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  • Mentimeter

    Mentimeter

    Mentimeter (or Menti for short) is a Swedish company based in Stockholm that develops and maintains an eponymous app used to create presentations with real-time feedback. == Foundation and background == Based in Stockholm, Sweden, the Mentimeter app was started by Swedish entrepreneur Johnny Warström and Niklas Ingvar as a response to unproductive meetings. The initial start-up budget was $500,000 raised by a group of prominent investors, including Per Appelgren in 2014, following the market's tendency to invest in Scandinavia. The app also focuses on online collaboration for the education sector, allowing students or public members to answer questions anonymously. The app enables users to share knowledge and real-time feedback on mobile devices with presentations, polls or brainstorming sessions in classes, meetings, gatherings, conferences and other group activities. == Achievements == By 2021, Mentimeter had over 270 million users and was one of Sweden's fastest-growing startups. The company also ranked #10 on 20 Fastest Growing 500 Startups Batch 16 Companies. It was ranked Stockholm's fastest growing company of the 2018 edition of the DI Gasell Award. Mentimeter has a freemium business model.

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  • Niceaunties

    Niceaunties

    Niceaunties is the pseudonym of a Singapore-based artist and designer whose work incorporates generative artificial intelligence, video, and digital installation. Her practice centers around the figure of the "auntie", a common term for older women in Southeast Asian contexts, and explores themes such as aging, care, domesticity, and gender roles. Her work has been featured in exhibitions and media platforms including TED, Christie's Art + Tech, Expanded.Art, and publications such as The Guardian, The Straits Times. == Early life and career == Niceaunties was born in 1981 in Singapore. She attributes her inspiration for "auntie culture" to the matriarchal environment and older women of her household, including her grandmother, while growing up. She is also an architectural designer with Spark Architect. The Niceaunties project began in 2023 after she encountered AI-generated images in her work as an architect. It draws inspiration from women in the artist's family and broader Southeast Asian cultural dynamics. Her work often features AI-generated visuals created with tools such as DALL-E, Krea, RunwayML, and SORA. Her imagery and narratives center on the fictional "Auntieverse", which features older women in imagined settings involving community, ecology, and labor. Her notable works include 'Auntlantis', a five-part video series imagining older women engaged in ocean clean-up and collective ritual, and 'Goddess,' a video created with Sora, featuring a character who gradually forgets her divine identity through years of domestic labor. == Exhibitions == 2024 – Expanded.Art, Berlin – Auntiedote solo exhibition 2024 – TED (conference), Vancouver – Speaker and screening 2024 – Victoria and Albert Museum, London – Digital Art Weekend 2024 – Louisiana Museum of Modern Art, Denmark – Ocean exhibition 2025 – Christie's Augmented Intelligence Auction, New York == Reception == In 2024, Niceaunties gave a TED Talk titled The Weird and Wonderful Art of Niceaunties. Journalist Rebecca Ratcliffe, writing for The Guardian, described her work as combining AI with "the surreal and the political," noting her focus on older women as central characters. Her work has also received criticism for being reliant on generative AI, which many feel exploits and steals from traditional artists.

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  • Murderbot (TV series)

    Murderbot (TV series)

    Murderbot is an American science fiction action comedy television series created by Paul Weitz and Chris Weitz for Apple TV+. It is based on All Systems Red, the first book of the series The Murderbot Diaries by Martha Wells, who serves as a consulting producer. The series stars Alexander Skarsgård as the titular character. The first season premiered on May 16, 2025 and received positive reviews. In July 2025, the series was renewed for a second season. == Premise == A media-obsessed private security construct (manufactured from cloned human tissue and mechanical parts) calling itself Murderbot must hide its newly acquired autonomy while completing dangerous assignments and being simultaneously drawn to humans, and appalled by their weakness. == Cast and characters == === Main === Alexander Skarsgård as Murderbot Noma Dumezweni as Ayda Mensah, a terraforming specialist, the President of Preservation Alliance and the leader of the science team protected by Murderbot David Dastmalchian as Gurathin, a tech expert and augmented human Sabrina Wu as Pin-Lee, a scientist and legal counsel to the team Akshay Khanna as Ratthi, a wormhole expert Tamara Podemski as Bharadwaj, a geochemist Tattiawna Jones as Arada, a biologist === Recurring === Cast of show-within-a-show The Rise and Fall of Sanctuary Moon John Cho as Eknie Jef Chem (playing Captain Hossein) Jack McBrayer as Breiller MocJac (playing Navigation Officer Hordööp-Sklanch) Clark Gregg as Arletty (playing Lieutenant Kullervv) DeWanda Wise as Pordron Bretney III Roche (playing NawBot 337 Alt 66) === Guest === Anna Konkle as Leebeebee, a member of another survey team on the planet. The character does not appear in the novella. Amanda Brugel as GrayCris Blue Leader David Reale as GrayCris Yellow == Episodes == == Production == The book series was optioned in the late 2010s, and its film adaptation was considered. In 2021, book series author Martha Wells said that a potential TV series adaptation was in development and that she had read the script and was "really excited about it". The series was green lit by Apple TV+ in 2022, with Wells serving as a consulting producer. The production design team, led by Sue Chan, started work in the autumn. Tommy Arnold, the Murderbot Diaries special edition illustrator, created the concept art for the show. After the casting was delayed by the 2023 SAG-AFTRA strike, in December 2023 it was announced that Alexander Skarsgård would produce and star in the series. He developed the character and the world of Murderbot with the showrunners. In February 2024, David Dastmalchian and Noma Dumezweni joined the cast. In March, Sabrina Wu, Tattiawna Jones, Akshay Khanna, and Tamara Podemski joined the cast. On July 10, 2025, the series was renewed for a second season. Showrunners Chris and Paul Weitz suggested the second season would combine the next three books of the series and will have longer episodes. === Filming === Principal photography for the first season took place from March–June 2024, in Toronto and parts of Ontario, Canada. Most of the filming was done on location, with the Sanctuary Moon scenes filmed on a virtual production stage. Principal photography for the second season began in mid-2026, in Madrid, Spain. It is planned to last 71 days, with Martha Wells also visiting the set. == Release == The first two episodes of Murderbot premiered on Apple TV+ on May 16, 2025, with subsequent episodes released weekly. The first season consists of ten episodes. == Reception == Even before the release of the show, numerous media sources had commented on the titular character as being coded as autistic and agender. On the review aggregator website Rotten Tomatoes, Murderbot has an approval rating of 96% with an average score of 7.5/10, based on 76 critics' reviews. The website's critical consensus states, "Alexander Skarsgård's superbly dry wit brings a lot of heart to Murderbot, making for a refreshingly jaunty sci-fi saga about finally coming out of one's shell". Metacritic, which uses a weighted average, assigned a score of 70 out of 100, based on 28 critics, indicating "generally favorable" reviews. Some reviewers have criticized Murderbot's changes to Wells' original books. Angela Watercutter of Wired noted that the series has significant tonal differences from the books and noted the show's changes to characters, particularly Murderbot and Dr. Mensah, and Wells' social commentary. === Accolades === Murderbot was a finalist for the 2025 Dragon Award for Best Science Fiction or Fantasy TV Series. Tommy Arnold won the 2025 Concept Art Association Award in the category of Live-Action Series Character Art for his work on Murderbot. Alexander Skarsgård was nominated for a Critics' Choice Award for Best Actor in a Comedy Series. Carrie Grace and Laura Jean Shannon were nominated for a Costume Designers Guild Award in the category of Excellence in Sci-Fi/Fantasy Television for their work on FreeCommerce. Amanda Jones was nominated for a Composers & Lyricists Award for Outstanding Original Title Sequence for a Television Production.

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  • Privacy Lost

    Privacy Lost

    Privacy Lost is a 2023 short science fiction film directed by Peter Stoel and Robert Berger. It follows a family using augmented reality (AR) and artificial intelligence (AI) devices capable of reading emotional states, raising questions about privacy and manipulation. == Premise == Privacy Lost follows a family using AR glasses that capture and interpret emotions in real time. As the parents argue in a restaurant, their emotional states and even hidden feelings become visible through these glasses. An AI-driven waiter adapts its appearance for each family member, employing emotional data to influence their decisions. == Cast == Brian Kant as Waiter Michael Krass as Husband Estelle Levinson as Waitress Thor van der Linden as Scotty Carlijn van Ramshorst as Wife == Production == Filming took place at HeadQ Productions, a virtual studio located in Amsterdam. The creators sought to depict a near-future scenario in which real-time emotion analysis becomes part of daily interactions. The film was screened at the Augmented World Expo (AWE), where it was noted for its thematic focus on AI-driven manipulation and emotional tracking. The depiction of AR glasses and AI characters integrates modern visual effects to show how devices might analyze emotional responses in real time. It also depicts how AI-driven interactions could influence consumer decisions, pointing to concerns over potential misuse. == Themes == Privacy Lost focuses on the intersection of advanced AI capabilities and AR environments, showing how real-time emotional analysis can be leveraged for targeted persuasion. The film aims to highlight the social and ethical implications of emerging AR and AI technologies, underlining how establishing clear regulatory frameworks for them is necessary to protect individual privacy, govern the storage of emotion-based data, and prevent manipulative practices. Critics describe the film’s theme as dystopian and note that such a reality is unlikely to occur in the near future. However, despite the exaggerated scenario, the film emphasizes the importance of a responsible approach by developers toward emerging technologies.

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  • Generative art

    Generative art

    Generative art is post-conceptual art that has been created (in whole or in part) with the use of an autonomous system. An autonomous system in this context is generally one that is non-human and can independently determine features of an artwork that would otherwise require decisions made directly by the artist. In some cases the human creator may claim that the generative system represents their own artistic idea, and in others that the system takes on the role of the creator. "Generative art" often refers to algorithmic art (algorithmically determined computer generated artwork) and synthetic media (general term for any algorithmically generated media), but artists can also make generative art using systems of chemistry, biology, mechanics and robotics, smart materials, manual randomization, mathematics, data mapping, symmetry, and tiling. Generative algorithms, algorithms programmed to produce artistic works through predefined rules, stochastic methods, or procedural logic, often yielding dynamic, unique, and contextually adaptable outputs—are central to many of these practices. == History == The use of the word "generative" in the discussion of art has developed over time. The use of "Artificial DNA" defines a generative approach to art focused on the construction of a system able to generate unpredictable events, all with a recognizable common character. The use of autonomous systems, required by some contemporary definitions, focuses a generative approach where the controls are strongly reduced. This approach is also named "emergent". Margaret Boden and Ernest Edmonds have noted the use of the term "generative art" in the broad context of automated computer graphics in the 1960s, beginning with artwork exhibited by Georg Nees and Frieder Nake in 1965: A. Michael Noll did his initial computer art, combining randomness with order, in 1962, and exhibited it along with works by Bell Julesz in 1965. The terms "generative art" and "computer art" have been used in tandem, and more or less interchangeably, since the very earliest days. The first such exhibition showed the work of Nees in February 1965, which some claim was titled "Generative Computergrafik". While Nees does not himself remember, this was the title of his doctoral thesis published a few years later. The correct title of the first exhibition and catalog was "computer-grafik". "Generative art" and related terms was in common use by several other early computer artists around this time, including Manfred Mohr and Ken Knowlton. Vera Molnár (born 1924) is a French media artist of Hungarian origin. Molnar is widely considered to be a pioneer of generative art, and is also one of the first women to use computers in her art practice. The term "Generative Art" with the meaning of dynamic artwork-systems able to generate multiple artwork-events was clearly used the first time for the "Generative Art" conference in Milan in 1998. The term has also been used to describe geometric abstract art where simple elements are repeated, transformed, or varied to generate more complex forms. Thus defined, generative art was practiced by the Argentinian artists Eduardo Mac Entyre and Miguel Ángel Vidal in the late 1960s. In 1972 the Romanian-born Paul Neagu created the Generative Art Group in Britain. It was populated exclusively by Neagu using aliases such as "Hunsy Belmood" and "Edward Larsocchi". In 1972 Neagu gave a lecture titled 'Generative Art Forms' at the Queen's University, Belfast Festival. In 1970 the School of the Art Institute of Chicago created a department called Generative Systems. As described by Sonia Landy Sheridan the focus was on art practices using the then new technologies for the capture, inter-machine transfer, printing and transmission of images, as well as the exploration of the aspect of time in the transformation of image information. Also noteworthy is John Dunn, first a student and then a collaborator of Sheridan. In 1988 Clauser identified the aspect of systemic autonomy as a critical element in generative art: It should be evident from the above description of the evolution of generative art that process (or structuring) and change (or transformation) are among its most definitive features, and that these features and the very term 'generative' imply dynamic development and motion. (the result) is not a creation by the artist but rather the product of the generative process - a self-precipitating structure. In 1989 Celestino Soddu defined the Generative Design approach to Architecture and Town Design in his book Citta' Aleatorie. In 1989 Franke referred to "generative mathematics" as "the study of mathematical operations suitable for generating artistic images." From the mid-1990s Brian Eno popularized the terms generative music and generative systems, making a connection with earlier experimental music by Terry Riley, Steve Reich and Philip Glass. From the end of the 20th century, communities of generative artists, designers, musicians and theoreticians began to meet, forming cross-disciplinary perspectives. The first meeting about generative Art was in 1998, at the inaugural International Generative Art conference at Politecnico di Milano University, Italy. In Australia, the Iterate conference on generative systems in the electronic arts followed in 1999. On-line discussion has centered around the eu-gene mailing list, which began late 1999, and has hosted much of the debate which has defined the field. These activities have more recently been joined by the Generator.x conference in Berlin starting in 2005. In 2012 the new journal GASATHJ, Generative Art Science and Technology Hard Journal was founded by Celestino Soddu and Enrica Colabella jointing several generative artists and scientists in the editorial board. Some have argued that as a result of this engagement across disciplinary boundaries, the community has converged on a shared meaning of the term. As Boden and Edmonds put it in 2011: Today, the term "Generative Art" is still current within the relevant artistic community. Since 1998 a series of conferences have been held in Milan with that title (Generativeart.com), and Brian Eno has been influential in promoting and using generative art methods (Eno, 1996). Both in music and in visual art, the use of the term has now converged on work that has been produced by the activation of a set of rules and where the artist lets a computer system take over at least some of the decision-making (although, of course, the artist determines the rules). In the call of the Generative Art conferences in Milan (annually starting from 1998), the definition of Generative Art by Celestino Soddu: Generative Art is the idea realized as genetic code of artificial events, as construction of dynamic complex systems able to generate endless variations. Each Generative Project is a concept-software that works producing unique and non-repeatable events, like music or 3D Objects, as possible and manifold expressions of the generating idea strongly recognizable as a vision belonging to an artist / designer / musician / architect /mathematician. Discussion on the eu-gene mailing list was framed by the following definition by Adrian Ward from 1999: Generative art is a term given to work which stems from concentrating on the processes involved in producing an artwork, usually (although not strictly) automated by the use of a machine or computer, or by using mathematic or pragmatic instructions to define the rules by which such artworks are executed. A similar definition is provided by Philip Galanter: Generative art refers to any art practice where the artist creates a process, such as a set of natural language rules, a computer program, a machine, or other procedural invention, which is then set into motion with some degree of autonomy contributing to or resulting in a completed work of art. Around the 2020s, generative AI models learned to imitate the distinct style of particular authors. For example, a generative image model such as Stable Diffusion is able to model the stylistic characteristics of an artist like Pablo Picasso (including his particular brush strokes, use of colour, perspective, and so on), and a user can engineer a prompt such as "an astronaut riding a horse, by Picasso" to cause the model to generate a novel image applying the artist's style to an arbitrary subject. Generative image models have received significant backlash from artists who object to their style being imitated without their permission, arguing that this harms their ability to profit from their own work. The emergence of text-to-image generative AI systems has expanded debates over authorship, copyright, and artistic labor. The main issues in these debates include the eligibility of AI-generated outputs for copyright protection and the legal and ethical questions of using existing copyrighted works as training data for generative AI systems. == Types == === Music === Johann Kirnberger's Mu

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  • Dominic Harris

    Dominic Harris

    Dominic Harris (born 16 November 1976) is a British artist known for integrating modern technology and classical design in his interactive artworks. == Background == Dominic Harris was born in London on 16 November 1976, and grew up in London, Brussels, and Michigan before returning to London in 1995. Harris attended the Cranbrook Kingswood Upper School, and then trained as an architect at the Bartlett School of Architecture, and has been ARB registered since 2011. Harris designs and fabricates his artworks at Dominic Harris Studio, a multi-disciplinary practice he founded in 2007. This studio consists of 25 people with diverse backgrounds including architecture, product design, electronics, programming, graphic design, and workshop skills. Harris uses the resources of his studio for the ongoing development, prototyping and production of his artworks. Harris also oversees the studio's international projects where his fascinations are translated into larger scale projects that span residential, retail, and public art projects. In 2015, Harris was granted permission by the Walt Disney Company to use their Intellectual Property for the purpose of making new interactive artworks. Harris is the only artist to gain permission to use Disney's back catalogue of characters, and led him to creating his interactive versions of "Snow White and the Seven Dwarfs" and "Mickey and Minnie: An Interactive Diptych". Harris is fascinated by the idea of using data streams, algorithms, and computer code to generate dynamic and ever-changing artworks. He sees data as a raw material that can be transformed into visual poetry. Many of his installations and sculptures are interactive, responding to the presence and movement of viewers/participants. This creates an immersive experience where the observer becomes part of the artwork itself. Harris is also the founding partner of a sister studio in London called Cinimod Studio that creates large commissioned installations, interactive events and lighting designs for large brands. == Works == == Exhibitions == The works of Dominic Harris have been exhibited internationally, both through direct and gallery representation. Solo shows: "Feeding Consciousness" at Halcyon Gallery, Mayfair, London, UK – 2023 "US: NOW" at Halcyon Gallery, Mayfair, London, UK – 2020 "Imagine" at Halcyon Gallery, Mayfair, London, UK – 2019 "5 Year Celebration", Priveekollektie Contemporary Art | Design, London, UK – 2016. "Moments of Reflection" at PHOS ART + DESIGN, Mayfair, London, UK – 2015 Recent exhibitions include: In Plain Sight, 2024 Halcyon Gallery Victoria & Albert Museum Dublin Science Museum Design Miami / Basel Design Miami Art Miami Art 14, London PAD Paris PAD London Art Geneva == Gallery Representation == 2010 to 2019: Dominic Harris was represented by Priveekollektie Contemporary Art | Design, a Dutch gallery based in Heusden, the Netherlands, and with a regular presence on the international art and design circuits. 2015: Dominic Harris was shown with PHOS ART + DESIGN Gallery, in Mayfair, London, UK. 2019 – ongoing: Dominic Harris is exclusively represented by the Halcyon Gallery, an established international gallery based in Mayfair, London. == Collections == The majority of Harris's work has been bought by private collectors. Since 2012 Harris's work is also being acquired by several large institutional collections, including the Borusan Contemporary Art Collection in Istanbul. Harris's artworks include some of the biggest and most respected international art collectors and are also displayed in public spaces. == Books == Dominic Harris: Feeding Consciousness. Halcyon Gallery, 2023. Imagine: Dominic Harris (exhibition catalogue). Halcyon Gallery, 2019. A Touch Of Code: Documents the "Beacon" art installation and "Flutter" artwork (ISBN 978-3899553314) Dominic Harris, Artworks, Edition Eight. (ISBN 978-0957306325) Digital Real: Kunst & Nachhaltigkeit Vol 8.

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  • Trevor Paglen

    Trevor Paglen

    Trevor Paglen (born 1974) is an American artist, geographer, and author whose work covers mass surveillance and data collection. In 2016, Paglen won the Deutsche Börse Photography Foundation Prize and he has also won The Cultural Award from the German Society for Photography. In 2017, he was a recipient of a MacArthur Fellowship. On March 17, 2026, Paglen was awarded the 2026 LG Guggenheim Award (a collaboration between LG and Guggenheim New York). == Early life and education == Paglen earned a B.A. degree in religious studies in 1998 from the University of California at Berkeley, a M.F.A. degree in 2002 from the School of the Art Institute of Chicago, and a Ph.D. in Geography in 2008 from the University of California at Berkeley. While at UC Berkeley, Paglen lived in the Berkeley Student Cooperative, residing in Chateau, Fenwick, and Rochdale co-ops. == Work == Sean O'Hagan, writing in The Guardian in 2015, said that Paglen, whose "ongoing grand project [is] the murky world of global state surveillance and the ethics of drone warfare", "is one of the most conceptually adventurous political artists working today, and has collaborated with scientists and human rights activists on his always ambitious multimedia projects." His visual work such as his "Limit Telephotography" and "The Other Night Sky" series have received widespread attention for both his technical innovations and for his conceptual project that involves simultaneously making and negating documentary-style truth-claims. Paglen’s work relies on contemporary technology in two meaningful ways. Firstly, the views he photographs would be impossible to shoot without media tech, that includes the cameras, the microscopes, and even helicopters. But interestingly enough, the shots would not be possible if not for the existence of the subject. The contrasts between secrecy and revelation, evidence and abstraction distinguish Paglen's work. With that the artist presents not so much "evidence" as admonitions to awareness. He was an Eyebeam Commissioned Artist in 2007. In 2008 the Berkeley Art Museum devoted a comprehensive solo exhibition to his work. In the next year, Paglen took part in the Istanbul Biennial, and in 2010 he exhibited at the Vienna Secession. Autonomy Cube was a project by Paglen and Jacob Appelbaum that placed relays for the anonymous communication network Tor in traditional art museums. He contributed to the Oscar-winning documentary film Citizenfour (2014), directed by Laura Poitras. Paglen features in the nerd-culture documentary Traceroute (2016). Orbital Reflector was a reflective, mylar sculpture by Paglen intended to be the first "purely artistic" object in space. The temporary satellite, containing an inflatable mylar balloon with reflective surface, launched into space 3 December 2018. A mid-career survey in 2018–2019, Trevor Paglen: Sites Unseen, was a traveling exhibition shown at the Smithsonian American Art Museum in Washington DC and the Museum of Contemporary Art San Diego. In September 2020, Pace Gallery in London held an exhibition of Paglen's work, exploring "the weird, partial ways computers look back at us". His work is included in the permanent collections of the San Francisco Museum of Modern Art, the Columbus Museum of Art, and the Metropolitan Museum. === Experimental Geography === Paglen is credited with coining the term "Experimental Geography" to describe practices coupling experimental cultural production and art-making with ideas from critical human geography about the production of space, materialism, and praxis. The 2009 book Experimental Geography: Radical Approaches to Landscape, Cartography, and Urbanism is largely inspired by Paglen's work. == Publications == Paglen has published a number of books. Torture Taxi (2006) (co-authored with investigative journalist A. C. Thompson) was the first book to comprehensively describe the CIA's extraordinary rendition program. I Could Tell You But Then You Would Have to be Destroyed by Me (2007), is a look at the world of black projects through unit patches and memorabilia created for top-secret programs. Blank Spots on the Map: The Dark Geography of the Pentagon's Secret World (2009) is a broader look at secrecy in the United States. The Last Pictures (2012) is a collection of 100 images to be placed on permanent media and launched into space on EchoStar XVI, as a repository available for future civilizations (alien or human) to find. === Publications by Paglen === I Could Tell You But Then You Would Have to be Destroyed by Me. Brooklyn, NY: Melville House, 2007. ISBN 1-933633-32-8. Blank Spots on the Map: The Dark Geography of the Pentagon's Secret World. New York: Dutton, 2009. ISBN 9781101011492. Invisible: Covert Operations and Classified Landscapes, Photographs by Trevor Paglen. New York: Aperture, 2010. ISBN 9781597111300. With an essay by Rebecca Solnit. The Last Pictures. Oakland, CA: University of California, 2012. ISBN 9780520275003. Trevor Paglen. London: Phaidon, 2018. ISBN 0714873446. With essays by Laren Cornell, Julia Bryan-Wilson, Omar Kholeif. === Publications co-authored === Torture Taxi. Co-authored with A. C. Thompson. Brooklyn, NY: Melville House Publishing, 2006. ISBN 1-933633-09-3. Icon, 2007. ISBN 9781840468304. === Publications with contributions by Paglen === Experimental Geography: Radical Approaches to Landscape, Cartography, and Urbanism. Brooklyn, NY: Melville House, 2009. ISBN 978-0091636586. Edited by Nato Thompson. With essays by Paglen, Thompson, and Jeffrey Kastner. Trevor Paglen and Jacob Appelbaum – Autonomy Cube. Revolver, 2016. ISBN 978-3957633026. Essays by Luke Skrebowski and Keller Easterling on Autonomy Cube, a piece of sculpture by Paglen and Jacob Appelbaum. In English and German. == Exhibitions == Bellwether Gallery, New York, November–December 2006 The Other Night Sky, Berkeley Art Museum, 2008 A Compendium of Secrets, Cologne Still Revolution: Suspended in Time, Museum of Contemporary Canadian Art, Toronto, May–June 2009. Group exhibition with Paglen, Barbara Astman, Walead Beshty, Mat Collishaw, Stan Douglas, Idris Khan, Martha Rosler, and Mikhael Subotzky A Hidden Landscape, Aksioma, Ljubljana, Slowenia Geographies of Seeing, Lighthouse, Brighton, England, October–November 2012 The Last Pictures, New York, 2012–13 Trevor Paglen, Altman Siegel gallery, San Francisco, CA, March–May 2015 The Octopus, Frankfurter Kunstverein, Frankfurt am Main, 2015 Autonomy Cube, Edith-Russ-Haus, Oldenburg, Germany, October 2015 – January 2016. Sculpture by Paglen and Jacob Appelbaum. Deutsche Börse Photography Foundation Prize 2016, The Photographers' Gallery, London, April–July 2016. Deutsche Börse Photography Prize shortlist with Paglen, Erik Kessels, Laura El-Tantawy, and Tobias Zielony. Radical Landscapes, di Rosa, Napa, February–April 2016 L’Image volée, Americas II, Bahamas Internet Cable System (BICS-1) and Globenet, Fondazione Prada, Milan (group exhibition), 2016 A Study of Invisible Images, Metro Pictures, New York, September–October 2017 == Awards == 2014: Pioneer Award from the Electronic Frontier Foundation. 2015: The Cultural Award from the German Society for Photography (DGPh) 2015: Academy Award as cameraman and director for the documentary film Citzenfour. 2016: Deutsche Börse Photography Foundation Prize 2017: MacArthur Fellowship, John D. and Catherine T. MacArthur Foundation, Chicago, IL 2018: Nam June Paik Art Center Prize == Films about Paglen == Unseen Skies (2021) == Works ==

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  • Evolving intelligent system

    Evolving intelligent system

    In computer science, an evolving intelligent system is a fuzzy logic system which improves the own performance by evolving rules. The technique is known from machine learning, in which external patterns are learned by an algorithm. Fuzzy logic based machine learning works with neuro-fuzzy systems. Intelligent systems have to be able to evolve, self-develop, and self-learn continuously in order to reflect a dynamically evolving environment. The concept of Evolving Intelligent Systems (EISs) was conceived around the turn of the century with the phrase EIS itself coined for the first time by Angelov and Kasabov in a 2006 IEEE newsletter and expanded in a 2010 text. EISs develop their structure, functionality and internal knowledge representation through autonomous learning from data streams generated by the possibly unknown environment and from the system self-monitoring. EISs consider a gradual development of the underlying (fuzzy or neuro-fuzzy) system structure and differ from evolutionary and genetic algorithms which consider such phenomena as chromosomes crossover, mutation, selection and reproduction, parents and off-springs. The evolutionary fuzzy and neuro systems are sometimes also called "evolving" which leads to some confusion. This was more typical for the first works on this topic in the late 1990s. == Implementations == EISs can be implemented, for example, using neural networks or fuzzy rule-based models. The first neural networks which consider an evolving structure were published in. These were later expanded by N. Kasabov and P. Angelov for the neuro-fuzzy models. P. Angelov introduced the evolving fuzzy rule-based systems (EFSs) as the first mathematical self-learning model that can dynamically evolve its internal structure and is human interpretable and coined the phrase EFS. Contemporarily, the offline incremental approach for learning an EIS, namely, EFuNN, was proposed by N. Kasabov. P. Angelov, D. Filev, N. Kasabov and O. Cordon organised the first IEEE Symposium on EFSs in 2006 (the proceedings of the conference can be found in). EFSs include a formal (and mathematically sound) learning mechanism to extract it from streaming data. One of the earliest and the most widely cited comprehensive survey on EFSs was done in 2008. Later comprehensive surveys on EFS methods with real applications were done in 2011 and 2016 by E. Lughofer. Other works that contributed further to this area in the following years expanded it to evolving participatory learning, evolving grammar, evolving decision trees, evolving human behaviour modelling, self-calibrating (evolving) sensors (eSensors), evolving fuzzy rule-based classifiers, evolving fuzzy controllers, autonomous fault detectors. More recently, the stability of the evolving fuzzy rule-based systems that consist of the structure learning and the fuzzily weighted recursive least square parameter update method has been proven by Rong. Generalized EFS, which allow rules to be arbitrarily rotated in the feature space and thus to improve their data representability, have been proposed in with significant extensions in towards 'smartness' of the rule bases (thus, termed as "Generalized Smart EFS"), allowing more interpretability and reducing curse of dimensionality. The generalized rule structure was also successfully used in the context of evolving neuro-fuzzy systems. Several facets and challenges for achieving more transparent and understandable rule bases in EFS have been discussed by E. Lughofer in. EISs form the theoretical and methodological basis for the Autonomous Learning Machines (ALMA) and autonomous multi-model systems (ALMMo) as well as of the Autonomous Learning Systems. Evolving Fuzzy Rule-based classifiers, in particular, is a very powerful new concept that offers much more than simply incremental or online classifiers – it can cope with new classes being added or existing classes being merged. This is much more than just adapting to new data samples being added or classification surfaces being evolved. Fuzzy rule-based classifiers are the methodological basis of a new approach to deep learning that was until now considered as a form of multi-layered neural networks. Deep Learning offers high precision levels surpassing the level of human ability and grabbed the imagination of the researchers, industry and the wider public. However, it has a number of intrinsic constraints and limitations. These include: The "black box", opaque internal structure which has millions of parameters and involves ad hoc decisions on the number of layers and algorithm parameters. The requirement for a huge amount of training data samples, computational resources (usually requiring GPUs and/or HPC) and time (usually requiring many hours of training). Iterative search. Requires retraining for new situations (is not evolving). Does not have proven convergence and stability. Most, if not all, of the above limitations can be avoided with the use of the Deep (Fuzzy) Rule-based Classifiers, which were recently introduced based on ALMMo, while achieving similar or even better performance. The resulting prototype-based IF...THEN...models are fully interpretable and dynamically evolving (they can adapt quickly and automatically to new data patterns or even new classes). They are non-parametric and, therefore, their training is non-iterative and fast (it can take few milliseconds per data sample/image on a normal laptop which contrasts with the multiple hours the current deep learning methods require for training even when they use GPUs and HPC). Moreover, they can be trained incrementally, online, or in real-time. Another aspect of Evolving Fuzzy Rule-based classifiers has been proposed in, which, in case of multi-class classification problems, achieves the reduction of class imbalance by cascadability into class sub-spaces and an increased flexibility and performance for adding new classes on the fly from streaming samples.

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  • Open-source robotics

    Open-source robotics

    Open-source robotics is a branch of robotics where robots are developed with open-source hardware and free and open-source software, publicly sharing blueprints, schematics, and source code. It is thus closely related to the open design movement, the maker movement and open science. == Requirements == Open source robotics means that information about the hardware is easily discerned, so that others can easily rebuild it. In turn, this requires design to use only easily available standard subcomponents and tools, and for the build process to be documented in detail including a bill of materials and detailed ('Ikea style') step-by-step building and testing instructions. (A CAD file alone is not sufficient, as it does not show the steps for performing or testing the build). These requirements are standard to open source hardware in general, and are formalised by various licences, certifications, especially those defined by the peer-reviewed journals Journal of Open Hardware and HardwareX. Licensing requirements for software are the same as for any open source software. But in addition, for software components to be of practical use in real robot systems, they need to be compatible with other software, usually as defined by some robotics middleware community standard. == Hardware systems == Applications to date include: Robot arms, e.g. PARA or Thor Wheeled mobile robots. e.g. OpenScout Four-legged robots such as the Open Dynamic Robot Initiative UAV quadcopters (drones) such as Agilicious Humanoid robots, e.g. iCub, Berkeley Humanoid Lite Self-driving cars, e.g. OpenPodcar (→ Personal rapid transit) Submersible robots, eg. OpenFish Laboratory robotics such as chemical liquid handling Vertical farming Swarm robots, e.g. HeRoSwarm Domestic tasks: vacuum cleaning, floor washing and grass mowing Robot sports including robot combat and autonomous racing Education == Hardware subcomponents == Most open source hardware definitions allow non-open subcomponents to be used in modular design, as long as they are easily available. However many designs try to push openness down into as many subcomponents as possible, with the aim of ultimately reaching fully open designs. Open hardware manual-drive vehicles and their subcomponents, such as from Open Source Ecology, are often used as starting points and extended with automation systems. Open subcomponents can include open-source computing hardware as subcomponents, such as Arduino and RISC-V, as well as open source motors and drivers such as the Open Source Motor Controller and ODrive. Open hardware robotics interface boards can simplify interfacing between middleware software and physical hardware. == Software subcomponents == === Middleware === Robotics middleware is software which links multiple other software components together. In robotics, this specifically means real-time communication systems with standardized message passing protocols. The predominant open source middleware is ROS2, the robot operating system, now as version 2. Other alternatives include ROS1, YARP — used in the iCub, URBI, and Orca. Open source middleware is usually run on an open source operating system, especially the Ubuntu distribution of Linux. === Driver software === Most robot sensors and actuators require software drivers. There is little standardization of open source software at this level, because each hardware device is different. Creating open drivers for closed hardware is difficult as it requires both low level programming and reverse engineering. === Simulation software === Open source robotics simulators include Gazebo, MuJoCo and Webots. Open source 3D game engines such as Godot are also sometimes used as simulators, when equipped with suitable middleware interfaces. === Automation software === At the level of AI, many standard algorithms have open source software implementations, mostly in ROS2. Major components include: Machine vision systems such as the YOLO object detector. 3D photogrammetry Navigation including SLAM and planning such as nav2 Arm inverse kinematics such as moveIt2 == Community == The first signs of the increasing popularity of building and sharing robot designs were found with the maker culture community. What began with small competitions for remote operated vehicles (e.g. Robot combat), soon developed to the building of autonomous telepresence robots such as Sparky and then true robots (being able to take decisions themselves) as the Open Automaton Project. Several commercial companies now also produce kits for making simple robots. The community has adopted open source hardware licenses, certifications, and peer-reviewed publications, which check that source has been made correctly and permanently available under community definitions, and which validate that this has been done. These processes have become critically important due to many historical projects claiming to be open source but them reverting on the promise due to commercialisation or other pressures. As with other forms of open source hardware, the community continues to debate precise criteria for 'ease of build'. A common standard is that designs should be buildable by a technical university student, in a few days, using typical fablab tools, but definitions of all of these subterms can also be debated. Compared to other forms of open source hardware, open source robotics typically includes a large software element, so involves software as well as hardware engineers. Open source concepts are more established in open source software than hardware, so robotics is a field in which those concepts can be shared and transferred from software to hardware. While the community in open source robotics is multi-faceted with a wide range of backgrounds, a sizable sub-community uses the ROS middleware and meets at the ROSCon conferences to discuss development of ROS itself and automation components built on it.

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  • International Aerial Robotics Competition

    International Aerial Robotics Competition

    The International Aerial Robotics Competition (IARC) is a university-based robotics competition held on the campus of the Georgia Institute of Technology, currently hosted by RoboNation. Since 1991, collegiate teams with the backing of industry and government have fielded autonomous flying robots in an attempt to perform missions requiring robotic behaviors not previously exhibited by a flying machine. The term “aerial robotics” was coined by competition creator Robert Michelson in 1990 to describe a new class of small highly intelligent flying machines. Successive years of competition saw these aerial robots grow from vehicles that could barely maintain themselves in the air, to automatons which are self-stable, self-navigating, and able to interact with their environment. The goal of the competition has been to provide a reason for the state-of-the-art of aerial robotics to move forward. Challenges have been geared towards producing advances. From 1991 through 2009, six missions were proposed. Each involved fully autonomous robotic behavior undemonstrated at the time. In October 2013 a seventh mission was proposed. It was the first to involve interaction between aerial robots and multiple ground robots. In 2016, the competition and its creator were recognized during the Georgia legislative session in the form of a senate resolution as the longest running aerial robotics competition in the world. == History == === First mission === The initial mission to move a metallic disc from one side of an arena to the other was seen by many as almost impossible. The college teams improved their entries over the next two years when the competition saw its first autonomous takeoff, flight, and landing by a team from the Georgia Institute of Technology. In 1995, a team from Stanford University was able to acquire a single disk and move it from one side of the arena to the other in a fully autonomous flight—half. === Second mission === The competition mission was toughened and made less abstract by requiring teams to search for a toxic waste dump, map the location of partially buried randomly oriented toxic waste drums, identify the contents of each drum from the hazard labels on the outside of each drum, and bring a sample back from one of the drums. In 1996, a team from the Massachusetts Institute of Technology and Boston University, with backing from Draper Labs, created a small fully autonomous flying robot that repeatedly and correctly mapped the location of all five of the toxic waste drums, and correctly identified the contents of two from the air, completing approximately seventy five percent of the mission. The following year, an aerial robot developed by a team from Carnegie Mellon University completed the entire mission. === Third mission === The third mission began in 1998. It was a search and rescue mission requiring fully autonomous robots to take off, fly to a disaster area and search amid fires, broken water mains, clouds of toxic gas, and rubble. The scenario was recreated at the U.S. Department of Energy's Hazardous Material Management and Emergency Response (HAMMER) training facility. Because of the realism of the scenario, animatrons were used instead of human actors to simulate survivors incapable of extracting themselves from the disaster area. An aerial robot from Germany's Technische Universität Berlin was able to detect and avoid all of the obstacles, identify all the dead on the ground and the survivors (distinguishing between the two based on movement), and relay pictures of the survivors along with their locations back to first responders who would attempt a rescue. This mission was completed in 2000. === Fourth mission === The fourth mission was initiated in 2001. It involved three scenarios requiring the same autonomous behavior: a hostage rescue mission where a submarine 3 kilometers off the coast must send an aerial robot to find a coastal city, identify the embassy where hostages are being held, locate valid openings in the embassy building, enter (or send in a sensor probe/subvehicle) and relay pictures of the hostages 3 km to the submarine prior to mounting an amphibious assault on the embassy to free the hostages; the discovery of an ancient mausoleum where a virus had killed the archaeological team, who had radioed that an important and undocumented tapestry was hanging inside, with 15 minutes to send an autonomous aerial robot to find the mausoleum, enter it (or send in a sensor probe/subvehicle) and relay pictures of the tapestry back prior to the destruction of the mausoleum and its contents; and an explosion at a nuclear reactor facility where scientists must send in an aerial robot to find the operating reactor building, enter the building (or send in a sensor probe/subvehicle) and relay pictures of the control panels to determine if a melt-down is imminent. All three missions involved the same elements of ingress, locating, identification, entry, and relaying pictures within 15 minutes. It was conducted at the U.S. Army's Fort Benning Soldier Battle Lab using the McKenna MOUT (Military Operations on Urban Terrain) site. The fourth mission was completed in 2008 with 27 teams who had demonstrated each of the required aerial robotic behaviors, except being able to demonstrate these behaviors in under 15 minutes—a feat considered by the judges to be inevitable given more time, and therefore no longer a significant challenge. Thus the fourth mission was terminated, $80,000 in awards distributed, and the fifth mission established. === Fifth mission === The fifth mission picked up where the fourth mission left off by demonstrating the fully autonomous aerial robotic behaviors necessary to rapidly negotiate the confined internal spaces of a structure once it has been penetrated by an air vehicle. The nuclear reactor complex explosion scenario of the fourth mission was used as the backdrop for the fifth mission. The fifth mission required a fully autonomous aerial vehicle to penetrate the structure and negotiate the more complex interior space containing hallways, small rooms, obstacles, and dead ends in order to search for a designated target without the aid of global-positioning navigational aids, and relay pictures back to a monitoring station some distance from the structure. The First Symposium on Indoor Flight Issues was held in conjunction with this 2009 IARC event. === Sixth mission === The sixth mission began in 2010 as an extension of the fifth mission theme of autonomous indoor flight behavior, however it demanded more advanced behaviors than were possible by any aerial robot extant in 2010. This espionage mission involved covertly stealing a flash drive from a particular room in a building and depositing an identical drive to avoid detection of the theft. The 2010 Symposium on Indoor Flight Issues was held concurrently at the University of Puerto Rico - Mayagüez during the 20th anniversary competition. === Seventh mission === The seventh mission began in 2014 demanding more advanced behaviors than were possible by any aerial robot extant in 2014. A single autonomous aerial robot had to herd up to 10 autonomous ground robot targets across one designated end of a 20m x 20m (65.62 feet x 65.62 feet) arena in under 10 minutes. The arena had neither walls for SLAM mapping nor GPS availability. Techniques such as optical flow or optical odometry were possible solutions to navigation within the arena. Collisions with obstacle ground robots ended the run with no score. The autonomous aerial robots interacted with the ground robots in the following way: if an aerial robot touched the ground robot on top, the ground robot would turn clockwise 45°. If the aerial robot blocked its forward motion by landing in front of it, the ground robot would reverse direction. Ground robots that feely escaped the arena, counted against the aerial robot's overall score, so the autonomous aerial robots had to decide which ground robots were in imminent danger of crossing any boundary except the designated one, and redirect them toward the designated boundary.Zhejiang University was the overall winner of Mission 7, of 52 teams from 12 nations entered as competitors. === Eighth mission === In 2018, the 8th mission was announced. Mission 8 focused on non-electronic human-machine interaction for the first time, with four aerial robots assisting humans to complete tasks that one person could not independently accomplish. The gist of mission 8 involved a swarm of autonomous aerial robots working with a human to achieve a task in the presence of hostile "Sentry aerial robots" which were trying to impede the human. In 2018, the inaugural year of mission 8, the American Venue was held on the campus of the Georgia Institute of Technology in Atlanta, Georgia, and the Asia/Pacific Venue was conducted at Beihang University in Beijing China. The following year, Mission 8 was successfully completed in Kunming China at the Yunnan Innovation

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  • Blocks world

    Blocks world

    The blocks world is a planning domain in artificial intelligence. It consists of a set of wooden blocks of various shapes and colors sitting on a table. The goal is to build one or more vertical stacks of blocks. Only one block may be moved at a time: it may either be placed on the table or placed atop another block. Because of this, any blocks that are, at a given time, under another block cannot be moved. Moreover, some kinds of blocks cannot have other blocks stacked on top of them. The simplicity of this toy world lends itself readily to classical symbolic artificial intelligence approaches, in which the world is modeled as a set of abstract symbols which may be reasoned about. == Motivation == Artificial Intelligence can be researched in theory and with practical applications. The problem with most practical applications is that the engineers don't know how to program an AI system. Instead of rejecting the challenge at all the idea is to invent an easy to solve domain which is called a toy problem. Toy problems were invented with the aim to program an AI which can solve it. The blocks world domain is an example of a toy problem. Its major advantage over more realistic AI applications is that many algorithms and software programs are available which can handle the situation. This allows comparing different theories against each other. In its basic form, the blocks world problem consists of cubes of the same size which have all the color black. A mechanical robot arm has to pick and place the cubes. More complicated derivatives of the problem consist of cubes of different sizes, shapes and colors. From an algorithmic perspective, blocks world is an NP-hard search and planning problem. The task is to bring the system from an initial state into a goal state. Automated planning and scheduling problems are usually described in the Planning Domain Definition Language (PDDL) notation which is an AI planning language for symbolic manipulation tasks. If something was formulated in the PDDL notation, it is called a domain. Therefore, the task of stacking blocks is a blocks world domain which stands in contrast to other planning problems like the dock worker robot domain and the monkey and banana problem. == Theses/projects which took place in a blocks world == Terry Winograd's SHRDLU Patrick Winston's Learning Structural Descriptions from Examples and Copy Demo Gerald Jay Sussman's Sussman anomaly Decision problem (Gupta and Nau, 1992): Given a starting Blocks World, an ending Blocks World, and an integer L > 0, is there a way to move the blocks to change the starting position to the ending position with L or less steps? This decision problem is NP-hard.

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  • Argüman

    Argüman

    Argüman is a free and open source software for collective structured argumentation and argument analysis via argumentation graphs or argument maps in which the type of connections can be specified. It allows users to create collaborative "semantic maps" of arguments in well structured tree formats and share them with an audience and potential participants. Arguman.org was an open structured social debate platform that implemented the software. It is down as of 2023. There also is a mobile version of the tool. The project was started, in 2014, and largely built by developers in Turkey. Some studies used or investigated excerpts of argumentations on the platform. Unlike the larger and functional alternative Kialo, which is structured using only 'Pro' and 'Con' relations, argüman arguments are structured by three types of premises – 'because', 'but', and 'however'. As of the latest version, debates are presented in their entirety as a large tree which may be harder to navigate than other formats – for instance, trees "can become extremely dense, and the interface does not make it obvious which arguments the user should pay attention to". Users can also flag arguments for fallacies. Arguman.org also had a Turkish-language subdomain. A researcher suggested the concept of the Semantic Web-interoperability could be useful for argumentative structures on the Web, going beyond the conventional flat structures of discussions and lack of characterizations of their components as implemented in argüman. There is research into how to automatically use these collaborative argumentation graphs, which is a "very active" topic in Artificial Intelligence. There also is research into applying conclusion-making methods to the debates or their data, such as bipolar weighted argumentation frameworks – this could be a way to find out what the current conclusion of debates like "Computer Science is not actually a science" is. A study suggests it could be useful for the development of critical thinking skills.

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  • ACL Data Collection Initiative

    ACL Data Collection Initiative

    The ACL Data Collection Initiative (ACL/DCI) was a project established in 1989 by the Association for Computational Linguistics (ACL) to create and distribute large text and speech corpora for computational linguistics research. The initiative aimed to address the growing need for substantial text databases that could support research in areas such as natural language processing, speech recognition, and computational linguistics. By 1993, the initiative’s activities had effectively ceased, with its functions and datasets absorbed by the Linguistic Data Consortium (LDC), which was founded in 1992. == Objectives == The ACL/DCI had several key objectives: To acquire a large and diverse text corpus from various sources To transform the collected texts into a common format based on the Standard Generalized Markup Language (SGML) To make the corpus available for scientific research at low cost with minimal restrictions To provide a common database that would allow researchers to replicate or extend published results To reduce duplication of effort among researchers in obtaining and preparing text data These objectives were designed to address the growing demand for very large amounts of text arising from applications in recognition and analysis of text and speech. Its core objective was to "oversee the acquisition and preparation of a large text corpus to be made available for scientific research at cost and without royalties". == History == By the late 1980s, researchers in computational linguistics and speech recognition faced a significant problem: the lack of large-scale, accessible text corpora for developing statistical models and testing algorithms. Existing generally available text databases were too small to meet the needs of developing applications in text and speech recognition. The initiative was formed to meet this need by collecting, standardizing, and distributing large quantities of text data with minimal restrictions for scientific research. As stated by Liberman (1990), "research workers have been severely hampered by the lack of appropriate materials, and specially by the lack of a large enough body of text on which published results can be replicated or extended by others." The ACL/DCI committee was established in February 1989. The committee included members from academic and industrial research laboratories in the United States and Europe. The initiative was chaired by Mark Liberman from the University of Pennsylvania (formerly of AT&T Bell Laboratories). Other committee members included representatives from organizations such as Bellcore, IBM T.J. Watson Research Center, Cambridge University, Virginia Polytechnic Institute & State University, Northeastern University, University of Pennsylvania, SRI International, MCC, Xerox PARC, ISSCO, and University of Pisa. The project operated initially without dedicated funding, relying on volunteer efforts from committee members and their affiliated institutions. Key supporters included AT&T Bell Labs, Bellcore, IBM, Xerox, and the University of Pennsylvania, which allowed the use of their computing facilities for ACL/DCI-related work. Previously running on volunteer effort pro bono, in 1991, it obtained funding from General Electric and the National Science Foundation (IRI-9113530). == Data == As of 1990, the ACL/DCI had collected hundreds of millions of words of diverse text. The collection included: Wall Street Journal articles (25 to 50 million words); Canadian Hansard (parliamentary records) in parallel English and French versions: cleaned-up English Hansard donated by the IBM alignment models group (100 million words), and original Bilingual Hansard (from a different time period) obtained directly (200 million words). Collins English Dictionary (1979 edition), both as fulltext (3 million words) and as various "database" versions, constructed using "typographers' tape" donated by Collins, which were computer tapes containing the structured digital data used to typeset and print the 1979 edition of the dictionary; Emails from ARPANET newsletters for the ACM Special Interest Group on Information Retrieval Forum (IRLIST) and AIList Digest issues distributed over the ARPANET (AILIST) (5 million words), both collected by Edward A. Fox at VIPSU; Articles on networking (2 million words); U.S. Department of Agriculture Extension Service Fact Sheets (>1 million words); 200,000 scientific abstracts of about 1,500 words each from the Department of Energy (25 million words); Archives of the Challenger Investigation Commission, including transcripts of depositions and hearings (2.5 million words); Books from the Library of America, including works by Mark Twain, Eugene O'Neill, Ralph Waldo Emerson, Herman Melville, W.E.B. DuBois, Willa Cather, and Benjamin Franklin (130 books, 20 million words); Public domain books like the King James Bible, Tristram Shandy, The Federalist Papers; Several million words of transcribed radiologists' reports, donated by Francis Ganong at Kurzweil Applied Intelligence Inc (about 5 million words); The Child Language Data Exchange corpus of child language acquisition transcripts; U.S. Department of Justice Justice Retrieval and Inquiry System (JURIS) materials; The Swiss Civil Code in parallel German, French and Italian; Economic reports from the Union Bank of Switzerland, in parallel English, German, French and Italian; About 12K words of administrative policy manuals and 14K words of administrative memos, contributed by Geoff Pullum of U.C.S.C.; Material from various ACM journals and the ACL journal Computational Linguistics; The CSLI publications series: 50-100 reports (8K words each) and 5-10 books (80K words each). The initiative started with North American English text but expanded to include Canadian French and planned to include Japanese, Chinese, and other Asian languages. At least 5 million words from the collection were tagged under the Penn Treebank project, and those tags were distributed by DCI as well. After DCI was absorbed by the LDC, the datasets were curated under LDC. == Format == The ACL/DCI corpus was coded in a standard form based on SGML (Standard Generalized Markup Language, ISO 8879), consistent with the recommendations of the Text Encoding Initiative (TEI), of which the DCI was an affiliated project. The TEI was a joint project of the ACL, the Association for Computers and the Humanities, and the Association for Literary and Linguistic Computing, aiming to provide a common interchange format for literary and linguistic data. The initiative planned to add annotations reflecting consensually approved linguistic features like part of speech and various aspects of syntactic and semantic structure over time. == Examples == As an example of the use of ACL/DCI, consider the Wall Street Journal (WSJ) corpus for speech recognition research. The WSJ corpus was used as the basis for the DARPA Spoken Language System (SLS) community's Continuous Speech Recognition (CSR) Corpus. The WSJ corpus became a standard benchmark for evaluating speech recognition systems and has been used in numerous research papers. The WSJ CSR Corpus provided DARPA with its first general-purpose English, large vocabulary, natural language, high perplexity corpus containing speech (400 hours) and text (47 million words) during 1987–89. The text corpus was 313 MB in size. The text was preprocessed to remove ambiguity in the word sequence that a reader might choose, ensuring that the unread text used to train language models was representative of the spoken test material. The preprocessing included converting numbers into orthographics, expanding abbreviations, resolving apostrophes and quotation marks, and marking punctuation. As another example, the Yarowsky algorithm used bitext data from DCI to train a simple word-sense disambiguation model that was competitive with advanced models trained on smaller datasets. == Distribution == Materials from the ACL/DCI collection were distributed to research groups on a non-commercial basis. By 1990, about 25 research groups and individual researchers had received tapes containing various portions of the collected material. To obtain the data, researchers had to sign an agreement not to redistribute the data or make direct commercial use of it. However, commercial application of "analytical materials" derived from the text, such as statistical tables or grammar rules, was explicitly permitted. The initiative first distributed data via 12-inch reels of 9-track tape, then via CD-ROMs. Each such tape could contain 30 million words compressed via the Lempel-Ziv algorithms. The first CD-ROM distribution was in 1991, funded by Dragon Systems Inc. It contained Collins English Dictionary, WSJ, scientific abstracts provided by the U.S. Department of Energy, and the Penn Treebank.

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  • Stable Diffusion

    Stable Diffusion

    Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. The generative artificial intelligence technology is the premier product of Stability AI and is considered to be a part of the ongoing AI boom. It is primarily used to generate detailed images conditioned on text descriptions, though it can also be applied to other tasks such as inpainting, outpainting, and generating image-to-image translations guided by a text prompt. Its development involved researchers from the CompVis Group at LMU Munich and Runway with a computational donation from Stability and training data from non-profit organizations. Stable Diffusion is a latent diffusion model, a kind of deep generative artificial neural network. Its code and model weights have been released publicly, and an optimized version can run on most consumer hardware equipped with a modest GPU with as little as 2.4 GB VRAM. This marked a departure from previous proprietary text-to-image models such as DALL-E and Midjourney which were accessible only via cloud services. == Development == Stable Diffusion originated from a project called Latent Diffusion, developed in Germany by researchers at LMU Munich in Munich and Heidelberg University. Four of the original 5 authors (Robin Rombach, Andreas Blattmann, Patrick Esser and Dominik Lorenz) later joined Stability AI and released subsequent versions of Stable Diffusion. The technical license for the model was released by the CompVis group at LMU Munich. Development was led by Patrick Esser of Runway and Robin Rombach of CompVis, who were among the researchers who had earlier invented the latent diffusion model architecture used by Stable Diffusion. Stability AI also credited EleutherAI and LAION (a German nonprofit which assembled the dataset on which Stable Diffusion was trained) as supporters of the project. == Technology == === Architecture === Diffusion models, introduced in 2015, are trained with the objective of removing successive applications of Gaussian noise on training images, which can be thought of as a sequence of denoising autoencoders. The name diffusion is from the thermodynamic diffusion, since they were first developed with inspiration from thermodynamics. Models in Stable Diffusion series before SD 3 all used a variant of diffusion models, called latent diffusion model (LDM), developed in 2021 by the CompVis (Computer Vision & Learning) group at LMU Munich. Stable Diffusion consists of 3 parts: the variational autoencoder (VAE), U-Net, and an optional text encoder. The VAE encoder compresses the image from pixel space to a smaller dimensional latent space, capturing a more fundamental semantic meaning of the image. Gaussian noise is iteratively applied to the compressed latent representation during forward diffusion. The U-Net block, composed of a ResNet backbone, denoises the output from forward diffusion backwards to obtain a latent representation. Finally, the VAE decoder generates the final image by converting the representation back into pixel space. The denoising step can be flexibly conditioned on a string of text, an image, or another modality. The encoded conditioning data is exposed to denoising U-Nets via a cross-attention mechanism. For conditioning on text, the fixed, pretrained CLIP ViT-L/14 text encoder is used to transform text prompts to an embedding space. Researchers point to increased computational efficiency for training and generation as an advantage of LDMs. With 860 million parameters in the U-Net and 123 million in the text encoder, Stable Diffusion is considered relatively lightweight by 2022 standards, and unlike other diffusion models, it can run on consumer GPUs, and even CPU-only if using the OpenVINO version of Stable Diffusion. ==== SD XL ==== The XL version uses the same LDM architecture as previous versions, except larger: larger UNet backbone, larger cross-attention context, two text encoders instead of one, and trained on multiple aspect ratios (not just the square aspect ratio like previous versions). The SD XL Refiner, released at the same time, has the same architecture as SD XL, but it was trained for adding fine details to preexisting images via text-conditional img2img. ==== SD 3.0 ==== The 3.0 version completely changes the backbone. Not a UNet, but a Rectified Flow Transformer, which implements the rectified flow method with a Transformer. The Transformer architecture used for SD 3.0 has three "tracks", for original text encoding, transformed text encoding, and image encoding (in latent space). The transformed text encoding and image encoding are mixed during each transformer block. The architecture is named "multimodal diffusion transformer (MMDiT), where the "multimodal" means that it mixes text and image encodings inside its operations. This differs from previous versions of DiT, where the text encoding affects the image encoding, but not vice versa. === Training data === Stable Diffusion was trained on pairs of images and captions taken from LAION-5B, a publicly available dataset derived from Common Crawl data scraped from the web, where 5 billion image-text pairs were classified based on language and filtered into separate datasets by resolution, a predicted likelihood of containing a watermark, and predicted "aesthetic" score (e.g. subjective visual quality). The dataset was created by LAION, a German non-profit which receives funding from Stability AI. The Stable Diffusion model was trained on three subsets of LAION-5B: laion2B-en, laion-high-resolution, and laion-aesthetics v2 5+. A third-party analysis of the model's training data identified that out of a smaller subset of 12 million images taken from the original wider dataset used, approximately 47% of the sample size of images came from 100 different domains, with Pinterest taking up 8.5% of the subset, followed by websites such as WordPress, Blogspot, Flickr, DeviantArt and Wikimedia Commons. An investigation by Bayerischer Rundfunk showed that LAION's datasets, hosted on Hugging Face, contain large amounts of private and sensitive data. === Training procedures === The model was initially trained on the laion2B-en and laion-high-resolution subsets, with the last few rounds of training done on LAION-Aesthetics v2 5+, a subset of 600 million captioned images which the LAION-Aesthetics Predictor V2 predicted that humans would, on average, give a score of at least 5 out of 10 when asked to rate how much they liked them. The LAION-Aesthetics v2 5+ subset also excluded low-resolution images and images which LAION-5B-WatermarkDetection identified as carrying a watermark with greater than 80% probability. Final rounds of training additionally dropped 10% of text conditioning to improve Classifier-Free Diffusion Guidance. The model was trained using 256 Nvidia A100 GPUs on Amazon Web Services for a total of 150,000 GPU-hours, at a cost of $600,000. === Limitations === Stable Diffusion has issues with degradation and inaccuracies in certain scenarios. Initial releases of the model were trained on a dataset that consists of 512×512 resolution images, meaning that the quality of generated images noticeably degrades when user specifications deviate from its "expected" 512×512 resolution; the version 2.0 update of the Stable Diffusion model later introduced the ability to natively generate images at 768×768 resolution. Another challenge is in generating human limbs due to poor data quality of limbs in the LAION database. The model is insufficiently trained to replicate human limbs and faces due to the lack of representative features in the database, and prompting the model to generate images of such type can confound the model. In addition to human limbs, Stable Diffusion is unable to generate legible ambigrams and some other forms of text and typography. Stable Diffusion XL (SDXL) version 1.0, released in July 2023, introduced native 1024x1024 resolution and improved generation for limbs and text. Accessibility for individual developers can also be a problem. In order to customize the model for new use cases that are not included in the dataset, such as generating anime characters ("waifu diffusion"), new data and further training are required. Fine-tuned adaptations of Stable Diffusion created through additional retraining have been used for a variety of different use-cases, from medical imaging to algorithmically generated music. However, this fine-tuning process is sensitive to the quality of new data; low resolution images or different resolutions from the original data can not only fail to learn the new task but degrade the overall performance of the model. Even when the model is additionally trained on high quality images, it is difficult for individuals to run models in consumer electronics. For example, the training process for waifu-diffusion requires a minimum 30 GB of VRAM, which exceeds the usual resource provided in such consumer GPUs as Nvidia's GeForce 30 series, w

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  • Micah Xavier Johnson

    Micah Xavier Johnson

    Micah Xavier Johnson (July 2, 1991 – July 8, 2016) was an American Army reserve Afghan war veteran, black nationalist, and mass murderer who perpetrated the 2016 shooting of Dallas police officers during a Black Lives Matter protest. He ambushed and killed five officers and wounded eleven others in Downtown, Dallas, Texas. He was killed by police during a standoff after expressing anger over police killings of black men. The shootings were the second-deadliest targeted attack on law enforcement officers in U.S. history, surpassed only by the September 11 attacks. == Early life == Micah Xavier Johnson was born in Magee, Mississippi, on July 2, 1991, and he was raised in Mesquite, Texas. When he was four years old, his parents divorced. At 17, Johnson enrolled at John Horn High School, where he joined the Junior Reserve Officers' Training Corps, as reported by the Mesquite Independent school district. He faced academic challenges, graduating in 2009 with a 1.98 GPA and ranking 430th out of 453 students in his class. In Spring 2011, Johnson registered for four courses at Richland college but did not complete any. Evidence suggests his enrollment at Richland gave him access to El Centro College, due to his pre-planned and coordinated movements throughout Building B during his standoff with police in 2016. == Military service == === Enlistment and early service === Micah Xavier Johnson enlisted in the U.S. Army Reserve in March 2009 at the age of 18, shortly after graduating high school in Mesquite, Texas. His initial service was primarily stateside, where he trained as a carpentry and masonry specialist (military occupational specialty 51B). This role involved engineering tasks such as construction and repair in support of military operations. During his reserve tenure, Johnson served part-time while living at home, and he was described by family and friends as initially idealistic about the military, even aspiring to become a police officer. === Deployment to Afghanistan === In September 2013, Johnson was activated for full-time duty and deployed to Afghanistan as part of the 420th Engineer Brigade, a unit based in Seagoville, Texas. His tour began in November 2013 and lasted approximately eight months, ending in July 2014. During this period, he performed non-combat engineering duties, though the stresses of serving in a combat zone were noted by those close to him. Associates from his service later suggested he experienced significant psychological strain, including the loss of friends and general disillusionment with military life, which contrasted with his pre-deployment enthusiasm. His mother later reflected that "the military was not what Micah thought it would be." === Sexual harassment allegation and early return === About six months into his deployment, in May 2014, Johnson faced a serious accusation of sexual harassment from a higher-ranking female soldier. She filed for a military protective order against him, prompting an investigation. As a result, his chain of command recommended an "other than honorable" discharge—the second (more severe is a dishonorable discharge, which does not require a court martial) most severe administrative separation short of a court-martial—and he was sent back to the United States ahead of schedule. Despite this, Johnson was not court-martialed, and the case did not lead to criminal charges. A military lawyer who represented him described the handling as unusual, noting that "someone really screwed up" in allowing him to avoid harsher consequences. === Post-deployment and discharge === Upon returning stateside in August 2014, Johnson resumed reserve duties with his engineering brigade until April 2015. He was honorably discharged at the rank of private first class (E-3), a relatively low junior enlisted rank after six years of service, which military sources attributed partly to the unresolved harassment allegation impacting his promotions and evaluations. Friends and family observed a marked change in his demeanor post-deployment: he became more reclusive, resentful toward the government, and withdrawn, with some speculating that the Afghanistan experience and the scandal contributed to a "small breakdown." In July 2016, following the Dallas shooting, the U.S. Army launched an internal review of his service record, including the harassment claims, to assess whether all misconduct allegations had been fully investigated. == Shootings == On July 7, 2016, a peaceful Black Lives Matter protest marched through downtown Dallas, Texas, drawing about 800 demonstrators. The event responded to the recent police killings of Alton Sterling in Baton Rouge, Louisiana, on July 5, and Philando Castile in Falcon Heights, Minnesota, on July 6—both black men shot during encounters captured on video. Around 100 officers monitored the march, which passed near El Centro College without incident until gunfire erupted around 8:45 p.m. Johnson arrived in a dark SUV, armed with an SKS semi-automatic rifle, a handgun, extra ammunition, and ballistic vests. He parked near the protest's end, chatted briefly with two officers, then opened fire on police from an elevated position on Lamar Street (now Botham Jean Boulevard). He shot from behind barriers, through windows, and while moving, targeting white officers specifically. The ambush killed five officers and wounded seven more, plus two civilians. Gunfire scattered protesters in panic as Johnson used military-style tactics, like quick position changes, to prolong the assault. === Standoff and Johnson's end === Johnson fled into El Centro College's Building C, then Building B, navigating pre-planned routes with familiarity from prior enrollment at nearby Richland College. He barricaded in a parking garage, wounding more officers in close-range fights. During two-hour negotiations, he taunted police via phone—laughing, singing, asking kill counts, and claiming planted bombs (none found). He admitted solo action, rage at White officers, and no group ties. At 2:30 a.m. on July 8, SWAT ended the standoff by detonating a bomb via remote-controlled robot in the garage, killing Johnson. This marked the first U.S. police use of such a tactic. === Victims and investigation findings === The slain officers were: Brent Thompson (Transit Authority, 36), Patrick Zamarripa (Dallas PD, 33), Michael Krol (Dallas PD, 40), Lorne Ahrens (Dallas PD, 48), and Michael Smith (Dallas PD, 55). Wounded officers included Sheik Smith, John Mitchell, and others; civilians She Tamara El-Sobky and Hillary Castro. Searches of Johnson's home revealed bomb-making materials, rifles, vests, and notes on tactics, suggesting plans for a larger attack. He had practiced explosions and honed skills post-discharge, including marksmanship. === Aftermath and impact === Dallas mourned with vigils and memorials, while national protests against police violence continued amid grief. President Barack Obama, the first African American president of the United States, called Johnson a "demented individual" and formed a task force on race and policing. The incident fueled debates on gun control, race relations, and veteran mental health—Johnson had sought VA treatment for stress and anxiety but showed no prior violent signs to friends. El Centro College canceled all classes on July 8. Police barricaded the perimeter and began canvassing the crime scene. The explosion that killed Johnson also destroyed the school's servers, further delaying reopening. The school partially reopened on July 20, with staff returning that day and students on the following day. Buildings A, B, and C remained closed pending the FBI investigation. == Motive == An investigation into his online activities uncovered his interest in black nationalist groups. The Southern Poverty Law Center (SPLC), and news outlets reported that Johnson "liked" the Facebook pages of black nationalist organizations such as the New Black Panther Party (NBPP), Nation of Islam, and Black Riders Liberation Party, three groups which are listed by the SPLC as hate groups. On Facebook, Johnson posted an angry and "disjointed" post against White people on July 2, several days before the attack. NBPP head Quanell X said after the shooting that Johnson had been a member of the NBPP's Houston chapter for about six months, several years before. Quanell X added that Johnson had been "asked to leave" the group for violating the organization's "chain of command" and espousing dangerous rhetoric, such as asking the NBPP why they had not purchased more weapons and ammunition, and expressing his desire to harm black church preachers because he believed they were more interested in money than God. Following the shooting, a national NBPP leader distanced the group from Johnson, saying that he "was not a member of" the party. Further investigation into his digital footprint showed that Johnson visited the sites of Marxist Leninist groups associated with "Revolutionary Black Nationalism",

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