AI Generator House Design

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  List of Java software and tools

  This is a list of software and programming tools for the Java programming language, which includes frameworks, libraries, IDEs, build tools, application servers, and related projects. == Java frameworks == == Libraries == Apache Ant – build automation tool Apache Batik – SVG processing Apache Cayenne – object-relational mapping Apache Xerces – collection of software libraries for parsing, validating, serializing and manipulating XML. Applet – applet API Ardor3D – 3D graphics engine Bonita BPM – workflow engine Cassowary – constraint solving Checkstyle – static code analysis GNU Classpath – standard library implementation Colt – scientific computing and technical computing Commons Daemon – manages applications as daemons DESMO-J – discrete event simulation Diagrams.net – diagramming Disruptor – high-performance messaging Dom4j – XML processing Dynamic Languages Toolkit – support for dynamic programming languages on the JVM Echo – GUI Flying Saucer – XHTML/CSS rendering Formatting Objects Processor – XSL-FO to PDF H2 Database Engine – relational database IAIK-JCE – cryptography Internet Foundation Classes – legacy GUI JavaBeans – reusable component architecture for enabling encapsulation, events, and properties for software components JavaCC – open-source parser generator and lexical analyzer Java Class Library – standard library of Java and other JVM languages Java Native Access – provides Java programs easy access to native shared libraries without using the Java Native Interface Javolution – real-time computing Jblas – linear algebra JDBCFacade – simplifies JDBC use JExcel – Excel API JFugue – music programming JMusic – music programming Joget Workflow – workflow engine JOOQ Object Oriented Querying – fluent API for SQL JPOS – financial messaging JUNG – open-source graph modeling and visualization LanguageWare – language processing LibGDX – game development Modular Audio Recognition Framework – collection of voice, sound, speech, text and natural language processing algorithms. ASM – bytecode manipulation Open Inventor – 3D graphics OpenPDF – PDF Parallel Colt – parallel computing Parboiled – parser PlayN – game development QOCA – constraint solving QtJambi – Qt bindings SLF4J – logging StableUpdate – update management SWT – GUI SuanShu – numerical computing SwingLabs – GUI extensions UBY – natural language processing Undecimber – calendar XDoclet – attribute-oriented programming XINS – XML network services XStream – object serialization == Machine learning and AI == Apache Mahout – scalable machine learning library focused on clustering, classification, and collaborative filtering Apache MXNet – deep learning framework with Java API support Apache OpenNLP – machine learning based toolkit for natural language processing of text Deeplearning4j – distributed deep learning library Deep Java Library – open-source deep learning framework developed by Amazon Web Services Encog – framework for neural networks, genetic algorithms, Hidden Markov model, and Bayesian networks. LIBSVM – Support Vector Machine implementation Mallet – machine learning toolkit for classification, clustering, and topic modeling. MLlib – distributed machine-learning framework on top of Apache Spark Core Neuroph – lightweight neural network framework Weka – collection of machine learning algorithms for data mining Yooreeka – machine learning == Data mining == Java Data Mining (JDM) – standard Java API for data mining Massive Online Analysis (MOA) – data stream mining with concept drift == Math and scientific libraries == Apache Commons Math – general-purpose mathematics library including statistics, linear algebra, and optimization. Colt – high-performance scientific computing, including linear algebra and random numbers. Efficient Java Matrix Library (EJML) – dense and sparse matrix computations and linear algebra Easy Java Simulations – Open Source Physics project designed to create discrete computer simulations Exp4j – evaluates mathematical expressions at runtime GroovyLab – numerical computational environment Hipparchus – fork of Apache Commons Math with updated algorithms for statistics, linear algebra, and optimization. JAMA – numerical linear algebra library Jblas: Linear Algebra for Java (Jblas) – linear algebra library using native BLAS/LAPACK bindings Java Astrodynamics Toolkit – numerical library of software components for use in spaceflight applications for Java or MATLAB Matrix Toolkit Java (MTJ) – linear algebra library with BLAS and LAPACK support OjAlgo – optimization, linear algebra, and financial calculations. OptimJ – extension for mathematical optimization and constraint programming Parallel Colt – A parallel extension of Colt SuanShu – numerical analysis, linear algebra, statistics, and optimization. == Integrated development environments == See also: Java IDEs on Wikibooks Android Studio – IDE for Google's Android operating system BlueJ – educational IDE for teaching Java DrJava – lightweight Java IDE for beginners Eclipse IDE – open-source IDE with extensive plugin ecosystem Greenfoot – educational IDE IntelliJ IDEA – commercial and community editions from JetBrains JDeveloper – freeware IDE supplied by Oracle Corporation jGRASP – software visualizations MyEclipse – Java EE IDE NetBeans IDE – Apache NetBeans Visual Studio Code – general-purpose editor with Java extensions === Online IDEs === Eclipse Che GitHub Codespaces JDoodle Replit == Text editors with Java support == == Build tools and package managers == Apache Ant – automating software build Apache Ivy – subproject of Apache Ant Apache Maven – build automation and dependency management Boot – build automation for Clojure CMake – build tool with limited support for java Gradle – modern build automation tool Go continuous delivery (GoCD) – continuous delivery and build automation server Jenkins – automation server continuous delivery JitPack – package repository for Git projects Leiningen – build automation for Clojure Simple build tool (sbt) – open-source build tool Spring Roo – rapid application development of Java-based enterprise software WaveMaker – low-code development platform == Java runtimes, compilers and virtual machines == Android Runtime – runtime environment javac – Java programming language compiler Java Virtual Machine (JVM) – virtual machine that executes Java bytecode JD Decompiler JEB decompiler – disassembler and decompiler software for Android applications GraalVM – Just-in-time compilation HotSpot – JVM implementation included in OpenJDK == JVM languages and dialects == Clojure – Lisp dialect Groovy JRuby – Ruby implementation Jython – Python implementation Kotlin – popular for Android app development Renjin – R implementation Scala == Application servers and containers == Apache Geronimo – open source application server Apache MINA – event-driven asynchronous network application framework Apache Tomcat – web container and web server Apache TomEE – Apache Tomcat with Java EE features Borland Enterprise Server – discontinued application server by Borland ColdFusion – commercial application server by Adobe Systems GlassFish – application server for Jakarta EE IBM WebSphere Application Server – enterprise application server by IBM IBM WebSphere Application Server Community Edition – open source edition of WebSphere (discontinued) JBoss Enterprise Application Platform – Red Hat's supported distribution of JBoss/WildFly JEUS – commercial Java EE application server from TmaxSoft Jetty – HTTP server and web container Lucee (formerly Railo) – open source CFML application server Netty – non-blocking I/O client–server framework for network applications Oracle Containers for J2EE – discontinued application server by Oracle Oracle WebLogic Server – enterprise application server by Oracle Orion Application Server – early commercial Java EE server by IronFlare Payara Server – fork of GlassFish for production use Resin – Java application server by Caucho (open source and professional editions) SAP NetWeaver Application Server – enterprise application server by SAP WildFly – application server == Debugging and profiling tools == jdb – Java debugger bundled with the JDK JConsole – JMX-compliant monitoring tool JDK Flight Recorder – method profiling, allocation profiling, and garbage collection related events. JProfiler – commercial Java profiler VisualVM – visual tool integrating commandline JDK tools for profiling and monitoring == Testing and quality assurance == Apache JMeter – load testing tool JaCoCo – Java code coverage library JArchitect – analyzes code quality, architecture, and dependencies. Jtest – software testing and static analysis JUnit – unit testing framework Mockito – open-source testing framework for Java PMD – static program analysis source code analyzer Selenium – browser automation for web app testing Spock – test framework SpotBugs (formerly FindBugs) – static analysis tool TestNG – testing framework inspired by JUnit and NUnit == Other == Apache XMLBeans –

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  Your AI Slop Bores Me

  Your AI Slop Bores Me (stylized in all lowercase) is a website and social experiment created by programmer Mihir Maroju. Serving as a parody of large language models (LLMs) like ChatGPT and Claude, all questions and image prompts posed by users are answered by other, randomly-selected human users of the site. As of March 2026, the site has reached 50 million hits and sits at 16,000 concurrent users. == Background == In an interview with Fast Company, Maroju said he was inspired to create the site by his frustration with AI proliferating the internet with AI generated content, saying the site came from "a frustration for AI art and its proliferation, making artists' lives worse and also just filling the internet with low-effort generic slop". == Overview == The site has a credit system, in which a first-time user will be given 1 credit for free. Every 10 minutes, if a user has 0 credits, they will receive 2 credits. Once the credits are used up, the user can no longer do prompts unless the user earns them. The user can earn credits by responding to other user's prompts by "larping as AI" while given a 75-second time limit. Prompts can either be for a written response, or a drawing for the other user to fulfill the prompt. The maximum amount of credits a user can have is 6 credits, and cannot exceed the maximum limit. If the prompting user activates "thinking mode", the countdown is extended to 150 seconds for the cost of 2 credits. == Reception == The site has garnered attention and praise from X users, and across many online communities. The Daily Dot's Rachel Kiley wrote that "the best part about the game is that there's really no right or wrong way to do it. Humans aren't LLMs trained on copyrighted material and the whole of the free internet, but we do retain a certain amount of the information we've learned from those things over the course of our lives, while also being capable of creativity". Chris Taylor of Mashable called the site "amateurish and charming". Aftermath's Nicole Carpenter wrote that the site reminded her of "the human touch of chaos".

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  Maschinen Krieger ZbV 3000

  Maschinen Krieger (Ma.K ZBV3000), often abbreviated as Ma.K., is a science fiction intellectual property created by Japanese artist and sculptor Kow Yokoyama in the 1980s. It consists of an illustrated series, a line of merchandise comprising display and action figures of mecha characters and a 1985 short film. == History == The franchise originally began as the science fiction series SF3D which ran as monthly installments in the Japanese hobby magazine Hobby Japan from 1982 to 1985. To develop the storyline, Kow Yokoyama collaborated with Hiroshi Ichimura as story editor and Kunitaka Imai as graphic designer. The three creators drew visual inspiration from their combined interest in World War I and World War II armor and aircraft, the American space program and films such as Star Wars, Blade Runner and The Road Warrior. Inspired by the ILM model builders who worked on Star Wars, Yokoyama built the original models from numerous kits including armor, aircraft, and automobiles. He mostly concentrated on powered armor suits, but later included bipedal walking tanks and aircraft with anti-gravity systems. In 1986, there was a dispute with Hobby Japan over the copyright of the series. The magazine dropped SF3D from its line-up of articles and Nitto ceased production of various kits of the series. The matter was tied up in the courts for years until Yokoyama was awarded the full copyright to the series in the 1990s. Yokoyama and Hobby Japan eventually reconciled and restarted their working relationship, ditching the old SF3D name in favor of Maschinen Krieger ZbV3000, otherwise known as Ma.K. == Story == A nuclear World War IV in 2807 kills most of Earth's population and renders the planet uninhabitable. Fifty-two years after the war, a research team from an interstellar union called the Galactic Federation is sent to Earth and discovers that the planet's natural environment has restored itself. The Federation decides to repopulate the planet and sends over colonists to the surface. Cities and towns are eventually reformed over the next 20 years, but this growth attracts the attention of criminals, military deserters, and other lawless elements who wanted to hide on Earth—away from the authorities. A few militias protect the colonists, but the new interlopers often defeat them. Fearing civil unrest and the colonists forming their own government, the Federation gives the Strahl Democratic Republic (SDR) the right to govern the planet in the late 2870s. The SDR sends three police battalions and three Foreign Legion corps to Earth and uses heavy-handed tactics such as travel restrictions and hard labor camps to restore order, which creates resentment amongst the colonists. In response, the colonists create the Earth Independent Provisional Government and declare independence from the SDR. The SDR immediately establishes a puppet government and attempts to quell the uprising. The wealthy colonists hire mercenaries who are descendants of WWIV veterans to form the Independent Mercenary Army (IMA), which is bolstered by the presence of SDR Foreign Legion defectors. They attack the SDR forces and the battle to control Earth begins in 2882. Over the next four years, the SDR and IMA fight each other at several locations worldwide while developing new technology along the way. The war turns up a notch in June 2883 when the IMA deploys a new weapon - the Armored Fighting Suit powered armor - to devastating effect. The SDR eventually builds their own AFS units. In the last SF3D installment published in the December 1986 issue of Hobby Japan, the IMA successfully defeats the new SDR Königs Kröte unmanned command-and-control mecha using a computer virus that also creates a new artificial intelligence system on the moon. == Merchandise == === Model kits === Fan interest from the installments in Hobby Japan resulted in a small Japanese model company, Nitto, securing the license and quickly released 21 injection molded kits from the series during its entire run in the magazine. Most of the Nitto model kits are in 1:20 scale, while others were made in 1:76 and 1:6 scale. Production of the kits stopped with the end of the Hobby Japan features in 1986, but Nitto reissued many of the original kits under the Maschinen Krieger name, albeit with new decals and box art. Some of the original Nitto kits such as the Krachenvogel are highly sought after by collectors. The Nitto models were also the basis for similar offerings from Japanese model companies Wave and ModelKasten. Wave, in particular, is currently producing original-tooled kits of various subjects in the franchise, such as the Armored Fighting Suits powered armor. Smaller companies such as Brick Works and Love Love Garden have made limited resin pilot figures to go with these model kits. At the 2008 Nuremberg Toy Fair in Germany, the Hasegawa company - known mostly for its line of military and civilian vehicles — announced plans to carry the Ma.K license, having successfully branched into pop culture franchises such as Macross. Hasegawa's venture into the franchise came with the release of the Pkf 85 Falke attack craft in March 2009. The company's Ma.K line has since expanded to at least ten kits either 1:35 or 1:20 scale, including a 1:35 Scale Nutrocker tank and the Mk44 humanoid mecha suit from Robot Battle V, a sidestory to the franchise. Wave corporation also has a line of 1/20 models. While Hasegawa largely maintained the yellow-box aesthetic from the older nitto kits, Wave has a more colorful box design. Certain garage kit manufacturers such as Rainbow-Egg are allowed to produce their own line of resin kits and accessories, upon securing special authorization from Yokoyama himself. === Toys === The franchise also contains a line of action and display figures. The Japanese hobby shop and toy company Yellow Submarine and garage kit maker Max Factory released several pre-finished figures in 1:35 and 1:16 scale. MediCom Toys included Chibi Ma.K. figures in their Kubrick line, plus two 1:6 SAFS figures with working lights and fully poseable pilot figures. === Books === Numerous sourcebooks and modeling guides that further flesh out the information in the series have been released. Hobby Japan published a compilation of the first 15 SF3D installments in 1983 and reprinted them in March 2010. Eventually, the magazine re-released all 43 installments in a slipcase compilation called "SF3D Chronicles" in August 2010, which organized the installments into two separate books: "Heaven" featuring articles on aerial models, and "Earth" for ground-based models. Model Graphix followed suit with their own line of sourcebooks, which provide tutorials from Yokoyama on how he makes his figures. Some sourcebooks also have custom decal sets. === Miniature wargaming === In 2019, Slave 2 Gaming gained the license to produce and sell 1:100 scale (15mm) metal and resin war gaming miniatures. This new range of Maschinen Krieger figures was given the name Ma.K in 15mm, so as to not complicate sales with customers, and rebrand the Ma.k name for the miniature wargaming world. The figures are designed and cast in Australia. They are sold exclusively through Slave 2 Gaming at this time due to the license agreement with Sensei Yokoyama. With the production of the miniatures, a set of gaming rules in the works, with the plan is to release all the current Maschinen Krieger models. == Short film == Yokoyama collaborated with Tsuburaya Productions to create a live-action SF3D film using miniatures in 1985. Directed by Shinichi Ohoka from a script penned by co-producer Hisao Ichikura, the 25-minute SF3D Original Video opens with wreckage left from a battle in the Wiltshire wastelands on Christmas Day 2884 before focusing on a badly damaged IMA SAFS unit. The pilot, Cpl Robert Bush (Tristan Hickey), who is still alive, seeks to get his armored suit back and running and leave the battle area, which is under heavy jamming. Seeing two of the SDR's new Nutrocker (Nutcracker) robot hovertanks arrive nearby, Bush tries to hide, but bodily functions give him away. One Nutcracker gives chase and the SAFS AI points out to Bush how to defeat it. He eventually clambers on to the tank, which passes through the rubble of a town and randomly shoots at high places to bring down objects that could snag him. With the SAFS' right arm sheared off by the Nutcracker's laser blasts and snow settling in, Bush is knocked unconscious all night long from the fall while the tank breaks down under the cold. The next day, the SAFS AI wakes up Bush because the Nutcracker is active again and is preparing to kill him. Bush gets up and faces the tank as it charges towards him. However, the Nutcracker gets too close to a cliff that buckles under its weight and Bush fires his laser into the tank's underbelly. The tank plunges into a ravine and explodes. Bush walks away and reestablishes radio contact with his base. It is revealed that the battle was a field test of th

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  Competitions and prizes in artificial intelligence

  There are a number of competitions and prizes to promote research in artificial intelligence. == General machine intelligence == The David E. Rumelhart Prize is an annual award for making a "significant contemporary contribution to the theoretical foundations of human cognition". The prize is $100,000. The Human-Competitive Award is an annual challenge started in 2004 to reward results "competitive with the work of creative and inventive humans". The prize is $10,000. Entries are required to use evolutionary computing. The Intel AI Global Impact Festival is an international annual competition held by Intel Corporation for school, and college students with prizes upwards of $15,000. It is about artificial intelligence technology. There are two age brackets in this competition, 13-18 Age Group, and 18 and Above Age Group. The IJCAI Award for Research Excellence is a biannual award given at the International Joint Conference on Artificial Intelligence (IJCAI) to researchers in artificial intelligence as a recognition of excellence of their career. The 2011 Federal Virtual World Challenge, advertised by The White House and sponsored by the U.S. Army Research Laboratory's Simulation and Training Technology Center, held a competition offering a total of US$52,000 in cash prize awards for general artificial intelligence applications, including "adaptive learning systems, intelligent conversational bots, adaptive behavior (objects or processes)" and more. The Machine Intelligence Prize is awarded annually by the British Computer Society for progress towards machine intelligence. The Kaggle – "the world's largest community of data scientists compete to solve most valuable problems". == Conversational behaviour == The Loebner prize is an annual competition to determine the best Turing test competitors. The winner is the computer system that, in the judges' opinions, demonstrates the "most human" conversational behaviour, they have an additional prize for a system that in their opinion passes a Turing test. This second prize has not yet been awarded. == Automatic control == === Pilotless aircraft === The International Aerial Robotics Competition is a long-running event begun in 1991 to advance the state of the art in fully autonomous air vehicles. This competition is restricted to university teams (although industry and governmental sponsorship of teams is allowed). Key to this event is the creation of flying robots which must complete complex missions without any human intervention. Successful entries are able to interpret their environment and make real-time decisions based only on a high-level mission directive (e.g., "find a particular target inside a building having certain characteristics which is among a group of buildings 3 kilometers from the aerial robot launch point"). In 2000, a $30,000 prize was awarded during the 3rd Mission (search and rescue), and in 2008, $80,000 in prize money was awarded at the conclusion of the 4th Mission (urban reconnaissance). === Driverless cars === The DARPA Grand Challenge is a series of competitions to promote driverless car technology, aimed at a congressional mandate stating that by 2015 one-third of the operational ground combat vehicles of the US Armed Forces should be unmanned. While the first race had no winner, the second awarded a $2 million prize for the autonomous navigation of a hundred-mile trail, using GPS, computers and a sophisticated array of sensors. In November 2007, DARPA introduced the DARPA Urban Challenge, a sixty-mile urban area race requiring vehicles to navigate through traffic. In November 2010 the US Armed Forces extended the competition with the $1.6 million prize Multi Autonomous Ground-robotic International Challenge to consider cooperation between multiple vehicles in a simulated-combat situation. Roborace will be a global motorsport championship with autonomously driving, electric vehicles. The series will be run as a support series during the Formula E championship for electric vehicles. This will be the first global championship for driverless cars. == Data-mining and prediction == The Netflix Prize was a competition for the best collaborative filtering algorithm that predicts user ratings for films, based on previous ratings. The competition was held by Netflix, an online DVD-rental service. The prize was $1,000,000. The Pittsburgh Brain Activity Interpretation Competition will reward analysis of fMRI data "to predict what individuals perceive and how they act and feel in a novel Virtual Reality world involving searching for and collecting objects, interpreting changing instructions, and avoiding a threatening dog." The prize in 2007 was $22,000. The Face Recognition Grand Challenge (May 2004 to March 2006) aimed to promote and advance face recognition technology. The American Meteorological Society's artificial intelligence competition involves learning a classifier to characterise precipitation based on meteorological analyses of environmental conditions and polarimetric radar data. == Cooperation and coordination == === Robot football === The RoboCup and Federation of International Robot-soccer Association (FIRA) are annual international robot soccer competitions. The International RoboCup Federation challenge is by 2050 "a team of fully autonomous humanoid robot soccer players shall win the soccer game, comply with the official rule of the FIFA, against the winner of the most recent World Cup." == Logic, reasoning and knowledge representation == The Herbrand Award is a prize given by Conference on Automated Deduction (CADE) Inc. to honour persons or groups for important contributions to the field of automated deduction. The prize is $1000. The CADE ATP System Competition (CASC) is a yearly competition of fully automated theorem provers for classical first order logic associated with the Conference on Automated Deduction (CADE) and International Joint Conference on Automated Reasoning (IJCAR). The competition was part of the Alan Turing Centenary Conference in 2012, with total prizes of 9000 GBP given by Google. The SUMO prize is an annual prize for the best open source ontology extension of the Suggested Upper Merged Ontology (SUMO), a formal theory of terms and logical definitions describing the world. The prize is $3000. The Hutter Prize for lossless compression of human knowledge is a cash prize which rewards compression improvements on a specific 100 MB English text file. The prize awards 500 euros for each one percent improvement, up to €50,000. The organizers believe that text compression and AI are equivalent problems and 3 prizes have been given, at around € 2k. The Cyc TPTP Challenge is a competition to develop reasoning methods for the Cyc comprehensive ontology and database of everyday common sense knowledge. The prize is 100 euros for "each winner of two related challenges". The Eternity II challenge was a constraint satisfaction problem very similar to the Tetravex game. The objective is to lay 256 tiles on a 16x16 grid while satisfying a number of constraints. The problem is known to be NP-complete. The prize was US$2,000,000. The competition ended in December 2010. == Games == The World Computer Chess Championship has been held since 1970. The International Computer Games Association continues to hold an annual Computer Olympiad which includes this event plus computer competitions for many other games. The Ing Prize was a substantial money prize attached to the World Computer Go Congress, starting from 1985 and expiring in 2000. It was a graduated set of handicap challenges against young professional players with increasing prizes as the handicap was lowered. At the time it expired in 2000, the unclaimed prize was 400,000 NT dollars for winning a 9-stone handicap match. The AAAI General Game Playing Competition is a competition to develop programs that are effective at general game playing. Given a definition of a game, the program must play it effectively without human intervention. Since the game is not known in advance the competitors cannot especially adapt their programs to a particular scenario. The prize in 2006 and 2007 was $10,000. The General Video Game AI Competition (GVGAI) poses the problem of creating artificial intelligence that can play a wide, and in principle unlimited, range of games. Concretely, it tackles the problem of devising an algorithm that is able to play any game it is given, even if the game is not known a priori. Additionally, the contests poses the challenge of creating level and rule generators for any game is given. This area of study can be seen as an approximation of General Artificial Intelligence, with very little room for game dependent heuristics. The competition runs yearly in different tracks: single player planning, two-player planning, single player learning, level and rule generation, and each track prizes ranging from 200 to 500 US dollars for winners and runner-ups. The 2007 Ultimate Computer Ches

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  Direct Graphics Access

  Direct Graphics Access is a plug-in for the X display servers that allows client programs direct access to the frame buffer. Graphics hardware communicates via a chunk of memory called a frame buffer. This is an array of values that represent pixel color values on the screen. Writing the appropriate values into the frame buffer therefore allows a program to paint areas of the screen. However, as with any shared resource, problems occur when multiple programs attempt to access the same resource, as they tend to write over each other's work. In the X Window System, this is solved by having a central display server that mediates between programs that want to draw on the screen. The display server also used to perform a lot of the drawing work, allowing programs to say Draw me a circle of this radius filled with this pattern or draw this text in this font. The X server does all this work, freeing programmers from having to write their own drawing code. Another advantage of the X architecture is that it works over a network, allowing programs on one machine to display output on the screen of another. Direct Graphics Access allows direct access to the frame buffer and the X-server hands over control of the frame buffer to the client program and waits for the client to hand it back. This means that the client program has control of the whole screen, and so it is mostly used for full-screen video/games.

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  DARPA Grand Challenge

  The DARPA Grand Challenge is a prize competition for American autonomous vehicles, funded by the Defense Advanced Research Projects Agency, the most prominent research organization of the United States Department of Defense. Congress has authorized DARPA to award cash prizes to further DARPA's mission to sponsor revolutionary, high-payoff research that bridges the gap between fundamental discoveries and military use. The initial DARPA Grand Challenge in 2004 was created to spur the development of technologies needed to create the first fully autonomous ground vehicles capable of completing a substantial off-road course within a limited time. The third event, the DARPA Urban Challenge in 2007, extended the initial Challenge to autonomous operation in a mock urban environment. The 2012 DARPA Robotics Challenge, focused on autonomous emergency-maintenance robots, and new Challenges are still being conceived. The DARPA Subterranean Challenge was tasked with building robotic teams to autonomously map, navigate, and search subterranean environments. Such teams could be useful in exploring hazardous areas and in search and rescue. In addition to the challenges in autonomous technology, DARPA has also conducted prize competitions in other areas of technology. == History and background == Fully autonomous vehicles have been an international pursuit for many years, from endeavors in Japan (starting in 1977), Germany (Ernst Dickmanns and VaMP), Italy (the ARGO Project), the European Union (EUREKA Prometheus Project), the United States of America, and other countries. DARPA funded the development of the first fully autonomous robot beginning in 1966 with the Shakey the robot project at Stanford Research Institute, now SRI International. The first autonomous ground vehicle capable of driving on and off roads was developed by DARPA as part of the Strategic Computing Initiative beginning in 1984 leading to demonstrations of autonomous navigation by the Autonomous Land Vehicle and the Navlab. The Grand Challenge was the first long distance competition for driverless cars in the world; other research efforts in the field of driverless cars take a more traditional commercial or academic approach. The U.S. Congress authorized DARPA to offer prize money ($1 million) for the first Grand Challenge to facilitate robotic development, with the ultimate goal of making one-third of ground military forces autonomous by 2015. Following the 2004 event, Dr. Tony Tether, the director of DARPA, announced that the prize money had been increased to $2 million for the next event, which was claimed on October 9, 2005. The first, second and third places in the 2007 Urban Challenge received $2 million, $1 million, and $500,000, respectively. 14 new teams have qualified in year 2015. The competition was open to teams and organizations from around the world, as long as there was at least one U.S. citizen on the roster. Teams have participated from high schools, universities, businesses and other organizations. More than 100 teams registered in the first year, bringing a wide variety of technological skills to the race. In the second year, 195 teams from 36 U.S. states and 4 foreign countries entered the race. == 2004 Grand Challenge == The first competition of the DARPA Grand Challenge was held on March 13, 2004 in the Mojave Desert region of the United States, along a 150-mile (240 km) route that follows along the path of Interstate 15 from just before Barstow, California to just past the California–Nevada border in Primm. None of the robot vehicles finished the route. Carnegie Mellon University's Red Team and car Sandstorm (a converted Humvee) traveled the farthest distance, completing 11.78 km (7.32 mi) of the course before getting hung up on a rock after making a switchback turn. No winner was declared, and the cash prize was not given. Therefore, a second DARPA Grand Challenge event was scheduled for 2005. == 2005 Grand Challenge == The second competition of the DARPA Grand Challenge began at 6:40 am on October 8, 2005. All but one of the 23 finalists in the 2005 race surpassed the 11.78 km (7.32 mi) distance completed by the best vehicle in the 2004 race. Five vehicles successfully completed the 212 km (132 mi) course: Vehicles in the 2005 race passed through three narrow tunnels and negotiated more than 100 sharp left and right turns. The race concluded through Beer Bottle Pass, a winding mountain pass with a sheer drop-off on one side and a rock face on the other. Although the 2004 course required more elevation gain and some very sharp switchbacks (Daggett Ridge) were required near the beginning of the route, the course had far fewer curves and generally wider roads than the 2005 course. The natural rivalry between the teams from Stanford and Carnegie Mellon (Sebastian Thrun, head of the Stanford team was previously a faculty member at Carnegie Mellon and colleague of Red Whittaker, head of the CMU team) was played out during the race. Mechanical problems plagued H1ghlander before it was passed by Stanley. Gray Team's entry was a miracle in itself, as the team from the suburbs of New Orleans was caught in Hurricane Katrina a few short weeks before the race. The fifth finisher, Terramax, a 30,000 pound entry from Oshkosh Truck, finished on the second day. The huge truck spent the night idling on the course, but was particularly nimble in carefully picking its way down the narrow roads of Beer Bottle Pass. == 2007 Urban Challenge == The third competition of the DARPA Grand Challenge, known as the "Urban Challenge", took place on November 3, 2007 at the site of the now-closed George Air Force Base (currently used as Southern California Logistics Airport), in Victorville, California (Google map). The course involved a 96 km (60 mi) urban area course, to be completed in less than 6 hours. Rules included obeying all traffic regulations while negotiating with other traffic and obstacles and merging into traffic. Unlike previous challenges, the 2007 Urban Challenge organizers divided competitors into two "tracks", A and B. All Track A and Track B teams were part of the same competition circuit, but the teams chosen for the Track A program received US $1 million in funding. These 11 teams largely represented major universities and large corporate interests such as CMU teaming with GM as Tartan Racing, Stanford teaming with Volkswagen, Virginia Tech teaming with TORC Robotics as VictorTango, Oshkosh Truck, Honeywell, Raytheon, Caltech, Autonomous Solutions, Cornell University, and MIT. One of the few independent entries in Track A was the Golem Group. DARPA has not publicly explained the rationale behind the selection of Track A teams. Teams were given maps sparsely charting the waypoints that defined the competition courses. At least one team, Tartan Racing, enhanced the maps through the insertion of additional extrapolated waypoints for improved navigation. A debriefing paper published by Team Jefferson illustrates graphically the contrast between the course map it was given by DARPA and the course map used by Tartan Racing. Tartan Racing claimed the $2 million prize with their vehicle "Boss", a Chevy Tahoe. The second-place finisher earning the $1 million prize was the Stanford Racing Team with their entry "Junior", a 2006 Volkswagen Passat. Coming in third place was team VictorTango, winning the $500,000 prize with their 2005 Ford Escape hybrid, "Odin". MIT placed 4th, with Cornell University and University of Pennsylvania/Lehigh University also completing the course. The six teams that successfully finished the entire course: While the 2004 and 2005 events were more physically challenging for the vehicles, the robots operated in isolation and only encountered other vehicles on the course when attempting to pass. The Urban Challenge required designers to build vehicles able to obey all traffic laws while they detect and avoid other robots on the course. This is a particular challenge for vehicle software, as vehicles must make "intelligent" decisions in real time based on the actions of other vehicles. Other than previous autonomous vehicle efforts that focused on structured situations such as highway driving with little interaction between the vehicles, this competition operated in a more cluttered urban environment and required the cars to perform sophisticated interactions with each other, such as maintaining precedence at a 4-way stop intersection. == 2012 Robotics Challenge == The DARPA Robotics Challenge is an ongoing competition focusing on humanoid robotics. The primary goal of the program is to develop ground robotic capabilities to execute complex tasks in dangerous, degraded, human-engineered environments. It launched in October 2012, and hosted the Virtual Robotics Competition in June 2013. Two more competitions are planned: the DRC Trials in December 2013, and the DRC Finals in December 2014. Unlike prior Challenges, the construction of the "vehicles" w

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  Distributed multi-agent reasoning system

  In artificial intelligence, the distributed multi-agent reasoning system (dMARS) was a platform for intelligent software agents developed at the AAII that makes uses of the belief–desire–intention software model (BDI). The design for dMARS was an extension of the intelligent agent cognitive architecture developed at SRI International called procedural reasoning system (PRS). The most recent incarnation of this framework is the JACK Intelligent Agents platform. == Overview == dMARS was an agent-oriented development and implementation environment written in C++ for building complex, distributed, time-critical systems.

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  AI Seoul Summit 2024

  The AI Seoul Summit 2024 was an event in May 2024 co-hosted by the South Korean and British governments. The Seoul Declaration was adopted to address artificial intelligence technology and related challenges and opportunities. == Background == The AI Seoul Summit is the second such meeting following the AI Safety Summit held in the United Kingdom in November 2023. In the Bletchley Declaration, the participating countries agreed to prioritize identifying AI safety risks of shared concern, a shared concern, but at the Seoul Summit, the leaders also recognized the importance of AI. == Notable attendees == The summit was attended by the leaders of Group of Seven countries, including the United States, Canada, France, and Germany, South Korea, Singapore and Australia, representatives of the United Nations, the Organisation for Economic Co-operation and Development, and the European Union. Also in attendance were representatives of global companies such as Tesla CEO Elon Musk, Samsung Electronics Chairman Lee Jae-yong, ChatGPT maker OpenAI, Google, Microsoft, Meta, and South Korea's top portal operator Naver. == Topics == === South Korean AI safety center === "South Korea will push forward with the establishment of an AI safety research center in Korea and join a network to boost the global safety of AI." Minister of Science, Lee Jong-ho said that South Korea was planning to open an AI Safety Institute in 2024. He also expressed his intention to strengthen cooperation for the development of international standards. === Seoul Declaration for Safe, Innovative and Inclusive AI === The Seoul Declaration was adopted at the summit by leaders representing the EU, the US, the UK, Australia, Canada, Germany, France, Italy, Japan, South Korea, and Singapore. The declaration is a commitment to foster international cooperation to help develop AI governance frameworks that are interoperable between countries, partly by integrating the Hiroshima Process International Code of Conduct for Organizations Developing Advanced AI Systems. It advocates for the development of human-centric AI in collaboration with the private sector, academia, and civil society. === Seoul Ministerial Statement for advancing AI safety === At the ministerial meeting of the summit, the Seoul Ministerial Statement, a joint statement calling for the improvement of the safety, innovation, and inclusivity of AI technologies, was adopted by ministers from Australia, Canada, Chile, France, Germany, India, Indonesia, Israel, Italy, Japan, Kenya, Mexico, the Netherlands, Nigeria, New Zealand, the Philippines, South Korea, Rwanda, Saudi Arabia, Singapore, Spain, Switzerland, Turkey, Ukraine, the United Arab Emirates, the UK, and the US, as well as an EU representative. It aims to develop low-power chips as the AI industry rapidly expands and massive consumption is expected. == Global AI Summit series ==

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  Fred (chatbot)

  Fred, or FRED, was an early chatbot written by Robby Garner. == History == The name Fred was initially suggested by Karen Lindsey, and then Robby jokingly came up with an acronym, "Functional Response Emulation Device." Fred has also been implemented as a Java application by Paco Nathan called JFRED Archived 2008-08-24 at the Wayback Machine. Fred Chatterbot is designed to explore Natural Language communications between people and computer programs. In particular, this is a study of conversation between people and ways that a computer program can learn from other people's conversations to make its own conversations. Fred used a minimalistic "stimulus-response" approach. It worked by storing a database of statements and their responses, and made its own reply by looking up the input statements made by a user and then rendering the corresponding response from the database. This approach simplified the complexity of the rule base, but required expert coding and editing for modifications. Fred was a predecessor to Albert One, which Garner used in 1998 and 1999 to win the Loebner Prize.

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  DialogOS

  DialogOS is a graphical programming environment to design computer system which can converse through voice with the user. Dialogs are clicked together in a Flowchart. DialogOS includes bindings to control Lego Mindstorms robots by voice and has bindings to SQL databases, as well as a generic plugin architecture to integrate with other types of backends. DialogOS is used in computer science courses in schools and universities to teach programming and to introduce beginners in the basic principles of human/computer interaction and dialog design. It has also been used in research systems. DialogOS was initially developed commercially by CLT Sprachtechnologie GmbH until its liquidation in 2017. The rights were then acquired by Saarland University and the software was released as open-source. == Bindings to Lego Mindstorms NXT == DialogOS can control the LEGO Mindstorms NXT Series. It uses sensor-nodes to obtain values for the following sensors: noise sensor ultrasonic sensor touch sensor luminosity sensor

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  International Conference on Automated Planning and Scheduling

  The International Conference on Automated Planning and Scheduling (ICAPS) is a leading international academic conference in automated planning and scheduling held annually for researchers and practitioners in planning and scheduling. ICAPS is supported by the National Science Foundation, the journal Artificial Intelligence, and other supporters. == The IPC and PDDL == ICAPS conducts the International Planning Competition (IPC), a competition scheduled every few years that empirically evaluates state-of-the-art planning systems on a collection of benchmark problems. The Planning Domain Definition Language (PDDL) was developed mainly to make the 1998/2000 International Planning Competition possible, and then evolved with each competition. PDDL is an attempt to standardize Artificial Intelligence (AI) planning languages. PDDL was first developed by Drew McDermott and his colleagues in 1998, inspired by STRIPS, ADL, and other sources. == History == The ICAPS conferences began in 2003 as a merge of two bi-annual conferences, the International Conference on Artificial Intelligence Planning and Scheduling (AIPS) and the European Conference on Planning (ECP). == List of events ==

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  Diagnosis (artificial intelligence)

  As a subfield in artificial intelligence, diagnosis is concerned with the development of algorithms and techniques that are able to determine whether the behaviour of a system is correct. If the system is not functioning correctly, the algorithm should be able to determine, as accurately as possible, which part of the system is failing, and which kind of fault it is facing. The computation is based on observations, which provide information on the current behaviour. The expression diagnosis also refers to the answer of the question of whether the system is malfunctioning or not, and to the process of computing the answer. This word comes from the medical context where a diagnosis is the process of identifying a disease by its symptoms. == Example == An example of diagnosis is the process of a garage mechanic with an automobile. The mechanic will first try to detect any abnormal behavior based on the observations on the car and his knowledge of this type of vehicle. If he finds out that the behavior is abnormal, the mechanic will try to refine his diagnosis by using new observations and possibly testing the system, until he discovers the faulty component; the mechanic plays an important role in the vehicle diagnosis. == Expert diagnosis == The expert diagnosis (or diagnosis by expert system) is based on experience with the system. Using this experience, a mapping is built that efficiently associates the observations to the corresponding diagnoses. The experience can be provided: By a human operator. In this case, the human knowledge must be translated into a computer language. By examples of the system behaviour. In this case, the examples must be classified as correct or faulty (and, in the latter case, by the type of fault). Machine learning methods are then used to generalize from the examples. The main drawbacks of these methods are: The difficulty acquiring the expertise. The expertise is typically only available after a long period of use of the system (or similar systems). Thus, these methods are unsuitable for safety- or mission-critical systems (such as a nuclear power plant, or a robot operating in space). Moreover, the acquired expert knowledge can never be guaranteed to be complete. In case a previously unseen behaviour occurs, leading to an unexpected observation, it is impossible to give a diagnosis. The complexity of the learning. The off-line process of building an expert system can require a large amount of time and computer memory. The size of the final expert system. As the expert system aims to map any observation to a diagnosis, it will in some cases require a huge amount of storage space. The lack of robustness. If even a small modification is made on the system, the process of constructing the expert system must be repeated. A slightly different approach is to build an expert system from a model of the system rather than directly from an expertise. An example is the computation of a diagnoser for the diagnosis of discrete event systems. This approach can be seen as model-based, but it benefits from some advantages and suffers some drawbacks of the expert system approach. == Model-based diagnosis == Model-based diagnosis is an example of abductive reasoning using a model of the system. In general, it works as follows: We have a model that describes the behaviour of the system (or artefact). The model is an abstraction of the behaviour of the system and can be incomplete. In particular, the faulty behaviour is generally little-known, and the faulty model may thus not be represented. Given observations of the system, the diagnosis system simulates the system using the model, and compares the observations actually made to the observations predicted by the simulation. The modelling can be simplified by the following rules (where A b {\displaystyle Ab\,} is the Abnormal predicate): ¬ A b ( S ) ⇒ I n t 1 ∧ O b s 1 {\displaystyle \neg Ab(S)\Rightarrow Int1\wedge Obs1} A b ( S ) ⇒ I n t 2 ∧ O b s 2 {\displaystyle Ab(S)\Rightarrow Int2\wedge Obs2} (fault model) The semantics of these formulae is the following: if the behaviour of the system is not abnormal (i.e. if it is normal), then the internal (unobservable) behaviour will be I n t 1 {\displaystyle Int1\,} and the observable behaviour O b s 1 {\displaystyle Obs1\,} . Otherwise, the internal behaviour will be I n t 2 {\displaystyle Int2\,} and the observable behaviour O b s 2 {\displaystyle Obs2\,} . Given the observations O b s {\displaystyle Obs\,} , the problem is to determine whether the system behaviour is normal or not ( ¬ A b ( S ) {\displaystyle \neg Ab(S)\,} or A b ( S ) {\displaystyle Ab(S)\,} ). This is an example of abductive reasoning. == Diagnosability == A system is said to be diagnosable if whatever the behavior of the system, we will be able to determine without ambiguity a unique diagnosis. The problem of diagnosability is very important when designing a system because on one hand one may want to reduce the number of sensors to reduce the cost, and on the other hand one may want to increase the number of sensors to increase the probability of detecting a faulty behavior. Several algorithms for dealing with these problems exist. One class of algorithms answers the question whether a system is diagnosable; another class looks for sets of sensors that make the system diagnosable, and optionally comply to criteria such as cost optimization. The diagnosability of a system is generally computed from the model of the system. In applications using model-based diagnosis, such a model is already present and doesn't need to be built from scratch.

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  Centurion Guard

  Centurion Guard is a PC hardware and software-based security product, developed by Centurion Technologies. It was first released in 1996. There were several different releases and versions of this product, and many were distributed in computers donated to libraries by the Bill & Melinda Gates Foundation. == Operating system compatibility == Microsoft Windows 7 Microsoft Windows Vista Microsoft Windows XP

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  Fuzzy pay-off method for real option valuation

  The fuzzy pay-off method for real option valuation (FPOM or pay-off method) is a method for valuing real options, developed by Mikael Collan, Robert Fullér, and József Mezei; and published in 2009. It is based on the use of fuzzy logic and fuzzy numbers for the creation of the possible pay-off distribution of a project (real option). The structure of the method is similar to the probability theory based Datar–Mathews method for real option valuation, but the method is not based on probability theory and uses fuzzy numbers and possibility theory in framing the real option valuation problem. == Method == The Fuzzy pay-off method derives the real option value from a pay-off distribution that is created by using three or four cash-flow scenarios (most often created by an expert or a group of experts). The pay-off distribution is created simply by assigning each of the three cash-flow scenarios a corresponding definition with regards to a fuzzy number (triangular fuzzy number for three scenarios and a trapezoidal fuzzy number for four scenarios). This means that the pay-off distribution is created without any simulation whatsoever. This makes the procedure easy and transparent. The scenarios used are a minimum possible scenario (the lowest possible outcome), the maximum possible scenario (the highest possible outcome) and a best estimate (most likely to happen scenario) that is mapped as a fully possible scenario with a full degree of membership in the set of possible outcomes, or in the case of four scenarios used - two best estimate scenarios that are the upper and lower limit of the interval that is assigned a full degree of membership in the set of possible outcomes. The main observations that lie behind the model for deriving the real option value are the following: The fuzzy NPV of a project is (equal to) the pay-off distribution of a project value that is calculated with fuzzy numbers. The mean value of the positive values of the fuzzy NPV is the "possibilistic" mean value of the positive fuzzy NPV values. Real option value, ROV, calculated from the fuzzy NPV is the "possibilistic" mean value of the positive fuzzy NPV values multiplied with the positive area of the fuzzy NPV over the total area of the fuzzy NPV. The real option formula can then be written simply as: R O V = A ( P o s ) A ( P o s ) + A ( N e g ) × E [ A + ] {\displaystyle \mathrm {ROV} ={\frac {A(\mathrm {Pos} )}{A(\mathrm {Pos} )+A(\mathrm {Neg} )}}\times E[A_{+}]} where A(Pos) is the area of the positive part of the fuzzy distribution, A(Neg) is the area of the negative part of the fuzzy distribution, and E[A+] is the mean value of the positive part of the distribution. It can be seen that when the distribution is totally positive, the real options value reduces to the expected (mean) value, E[A+]. As can be seen, the real option value can be derived directly from the fuzzy NPV, without simulation. At the same time, simulation is not an absolutely necessary step in the Datar–Mathews method, so the two methods are not very different in that respect. But what is totally different is that the Datar–Mathews method is based on probability theory and as such has a very different foundation from the pay-off method that is based on possibility theory: the way that the two models treat uncertainty is fundamentally different. == Use of the method == The pay-off method for real option valuation is very easy to use compared to the other real option valuation methods and it can be used with the most commonly used spreadsheet software without any add-ins. The method is useful in analyses for decision making regarding investments that have an uncertain future, and especially so if the underlying data is in the form of cash-flow scenarios. The method is less useful if optimal timing is the objective. The method is flexible and accommodates easily both one-stage investments and multi-stage investments (compound real options). The method has been taken into use in some large international industrial companies for the valuation of research and development projects and portfolios. In these analyses triangular fuzzy numbers are used. Other uses of the method so far are, for example, R&D project valuation IPR valuation, valuation of M&A targets and expected synergies, valuation and optimization of M&A strategies, valuation of area development (construction) projects, valuation of large industrial real investments. The use of the pay-off method is lately taught within the larger framework of real options, for example at the Lappeenranta University of Technology and at the Tampere University of Technology in Finland.

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  Death of Elaine Herzberg

  The death of Elaine Herzberg (August 2, 1968 – March 18, 2018) was the first recorded case of a pedestrian fatality involving a self-driving car, after a collision that occurred late in the evening of March 18, 2018. Herzberg was pushing a bicycle across a four-lane road in Tempe, Arizona, United States, when she was struck by an Uber test vehicle, which was operating in self-drive mode with a human safety backup driver sitting in the driving seat. Herzberg was taken to the local hospital where she died of her injuries. Following the fatal incident, the National Transportation Safety Board (NTSB) issued a series of recommendations and sharply criticized Uber. The company suspended testing of self-driving vehicles in Arizona, where such testing had been approved since August 2016. Uber chose not to renew its permit for testing self-driving vehicles in California when it expired at the end of March 2018. Uber resumed testing in December 2018, starting in Pittsburgh, Pennsylvania. In March 2019, Arizona prosecutors ruled that Uber was not criminally responsible for the crash. The back-up driver of the vehicle was charged with negligent homicide, pled guilty to endangerment, and was sentenced to three years' probation. While Herzberg was the first pedestrian killed by a self-driving car, driver Gao Yaning died in a Tesla semi-autonomous car two years earlier. A reporter for The Washington Post compared Herzberg's fate with that of Bridget Driscoll who, in the United Kingdom in 1896, was the first pedestrian to be killed by an automobile. The Arizona incident has magnified the importance of collision avoidance systems for self-driving vehicles. == Collision summary == Herzberg was crossing Mill Avenue (North) from west to east, approximately 360 feet (110 m) south of the intersection with Curry Road, outside the designated pedestrian crosswalk, close to the Red Mountain Freeway. She was pushing a bicycle laden with shopping bags, and had crossed at least two lanes of traffic when she was struck at approximately 9:58 pm MST (UTC−07:00) by a prototype Uber self-driving car based on a Volvo XC90, which was traveling north on Mill. The vehicle had been operating in autonomous mode since 9:39 pm, nineteen minutes before it struck and killed Herzberg. The car's human safety backup driver, Rafaela Vasquez, did not intervene in time to prevent the collision. Vehicle telemetry obtained after the crash showed that the human operator responded by moving the steering wheel less than a second before impact, and she engaged the brakes less than a second after impact. == Cause investigation == The county district attorney's office recused itself from the investigation, due to a prior joint partnership with Uber promoting their services as an alternative to driving under the influence of alcohol. Accounts differ on the speed limit at the place of the incident. According to Tempe police the car was traveling in a 35 mph (56 km/h) zone, but this is contradicted by a posted speed limit of 45 mph (72 km/h). The National Transportation Safety Board (NTSB) sent a team of federal investigators to gather data from vehicle instruments, and to examine vehicle condition along with the actions taken by the safety driver. Their preliminary findings were substantiated by multiple event data recorders and proved the vehicle was traveling 43 miles per hour (69 km/h) when Herzberg was first detected 6 seconds (378 feet (115 m)) before impact; during 4.7 seconds the self driving system did not infer that emergency braking was needed. A vehicle traveling 43 mph (69 km/h) can generally stop within 89 feet (27 m) once the brakes are applied. The machine needed to be 1.3 seconds (82 feet (25 m)) away prior to discerning that emergency braking was required, whereas at least that much distance was required to stop. The system failed to behave properly. A total stopping distance of 76 feet itself would imply a safe speed under 25 mph (40 km/h). Human intervention was still legally required. Computer perception–reaction time would have been a speed limiting factor had the technology been superior to humans in ambiguous situations; however, the nascent computerized braking technology was disabled the day of the crash, and the machine's apparent 4.7-second perception–reaction (alarm) time allowed the car to travel 250 feet (76 m). Video released by the police on March 21 showed the safety driver was not watching the road moments before the vehicle struck Herzberg. === Environment === In widely disseminated remarks that would shape the narrative about the crash, which were later seen as prejudicial and subsequently contradicted by her own department, Tempe Police Chief Sylvia Moir was quoted stating that the collision was "unavoidable" based on the initial police investigation, which included a review of the video captured by an onboard camera. Moir faulted Herzberg for crossing the road in an unsafe manner: "It is dangerous to cross roadways in the evening hour when well-illuminated, managed crosswalks are available." According to Uber, safety drivers were trained to keep their hands very close to the wheel all the time while driving the vehicle so they were ready to quickly take control if necessary. The driver said it was like a flash, the person walked out in front of them. His [sic] first alert to the collision was the sound of the collision. [...] it's very clear it would have been difficult to avoid this collision in any kind of mode (autonomous or human-driven) based on how she came from the shadows right into the roadway. Tempe police released video on March 21, 2018, showing footage recorded by two onboard cameras: one forward-looking, and one capturing the safety driver's actions. The forward-facing video shows that the self-driving car was traveling in the far right lane when it struck Herzberg. The driver-facing video shows the safety driver was looking down prior to the collision. The Uber operator is responsible for intervening and taking manual control when necessary as well as for monitoring diagnostic messages, which are displayed on a screen in the center console. In an interview conducted after the crash with NTSB, the driver stated she was monitoring the center stack at the time of the collision. After the Uber video was released, journalist Carolyn Said noted the police explanation of Herzberg's path meant she had already crossed two lanes of traffic before she was struck by the autonomous vehicle. The Marquee Theatre and Tempe Town Lake are west of Mill Avenue, and pedestrians commonly cross mid-street without detouring north to the crosswalk at Curry. According to reporting by the Phoenix New Times, Mill Avenue contains what appears to be a brick-paved path in the median between the northbound and southbound lanes; however, posted signs prohibit pedestrians from crossing in that location. When the second of the Mill Avenue bridges over the town lake was added in 1994 for northbound traffic, the X-shaped crossover in the median was installed to accommodate the potential closing of one of the two road bridges. The purpose of this brick-paved structure is purely to divert cars from one side to the other if a bridge is closed to traffic, and although it may look like a crosswalk for pedestrians, it is in fact a temporary roadway with vertical curbs and warning signs. === Software issues === Michael Ramsey, a self-driving car expert with Gartner, characterized the video as showing "a complete failure of the system to recognize an obviously seen person who is visible for quite some distance in the frame. Uber has some serious explaining to do about why this person wasn't seen and why the system didn't engage." The NTSB preliminary report, however, noted that the software did order the car to brake 1.3 seconds before the collision. A video shot from the vehicle's dashboard camera showed the safety driver looking down, away from the road. It also appeared that the driver's hands were not hovering above the steering wheel, which is what drivers are instructed to do so they can quickly retake control of the car. Uber had moved from two employees in every car to one. The paired employees had been splitting duties: one ready to take over if the autonomous system failed, and another to keep an eye on what the computers were detecting. The second person was responsible for keeping track of system performance as well as labeling data on a laptop computer. Mr. Kallman, the Uber spokesman, said the second person was in the car for purely data related tasks, not safety. When Uber moved to a single operator, some employees expressed safety concerns to managers, according to the two people familiar with Uber's operations. They were worried that going solo would make it harder to remain alert during hours of monotonous driving. The recorded telemetry showed the system had detected Herzberg six seconds before the crash, and classified her first as an unknown object, then as a

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