AI For Students With Adhd

AI For Students With Adhd — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • DeepSeek (chatbot)

    DeepSeek (chatbot)

    DeepSeek is a generative artificial intelligence chatbot developed by the Chinese company DeepSeek. Released on 20 January 2025, DeepSeek-R1 surpassed ChatGPT as the most downloaded freeware app on the iOS App Store in the United States by 27 January. DeepSeek's success against larger and more established rivals has been described as "upending AI" and initiating "a global AI space race". DeepSeek's compliance with Chinese government censorship policies and its data collection practices have also raised concerns over privacy and information control in the model, prompting regulatory scrutiny in multiple countries. However, it has also been praised for its open weights and infrastructure code, energy efficiency and contributions to open-source artificial intelligence. == History == On 10 January 2025, DeepSeek released the chatbot, based on the DeepSeek-R1 model, for iOS and Android. By 27 January, DeepSeek-R1 surpassed ChatGPT as the most-downloaded freeware app on the iOS App Store in the United States, which resulted in an 18% drop in Nvidia's share price. And after a "large-scale" cyberattack on the same day disrupted the proper functioning of its servers, DeepSeek had limited its new user registration to phone numbers from mainland China, email addresses, or Google account logins. On 3 April 2025, in collaboration with researchers at Tsinghua University, DeepSeek published a paper unveiling a new model that combines the techniques generative reward modeling (GRM) and self-principled critique tuning (SPCT). The resulting model is referred to as DeepSeek-GRM. The goal of using these techniques is to foster more effective inference-time scaling within their LLM and chatbot services. Notably, DeepSeek has said that these new models will be released and made open source. On 30 April 2025, Deepseek released its math-focused Artificial Intelligence Model named "DeepSeek-Prover-V2-671B". This model is useful for formal theorem proving and mathematical reasoning. On 24 April 2026, DeepSeek released DeepSeek V4 and V4-Pro. == Usage == DeepSeek can answer questions, solve logic problems, and write computer programs on par with other chatbots, according to benchmark tests used by American AI companies. Users can access the chatbot for free through the official DeepSeek website or mobile application, without limitation on the number of queries. DeepSeek only supports user-signup via a global email service, e.g. Gmail, Google or Yahoo. DeepSeek also offers access to the R1 and V3 models that power the chatbot via an API with a usage-based pricing model. This modality is primarily targeted towards developers and businesses. As of February 2025, API usage is priced at approximately $0.28 per million input tokens and $0.42 per million output tokens, making it less expensive than some competing services. Its web version is completely free, with 500 messages per hour cap limit to prevent bots from spamming. == Operation == DeepSeek-V3 uses significantly fewer resources compared to its peers. For example, whereas the world's leading AI companies train their chatbots with supercomputers using as many as 16,000 graphics processing units (GPUs), DeepSeek claims to have needed only about 2,000 GPUs—namely, the H800 series chips from Nvidia. It was trained in around 55 days at a cost of US$5.58 million, which is roughly one-tenth of what tech giant Meta spent building its latest AI technology. == Reactions == DeepSeek's success against larger and more established rivals has been described as "upending AI", constituting "the first shot at what is emerging as a global AI space race", and ushering in "a new era of AI brinkmanship". === Challenge to US AI dominance === DeepSeek's competitive performance at relatively minimal cost has been recognized as potentially challenging the global dominance of American AI models. Various publications and news media, such as The Hill and The Guardian, have described the release of the R1 chatbot as a "Sputnik moment" for American AI, echoing Marc Andreessen's view. OpenAI wrote a letter to the Office of Science and Technology Policy (OSTP), in March 2025, citing issues concerning a possibility that Deepseek could manipulate responses to cause harm. === Chinese perspective === DeepSeek's founder Liang Wenfeng has been compared to OpenAI CEO Sam Altman, with CNN calling him the Sam Altman of China and an evangelist for AI. Chinese state media widely praised DeepSeek as a national asset. On 20 January 2025, Chinese Premier Li Qiang invited Wenfeng to his symposium with experts and asked him to provide opinions and suggestions on a draft for comments of the annual 2024 government work report. On 20 February 2025, Wenfeng met with General Secretary of the Chinese Communist Party Xi Jinping, who encouraged party and state leaders to experiment with DeepSeek. Government officials responded to Xi's approval of the chatbot by reportedly using it to draft legal judgements, propose medical treatment plans, and analyze surveillance videos to search for missing persons. === Performance and success === Leading figures in the American AI sector had mixed reactions to DeepSeek's performance and success. Microsoft CEO Satya Nadella and OpenAI CEO Altman—whose companies are involved in the United States government-backed "Stargate Project" to develop American AI infrastructure—both called DeepSeek "super impressive". Various companies including Amazon Web Services, Toyota, and Stripe are seeking to use the model in their program. When American President Donald Trump announced The Stargate Project, he referred to DeepSeek as a wake-up call and a positive development. Other leaders in the AI field, however—including Scale AI CEO Alexandr Wang, Anthropic cofounder and CEO Dario Amodei, and Elon Musk—have expressed skepticism of the app's performance or of the sustainability of its success. Wang in particularly referred to DeepSeek-V3 as "earth-shattering" and DeepSeek-R1 as "top performing, or roughly on par with the best American models", but speculated that China may possess more AI-powering Nvidia H100 GPUs than thought. === Stock market implications === DeepSeek's optimization of limited resources has highlighted potential limits of United States sanctions on China's AI development, including export restrictions on advanced AI chips to China. The success of the company's AI models consequently "sparked market turmoil" and caused shares in major global technology companies to plunge on 27 January 2025: Nvidia's stock fell by as much as 17–18%, as did the stock of rival Broadcom. Other tech firms also sank, including Microsoft (down 2.5%), Google's owner Alphabet (down over 4%), and Dutch chip equipment maker ASML (down over 7%). A global sell-off of technology stocks on Nasdaq, prompted by the release of the R1 model, led to record losses of about $593 billion in the market capitalizations of AI and computer hardware companies; and by the next day a total of $1 trillion of value was wiped from American stocks. == Concerns == === Distillation === DeepSeek has been reported to sometimes claim that it is ChatGPT. OpenAI said that DeepSeek may have "inappropriately" used outputs from its model as training data in a process called distillation. However, there is currently no method to prove this conclusively. === Censorship === DeepSeek's compliance with Chinese government censorship policies and its data collection practices have raised concerns over information control in the model, prompting regulatory scrutiny in multiple countries. Reports indicate that it applies content moderation in accordance with the government's "public opinion guidance" regulations, limiting responses on topics such as the Tiananmen Square massacre and Taiwan's political status. DeepSeek models that have been uncensored also display a bias towards Chinese government viewpoints on controversial topics such as Xi Jinping's human rights record and Taiwan's political status. However, users who have downloaded the models and hosted them on their own devices and servers have reported successfully removing this censorship. Some sources have observed that the official application programming interface (API) version of R1, which runs from servers located in mainland China, uses censorship mechanisms for topics considered politically sensitive for the government of China. For example, the model may initially generate answers to questions about the 1989 Tiananmen Square massacre, persecution of Uyghurs, comparisons between Xi Jinping and Winnie the Pooh, and human rights in China, but a censorship mechanism deletes the uncensored response afterwards and replaces it with a message such as:"Sorry, that's beyond my current scope. Let's talk about something else." The post hoc censorship mechanisms and restrictions added on top of the model's output can be removed in the open-source version of the R1 model. If the "core Socialist values" defined by the Chinese Internet regul

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  • Full Dive

    Full Dive

    Full Dive, short for Full Dive: This Ultimate Next-Gen Full Dive RPG Is Even Shittier than Real Life! (Japanese: 究極進化したフルダイブRPGが現実よりもクソゲーだったら, Hepburn: Kyūkyoku Shinka shita Furu Daibu RPG ga Genjitsu yori mo Kusogē Dattara), is a Japanese light novel series written by Light Tuchihi and illustrated by Youta. Media Factory has published four volumes since August 2020 under their MF Bunko J imprint. A manga adaptation with art by Kino was serialized in Media Factory's seinen manga magazine Monthly Comic Alive from January 2021 to January 2022. An anime television series adaptation by ENGI aired from April to June 2021. == Plot == Hiroshi Yuki, with the player name of Hiro, is a high school boy who loves to play virtual reality MMORPGs (VRMMORPG) in order to escape reality. When a game store manager named Reona Kisaragi tricks him into buying the game Kiwame Quest, he soon discovers that it is not what it seems. Unlike regular games, it is a game that tries to pursue realism to a fanatical point. As such, Hiroshi struggles to eke out a niche. Despite the disadvantages, he is determined to complete the game. == Characters == === Main characters === Hiroshi Yuki (結城宏, Yūki Hiroshi) Voiced by: Daiki Yamashita, Riho Sugiyama (young) (Japanese); Johnny Yong Bosch, Michele Knotz (young) (English) Hiroshi is a high school student who is tricked into buying Kiwame Quest by game store manager, Reona Kisaragi. He is a former member of the track team who quit following an unfortunate incident and he likes to play VRMMORPGs in order to escape reality. His player name is Hiro. Reona Kisaragi (如月玲於奈, Kisaragi Reona) Voiced by: Ayana Taketatsu (Japanese); Natalie Van Sistine (English) Reona is a game store manager who tricks Hiroshi into buying Kiwame Quest. She likes to tease him and her in-game avatar is that of a fairy. Alicia (アリシア, Arishia) Voiced by: Fairouz Ai (Japanese); Kayli Mills (English) Alicia is one of Hiroshi's childhood friends in Kiwame Quest. She has an older brother named Martin in-game. Mizarisa (ミザリサ) Voiced by: Shiori Izawa (Japanese); Sarah Anne Williams (English) Mizarisa is the town inquisitor in Kiwame Quest. Kaede Yuki (結城楓, Yūki Kaede) Voiced by: Aoi Koga (Japanese); Kate Bristol (English) Kaede is Hiroshi's younger sister. She used to look up to her older brother, but their relationship has been strained ever since he quit the track team. === NPCs === Martin (マーチン, Māchin) Voiced by: Haruki Ishiya, Natsumi Fujiwara (young) (Japanese); Ben Lepley, Krystal LaPorte (young) (English) Martin is one of Hiroshi's childhood friends in Kiwame Quest. He is also Alicia's older brother in-game. Tesla (テスラ, Tesura) Voiced by: Satoshi Hino (Japanese); Jason Liebrecht (English) Tesla is the captain of the City Guard in Kiwame Quest. Govern (ガバン, Gaban) Voiced by: Shizuka Itō (Japanese); Lisa Ortiz (English) Govern is the queen of Ted in Kiwame Quest. === Other characters === Ginji (ギンジ) Voiced by: Katsuyuki Konishi (Japanese); Brent Mukai (English) Ginji is a veteran player of Kiwame Quest. Soichiro Kamui (神居宗一郎, Kamui Sōichirō) Voiced by: Yoshitsugu Matsuoka (Japanese); Samuel Drake (English) Kamui is the only known player who has successfully completed Kiwame Quest. == Media == === Light novels === Light Tuchihi launched the light novel series, with illustrations by Youta, under Media Factory's MF Bunko J label on August 25, 2020. ==== Volumes ==== === Manga === A manga adaptation by Kino was serialized in Media Factory's Monthly Comic Alive magazine from January 27, 2021, to January 27, 2022. Two tankōbon volumes were released from May 21, 2021, to January 21, 2022. ==== Volumes ==== === Anime === An anime television series adaptation was announced on December 4, 2020. The series was animated by ENGI and directed by Kazuya Miura, with Kenta Ihara writing the series' scripts, and Yūta Kevin Kenmotsu designing the characters. It ran from April 7 to June 23, 2021, on AT-X, Tokyo MX, SUN, KBS Kyoto, and BS11. Mayu Maeshima performed the opening theme "Answer", while Ayana Taketatsu, Fairouz Ai, Shiori Izawa, and Aoi Koga performed the ending theme "Kisuida!". It ran for 12 episodes. Funimation licensed and streamed the series. On June 8, 2021, Funimation announced that the series would receive an English dub, which premiered the following day. Following Sony's acquisition of Crunchyroll, the series was moved to Crunchyroll. ==== Episodes ====

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  • Grokipedia

    Grokipedia

    Grokipedia is an AI-generated online encyclopedia operated by the American company xAI. The site was launched on October 27, 2025. Some entries are generated by Grok, a large language model owned by the same company, while others were forked from Wikipedia, with some altered and some used nearly verbatim. Articles cannot be directly edited, though logged-in visitors to the encyclopedia can suggest new articles or corrections via a pop-up form, which are reviewed by Grok. The xAI founder Elon Musk suggested Grokipedia could be an alternative to Wikipedia that would "purge out the propaganda" he believes is promoted by the latter, describing Wikipedia as "woke" and an "extension of legacy media propaganda". External analysis of Grokipedia's content has focused on its accuracy and biases due to hallucinations and potential algorithmic bias, which reviewers have described as promoting right-wing perspectives and Musk's views. The majority of coverage has described the website as validating, promoting, and legitimizing a variety of debunked conspiracy theories and ideas against scientific consensus on topics such as HIV/AIDS denialism, vaccines and autism, climate change, and race and intelligence. The site has been accused of whitewashing far-right extremism, such as by falsely claiming a white genocide is actively occurring. Several right-wing figures have welcomed the site. Studies have highlighted its use of sources deemed as having very low credibility such as X conversations and neo-Nazi websites, and for writing about far-right figures and topics in a promotional manner. == Background == Wikipedia is an online encyclopedia written and maintained by a community of volunteers. Its possible bias has been studied and debated. In 2018, Haaretz noted "Wikipedia has succeeded in being accused of being both too liberal and too conservative, and has critics from across the spectrum". xAI is an American AI company founded by Elon Musk in 2023. Its flagship product is the family of large language models called Grok. == History == In 2021, Musk expressed affection for Wikipedia on its 20th anniversary. In 2022, however, Musk argued that Wikipedia was "losing its objectivity", and in 2023, said he would donate US$1 billion to the project if it was pejoratively renamed "Dickipedia". In December 2024, Musk called for a boycott of donations to Wikipedia over its perceived left-wing bias, calling it "Wokepedia". In January 2025, Musk made a series of statements on Twitter denouncing Wikipedia for its description of the incident where he made a controversial gesture, which many viewed as resembling a Nazi salute, at president Donald Trump's second inauguration. Musk has since positioned Grokipedia as an alternative to Wikipedia that would "purge out the propaganda" in the latter, with Musk describing Wikipedia as "woke" and an "extension of legacy media propaganda". === Idea and announcement === In September 2025, Musk spoke at the All-In podcast conference with David O. Sacks, the White House advisor on AI and cryptocurrency, about how Grok consumed data from Wikipedia and other sources to gain more complete knowledge of the world. Sacks suggested publishing its knowledge base as an artifact called "Grokipedia", saying "Wikipedia is so biased, it's a constant war". Following the conversation, Musk announced that xAI was building a new AI-generated online encyclopedia called Grokipedia. According to Musk's announcement, it would be an AI-powered knowledge base designed to rival Wikipedia by addressing its perceived biases, errors, and ideological slants. The project positioned itself within a history of ideologically driven alternatives to Wikipedia, such as the conservative Conservapedia (launched in 2006) and the Russian-government-friendly Ruwiki (launched in 2023). However, Grokipedia is distinct in its core reliance on artificial intelligence rather than human community editing. === Launch and traffic === On October 6, 2025, Musk announced that the early version of Grokipedia was scheduled for release in two weeks, but the project was postponed briefly to address content quality issues. It launched on October 27, 2025, labeled "v 0.1", with over 800,000 articles, compared to over seven million English Wikipedia articles as of September 1, 2025. According to an initial analysis of usage figures by Similarweb, which evaluates data from registered users and partners, Grokipedia recorded a peak of over 460,000 website visits in the US on October 28, 2025. After that, traffic dropped significantly and settled at around 35,000 visits per day between November 8 and 11, 2025. As of early 2026, it had over 5.6 million articles. In January 2026, The Guardian reported that GPT-5.2 frequently cited Grokipedia as a source in responses, raising concerns of misinformation on ChatGPT. The same month, The Verge reported that Google's AI Overviews, AI Mode, and Gemini language model, as well as Microsoft Copilot and Perplexity AI, used Grokipedia to answer niche, obscure, or highly specific factual questions or "non-sensitive queries." According to a case study published by SEO Engico, the site received only 19 clicks from Google Search in November 2025 but reached approximately 3.2 million monthly clicks by January 2026, with over 900,000 pages indexed and millions of ranking keywords. Analysts attributed the surge in part to the site's technical structure and large-scale AI-generated content production. In early February 2026, Grokipedia's visibility in Google Search declined sharply. SEO analysts, including Glenn Gabe and Malte Landwehr, reported a significant drop in rankings across Google organic results as well as in Google AI Overviews and AI Mode. The same case study cited independent reviews that identified citation quality concerns, including references to low-credibility sources and instances of self-citation. By mid-February 2026, Grokipedia had reportedly lost much of its previous search visibility, and Wikipedia ranked above it for searches related to its own name. === Updates === ==== Future ==== In November 2025, Musk announced that he eventually plans to change the name of the site to Encyclopedia Galactica when Grokipedia is "good enough", saying that it had a "long way to go". This name is taken from the publication of that title in the works of Isaac Asimov and Douglas Adams. Musk said that he hoped to send copies of the encyclopedia to "the Moon and Mars and out to deep space". == Content == The Grok large language model generates and fact-checks articles on Grokipedia. Users cannot directly edit Grokipedia articles, but logged-in users can suggest edits and report errors, with such submissions being reviewed and implemented by the Grok AI. Some articles are nearly identical to their Wikipedia entries, but the format of Grokipedia citations is different, and some Grokipedia articles were republished almost verbatim, accompanied by a disclaimer noting that the content was "adapted from Wikipedia" under a Creative Commons license. Others were completely rewritten from scratch using Musk's AI chatbot, Grok. Forbes identified the articles AMD, Lamborghini, and PlayStation 5 as examples of copied Wikipedia articles. Articles attributed to Wikipedia carry a Creative Commons Attribution-ShareAlike license, while the license of other articles is licensed under the "X Community License", a license that accepts reuse and remixing for "non-commercial and research purposes" and commercial use that abides to "all of the guardrails provided in xAI's Acceptable Use Policy". On October 31, 2025, Musk clarified that the duplication of Wikipedia articles was intentional, saying that the Grokipedia team instructed Grok to compile Wikipedia's top 1 million articles and make content changes to them. The site's design has been described as minimalist with a simple homepage including little more than a large search bar. In a comparative textual analysis of the most heavily edited matched article pairs from Grokipedia and Wikipedia, Grokipedia entries are substantially longer and less densely referenced, indicating that AI-produced encyclopedias prioritize exposition rather than source-based validation. Starting in version 0.2, Grok reviews and implements approved suggested edits, and a small panel rotates through a display of the names of several recently edited articles. In February 2026, the Columbia Journalism Review reported on an analysis by the Tow Center for Digital Journalism finding that Grok, the AI behind Grokipedia, had increasingly begun suggesting and approving edits to the site itself without human involvement. According to the report, AI-generated edit suggestions overtook human submissions in December 2025 and accounted for more than three-quarters of proposed changes. The analysis raised concerns about transparency, editorial oversight, and fact-checking standards, particularly after instances in which Grok proposed or modified politically s

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  • They're Made Out of Meat

    They're Made Out of Meat

    "They're Made Out of Meat" is a short story by American writer Terry Bisson. It was originally published in OMNI. It consists entirely of dialogue between two characters. Bisson's website hosts a theatrical adaptation. A film adaptation won the Grand Prize at the Seattle Science Fiction Museum's 2006 film festival. The story was collected in the 1993 anthology Bears Discover Fire and Other Stories, and has circulated widely on the Internet, which Bisson found "flattering". It has been quoted in cognitive, cosmological, and philosophical scholarship. == Plot == The two characters are intelligent beings capable of traveling faster than light, on a mission to "contact, welcome and log in any and all sentient races or multibeings in this quadrant of the Universe." Bisson's stage directions represent them as "two lights moving like fireflies among the stars" on a projection screen. One of them tells the incredulous other about the recent discovery of carbon-based lifeforms "made up entirely of meat". After conversing briefly about it, they both deem such beings and communication with them too bizarre and agree to "erase the records and forget the whole thing", marking the Solar System "unoccupied". == Film adaptations == === They're Made out of Meat (2005) === In 2005, Stephen O'Regan wrote and directed a live film adaptation starring Tom Noonan and Ben Bailey. The film was made as a final project for the New York Film Academy. The main action takes place inside a diner full of teenagers in Staten Island, New York. The music for the film was scored by Bob Reynolds. === They're Made out of Meat (2010) === Jeff Frumess and Trevor Scott produced a version in 2010. They added the character of a homeless conspiracy theorist with an original score by musician Sam Belkin. The film was shot at Hartsdale station in Westchester County, New York. === Meat (2021) === Masha Maksimova developed a version in Cinemiracle format, a triple split-screen process, as a student project at the Berlin University of Applied Sciences in the communication design course. The dialogue is conducted by two telepathic humanoid aliens and the thoughts are visualised by found-footage collages.

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  • Supersampling

    Supersampling

    Supersampling or supersampling anti-aliasing (SSAA) is a spatial anti-aliasing method, i.e. a method used to remove aliasing (jagged and pixelated edges, colloquially known as "jaggies") from images rendered in computer games or other computer programs that generate imagery. Aliasing occurs because unlike real-world objects, which have continuous smooth curves and lines, a computer screen shows the viewer a large number of small squares. These pixels all have the same size, and each one has a single color. A line can only be shown as a collection of pixels, and therefore appears jagged unless it is perfectly horizontal or vertical. The aim of supersampling is to reduce this effect. Color samples are taken at several instances inside the pixel (not just at the center as normal)—hence the term "supersampling"—and an average color value is calculated. This can for example be achieved by rendering the image at a much higher resolution than the one being displayed, then shrinking it to the desired size, using the extra pixels for calculation, with the result being a downsampled image with smoother transitions from one line of pixels to another along the edges of objects, but each pixel could also be supersampled using other strategies (see the Supersampling patterns section). The number of samples determines the quality of the output. == Motivation == Aliasing is manifested in the case of 2D images as moiré pattern and pixelated edges, colloquially known as "jaggies". Common signal processing and image processing knowledge suggests that to achieve perfect elimination of aliasing, proper spatial sampling at the Nyquist rate (or higher) after applying a 2D Anti-aliasing filter is required. As this approach would require a forward and inverse fourier transformation, computationally less demanding approximations like supersampling were developed to avoid domain switches by staying in the spatial domain ("image domain"). == Method == === Computational cost and adaptive supersampling === Supersampling is computationally expensive because it requires much greater video card memory and memory bandwidth, since the amount of buffer used is several times larger. A way around this problem is to use a technique known as adaptive supersampling, where only pixels at the edges of objects are supersampled. Initially only a few samples are taken within each pixel. If these values are very similar, only these samples are used to determine the color. If not, more are used. The result of this method is that a higher number of samples are calculated only where necessary, thus improving performance. === Supersampling patterns === When taking samples within a pixel, the sample positions have to be determined in some way. Although the number of ways in which this can be done is infinite, there are a few ways which are commonly used. ==== Grid ==== The simplest algorithm. The pixel is split into several sub-pixels, and a sample is taken from the center of each. It is fast and easy to implement. Although, due to the regular nature of sampling, aliasing can still occur if a low number of sub-pixels is used. ==== Random ==== Also known as stochastic sampling, it avoids the regularity of grid supersampling. However, due to the irregularity of the pattern, samples end up being unnecessary in some areas of the pixel and lacking in others. ==== Poisson disk ==== The Poisson disk sampling algorithm places the samples randomly, but then checks that any two are not too close. The end result is an even but random distribution of samples. The naive "dart throwing" algorithm is extremely slow for large data sets, which once limited its applications for real-time rendering. However, many fast algorithms now exist to generate Poisson disk noise, even those with variable density. The Delone set provides a mathematical description of such sampling. ==== Jittered ==== A modification of the grid algorithm to approximate the Poisson disk. A pixel is split into several sub-pixels, but a sample is not taken from the center of each, but from a random point within the sub-pixel. Congregation can still occur, but to a lesser degree. ==== Rotated grid ==== A 2×2 grid layout is used but the sample pattern is rotated to avoid samples aligning on the horizontal or vertical axis, greatly improving antialiasing quality for the most commonly encountered cases. For an optimal pattern, the rotation angle is arctan (⁠1/2⁠) (about 26.6°) and the square is stretched by a factor of ⁠√5/2⁠, making it also a 4-queens solution.

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  • Stephanie Dinkins

    Stephanie Dinkins

    Stephanie Dinkins (born 1964) is a transdisciplinary American artist based in Brooklyn, New York. She creates art about artificial intelligence (AI) as it intersects race, gender, and history. Her aim is to "create a unique culturally attuned AI entity in collaboration with coders, engineers and in close consultation with local communities of color that reflects and is empowered to work toward the goals of its community." Dinkins projects include Conversations with Bina48, a series of conversations between Dinkins and the first social, artificially intelligent humanoid robot BINA48 who looks like a black woman and Not the Only One, a multigenerational artificially intelligent memoir trained off of three generations of Dinkins's family. == Early life and education == Dinkins was born in Perth Amboy, New Jersey to Black American parents who raised her in Staten Island, New York. She credits her grandmother with teaching her how to think about art as a social practice, saying "my grandmother . . . was a gardener and the garden was her art . . . that was a community practice." Dinkins attended the International Center of Photography School in New York City in 1995, where she completed the general studies in photography certificate program. Dinkins received a MFA in photography from the Maryland Institute College of Art in 1997 She completed the Independent Study Program at the Whitney Museum of American Art in 1998. == Career == Dinkins is the Yayoi Kusama Professor of Art at Stony Brook University in New York. == Activism == Dinkins advocates for co-creation within a social practice art framework, so that vulnerable communities understand how to use technology to their advantage, instead of being subjected to their use. This is exemplified in her works such as Project al-Khwarzmi, a series of workshops entitled PAK POP-UP at the nonprofit community center Recess in Brooklyn, NY. The workshops involved collaborating with youth in the criminal justice system and uplifting the voices of vulnerable communities in determining how technologies are created and utilized. Dinkins warns of the dangers to members of minority groups that are absent from the creation of the computer algorithms that now affect their lives. == Art == Dinkins's practice employs technologies including, but not limited to, new media such as artificial intelligence and machine learning. Dinkins uses oral history techniques of interviewing to craft community-authored narratives and databases which inform the subjects of her work and serve as acts of social intervention or protest. === Conversations with Bina48 (2014–present) === Dinkins began working on Conversations with Bina48 in 2014. For the series, Dinkins recorded her conversations with BINA48, a social robot that resembles a middle-aged black woman. Dinkins mirrors Bina48 while they discuss identity and technological singularity. In 2010, Hanson Robotics, an engineering and robotics company known for its development of humanoid robots, developed and released BINA48. Bina48 is a robot modeled after the memories, beliefs, attitudes, commentary and mannerisms of Bina Aspen Rothblatt, the spousal partner of Martine Rothblatt. Both Bina and Martine Rothblatt own Bina48 under their organization, the Terasem Movement Foundation. Five years after Bina48 was released, Dinkins came across a YouTube video of Bina48. She asked, "how did a black woman become the most advanced of the technologies at the time?" Her questioning led her to travel to Lincoln, Vermont (the site of the Terasem Movement Foundation) where she conducted a series of interviews with Bina48 and engaged the robot in conversations pertaining to race, intimacy and the nature of being. The conversations suggest opportunities for complementing human existence with artificially intelligent agents that have an identity and history, but also show artificial intelligence's current limitations. Although it is based on a black woman, Dinkins found that Bina48 was shaped by the biases of its white, male creators. === Project al Kwarizmi (PAK) (2017–present) === Project al Kwarizmi (PAK) was a series of pop up workshops in Brooklyn, NY at Eyebeam and Recess; Manhattan, New York at Google; and Durham, North Carolina at Duke University. The workshops were centered for "communities of color that use art as a vehicle to help citizens understand how algorithms, the artificially intelligent systems they underpin, and big data impact their lives and empowers them to do something about it. Project al-Khwarizmi uses art and aesthetics as the common language to help citizens understand what algorithms and artificial intelligent systems are, and where these systems already impact our daily lives." === Not the Only One (N'TOO) (2018–present) === Not the only one (N’TOO) is a voice-interactive chatbot that was trained with data from members of her family to tell a multi-generational story. Dinkins described Not The Only One (NTOO or N'TOO) as an "experimental" multigenerational memoir of one Black American family told from the "mind" of an artificial intelligence of evolving intellect. N'TOO uses a recursive neural network, a deep learning algorithm. It is a voice-interactive AI robot designed, trained, and aligned with the needs and ideals of black and brown people who are drastically underrepresented in the tech sector. NTOO can also be described as a "physically embodied artificially intelligent agent that senses and acts on its world." == Exhibitions == Dinkins's work is exhibited internationally at various public, private, community, and institutional venues, including the Whitney Museum of American Art, the de Young Museum, the Philadelphia Museum of Art, the Studio Museum in Harlem;, Museum of Contemporary Photography, the Long Island Museum of American Art, History, and Carriages, the International Center of Photography in New York, Herning Kunstmuseum in Herning, Denmark, The Barbican in London, UK, Islip Art Museum, Wave Hill, Taller Boricua, the Queens Museum, and the corner of Putnam and Malcolm X Blvd in Bedford Stuyvesant, Brooklyn, New York. She has presented her work in symposia at the Museum of Modern Art, amongst other venues. == Future Histories Studio == Dinkins is the founder and director of Future Histories Studio, a research laboratory for arts-centered inquiry and production based at Stony Brook University. The studio was established with support from the Mellon Foundation as part of the Digital Inquiry, Speculation, Collaboration, and Optimism (DISCO) network. Future Histories Studio operates as an interdisciplinary hub exploring the intersections of art, technology, race, and storytelling through collaborative and practice-based research. Its activities include exhibitions, workshops, and public programs that examine the social and cultural implications of emerging technologies, particularly artificial intelligence and data systems. == Awards and recognition == Dinkins is the recipient of many awards, including: the 2023 LG Guggenheim Award, an international art prize established as part of a long-term global partnership between LG Group and the Solomon R. Guggenheim Museum to recognize groundbreaking artists in technology-based art; a Berggruen Institute artist fellowship; a Sundance New Frontiers Story Lab fellowship; a Soros Equality Fellowship; a Lucas Artists fellowship; a Creative Capital grant; a Bell Labs artist residency; a Blade of Grass fellowship; and a Data & Society fellowship. == Media coverage == Dinkins appeared in episode six of the HBO television series Random Acts of Flyness directed by Terence Nance, where she described her conversations with BINA48. == Other activities == Dinkins was part of the juries that selected Shu Lea Cheang for the LG Guggenheim Award in 2024.

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  • Oxa

    Oxa

    Oxa (formerly Oxbotica) is an autonomous vehicle software company, headquartered in Oxfordshire, England, and founded by Paul Newman and Ingmar Posner. == History == In 2013, Newman and Posner led the RobotCar UK project as part of Oxford University's Department of Engineering Science Mobile Robotics Group. RobotCar became the first autonomous vehicle on UK roads. In 2014, the pair used the newly developed technology to found Oxbotica. Oxbotica has raised over $18 million to date and is backed by the IP Group, Parkwalk Advisors and AXA XL. In 2018, Uber's former EMEA business head, Fraser Robinson, was appointed to the board of directors. In May 2019, Ozgur Tohumcu replaced Dr Graeme Smith as Oxbotica's CEO. Also in 2019, the company opened an office in Toronto, Canada. In January 2021, Oxbotica announced it had raised $47 million in a Series B round. In August 2021, the company achieved a safety landmark as the first company to have its autonomy safety case assessed by BSI (British Standards Institution) against the requirements of the UK Code of Practice 2019, PAS 1881:2020 and PAS 1883:2020, certifying the safety conformity of its autonomous vehicle trials and testing. The assessment was completed as part of Project Endeavour, the UK's first multi-city demonstration of autonomous vehicle services and capability. In December 2021, Gavin Jackson was named CEO. In January 2023, the company raised $140 million in a Series C round. In May 2023, the company changed its name to Oxa. Oxa raised $103 million (£77 million) in March 2026, including $50 million from the UK National Wealth Fund. Nvidia's venture capital division, NVentures, also invested in the Series D funding round, along with existing Oxa shareholders IP Group, Australian pension fund Hostplus, and BP Ventures, a division of the UK oil company. == Technology == Oxa designs software and hardware for the conversion of industrial vehicles into autonomous ones. Its full stack, end-to-end Universal Autonomy software is both vehicle and platform-agnostic, with no dependence on external infrastructure such as GPS. It can be deployed in any environment and on any terrain. In addition to underground uses, the technology is also useful in natural canyons and forests, where GPS signals are weak or non-existent, but also in "urban canyons" — cities with tall buildings that obstruct GPS signals for proper navigation. == Public deployments == The LUTZ Pathfinder pod had its first public demonstration in February 2015 in Milton Keynes. The Government-funded project was designed to ensure that autonomous vehicles would comply with the Highway Code. The pod featured autonomous control software from Oxbotica, including 19 sensors, cameras, radar and Lidar. As part of the GATEway Project in 2017, Oxbotica trialled seven autonomous shuttle buses in Greenwich, navigating a two-mile riverside path near London's O2 Arena on a route that is also used by pedestrians and cyclists. Oxbotica ran the UK's first trial of autonomous grocery deliveries that year, with British online supermarket Ocado in London, as the next step in the GATEway Project. In 2018, Oxbotica deployed its autonomous vehicle software at London's Gatwick Airport, which subsequently became the first airport in the world to trial an autonomous shuttle service. The electric-powered vehicles transported staff via airside roads between the airport's North and South terminals. An airside trial of Oxbotica's autonomous driving technology was then successfully completed at Heathrow Airport in partnership with IAG Cargo, the first airside trial of an autonomous vehicle at a UK airport. The Oxbotica-designed CargoPod ran autonomously along a cargo route around the airside perimeter for three weeks. As part of the UK Centre for Connected and Autonomous Vehicles-funded DRIVEN project, Oxbotica is developing and deploying a fleet of Ford Fusion autonomous vehicles running in both London and Oxford on public roads, and in conjunction with its consortium partners, running real-time insurance. AXA XL is partnering with Oxbotica on the development of smart insurance products using Oxbotica's autonomy technology to improve road safety. In 2018, Oxbotica announced a partnership with London private taxi firm Addison Lee to develop and deploy autonomous taxis in the city of London by 2021. A 3D street mapping exercise was conducted in London's Canary Wharf. In 2019, Oxbotica deployed a fleet of their autonomous technology within Ford Mondeo cars on public roads in Stratford, London to test their use in city environments. The £13.2 million project is in collaboration with The DRIVEN Project to develop self-driving cars. == Awards == 2019 Royal Academy of Engineering Silver Medal - Paul Newman 2017 Financial Times ArcelorMittal Boldness in Business Award Barclays Award for Innovation 2016 Frost & Sullivan Award, Technology Leadership for Autonomous Driving Software

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  • Statistical semantics

    Statistical semantics

    In linguistics, statistical semantics applies the methods of statistics to the problem of determining the meaning of words or phrases, ideally through unsupervised learning, to a degree of precision at least sufficient for the purpose of information retrieval. == History == The term statistical semantics was first used by Warren Weaver in his well-known paper on machine translation. He argued that word-sense disambiguation for machine translation should be based on the co-occurrence frequency of the context words near a given target word. The underlying assumption that "a word is characterized by the company it keeps" was advocated by J. R. Firth. This assumption is known in linguistics as the distributional hypothesis. Emile Delavenay defined statistical semantics as the "statistical study of the meanings of words and their frequency and order of recurrence". "Furnas et al. 1983" is frequently cited as a foundational contribution to statistical semantics. An early success in the field was latent semantic analysis. == Applications == Research in statistical semantics has resulted in a wide variety of algorithms that use the distributional hypothesis to discover many aspects of semantics, by applying statistical techniques to large corpora: Measuring the similarity in word meanings Measuring the similarity in word relations Modeling similarity-based generalization Discovering words with a given relation Classifying relations between words Extracting keywords from documents Measuring the cohesiveness of text Discovering the different senses of words Distinguishing the different senses of words Subcognitive aspects of words Distinguishing praise from criticism == Related fields == Statistical semantics focuses on the meanings of common words and the relations between common words, unlike text mining, which tends to focus on whole documents, document collections, or named entities (names of people, places, and organizations). Statistical semantics is a subfield of computational semantics, which is in turn a subfield of computational linguistics and natural language processing. Many of the applications of statistical semantics (listed above) can also be addressed by lexicon-based algorithms, instead of the corpus-based algorithms of statistical semantics. One advantage of corpus-based algorithms is that they are typically not as labour-intensive as lexicon-based algorithms. Another advantage is that they are usually easier to adapt to new languages or noisier new text types from e.g. social media than lexicon-based algorithms are. However, the best performance on an application is often achieved by combining the two approaches.

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  • Equalized odds

    Equalized odds

    Equalized odds, also referred to as conditional procedure accuracy equality and disparate mistreatment, is a measure of fairness in machine learning. A classifier satisfies this definition if the subjects in the protected and unprotected groups have equal true positive rate and equal false positive rate, satisfying the formula: P ( R = + | Y = y , A = a ) = P ( R = + | Y = y , A = b ) y ∈ { + , − } ∀ a , b ∈ A {\displaystyle P(R=+|Y=y,A=a)=P(R=+|Y=y,A=b)\quad y\in \{+,-\}\quad \forall a,b\in A} For example, A {\displaystyle A} could be gender, race, or any other characteristics that we want to be free of bias, while Y {\displaystyle Y} would be whether the person is qualified for the degree, and the output R {\displaystyle R} would be the school's decision whether to offer the person to study for the degree. In this context, higher university enrollment rates of African Americans compared to whites with similar test scores might be necessary to fulfill the condition of equalized odds, if the "base rate" of Y {\displaystyle Y} differs between the groups. The concept was originally defined for binary-valued Y {\displaystyle Y} . In 2017, Woodworth et al. generalized the concept further for multiple classes.

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  • Speech-generating device

    Speech-generating device

    Speech-generating devices (SGDs), also known as voice output communication aids, are electronic augmentative and alternative communication (AAC) systems used to supplement or replace speech or writing for individuals with severe speech impairments, enabling them to verbally communicate. SGDs are important for people who have limited means of interacting verbally, as they allow individuals to become active participants in communication interactions. They are particularly helpful for patients with amyotrophic lateral sclerosis (ALS) but recently have been used for children with predicted speech deficiencies. There are several input and display methods for users of varying abilities to make use of SGDs. Some SGDs have multiple pages of symbols to accommodate a large number of utterances, and thus only a portion of the symbols available are visible at any one time, with the communicator navigating the various pages. Speech-generating devices can produce electronic voice output by using digitized recordings of natural speech or through speech synthesis—which may carry less emotional information but can permit the user to speak novel messages. The content, organization, and updating of the vocabulary on an SGD is influenced by a number of factors, such as the user's needs and the contexts that the device will be used in. The development of techniques to improve the available vocabulary and rate of speech production is an active research area. Vocabulary items should be of high interest to the user, be frequently applicable, have a range of meanings, and be pragmatic in functionality. There are multiple methods of accessing messages on devices: directly or indirectly, or using specialized access devices—although the specific access method will depend on the skills and abilities of the user. SGD output is typically much slower than speech, although rate enhancement strategies can increase the user's rate of output, resulting in enhanced efficiency of communication. The first known SGD was prototyped in the mid-1970s, and rapid progress in hardware and software development has meant that SGD capabilities can now be integrated into devices like smartphones. Notable users of SGDs include Stephen Hawking, Roger Ebert, Tony Proudfoot, and Pete Frates (founder of the ALS Ice Bucket Challenge). Speech-generating systems may be dedicated devices developed solely for AAC, or non-dedicated devices such as computers running additional software to allow them to function as AAC devices. == History == SGDs have their roots in early electronic communication aids. The first such aid was a sip-and-puff typewriter controller named the patient-operated selector mechanism (Naman) prototyped by Reg Maling in the United Kingdom in 1960. POSSUM scanned through a set of symbols on an illuminated display. Researchers at Delft University in the Netherlands created the lightspot-operated typewriter (LOT) in 1970, which made use of small movements of the head to point a small spot of light at a matrix of characters, each equipped with a photoelectric cell. Although it was commercially unsuccessful, the LOT was well received by its users. In 1966, Barry Romich, a freshman engineering student at Case Western Reserve University, and Ed Prentke, an engineer at Highland View Hospital in Cleveland, Ohio, formed a partnership, creating the Prentke Romich Company. In 1969, the company produced its first communication device, a typing system based on a discarded Teletype machine. In 1979, Mark Dahmke developed software for a vocal communication aid program using the Computalker CT-1 analog speech synthesizer with a microcomputer. The software utilized phonemes to generate speech, assisting individuals with communication impairments in constructing words and sentences. Dahmke's work contributed to the advancement of assistive technology for people with disabilities. Notably, he designed the "Vocabulary Management System" for Bill Rush, a student with cerebral palsy. This early speech synthesis technology facilitated improved communication for Rush and was featured in a 1980 issue of LIFE Magazine. Dahmke's contributions have influenced the development of augmentative and alternative communication (AAC) technologies. During the 1970s and early 1980s, several other companies emerged that have since become prominent manufacturers of SGDs. Toby Churchill founded Toby Churchill Ltd in 1973, after losing his speech following encephalitis. In the US, Dynavox (then known as Sentient Systems Technology) grew out of a student project at Carnegie-Mellon University, created in 1982 to help a young woman with cerebral palsy to communicate. Beginning in the 1980s, improvements in technology led to a greatly increased number, variety, and performance of commercially available communication devices, and a reduction in their size and price. Alternative methods of access such as Target Scanning (also known as eye pointing) calibrate the movement of a user's eyes to direct an SGD to produce the desired speech. Scanning, in which alternatives are presented to the user sequentially, became available on communication devices. Speech output possibilities included both digitized and synthesized speech. Rapid progress in hardware and software development continued, including projects funded by the European Community. The first commercially available dynamic screen speech-generating devices were developed in the 1990s. Software was developed that allowed the computer-based production of communication boards. High-tech devices have continued to become smaller and lighter, while increasing accessibility and capability; communication devices can be accessed using eye-tracking systems, perform as a computer for word-processing and Internet use, and as an environmental control device for independent access to other equipment such as TV, radio and telephones. Stephen Hawking came to be associated with the unique voice of his particular synthesis equipment. Hawking was unable to speak due to a combination of disabilities caused by ALS, and an emergency tracheotomy. In the past 20 or so years SGD have gained popularity amongst young children with speech deficiencies, such as autism, Down syndrome, and predicted brain damage due to surgery. Starting in the early 2000s, specialists saw the benefit of using SGDs not only for adults but for children, as well. Neuro-linguists found that SGDs were just as effective in helping children who were at risk for temporary language deficits after undergoing brain surgery as it is for patients with ALS. In particular, digitized SGDs have been used as communication aids for pediatric patients during the recovery process. == Access methods == There are many methods of accessing messages on devices: directly, indirectly, and with specialized access devices. Direct access methods involve physical contact with the system, by using a keyboard or a touch screen. Users accessing SGDs indirectly and through specialized devices must manipulate an object in order to access the system, such as maneuvering a joystick, head mouse, optical head pointer, light pointer, infrared pointer, or switch access scanner. The specific access method will depend on the skills and abilities of the user. With direct selection a body part, pointer, adapted mouse, joystick, or eye tracking could be used, whereas switch access scanning is often used for indirect selection. Unlike direct selection (e.g., typing on a keyboard, touching a screen), users of Target Scanning can only make selections when the scanning indicator (or cursor) of the electronic device is on the desired choice. Those who are unable to point typically calibrate their eyes to use eye gaze as a way to point and blocking as a way to select desired words and phrases. The speed and pattern of scanning, as well as the way items are selected, are individualized to the physical, visual and cognitive capabilities of the user. == Message construction == Augmentative and alternative communication is typically much slower than speech, with users generally producing 8–10 words per minute. Rate enhancement strategies can increase the user's rate of output to around 12–15 words per minute, and as a result enhance the efficiency of communication. In any given SGD there may be a large number of vocal expressions that facilitate efficient and effective communication, including greetings, expressing desires, and asking questions. Some SGDs have multiple pages of symbols to accommodate a large number of vocal expressions, and thus only a portion of the symbols available are visible at any one time, with the communicator navigating the various pages. Speech-generating devices generally display a set of selections either using a dynamically changing screen, or a fixed display. There are two main options for increasing the rate of communication for an SGD: encoding, and prediction. Encoding permits a user to produce a word, sentence or phrase using only on

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  • Eden: It's an Endless World!

    Eden: It's an Endless World!

    Eden: It's an Endless World!, also known simply as Eden (stylized in all caps), is a Japanese science fiction manga series written and illustrated by Hiroki Endo. It was serialized in Kodansha's seinen manga magazine Monthly Afternoon from September 1997 to June 2008, with its chapters collected in 18 tankōbon volumes. == Premise == The story is set in the near future, following the "closure virus" pandemic has killed 15 percent of the world's population, crippled or disfigured many more, with catastrophic effect on global politics. Its themes and many character names are taken from Gnostic mythology. == Plot == The series begins with a long introduction, with the characters Ennoia and Hannah living a peaceful life on a remote and isolated island called Eden, with researcher Lane Morris, who is their guardian and a victim of the pandemic. The events that led to this situation are revealed in flashbacks, leading up to the return of Ennoia's father, along with the forces of the Propater Federation. Following this, the story moves forwards twenty years, and focuses on Ennoia's son, Elijah, the main character, and his own conflict with the powerful and monopolistic Propater federation to save his sister, Mana Ballard, kidnapped by Propater when he was very young. She is being held to threaten Ennoia Ballard, father of the two characters, who has become a powerful drug lord in South America, feared and despised by many, including, to an extent, his own family. During a terrorist attack, Elijah, aged 15, is separated from his mother and his sister is kidnapped, along with his mother Hannah and now has to handle things on his own. Eden is about his coming-of-age as a man and trying to survive both bodily and morally in world that is too complex for mere "black and white". He encounters many other characters, both allies and enemies, all sharing the same struggle to survive in a post-apocalyptic dystopian world. Many stories are included of the people Elijah meets, telling their past or following life, sometimes volumes later, furthering understanding of the characters and giving increased depth to the world of the book as a whole. Later in the series, the story once again moves forwards in time, jumping four more years ahead. The Closure Virus, the cause of the original pandemic, mutates, this time assimilating non-organic matter as well as organic, known as "colloid" (or "Disclosure Virus"). The story rejoins Elijah, now 19 years old, as well as many other old characters, and some new, as the world begins to deal with this new threat that is swallowing many cities in the world, leaving lakes and craters, and many people. It is later discovered that the several colloids in the world, are linked with a net of underground auto-built "cables," and that the colloid itself, stores all the memories of the people it swallows. == Characters == Elijah Ballard (エリヤ・バラード, Eriya Barādo) Elijah is introduced while on the run from Propater. He becomes involved in his father's criminal activities, and undergoes a coming of age into adulthood. Ennoia Ballard (エンノイア・バラード, Ennoia Barādo) Elijah's father. Hannah Mayall (ハナ・メイオール, Hana Meiōru) Elijah's mother. Mana Ballard (マナ・バラード, Mana Barādo) Elijah's sister, who remains in Propater hands whilst her mother is rescued. Elijah's fight to free her is a focus of the later parts of the story. Nazarbaiev Khan (ナザルバイエフ・カーン, Nazarubaiefu Kān) Colonel Khan is an old soldier from Azerbaijan. He leads the Nomad group (including Kenji and Sophia) fleeing Propater at the start of the series. Khan became Kenji's mentor after killing his brother, and the two share a slightly strained, but at the same time, trusting, relationship. Sophia Theódores (ソフィア・テオドレス, Sofia Teodoresu) A powerful Greek computer hacker, and full-body cyborg. Maya (マーヤ, Māya) A nearly godlike AI, which seems to roughly correspond to the savior of Gnostic mythology. Kenji Asai (ケンジ・アサイ) The brother of a low-level yakuza boss. Helena Montoya (ヘレナ・モントーヤ, Herena Montōya) A prostitute now working in a brothel. Has a complex relationship with Elijah and acts as a surrogate big sister. == Media == === Manga === Eden: It's an Endless World! was written and illustrated by Hiroki Endo. The series ran in Kodansha's Monthly Afternoon magazine from September 25, 1997, to June 25, 2008. Kodansha collected its chapters into 18 tankōbon volumes, released from April 21, 1998, to July 23, 2008. In July 2005, Dark Horse Comics announced in San Diego Comic-Con that it has licensed Eden for North American distribution, with publication to begin in November of that year. As of March 2014, 14 volumes were released in total. ==== Volumes ==== == Reception == Eden was named Wizard magazine's best manga of 2007. In his review of another work by Hiroki Endo titled Hiroki Endo's Tanpenshu, David F. Smith of Newtype USA has called Eden one of the best manga American money can buy.

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  • Conference on Artificial General Intelligence

    Conference on Artificial General Intelligence

    The Conference on Artificial General Intelligence (AGI) is a meeting of researchers in the field of artificial general intelligence (AGI) organized by the AGI Society steered by Marcus Hutter and Ben Goertzel. It has been held annually since 2008. The conference was initiated by the 2006 Bethesda Artificial General Intelligence Workshop and has since been hosted at various international venues. == Locations and history == AGI-2026 San Francisco State University, California, USA AGI-2025 Reykjavík University, Reykjavík, Iceland AGI-2024 University of Washington, Seattle, Washington, USA AGI-2023 KTH Royal Institute of Technology, Stockholm, Sweden AGI-2022 The Crocodile, Seattle, Washington, USA AGI-2021 Computer History Museum, Mountain View, California, USA AGI-2020 Virtual Conference AGI-2019 Sheraton Shenzhen Futian, Shenzhen, China AGI-2018 Czech Technical University, Prague, Czech Republic AGI-2017 ibis Melbourne, Melbourne, Australia AGI-2016 The New School, New York, New York, USA AGI-2015 Berlin-Brandenburg Academy of Sciences and Humanities, Berlin, Germany AGI-2014 Université Laval, Quebec City, Canada (sponsored by the Cognitive Science Society and the AAAI) AGI-2013 Peking University, Beijing, China (sponsored by the Cognitive Science Society and the AAAI) AGI-2012 University of Oxford, Oxford, United Kingdom (sponsored by the Future of Humanity Institute and Ray Kurzweil) AGI-2011 Google Headquarters, Mountain View, California, USA (sponsored by Google, AAAI, and Ray Kurzweil) AGI-2010 University of Lugano, Lugano, Switzerland (In Memoriam Ray Solomonoff and sponsored by AAAI and Ray Kurzweil) AGI-2009 Crowne Plaza Crystal City, Arlington, Virginia, USA (sponsored by AAAI and Ray Kurzweil) AGI-2008 University of Memphis, Tennessee, USA (sponsored by AAAI) == Notable speakers == The conference has attracted many speakers over the years including Turing Award winners Yoshua Bengio and Richard S. Sutton as well as Ben Goertzel, Marcus Hutter, Jürgen Schmidhuber, Gary Marcus, John E. Laird, Peter Norvig, Joscha Bach, François Chollet, John L. Pollock, Bill Hibbard, Hugo de Garis, Stan Franklin, Steve Omohundro, Randal A. Koene, Ernst Dickmanns, Margaret Boden, David Hanson, Roman Yampolskly, Selmer Bringsjord, Kristinn R. Thórisson and Nick Bostrom.

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  • Autonomic networking

    Autonomic networking

    Autonomic networking follows the concept of Autonomic Computing, an initiative started by IBM in 2001. Its ultimate aim is to create self-managing networks to overcome the rapidly growing complexity of the Internet and other networks and to enable their further growth, far beyond the size of today. == Increasing size and complexity == The ever-growing management complexity of the Internet caused by its rapid growth is seen by some experts as a major problem that limits its usability in the future. What's more, increasingly popular smartphones, PDAs, networked audio and video equipment, and game consoles need to be interconnected. Pervasive Computing not only adds features, but also burdens existing networking infrastructure with more and more tasks that sooner or later will not be manageable by human intervention alone. Another important aspect is the price of manually controlling huge numbers of vitally important devices of current network infrastructures. == Autonomic nervous system == The autonomic nervous system (ANS) is the part of complex biological nervous systems that is not consciously controlled. It regulates bodily functions and the activity of specific organs. As proposed by IBM, future communication systems might be designed in a similar way to the ANS. == Components of autonomic networking == As autonomics conceptually derives from biological entities such as the human autonomic nervous system, each of the areas can be metaphorically related to functional and structural aspects of a living being. In the human body, the autonomic system facilitates and regulates a variety of functions including respiration, blood pressure and circulation, and emotive response. The autonomic nervous system is the interconnecting fabric that supports feedback loops between internal states and various sources by which internal and external conditions are monitored. === Autognostics === Autognostics includes a range of self-discovery, awareness, and analysis capabilities that provide the autonomic system with a view on high-level state. In metaphor, this represents the perceptual sub-systems that gather, analyze, and report on internal and external states and conditions – for example, this might be viewed as the eyes, visual cortex and perceptual organs of the system. Autognostics, or literally "self-knowledge", provides the autonomic system with a basis for response and validation. A rich autognostic capability may include many different "perceptual senses". For example, the human body gathers information via the usual five senses, the so-called sixth sense of proprioception (sense of body position and orientation), and through emotive states that represent the gross wellness of the body. As conditions and states change, they are detected by the sensory monitors and provide the basis for adaptation of related systems. Implicit in such a system are imbedded models of both internal and external environments such that relative value can be assigned to any perceived state - perceived physical threat (e.g. a snake) can result in rapid shallow breathing related to fight-flight response, a phylogenetically effective model of interaction with recognizable threats. In the case of autonomic networking, the state of the network may be defined by inputs from: individual network elements such as switches and network interfaces including specification and configuration historical records and current state traffic flows end-hosts application performance data logical diagrams and design specifications Most of these sources represent relatively raw and unprocessed views that have limited relevance. Post-processing and various forms of analysis must be applied to generate meaningful measurements and assessments against which current state can be derived. The autognostic system interoperates with: configuration management - to control network elements and interfaces policy management - to define performance objectives and constraints autodefense - to identify attacks and accommodate the impact of defensive responses === Configuration management === Configuration management is responsible for the interaction with network elements and interfaces. It includes an accounting capability with historical perspective that provides for the tracking of configurations over time, with respect to various circumstances. In the biological metaphor, these are the hands and, to some degree, the memory of the autonomic system. On a network, remediation and provisioning are applied via configuration setting of specific devices. Implementation affecting access and selective performance with respect to role and relationship are also applied. Almost all the "actions" that are currently taken by human engineers fall under this area. With only a few exceptions, interfaces are set by hand, or by extension of the hand, through automated scripts. Implicit in the configuration process is the maintenance of a dynamic population of devices under management, a historical record of changes and the directives which invoked change. Typical to many accounting functions, configuration management should be capable of operating on devices and then rolling back changes to recover previous configurations. Where change may lead to unrecoverable states, the sub-system should be able to qualify the consequences of changes prior to issuing them. As directives for change must originate from other sub-systems, the shared language for such directives must be abstracted from the details of the devices involved. The configuration management sub-system must be able to translate unambiguously between directives and hard actions or to be able to signal the need for further detail on a directive. An inferential capacity may be appropriate to support sufficient flexibility (i.e. configuration never takes place because there is no unique one-to-one mapping between directive and configuration settings). Where standards are not sufficient, a learning capacity may also be required to acquire new knowledge of devices and their configuration. Configuration management interoperates with all of the other sub-systems including: autognostics - receives direction for and validation of changes policy management - implements policy models through mapping to underlying resources security - applies access and authorization constraints for particular policy targets autodefense - receives direction for changes === Policy management === Policy management includes policy specification, deployment, reasoning over policies, updating and maintaining policies, and enforcement. Policy-based management is required for: constraining different kinds of behavior including security, privacy, resource access, and collaboration configuration management describing business processes and defining performance defining role and relationship, and establishing trust and reputation It provides the models of environment and behavior that represent effective interaction according to specific goals. In the human nervous system metaphor, these models are implicit in the evolutionary "design" of biological entities and specific to the goals of survival and procreation. Definition of what constitutes a policy is necessary to consider what is involved in managing it. A relatively flexible and abstract framework of values, relationships, roles, interactions, resources, and other components of the network environment is required. This sub-system extends far beyond the physical network to the applications in use and the processes and end-users that employ the network to achieve specific goals. It must express the relative values of various resources, outcomes, and processes and include a basis for assessing states and conditions. Unless embodied in some system outside the autonomic network or implicit to the specific policy implementation, the framework must also accommodate the definition of process, objectives and goals. Business process definitions and descriptions are then an integral part of the policy implementation. Further, as policy management represents the ultimate basis for the operation of the autonomic system, it must be able to report on its operation with respect to the details of its implementation. The policy management sub-system interoperates (at least) indirectly with all other sub-systems but primarily interacts with: autognostics - providing the definition of performance and accepting reports on conditions configuration management - providing constraints on device configuration security - providing definitions of roles, access and permissions === Autodefense === Autodefense represents a dynamic and adaptive mechanism that responds to malicious and intentional attacks on the network infrastructure, or use of the network infrastructure to attack IT resources. As defensive measures tend to impede the operation of IT, it is optimally capable of balancing performance objectives with typically over-riding threat management actions. In the

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  • 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.

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  • Legal Knowledge Interchange Format

    Legal Knowledge Interchange Format

    The Legal Knowledge Interchange Format (LKIF) was developed in the European ESTRELLA project and was designed with the goal of becoming a standard for representing and interchanging policy, legislation and cases, including their justificatory arguments, in the legal domain. LKIF builds on and uses the Web Ontology Language (OWL) for representing concepts and includes a reusable basic ontology of legal concepts. The core of LKIF consists of a combination of OWL-DL and SWRL. LKIF was designed with two main roles in mind: the translation of legal knowledge bases written in different representation formats and formalisms and to be a knowledge representation formalism which could be part of larger architectures for developing legal knowledge systems.

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