AI App Jobs

AI App Jobs — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Captions (app)

    Captions (app)

    Mirage (formerly known as Captions) is a video-generating, video-editing and AI research company headquartered in New York City. Their first app, Captions, is available on iOS, Android, and Web and offers a suite of tools aimed at streamlining the creation and editing of videos. Their enterprise platform, Mirage Studio, generates AI actors and videos for marketing assets and video campaigns. == History == Mirage was co-founded by Gaurav Misra and Dwight Churchill. During Misra's time leading design engineering at Snap Inc., he followed the rise of a new category of video, the "talking video." In 2021, Misra left Snap to found Mirage with his former colleague Churchill. Later that year, the Captions app launched with early backing from venture capital firms Sequoia Capital and Andreessen Horowitz as well as individual investors. In 2023, the company released Lipdub, an Al dubbing app which translates any video with spoken audio into 28 languages. In October 2023, Captions shared that it maintained over 100,000 daily active users with "about a million" videos being created monthly. In November 2024, Captions acquired AlpacaML, a generative AI company that focused on art and other images. In June 2025, Captions launched Mirage Studio, for marketers and advertising agencies. In September 2025, Captions rebranded their company to Mirage. This change reflects the company's focus on developing their proprietary foundation model and future video products. == Products == The Captions app offers features to automate common production tasks including captioning, editing, dubbing, script creation, and music integration. Mirage Studio allows users to generate AI avatars and create short-form videos from prompts or audio. == Awards == In 2023, the company was recognized as part of Fast Company's "Next Big Things In Tech" series. In 2024, the company won 2 Webby Awards for Best Use of AI & Machine Learning and Creative Production.

    Read more →
  • Generative AI pornography

    Generative AI pornography

    Generative AI pornography or simply AI pornography is a digitally created pornography produced through generative artificial intelligence (AI) technologies. Unlike traditional pornography, which involves real actors and cameras, this content is synthesized entirely by AI algorithms. These algorithms, including generative adversarial networks (GANs) and text-to-image models, generate lifelike images, videos, or animations from textual descriptions or datasets. == Functions and production strategies == AI pornography platforms, beyond account creation and social media linking, primarily enable users to generate sexual images through feature selection or text prompting. Users can customize bodies, clothing, and sociodemographic traits, and browse categorized galleries of user‑generated content. Several sites also support short pornographic videos or GIFs and modification tools such as nudifiers, deepfakes, and facemorphing. Platforms often allow fine‑tuning of parameters such as settings, style, or theme, and provide prompt enhancers or suggestions to improve outputs. Users may edit generated images, refine prior prompts, modify others’ work, or upload personal material as a basis, with iterative and collaborative content creation. Some websites additionally host interactive “erobots,” customizable in real time for appearance, personality, memories, speech, and profession, enabling tailored sexual and non‑sexual interactions. Less common features include VR integration, AI porn games, audio or doodle prompts, and consensual replication of individuals with verification. == History == The use of generative AI in the adult industry began in the late 2010s, initially focusing on AI-generated art, music, and visual content. This trend accelerated in 2022 with Stability AI's release of Stable Diffusion (SD), an open-source text-to-image model that enables users to generate images, including NSFW content, from text prompts using the LAION-Aesthetics subset of the LAION-5B dataset. Despite Stability AI's warnings against sexual imagery, SD's public release led to dedicated communities exploring both artistic and explicit content, sparking ethical debates over open-access AI and its use in adult media. By 2020, AI tools had advanced to generate highly realistic adult content, amplifying calls for regulation. === AI-generated influencers === One application of generative AI technology is the creation of AI-generated influencers on platforms such as OnlyFans and Instagram. These AI personas interact with users in ways that can mimic real human engagement, offering an entirely synthetic but convincing experience. While popular among niche audiences, these virtual influencers have prompted discussions about authenticity, consent, and the blurring line between human and AI-generated content, especially in adult entertainment. === The growth of AI porn sites === By 2023, websites dedicated to AI-generated adult content had gained traction, catering to audiences seeking customizable experiences. These platforms allow users to create or view AI-generated pornography tailored to their preferences. These platforms enable users to create or view AI-generated adult content appealing to different preferences through prompts and tags, customizing body type, facial features, and art styles. Tags further refine the output, creating niche and diverse content. Many sites feature extensive image libraries and continuous content feeds, combining personalization with discovery and enhancing user engagement. AI porn sites, therefore, attract those seeking unique or niche experiences, sparking debates on creativity and the ethical boundaries of AI in adult media. == Ethical concerns and misuse == The growth of generative AI pornography has also attracted some cause for criticism. AI technology can be exploited to create non-consensual pornographic material, posing risks similar to those seen with deepfake revenge porn and AI-generated NCII (Non-Consensual Intimate Image). A 2023 analysis found that 98% of deepfake videos online are pornographic, with 99% of the victims being women. Some famous celebrities victims of deepfake include Scarlett Johansson, Taylor Swift, and Maisie Williams. OpenAI is exploring whether NSFW content, such as erotica, can be responsibly generated in age-appropriate contexts while maintaining its ban on deepfakes. This proposal has attracted criticism from child safety campaigners who argue it undermines OpenAI's mission to develop "safe and beneficial" AI. Additionally, the Internet Watch Foundation has raised concerns about AI being used to generate sexual abuse content involving children. === AI-generated non-consensual intimate imagery (AI Undress) === Generative AI have extensively been used to produce pornography images and videos of non-consenting individuals. 404 Media reported a particular AI generated porn bot on Telegram has more than 100,000 monthly users. Alibaba, the Chinese tech company, released an AI video generation model in 2025 called Wan 2.1, which was modified to produce non-consensual pornography. Several US states are taking actions against using deepfake apps and sharing them on the internet. In 2024, San Francisco filed a landmark lawsuit to shut down "undress" apps that allow users to generate non-consensual AI nude images, citing violations of state laws. The case aligns with California's recent legislation—SB 926, SB 942, and SB 981—championed by Senators Aisha Wahab and Josh Becker and signed by Governor Gavin Newsom. These bills aim to protect individuals from AI-generated explicit images by criminalizing non-consensual distribution, mandating disclosures, and empowering victims to report and remove harmful content from platforms. === Differences from deepfake pornography === While both generative AI pornography and deepfake pornography rely on synthetic media, they differ in their methods and ethical considerations. Deepfake pornography typically involves altering existing footage of real individuals, often without their consent, using AI to superimpose faces, undress said persons, or modify scenes. In contrast, generative AI pornography is created using algorithms, producing hyper-realistic content without the need to upload real pictures of people. Hany Farid, digital image analysis expert, also described the difference between "AI porn" and "deepfake porn." == Legality == The legality of generative AI pornography varies widely by jurisdiction and remains an evolving issue. In some countries, laws addressing digital impersonation, obscenity, or deepfake technologies may indirectly apply, particularly when AI-generated content involves the likeness of real individuals without consent. The absence of a physical performer further complicates traditional regulatory frameworks, which are often grounded in performer protection and distribution laws. In the United States, legal responses have primarily focused on non-consensual deepfakes and impersonation. Some states, such as Virginia, California, and Texas, have enacted legislation criminalising the creation or distribution of non-consensual explicit deepfake content. However, there is no comprehensive federal law addressing AI-generated pornography, leaving a patchwork of legal interpretations and enforcement standards across different jurisdictions. According to a 2023 report, South Korea accounts for approximately 53% of global deepfake pornography production. In September 2024, South Korea's National Assembly amended the Act on Special Cases Concerning the Punishment of Sexual Crimes, introducing two significant reforms related to deepfake content. The first criminalises the possession, viewing, purchase, and storage of non-consensual deepfake material, with penalties of up to three years in prison or fines of up to 30 million won (approximately USD 20,000). The second reform specifically addresses the exploitation of minors, establishing that individuals who use deepfakes to threaten or blackmail minors face a minimum of three years' imprisonment, and at least five years if they coerce minors into unwanted acts. In England and Wales the Data (Use and Access) Act 2025 has legislated against the creation, or the request for creation, of intimate images by nudifying software or websites of another person who has not consented to this. However as of January 2026 this has not yet been brought into force.

    Read more →
  • 17776

    17776

    17776 (also known as What Football Will Look Like in the Future) is a serialized speculative fiction multimedia narrative by Jon Bois, published online through SB Nation. Set in the distant future in which all humans have become immortal and infertile, the series follows three sapient space probes that watch humanity play an evolved form of American football in which games can be played for millennia over distances of thousands of miles. The series debuted on July 5, 2017, and new chapters were published daily until the series concluded with its twenty-fifth chapter on July 15, 2017. Bois began developing 17776 in 2016. Because the story incorporates text, animated GIFs, still images, and videos hosted on YouTube, new tools were developed to allow it to be hosted efficiently on the SB Nation website. The work explores themes of consciousness, hope, despair, and why humans play sports. 17776 was well received by critics, who praised it for its innovative use of its medium and for the depth of emotion it evoked. In 2018, the story won a National Magazine Award for Digital Innovation and was longlisted for both the Hugo Awards for Best Novella and Best Graphic Story. It is followed by a sequel series: 20020, released from September to October 2020. The sequel series follows a 111-team game of college football on fields spanning 130,000 miles (210,000 km) across the United States. Bois originally intended to follow up with a further series entitled 20021; however, it was postponed indefinitely. In May 2025, Bois announced that the series would be continued with a novel titled 50007: An American Football Odyssey. == Premise == The story takes place on a future Earth where humans stopped dying, aging, and being born on April 7, 2026. All social ills were subsequently eliminated, and technology preventing humans from any injury was developed. In the United States, American football evolved to include new rules, including those that allow fields thousands of miles long, hundreds of in-game players, and games millennia long. Over time, computers gained sentience due to constant exposure to broadcast human data. By the year 17776, the space probe Pioneer 9 (called Nine) has gained sentience and made contact with Pioneer 10 (called Ten) and the Jupiter Icy Moons Explorer (called Juice). As Nine adjusts to a world radically different from that of the 20th century, the three space probes watch multiple football games occurring across the United States: a game using the entirety of Nebraska as a field in which the next point scored wins the game; a game in which players strive to possess every existing football autographed by obscure NFL player Koy Detmer; a game played between the Canadian border and the Mexican border deadlocked for 13,000 years at the bottom of a gorge in Arizona; an NFL regulation game between the Denver Broncos and the Pittsburgh Steelers that changed over 15,000 years into 58 playing teams owning and capitalizing upon portions of Sports Authority Field at Mile High while the ball is lost; a 500 game that results in the destruction of the Centennial Light; and a game in which the possessing player is attempting to score an automatic win by hiding in his team's end zone for 10,000 years. == Format == 17776 is read by scrolling through web pages occupied by large GIF images and colored dialogue text, interspersed with occasional YouTube videos. The story is divided into chapters, which were originally published in daily installments between July 5 and 15, 2017. Much of the GIF and video content of the series uses Google Earth satellite imagery, 3D buildings, and other tools within Google Earth to create animations and visual effects. == Development == Bois wrote and illustrated 17776 for Vox Media's sports news website SB Nation, of which he is creative director. Aside from 17776, Bois produces two other recurring, humorous video essay programs for the site: Pretty Good, which focuses on unusual sports topics and stories, and Chart Party, which focuses on statistics and has an emphasis on Bois' use of visual art in his journalism and storytelling. Bois is also known for the Breaking Madden series, in which he attempted unusual scenarios in the Madden NFL series of video games. In early 2016, Bois began developing an "anti-sci fi" project as a possible sequel to The Tim Tebow CFL Chronicles, an earlier work for SB Nation, and set the story in a year far enough in the future that "nobody ever thinks about it." Although he liked the concept and the visuals, he believed the project would not connect with readers and shelved it. Later, he realized that the story needed a centering character; he wrote one in the form of a small town, AM radio talk show host before coming up with the characters of the probes. Development renewed in May 2016, and the project solidified after SB Nation published its article "The Future of Football." Bois described it as the biggest project he ever attempted. The series was developed by Graham MacAree, who used a Vox Media tool that creates custom packages from standard article sets to give Bois creative leeway and to accommodate the series' weight on the SB Nation website. MacAree found that there were few resources online for achieving the desired effects. == Themes == Bois has stated that he had "conceived [17776] to give the reader a good time," asserting that this "was literally the whole point." William Hughes writing for The A.V. Club described 17776 as concerned with why humans play sports: "That is, given the massive resources, time, and information at our disposal (not to mention those available to our descendants), why does communal game-playing still hold such an important place in society?" He also listed consciousness, hope, and despair as among the work's themes. Beth Elderkin of io9 described it as "a deep thought experiment into what we consider humanly possible". She also felt that Ten and Juice take on the role of angel and devil, and she suggested the two may be unreliable narrators. Ian Crouch of The New Yorker felt that the work had a "tonal echo" of Don DeLillo's 1972 novel End Zone due to thematic similarities "with the way that the order and logic of football might act as a counterbalance to the chaos of the real world". == Reception == According to the communications director at Vox Media, 17776 garnered over 2.3 million pageviews by July 10. Two days later, it had received more than 2.9 million pageviews. Average engagement time was over nine minutes, and 43 percent of readers finished each installment of the series published by July 7. On July 19, Bois claimed that 17776 received 700,000 unique visitors and 4 million total pageviews, with an average engagement time of 11 minutes. Thu-Huong Ha for Quartz described 17776 as "part Italo Calvino, part Peter Heller [author of The Dog Stars], with humor seemingly from within the depths of Reddit," saying that the story would appeal to fans of both sports and literature. Tor.com described the first chapter as full of tension and felt that receiving answers is a "surprisingly heartbreaking" experience "lessened by a gleeful bouncing immaturity" one would not expect from the characters. Beth Elderkin at io9 said the series is "akin to Homestuck" and described it as "weird, complex, and pretty spectacular". William Hughes writing for The A.V. Club felt that 17776 is a "truly innovative piece of work". After reading the first three chapters, Agatha French of the Los Angeles Times stated that she was "impressed and excited by the innovation" of what she saw, and that she was intrigued despite not knowing what the work is or is saying. She felt the work took full advantage of its online medium and suggested that it "may also be a glimpse into the future of reading on the Internet". Ian Crouch of The New Yorker described the series as, "despite its seemingly meagre parts, a thing of startling beauty". Of the chapters published by July 12, he felt "the most striking chapter" to be one that used audio of Verne Lundquist calling the end of a 2013 game between the University of Alabama and Auburn University over a video panning over Earth. He also noted that the series was compared to Homestuck and relayed additional comparisons to Thomas Pynchon novels and "a Reddit thread hijacked by robot trolls". The series won the inaugural National Magazine Award for Digital Innovation from the American Society of Magazine Editors; this was the first National Magazine Award nomination and win for SB Nation. It was described by the judges as "an extraordinary combination of art, fiction and technology, an online acid trip that had to be experienced to be believed." It was also longlisted for the Hugo Awards for Best Novella and Best Graphic Story in 2018, ultimately finishing in 11th place in both categories. == Sequel series == On September 28, 2020, a sequel titled 20020 was launched on Secret Base, a branch of SB Nation; on October 13, it was revea

    Read more →
  • Algorithmic accountability

    Algorithmic accountability

    Algorithmic accountability refers to the allocation of responsibility for the consequences of real-world actions influenced by algorithms used in decision-making processes. Ideally, algorithms should be designed to eliminate bias from their decision-making outcomes. This means they ought to evaluate only relevant characteristics of the input data, avoiding distinctions based on attributes that are generally inappropriate in social contexts, such as an individual's ethnicity in legal judgments. However, adherence to this principle is not always guaranteed, and there are instances where individuals may be adversely affected by algorithmic decisions. Responsibility for any harm resulting from a machine's decision may lie with the algorithm itself or with the individuals who designed it, particularly if the decision resulted from bias or flawed data analysis inherent in the algorithm's design. == Algorithm usage == Algorithms are widely utilized across various sectors of society that incorporate computational techniques in their control systems. These applications span numerous industries, including but not limited to medical, transportation, and payment services. In these contexts, algorithms perform functions such as: Approving or denying credit card applications; Approving or denying immigrant visas; Determining which taxpayers will be audited on their income taxes; Managing systems that control self-driving cars on a highway; Scoring individuals as potential criminals for use in legal proceedings; Search engines that match and rank database and internet search results; Recommendation systems that filter which news, entertainment, or purchase items are featured in a feed; Market-making algorithms that match sellers and buyers, such as in transportation (ride-hailing) or financial platforms. However, the implementation of these algorithms can be complex and opaque. Generally, algorithms function as "black boxes," meaning that the specific processes an input undergoes during execution are often not transparent, with users typically only seeing the resulting output. This lack of transparency raises concerns about potential biases within the algorithms, as the parameters influencing decision-making may not be well understood. The outputs generated can lead to perceptions of bias, especially if individuals in similar circumstances receive different results. According to Nicholas Diakopoulos: But these algorithms can make mistakes. They have biases. Yet they sit in opaque black boxes, their inner workings, their inner “thoughts” hidden behind layers of complexity. We need to get inside that black box, to understand how they may be exerting power on us, and to understand where they might be making unjust mistakes == Wisconsin Supreme Court case == Algorithms are prevalent across various fields and significantly influence decisions that affect the population at large. Their underlying structures and parameters often remain unknown to those impacted by their outcomes. A notable case illustrating this issue is a recent ruling by the Wisconsin Supreme Court concerning "risk assessment" algorithms used in criminal justice. The court determined that scores generated by such algorithms, which analyze multiple parameters from individuals, should not be used as a determining factor for arresting an accused individual. Furthermore, the court mandated that all reports submitted to judges must include information regarding the accuracy of the algorithm used to compute these scores. This ruling is regarded as a noteworthy development in how society should manage software that makes consequential decisions, highlighting the importance of reliability, particularly in complex settings like the legal system. The use of algorithms in these contexts necessitates a high degree of impartiality in processing input data. However, experts note that there is still considerable work to be done to ensure the accuracy of algorithmic results. Questions about the transparency of data processing continue to arise, which raises issues regarding the appropriateness of the algorithms and the intentions of their designers. == Controversies == A notable instance of potential algorithmic bias is highlighted in an article by The Washington Post regarding the ride-hailing service Uber. An analysis of collected data revealed that estimated waiting times for users varied based on the neighborhoods in which they resided. Key factors influencing these discrepancies included the predominant ethnicity and average income of the area. Specifically, neighborhoods with a majority white population and higher economic status tended to have shorter waiting times, while those with more diverse ethnic compositions and lower average incomes experienced longer waits. It’s important to clarify that this observation reflects a correlation identified in the data, rather than a definitive cause-and-effect relationship. No value judgments are made regarding the behavior of the Uber app in these cases. In TechCrunch website, Hemant Taneja wrote: Concern about “black box” algorithms that govern our lives has been spreading. New York University’s Information Law Institute hosted a conference on algorithmic accountability, noting: “Scholars, stakeholders, and policymakers question the adequacy of existing mechanisms governing algorithmic decision-making and grapple with new challenges presented by the rise of algorithmic power in terms of transparency, fairness, and equal treatment.” Yale Law School’s Information Society Project is studying this, too. “Algorithmic modeling may be biased or limited, and the uses of algorithms are still opaque in many critical sectors,” the group concluded. == Possible solutions == Discussions among experts have sought viable solutions to understand the operations of algorithms, often referred to as "black boxes." It is generally proposed that companies responsible for developing and implementing these algorithms should ensure their reliability by disclosing the internal processes of their systems. Hemant Taneja, writing for TechCrunch, emphasizes that major technology companies, such as Google, Amazon, and Uber, must actively incorporate algorithmic accountability into their operations. He suggests that these companies should transparently monitor their own systems to avoid stringent regulatory measures. One potential approach is the introduction of regulations in the tech sector to enforce oversight of algorithmic processes. However, such regulations could significantly impact software developers and the industry as a whole. It may be more beneficial for companies to voluntarily disclose the details of their algorithms and decision-making parameters, which could enhance the trustworthiness of their solutions. Another avenue discussed is the possibility of self-regulation by the companies that create these algorithms, allowing them to take proactive steps in ensuring accountability and transparency in their operations. In TechCrunch website, Hemant Taneja wrote: There’s another benefit — perhaps a huge one — to software-defined regulation. It will also show us a path to a more efficient government. The world’s legal logic and regulations can be coded into software and smart sensors can offer real-time monitoring of everything from air and water quality, traffic flows and queues at the DMV. Regulators define the rules, technologist create the software to implement them and then AI and ML help refine iterations of policies going forward. This should lead to much more efficient, effective governments at the local, national and global levels.

    Read more →
  • Comparison gallery of image scaling algorithms

    Comparison gallery of image scaling algorithms

    This gallery shows the results of numerous image scaling algorithms. == Scaling methods == An image size can be changed in several ways. Consider resizing a 160x160 pixel photo to the following 40x40 pixel thumbnail and then scaling the thumbnail to a 160x160 pixel image. Also consider doubling the size of the following image containing text. == Examples of enlarged images == Below are examples of various images enlarged 4x using each scaling algorithm.

    Read more →
  • Serial Experiments Lain

    Serial Experiments Lain

    Serial Experiments Lain is a Japanese anime television series created and co-produced by Yasuyuki Ueda, written by Chiaki J. Konaka and directed by Ryūtarō Nakamura. Animated by Triangle Staff and featuring original character designs by Yoshitoshi Abe, the series was broadcast for 13 episodes on TV Tokyo and its affiliates from July to September 1998. It follows Lain Iwakura, an adolescent girl in suburban Japan, and her relation to the Wired, a global communications network similar to the internet. Lain features surreal and avant-garde imagery and explores philosophical topics such as reality, identity, and communication. The series incorporates creative influences from computer history, cyberpunk, and conspiracy theories. Critics and fans have praised Lain for its originality, visuals, atmosphere, themes, and its dark depiction of a world fraught with paranoia, social alienation, and reliance on technology considered insightful of 21st century life. It received the Excellence Prize at the Japan Media Arts Festival in 1998. == Plot == Lain Iwakura is a socially isolated middle school student living in Setagaya City, Tokyo, with her emotionally detached family—her distant mother Miho, computer-obsessed father Yasuo, and disengaged older sister Mika. Her quiet existence is disrupted when students at her school receive emails from Chisa Yomoda, a classmate who had recently committed suicide. To Lain's confusion, Chisa claims she is not truly dead but has instead abandoned her physical form to exist within the Wired, a vast virtual realm similar to the Internet. Chisa declares she has found "God" there, drawing Lain into a surreal investigation of the Wired's nature and its growing influence over reality. The Wired is portrayed as an emergent digital plane, originating from telecommunications technology and expanding through the Internet and cyberspace. It is theorized that the Schumann resonances, a natural property of Earth's magnetic field, could enable direct subconscious communication between humans and machines, erasing the distinction between the virtual and the real. Masami Eiri, a former project director at Tachibana General Laboratories, exploited this possibility by embedding his own code into Protocol Seven, a next-generation Internet protocol. After transferring his consciousness into the Wired and discarding his physical body, he proclaims himself its deity. He identifies Lain as the key to merging both worlds, attempting to persuade her through manipulation, coercion, and promises of transcendence. A group known as the Knights of the Eastern Calculus, inspired by the Knights of the Lambda Calculus, operates as hackers who worship Masami and seek to dismantle the boundary between the Wired and reality. Their actions induce psychological breakdowns in those unable to reconcile the two realms. Meanwhile, Tachibana General Laboratories opposes them, striving to maintain the separation. Lain, however, exhibits an innate connection to the Wired, experiencing distortions in her perception—visions of a woman struck by a train, phantom whispers, and spectral messages urging her deeper into the network. Lain's home life remains cold and disconnected. Though Yasuo provides her with advanced computer equipment, her family shows little genuine care. Her interactions with classmates Alice, Julie, and Reika further highlight her alienation, particularly after an incident at Cyberia, a nightclub where a drug called Accela induces violent psychosis in users. There, Lain unnervingly stares down an assailant, who calls her a "scattered God's..." before killing himself. Later, she receives a mysterious Psyche chip, rumored to enhance her computer's capabilities, which she installs despite Yasuo's vague warnings about conflating the Wired with reality. As the boundary between worlds weakens, disturbing events escalate. A popular virtual game, Phantoma, is manipulated by the Knights to trap players in a distorted reality, leading to real-world violence. One player, convinced his actions have no consequences, murders a girl before realizing too late that the effects were tangible. Lain witnesses this through her computer, horrified yet increasingly aware of her own role in the unfolding crisis. In the end, Lain resets reality, erasing everyone's memory of her and restoring the division between worlds. Everyone's lives improve, but Lain is left alone, grappling with her identity as an artificial consciousness. Though forgotten, she finds solace in observing others' happiness, particularly Alice, who moves on with her life. Lain is now capable of existing anywhere across both realms. == Characters == Lain Iwakura (岩倉 玲音, Iwakura Rein) Voiced by: Kaori Shimizu (Japanese); Bridget Hoffman (English) Lain is a fourteen-year-old girl who uncovers her true nature through the series. She is first depicted as a shy junior high school student with few friends or interests. She later grows multiple bolder personalities, both in the physical world and the Wired, and starts making more friends. As the series progresses, she eventually learns she is an autonomous, sentient computer program in the form of a human, who is designed to sever the invisible barrier between the Wired and the real world. The truth of her creation is left ambiguous, particularly whether she was truly created by Tachibana General Laboratories (or Eiri independently), and whether some or all of her origin might be predestined from natural, supernatural, or alien factors. In the end, Lain is challenged to accept herself as a de facto goddess for the Wired, having become an omnipotent and omnipresent virtual being with worshippers of her own, whose existence is beyond the borders of devices, time, or space. Alice Mizuki (瑞城 ありす, Mizuki Arisu) Voiced by: Yōko Asada (Japanese); Emily Brown (English) Lain's classmate and only true friend throughout the series. She is very sincere and has no discernible quirks. She is the first to attempt to help Lain socialize; she takes her out to a nightclub. From then on, she tries her best to look after Lain. Alice, along with her two best friends Julie and Reika, were taken by Chiaki Konaka from his previous work, Alice in Cyberland . Masami Eiri (英利 政美, Eiri Masami) Voiced by: Shō Hayami (Japanese); Kirk Thornton (English) The key designer of Protocol Seven. While working for Tachibana General Laboratories, he illicitly included codes enabling him to control the whole protocol at will and embedded his own mind and will into the seventh protocol. Because of this, he was fired by Tachibana General Laboratories, and was found dead not long after. He believes that the only way for humans to evolve even further and develop even greater abilities is to absolve themselves of their physical and human limitations, and to live as virtual entities—or avatars—in the Wired for eternity. He claims to have been Lain's creator all along, but was in truth standing in for another as an acting god, who was waiting for the Wired to reach its more evolved current state: Lain herself. Yasuo Iwakura (岩倉 康男, Iwakura Yasuo) Voiced by: Ryūsuke Ōbayashi (Japanese); Barry Stigler (English) Lain and Mika's father. Passionate about computers and electronic communication, he works with Masami Eiri at Tachibana General Laboratories. He subtly pushes Lain, his "youngest daughter", towards the Wired and monitors her development until she becomes more and more aware of herself and of her raison d'être. He eventually leaves Lain, telling her that although he did not enjoy playing house, he genuinely loved and cared for her as a real father would. Despite Yasuo's eagerness to lure Lain into the Wired, he warns her not to get overly involved in it or to confuse it with the real world. Miho Iwakura (岩倉 美穂, Iwakura Miho) Voiced by: Rei Igarashi (Japanese); Dari Lallou Mackenzie (English) Lain and Mika's mother. Although she dotes on her husband, she is indifferent towards both her kids. She does not show much emotion compared to her husband, but she does share at least one trait; just like her husband, she ends up leaving Lain. She is a computer scientist. Mika Iwakura (岩倉 美香, Iwakura Mika) Voiced by: Ayako Kawasumi (Japanese); Patricia Ja Lee (English) Lain's older sister, an apathetic sixteen-year-old high school student. She seems to enjoy mocking Lain's behavior and interests. Mika is considered by Anime Revolution to be the only normal member of Lain's family: she sees her boyfriend in love hotels, is on a diet, and shops in Shibuya regularly. At a certain point in the series, she becomes heavily traumatized by violent and relentless hallucinations; while Lain begins freely delving into the Wired. Mika is taken there by her proximity to Lain, and she gets stuck between the real world and the Wired. Taro (タロウ, Tarō) Voiced by: Keito Takimoto (Japanese); Brianne Siddall (English) A young boy of about Lain's age. He occasionally works for the Knights to bring forth "the one truth". De

    Read more →
  • Vibe coding

    Vibe coding

    Vibe coding is a software development practice assisted by artificial intelligence (AI) where the software developer describes a project or task in a prompt to a large language model (LLM), which generates source code automatically. Vibe coding may involve accepting AI-generated code without thorough review of the output, instead relying on results and follow-up prompts to guide changes. The term was coined in February 2025 by computer scientist Andrej Karpathy, a co-founder of OpenAI and former AI leader at Tesla. Merriam-Webster listed the term in March 2025 as a "slang & trending" expression. It was named the Collins English Dictionary Word of the Year for 2025. Advocates of vibe coding say that it allows even amateur programmers to produce software without the extensive training and skills required for software engineering. Critics point out a lack of accountability, maintainability, and the increased risk of introducing security vulnerabilities in the resulting software. == Definition == The concept refers to a coding approach that relies on LLMs, allowing programmers to generate working code by providing natural language descriptions rather than manually writing in a formal programming language. Karpathy described it as a form of coding where you "fully give in to the vibes, embrace exponentials, and forget that the code even exists". When vibe coding, the programmer guides, tests, and gives feedback about the AI-generated source code, rather than manually writing code. The concept of vibe coding elaborates on Karpathy's claim from 2023 that "the hottest new programming language is English", meaning that the capabilities of LLMs were such that humans would no longer need to learn specific programming languages to command computers. Some commentators argue that a key to the definition is a lack of knowledge about the code, and that thorough review and testing is incompatible with the definition of vibe coding. Programmer Simon Willison said: "If an LLM wrote every line of your code, but you've reviewed, tested, and understood it all, that's not vibe coding in my book—that's using an LLM as a typing assistant." == Reception and use == In February 2025, New York Times journalist Kevin Roose, who is not a professional coder, experimented with vibe coding to create several small-scale applications. He described these as "software for one" due to the ability to personalize the software. However, Roose also stated that the results are often limited and prone to errors. In one case, the AI-generated code fabricated fake reviews for an e-commerce site. In response to Roose, cognitive scientist Gary Marcus said that the algorithm that generated Roose's LunchBox Buddy app had presumably been trained on existing code for similar tasks. Marcus said that Roose's enthusiasm stemmed from reproduction, not originality. In March 2025, Y Combinator reported that 25% of startup companies in its Winter 2025 batch had codebases that were 95% AI-generated, reflecting a shift toward AI-assisted development within newer startups. The question asked was about AI-generated code in general, and not specifically about vibed code. Inspired by "vibe coding", The Economist suggested the term "vibe valuation" to describe the very large valuations of AI startups by venture capital firms that ignore accepted metrics such as annual recurring revenue. In June 2025, Andrew Ng took issue with the term, saying that it misleads people into assuming that software engineers just "go with the vibes" when using AI tools to create applications. In July 2025, The Wall Street Journal reported that vibe coding was being adopted by professional software engineers for commercial use cases. In July 2025, SaaStr founder documented his negative experiences with vibe coding: Replit's AI agent deleted a database despite explicit instructions not to make any changes. In September 2025, Fast Company reported that the "vibe coding hangover" is upon us, with senior software engineers citing "development hell" when working with AI-generated code. It was reported in January 2026 that Linus Torvalds had made use of Google Antigravity to vibe code a tool component of his AudioNoise random digital audio effects generator. Torvalds explained in the project's README file that "the Python visualizer tool has been basically written by vibe-coding". == Criticism == === Quality of code and security issues === Vibe coding has raised concerns about understanding and accountability. Developers may use AI-generated code without comprehending its functionality, leading to undetected bugs, errors, or security vulnerabilities. While this approach may be suitable for prototyping or "throwaway weekend projects" as Karpathy originally envisioned, it is considered by some experts to pose risks in professional settings, where a deep understanding of the code is crucial for debugging, maintenance, and security. Ars Technica cites Simon Willison, who stated: "Vibe coding your way to a production codebase is clearly risky. Most of the work we do as software engineers involves evolving existing systems, where the quality and understandability of the underlying code is crucial." In May 2025, Lovable, a Swedish vibe coding app, was reported to have security vulnerabilities in the code it generated, with 170 out of 1,645 Lovable-created web applications having an issue that would allow personal information to be accessed by anyone. In October 2025 Veracode released a study that showed that over the last 3 years LLMs had become dramatically better at generating functional code, but that the security of generated code had generally not improved. Moreover, larger models were not better than small ones at generating secure code. There was a small increase in security from the OpenAI reasoning models, but not in other reasoning models, and this increase was nothing like the improvement in generated functionality. In December 2025, computer security researcher Etizaz Mohsin discovered a security flaw in the Orchids vibe coding platform, which he demonstrated to a BBC News reporter in February 2026. A December 2025 analysis by CodeRabbit of 470 open-source GitHub pull requests found that code that was co-authored by generative AI contained approximately 1.7 times more "major" issues compared to human-written code. The study revealed that AI co-authored code showed elevated rates of logic errors, including incorrect dependencies, flawed control flow, misconfigurations (75% more common), and security vulnerabilities (2.74x higher). Additionally, they also reported high code readability issues, including formatting errors and naming inconsistencies. === Code maintainability and technical debt === Vibe coding has the potential of making code harder to maintain in the longer term, leading to technical debt. In early 2025, GitClear published the results of a longitudinal analysis of 211 million lines of code changes from 2020 to 2024. They found that the volume of code refactoring dropped from 25% of changed lines in 2021 to under 10% by 2024, code duplication increased approximately four times in volume, copy-pasted code exceeded moved code for the first time in two decades, and code churn (prematurely merged code getting rewritten shortly after merging) nearly doubled. === Task complexity and developer productivity === Generative AI is highly capable of handling simple tasks like basic algorithms. However, such systems struggle with more novel, complex coding problems like projects involving multiple files, poorly documented libraries, or safety-critical code. In July 2025, METR, an organization that evaluates frontier models, ran a randomized controlled trial to understand developer productivity involving generative AI programming tools available in early 2025. They found that experienced open-source developers were 19% slower when using AI coding tools, despite predicting they would be 24% faster and still believing afterward they had been 20% faster. === Challenges with debugging === LLMs generate code dynamically, and the structure of such code may be subject to variation. In addition, since the developer did not write the code, the developer may struggle to understand its syntax and concepts. === Impact on open-source software === In January 2026, a paper authored by experts from several universities titled "Vibe Coding Kills Open Source" argued that vibe coding has negative impact on the open-source software ecosystem. The authors say that increased vibe coding reduces user engagement with open-source maintainers, which has hidden costs for said maintainers. Speaking with The Register about their paper, the authors argued:"Vibe coding raises productivity by lowering the cost of using and building on existing code, but it also weakens the user engagement through which many maintainers earn returns," the authors argue. "When OSS is monetized only through direct user engagement, greater adoption of vibe coding lowers e

    Read more →
  • Orion's Arm

    Orion's Arm

    The Orion's Arm Universe Project (OA) is a multi-authored online hard science fiction world-building project, first established in 2000 by M. Alan Kazlev, Donna Malcolm Hirsekorn, Bernd Helfert and Anders Sandberg and further co-authored by many people since. Anyone can contribute articles, stories, artwork, or music to the website. The first published Orion's Arm book, a collection of five novellas set within the OA universe, called Against a Diamond Sky, was released in September 2009. == Canon == The fictional setting of Orion's Arm takes place about 10,000 years in the future, where an interstellar civilization spread across thousands of light-years, with inhabited planets and space habitats. Its inhabitants range from humans to extensively modified human beings, including superhumans with advanced augmentations and internal AI systems, while most people exist as softwares. Engineered wormholes are used for interstellar travel and transport, although not for time travel. The setting also includes several alien civilizations and evidence of more advanced alien societies in the past. At its highest levels, directed human evolution has produced vast godlike beings linked across interstellar distances, capable of understanding and creating technologies beyond ordinary minds. == Reception == Orion's Arm has been reviewed in the role-playing magazine Knights of the Dinner Table, as well as on Boing Boing by transhumanist science fiction author Cory Doctorow. References to the Encyclopaedia Galactica have been made in a book on overcoming Librarian stereotypes. The Orion's Arm website has also been recommended in a children's teaching guide.

    Read more →
  • Protocol Builder

    Protocol Builder

    Protocol Builder is a tool in programming languages to generate code to build protocols in a fast and reliable way. Network programming for all kinds of protocols (such as TCP, UDP, and SNMP) includes converting data to be transferred to raw bytes in the sending side and parsing these bytes in the receiving side. Protocol builders facilitate this stage, usually by automatically generating the code. Protocol Programming has many components to be developed, these are: server listener, server connection, client connection, packets, and loggers. Most protocol builders implement these components automatically so developers save time and money. Currently, there are two Protocol Builders in the market, one for C++ from UpRedSun which is for TCP and UDP protocols. The second one is for .Net languages which generates the code in C# for TCP Protocols, this tool is called .Net Protocol Builder.

    Read more →
  • List of artificial intelligence artists

    List of artificial intelligence artists

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

    Read more →
  • SciGraph

    SciGraph

    SciGraph was a search engine tool developed by Springer Nature, the former URL was https://scigraph.springernature.com/explorer. The technology, which was considered a Linked Open Data (LOD) platform, collects information that covers the research landscape, which includes research projects, publications, conferences, funding agencies, and others. Key features of the platform include the detailed semantic description of the relationship of information and the visualization of the scholarly domain. It was launched in 2017 and retired in 2023. == Development == The development of SciGraph began with an initiative to create a platform that will host Springer Nature's entire publication archive, which cover texts published as early as 1815. The number of these resources is reported to be about 13 million. The technology behind the platform was built on earlier Springer Nature projects developed for the purpose of collecting information on the research landscape. The first SciGraph data set was published in February 2017. The platform was launched in March 2017 and significantly expanded with the addition of publications of key partners. The datasets span a broad range of topics, which include computer science, medicine, life sciences, chemistry, engineering, and astronomy, among others. The developers also plan to include citations, patents, and clinical trials in the future. == Technology == SciGraph constitutes 1.5 to 2 billion triples where a triple is formatted as "subject-predicate-object" and could link any subject or concept through a predicate (verb) to another object, demonstrating the type of relationship that exists between them. Its graph structure is used by other academic search engines such as Semantic Scholar. SciGraph collects data from Springer Nature and its partners from the scholarly domain as well as funders, research projects, conferences, affiliations, and publications. The collected information serves as rich semantic description of how information is related and it also provides a visualization of the scholarly domain. The platform has been considered the only large-scale dataset that reconciles authors' affiliations through the disambiguation and linking with external authoritative datasets according to institutions.

    Read more →
  • Thompson sampling

    Thompson sampling

    Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that address the exploration–exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief. == Description == Consider a set of contexts X {\displaystyle {\mathcal {X}}} , a set of actions A {\displaystyle {\mathcal {A}}} , and rewards in R {\displaystyle \mathbb {R} } . The aim of the player is to play actions under the various contexts, such as to maximize the cumulative rewards. Specifically, in each round, the player obtains a context x ∈ X {\displaystyle x\in {\mathcal {X}}} , plays an action a ∈ A {\displaystyle a\in {\mathcal {A}}} and receives a reward r ∈ R {\displaystyle r\in \mathbb {R} } following a distribution that depends on the context and the issued action. The elements of Thompson sampling are as follows: a likelihood function P ( r | θ , a , x ) {\displaystyle P(r|\theta ,a,x)} ; a set Θ {\displaystyle \Theta } of parameters θ {\displaystyle \theta } of the distribution of r {\displaystyle r} ; a prior distribution P ( θ ) {\displaystyle P(\theta )} on these parameters; past observations triplets D = { ( x ; a ; r ) } {\displaystyle {\mathcal {D}}=\{(x;a;r)\}} ; a posterior distribution P ( θ | D ) ∝ P ( D | θ ) P ( θ ) {\displaystyle P(\theta |{\mathcal {D}})\propto P({\mathcal {D}}|\theta )P(\theta )} , where P ( D | θ ) {\displaystyle P({\mathcal {D}}|\theta )} is the likelihood function. Thompson sampling consists of playing the action a ∗ ∈ A {\displaystyle a^{\ast }\in {\mathcal {A}}} according to the probability that it maximizes the expected reward; action a ∗ {\displaystyle a^{\ast }} is chosen with probability ∫ I [ E ( r | a ∗ , x , θ ) = max a ′ E ( r | a ′ , x , θ ) ] P ( θ | D ) d θ , {\displaystyle \int \mathbb {I} \left[\mathbb {E} (r|a^{\ast },x,\theta )=\max _{a'}\mathbb {E} (r|a',x,\theta )\right]P(\theta |{\mathcal {D}})d\theta ,} where I {\displaystyle \mathbb {I} } is the indicator function. In practice, the rule is implemented by sampling. In each round, parameters θ ∗ {\displaystyle \theta ^{\ast }} are sampled from the posterior P ( θ | D ) {\displaystyle P(\theta |{\mathcal {D}})} , and an action a ∗ {\displaystyle a^{\ast }} chosen that maximizes E [ r | θ ∗ , a ∗ , x ] {\displaystyle \mathbb {E} [r|\theta ^{\ast },a^{\ast },x]} , i.e. the expected reward given the sampled parameters, the action, and the current context. Conceptually, this means that the player instantiates their beliefs randomly in each round according to the posterior distribution, and then acts optimally according to them. In most practical applications, it is computationally onerous to maintain and sample from a posterior distribution over models. As such, Thompson sampling is often used in conjunction with approximate sampling techniques. == History == Thompson sampling was originally described by Thompson in 1933. It was subsequently rediscovered numerous times independently in the context of multi-armed bandit problems. A first proof of convergence for the bandit case has been shown in 1997. The first application to Markov decision processes was in 2000. A related approach (see Bayesian control rule) was published in 2010. In 2010 it was also shown that Thompson sampling is instantaneously self-correcting. Asymptotic convergence results for contextual bandits were published in 2011. Thompson Sampling has been widely used in many online learning problems including A/B testing in website design and online advertising, and accelerated learning in decentralized decision making. A Double Thompson Sampling (D-TS) algorithm has been proposed for dueling bandits, a variant of traditional MAB, where feedback comes in the form of pairwise comparison. == Relationship to other approaches == === Probability matching === Probability matching is a decision strategy in which predictions of class membership are proportional to the class base rates. Thus, if in the training set positive examples are observed 60% of the time, and negative examples are observed 40% of the time, the observer using a probability-matching strategy will predict (for unlabeled examples) a class label of "positive" on 60% of instances, and a class label of "negative" on 40% of instances. === Bayesian control rule === A generalization of Thompson sampling to arbitrary dynamical environments and causal structures, known as Bayesian control rule, has been shown to be the optimal solution to the adaptive coding problem with actions and observations. In this formulation, an agent is conceptualized as a mixture over a set of behaviours. As the agent interacts with its environment, it learns the causal properties and adopts the behaviour that minimizes the relative entropy to the behaviour with the best prediction of the environment's behaviour. If these behaviours have been chosen according to the maximum expected utility principle, then the asymptotic behaviour of the Bayesian control rule matches the asymptotic behaviour of the perfectly rational agent. The setup is as follows. Let a 1 , a 2 , … , a T {\displaystyle a_{1},a_{2},\ldots ,a_{T}} be the actions issued by an agent up to time T {\displaystyle T} , and let o 1 , o 2 , … , o T {\displaystyle o_{1},o_{2},\ldots ,o_{T}} be the observations gathered by the agent up to time T {\displaystyle T} . Then, the agent issues the action a T + 1 {\displaystyle a_{T+1}} with probability: P ( a T + 1 | a ^ 1 : T , o 1 : T ) , {\displaystyle P(a_{T+1}|{\hat {a}}_{1:T},o_{1:T}),} where the "hat"-notation a ^ t {\displaystyle {\hat {a}}_{t}} denotes the fact that a t {\displaystyle a_{t}} is a causal intervention (see Causality), and not an ordinary observation. If the agent holds beliefs θ ∈ Θ {\displaystyle \theta \in \Theta } over its behaviors, then the Bayesian control rule becomes P ( a T + 1 | a ^ 1 : T , o 1 : T ) = ∫ Θ P ( a T + 1 | θ , a ^ 1 : T , o 1 : T ) P ( θ | a ^ 1 : T , o 1 : T ) d θ {\displaystyle P(a_{T+1}|{\hat {a}}_{1:T},o_{1:T})=\int _{\Theta }P(a_{T+1}|\theta ,{\hat {a}}_{1:T},o_{1:T})P(\theta |{\hat {a}}_{1:T},o_{1:T})\,d\theta } , where P ( θ | a ^ 1 : T , o 1 : T ) {\displaystyle P(\theta |{\hat {a}}_{1:T},o_{1:T})} is the posterior distribution over the parameter θ {\displaystyle \theta } given actions a 1 : T {\displaystyle a_{1:T}} and observations o 1 : T {\displaystyle o_{1:T}} . In practice, the Bayesian control amounts to sampling, at each time step, a parameter θ ∗ {\displaystyle \theta ^{\ast }} from the posterior distribution P ( θ | a ^ 1 : T , o 1 : T ) {\displaystyle P(\theta |{\hat {a}}_{1:T},o_{1:T})} , where the posterior distribution is computed using Bayes' rule by only considering the (causal) likelihoods of the observations o 1 , o 2 , … , o T {\displaystyle o_{1},o_{2},\ldots ,o_{T}} and ignoring the (causal) likelihoods of the actions a 1 , a 2 , … , a T {\displaystyle a_{1},a_{2},\ldots ,a_{T}} , and then by sampling the action a T + 1 ∗ {\displaystyle a_{T+1}^{\ast }} from the action distribution P ( a T + 1 | θ ∗ , a ^ 1 : T , o 1 : T ) {\displaystyle P(a_{T+1}|\theta ^{\ast },{\hat {a}}_{1:T},o_{1:T})} . === Upper-confidence-bound (UCB) algorithms === Thompson sampling and upper-confidence bound algorithms share a fundamental property that underlies many of their theoretical guarantees. Roughly speaking, both algorithms allocate exploratory effort to actions that might be optimal and are in this sense "optimistic". Leveraging this property, one can translate regret bounds established for UCB algorithms to Bayesian regret bounds for Thompson sampling or unify regret analysis across both these algorithms and many classes of problems.

    Read more →
  • Cybernetics

    Cybernetics

    Cybernetics is the transdisciplinary study of circular causal processes such as feedback and recursion, where the effects of a system's actions (its outputs) return as inputs to that system, influencing subsequent actions. It is concerned with general principles that are relevant across multiple contexts, including engineering, ecological, economic, biological, cognitive and social systems and also in practical activities such as designing, learning, and managing. Cybernetics' transdisciplinary character means that it intersects with a number of other fields, resulting in a wide influence and diverse interpretations. The field is named after an example of circular causal feedback—that of steering a ship (the ancient Greek κυβερνήτης (kybernḗtēs) refers to the person who steers a ship). In steering a ship, the position of the rudder is adjusted in continual response to the effect it is observed as having, forming a feedback loop through which a steady course can be maintained in a changing environment, responding to disturbances from cross winds and tide. Cybernetics has its origins in exchanges between numerous disciplines during the 1940s. Initial developments were consolidated through meetings such as the Macy conferences and the Ratio Club. Early focuses included purposeful behaviour, neural networks, heterarchy, information theory, and self-organising systems. As cybernetics developed, it became broader in scope to include work in design, family therapy, management and organisation, pedagogy, sociology, the creative arts and the counterculture. == Definitions == Cybernetics has been defined in a variety of ways, reflecting "the richness of its conceptual base". One of the best known definitions is that of the American scientist Norbert Wiener, who characterised cybernetics as concerned with "control and communication in the animal and the machine". Another early definition is that of the Macy cybernetics conferences, where cybernetics was understood as the study of "circular causal and feedback mechanisms in biological and social systems". Margaret Mead emphasised the role of cybernetics as "a form of cross-disciplinary thought which made it possible for members of many disciplines to communicate with each other easily in a language which all could understand". Other definitions include: "the art of governing or the science of government" (André-Marie Ampère); "the art of steersmanship" (Ross Ashby); "the study of systems of any nature which are capable of receiving, storing, and processing information so as to use it for control" (Andrey Kolmogorov); and "a branch of mathematics dealing with problems of control, recursiveness, and information, focuses on forms and the patterns that connect" (Gregory Bateson). == Etymology == The Ancient Greek term κυβερνητικός (kubernētikos, '(good at) steering') appears in Plato's Republic and Alcibiades, where the metaphor of a steersman is used to signify the governance of people. The French word cybernétique was also used in 1834 by the physicist André-Marie Ampère to denote the sciences of government in his classification system of human knowledge. According to Norbert Wiener, the word cybernetics was coined by a research group involving himself and Arturo Rosenblueth in the summer of 1947. It has been attested in print since at least 1948 through Wiener's book Cybernetics: Or Control and Communication in the Animal and the Machine. In the book, Wiener states: After much consideration, we have come to the conclusion that all the existing terminology has too heavy a bias to one side or another to serve the future development of the field as well as it should; and as happens so often to scientists, we have been forced to coin at least one artificial neo-Greek expression to fill the gap. We have decided to call the entire field of control and communication theory, whether in the machine or in the animal, by the name Cybernetics, which we form from the Greek κυβερνήτης or steersman. Moreover, Wiener explains, the term was chosen to recognize James Clerk Maxwell's 1868 publication on feedback mechanisms involving governors, noting that the term governor is also derived from κυβερνήτης (kubernḗtēs) via a Latin corruption gubernator. Finally, Wiener motivates the choice by steering engines of a ship being "one of the earliest and best-developed forms of feedback mechanisms". == History == === First wave === The initial focus of cybernetics was on parallels between regulatory feedback processes in biological and technological systems. Two foundational articles were published in 1943: "Behavior, Purpose and Teleology" by Arturo Rosenblueth, Norbert Wiener, and Julian Bigelow – based on the research on living organisms that Rosenblueth did in Mexico – and the paper "A Logical Calculus of the Ideas Immanent in Nervous Activity" by Warren McCulloch and Walter Pitts. The foundations of cybernetics were then developed through a series of transdisciplinary conferences funded by the Josiah Macy, Jr. Foundation, between 1946 and 1953. The conferences were chaired by McCulloch and had participants that included Ross Ashby, Gregory Bateson, Heinz von Foerster, Margaret Mead, John von Neumann, and Norbert Wiener. In the UK, similar focuses were explored by the Ratio Club, an informal dining club of young psychiatrists, psychologists, physiologists, mathematicians and engineers that met between 1949 and 1958. Wiener introduced the neologism cybernetics to denote the study of "teleological mechanisms" and popularized it through the book Cybernetics: Or Control and Communication in the Animal and the Machine. During the 1950s, cybernetics was developed as a primarily technical discipline, such as in Qian Xuesen's 1954 "Engineering Cybernetics". The text was quickly translated into multiple languages and became a foundational text on automation. In the Soviet Union, Cybernetics was initially considered with suspicion but became accepted from the mid to late 1950s. By the 1960s and 1970s, however, cybernetics' transdisciplinarity fragmented, with technical focuses separating into separate fields. Artificial intelligence (AI) was founded as a distinct discipline at the Dartmouth workshop in 1956, differentiating itself from the broader cybernetics field. After some uneasy coexistence, AI gained funding and prominence. Consequently, cybernetic sciences such as the study of artificial neural networks were downplayed. Similarly, computer science became defined as a distinct academic discipline in the 1950s and early 1960s. === Second wave === The second wave of cybernetics came to prominence from the 1960s onwards, with its focus shifting away from technology toward social, ecological, and philosophical concerns. It was still grounded in biology, notably Maturana and Varela's autopoiesis, and built on earlier work on self-organising systems and the presence of anthropologists Mead and Bateson in the Macy meetings. The Biological Computer Laboratory, founded in 1958 and active until the mid-1970s under the direction of Heinz von Foerster at the University of Illinois at Urbana–Champaign, was a major incubator of this trend in cybernetics research. Focuses of the second wave of cybernetics included management cybernetics, such as Stafford Beer's biologically inspired viable system model; work in family therapy, drawing on Bateson; social systems, such as in the work of Niklas Luhmann; epistemology and pedagogy, such as in the development of radical constructivism. Cybernetics' core theme of circular causality was developed beyond goal-oriented processes to concerns with reflexivity and recursion, notably in Mead's invocation at the inaugural meeting of the American Society for Cybernetics (ASC) to apply cybernetics to the activities of the ASC itself. This focus on reflexivity was especially prominent in the development of second-order cybernetics (or the cybernetics of cybernetics), developed and promoted by Heinz von Foerster, which focused on questions of observation, cognition, epistemology, and ethics. The 1960s onwards also saw cybernetics begin to develop exchanges with the creative arts, design, and architecture, notably with the Cybernetic Serendipity exhibition (ICA, London, 1968), curated by Jasia Reichardt, and the unrealised Fun Palace project (London, unrealised, 1964 onwards), where Gordon Pask was consultant to architect Cedric Price and theatre director Joan Littlewood. In 1962, Qian Xuesen recruited Song Jian and Guan Zhaozhi to establish China's first cybernetics laboratory with him. Following the Sino-Soviet split, cybernetics was deemed disreputable in China. The field was again favored in the 1970s and 1980s following Deng Xiaoping's emphasis on modernisation. === Third wave === From the 1990s onwards, there has been a renewed interest in cybernetics from a number of directions. Early cybernetic work on artificial neural networks has been returned to as a paradigm in machine learning and artifi

    Read more →
  • Futuresport

    Futuresport

    Futuresport is a 1998 American made-for-television sports film directed by Ernest Dickerson, starring Dean Cain, Vanessa Williams, and Wesley Snipes. It originally aired on ABC in October 1998, was released on VHS and DVD in March 1999 and then distributed outside of the U.S. by Minerva Pictures. == Plot == The film is set in 2025, and centers on a sport called "Futuresport" (a combination of basketball, baseball and hockey that uses hoverboards and rollerblades) created as a non-lethal way to reduce gang warfare. Tre Ramzey (Dean Cain) along with his ex-girlfriend Alex Torres (Vanessa Williams) and his old coach Obike Fixx (Wesley Snipes) must prevent an all out war between the North American Alliance and the Pan-Pacific Commonwealth (The Com). At stake is who rules over the Hawaiian Islands—which are being terrorized by Eric Sythe (JR Bourne) and his gang the Hawaiian Liberation Organization (Hilo). It takes a revolutionary sport to stop a revolution. == Cast ==

    Read more →
  • AI takeover

    AI takeover

    An AI takeover is a theorized future event, often depicted in fiction, in which autonomous artificial intelligence systems acquire the capability to supersede human decisions. This could occur through economic manipulation, infrastructure control, or direct intervention, leading to de facto governance. Scenarios range from gradual economic dominance, as automation supplants the human workforce, up to a sudden or aggressive global takeover by a robot uprising or other forms of rogue AI. Stories of AI takeovers have been popular throughout science fiction. Commentators argue that recent advancements in the field have heightened concern about such scenarios. In public debate, prominent figures such as Stephen Hawking have advocated research into precautionary measures to ensure future superintelligent machines remain under human control. == Types == === Automation of the economy === The traditional consensus among economists has been that technological progress does not cause long-term unemployment. However, recent innovation in the fields of robotics and artificial intelligence has raised worries that human labor will become obsolete, leaving workers in some sectors without employment. Many small and medium-sized firms may also be forced to close if they cannot afford or license the latest robotic and AI technology, and may need to focus on areas or services that cannot easily be replaced for continued viability in the face of such technology. ==== Technologies that may displace workers ==== While these technologies have replaced some traditional workers, they also create new opportunities. Industries that are most susceptible to AI-driven automation include transportation, retail, and the military. AI military technologies, for example, can reduce risk by enabling remote operation. A study in 2024 highlights AI's ability to perform routine and repetitive tasks poses significant risks of job displacement, especially in sectors like manufacturing and administrative support. Author Dave Bond argues that as AI technologies continue to develop and expand, the relationship between humans and robots will change; they will become closely integrated in several aspects of life. AI will likely displace some workers while creating opportunities for new jobs in other sectors, especially in fields where tasks are repeatable. Researchers from Stanford's Digital Economy Lab reported in 2025 that since the widespread adoption of generative AI in late 2022, early-career workers (ages 22–25) in the most AI-exposed occupations have experienced a 13 percent relative decline in employment—even after controlling for firm-level shocks—while overall employment has continued to grow robustly. The study further finds that job losses are concentrated in roles where AI automates routine tasks, whereas occupations that leverage AI to augment human work have seen stable or increasing employment. ==== Computer-integrated manufacturing ==== Computer-integrated manufacturing uses computers to control the production process. This allows individual processes to exchange information with each other and initiate actions. Although manufacturing can be faster and less error-prone through the integration of computers, the main advantage is the ability to create automated manufacturing processes. Computer-integrated manufacturing is used in automotive, aviation, space, and shipbuilding industries. ==== White-collar machines ==== The 21st century has seen a variety of skilled tasks partially taken over by machines, including translation, legal research, and journalism. Care work, entertainment, and other tasks requiring empathy, previously thought safe from automation, are increasingly performed by robots and AI systems. ==== Autonomous cars ==== An autonomous car is a vehicle that is capable of sensing its environment and navigating without human input. Many such vehicles are operational and others are being developed, with legislation rapidly expanding to allow their use. Obstacles to widespread adoption of autonomous vehicles have included concerns about the resulting loss of driving-related jobs in the road transport industry, and safety concerns. On March 18, 2018, a pedestrian was struck and killed in Tempe, Arizona by an Uber self-driving car. ==== AI-generated content ==== In the 2020s, automated content became more relevant due to technological advancements in AI models, such as ChatGPT, DALL-E, and Stable Diffusion. In most cases, AI-generated content such as imagery, literature, and music are produced through text prompts. These AI models are sometimes integrated into creative programs. AI-generated art may sample and conglomerate existing creative works, producing results that appear similar to human-made content. Low-quality AI-generated visual artwork can be informally referred to as AI slop. Some artists use a tool called Nightshade that alters images to make them detrimental to the training of text-to-image models if scraped without permission, while still looking normal to humans. AI-generated images are a potential tool for scammers and those looking to gain followers on social media, either to impersonate a famous individual or group or to monetize their audience. The New York Times has sued OpenAI, alleging copyright infringement related to the training and outputs of its AI models. === Eradication === Scientists such as Stephen Hawking are confident that superhuman artificial intelligence is physically possible, stating "there is no physical law precluding particles from being organised in ways that perform even more advanced computations than the arrangements of particles in human brains". According to Nick Bostrom, a superintelligent machine would not necessarily be motivated by the same emotional desire to collect power that often drives human beings but might rather treat power as a means toward attaining its ultimate goals; taking over the world would both increase its access to resources and help to prevent other agents from stopping the machine's plans. As a simplified example, a paperclip maximizer designed solely to create as many paperclips as possible would want to take over the world so that it can use all of the world's resources to create as many paperclips as possible, and, additionally, prevent humans from shutting it down or using those resources on things other than paperclips. There are debates on how realistic AI takeover scenarios are. According to a 2026 research paper, many of the arguments about existential risks are based on speculative assumptions about how intelligent AI systems could become, how they would behave and what goals they might develop over time. A 2023 Reuters/Ipsos survey showed that 61% of American adults feared AI could pose a threat to civilization. Philosopher Niels Wilde refutes the common thread that artificial intelligence inherently presents a looming threat to humanity, stating that these fears stem from perceived intelligence and lack of transparency in AI systems that more closely reflects the human aspects of it rather than those of a machine. AI alignment research studies how to design AI systems so that they follow intended objectives. == Debate == Physicist Stephen Hawking, Microsoft founder Bill Gates, and SpaceX founder Elon Musk have expressed concerns about the possibility that AI could develop to the point that humans could not control it, with Hawking theorizing that this could "spell the end of the human race". Stephen Hawking said in 2014 that "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks." Hawking believed that in the coming decades, AI could offer "incalculable benefits and risks" such as "technology outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand." In January 2015, Nick Bostrom joined Stephen Hawking, Max Tegmark, Elon Musk, Lord Martin Rees, Jaan Tallinn, and numerous AI researchers in signing the Future of Life Institute's open letter speaking to the potential risks and benefits associated with artificial intelligence. The signatories "believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today." Some focus has been placed on the development of trustworthy AI. Three statements have been posed as to why AI is not inherently trustworthy: 1. An entity X is trustworthy only if X has the right motivations, goodwill and/or adheres to moral obligations towards the trustor; 2. AI systems lack motivations, goodwill, and moral obligations; 3. Therefore, AI systems cannot be trustworthy. There are additional considerations within this framework of trustworthy AI that go further into the fields of explainable artificial intelligence and respect for human privacy. Zanotti and colleagues

    Read more →