A-i Generator Reviews

A-i Generator Reviews — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Puck App

Puck App is a mobile application that allows hockey players to quickly find and rent a hockey goalie. Founded in 2015 in Toronto, the application primarily operates throughout Canada. It is available on Apple's App Store and Google Play. == History == Puck App was founded in 2016 by Niki Sawni. Users can rate the goalies, message with available goalies, and coordinate skill levels. In 2017, Puck App expanded to Western Canada and has over 1,000 goalies registered. In 2018, Puck App charged approximately $40 CDN to rent a goalie with more than 2 hours notice. Previously, Puck App was a competitor to a similar application called GoalieUp. As of 2024, both companies have agreed to a merger deal.
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Database-as-IPC

In computer programming, Database-as-IPC may be considered an anti-pattern where a disk persisted table in a database is used as the message queue store for routine inter-process communication (IPC) or subscribed data processing. If database performance is of concern, alternatives include sockets, network socket, or message queue. British computer scientist, Junade Ali, defined the Database-as-IPC Anti-Pattern as using a database to "schedule jobs or queue up tasks to be completed", noting that this anti-pattern centres around using a database for temporary messages instead of persistent data. == Controversy == The issue arises if there is a performance issue, and if additional systems (and servers) can be justified. In terms of performance, recent advancements in database systems provide more efficient mechanisms for signaling and messaging, and database systems also support memory (non-persisted) tables. There are databases with built-in notification mechanisms, such as PostgreSQL, SQL Server, and Oracle. These mechanisms and future improvements of database systems can make queuing much more efficient and avoid the need to set up a separate signaling or messaging queue system along with the server and management overhead. While MySQL doesn't have direct support for notifications, some workarounds are possible. However, they would be seen as non-standard and therefore more difficult to maintain.
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Sprayprinter

SprayPrinter is a device that attaches to aerosol paint cans whereby users can print images via Bluetooth from a smartphone onto a wall or almost any surface. == History == The technology behind SprayPrinter was developed by Mihkel Joala. He explained in a 2016 interview with New Atlas that his idea was inspired by the modern car engine and the Nintendo Wii console. "Engines nowadays use extremely fast valves to spray fuel to [the] combustion chamber," says Joala. "I realized I can use them to shoot paint with pinpoint accuracy." As of December 2021, the company appears to be no longer selling products. == Awards and Recognitions == In 2015, SprayPrinter received €8,000 from the Estonian prototyping contest Prototron for its initial prototype. In 2016, the SprayPrinter team won the grand prize of €30,000 from the televised pitching competition Ajujaht.
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Transparency in the software supply chain

Transparency in the software supply chain is a condition in which participants involved in the development, procurement, operation, auditing, or regulation of software can determine which components, dependencies, build stages, identifiers, and relationships within the supply chain make up the delivered product. The disclosure of information about software components, their interrelationships, origins, and development methods—for the purposes of risk management, vulnerability detection, and compliance—takes place throughout the software lifecycle. Transparency is one of the key security attributes of the software supply chain, as a deeper understanding of the chain enables participants to identify vulnerabilities and mitigate threats. Problems in the software supply chain can cause billions in losses and create operational challenges for government and commercial entities, as demonstrated by incidents involving SolarWinds, Bybit, 3CX, Jaguar Land Rover, GitHub, and NotPetya. Modern software is often assembled from third-party libraries and open-source components. According to research by the Linux Foundation and Synopsys, 96% of the commercial codebases analyzed contained open-source software, and 70–90% of a typical codebase may consist of open-source components. Without transparency, any software component can become a threat. As a result, companies may spend billions of dollars building robust external defenses, but this will not protect against vulnerabilities in legitimate software inside the perimeter. At the same time, supply chain attacks also erode trust between customers and their IT providers, as malicious code is often embedded in official updates with certificates and digital signatures. One of the primary ways to ensure transparency is through a software bill of materials, which documents the components used to create the software and the relationships within the supply chain. == Concept == The software supply chain is the collection of systems, devices, people, artifacts, and processes involved in the creation of the final software product. Attacks on the software supply chain differ from conventional attacks in that they follow a four-stage pattern: compromise, modification, distribution, and subsequent exploitation of the compromised or modified component. A defining feature of a supply chain attack is the introduction or manipulation of a change at an upstream stage, which is subsequently exploited at a downstream stage. Transparency refers to the availability of knowledge about the chain, while validity concerns the integrity of operations and artifacts and the authentication of participants, and separation involves reducing unnecessary trust relationships and the radius of impact through compartmentalization. In this framework, transparency primarily helps during the pre-compromise and detection phases, as a clearer understanding of participants, operations, and artifacts makes it easier to identify weak links before attackers exploit them. Current major attack vectors include dependencies and containers, build infrastructure, and human participants, such as maintainers or developers. == History == Software supply-chain transparency developed from earlier efforts to document software components, long before the term came into widespread use in the cybersecurity field. Early component-documentation formats included SPDX, first published in 2011, and CycloneDX, first published in 2017. Initially, these formats were created to support license compliance, package identification, and tool compatibility. Their development helped shape a broader concept of software supply chain transparency, encompassing component documentation, disclosure practices, risk management, security analysis, and regulatory compliance. In 2018, the U.S. National Telecommunications and Information Administration launched a multistakeholder process on promoting software component transparency. This process helped move work on SBOMs from a specialized technical practice into the realm of policy and procurement to identify components used in software products. The 2020 compromise of the SolarWinds Orion platform made software supply chain security a central issue in government cybersecurity policy. An analysis of the “Sunburst” campaign prepared by the Atlantic Council noted that the vulnerability of the software supply chain had become a realized risk for national-security agencies. In May 2021, U.S. President Joe Biden issued Executive Order 14028, which directed federal agencies to improve cybersecurity and increase transparency in the software supply chain, including requirements related to SBOMs. Reuters reported that the executive order required software developers selling their products to the federal government to provide greater visibility into their software and make security data available. In July 2021, the NTIA published the document “The Minimum Elements for a Software Bill of Materials (SBOM)”, defining the basic data fields and practices for creating SBOMs. Between 2021 and 2025, the U.S. Cybersecurity and Infrastructure Security Agency updated its guidance on “Framing Software Component Transparency”, expanding the set of SBOM attributes, metadata requirements, and operational recommendations for the creation, exchange, and use of SBOMs. Major incidents that occurred following the SolarWinds attack have underscored the importance of transparency in vulnerability management and supply chain security. The Log4Shell vulnerability in the Log4j library, disclosed in December 2021, demonstrated how difficult it can be for organizations to identify a vulnerable component deeply embedded within applications and services. In 2024, an attempt to plant a backdoor in XZ Utils showed how attackers could exploit trust in open-source maintenance processes to introduce malicious code into widely used infrastructure software. By the mid-2020s, software supply chain transparency had become part of international cybersecurity coordination and regulation. On September 3, 2025, Japan's Ministry of Economy, Trade and Industry and the National Cybersecurity Office, in collaboration with cybersecurity agencies from 15 countries, released the document “A Shared Vision of Software Bill of Materials (SBOM) for Cybersecurity.” In the European Union, the Cyber Resilience Act required manufacturers of products with digital elements to create, maintain, and retain SBOMs as part of the technical documentation for software placed on the EU market. == Transparency mechanisms == The primary mechanism for ensuring transparency is the software bill of materials (SBOM). An SBOM is a structured list of components, libraries, and tools used to build and distribute a software product, and it records dependencies in a way that helps organizations understand and assess their software supply chains. It can also be described as a formal record of components and their interdependencies, which gives users insight into their actual exposure to risks and threats. Five key areas of SBOM application in software supply chain security have been identified: vulnerability management, ensuring transparency, component evaluation, risk assessment, and ensuring supply chain integrity. In software supply chains, an SBOM documents all components, both open-source and proprietary. Under Executive Order 14028, U.S. federal agencies require software suppliers to provide SBOMs for government-procured software. The list of minimum required SBOM elements defined by NTIA includes three main categories: required data fields for describing each component (name, version, identifiers), automation support (machine-readable format, generation tools), and recommendations for creating SBOMs during development and purchasing. The post-2021 push for SBOMs was intended to provide visibility into the components used within software and to expose parts of an application that would otherwise remain hidden. This information can be used to prioritize patches, manage vulnerabilities, and support compliance work. Transparency also supports software traceability, which is becoming a standard feature of developer platforms. Traceability has become important because organizations are increasingly required to demonstrate how software was created, rather than simply listing its components. Higher levels of assurance require signed, tamper-proof traceability and more isolated, verifiable build environments. A related mechanism is build reproducibility. Reproducible builds are defined as build processes that make the compilation process deterministic, ensuring that the same source code always produces the same binary file. These builds are considered a foundational element for distributed verification, transparency-log maintenance, supply-chain workflow integration, and the creation of keyless signatures based on verifiable logs. Although reproducibility does not replace inventory or attestation, it gives external par
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Managed private cloud

Managed private cloud (also known as "hosted private cloud" or "single-tenant SaaS") refers to a principle in software architecture where a single instance of the software runs on a server, serves a single client organization (tenant), and is managed by a third party. The third-party provider is responsible for providing the hardware for the server and also for preliminary maintenance. This is in contrast to multitenancy, where multiple client organizations share a single server, or an on-premises deployment, where the client organization hosts its software instance. Managed private clouds also fall under the larger umbrella of cloud computing. == Adoption == The need for private clouds arose due to enterprises requiring a dedicated service and infrastructure for their cloud computing needs, such as for business-critical operations, improved security, and better control over their resources. Managed private cloud adoption is a popular choice among organizations. It has been on the rise due to enterprises requiring a dedicated cloud environment and preferring to avoid having to deal with management, maintenance, or future upgrade costs for the associated infrastructure and services. Such operational costs are unavoidable in on-premises private cloud data centers. == Advantages and challenges of managed private cloud == A managed private cloud cuts down on upkeep costs by outsourcing infrastructure management and maintenance to the managed cloud provider. It is easier to integrate an organization's existing software, services, and applications into a dedicated cloud hosting infrastructure which can be customized to the client's needs instead of a public cloud platform, whose hardware or infrastructure/software platform cannot be individualized to each client. Customers who choose a managed private cloud deployment usually choose them because of their desire for efficient cloud deployment, but also have the need for service customization or integration only available in a single-tenant environment. This chart shows the key benefits of the different types of deployments, and shows the overlap between these cloud solutions. This chart shows key drawbacks. Since deployments are done in a single-tenant environment, it is usually cost-prohibitive for small and medium-sized businesses. While server upkeep and maintenance are handled by the service provider, including network management and security, the client is charged for all such services. It is up to the potential client to determine if a managed private cloud solution aligns with their business objectives and budget. While the service provider maintains the upkeep of servers, network, and platform infrastructure, sensitive data is typically not stored on managed private clouds as it may leave business-critical information prone to breaches via third-party attacks on the cloud service provider. Common customizations and integrations include: Active Directory Single Sign-on Learning Management Systems Video Teleconferencing == Deployment strategies and service providers == Software companies have taken a variety of strategies in the Managed Private Cloud realm. Some software organizations have provided managed private cloud options internally, such as Microsoft. Companies that offer an on-premises deployment option, by definition, enable third-party companies to market Managed Private Cloud solutions. A few managed private cloud service providers are: Adobe Connect: Adobe Connect may be purchased for on-premises deployment, multi-tenant hosted deployment, managed private cloud as ACMS, or managed by third-party managed private cloud provider ConnectSolutions. Rackspace CenturyLink Microsoft licenses for Lync, SharePoint and Exchange may be purchased for on-premises deployment, a multi-tenant hosted deployment via Office 365, or managed by third-party cloud hosting from Azaleos, ConnectSolutions and others.
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Computer security compromised by hardware failure

Computer security compromised by hardware failure is a branch of computer security applied to hardware. The objective of computer security includes protection of information and property from theft, corruption, or natural disaster, while allowing the information and property to remain accessible and productive to its intended users. Such secret information could be retrieved by different ways. This article focus on the retrieval of data thanks to misused hardware or hardware failure. Hardware could be misused or exploited to get secret data. This article collects main types of attack that can lead to data theft. Computer security can be compromised by devices, such as keyboards, monitors or printers (thanks to electromagnetic or acoustic emanation for example) or by components of the computer, such as the memory, the network card or the processor (thanks to time or temperature analysis for example). == Devices == === Monitor === The monitor is the main device used to access data on a computer. It has been shown that monitors radiate or reflect data on their environment, potentially giving attackers access to information displayed on the monitor. ==== Electromagnetic emanations ==== Video display units radiate: narrowband harmonics of the digital clock signals; broadband harmonics of the various 'random' digital signals such as the video signal. Known as compromising emanations or TEMPEST radiation, a code word for a U.S. government programme aimed at attacking the problem, the electromagnetic broadcast of data has been a significant concern in sensitive computer applications. Eavesdroppers can reconstruct video screen content from radio frequency emanations. Each (radiated) harmonic of the video signal shows a remarkable resemblance to a broadcast TV signal. It is therefore possible to reconstruct the picture displayed on the video display unit from the radiated emission by means of a normal television receiver. If no preventive measures are taken, eavesdropping on a video display unit is possible at distances up to several hundreds of meters, using only a normal black-and-white TV receiver, a directional antenna and an antenna amplifier. It is even possible to pick up information from some types of video display units at a distance of over 1 kilometer. If more sophisticated receiving and decoding equipment is used, the maximum distance can be much greater. ==== Compromising reflections ==== What is displayed by the monitor is reflected on the environment. The time-varying diffuse reflections of the light emitted by a CRT monitor can be exploited to recover the original monitor image. This is an eavesdropping technique for spying at a distance on data that is displayed on an arbitrary computer screen, including the currently prevalent LCD monitors. The technique exploits reflections of the screen's optical emanations in various objects that one commonly finds close to the screen and uses those reflections to recover the original screen content. Such objects include eyeglasses, tea pots, spoons, plastic bottles, and even the eye of the user. This attack can be successfully mounted to spy on even small fonts using inexpensive, off-the-shelf equipment (less than 1500 dollars) from a distance of up to 10 meters. Relying on more expensive equipment allowed to conduct this attack from over 30 meters away, demonstrating that similar attacks are feasible from the other side of the street or from a close by building. Many objects that may be found at a usual workplace can be exploited to retrieve information on a computer's display by an outsider. Particularly good results were obtained from reflections in a user's eyeglasses or a tea pot located on the desk next to the screen. Reflections that stem from the eye of the user also provide good results. However, eyes are harder to spy on at a distance because they are fast-moving objects and require high exposure times. Using more expensive equipment with lower exposure times helps to remedy this problem. The reflections gathered from curved surfaces on close by objects indeed pose a substantial threat to the confidentiality of data displayed on the screen. Fully invalidating this threat without at the same time hiding the screen from the legitimate user seems difficult, without using curtains on the windows or similar forms of strong optical shielding. Most users, however, will not be aware of this risk and may not be willing to close the curtains on a nice day. The reflection of an object, a computer display, in a curved mirror creates a virtual image that is located behind the reflecting surface. For a flat mirror this virtual image has the same size and is located behind the mirror at the same distance as the original object. For curved mirrors, however, the situation is more complex. === Keyboard === ==== Electromagnetic emanations ==== Computer keyboards are often used to transmit confidential data such as passwords. Since they contain electronic components, keyboards emit electromagnetic waves. These emanations could reveal sensitive information such as keystrokes. Electromagnetic emanations have turned out to constitute a security threat to computer equipment. The figure below presents how a keystroke is retrieved and what material is necessary. The approach is to acquire the raw signal directly from the antenna and to process the entire captured electromagnetic spectrum. Thanks to this method, four different kinds of compromising electromagnetic emanations have been detected, generated by wired and wireless keyboards. These emissions lead to a full or a partial recovery of the keystrokes. The best practical attack fully recovered 95% of the keystrokes of a PS/2 keyboard at a distance up to 20 meters, even through walls. Because each keyboard has a specific fingerprint based on the clock frequency inconsistencies, it can determine the source keyboard of a compromising emanation, even if multiple keyboards from the same model are used at the same time. The four different kinds way of compromising electromagnetic emanations are described below. ===== The Falling Edge Transition Technique ===== When a key is pressed, released or held down, the keyboard sends a packet of information known as a scan code to the computer. The protocol used to transmit these scan codes is a bidirectional serial communication, based on four wires: Vcc (5 volts), ground, data and clock. Clock and data signals are identically generated. Hence, the compromising emanation detected is the combination of both signals. However, the edges of the data and the clock lines are not superposed. Thus, they can be easily separated to obtain independent signals. ===== The Generalized Transition Technique ===== The Falling Edge Transition attack is limited to a partial recovery of the keystrokes. This is a significant limitation. The GTT is a falling edge transition attack improved, which recover almost all keystrokes. Indeed, between two traces, there is exactly one data rising edge. If attackers are able to detect this transition, they can fully recover the keystrokes. ===== The Modulation Technique ===== Harmonics compromising electromagnetic emissions come from unintentional emanations such as radiations emitted by the clock, non-linear elements, crosstalk, ground pollution, etc. Determining theoretically the reasons of these compromising radiations is a very complex task. These harmonics correspond to a carrier of approximately 4 MHz which is very likely the internal clock of the micro-controller inside the keyboard. These harmonics are correlated with both clock and data signals, which describe modulated signals (in amplitude and frequency) and the full state of both clock and data signals. This means that the scan code can be completely recovered from these harmonics. ===== The Matrix Scan Technique ===== Keyboard manufacturers arrange the keys in a matrix. The keyboard controller, often an 8-bit processor, parses columns one-by-one and recovers the state of 8 keys at once. This matrix scan process can be described as 192 keys (some keys may not be used, for instance modern keyboards use 104/105 keys) arranged in 24 columns and 8 rows. These columns are continuously pulsed one-by-one for at least 3μs. Thus, these leads may act as an antenna and generate electromagnetic emanations. If an attacker is able to capture these emanations, he can easily recover the column of the pressed key. Even if this signal does not fully describe the pressed key, it still gives partial information on the transmitted scan code, i.e. the column number. Note that the matrix scan routine loops continuously. When no key is pressed, we still have a signal composed of multiple equidistant peaks. These emanations may be used to remotely detect the presence of powered computers. Concerning wireless keyboards, the wireless data burst transmission can be used as an electromagnetic trigger to detect exactly when a key is pressed, while the matrix s
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Intel Threat Detection Technology

Intel Threat Detection Technology (TDT) is a CPU-level technology created by Intel in 2018 to enable host endpoint protections to use a CPU's low-level access to detect threats to a system. TDT consists of multiple components including Accelerated Memory Scanning, which uses the CPU's integrated GPU to scan memory, and Advanced Platform Telemetry, which uses processor-level activity monitoring to detect unusual activity. It is supported on sixth-generation or newer Intel Core CPUs and additional capabilities were added to the 11th generation Core processors. Intel TDT is integrated into several third-party anti-malware solutions including Microsoft Defender, Check Point Harmony Endpoint, CrowdStrike Falcon, and others. == Accelerated Memory Scanning == Accelerated Memory Scanning (also referred to as "Advanced Memory Scanning") uses the CPU's integrated GPU to scan memory for malicious code, instead of using the CPU directly. This improves system responsiveness during anti-malware scanning. and lowers power consumption. Features include pattern matching, using random forest decision trees, string extraction, entropy calculation, and Euclidean clustering. == Advanced Platform Telemetry == Advanced Platform Telemetry collects CPU-level telemetry to detect uncommon activity patterns which might be indicative of malware. The telemetry data is collected from the CPU performance monitoring unit (PMU) and doesn't require a large signature database to detect malware. Instead, it uses machine-learning based correlations to identify indicators of attack For example, Microsoft Defender is able to use TDT's Advanced Platform Telemetry features to detect processor usage patterns indicative of ransomware and cryptojacking with TDT so it can detect them.
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Generatrix

In geometry, a generatrix () or describent is a point, curve or surface that, when moved along a given path, generates a new shape. The path directing the motion of the generatrix motion is called a directrix or dirigent. == Examples == A cone can be generated by moving a line (the generatrix) fixed at the future apex of the cone along a closed curve (the directrix); if that directrix is a circle perpendicular to the line connecting its center to the apex, the motion is rotation around a fixed axis and the resulting shape is a circular cone. The generatrix of a cylinder, a limiting case of a cone, is a line that is kept parallel to some axis.
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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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IPUMS

IPUMS, originally the Integrated Public Use Microdata Series, is the world's largest individual-level population database. IPUMS consists of microdata samples from United States (IPUMS-USA) and international (IPUMS-International) census records, as well as data from U.S. and international surveys. The records are converted into a consistent format and made available to researchers through a web-based data dissemination and analysis system. IPUMS is housed at the Institute for Social Research and Data Innovation (ISRDI), an interdisciplinary research center at the University of Minnesota, under the direction of Professor Steven Ruggles. == Description == IPUMS includes all persons enumerated in the United States censuses from 1850 to 1950 (though, the 1890 census is missing because it was destroyed in a fire) and from the American Community Survey since 2000 and the Current Population Survey since 1962. IPUMS includes household-level data for United States Censuses from 1790 to 1840, due to the first six censuses only including the name of the head of household, with tallied household totals following. IPUMS provides consistent variable names, coding schemes, and documentation across all the samples, facilitating the analysis of long-term change. IPUMS-International includes countries from Africa, Asia, Europe, and Latin America for 1960 forward. The database currently includes more than a billion individuals enumerated in 365 censuses from 94 countries around the world. IPUMS-International converts census microdata for multiple countries into a consistent format, allowing for comparisons across countries and time periods. Special efforts are made to simplify use of the data while losing no meaningful information. Comprehensive documentation is provided in a coherent form to facilitate comparative analyses of social and economic change. Additional databases in the IPUMS family include the: North Atlantic Population Project (NAPP) IPUMS National Historical Geographic Information System (NHGIS) IPUMS Health Surveys IPUMS Global Health IPUMS Time Use The Journal of American History described the effort as "One of the great archival projects of the past two decades." Liens Socio, the French portal for the social sciences, gave IPUMS the only “best site” designation that has gone to any non-French website, writing “IPUMS est un projet absolument extraordinaire...époustouflante [mind-blowing]!” The official motto of IPUMS is "use it for good, never for evil." All public IPUMS data and documentation are available online free of charge.
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Deductive language

A deductive language is a computer programming language in which the program is a collection of predicates ('facts') and rules that connect them. Such a language is used to create knowledge based systems or expert systems which can deduce answers to problem sets by applying the rules to the facts they have been given. An example of a deductive language is Prolog, or its database-query cousin, Datalog. == History == As the name implies, deductive languages are rooted in the principles of deductive reasoning; making inferences based upon current knowledge. The first recommendation to use a clausal form of logic for representing computer programs was made by Cordell Green (1969) at Stanford Research Institute (now SRI International). This idea can also be linked back to the battle between procedural and declarative information representation in early artificial intelligence systems. Deductive languages and their use in logic programming can also be dated to the same year when Foster and Elcock introduced Absys, the first deductive/logical programming language. Shortly after, the first Prolog system was introduced in 1972 by Colmerauer through collaboration with Robert Kowalski. == Components == The components of a deductive language are a system of formal logic and a knowledge base upon which the logic is applied. === Formal Logic === Formal logic is the study of inference in regards to formal content. The distinguishing feature between formal and informal logic is that in the former case, the logical rule applied to the content is not specific to a situation. The laws hold regardless of a change in context. Although first-order logic is described in the example below to demonstrate the uses of a deductive language, no formal system is mandated and the use of a specific system is defined within the language rules or grammar. As input, a predicate takes any object(s) in the domain of interest and outputs either one of two Boolean values: true or false. For example, consider the sentences "Barack Obama is the 44th president" and "If it rains today, I will bring an umbrella". The first is a statement with an associated truth value. The second is a conditional statement relying on the value of some other statement. Either of these sentences can be broken down into predicates which can be compared and form the knowledge base of a deductive language. Moreover, variables such as 'Barack Obama' or 'president' can be quantified over. For example, take 'Barack Obama' as variable 'x'. In the sentence "There exists an 'x' such that if 'x' is the president, then 'x' is the commander in chief." This is an example of the existential quantifier in first order logic. Take 'president' to be the variable 'y'. In the sentence "For every 'y', 'y' is the leader of their nation." This is an example of the universal quantifier. === Knowledge Base === A collection of 'facts' or predicates and variables form the knowledge base of a deductive language. Depending on the language, the order of declaration of these predicates within the knowledge base may or may not influence the result of applying logical rules. Upon application of certain 'rules' or inferences, new predicates may be added to a knowledge base. As new facts are established or added, they form the basis for new inferences. As the core of early expert systems, artificial intelligence systems which can make decisions like an expert human, knowledge bases provided more information than databases. They contained structured data, with classes, subclasses, and instances. == Prolog == Prolog is an example of a deductive, declarative language that applies first- order logic to a knowledge base. To run a program in Prolog, a query is posed and based upon the inference engine and the specific facts in the knowledge base, a result is returned. The result can be anything appropriate from a new relation or predicate, to a literal such as a Boolean (true/false), depending on the engine and type system.
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Tessellation (computer graphics)

In computer graphics, tessellation is the dividing of datasets of polygons (sometimes called vertex sets) presenting objects in a scene into suitable structures for rendering. Especially for real-time rendering, data is tessellated into triangles, for example in OpenGL 4.0 and Direct3D 11. == In graphics rendering == A key advantage of tessellation for realtime graphics is that it allows detail to be dynamically added and subtracted from a 3D polygon mesh and its silhouette edges based on control parameters (often camera distance). In previously leading realtime techniques such as parallax mapping and bump mapping, surface details could be simulated at the pixel level, but silhouette edge detail was fundamentally limited by the quality of the original dataset. In Direct3D 11 pipeline (a part of DirectX 11), the graphics primitive is the patch. The tessellator generates a triangle-based tessellation of the patch according to tessellation parameters such as the TessFactor, which controls the degree of fineness of the mesh. The tessellation, along with shaders such as a Phong shader, allows for producing smoother surfaces than would be generated by the original mesh. By offloading the tessellation process onto the GPU hardware, smoothing can be performed in real time. Tessellation can also be used for implementing subdivision surfaces, level of detail scaling and fine displacement mapping. OpenGL 4.0 uses a similar pipeline, where tessellation into triangles is controlled by the Tessellation Control Shader and a set of four tessellation parameters. == In computer-aided design == In computer-aided design the constructed design is represented by a boundary representation topological model, where analytical 3D surfaces and curves, limited to faces, edges, and vertices, constitute a continuous boundary of a 3D body. Arbitrary 3D bodies are often too complicated to analyze directly. So they are approximated (tessellated) with a mesh of small, easy-to-analyze pieces of 3D volume—usually either irregular tetrahedra, or irregular hexahedra. The mesh is used for finite element analysis. The mesh of a surface is usually generated per individual faces and edges (approximated to polylines) so that original limit vertices are included into mesh. To ensure that approximation of the original surface suits the needs of further processing, three basic parameters are usually defined for the surface mesh generator: The maximum allowed distance between the planar approximation polygon and the surface (known as "sag"). This parameter ensures that mesh is similar enough to the original analytical surface (or the polyline is similar to the original curve). The maximum allowed size of the approximation polygon (for triangulations it can be maximum allowed length of triangle sides). This parameter ensures enough detail for further analysis. The maximum allowed angle between two adjacent approximation polygons (on the same face). This parameter ensures that even very small humps or hollows that can have significant effect to analysis will not disappear in mesh. An algorithm generating a mesh is typically controlled by the above three and other parameters. Some types of computer analysis of a constructed design require an adaptive mesh refinement, which is a mesh made finer (using stronger parameters) in regions where the analysis needs more detail.
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Foveated rendering

Foveated rendering is a rendering technique which uses an eye tracker integrated with a virtual reality headset to reduce the rendering workload by greatly reducing the image quality in the peripheral vision (outside of the zone gazed by the fovea). A less sophisticated variant called fixed foveated rendering doesn't utilise eye tracking and instead assumes a fixed focal point. == History == Research into foveated rendering dates back at least to 1991. At Tech Crunch Disrupt SF 2014, Fove unveiled a headset featuring foveated rendering. This was followed by a successful kickstarter in May 2015. At CES 2016, SensoMotoric Instruments (SMI) demoed a new 250 Hz eye tracking system and a working foveated rendering solution. It resulted from a partnership with camera sensor manufacturer Omnivision who provided the camera hardware for the new system. In July 2016, Nvidia demonstrated during SIGGRAPH a new method of foveated rendering claimed to be invisible to users. In February 2017, Qualcomm announced their Snapdragon 835 Virtual Reality Development Kit (VRDK) which includes foveated rendering support called Adreno Foveation. == Use == According to chief scientist Michael Abrash at Oculus, utilising foveated rendering in conjunction with sparse rendering and deep learning image reconstruction has the potential to require an order of magnitude fewer pixels to be rendered in comparison to a full image. Later, these results have been demonstrated and published. In December 2019, fixed foveated rendering support was added to the Oculus Quest SDK. A number of VR headsets have included on-board eye tracking to provide support for foveated rendering, including HTC's Vive Pro Eye (2019), Meta Quest Pro (2022), PlayStation VR2 (2023), and Apple Vision Pro (2024). In 2025, Valve announced the upcoming Steam Frame headset, which applies a variation of the technique known as "foveated streaming" for wireless streaming from a PC to the headset; the method similarly uses variance in bit rate, and is performed at the encoder level rather than the software level.
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Voyages: The Trans-Atlantic Slave Trade Database

Voyages: The Trans-Atlantic Slave Trade Database is a database hosted at Rice University that aims to present all documentary material pertaining to the transatlantic slave trade. It is a sister project to African Origins. The database breaks down the kingdoms and countries that engaged in the Atlantic trade. By 2008, the project had gathered data on nearly 35,000 transatlantic slave voyages from 1501 to 1867. For each voyage they sought to establish dates, owners, vessels, captains, African visits, American destinations, numbers of slaves embarked, and numbers landed. They have been able to find much of this material for an estimated 80 percent of the entire transatlantic African slave trade. With corrections for missing voyages, the Project has estimated the entire size of the transatlantic slave trade with more comprehension, precision, and accuracy than before. They reckon that in 366 years, slaving vessels embarked about 12.5 million captives in Africa, and landed 10.7 million in the New World. A horrific discovery is a careful estimate that the Middle Passage took a toll of more than 1.8 million African lives. In this quantitative database, the numbers are enslaved people.
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Viewport

A viewport is a polygon viewing region in computer graphics. In computer graphics theory, there are two region-like notions of relevance when rendering some objects to an image. In textbook terminology, the world coordinate window is the area of interest (meaning what the user wants to visualize) in some application-specific coordinates, e.g. miles, centimeters etc. The word window as used here should not be confused with the GUI window, i.e. the notion used in window managers. Rather it is an analogy with how a window limits what one can see outside a room. In contrast, the viewport is an area (typically rectangular) expressed in rendering-device-specific coordinates, e.g. pixels for screen coordinates, in which the objects of interest are going to be rendered. Clipping to the world-coordinates window is usually applied to the objects before they are passed through the window-to-viewport transformation. For a 2D object, the latter transformation is simply a combination of translation and scaling, the latter not necessarily uniform. An analogy of this transformation process based on traditional photography notions is to equate the world-clipping window with the camera settings and the variously sized prints that can be obtained from the resulting film image as possible viewports. Because the physical-device-based coordinates may not be portable from one device to another, a software abstraction layer known as normalized device coordinates is typically introduced for expressing viewports; it appears for example in the Graphical Kernel System (GKS) and later systems inspired from it. In 3D computer graphics, the viewport refers to the 2D rectangle used to project the 3D scene to the position of a virtual camera. A viewport is a region of the screen used to display a portion of the total image to be shown. In virtual desktops, the viewport is the visible portion of a 2D area which is larger than the visualization device. When viewing a document in a web browser, the viewport is the region of the browser window which contains the visible portion of the document. If the size of the viewport changes, for example as a result of the user resizing the browser window, then the browser may reflow the document (recalculate the locations and sizes of elements of the document). If the document is larger than the viewport, the user can control the portion of the document which is visible by scrolling in the viewport.
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