in films, the director has complete control of what has to be drawn on each frame, which can sometimes involve lengthy decision-making. Teams of people are typically involved in the making of these decisions. In real-time computer graphics, the user typically operates an input device to influence what is about to be drawn on the display. For example, when the user wants to move a character on the screen, the system updates the character's position before drawing the next frame. Usually, the display's response-time is far slower than the input device—this is justified by the immense difference between the (fast) response time of a human being's motion and the (slow) perspective speed of the human visual system. This difference has other effects too: because input devices must be very fast to keep up with human motion response, advancements in input devices (e.g., the current Wii remote) typically take much longer to achieve than comparable advancements in display devices. Another important factor controlling real-time computer graphics is the combination of physics and animation. These techniques largely dictate what is to be drawn on the screen—especially where to draw objects in the scene. These techniques help realistically imitate real world behavior (the temporal dimension, not the spatial dimensions), adding to the computer graphics' degree of realism. Real-time previewing with graphics software, especially when adjusting lighting effects, can increase work speed. Some parameter adjustments in fractal generating software may be made while viewing changes to the image in real time. == Rendering pipeline == The graphics rendering pipeline ("rendering pipeline" or simply "pipeline") is the foundation of real-time graphics. Its main function is to render a two-dimensional image in relation to a virtual camera, three-dimensional objects (an object that has width, length, and depth), light sources, lighting models, textures and more. === Architecture === The architecture of the real-time rendering pipeline can be divided into conceptual stages: application, geometry and rasterization. === Application stage === The application stage is responsible for generating "scenes", or 3D settings that are drawn to a 2D display. This stage is implemented in software that developers optimize for performance. This stage may perform processing such as collision detection, speed-up techniques, animation and force feedback, in addition to handling user input. Collision detection is an example of an operation that would be performed in the application stage. Collision detection uses algorithms to detect and respond to collisions between (virtual) objects. For example, the application may calculate new positions for the colliding objects and provide feedback via a force feedback device such as a vibrating game controller. The application stage also prepares graphics data for the next stage. This includes texture animation, animation of 3D models, animation via transforms, and geometry morphing. Finally, it produces primitives (points, lines, and triangles) based on scene information and feeds those primitives into the geometry stage of the pipeline. === Geometry stage === The geometry stage manipulates polygons and vertices to compute what to draw, how to draw it and where to draw it. Usually, these operations are performed by specialized hardware or GPUs. Variations across graphics hardware mean that the "geometry stage" may actually be implemented as several consecutive stages. ==== Model and view transformation ==== Before the final model is shown on the output device, the model is transformed onto multiple spaces or coordinate systems. Transformations move and manipulate objects by altering their vertices. Transformation is the general term for the four specific ways that manipulate the shape or position of a point, line or shape. ==== Lighting ==== In order to give the model a more realistic appearance, one or more light sources are usually established during transformation. However, this stage cannot be reached without first transforming the 3D scene into view space. In view space, the observer (camera) is typically placed at the origin. If using a right-handed coordinate system (which is considered standard), the observer looks in the direction of the negative z-axis with the y-axis pointing upwards and the x-axis pointing to the right. ==== Projection ==== Projection is a transformation used to represent a 3D model in a 2D space. The two main types of projection are orthographic projection (also called parallel) and perspective projection. The main characteristic of an orthographic projection is that parallel lines remain parallel after the transformation. Perspective projection utilizes the concept that if the distance between the observer and model increases, the model appears smaller than before. Essentially, perspective projection mimics human sight. ==== Clipping ==== Clipping is the process of removing primitives that are outside of the view box in order to facilitate the rasterizer stage. Once those primitives are removed, the primitives that remain will be drawn into new triangles that reach the next stage. ==== Screen mapping ==== The purpose of screen mapping is to find out the coordinates of the primitives during the clipping stage. ==== Rasterizer stage ==== The rasterizer

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  NNDB

  The Notable Names Database (NNDB) is an online database of biographical details of over 40,000 people. Soylent Communications, a sole proprietorship that also hosted the later defunct Rotten.com, describes NNDB as an "intelligence aggregator" of noteworthy persons, highlighting their interpersonal connections. The Rotten.com domain was registered in 1996 by former Apple and Netscape software engineer Thomas E. Dell, who was also known by his internet alias, "Soylent". == Entries == Each entry has an executive summary followed by a brief narrative about their life. It also lists date and cause of death if deceased. Businesspeople and government officials are listed with chronologies of their posts, positions, and board memberships. As of 2022, the site is no longer updated. == NNDB Mapper == The NNDB Mapper, a visual tool for exploring connections between people, was made available in May 2008. It required Adobe Flash 7.

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  IT8

  IT8 is a set of American National Standards Institute (ANSI) standards for color communications and control specifications. Formerly governed by the IT8 Committee, IT8 activities were merged with those of the Committee for Graphics Arts Technologies Standards (CGATS Archived November 9, 2018, at the Wayback Machine) in 1994. == Standards list == The following is a list of the IT8 standards, according to the NPES Standards Blue Book Archived July 19, 2011, at the Wayback Machine: === IT8.6 - 2002 - Graphic technology - Prepress digital data exchange - Diecutting data (DDES3) === This standard establishes a data exchange format to enable transfer of numerical control information between diecutting systems and electronic prepress systems. The information will typically consist of numerical control information used in the manufacture of dies. 37 pp. === IT8.7/1 - 1993 (R2003) - Graphic technology - Color transmission target for input scanner calibration === This standard defines an input test target that will allow any color input scanner to be calibrated with any film dye set used to create the target. It is intended to address the color transparency products that are generally used for input to the preparatory process for printing and publishing. This standard defines the layout and colorimetric values of a target that can be manufactured on any positive color transparency film and that is intended for use in the calibration of a photographic film/scanner combination. 32 pp. === IT8.7/2 - 1993 (R2003) Graphic technology - Color reflection target for input scanner calibration === This standard defines an input test target that will allow any color input scanner to be calibrated with any film dye set used to create the target. It is intended to address the color photographic paper products that are generally used for input to the preparatory process for printing and publishing. It defines the layout and colorimetric values of the target that can be manufactured on any color photographic paper and is intended for use in the calibration of a photographic paper/scanner combination. 29 pp. === IT8.7/3 - 1993 (R2003) Graphic technology - Input data for characterization of 4-color process printing === The purpose of this standard is to specify an input data file, a measurement procedure and an output data format to characterize any four-color printing process. The output data (characterization) file should be transferred with any four-color (cyan, magenta, yellow and black) halftone image files to enable a color transformation to be undertaken when required. 29 pp. == Targets == Calibrating all devices involved in the process chain (original, scanner/digital camera, monitor/printer) is required for an authentic color reproduction, because their actual color spaces differ device-specifically from the reference color spaces. An IT8 calibration is done with what are called IT8 targets, which are defined by the IT8 standards. Example Special targets, implementing the IT8.7/1 (transparent target) or IT8.7/2 (reflective target) standards, are needed for calibrating scanners. These targets consists of 24 grey fields and 264 color fields in 22 columns: Column 01 to 12: HCL color model, which differ in Hue, Chroma, and Lightness Column 13 to 16: CMYK-Colors Cyan, Magenta, Yellow, and Key (black) in different steps of brightness Column 17 to 19: RGB-Colors Red, Green, and Blue in different steps of brightness Column 20 to 22: undefined, producers' choice After scanning such a target, an ICC profile gets calculated on the basis of reference values. This profile is used for all subsequent scans and assures color fidelity.

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  Automated storage and retrieval system

  An automated storage and retrieval system (ASRS or AS/RS) consists of a variety of computer-controlled systems for automatically placing and retrieving loads from defined storage locations. Automated storage and retrieval systems (AS/RS) are typically used in applications where: There is a very high volume of loads being moved into and out of storage Storage density is important because of space constraints No value is added in this process (no processing, only storage and transport) Accuracy is critical because of potential expensive damages to the load An AS/RS can be used with standard loads as well as nonstandard loads, meaning that each standard load can fit in a uniformly-sized volume; for example, the film canisters in the image of the Defense Visual Information Center are each stored as part of the contents of the uniformly sized metal boxes, which are shown in the image. Standard loads simplify the handling of a request of an item. In addition, audits of the accuracy of the inventory of contents can be restricted to the contents of an individual metal box, rather than undergoing a top-to-bottom search of the entire facility, for a single item. They can also be used in self storage places. == Overview == AS/RS systems are designed for automated storage and retrieval of parts and items in manufacturing, distribution, retail, wholesale and institutions. They first originated in the 1960s, initially focusing on heavy pallet loads but with the evolution of the technology the handled loads have become smaller. The systems operate under computerized control, maintaining an inventory of stored items. Retrieval of items is accomplished by specifying the item type and quantity to be retrieved. The computer determines where in the storage area the item can be retrieved from and schedules the retrieval. It directs the proper automated storage and retrieval machine (SRM) to the location where the item is stored and directs the machine to deposit the item at a location where it is to be picked up. A system of conveyors and or automated guided vehicles is sometimes part of the AS/RS system. These take loads into and out of the storage area and move them to the manufacturing floor or loading docks. To store items, the pallet or tray is placed at an input station for the system, the information for inventory is entered into a computer terminal and the AS/RS system moves the load to the storage area, determines a suitable location for the item, and stores the load. As items are stored into or retrieved from the racks, the computer updates its inventory accordingly. The benefits of an AS/RS system include reduced labor for transporting items into and out of inventory, reduced inventory levels, more accurate tracking of inventory, and space savings. Items are often stored more densely than in systems where items are stored and retrieved manually. Within the storage, items can be placed on trays or hang from bars, which are attached to chains/drives in order to move up and down. The equipment required for an AS/RS include a storage & retrieval machine (SRM) that is used for rapid storage and retrieval of material. SRMs are used to move loads vertically or horizontally, and can also move laterally to place objects in the correct storage location. The trend towards Just In Time production often requires sub-pallet level availability of production inputs, and AS/RS is a much faster way of organizing the storage of smaller items next to production lines. The Material Handling Institute of America (MHIA), the non-profit trade association for the material handling world, and its members have categorised AS/RS into two primary segments: Fixed Aisle and Carousels/Vertical Lift Modules (VLMs). Both sets of technologies provide automated storage and retrieval for parts and items, but use different technologies. Each technology has its unique set of benefits and disadvantages. Fixed Aisle systems are characteristically larger systems whereas carousels and Vertical Lift Modules are used individually or grouped, but in small to medium-sized applications. A fixed-aisle AS/R machine (stacker crane) is one of two main designs: single-masted or double masted. Most are supported on a track and ceiling guided at the top by guide rails or channels to ensure accurate vertical alignment, although some are suspended from the ceiling. The 'shuttles' that make up the system travel between fixed storage shelves to deposit or retrieve a requested load (ranging from a single book in a library system to a several ton pallet of goods in a warehouse system). The entire unit moves horizontally within an aisle, while the shuttles are able to elevate up to the necessary height to reach the load, and can extend and retract to store or retrieve loads that are several positions deep in the shelving. A semi-automated system can be achieved by utilizing only specialized shuttles within an existing rack system. Another AS/RS technology is known as shuttle technology. In this technology the horizontal movement is made by independent shuttles each operating on one level of the rack while a lift at a fixed position within the rack is responsible for the vertical movement. By using two separate machines for these two axes the shuttle technology is able to provide higher throughput rates than stacker cranes. Storage and Retrieval Machines pick up or drop off loads to the rest of the supporting transportation system at specific stations, where inbound and outbound loads are precisely positioned for proper handling. In addition, there are several types of Automated Storage & Retrieval Systems (AS/RS) devices called Unit-load AS/RS, Mini-load AS/RS, Mid-Load AS/RS, Vertical Lift Modules (VLMs), Horizontal Carousels and Vertical Carousels. These systems are used either as stand-alone units or in integrated workstations called pods or systems. These units are usually integrated with various types of pick to light systems and use either a microprocessor controller for basic usage or inventory management software. These systems are ideal for increasing space utilization up to 90%, productivity levels by 90%, accuracy to 99.9%+ levels and throughput up to 750 lines per hour/per operator or more depending on the configuration of the system. == Horizontal carousels == Robotic Inserter/Extractor devices can be used for horizontal carousels. The robotic device is positioned in the front or rear of up to three horizontal carousels tiered high. The robot grabs the tote required in the order and often replenishes at the same time to speed up throughput. The tote(s) are then delivered to a conveyor, which routes it to a work station for picking or replenishing. Up to eight transactions per minute per unit can be done. Totes or containers up to 36" x 36" x 36" can be used in a system. On a simplistic level, horizontal carousels are also often used as "rotating shelving". With simple "fetch" command, items are brought to the operator and otherwise wasted space is eliminated. AS/RS Applications: Most applications of AS/RS technology have been associated with warehousing and distribution operations. An AS/RS can also be used to store raw materials and work in process in manufacturing. Three application areas can be distinguished for AS/RS: (1) Unit load storage and handling, (2) Order picking, and (3) Work in process storage. Unit load storage and retrieval applications are represented by unit load AS/RS and deep-lane storage systems. These kinds of applications are commonly found in warehousing for finishing goods in a distribution center, rarely in manufacturing. Deep-lane systems are used in the food industry. As described above, order picking involves retrieving materials in less than full unit load quantities. Minilpass, man-on board, and items retrieval systems are used for this second application area. Work in process storage is a more recent application of automated storage technology. While it is desirable to minimize the amount of work in process, WIP is unavoidable and must be effectively managed. Automated storage systems, either automated storage/retrieval systems or carousel systems, represent an efficient way to store materials between processing steps, particularly in batch and job shop production. In high production, work in process is often carried between operations by conveyor system, which this serve both storage and transport functions. === Inventory Category-specific AS/RS === Each inventory category—raw materials, work-in-process, and finished goods—requires its own specialized Automated Storage and Retrieval System (AS/RS). Particularly for work-in-process (WIP) inventories, due to variations in manufacturing processes, the AS/RS systems are significantly different in design and function, tailored specifically to match unique handling, storage, and retrieval requirements === Installed applications === Installed applications of this technology can be wide-ranging. In some librarie

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  Render layers

  When creating computer-generated imagery, final scenes appearing in movies and television productions are usually produced by rendering more than one "layer" or "pass," which are multiple images designed to be put together through digital compositing to form a completed frame. Rendering in passes is based on a traditions in motion control photography which predate CGI. As an example, for a visual effects shot, a camera could be programmed to move past a physical model of a spaceship in one pass to film the fully lit beauty pass of the ship, and then to repeat exactly the same camera move passing the ship again to photograph additional elements such as the illuminated windows in the ship or its thrusters. Once all of the passes were filmed, they could then be optically printed together to form a completed shot. The terms render layers and render passes are sometimes used interchangeably. However, rendering in layers refers specifically to separating different objects into separate images, such as a layer each for foreground characters, sets, distant landscape, and sky. On the other hand, rendering in passes refers to separating out different aspects of the scene, such as shadows, highlights, or reflections, into separate images.

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  INaturalist

  iNaturalist is an American 501(c)(3) nonprofit social network of naturalists, citizen scientists, and biologists built on the concept of mapping and sharing observations of biodiversity across the globe. iNaturalist may be accessed via its website or from its mobile applications. iNaturalist includes an automated species identification tool, and users further assist each other in identifying organisms from photographs and sound recordings. As of 5 August 2025, iNaturalist users had contributed nearly 300 million observations of plants, animals, fungi, and other organisms worldwide, and 400,000 users were active in the previous 30 days. iNaturalist serves as an important resource of open data for biodiversity research, conservation, and education, describing itself as "an online social network of people sharing biodiversity information to help each other learn about nature." It is the primary application for crowd-sourced biodiversity data in places such as Mexico, southern Africa, and Australia, and the project has been called "a standard-bearer for natural history mobile applications." Most of iNaturalist's software is open source. It has contributed to over 4,000 research papers and is widely used by scientists, land managers, and conservationists worldwide. The platform has also been active in the discovery of new species and rediscovery of species previously assumed to be extinct. == History == iNaturalist began in 2008 as a UC Berkeley School of Information Master's final project of Nate Agrin, Jessica Kline, and Ken-ichi Ueda. Agrin and Ueda continued work on the site with Sean McGregor, a web developer. In 2011, Ueda began collaboration with Scott Loarie, a research fellow at Stanford University and lecturer at UC Berkeley. Ueda and Loarie are the current co-directors of iNaturalist.org. The organization merged with the California Academy of Sciences on 24 April 2014. In 2017, iNaturalist became a joint initiative between the California Academy of Sciences and the National Geographic Society. With these collaborations and growing popularity of the site since 2012, the number of participants and observations has roughly doubled each year. In 2014, iNaturalist reached 1 million observations. Later, as of October 2023, there were 181 million observations (163 million verifiable). On 11 July 2023 iNaturalist announced its status as a newly independent 501(c)(3) nonprofit organization. === Google AI controversy === On 9 June 2025 Google announced that iNaturalist would be part of its "Generative AI Accelerator". This announcement, paired with the initial lack of information on the iNaturalist site, led to outcry from many iNaturalist users in the blog comments and forum, worrying about the consequences for the environment, volunteer engagement, reliability and raised questions about the decision making within iNaturalist, while some saw the backlash as a sign that people want to resist 'corrosive technologies'. PZ Myers, a biology professor who uses iNaturalist in his teaching, published an article on his website Pharyngula stating that "any decision that drives people away and replaces them with a hallucinating bot is a bad decision". == Platforms == Users can interact with iNaturalist in the following ways: through the iNaturalist.org website, through two mobile apps: iNaturalist (iOS/Android) and Seek by iNaturalist (iOS/Android), or through partner organizations such as the Global Biodiversity Information Facility (GBIF) website. On the iNaturalist.org website, visitors can search the public dataset and interact with other people adding observations and identifications. The website provides tools for registered users to add, identify, and discuss observations, write journal posts, explore information about species, create project pages to recruit participation, and coordinate work on their topics of interest. On the iNaturalist mobile app, users can create and share nature observations to the online dataset, explore observations both nearby and around the world, and learn about different species. Seek by iNaturalist, a separate app marketed to families, requires no online account registration and all observations may remain private. Seek incorporates features of gamification, such as providing a list of nearby organisms to find and encouraging the collection of badges and participation in challenges. Seek was initially released in the spring of 2018. == Observations == The iNaturalist platform is based on crowdsourcing of observations and identifications. An iNaturalist observation records a person's encounter with an individual organism at a particular time and place. An iNaturalist observation may also record evidence of an organism, such as animal tracks, nests, or scat. The scope of iNaturalist excludes natural but inert subjects such as geologic or hydrologic features. Users typically upload photos as evidence of their findings, though audio recordings are also accepted, and such evidence is not a strict requirement. Users may share observation locations publicly, "obscure" them to display a less precise location or make the locations completely private. iNaturalist users can add identifications to each other's observations in order to confirm or improve the identification of the observation. Observations are classified as "Casual", "Needs ID" (needs identification), or "Research Grade" based on the quality of the data provided and the community identification process. Any quality of data can be downloaded from iNaturalist and "Research Grade" observations are often incorporated into other online databases such as the Global Biodiversity Information Facility and the Atlas of Living Australia. === Automated species identification === In addition to observations being identified by others in the community, iNaturalist includes an automated species identification tool, first released in 2017. Images can be identified via a computer vision model which has been trained on the large database of the observations on iNaturalist. Multiple species suggestions are typically provided with the suggestion that the software guesses to be most likely is at the top of the list. A broader taxon such as a genus or family is commonly provided if the model is unsure of the species. It is trained once or twice a year, and the threshold for species included in the training set has changed over time. It can be difficult for the model to guess correctly if the species in question is infrequently observed or hard to identify from images alone, or if the image submitted has poor lighting, is blurry, or contains multiple subjects. In February 2023, iNaturalist released v2.1 of its computer vision model, which was trained on a new source model which performed significantly better than the previous models trained using a different source model. In April 2025 iNaturalist released an updated app for iOS, changing the original version to "iNaturalist Classic." == Projects == Users have created and contributed to tens of thousands of different projects on iNaturalist. The platform is commonly used to record observations during bioblitzes, which are biological surveying events that attempt to record all the species that occur within a designated area, and a specific project type on iNaturalist. Other project types include collections of observations by location or taxon or documenting specific types of observations such as animal tracks and signs, the spread of invasive species, roadkill, fishing catches, or discovering new species. In 2011, iNaturalist was used as a platform to power the Global Amphibian and Global Reptile BioBlitzes, in which observations were used to help monitor the occurrence and distribution of the world's reptiles and amphibian species. The US National Park Service partnered with iNaturalist to record observations from the 2016 National Parks BioBlitz. That project exceeded 100,000 observations in August 2016. In 2017, the United Nations Environment Programme teamed up with iNaturalist to celebrate World Environment Day.. In 2022, Reef Ecologic teamed up with iNaturalist to celebrate World Oceans Day. === City Nature Challenge === In 2016, Lila Higgins from the Natural History Museum of Los Angeles County and Alison Young from the California Academy of Sciences co-founded the City Nature Challenge (CNC). In the first City Nature Challenge, naturalists in Los Angeles and the San Francisco Bay Area documented over 20,000 observations with the iNaturalist platform. In 2017, the CNC expanded to 16 cities across the United States and collected over 125,000 observations of wildlife in 5 days. The CNC expanded to a global audience in 2018, with 68 cities participating from 19 countries, with some cities using community science platforms other than iNaturalist to participate. In 4 days, over 17,000 people cataloged over 440,000 nature observations in urban regions around the world. In 2019, the CNC once again expanded, with 35,000 parti

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