AI Chatbot Q

AI Chatbot Q — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Catie Cuan

    Catie Cuan

    Catie Cuan is an artist, entrepeuneur, and innovator in the field of robotic art and human-robot interaction, where she specializes in choreorobotics, an emerging field at the intersection of choreographic dance and robotics. Catie Cuan is currently one of the academic researchers pioneering the field of choreorobotics and currently holds a post-doctoral fellowship at Stanford University. == Career == Catie Cuan earned a bachelor's degree from the University of California, Berkeley. She graduated with a Ph.D. from the Department of Mechanical Engineering at Stanford University, focusing in robotics. Her most cited publication is about how to improve robotic expressive systems using tools from dance theory, such as the Laban/Bartenieff Movement Analysis. In her most recent research projects, she explores a predictive model of imitation learning for robots moving around humans, a project that advances the field of social robotics. Cuan credits her work in robotics to the experience with her father when he had a stroke and was surrounded by many medical machines, which made her think about how people might feel empowered and hopeful rather than afraid. As a ballet dancer and choreographer, she has performed with the Metropolitan Opera Ballet and the Lyric Opera of Chicago. In 2020, she was the dancer and choreographer of the show Output, which was part of a collaboration with ThoughtWorks Arts and the Pratt Institute. In the production, she danced with an ABB IRB 6700 industrial robot. In 2022, she was named as an IF/THEN ambassador for the American Association for the Advancement of Science. The same year, she was appointed Futurist-in-Residence at the Smithsonian Arts and Industries Building, where she performed at the closing ceremonies of the FUTURES exhibit on July 6, 2022. Cuan has also contributed to product designs, working with IDEO and Dutch interior design firm moooi on their Piro project, which launched a dancing scent diffuser robot during Milan Design Week in June 2022. She is a TED speaker with talks about how to teach robots to dance, and what is coming up for dancing robots in the AI era.

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  • Hardware security

    Hardware security

    Hardware security is a discipline originated from the cryptographic engineering and involves hardware design, access control, secure multi-party computation, secure key storage, ensuring code authenticity, measures to ensure that the supply chain that built the product is secure among other things. A hardware security module (HSM) is a physical computing device that safeguards and manages digital keys for strong authentication and provides cryptoprocessing. These modules traditionally come in the form of a plug-in card or an external device that attaches directly to a computer or network server. Some providers in this discipline consider that the key difference between hardware security and software security is that hardware security is implemented using "non-Turing-machine" logic (raw combinatorial logic or simple state machines). One approach, referred to as "hardsec", uses FPGAs to implement non-Turing-machine security controls as a way of combining the security of hardware with the flexibility of software. Hardware backdoors are backdoors in hardware. Conceptionally related, a hardware Trojan (HT) is a malicious modification of electronic system, particularly in the context of integrated circuit. A physical unclonable function (PUF) is a physical entity that is embodied in a physical structure and is easy to evaluate but hard to predict. Further, an individual PUF device must be easy to make but practically impossible to duplicate, even given the exact manufacturing process that produced it. In this respect it is the hardware analog of a one-way function. The name "physical unclonable function" might be a little misleading as some PUFs are clonable, and most PUFs are noisy and therefore do not achieve the requirements for a function. Today, PUFs are usually implemented in integrated circuits and are typically used in applications with high security requirements. Many attacks on sensitive data and resources reported by organizations occur from within the organization itself.

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  • Video game

    Video game

    A video game, computer game, or simply game is an electronic game that involves interaction with a user interface or input device (such as a joystick, controller, keyboard, or motion sensing device) to generate visual feedback from a display device, most commonly shown in a video format on a television set, computer monitor, flat-panel display or touchscreen on handheld devices, or a virtual reality headset. Most modern video games are audiovisual, with audio complement delivered through speakers or headphones, and sometimes also with other types of sensory feedback (e.g., haptic technology that provides tactile sensations). Some video games also allow microphone and webcam inputs for in-game chatting and livestreaming. Video games are typically categorized according to their hardware platform, which traditionally includes arcade video games, console games, and computer games (which includes LAN games, online games, and browser games). More recently, the video game industry has expanded onto mobile gaming through mobile devices (such as smartphones and tablet computers), virtual and augmented reality systems, and remote cloud gaming. Video games are also classified into a wide range of genres based on their style of gameplay and target audience. The first video game prototypes in the 1950s and 1960s were simple extensions of electronic games using video-like output from large, room-sized mainframe computers. The first consumer video game was the arcade video game Computer Space in 1971, which took inspiration from the earlier 1962 computer game Spacewar!. In 1972 came the now-iconic video game Pong and the first home console, the Magnavox Odyssey. The industry grew quickly during the "golden age" of arcade video games from the late 1970s to early 1980s but suffered from the crash of the North American video game market in 1983 due to loss of publishing control and saturation of the market. Following the crash, the industry matured, was dominated by Japanese companies such as Nintendo, Sega, and Sony, and established practices and methods around the development and distribution of video games to prevent a similar crash in the future, many of which continue to be followed. In the 2000s, the core industry centered on "AAA" games, leaving little room for riskier experimental games. Coupled with the availability of the Internet and digital distribution, this gave room for independent video game development (or "indie games") to gain prominence into the 2010s. Since then, the commercial importance of the video game industry has been increasing. The emerging Asian markets and proliferation of smartphone games in particular are altering player demographics towards casual and cozy gaming, and increasing monetization by incorporating games as a service. Today, video game development requires numerous skills, vision, teamwork, and liaisons between different parties, including developers, publishers, distributors, retailers, hardware manufacturers, and other marketers, to successfully bring a game to its consumers. As of 2020, the global video game market had estimated annual revenues of US$159 billion across hardware, software, and services, which is three times the size of the global music industry and four times that of the film industry in 2019, making it a formidable heavyweight across the modern entertainment industry. The video game market is also a major influence behind the electronics industry, where personal computer component, console, and peripheral sales, as well as consumer demands for better game performance, have been powerful driving factors for hardware design and innovation. == Origins == Early video games used interactive electronic devices with various display formats. The earliest example dates to 1947—a "cathode-ray tube amusement device" was filed for a patent on 25 January 1947, by Thomas T. Goldsmith Jr. and Estle Ray Mann, and issued on 14 December 1948, as U.S. Patent 2455992. Inspired by radar display technology, it consisted of an analog device allowing a user to control the parabolic arc of a dot on the screen to simulate a missile being fired at targets, which were paper drawings fixed to the screen. Other early examples include the Nimrod computer at the 1951 Festival of Britain; Christopher Strachey's Checkers, possibly the first game to display visuals on an electronic screen in 1952; OXO, a tic-tac-toe computer game by Alexander S. Douglas for the EDSAC in 1952; Tennis for Two, an electronic interactive game engineered by William Higinbotham in 1958; and Spacewar!, written by Massachusetts Institute of Technology students Martin Graetz, Steve Russell, and Wayne Wiitanen's on a DEC PDP-1 computer in 1962. Each game had different means of display: NIMROD had a panel of lights to play the game of Nim, OXO had a graphical display to play tic-tac-toe, Tennis for Two had an oscilloscope to display a side view of a tennis court, and Spacewar! had the DEC PDP-1's vector display to have two spaceships battle each other. These inventions laid the foundation for modern video games. In 1966, while working at Sanders Associates, Ralph H. Baer devised a system to play a basic table tennis game on a television screen. With the company's approval, Baer created the prototype known as the "Brown Box". Sanders patented Baer's innovations and licensed them to Magnavox, which commercialized the technology as the first home video game console, the Magnavox Odyssey, released in 1972. Separately, Nolan Bushnell and Ted Dabney, inspired by seeing Spacewar! running at Stanford University, devised a similar version running in a smaller coin-operated arcade cabinet using a less expensive computer. This was released as Computer Space, the first arcade video game, in 1971. Bushnell and Dabney went on to form Atari, Inc., and with Allan Alcorn, created their second arcade game in 1972, the hit ping pong-style Pong, which was directly inspired by the table tennis game on the Odyssey. Atari made a home version of Pong, which was released by Christmas 1975. The success of the Odyssey and Pong, both as an arcade game and home machine, launched the video game industry. Both Baer and Bushnell have been titled "Father of Video Games" for their contributions. == Terminology == The term "video game" was developed to describe electronic games played on a video display rather than on a teletype printer, audio speaker, or similar device. This also distinguished from handheld electronic games such as Merlin, which commonly used LED lights for indicators not in combination for imaging purposes. "Computer game" may also be used as a descriptor, as all these types of games essentially require the use of a computer processor; in some cases, it is used interchangeably with "video game". Particularly in the United Kingdom and Western Europe, this is common due to the historic relevance of domestically produced microcomputers. Other terms used include digital game, for example, by the Australian Bureau of Statistics. The term "computer game" can also refer to PC games, which are played primarily on personal computers or other flexible hardware systems, to distinguish them from console games, arcade games, or mobile games. Other terms, such as "television game", "telegame", or "TV game", had been used in the 1970s and early 1980s, particularly for home gaming consoles that rely on connection to a television set. However, these terms were also used interchangeably with "video game" in the 1970s, primarily due to "video" and "television" being synonymous. In Japan, where consoles like the Odyssey were first imported and then made within the country by the large television manufacturers such as Toshiba and Sharp Corporation, such games are known as "TV games", "TV geemu", or "terebi geemu". The term "TV game" is still commonly used into the 21st century. "Electronic game" may also be used to refer to video games, but this also incorporates devices like early handheld electronic games that lack any video output. The first appearance of the term "video game" emerged around 1973. The Oxford English Dictionary cited a 10 November 1973 BusinessWeek article as the first printed use of the term. Though Bushnell believed the term came from a vending magazine review of Computer Space in 1971, a review of the major vending magazines Vending Times and Cashbox showed that the term may have come even earlier, appearing first in a letter dated July 10, 1972. In the letter, Bushnell uses the term "video game" twice. Per video game historian Keith Smith, the sudden appearance suggested that the term had been proposed and readily adopted by those in the field. Around March 1973, Ed Adlum, who ran Cashbox's coin-operated section until 1972 and then later founded RePlay Magazine, covering the coin-op amusement field, in 1975, used the term in an article in March 1973. In a September 1982 issue of RePlay, Adlum is credited with first naming these games as "video games": "RePlay

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  • Algorithmic amplification

    Algorithmic amplification

    Algorithmic amplification is the process by which automated ranking and recommendation systems on digital platforms increase the visibility of certain content beyond its initial audience. Major platforms including Facebook, YouTube, TikTok, and X (formerly Twitter) use such systems to determine what appears in users' feeds and search results. The term is used in research on social media and digital media regulation to describe how platform design choices influence the distribution of online information. Unlike chronological feeds, algorithmic systems evaluate content using signals such as engagement rates, viewing duration, and predicted relevance to individual users. Content that performs strongly on these metrics may be promoted to progressively larger audiences through feeds, search rankings, or autoplay systems. The process is distinct from content moderation, which involves removing, labelling, or restricting content under platform rules, although the two can interact in practice. The concept is closely connected to the attention economy. Research has linked algorithmic amplification to the spread of misinformation and the circulation of political content, as well as to effects on young users' mental health. The scale and direction of those effects remain debated, in part because independent researchers have limited access to the internal workings of platform recommendation systems. Governments in the European Union, United Kingdom, United States, and China have pursued differing regulatory approaches to recommendation algorithms. The EU's Digital Services Act and the UK's Online Safety Act 2023 impose obligations on large platforms related to recommendation system transparency and risk, while China became the first country to enact binding legislation specifically targeting such systems. Internal documents and whistleblower testimony reported by the BBC in 2026 described how competitive pressure between Meta and TikTok led to trade-offs between engagement and user safety in the design of their recommendation systems. == Terminology == The term algorithmic amplification is used in media studies, platform governance scholarship and regulatory literature to describe how automated systems influence the distribution of content beyond what organic user sharing alone would produce. It is distinct from viral spread, which refers primarily to user-driven sharing behaviour, and from algorithmic bias, which describes systematic errors or unfairness in algorithmic outputs. The related term algorithmic curation is used for the broader process of selecting and ordering content, of which amplification is one possible outcome. The phrase also appears in regulatory and legislative discussion of recommendation systems. The European Union's Digital Services Act (DSA) identifies recommendation systems as a potential source of systemic risk, and the term appears frequently in academic and policy commentary on the regulation. In the United States, proposals including the Filter Bubble Transparency Act and the Kids Online Safety Act (KOSA) have used it to frame requirements around recommendation system transparency. In the United Kingdom, the House of Commons Science, Innovation and Technology Committee used the term in a 2025 report on how recommendation algorithms contributed to the spread of misinformation during the 2024 Southport riots. A Joint Declaration on AI and Freedom of Expression adopted in October 2025 by four international freedom of expression mandate holders, including the UN Special Rapporteur on Freedom of Opinion and Expression and the OSCE Representative on Freedom of the Media, stated that recommender systems and other AI-powered curation tools exert "a large hidden influence and gatekeeper role" over what information people access and consume. == Background == Early internet platforms typically displayed content in reverse-chronological order or through keyword-based search systems. Although the term is most often applied to social media, the underlying logic predates social media itself. A 2021 overview traced the origins of modern recommendation systems to the early 1990s, when they were first used experimentally for personal email and information filtering. The 1992 Tapestry mail system and the 1994 GroupLens news filtering system were early milestones before recommendation systems spread into e-commerce and other online services. As user bases and content volumes grew during the 2000s, major platforms including Google, YouTube, and Facebook developed machine-learning systems to personalise content delivery and prioritise material predicted to generate engagement. Facebook introduced its News Feed in 2006, which gradually shifted from chronological presentation towards algorithmically ranked content. YouTube altered its recommendation system in 2012 to prioritise watch time rather than clicks, a change the platform said was prompted by concerns that click-based metrics encouraged misleading thumbnails and low-quality videos. TikTok, launched internationally in 2018, adopted a model in which its primary content surface, the For You feed, is driven almost entirely by algorithmic recommendation rather than by a user's social graph. An internal document obtained by The New York Times in 2021 showed that the platform's algorithm optimised for retention and time spent, using signals such as watch duration, replays, likes, and comments to score and rank videos. Algorithmic recommendation also became central to platforms outside social media. Spotify's personalised features, including Discover Weekly, Release Radar, and Home recommendations, use behavioural signals and inferred "taste profiles" to surface tracks and artists beyond a listener's existing library. An ethnographic study of music curators at streaming platforms described this blend of algorithmic and human editorial selection as an "algo-torial" model of gatekeeping. Amazon adopted item-based collaborative filtering for product recommendations in 1998, and its recommendation engine has been described as one of the earliest large-scale deployments of recommendation technology in e-commerce. The same dynamics operate on adult content platforms. Law professor Amy Adler has argued that from 2007 onwards the pornography industry migrated to algorithm-driven streaming platforms, most of which are controlled by a single near-monopoly company, Aylo (formerly MindGeek). These platforms use algorithmic search engines, suggestions, rigid categorisation of content, and AI-driven search term optimisation in ways that produce the same distorting effects found on mainstream speech platforms, including filter bubbles, feedback loops, and the tendency of algorithmic recommendations to alter individual preferences. == Mechanisms == Recommendation systems commonly combine collaborative filtering, which predicts a user's preferences from the behaviour of similar users, with machine-learning models that predict which content a user is likely to engage with from their prior activity. In a common two-stage design, a platform first generates a set of candidate items from a large content pool and then ranks them using a scoring model with objectives such as predicted engagement or user satisfaction. Small changes in ranking criteria can shift exposure at scale, particularly when applied repeatedly across multiple browsing sessions. These systems typically rely on signals including engagement rates, viewing duration, click-through rates, and network relationships between users. Modern recommendation pipelines continuously update predictions as new behavioural data arrives, allowing platforms to adjust rankings in near real time. Users' revealed preferences, expressed through behaviour such as clicks and viewing time, do not always align with their stated preferences, expressed through explicit feedback such as surveys or content controls. Popularity signals can create feedback dynamics in which early engagement increases the likelihood that content will be shown to additional users. Experimental research on online cultural markets has demonstrated how such feedback processes can produce unequal visibility outcomes even when initial differences in content quality are small. == Beneficial and public-interest uses == Recommendation systems can help users navigate large volumes of content by surfacing material predicted to match their interests or needs, which can improve discoverability on platforms with large content libraries. In public health communication, platforms can help health authorities distribute timely information at scale, though the same recommendation systems also risk amplifying misinformation alongside official guidance. Sociologist Zeynep Tufekci has argued that the shift from independent blogs to large centralised platforms transferred gatekeeping power from traditional media to corporate algorithms. In the case of the Egyptian uprising of 2011, she noted that ordinary users

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  • Content Security Policy

    Content Security Policy

    Content Security Policy (CSP) is a computer security standard introduced to prevent cross-site scripting (XSS), clickjacking and other code injection attacks resulting from execution of malicious content in the trusted web page context. It is a Candidate Recommendation of the W3C working group on Web Application Security, widely supported by modern web browsers. CSP provides a standard method for website owners to declare approved origins of content that browsers should be allowed to load on that website—covered types are JavaScript, CSS, HTML frames, web workers, fonts, images, embeddable objects such as Java applets, ActiveX, audio and video files, and other HTML5 features. == Status == The standard, originally named Content Restrictions, was proposed by Robert Hansen in 2004, first implemented in Firefox 4 and quickly picked up by other browsers. Version 1 of the standard was published in 2012 as W3C candidate recommendation and quickly with further versions (Level 2) published in 2014. As of 2023, the draft of Level 3 is being developed with the new features being quickly adopted by the web browsers. The following header names are in use as part of experimental CSP implementations: Content-Security-Policy – standard header name proposed by the W3C document. Google Chrome supports this as of version 25. Firefox supports this as of version 23, released on 6 August 2013. WebKit supports this as of version 528 (nightly build). Chromium-based Microsoft Edge support is similar to Chrome's. X-WebKit-CSP – deprecated, experimental header introduced into Google Chrome, Safari and other WebKit-based web browsers in 2011. X-Content-Security-Policy – deprecated, experimental header introduced in Gecko 2 based browsers (Firefox 4 to Firefox 22, Thunderbird 3.3, SeaMonkey 2.1). A website can declare multiple CSP headers, also mixing enforcement and report-only ones. Each header will be processed separately by the browser. CSP can also be delivered within the HTML code using a meta tag, although in this case its effectiveness will be limited. Internet Explorer 10 and Internet Explorer 11 also support CSP, but only sandbox directive, using the experimental X-Content-Security-Policy header. A number of web application frameworks support CSP, for example AngularJS (natively) and Django (middleware). Instructions for Ruby on Rails have been posted by GitHub. Web framework support is however only required if the CSP contents somehow depend on the web application's state—such as usage of the nonce origin. Otherwise, the CSP is rather static and can be delivered from web application tiers above the application, for example on load balancer or web server. === Bypasses === In December 2015 and December 2016, a few methods of bypassing 'nonce' allowlisting origins were published. In January 2016, another method was published, which leverages server-wide CSP allowlisting to exploit old and vulnerable versions of JavaScript libraries hosted at the same server (frequent case with CDN servers). In May 2017 one more method was published to bypass CSP using web application frameworks code. == Mode of operation == If the Content-Security-Policy header is present in the server response, a compliant client enforces the declarative allowlist policy. One example goal of a policy is a stricter execution mode for JavaScript in order to prevent certain cross-site scripting attacks. In practice this means that a number of features are disabled by default: Inline JavaScript code