AI Email Response Generator Free

AI Email Response Generator Free — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Tute Genomics

    Tute Genomics

    Tute Genomics was an American genomics startup that provided a cloud-based web application for rapid and accurate annotation of human genomic data. It was built on the expertise of ANNOVAR. Tute Genomics assisted researchers in identifying disease genes and biomarkers, and assisted clinicians/labs in performing genetic diagnosis. Based in Provo, Utah, Tute was co-founded by Dr. Kai Wang, an assistant professor at the University of Southern California (USC); and Dr. Reid J. Robison, a board-certified psychiatrist with fellowship training in both neurodevelopmental genetics and bioinformatics. Tute Genomics was acquired by PierianDX in 2016. == History == The word "tute" means "personal" in the Na’vi language created for the 2009 film Avatar by Paul Frommer, a linguist and communications professor at the USC Marshall School of Business. === Timeline === 2013 Tute Genomics launched in 2013 and entered the accelerator, BoomStartup. By "demo day" of BoomStartup, Tute had raised their seed round of funding and expanded the round to include angel investors from SLC Angels, Park City Angels, Life Science Angels. Tute was the tenth ever online syndicate for AngelList and in all raised a seed round of $1.5 million. 2014 In March 2014, the company announced that Affiliated Genetics, a Utah-based CLIA-certified laboratory, selected Tute Genomics for its next-generation sequencing (NGS) analytics pipeline. In May 2014, the company announced joining the Global Alliance for Genomics and Health. In June 2014, Advanced Biological Laboratories (ABL), S.A., announced a licensing and collaboration agreement with Tute Genomics and the commercial launch of OncoChek for managing and analysing genomics data in the field of oncology. In July 2014, the company announced an agreement with Lineagen, Inc., to provide next-generation sequencing analytics for Lineagen’s NextStepDx Plus assay. Also, Brigham Young University selected the Tute Genomics genome annotation and discovery platform for analysis and interpretation of 1,000 exomes and genomes. In November 2014, the company announced addition of the Tute platform to Illumina’s BaseSpace. The company announced a Series A1 funding round of $2.3 million in December 2014. The round was led by UK-based Eurovestech. Peak Ventures and a number of angel investors also participated in this round. 2015 Tute recruits David Mittelman, founder of Arpeggi, Inc. and former CSO at FamilyTreeDNA, to Tute Genomics as Chief Scientific Officer. Tute acquires Knome and integrates the KnoSys platform into its software product. 2016 Reid Robison, Tute CEO, launches a Kickstarter campaign to sell Tute interpreted whole genome and whole exome sequencing directly to consumers. The campaign was suspended within the same month after receiving a letter from the United States Food and Drug Administration. Tute is acquired by PierianDX.

    Read more →
  • 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 ==

    Read more →
  • Hundred (novel series)

    Hundred (novel series)

    Hundred (ハンドレッド, Handoreddo) is a Japanese light novel series written by Jun Misaki and illustrated by Nekosuke Ōkuma. SB Creative published 16 novels between November 15, 2012, and October 15, 2018, under their GA Bunko imprint. A manga adaptation with art by Sasayuki was serialized in Fujimi Shobo's Monthly Dragon Age magazine. An anime television series adaptation, produced by Production IMS and directed by Tomoki Kobayashi, aired from April to June 2016. == Plot == "Hundreds" are a kind of weapon that get their name from their ability to change into many different forms, and are the only thing that can counter the mysterious life forms called Savage that are attacking Earth. Those who can wield a Hundred are sought out to be made into Slayers, trained individuals who can use them in combat. To become a Slayer, Hayato Kisaragi successfully enrolls in the marine academy city ship Little Garden. However he feels a strange yet familiar sense of incongruity towards Emile Crossford, his roommate who somehow knows him from somewhere. On top of that, shortly after he enters the school, he ends up getting challenged to a duel by the "Queen" and the school's most powerful Slayer, Claire Harvey. == Characters == Hayato Kisaragi (如月 ハヤト, Kisaragi Hayato) Voiced by: Yoshiaki Hasegawa (Japanese); Ricco Fajardo (English) Hayato is the male protagonist of Hundred. Originally from Yamato, Hayato became a Slayer in order to obtain state-of-the-art medical treatment for his sister. His previous encounter with a Savage 10 years ago resulted in him becoming a Variant - one of a very small fraction of people (fewer than 10 in the world, according to Emile) who have survived exposure to the Savages and obtained a greatly increased affinity for Hundreds as a result. He has the highest known compatibility with a Hundred and his Hundred, the Flying Swallow, is a chevalier-type that takes the form of a sword and a shoulder guard. When he first met Emilia he didn't realize that she was really a girl, but upon discovering the truth, he agreed to keep her secret. He is shown to be slightly uncomfortable whenever Emilia was showing him affection and would always blush when around her or other women who show their romantic feelings toward him. Emilia Hermit (エミリア・ハーミット, Emiria Hāmitto) Voiced by: Rumi Ōkubo (Japanese); Mikaela Krantz (English) Emilia is the female protagonist of Hundred. She is a silver-haired girl from the Britannia Empire and Hayato's roommate. She initially poses as a boy under the name Emile Crossfode (エミール・クロスフォード, Emīru Kurosufōdo) with only a few people aware of her secret until she eventually reveals the truth about herself. She and Hayato were survivors from the second Savage attack 10 years earlier, which resulted in her and Hayato becoming Variants. Hayato only has vague recollections of the prior event and it isn't until their encounter with the Savages at Zwei Island that Hayato realizes her true identity. She is a citizen of the Gudenburg Empire by birth and eventually reveals that she is Emilia Gudenburg (エミリア・グーデンブルグ, Emiria Gūdenburugu), the Empire's third princess. Her Hundred is the Arms Shroud that is an innocence type able to change into any form of weapon, something no other Slayer's Hundred can do. Like Hayato, she too is a Variant. Ten years ago she and Hayato where fleeing from the Savages' onslaught when she was attacked by one and almost died. The attack left a potent amount of virus in her gaping wound. Hayato, in an attempt to save her life sucked some of the fluids out, causing him to become a Variant as well. A substantial amount was still left in her system. She is in love with Hayato and is known to be very affectionate towards him and does not care about the rumors circulating about their relationship since everyone assumes them to be gay. Eventually, her status as a princess and girl are revealed to her peers, who were shocked at her heritage and finally understand her feelings to Hayato. Claire Harvey (クレア・ハーヴェイ, Kurea Hāvei) Voiced by: M.A.O (Japanese); Caitlin Glass (English) The highest-ranked Slayer in Little Garden who is from the United States of Liberia, she is called the Queen. The newly-arrived Hayato is forced to duel her to prevent the expulsion of two students who arrived late to the entrance ceremony because they are looking for him at the airport when he arrived. During the duel Hayato accidentally gropes her and she goes all out and defeats him, but the duel is called a draw and the students are allowed to stay. After Hayato saves her from a Savage and, later, accidentally kisses her, she falls in love with him. Her Hundred is a Dragoon Type which utilizes multiple cannons or transforms into a large powerful rifle, in doing so it drains much of her energy. She is also one of the few people who are aware that Emilia is secretly a girl. Karen Kisaragi (如月 カレン, Kisaragi Karen) Voiced by: Kaya Okuno (Japanese); Dawn M. Bennett (English) Hayato's younger sister who is ill. Hayato became a Slayer in order to obtain first-class treatment for her. While staying in the hospital she is often seen playing tarot cards, where she has become sort of a clairvoyant. Unlike her brother, Hayato, she suspected that Emilia was really a girl the moment she met her, until she was later convinced otherwise. She later becomes good friends with popular idol Sakura. Sakura Kirishima (霧島 サクラ, Kirishima Sakura) Voiced by: Mayu Yoshioka (Japanese); Amber Lee Connors (English) She is a popular idol who falls in love with Hayato after seeing him defeat the Trenta Savage at Zwei Island. She originally met Hayato and Karen at a shelter in Gudenberg during the second Savage attack. She remembers Karen but wasn't able to get Hayato's name at the time. After that incident, she lives with her father whom she never meets. When she later falls ill from an unknown illness, her father sells her to the Warslran Research Facility, where subjects like her are injected with vaccines that are developed from the fluids recovered from defeated Savages. She is the only one of the test subjects to have survived and, like Hayato and Emilia, she is also a Variant and a Slayer. Liza Harvey (リザ・ハーヴェイ, Riza Hāvei) Voiced by: Nichika Ōmori (Japanese); Megan Shipman (English) Claire's younger sister. Liddy Steinberg (リディ・スタインバーグ, Ridi Sutainbāgu) Voiced by: Rika Kinugawa (Japanese); Alex Moore (English) Little Garden's student council Vice President who is in charge of enforcement, she is very loyal to Claire and can be very uptight when enforcing the school's rules and regulations. Her Hundred takes the form of a lance and a shield. Erica Candle (エリカ・キャンドル, Erika Kyandoru) Voiced by: Yui Makino (Japanese); Natalie Hoover (English) She is also student council Vice President, however, she is mostly in charge of strategic planning, she has a high admiration for Claire, and it is suggested that she has certain feelings for her. Her Hundred, the Everlasting, is an Arsene type, which takes the form of a massive chained yoyo that she uses for restraining. Unfortunately her Hundred is ineffective against much stronger Savages. She is also one of the few people who became aware of Emilia's secret. Fritz Granz (フリッツ・グランツ, Furittsu Gurantsu) Voiced by: Wataru Hatano (Japanese); Jason Liebrecht (English) Hayato's classmate and Latia's partner. His Hundred takes the form of a sniper rifle. He and Latia were childhood friends, he often pokes fun at her. He is curious about the relationship between Hayato and Emilie and often teases them about their relationship, including sometimes referring to them as a couple on occasion. Latia Saintemilion (レイティア・サンテミリオン, Reitia Santemirion) Voiced by: Yuka Ōtsubo (Japanese); Elizabeth Maxwell (English) She is classmates with Hayato and Emilia, she is also Fritz's partner. Her Hundred is a close quarter melee type. She is Fritz's childhood friend. Charlotte Dimandias (シャーロット・ディマンディウス, Shārotto Dimandiusu) Voiced by: Miyu Matsuki (1st drama CD), Yui Horie (2nd drama CD, anime); Sarah Wiedenheft (English) She is a child prodigy who serves as the Little Garden's only main technical expert and chief researcher on Hundreds. Her authority is equal to that of the student council, that she can go against them or question their decisions. She is best friends with Emilia, and she is one of the characters who knows her secret. Meimei (メイメイ, Meimei) Voiced by: Ayaka Imamura (Japanese); Jill Harris (English) Miharu Kashiwagi (柏木 ミハル, Kashiwagi Miharu) Voiced by: Yuna Yoshino (Japanese); Rachel Glass (English) Miharu is a nurse at the hospital where Karen is staying. She is known for her very sweet demeanor and large breasts. Chris Steinbelt (クリス・シュタインベルト, Kurisu Shutainberuto) Voiced by: Emiri Kato (Japanese); Howard Wang (English) Noa Sheldon (ノア・シェルダン, Noa Sherudan) Voiced by: Yurika Kubo (Japanese); Madeleine Morris (English) Xue-Mei Liu (劉雪梅, Ryū Shuemei) Voiced by: Eri Suzuki (Japanese); Apphia Yu (English) Alphonse Brustad (アルフォ

    Read more →
  • Emma Hart (computer scientist)

    Emma Hart (computer scientist)

    Professor Emma Hart, FRSE (born 1967) is an English computer scientist known for her work in artificial immune systems (AIS), evolutionary computation and optimisation. She is a professor of computational intelligence at Edinburgh Napier University, editor-in-chief of the Journal of Evolutionary Computation (MIT Press), and D. Coordinator of the Future & Emerging Technologies (FET) Proactive Initiative, Fundamentals of Collective Adaptive Systems. == Early life and education == Hart was born in Middlesbrough, England in 1967. In 1990 she graduated from the University of Oxford with a first class BA(Hons) in Chemistry. She then continued her studies at the University of Edinburgh, graduating with an MSc in Artificial Intelligence in 1994, followed by a PhD that explored the use of immunology as an inspiration for computing, examining a range of techniques applied to optimization and data classification problems. Her dissertation was titled Immunology as a metaphor for computational information processing: Fact or fiction?, and her doctoral advisor was Peter Ross. == Career == In 2000 Hart took a position as a lecturer at Edinburgh Napier University, and was promoted to a Reader, Professor, and in 2008 Chair in Natural Computation. She is now director of the Centre of Algorithms, Visualisation and Evolving Systems (CAVES) group in the School of Computing. She continues to research in the area of developing novel bio-inspired techniques for solving a range of real-world optimisation and classification problems, as well as exploring the fundamental properties of immune-inspired computing through modelling and simulation. She is also involved in editorial activity and currently occupies the position of Editor-in-Chief of the Journal of Evolutionary Computation (MIT Press). Her interests lie in the area of bio-inspired computing, in particular artificial immune systems (AIS). She also undertakes research in three main areas: optimisation, self-organising/self-adaptive systems, and artificial intelligence. Hart is D. Coordinator of Fundamentals of Collective Adaptive Systems (FoCAS), a Future and Emerging Technologies Proactive Initiative funded by the European Commission under FP7. == Selected works == === Conference talks === Hart, Emma. "Lifelong learning in optimization (video)". 28th European Conference on Operational Research. The Association of European Operational Research Societies. Hart, Emma (December 2021). "Self-assembling robots and the potential of artificial evolution". TED talk 2021. === Journal articles === "An immune system approach to scheduling in changing environments". E.Hart, P.Ross. 1999. Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation (2), 1559–1566. "Exploiting the analogy between immunology and sparse distributed memories: A system for clustering non-stationary data". E.Hart, P.Ross. 2002. 1st International Conference on Artificial Immune Systems. "Evolutionary scheduling: A review". E Hart, P Ross, D Corne. 2005. Genetic Programming and Evolvable Machines 6(2), 191–220. DOI: https://doi.org/10.1007/s10710-005-7580-7 "Application areas of AIS: The past, the present and the future". E.Hart, J.Timmis. 2008. Applied soft computing 8(1), 191–201. DOI: https://doi.org/10.1016/j.asoc.2006.12.004 "Structure versus function: a topological perspective on immune networks". E.Hart, H.Bersini, F.Santos. 2010. Natural computing 9(3), 603–624. DOI: https://doi.org/10.1007/s11047-009-9138-8 "On the life-long learning capabilities of a nelli: A hyper-heuristic optimisation system". E.Hart, K.Sim. 2014. International Conference on Parallel Problem Solving from Nature, 282–291. DOI: https://doi.org/10.1007/978-3-319-10762-2_28 "A hyper-heuristic ensemble method for static job-shop scheduling". E.Hart, K.Sim. 2016. Evolutionary computation 24(4), 609-635. DOI: https://dx.doi.org/10.1162/EVCO_a_00183 == Awards and recognition == 2016, Featured article on Lifelong Learning in Optimisation, IFORS newsletter 2016, "A Combined Generative and Selective Hyper-heuristic for the Vehicle Routing Problem" presented at GECCO 2016 (Denver, USA), ACM 2016, "A Hybrid Parameter Control Approach Applied to a Diversity-based Multi-objective Memetic Algorithm for Frequency Assignment Problems" presented at WCCI 2016 (Vancouver, Canada), IEEE 2017, Keynote Speaker, 2017 International Joint Conference on Computational Intelligence 2018, Bronze Award in International Human-Competitive Awards (Humies), International Conference on Genetic and Evolutionary Computation, Kyoto Japan 2018, Nomination for best paper award, GECCO 18, Kyoto, Japan 2022, Elected Fellow of the Royal Society of Edinburgh

    Read more →
  • Connected-component labeling

    Connected-component labeling

    Connected-component labeling (CCL), connected-component analysis (CCA), blob extraction, region labeling, blob discovery, or region extraction is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic. Connected-component labeling is not to be confused with segmentation. Connected-component labeling is used in computer vision to detect connected regions in binary digital images, although color images and data with higher dimensionality can also be processed. When integrated into an image recognition system or human-computer interaction interface, connected component labeling can operate on a variety of information. Blob extraction is generally performed on the resulting binary image from a thresholding step, but it can be applicable to gray-scale and color images as well. Blobs may be counted, filtered, and tracked. Blob extraction is related to but distinct from blob detection. == Overview == A graph, containing vertices and connecting edges, is constructed from relevant input data. The vertices contain information required by the comparison heuristic, while the edges indicate connected 'neighbors'. An algorithm traverses the graph, labeling the vertices based on the connectivity and relative values of their neighbors. Connectivity is determined by the medium; image graphs, for example, can be 4-connected neighborhood or 8-connected neighborhood. Following the labeling stage, the graph may be partitioned into subsets, after which the original information can be recovered and processed . == Definition == The usage of the term connected-component labeling (CCL) and its definition is quite consistent in the academic literature, whereas connected-component analysis (CCA) varies both in terminology and in its definition of the problem. Rosenfeld et al. define connected components labeling as the “[c]reation of a labeled image in which the positions associated with the same connected component of the binary input image have a unique label.” Shapiro et al. define CCL as an operator whose “input is a binary image and [...] output is a symbolic image in which the label assigned to each pixel is an integer uniquely identifying the connected component to which that pixel belongs.” There is no consensus on the definition of CCA in the academic literature. It is often used interchangeably with CCL. A more extensive definition is given by Shapiro et al.: “Connected component analysis consists of connected component labeling of the black pixels followed by property measurement of the component regions and decision making.” The definition for connected-component analysis presented here is more general, taking the thoughts expressed in into account. == Algorithms == The algorithms discussed can be generalised to arbitrary dimensions, albeit with increased time and space complexity. === One component at a time === This is a fast and very simple method to implement and understand. It is based on graph traversal methods in graph theory. In short, once the first pixel of a connected component is found, all the connected pixels of that connected component are labelled before going onto the next pixel in the image. This algorithm is part of Vincent and Soille's watershed segmentation algorithm, other implementations also exist. In order to do that a linked list is formed that will keep the indexes of the pixels that are connected to each other, steps (2) and (3) below. The method of defining the linked list specifies the use of a depth or a breadth first search. For this particular application, there is no difference which strategy to use. The simplest kind of a last in first out queue implemented as a singly linked list will result in a depth first search strategy. It is assumed that the input image is a binary image, with pixels being either background or foreground and that the connected components in the foreground pixels are desired. The algorithm steps can be written as: Start from the first pixel in the image. Set current label to 1. Go to (2). If this pixel is a foreground pixel and it is not already labelled, give it the current label and add it as the first element in a queue, then go to (3). If it is a background pixel or it was already labelled, then repeat (2) for the next pixel in the image. Pop out an element from the queue, and look at its neighbours (based on any type of connectivity). If a neighbour is a foreground pixel and is not already labelled, give it the current label and add it to the queue. Repeat (3) until there are no more elements in the queue. Go to (2) for the next pixel in the image and increment current label by 1. Note that the pixels are labelled before being put into the queue. The queue will only keep a pixel to check its neighbours and add them to the queue if necessary. This algorithm only needs to check the neighbours of each foreground pixel once and doesn't check the neighbours of background pixels. The pseudocode is: algorithm OneComponentAtATime(data) input : imageData[xDim][yDim] initialization : label = 0, labelArray[xDim][yDim] = 0, statusArray[xDim][yDim] = false, queue1, queue2; for i = 0 to xDim do for j = 0 to yDim do if imageData[i][j] has not been processed do if imageData[i][j] is a foreground pixel do check its four neighbors(north, south, east, west) : if neighbor is not processed do if neighbor is a foreground pixel do add it to queue1 else update its status to processed end if labelArray[i][j] = label (give label) statusArray[i][j] = true (update status) while queue1 is not empty do For each pixel in the queue do : check its four neighbors if neighbor is not processed do if neighbor is a foreground pixel do add it to queue2 else update its status to processed end if give it the current label update its status to processed remove the current element from queue1 copy queue2 into queue1 end While increase the label end if else update its status to processed end if end if end if end for end for === Two-pass === Relatively simple to implement and understand, the two-pass algorithm, (also known as the Hoshen–Kopelman algorithm) iterates through 2-dimensional binary data. The algorithm makes two passes over the image: the first pass to assign temporary labels and record equivalences, and the second pass to replace each temporary label by the smallest label of its equivalence class. The input data can be modified in situ (which carries the risk of data corruption), or labeling information can be maintained in an additional data structure. Connectivity checks are carried out by checking neighbor pixels' labels (neighbor elements whose labels are not assigned yet are ignored), or say, the north-east, the north, the north-west and the west of the current pixel (assuming 8-connectivity). 4-connectivity uses only north and west neighbors of the current pixel. The following conditions are checked to determine the value of the label to be assigned to the current pixel (4-connectivity is assumed) Conditions to check: Does the pixel to the left (west) have the same value as the current pixel? Yes – We are in the same region. Assign the same label to the current pixel No – Check next condition Do both pixels to the north and west of the current pixel have the same value as the current pixel but not the same label? Yes – We know that the north and west pixels belong to the same region and must be merged. Assign the current pixel the minimum of the north and west labels, and record their equivalence relationship No – Check next condition Does the pixel to the left (west) have a different value and the one to the north the same value as the current pixel? Yes – Assign the label of the north pixel to the current pixel No – Check next condition Do the pixel's north and west neighbors have different pixel values than current pixel? Yes – Create a new label id and assign it to the current pixel The algorithm continues this way, and creates new region labels whenever necessary. The key to a fast algorithm, however, is how this merging is done. This algorithm uses the union-find data structure which provides excellent performance for keeping track of equivalence relationships. Union-find essentially stores labels which correspond to the same blob in a disjoint-set data structure, making it easy to remember the equivalence of two labels by the use of an interface method E.g.: findSet(l). findSet(l) returns the minimum label value that is equivalent to the function argument 'l'. Once the initial labeling and equivalence recording is completed, the second pass merely replaces each pixel label with its equivalent disjoint-set representative element. A faster-scanning algorithm for connected-region extraction is presented below. On the first pass: Iterate through each element of the data by column, then by row (Raster Scanning) If the element is not the background Get the neighboring elements of the current element If there are no neighbors, uniquely

    Read more →
  • AI Snake Oil

    AI Snake Oil

    AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference is a 2024 non-fiction book written by scholars Arvind Narayanan and Sayash Kapoor. It is a critique of the tech industry's overly inflated promises and capabilities of artificial intelligence (AI) as well as a debunking of the flawed science fueling AI hype while attempting to outline both the potential positives and negatives that come with different modes of the technology. == Contents == === Publication === The book was published in September 2024 by the Princeton University Press. AI Snake Oil consists of 360 pages and features eight chapters, and sections for acknowledgements, references, and an index. An updated edition with a new preface and epilogue by the authors was published in September 2025. The authors use the term "AI snake oil" derived from the U.S. idiom for a fraudulent remedy, to describe overhyped AI systems. === Chapter one: Introduction === Narayanan and Kapoor argue that many individuals do not yet have the literacy to detect functioning aspects of AI compared to potential snake oil, which they identify as "AI that does not and cannot work as advertised". Some of the major examples utilized by the authors include Allstate's 2013 use of predictive AI, as well as the concern surrounding actors and AI attempting to replicate or use their likeness. Important discussions regarding discrimination are brought up and explored in the first chapter, including the false arrests of six Black individuals due to errors with AI facial recognition tools. The chapter concludes with a comparison to the Industrial Revolution, where Narayanan and Kapoor highlight the extensive human labour that is necessary for artificial intelligence technologies to function. === Chapter two: How Predictive AI Goes Wrong === Chapter two focuses on predictive artificial intelligence, and criticizes the overestimation of the capabilities of the technology. === Chapter three: Why Can't AI Predict the Future? === Chapter three works to inform the reader about the history of early computational prediction attempts, with examples from companies like Simulatics. === Chapter four: The Long Road to Generative AI === The fourth chapter goes in more in-depth in explorations of generative AI. Generative AI software examples include ChatGPT, Midjourney, and DALL-E. The section begins with a positive example of generative AI. As the chapter progresses, the authors begin to provide examples of harm produced by generative AI, including the suicide of a Belgian man after connecting with Chai, a generative chatbot. Issues of deepfakes and preservation of artistic property are also discussed. The use of generative AI to create non-consensual pornographic deepfake content is discussed in relation to female celebrities. === Chapter five: Is Advanced AI an Existential Threat? === The fifth chapter draws attention the AGI, or Artificial General Intelligence. The authors describe AGI as "AI that can perform most or economically relevant tasks as effectively as any human". They summarize that many contributors to the field of artificial intelligence believe AGI to be an impending threat that demands attention. However, they argue that the perceived threat of AGI would only exist if the technology continually functioned reliably. In order to better illustrate the hype surrounding AGI, Narayanan and Kapoor use the Ladder of Generality, which is described as a visual tool in which "each rung represents a way of computing that is more flexible, and more general, than the previous one". They note that we are not yet aware of the next rungs on the ladder, or if the ladder will eventually result in a dead end. The rungs that have been identified so far are as follows: (0, or floor) special purpose hardware, (1) programmable computers, (2) stored program computers, (3) machine learning, (4) deep learning, (5) pretrained models, and, finally, (6) instruction-tuned models. The potential for future rungs and what those rungs might be are currently undetermined. The chapter also discusses the ELIZA effect, which Lawrence Switzky discusses in his article "ELIZA Effects". Switzky attributes the coined term ELIZA Effect to Sherry Turke, who defined it as "our more general tendency to treat responsive computer programs as more intelligent than they really are". === Chapter six: Why Can't AI Fix Social Media? === The sixth chapter focuses on content moderation, why it is important, and how it has been and could be affected by artificial automation. The first issue raised in regard to AI-driven content moderation is the inability for computers and machines to understand context and nuance, resulting in potential for discriminatory moderation and shadow banning. While they note that there are issues with automating content moderation, Narayanan and Kapoor also highlight the psychological impact on human content moderators and their labour. They indicate the hidden labour behind moderation, which is often outsourced to less developed countries, where labourers sort through potentially traumatizing content for pay. However, the discussion focuses more heavily on why automated moderation can be problematic, including discriminatory algorithms and lack of nuance. To balance their argument, issues of discrimination and bias are also discussed in relation the human content moderators. To automate moderation, there are two types of AI used, which are fingerprint matching and machine learning. === Chapter seven: Why Do Myths about AI Persist? === The seventh chapter outlines possible factors that contribute to hype surrounding AI. Narayanan and Kapoor explain how companies often promote their new AI models without properly disclosing how the model works, and what it is learning from. They attribute hype to several different groups, including journalists, researchers, and companies. They explain the impact of companies and the misplaced hype that they spread can be attributed to greed and a desire to grow corporate funds. For journalists, one of the stated sources of hype, they argue that news media has a tendency to prioritize financial incentives over validity and quality of writing. As well, Narayanan and Kapoor point out the emergence of company statement regurgitation in news media, leading to clickbait. Hype from researchers is potentially linked to lack of reproducibility in studies as well as leakage, which occurs when AI models are tested on their training data. === Chapter eight: Where do we go from here? === The final chapter, chapter eight, turns its attention to the future. The authors express their ideas and predictions for how the technology will evolve and be utilized in the upcoming years. == Authors == Author Narayanan is a computer science professor at Princeton University. Kapoor is a doctoral candidate at the same university, and both scholars are located at the Center for Information Technology at Princeton. In 2023, Narayanan and Kapoor appeared on the TIME100 Artificial Intelligence list, which features influential figures in the field. == Reception == Nature, a science and technology peer-reviewed journal, released an article highlighting the top "10 essential reads from the past year", listing Arvind Narayanan and Sayash Kapoor's AI Snake Oil. The article states the that text is "one of the best on this controversial subject". Elizabeth Quill, in her review of the text in Science News, writes that the authors "squarely achieve their stated goal: to empower people to distinguish AI that works well from AI snake oil". Joshua Rothman of The New Yorker writes that "compared with many technologists, Narayanan, Kapoor, and Vallor [Shannon Vallor, University of Edinburgh], are deeply skeptical about today's A.I. technology and what it can achieve. Perhaps they shouldn't be". Rothman argues, following an interview with prominent computer scientist Geoffrey Hinton of University of Toronto, that the potential for AI to replicate complexity is already here and continues to be heavily funded, enhancing the prospective capabilities of the technology. However, he does praise the author's ability to address questions regarding the existential human experience. Alexya Martinez discusses the text in a book review for Journalism and Mass Communication Quarterly, critiquing AI Snake Oil for its extensive focus on the West. Martinez writes that Narayanan and Kapoor "do not fully explore how AI impacts other countries", and suggests more focus on countries outside of the United States to enhance their argument.

    Read more →
  • Agent mining

    Agent mining

    Agent mining is a research field that combines two areas of computer science: multiagent systems and data mining. It explores how intelligent computer agents can work together to discover, analyze, and learn from large amounts of data more effectively than traditional methods. == Historical context == The interaction and the integration between multiagent systems and data mining have a long history. The very early work on agent mining focused on agent-based knowledge discovery, agent-based distributed data mining, and agent-based distributed machine learning, and using data mining to enhance agent intelligence. The International Workshop on Agents and Data Mining Interaction has been held for more than 10 times, co-located with the International Conference on Autonomous Agents and Multi-Agent Systems. Several proceedings are available from Springer Lecture Notes in Computer Science.

    Read more →
  • Minne Atairu

    Minne Atairu

    Minne Atairu is a Nigerian interdisciplinary artist, a recipient of the 2021 Global South Award Lumen Prize for Art and Technology. She generates synthetic Benin Bronzes through recombination of historical fragments, sculptures, texts, images, and sounds. == Early life and education == Atairu was born in Benin, Nigeria. She holds a bachelor's degree in art history from the University of Maiduguri in Maiduguri, Nigeria; a master's degree in museum studies from the George Washington University in Washington, D.C.; and a doctorate in art education from Teachers College, Columbia University in New York City. Her academic research integrates artificial intelligence, art/museum education and hip-hop based education. == Works == Atairu's artmaking involves using artificial intelligence (AI; such as StyleGAN, GPT-3) to make artwork. She uses tools such as Midjourney and Blender software to develop her works. === Mami Wata === Her first work is a Yoruba goddess called Mami Wata where she used Midjourney in generating the images. === To the Hand === For her 2023 installation To the Hand at The Shed arts center, she worked with Blender to convert text into 3D-printed sculptures made of corn starch or sugarcane infused with bronze. The rings of ground terra-cotta that surround the sculpture represent the walls and deep moats of Benin. == Publications == Atairu, Minne (February 1, 2024). "Reimagining Benin Bronzes using generative adversarial networks". AI & Society. 39 (1): 91–102. doi:10.1007/s00146-023-01761-7. ISSN 1435-5655.

    Read more →
  • Direct voice input

    Direct voice input

    Direct voice input (DVI), sometimes called voice input control (VIC), is a style of human–machine interaction "HMI" in which the user makes voice commands to issue instructions to the machine through speech recognition. In the field of military aviation, DVI has been introduced into the cockpits of several modern military aircraft, such as the Eurofighter Typhoon, the Lockheed Martin F-35 Lightning II, the Dassault Rafale, the KF-21 Boramae and the Saab JAS 39 Gripen. Such systems have also been used for various other purposes, including industry control systems and speech recognition assistance for impaired individuals. == Overview == DVI systems can be divided into two major categories of functionality: "user-dependent" or "user-independent". A user-dependent system requires that a personal voice template to be generated for a specific person; the template for this individual has to be loaded onto their assigned machine prior to use of the DVI system for it to function properly. In contrast, a user-independent system does not require any personal voice template, being intended to respond correctly to the voice of any user. They can also be categorised between "discrete recognition" and "continuous recognition". Users of a discrete recognition system must pause between each word so that the DVI system can identify the separations between each word, while a continuous speech recognition system is capable of understanding a normal rate of speech. During the mid-2000s, researchers at the National Aerospace Laboratory in the Netherlands examined the use of DVI in the "GRACE" simulator; a total of twelve pilots participated in the ensuing experiment. The tests performed reportedly revealed that, while the hardware itself functioned well, several improvements were desirable prior to real-world deployment on aircraft since DVI operations actually consumed more time in comparison to traditional existing methods. Recommendations for improvements included the adoption of simpler syntax, the achievement of a greater recognition rate, and a decrease in response times; all of the issues encountered were determined to be of a technological nature, and were deemed feasible to resolve. The researchers concluded that in cockpits, especially during emergencies where pilots have to operate entirely on their own, a DVI system could be highly relevant, but that it was not of crucial importance during most other conceivable scenarios. Around the same time, evaluations of DVI systems for civil aviation purposes were conducted within the framework of Project SafeSound, coordinated by the European Union. It involved the observation of pilot workloads in real-world cockpits and contrasting them against pilot activity in flight simulators using both conventional systems and DVI assistance. The project aimed to enhance aviation safety and to decrease the workload in both ground and flight operations via the application of enhanced audio functions. == Applications == === Aviation === Prior to its widespread deployment, a handful of conventional military aircraft were converted to trial DVI systems; examples include the Harrier AV-8B and F-16 VISTA. In another case, a General Dynamics F-16 Fighting Falcon simulator was modified with DVI for a voice control study that was undertaken by the Royal Netherlands Air Force. DVI trials have also been conducted on helicopters, including the Boeing AH-64 Apache, showing the potential to improve flight safety and mission effectiveness. Numerous modern fighter aircraft have been outfitted with DVI systems, often in combination with various other man-machine interface schemes, such as HOTAS-compliant controls and other advanced control technologies. The combination of Voice and HOTAS control schemes has sometimes been referred to as the "V-TAS" concept. A prominent fighter aircraft to be furnished with a V-TAS cockpit is the Eurofighter Typhoon. The Lockheed Martin F-35 Lightning II also features a DVI system, which was developed by Adacel. Other examples includes the Dassault Rafale and the Saab JAS 39 Gripen. Numerous aircraft have been planned to use DVI. At one stage, the United States Air Force had sought to integrate DVI upon the Lockheed Martin F-22 Raptor; however, the technology was eventually judged to pose too many technical risks at that point in time, and thus such efforts were abandoned. === Personal === By 1990, working prototypes of speech recognition systems were being demonstrated; these were being promoted for the purpose of providing an effective man-machine interface for individuals with impaired speech. Techniques employed included time-encoded digital speech and automatic token set selection. Investigations of these early DVI systems reportedly included the use of automatic diagnostic routines and limited-scale trials using volunteers. During the 2010s, various companies were offering voice recognition systems to the general public in the form of personal digital assistants. One example is the Google Voice service, which allows users to pose questions via a DVI package installed on either a personal computer, tablet, or mobile phone. Numerous digital assistants have been developed, such as Amazon Echo, Siri, and Cortana, that use DVI to interact with users. === Commercial === DVI technology has enabled automated telephone systems to be widely deployed. Many companies commonly use centralised phone systems that route callers to the correct department via such methods. Various car manufacturers have also furnished their road vehicles with DVI systems; these typically allow drivers to control infotainment systems and interact with mobile phones with more convenience than legacy methods. During the late 1980s, investigations into the use of DVI systems for controlling CNC machines and other manufacturing apparatus were underway. During the 2010s, such systems were being used for logistics and warehouse management purposes.

    Read more →
  • The Stories of Ibis

    The Stories of Ibis

    The Stories of Ibis (アイの物語, Ai no Monogatari) is a Japanese science-fiction light novel by Hiroshi Yamamoto (山本 弘) and translated by Takami Nieda. Yamamoto considered this to be an easier read than his earlier science fiction novel 'God Never Keeps Silent' because of its "light novel touch". The light novel was published in Japanese by Kadokawa Shoten and in English by Viz Media under their 'Haikasoru' imprint. The Stories of Ibis is told through a collection of short stories. All but two had been previously published. The two that Yamamoto wrote for the novel were 'The Day Shion Came' and 'AI's Story'. This is similar to The Illustrated Man by Ray Bradbury. Yamamoto drew from Bradbury's idea of short stories that were loosely connected. He represented this influence in the novel by giving Ibis a facial tattoo. == Plot == The Stories of Ibis begins with a wandering storyteller who encounters Ibis. He has the mindset that all robots are a threat to humanity and must be fought against for survival. He attacks the robot Ibis, not aware of who she is, as a result of his mindset. Ibis tells the storyteller that she is far more proficient in battle. During the battle the storyteller becomes injured and Ibis takes him to an android hospital to care for him. While he is recovering Ibis offers to tell him stories. While originally skeptical he agrees after Ibis makes it clear that the stories are not taboo. The space after each story is referred to as intermission and is a time for Ibis to comment on the story she just told. === The Universe on my Hands === The story is about a group of friends who are writing a science fiction story over the internet. One of the group members kills someone in real life. The rest of the short story is about how the group fights to convince this man to not commit suicide, but to turn himself in. He resolves to turn himself in, being hopeful to the future because he knows he has friends who care about him. The ending words of the story are a commentary. While the story they were writing was not real, the emotions they were feeling were real. === A Romance in Virtual Space === This is another story about human interactions over the internet. The device that allows people to enter virtual reality (VR) is MUGEN Net. Such devices are extremely expensive and most people need to go to a public server to use one. However the girl's parents in this story are wealthy enough to own one. This girl is shopping in VR when a boy meets her and asks her out for ice cream. All goes well and they plan for another. After some time of VR dating and awesome adventures with a female heroine, they agree to meet up in real life. He discovers that in reality, she is blind, yet he thinks she is brave and they continue dating. It's a wonderful short story of a secret utopia inside a dystopian culture of technology. === Mirror Girl === A short story about an artificial intelligence that grows over time with human interaction. The inspiration for this story was Ray Bradbury's I Sing the Body Electric. The mirror girl Shalice starts off with basic knowledge and by interacting with her owner develops. The owner grows up and marries a technician who incubates Shalice by teaching her in the virtual world at many thousand times faster than average life. When he is done, Strong Eye is created. Strong Eye is the fully developed and completely intelligent AI. === Black Hole Diver === A futuristic story about an artificial space station and people who go diving into a black hole. The space station cannot stop people but is sorry that they go to their deaths because none of them get past the event horizon. Then one girl comes who has the space ship, the training, and the research necessary to attempt to dive into the black hole. As she goes into the black hole the space station can no longer observe. She may have made it, she could have been destroyed. === A World Where Justice is Just === An anime flavored story about the intelligence of people being scanned onto a computer network. The AIs in the network fight crime and live repeating lives. At the end of each year they start anew, but different story lines. Thousands of 'extras' populate the network and are the ones subject to harm and deletion. The protagonist has a pen pal in real life who explains to her that the real world is under attack and that there are no respawns and no extras. The AI finds this so cruel that people would willingly kill each other when they can't come back. === The Day Shion Came === The stories leading up to this were all relatively short. This and the next took up over 100 pages each. This is a story about an android named Shion who works in a Japanese nursing facility. Shion comes with only extensive nursing training but lacks the knowledge of how to communicate with the residents. After months of training she informs her adviser that she believes all humans have dementia, which explains their irrational behavior. Near the end of the story one of the residents threatens suicide but Shion convinces him to step down and be rational. === AI's Story === The culminating story of the entire novel. It is about Ibis herself. She starts off as a virtual reality fighting program and over time develops intelligence. Her master gains enough funds to create her a body in the real world or level 0. There is significant hate against TAIs (True Artificial Intelligence) in the real world. Ibis and her friend Raven rebel against their masters to make a point. Human hatred was destroying them. After many years robots took prevalence and most humans realized they were not worthy to be the guardians of Earth and died in peace. The remaining population was stubborn and fought against the robots for centuries. The storyteller is a child of this generation, being raised in hatred and ignorance. The robots sought to take him captive, and teach him the truth so that he could go to the villages where people lived and teach them the truth. The whole point was they cared for the humans and wanted them to live in peace, rather than fighting for their survival. == Reception == It was reviewed by the Denver Post to be an "excellent novel". Being a Japanese novel translated to English, it has a small audience. The novel was given a 3.85 of 5 by the reviewers at Librarything.com. The reviewers of Google Books gave it a 4.33 of 5.

    Read more →
  • Riffusion

    Riffusion

    Riffusion is a neural network, designed by Seth Forsgren and Hayk Martiros, that generates music using images of sound rather than audio. The resulting music has been described as "de otro mundo" (otherworldly), although unlikely to replace man-made music. The model was made available on December 15, 2022, with the code also freely available on GitHub. The first version of Riffusion was created as a fine-tuning of Stable Diffusion, an existing open-source model for generating images from text prompts, on spectrograms, resulting in a model which used text prompts to generate image files which could then be put through an inverse Fourier transform and converted into audio files. While these files were only several seconds long, the model could also use latent space between outputs to interpolate different files together (using the img2img capabilities of SD). It was one of many models derived from Stable Diffusion. In December 2022, Mubert similarly used Stable Diffusion to turn descriptive text into music loops. In January 2023, Google published a paper on their own text-to-music generator called MusicLM. Forsgren and Martiros formed a startup, also called Riffusion, and raised $4 million in venture capital funding in October 2023.

    Read more →
  • KitKat (cat)

    KitKat (cat)

    KitKat was a bodega cat from the Mission District of San Francisco who was killed by a Waymo car on October 27, 2025. Locals built altars and the death has raised comments about the safety of self-driving cars. == Life == Mike Zeidan, the owner of Randa's Market, adopted KitKat as a stray to help keep rodents out of his store. KitKat lived in Randa's Market for six years and was well-loved by the neighborhood, including an appearance on a shop cats map that went viral in 2022 as a "particularly friendly cat". After KitKat arrived at the bodega, customers were said to come more often, and regularly brought the cat food and gifts. == Death == At around 11:40 pm on October 27, 2025, witnesses saw KitKat sitting in front of a stopped Waymo car for seven seconds. He walked under the car as the car pulled out, and the right rear tire ran over the back half of his body. A bartender who was taking a cigarette break used a sandwich board sign as a stretcher and took KitKat to an emergency animal clinic. An hour later, KitKat was pronounced dead. Waymo confirmed that the cat was killed by one of its vehicles on October 30. Surveillance footage of the incident was released in December. From Waymo's report to the National Highway Traffic Safety Administration (NHTSA): The Waymo AV was stopped next to the curb for a passenger pickup facing east on 16th Street. As the passengers were boarding the Waymo AV, a cat approached the Waymo AV from the southern sidewalk of 16th Street and sat in the roadway partially under the front right corner of the Waymo AV. A pedestrian approached the Waymo AV from the east on the southern sidewalk of 16th Street and began crouching near the front of the Waymo AV, stepping partially into the roadway, appearing to reach for the cat. As they did so, the cat moved farther from the sidewalk under the Waymo AV and the pedestrian stepped back onto the sidewalk. The Waymo AV then departed the pickup location and the rear right tire made contact with the cat. At the time of impact, the Waymo AV's Level 4 ADS was engaged in autonomous mode. Waymo later received notice that the cat did not survive. The passengers in the Waymo AV did not have seatbelts fastened at the time, having just boarded the Waymo AV. Prior to KitKat's death, the NHTSA had logged 14 collisions between Waymo cars and animals, of which 5 were confirmed fatalities. == Aftermath == After KitKat's death, an altar was created outside Randa's Market. People left flowers, candles, cat food, written notes, and Kit Kat candy bars in the cat's honor. A city worker took down the memorial for fire safety reasons, but neighbors built it again. Local supervisor Jackie Fielder held a rally called "Justice for KitKat" in support of a non-binding San Francisco resolution to shift decision-making about the operation of self-driving cars from the state to individual counties. Critics say that the resolution is performative because it is non-binding, that local control would make autonomous vehicle operation impractical, and that Waymo is still far less dangerous to animals than human drivers. Elon Musk commented that "many pets will be saved by autonomy". There are multiple meme coins inspired by KitKat.

    Read more →
  • Tesla Dojo

    Tesla Dojo

    Tesla Dojo is a series of supercomputers designed and built by Tesla for computer vision video processing and recognition. It was used for training Tesla's machine learning models to improve its Full Self-Driving (FSD) advanced driver-assistance system. It went into production in July 2023. Dojo's goal was to efficiently process millions of terabytes of video data captured from real-life driving situations from Tesla's 4+ million cars. This goal led to a considerably different architecture than conventional supercomputer designs. In August 2025, Bloomberg News reported that the Dojo project had been disbanded, though it was restarted in January 2026. == History == Tesla operates several massively parallel computing clusters for developing its Autopilot advanced driver assistance system. Its primary unnamed cluster using 5,760 Nvidia A100 graphics processing units (GPUs) was touted by Andrej Karpathy in 2021 at the fourth International Joint Conference on Computer Vision and Pattern Recognition (CCVPR 2021) to be "roughly the number five supercomputer in the world" at approximately 81.6 petaflops, based on scaling the performance of the Nvidia Selene supercomputer, which uses similar components. However, the performance of the primary Tesla GPU cluster has been disputed, as it was not clear if this was measured using single-precision or double-precision floating point numbers (FP32 or FP64). Tesla also operates a second 4,032 GPU cluster for training and a third 1,752 GPU cluster for automatic labeling of objects. The primary unnamed Tesla GPU cluster has been used for processing one million video clips, each ten seconds long, taken from Tesla Autopilot cameras operating in Tesla cars in the real world, running at 36 frames per second. Collectively, these video clips contained six billion object labels, with depth and velocity data; the total size of the data set was 1.5 petabytes. This data set was used for training a neural network intended to help Autopilot computers in Tesla cars understand roads. By August 2022, Tesla had upgraded the primary GPU cluster to 7,360 GPUs. Dojo was first mentioned by Elon Musk in April 2019 during Tesla's "Autonomy Investor Day". In August 2020, Musk stated it was "about a year away" due to power and thermal issues. Dojo was officially announced at Tesla's Artificial Intelligence (AI) Day on August 19, 2021. Tesla revealed details of the D1 chip and its plans for "Project Dojo", a datacenter that would house 3,000 D1 chips; the first "Training Tile" had been completed and delivered the week before. In October 2021, Tesla released a "Dojo Technology" whitepaper describing the Configurable Float8 (CFloat8) and Configurable Float16 (CFloat16) floating point formats and arithmetic operations as an extension of Institute of Electrical and Electronics Engineers (IEEE) standard 754. At the follow-up AI Day in September 2022, Tesla announced it had built several System Trays and one Cabinet. During a test, the company stated that Project Dojo drew 2.3 megawatts (MW) of power before tripping a local San Jose, California power substation. At the time, Tesla was assembling one Training Tile per day. In August 2023, Tesla powered on Dojo for production use as well as a new training cluster configured with 10,000 Nvidia H100 GPUs. In January 2024, Musk described Dojo as "a long shot worth taking because the payoff is potentially very high. But it's not something that is a high probability." In June 2024, Musk explained that ongoing construction work at Gigafactory Texas is for a computing cluster claiming that it is planned to comprise an even mix of "Tesla AI" and Nvidia/other hardware with a total thermal design power of at first 130 MW and eventually exceeding 500 MW. In August 2025, Bloomberg News reported that the Dojo project was disbanded, though Musk announced it would be restarted in January 2026 with a new chip iteration. == Technical architecture == The fundamental unit of the Dojo supercomputer is the D1 chip, designed by a team at Tesla led by ex-AMD CPU designer Ganesh Venkataramanan, including Emil Talpes, Debjit Das Sarma, Douglas Williams, Bill Chang, and Rajiv Kurian. The D1 chip is manufactured by the Taiwan Semiconductor Manufacturing Company (TSMC) using 7 nanometer (nm) semiconductor nodes, has 50 billion transistors and a large die size of 645 mm2 (1.0 square inch). Updating at Artificial Intelligence (AI) Day in 2022, Tesla announced that Dojo would scale by deploying multiple ExaPODs, in which there would be: 10 Cabinets per ExaPOD (1,062,000 cores, 3,000 D1 chips) 2 System Trays per Cabinet (106,200 cores, 300 D1 chips) 6 Training Tiles per System Tray (53,100 cores, along with host interface hardware) 25 D1 chips per Training Tile (8,850 cores) 354 computing cores per D1 chip According to Venkataramanan, Tesla's senior director of Autopilot hardware, Dojo will have more than an exaflop (a million teraflops) of computing power. For comparison, according to Nvidia, in August 2021, the (pre-Dojo) Tesla AI-training center used 720 nodes, each with eight Nvidia A100 Tensor Core GPUs for 5,760 GPUs in total, providing up to 1.8 exaflops of performance. === D1 chip === Each node (computing core) of the D1 processing chip is a general purpose 64-bit CPU with a superscalar core. It supports internal instruction-level parallelism, and includes simultaneous multithreading (SMT). It doesn't support virtual memory and uses limited memory protection mechanisms. Dojo software/applications manage chip resources. The D1 instruction set supports both 64-bit scalar and 64-byte single instruction, multiple data (SIMD) vector instructions. The integer unit mixes reduced instruction set computer (RISC-V) and custom instructions, supporting 8, 16, 32, or 64 bit integers. The custom vector math unit is optimized for machine learning kernels and supports multiple data formats, with a mix of precisions and numerical ranges, many of which are compiler composable. Up to 16 vector formats can be used simultaneously. ==== Node ==== Each D1 node uses a 32-byte fetch window holding up to eight instructions. These instructions are fed to an eight-wide decoder which supports two threads per cycle, followed by a four-wide, four-way SMT scalar scheduler that has two integer units, two address units, and one register file per thread. Vector instructions are passed further down the pipeline to a dedicated vector scheduler with two-way SMT, which feeds either a 64-byte SIMD unit or four 8×8×4 matrix multiplication units. The network on-chip (NOC) router links cores into a two-dimensional mesh network. It can send one packet in and one packet out in all four directions to/from each neighbor node, along with one 64-byte read and one 64-byte write to local SRAM per clock cycle. Hardware native operations transfer data, semaphores and barrier constraints across memories and CPUs. System-wide double data rate 4 (DDR4) synchronous dynamic random-access memory (SDRAM) memory works like bulk storage. ==== Memory ==== Each core has a 1.25 megabytes (MB) of SRAM main memory. Load and store speeds reach 400 gigabytes (GB) per second and 270 GB/sec, respectively. The chip has explicit core-to-core data transfer instructions. Each SRAM has a unique list parser that feeds a pair of decoders and a gather engine that feeds the vector register file, which together can directly transfer information across nodes. ==== Die ==== Twelve nodes (cores) are grouped into a local block. Nodes are arranged in an 18×20 array on a single die, of which 354 cores are available for applications. The die runs at 2 gigahertz (GHz) and totals 440 MB of SRAM (360 cores × 1.25 MB/core). It reaches 376 teraflops using 16-bit brain floating point (BF16) numbers or using configurable 8-bit floating point (CFloat8) numbers, which is a Tesla proposal, and 22 teraflops at FP32. Each die comprises 576 bi-directional serializer/deserializer (SerDes) channels along the perimeter to link to other dies, and moves 8 TB/sec across all four die edges. Each D1 chip has a thermal design power of approximately 400 watts. === Training Tile === The water-cooled Training Tile packages 25 D1 chips into a 5×5 array. Each tile supports 36 TB/sec of aggregate bandwidth via 40 input/output (I/O) chips - half the bandwidth of the chip mesh network. Each tile supports 10 TB/sec of on-tile bandwidth. Each tile has 11 GB of SRAM memory (25 D1 chips × 360 cores/D1 × 1.25 MB/core). Each tile achieves 9 petaflops at BF16/CFloat8 precision (25 D1 chips × 376 TFLOP/D1). Each tile consumes 15 kilowatts; 288 amperes at 52 volts. === System Tray === Six tiles are aggregated into a System Tray, which is integrated with a host interface. Each host interface includes 512 x86 cores, providing a Linux-based user environment. Previously, the Dojo System Tray was known as the Training Matrix, which includes six Training Tiles, 20 Dojo Interface Processor cards across four host servers, and Ethernet-l

    Read more →
  • Pommerman Challenge

    Pommerman Challenge

    The Pommerman Challenge is a multi-agent game to test autonomous artificial intelligence systems. == Game structure == Two-agent team compete against each other on an 11 x 11 board. Each agent can observe only part of the board, and the agents cannot communicate. The goal is to knock down the opponents. Agents place explosives to destroy walls and collect power-ups that appear from those walls, while avoiding death. Game objects can move unpredictably or be moved by an agent. == Play == The game involves real-time decision making. Agents must choose moves in about .1 seconds. == Algorithms == The real-time requirement limits the use of compute-heavy techniques such as Monte Carlo tree search. The branching factor at each move can be as large as 1,296, because all four agents act in each step, choosing among six possibilities. The agents choose by accounting for explosions, which have lifetimes of 10 steps. Explosions derail tree search techniques, as searches with less than 10 levels ignore explosions while deeper searches consider too many choices (given the branching factor). A hybrid approach uses a limited-depth tree search followed by exploring a deterministic/pessimistic scenario. Limiting the depth keeps the search tree small. The deterministic approach can predict far in the future, by omitting branching. "Good" actions are often those that perform well under pessimistic scenarios, particularly if safety is important. Identifying the worst sequence of positions for an object can suggest where to move it. After generating pessimistic scenarios, the agent quantifies the survivability of each move, notionally the number of positions in which the agent can then remain safely (without encountering other agents). == Competitions == 3 competitions were organized with slightly changing rules during 2018–2019. === Online - FFA === This round was a warm-up online event, where each competitor controlled only one agent. Results: 1st: Agent47Agent by Yichen Gong 2nd: aiKiller by Márton Görög === NeurIPS 2018 - Team === The first Pommerman competition with in-person finals. Results: 1st: hakozakijunctions by Toshihiro Takahashi 2nd: eisenach by Márton Görög 3rd: dypm by Takayuki Osogami The 3 best performing solutions used online tree search. === NeurIPS 2019 - Team Radio === The second competition with in-person finals improved communication between teammate agents. Results: 1st: Márton Görög 2nd: Paul Jasek 3rd: Yifan Zhang

    Read more →
  • Anytime algorithm

    Anytime algorithm

    In computer science, an anytime algorithm is an algorithm that can return a valid solution to a problem even if it is interrupted before it ends. The algorithm is expected to find better and better solutions the longer it keeps running. Most algorithms run to completion: they provide a single answer after performing some fixed amount of computation. In some cases, however, the user may wish to terminate the algorithm prior to completion. The amount of computation required may be substantial, for example, and computational resources might need to be reallocated. Most algorithms either run to completion or they provide no useful solution information. Anytime algorithms, however, are able to return a partial answer, whose quality depends on the amount of computation they were able to perform. The answer generated by anytime algorithms is an approximation of the correct answer. == Names == An anytime algorithm may be also called an "interruptible algorithm". They are different from contract algorithms, which must declare a time in advance; in an anytime algorithm, a process can just announce that it is terminating. == Goals == The goal of anytime algorithms are to give intelligent systems the ability to make results of better quality in return for turn-around time. They are also supposed to be flexible in time and resources. They are important because artificial intelligence or AI algorithms can take a long time to complete results. This algorithm is designed to complete in a shorter amount of time. Also, these are intended to have a better understanding that the system is dependent and restricted to its agents and how they work cooperatively. An example is the Newton–Raphson iteration applied to finding the square root of a number. Another example that uses anytime algorithms is trajectory problems when you're aiming for a target; the object is moving through space while waiting for the algorithm to finish and even an approximate answer can significantly improve its accuracy if given early. What makes anytime algorithms unique is their ability to return many possible outcomes for any given input. An anytime algorithm uses many well defined quality measures to monitor progress in problem solving and distributed computing resources. It keeps searching for the best possible answer with the amount of time that it is given. It may not run until completion and may improve the answer if it is allowed to run longer. This is often used for large decision set problems. This would generally not provide useful information unless it is allowed to finish. While this may sound similar to dynamic programming, the difference is that it is fine-tuned through random adjustments, rather than sequential. Anytime algorithms are designed so that it can be told to stop at any time and would return the best result it has found so far. This is why it is called an interruptible algorithm. Certain anytime algorithms also maintain the last result, so that if they are given more time, they can continue from where they left off to obtain an even better result. == Decision trees == When the decider has to act, there must be some ambiguity. Also, there must be some idea about how to solve this ambiguity. This idea must be translatable to a state to action diagram. == Performance profile == The performance profile estimates the quality of the results based on the input and the amount of time that is allotted to the algorithm. The better the estimate, the sooner the result would be found. Some systems have a larger database that gives the probability that the output is the expected output. One algorithm can have several performance profiles. Most of the time performance profiles are constructed using mathematical statistics using representative cases. For example, in the traveling salesman problem, the performance profile was generated using a user-defined special program to generate the necessary statistics. In this example, the performance profile is the mapping of time to the expected results. This quality can be measured in several ways: certainty: where probability of correctness determines quality accuracy: where error bound determines quality specificity: where the amount of particulars determine quality == Algorithm prerequisites == Initial behavior: While some algorithms start with immediate guesses, others take a more calculated approach and have a start up period before making any guesses. Growth direction: How the quality of the program's "output" or result, varies as a function of the amount of time ("run time") Growth rate: Amount of increase with each step. Does it change constantly, such as in a bubble sort or does it change unpredictably? End condition: The amount of runtime needed

    Read more →