AI Coding Kya Hota Hai

AI Coding Kya Hota Hai — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Open Cloud Computing Interface

    Open Cloud Computing Interface

    The Open Cloud Computing Interface (OCCI) is a set of specifications delivered through the Open Grid Forum, for cloud computing service providers. OCCI has a set of implementations that act as proofs of concept. It builds upon World Wide Web fundamentals by using the Representational State Transfer (REST) approach for interacting with services. == Scope == The aim of the Open Cloud Computing Interface is the development of an open specification and API for cloud offerings. The focus was on Infrastructure-as-a-Service (IaaS) based offerings but the interface can be extended to support Platform and Software as a Service offerings as well. IaaS is one of three primary segments of the cloud computing industry in which compute, storage and network resources are provided as services. The API is based on a review of existing service-provider functionality and a set of use cases contributed by the working group. OCCI is a boundary API that acts as a service front-end to an IaaS provider’s internal infrastructure management framework. OCCI provides commonly understood semantics, syntax and a means of management in the domain of consumer-to-provider IaaS. It covers management of the entire life-cycle of OCCI-defined model entities and is compatible with existing standards such as the Open Virtualization Format (OVF) and the Cloud Data Management Interface (CDMI). Notably, it serves as an integration point for standardization efforts including Distributed Management Task Force, Internet Engineering Task Force and the Storage Networking Industry Association. == Context == OCCI began in March 2009 and was initially led by RabbitMQ and the Complutense University of Madrid. Today, the working group has over 250 members and includes numerous individuals, industry and academic parties. The OCCI operates under the umbrella of the Open Grid Forum (OGF), using a wiki and a mailing list for collaboration. == Goals == Interoperability: allow different Cloud providers to work together without data schema/format translation, facade/proxying between APIs and understanding and/or dependency on multiple APIs Portability: no technical/vendor lock-in and enable services to move between providers allows clients to easily switch between providers based on business objectives (e.g., cost) with minimal technical costs, thus enabling and fostering competition. Integration: the specification can be implemented with both the latest infrastructures or legacy ones. Extensibility: thanks to the use of a meta-model and capabilities discovery features, an OCCI client is able to interact with any OCCI server using provider-specific OCCI extensions. == Specific Implementations == They implement specific extensions of OCCI for a particular service: IaaS, PaaS, brokering, etc. Several implementations have been announced or released. == Generic Implementations (frameworks) == Here are frameworks to build OCCI APIs. Complementing these are a variety of developer tools. == Alternatives == Alternative approaches include the use of the Cloud Infrastructure Management Interface (CIMI) and related standards set from DMTF and the Amazon Web Services interfaces from Amazon. (The latter have not been endorsed by any known Standards organization). OpenNebula conducted a survey of their users in which the results showed, 38% do not expose cloud APIs, their users only interface through the Sunstone GUI, 36% mostly use the Amazon Web Services API, and 26% mostly use the OpenNebula’s OCCI API or the OCCI API offered by rOCCI.

    Read more →
  • Fuzzy Control Language

    Fuzzy Control Language

    Fuzzy Control Language, or FCL, is a language for implementing fuzzy logic, especially fuzzy control. It was standardized by IEC 61131-7. It is a domain-specific programming language: it has no features unrelated to fuzzy logic, so it is impossible to even print "Hello, world!". Therefore, one does not write a program in FCL, but one may write part of it in FCL. == Example == RULE 0: IF (temperature IS cold) THEN (output IS low) RULE 1: IF (temperature IS very cold) THEN (output IS high) == Limitations == FCL is not an entirely complete fuzzy language, for instance, it does not support "hedges", which are adverbs that modify the set. For instance, the programmer cannot write: RULE 0: If (Temperature is VERY COLD) then (Output is VERY HIGH) However, the programmer can simply define new sets for "very cold" and "very high". FCL also lacks support for higher-order fuzzy sets, subsets, and so on. None of these features are essential to fuzzy control, although they may be nice to have.

    Read more →
  • DARPA Prize Competitions

    DARPA Prize Competitions

    Over the years, the U.S. Defense Advanced Research Projects Agency (DARPA) has conducted numerous prize competitions to spur innovation. A prize competition allows DARPA to establish an ambitious goal, opening the door to novel approaches from the public that might otherwise appear too risky for experts in a particular field to pursue. == Statutory authorities == In 1999, Congress provided prize competition authority to DARPA in the National Defense Authorization Act for Fiscal Year 2000 (P.L. 106–65), 10 U.S.C. § 4025, formerly 10 U.S.C. §2374a. DARPA also conducts prize competitions under the America COMPETES Act, 15 U.S.C. § 3719. == Recent prize competitions == DARPA Grand Challenge (2004 and 2005) was a prize competition to spur the development of autonomous vehicle technologies. The $1 million prize went unclaimed as no vehicles could complete the challenging desert route from Barstow, CA, to Primm, NV, on March 13, 2004. A year later, on October 8, 2005, the Stanford Racing Team won the $2 million prize during the second competition of the Grand Challenge in the desert Southwest near the California/Nevada state line. DARPA Urban Challenge (2007) required the competitors to build an autonomous vehicle capable of driving in traffic and performing complex maneuvers such as merging, passing, parking, and negotiating intersections. On November 3, 2007, the Carnegie Mellon Team won the $2 million prize, and its vehicle became the first autonomous vehicle that interacted with both manned and unmanned vehicle traffic in an urban environment. DARPA Network Challenge (Red Balloon Challenge) (2009) explored the roles that the Internet and social networking play in solving broad-scope, time-critical problems. On December 5, 2009, the Massachusetts Institute of Technology team won $40,000 by locating the ten moored, eight-foot, red weather balloons at ten places in the United States within seven hours. DARPA Digital Manufacturing Analysis, Correlation and Estimation Challenge (DMACE) (2010) was a three-month contest to showcase the potential of digital manufacturing of advanced materials. The University of California at Santa Barbara team won a $50,000 prize for crushing 180 digitally manufactured (DM) titanium mesh spheres with the most accurate predictive model of the components’ properties. DARPA Shredder Challenge (2011) was to identify and assess potential capabilities and vulnerabilities to sensitive information in the national security community. Participating teams must download the images of the documents shredded into more than 10,000 pieces from the Challenge website, reconstruct the documents, and solve the five puzzles. Of almost 9,000 teams, the San Francisco-based All Your Shreds Are Belong to U.S team won the $50,000 prize. DARPA UAVForge Challenge (2011-2012) aimed to build and test a user-intuitive, backpack-portable unmanned aerial vehicle (UAV) that could quietly fly in and out of critical environments to conduct sustained surveillance for up to three hours. The $100,000 prize was not claimed because none of the 140 teams met the technical matrix. DARPA Cash for Locating & Identifying Quick Response Codes (CLIQR) Quest Challenge (2012) explored the role the Internet and social media played in the timely communication, wide-area team-building, and urgent mobilization required to solve broad scope, time-critical problems. The challenge offered $40,000 to the first individual or team that could locate seven posters appearing in U.S. cities bearing the DARPA logo and a quick response code (QR) within 15 days. No team found and submitted all seven codes. DARPA Fast Adaptable Next-Generation Ground Vehicle (FANG) Challenge (2012-2013) was to use three competitions for the design of an infantry fighting vehicle, culminating in prototypes. In April 2013, DARPA awarded US$1 million to a three-man team during the first competition. DARPA decided not to proceed with the second and third competitions as originally planned and transitioned the technologies to the defense and commercial industry through the Digital Manufacturing and Design Innovation Institute (DMDII). DARPA Spectrum Challenge (2013-2014) sought to demonstrate how a software-defined radio can use a given communication channel in the presence of other users and interfering signals. Three teams emerged as the overall winners, winning a total of $150,000 in prizes. DARPA Chikungunya (CHIKV) Challenge (2014-2015) was a health-related effort to develop the most accurate predictions of CHIKV cases for all Western Hemisphere countries and territories between September 2014 and March 2015. On May 12, 2015, DARPA awarded $500,000 in prizes to the 11 winners of the competition during a scientific review DARPA Robotics Challenge (DRC) (2013-2015) aimed to develop semi-autonomous ground robots that could do "complex tasks in dangerous, degraded, human-engineered environments." A South Korean team won the first prize of $2 million, and two U.S. teams won $1 million and $500,000 as second and third winners. DARPA Cyber Grand Challenge (CGC) (2014 - 2016) was to “create automatic defensive systems capable of reasoning about flaws, formulating patches and deploying them on a network in real time.” The top three winners were awarded prizes of $2 million, $1 million, and $750,000, respectively. DARPA Spectrum Collaboration Challenge (SC2) (2016-2019) aimed to encourage the development of AI-enabled wireless networks to “ensure that the exponentially growing number of military and civilian wireless devices would have full access to the increasingly crowded electromagnetic spectrum.” A team from the University of Florida won the overall top prize of US$2 million at the final SC2 competition. DARPA Subterranean (SubT) Challenge (2017-2021) was to develop robotic technologies to map, navigate, search and exploit complex underground environments. The first-place winners of the system final competition and of the virtual final competition were awarded $2 million and $750,000, respectively, with multiple prizes awarded to the second and third-place winners. DARPA Launch Challenge (2018-2020) was a $12 million satellite launch challenge to demonstrate responsive and flexible space launch capabilities from the small launch providers and was to culminate in two separate launch competitions where the competitors must launch a satellite to low Earth orbit (LEO) within days of each other at different locations in the United States. The competition ended without a winner. DARPA Forecasting Floats in Turbulence (FFT) Challenge (2021) was to spur technologies that could predict the location of sea drifters or floats within 10 days. DARPA awarded $25,000 for first place, with prizes of $15,000 and $10,000 for second place and third place. DARPA Artificial Intelligence Cyber Challenge (AIxCC) (2023–2025) was a two-year challenge and asks competitors to design novel AI systems to secure critical software code on which Americans rely. The total prize money is $29.5 million. In March 2024, the Advanced Research Projects Agency for Health (ARPA-H) partnered with DARPA, contributing an additional $20 million to the competition's prize pool to address software vulnerabilities in medical devices, hospital IT, and biotech equipment. AIxCC collaborates with Google, Microsoft, OpenAI, Anthropic, Linux Foundation, Open Source Security Foundation, Black Hat USA, and DEF CON, all of which provide AIxCC with access to large language models. In August 2024, AIxCC held the semifinal at DEF CON in Las Vegas. DARPA and ARPA-H tested all 42 submissions by running them through various open-source coding projects with deliberately injected vulnerabilities and scored the tools based on their effectiveness in identifying and fixing security flaws. Seven teams, each winning $2 million in the semifinals, competed in the final round of the AIxCC at the August 2025 DEF CON conference. Team Atlanta won first place with a $4 million prize for its cyber reasoning systems, which identified and patched vulnerabilities across 54 million lines of code. DARPA Triage Challenge (2023 – 2026) aims to spur the development of novel physiological features for medical triage, with a total prize money of $7 million. In October 2024, Challenge Event 1 was held in Perry, Georgia, featuring to-scale replicas of disaster sites such as an airplane crash and Hurricane Katrina, and teams competed based on how closely their data aligned with the agency’s official data and how quickly and accurately their autonomous systems could identify individuals most urgently in need of medical care. DARPA concluded the second year of competitions and, in November 2025, named the top performers in systems and data categories, which will advance to the final 2026 competition. The DARPA Lift Challenge (2025-2026) is for participants to design unmanned aerial systems capable of carrying up to four times their own weight, with a minimum payload of 110 pounds. Acco

    Read more →
  • Wayve

    Wayve

    Wayve Technologies Ltd is a British autonomous driving technology company focused on developing self-driving vehicle systems through end-to-end deep learning. Founded in 2017 by researchers from the University of Cambridge, Wayve’s approach eschews detailed 3D maps and hand-coded rules, in favor of a self-learning “AI driver” that learns from camera data and driving experience. The London-headquartered startup has garnered significant attention and funding for its visually-based method. == History == Wayve was founded in Cambridge, England, on August 21, 2017, by Amar Shah and Alex Kendall, two machine learning PhD students at the University of Cambridge. Shah initially served as CEO while Kendall was CTO, and the pair set out to develop an unconventional self-driving car system using machine learning at every layer of the driving task. In May 2018, Wayve emerged from stealth mode with backing from early-stage investors. At this time the company had around 10 employees, and its advisory investors included Uber’s Chief Scientist, Zoubin Ghahramani, who shared Wayve’s vision of a learning-centric driving AI. In 2019, Wayve achieved a milestone by training a car to drive autonomously on public roads it had never seen before, using only cameras, a basic GPS map, and end-to-end deep learning control. The company moved its base to London and secured a $20 million Series A funding round in November 2019. This investment enabled Wayve to launch a pilot fleet of autonomous electric vehicles in central London for real-world testing. During these trials, Wayve’s cars (such as retrofitted Jaguar I-Pace SUVs) began navigating the complex, narrow streets of London to prove the system’s ability to adapt to challenging urban scenarios. In 2020, co-founder Amar Shah departed the company, and Alex Kendall assumed the role of CEO. The startup joined the Microsoft for Startups: Autonomous Driving program in 2020, leveraging Microsoft Azure’s cloud computing for training its machine learning models at scale. It also committed to testing exclusively on electric vehicles, and a goal to reduce carbon emissions. In 2021, Wayve entered pilot programs with major UK retailers. It launched a 12-month autonomous delivery trial with supermarket chain Asda, and received a £10 million ($13.6 million) investment from online grocer Ocado Group as part of a partnership to develop self-driving grocery delivery vans. Ocado’s backing gave Wayve access to a fleet of delivery vans for data collection and testing on busy London routes (with human safety drivers present) to train its AI in urban traffic. In 2022, after a successful Series B funding round, the company extended road testing beyond the UK to other regions, and, by 2023, in multiple countries. The company had begun operating in the United States and in continental Europe, in preparation for larger commercial deployments. In 2023, Wayve announced a collaboration with Nissan to integrate Wayve’s AI-driven software into its ProPilot ADAS system, slated to launch in fiscal year 2027. Wayve received strategic investment from Uber, in 2024, to jointly develop autonomous ride-hailing services. The two companies plan to trial a fully driverless robotaxi service in London, supported by a UK government program to accelerate commercial self-driving pilots to as early as 2026. To demonstrate the scalability of its technology, Wayve conducted an “AI-500” roadshow project, driving in dozens of cities across Asia, Europe, and North America using the same AI model. By mid-2025, it had completed autonomous driving demos in 90 cities without prior HD mapping. In April 2025, Wayve opened its first Asian research hub in Japan, with investment by SoftBank, to improve its model’s generalization using local driving data. That year, the company conducted driving tests in over 500 cities in Europe, North America and Japan without city-specific programming. In February 2026, Nissan, Uber and Wayve announced their collaboration on robotaxi development, with the aim of launching a pilot programme in Tokyo by late 2026. Wayve also formed a strategic alliance with Mercedes-Benz and Stellantis on personal vehicle and robotaxi applications. == Financing and investors == Wayve has been backed by a mix of venture capital (VC) firms, corporate investors, and individuals. Its initial seed funding came from funds such as Compound (NYC) and Firstminute Capital (London), as well as Cambridge-based angel investors, in 2018. Academic Pieter Abbeel and Uber’s chief scientist, Zoubin Ghahramani, were early backers. In November 2019, Wayve raised a $20 million Series A led by Eclipse Ventures, with participation from Balderton Capital and other prior investors. The Series A financing was used to fund the company’s first autonomous trials in London, and marked the first time a European self-driving car startup had secured a U.S. VC as lead investor. In October 2021, Ocado Group invested £10 million (approximately $13.6 million) in Wayve as a strategic partner in autonomous grocery delivery. This brought Wayve’s total funding to around $60 million at that time. The Series B round followed in January 2022, when Wayve announced $200 million in new funding led by Eclipse Ventures, with D1 Capital Partners, Moore Strategic Ventures, and Linse Capital. Balderton, Microsoft and Virgin Group joined as strategic backers. Baillie Gifford and Compound also participated; Ocado increased its stake as a strategic investor; and Meta AI head Yann LeCun and Richard Branson also became investors. Wayve’s Series C in May 2024 closed a $1.05 billion, led by Japan’s SoftBank Group. The funding round was the largest-ever for a UK AI company, and included new investor Nvidia, and returning investors Microsoft and Eclipse Ventures, among others. Uber also joined as a stratgic partner and a stakeholder. The Series C round increased Wayve’s total funding raised to about $1.3 billion to date from investors including SoftBank, Microsoft and Nvidia, and lifted Wayve’s valuation into “unicorn” status. In February 2026, Wayve announced a $1.2 billion Series D funding round; later that month, the company reported that $1.5 billion had been raised from, primarily, Mercedes-Benz, Stellantis, Nissan, and existing backers Uber, Microsoft and Nvidia, increasing Wayve's overall valuation to $8.6 billion. == Technology == Wayve’s self-driving approach centers on end-to-end deep learning and a vision-based AI system. Unlike conventional autonomous vehicles that depend on high-definition maps, hand-coded rules, and arrays of expensive lidar sensors, Wayve’s platform learns to drive predominantly using camera data and machine learning algorithms. The company refers to its AI-driven driving software as an “Embodied AI” or AI Driver, emphasizing that the system learns from experience (both real and simulated) to handle complex or novel situations rather than following pre-programmed instructions, not unlike Tesla's approach. The Wayve hardware-agnostic autonomy stack consists of a suite of video cameras, with basic automotive sensors, mounted on the vehicle, and paired with onboard compute units that are powered by GPUs to run the AI models. This vision-only philosophy is similar to Tesla’s Autopilot/FSDB model, but Wayve’s solution is vehicle-agnostic and mapless. Wayve’s strategy is to provide its driving AI as an OEM-ready platform; it plans to license or embed its technology into vehicles made by established automakers rather than build its own cars. Wayve’s development vehicles currently use Nvidia’s Orin system-on-chip as the onboard computer for running the AI model, but CEO Kendall has noted that the software can run on “whatever GPU [an automaker] already has in their vehicles” Wayve has built a cloud infrastructure, largely on Microsoft Azure, to process petabytes of this data, and uses simulation tools (known internally as the “Wayve Infinity” simulator) to synthetically generate and practice rare or dangerous scenarios for the AI to learn from. == Corporate affairs == Wayve is a privately held company headquartered in London, England, with its primary research and development office in the Kings Cross area of London. The company was initially incorporated as Wayve Technologies Ltd in the UK. Wayve has also established a presence in the U.S., in Silicon Valley); in Canada, with a research hub in Vancouver; in Yokohama, Japan; in Leonberg, Germany; and in Herzliya, Israel. The Leadership team includes research scientists and engineers with backgrounds in computer vision, robotics, and automotive systems. President Erez Dagan was hired in 2024, following two decades at Mobileye; chief scientist Jamie Shotton is formerly of Microsoft Research; CEO Alex Kendall, originally from New Zealand with a PhD in computer vision from Cambridge, took over as CEO in 2020 after the departure of his co-founder Amar Shah.

    Read more →
  • Language technology

    Language technology

    Language technology, often called human language technology (HLT), studies methods of how computer programs or electronic devices can analyze, produce, modify or respond to human texts and speech. Working with language technology often requires broad knowledge not only about linguistics but also about computer science. It consists of natural language processing (NLP) and computational linguistics (CL) on the one hand, many application oriented aspects of these, and more low-level aspects such as encoding and speech technology on the other hand. Note that these elementary aspects are normally not considered to be within the scope of related terms such as natural language processing and (applied) computational linguistics, which are otherwise near-synonyms. As an example, for many of the world's lesser known languages, the foundation of language technology is providing communities with fonts and keyboard setups so their languages can be written on computers or mobile devices. Other tools also are part of modern language technology and include machine translation, speech recognition, text processing and natural language processing. Large scale AI models have recently advanced the field and enhanced the ability of machines to interpret complex human context.

    Read more →
  • Vidby

    Vidby

    Vidby AG (stylized in lower-case) is a start-up based in Rotkreuz, Switzerland specializing in AI language translation for videos. Founded by Alexander Konovalov (uk:Олександр Коновалов) and Eugen von Rubinberg in September 2021, the company has especially garnered attention for its use in translating speeches given by President Volodymyr Zelenskyy during the Russian invasion of Ukraine. == History == Vidby AG was founded by Alexander Konovalov and Eugen von Rubinberg. Konovalov is a native of Ukraine and retains Ukrainian citizenship; Rubinberg came to Switzerland from Germany and holds German citizenship. Both are residents of Switzerland. The latter founded his first business, a trading company, at age 16. In 2013, the business partners launched a consumer-oriented video-call translation service called DROTR (Droid Translator) AG, utilizing a Konovalov-created AI-powered language translation technology enabling simultaneous translation of messages, voice and video calls in 104 languages (written), with 44 available in spoken form. This was the world's first video calling app with translation. The technology was pronounced a competitor of Skype and Viber by Forbes and claimed first prize at the "Innovative Breakthrough 2013" Competition. In 2021, with a new business-oriented focus, DROTR became Vidby, with the former Google technology partners Konovalov and Rubinberg remaining at the helm, each with the title Co-CEO. While headquartered in Switzerland, Vidby's development team is, according to the company's founders, based in Ukraine. The technology behind Vidby has an accuracy level variously reported as up to 99 percent or 99 to 100 percent, equalling the highest level of human translation. Additionally, the technology is capable of removing the original language while maintaining ambient sounds. Currently, some 70 languages plus 60 dialects are possible with the algorithm-based technology. == Notable use == In addition to its use with speeches delivered by Pope Francis, the technology has been provided to Ukrainian authorities and embassies during the ongoing military conflict with Russia free of remuneration. By July, 2022, some 70 speeches given by President Zelenskyy totalling 650 minutes had been translated into 30 languages, for a total of over 10,000 minutes of video material. Of its use in translating Zelenskyy's wartime speeches, Konovalov has said, "Like any citizen, I want to help defend my country." Notable corporate clients of Vidby include Samsung, Siemens, Cisco, Kärcher, Generali and McDonald's Corporation; an academic client is Harvard University. Google Cloud Technology Partner status of Vidby was confirmed officially after a six-month audit in December 2022. Denys Krasnikov, a Vidby co-founder, is responsible for cooperation with Google, YouTube, Microsoft, and other key partners. After the launch of multilingual YouTube channels, Vidby started AI translating and dubbing creators' videos for this new type of channel at the end of February 2023. == Accolades == Vidby headed a list of the five best video translation services as named by TechRadar Deutschland in September, 2022. In the same month, Tech Times named Vidby #1 in their list of the five best such services. It similarly topped a list of the five best content translation technologies as judged by European Business Review in October, 2022. Prior to these lead-position rankings (August, 2022), it was featured as Business Insider's special start-up recommendation (German: "Unser Lesetipp auf Gründerszene"). In 2023, YouTube recognized Vidby as its recommended vendor.

    Read more →
  • Plants vs. Zombies: Replanted

    Plants vs. Zombies: Replanted

    Plants vs. Zombies: Replanted is a 2025 tower defense video game developed by PopCap Seattle, The Lost Pixels, and published by Electronic Arts. It is a remaster of the 2009 game Plants vs. Zombies, introducing upscaled graphics and new additional content. Plants vs. Zombies: Replanted was released for video game consoles and personal computers on October 23, 2025. It received generally positive reviews from critics, but was criticized by the original game's development team for including fabricated concept art and for mishandling the soundtrack. == Gameplay == Plants vs. Zombies: Replanted follows the same gameplay of the original Plants vs. Zombies game with very minor changes. It is a lane-based tower defense game where the player has to defend their home from incoming zombies. The player can place various plants by spending "sun", the game's currency during levels. Sun icons can be collected from the sky during daytime and from sun-producing plants such as sunflowers. Some plants can attack zombies while some can act as defense. If all zombies are defeated in a level, the player wins. If a zombie reaches the left side of the line, a lawn mower—or other similar, relevant object—will activate and clear the row of any zombies, but if the lawn mower has already been used, and another zombie crosses, the game is over. === Replanted features === Plants vs. Zombies: Replanted contains up to 4K upscaled graphics and widescreen support, in comparison to the original game's static 800x600 resolution and 4:3 aspect ratio. Replanted now has full controller support and features local multiplayer modes ported from the original game's seventh generation console ports: co-op, where two players play together with assigned roles; and Versus, where one plays as the plants and the other as the zombies. No online multiplayer is planned, however support for Steam Remote Play was later added in a patch as an alternative for Windows users. Replanted also contains quality-of-life features. Gameplay can now be sped up by the player's will, with a max speed increase of 2.5x. Sun icons can now be mass collected using the "Sun Magnet." On Windows, players can quick-select plants from their seed bank using the number keys as hotkeys. Replanted also introduces two new additional game modes. "Cloudy Day" is a set of non-linear levels in the Adventure campaign. These levels only allow Sunflowers as sun-producing plants. During these levels, the amount of sun dropped from the sky and produced by plants are lowered. At certain times, rain clouds will move over the lawn. While these clouds are present, sun will stop appearing from the sky and from Sunflowers. However, all plants will cost around half their original price and have significantly faster recharge times. "R.I.P. Mode" is a harder difficulty of the Adventure campaign, but the player is forced back to the beginning if they lose a single level. Replanted additionally features "bonus levels" included as non-linear levels in the Adventure campaign. These include 10 new minigames that were previously unused in the original game. In a later update, Replanted added "Survival: Endless" levels to all five areas of the game instead of just the daytime pool. == Development == The existence of a Plants vs. Zombies remaster was revealed in an interview with Janet Robin from The String Revolution, who they did a vinyl collaboration with the franchise in 2025 with Iam8bit. Janet stated that EA commissioned them to record an acoustic composition of the track "Crazy Dave" to be used for an "anniversary edition" of the game. The song would be additionally be a tribute to the song "Bad Guy", which artist Billie Eilish has stated to be somewhat similar to the track. Plants vs. Zombies Replanted was officially announced in a Nintendo Direct presentation in late July 2025. As an incentive, people who pre-ordered the game are given an in-game retro-styled skin of the Peashooter. Replanted was showcased at PAX West on August 25, 2025. A dev diary for Plants vs. Zombies: Replanted was uploaded to YouTube on October 17, 2025. The video features Nick Reinhart, Jake Neri, and Matt Townsend. A developer panel for the game was available during TwitchCon 2025. == Release == Plants vs. Zombies: Replanted was released for Nintendo Switch, Nintendo Switch 2, PlayStation 4, PlayStation 5, Xbox One, Xbox Series X and Series S, and personal computers on October 23, 2025. It was leaked onto the internet on October 17, 2025. Players discovered multiple software bugs, and multiple assets alleged to be upscaled by generative artificial intelligence were found, leading to backlash. Numerous bugs were fixed in a day-one patch on October 23, 2025. == Reception == === Critical response === The versions of Plants vs. Zombies: Replanted for Windows, PlayStation 5, and Nintendo Switch 2 received "generally favorable" reviews from critics, according to review aggregator website Metacritic, while the Xbox Series X version received "mixed or average" reviews. According to OpenCritic, 57% of critics recommended it. IGN's Alessandro Fillari called it "a good way to get re-acquainted with one of the quirkiest puzzle-strategy games of the 2000s", while acknowledging its questionable decisions. Shacknews' David Craddock said it was his favorite version of Plants vs. Zombies, stating, "it packs everything fans loved about the original game, plus lots more" while justifying its US$20 price. The Verge described Replanted as "a time capsule from a simpler, happier time". Kyle Hilliard from Game Informer praised its faithfulness, complimenting the new animations and character designs that did not alter its memorability. Noah Hunter for Final Weapon described the remake as solid, though criticized the lack of certain features and containing bugs that gate it from being excellent. Ben Lyons from Gamereactor stated Replanted is the same as the original overall, despite believing the £18 price is not justified. === Original developers === Rich Werner, the original game's character designer, claims that some concept art contained in the game, speculated to be for Plants vs. Zombies: Garden Warfare (2014), did not originate from the original's development. Werner also stated that concept art for the Disco Zombie is fabricated; the design for the Disco Zombie was created after the estate of Michael Jackson requested the original Dancing Zombie, who resembles Michael Jackson from his Thriller music video, be removed from the game. On October 19, 2026, composer Laura Shigihara expressed her dissatisfaction with the lack of dynamic music in the game. Dynamic music would later be implemented in a later patch. In an interview featuring Rich Werner and user interface designer Matt Holmberg on April 29, 2026, Werner revealed that he and Shigihara were contacted by EA to make a music video to market Replanted. However, after the game was leaked, Werner's response on social media led EA to cancel the collaboration.

    Read more →
  • Sora (text-to-video model)

    Sora (text-to-video model)

    Sora was a text-to-video model and social media app developed by OpenAI. Using artificial intelligence, the model generated short video clips based on prompts, and could also extend existing short videos. In February 2024, OpenAI previewed examples of its output to the public, with the first generation of Sora released publicly for ChatGPT Plus and ChatGPT Pro users in the United States and Canada in December 2024. The second generation of Sora was released to select users in the US and Canada at the end of September 2025. Sora 2 integrated social media features into the app. The app was shut down on April 26, 2026 and the application programming interface (API) is planned to be discontinued on September 24, 2026, marking the end of the Sora AI brand as a whole. By default, the generator used copyrighted material in its videos, unless copyright holders actively opt out of having their content included. Videos contained a visible, moving digital watermark to prevent misuse, but a week after Sora 2's release, third-party programs became available which could remove the watermark. == Background == Several other models capable of generating video from text had been created prior to Sora, including Meta's Make‑A‑Video, Runway's Gen‑2 and Google Veo. OpenAI, the company behind Sora, had released DALL·E 3, the third of its DALL-E text-to-image models, in September 2023. == History == === Initial release === The team that developed Sora named it after the Japanese word for 'sky' to signify its "limitless creative potential". On February 15, 2024, OpenAI first previewed Sora by releasing multiple clips of high-definition videos that it had created, including an SUV driving down a mountain road, an animation of a "short fluffy monster" next to a candle, two people walking through Tokyo in the snow, and fake historical footage of the California gold rush. OpenAI stated that it was able to generate videos as long as one minute. The company then shared a technical report that highlighted the methods used to train the model. OpenAI CEO Sam Altman also posted a series of tweets responding to Twitter users' prompts with Sora-generated videos of the prompts. As of December 9, 2024, OpenAI had gradually made Sora available to the public for ChatGPT Pro and ChatGPT Plus users in the U.S. and Canada. Prior to this, the company had provided limited access to a small "red team", including experts in misinformation and bias, to perform adversarial testing on the model. The company also shared Sora with a small group of creative professionals, including video makers and artists, to seek feedback on its usefulness in creative fields. In February 2025, OpenAI announced plans to integrate Sora into ChatGPT by letting users generate Sora videos from the chatbot. === Sora 2 === Sora 2 was unveiled on September 30, 2025, with an iOS app at the same time, as well as an Android app two months later. All videos generated by the model feature a visible, moving watermark to prevent misuse of the tool. The previous version of Sora also added a safety watermark to allow viewers to distinguish between real and fictional content. On October 7, 404 Media reported that third-party programs that could remove the watermark from Sora 2 videos had become prevalent. Many outlets, such as Wired magazine, have noted that the Sora 2 app is overtly similar to TikTok in style and features. === Discontinuation === On March 24, 2026, OpenAI announced on X that it was discontinuing Sora in both the mobile app and the API. The Sora app was shut down on April 26, 2026, while the API is planned to be shut down on September 24, 2026. OpenAI's partnership with Disney, which included a licensing agreement allowing Disney characters to be used within Sora, was also coming to an end. The decision prompted British technology news website The Register to label OpenAI a "product-killer", following in the footsteps of other technology companies such as Google, Amazon Web Services, Broadcom, Cloud Software Group, and Netscape. OpenAI did not provide a specific reason for discontinuing Sora in its shutdown notice. The reports that emerged regarding this discontinuity linked the decision to computation shortages, cost pressures, and a broader shift toward core enterprise products. Following its public launch, Sora's worldwide users peaked at around a million before declining to fewer than 500,000, while the service cost an estimated $1 million per day to operate due to the computational demands of video generation. == Legal regulation == In November 2024, an API key for Sora access was leaked by a group of testers on Hugging Face who posted a manifesto stating that they were protesting that Sora was used for "art washing". OpenAI revoked all access three hours after the leak was made public and stated that "hundreds of artists" have shaped the development and that "participation is voluntary". At the time of its launch, Sora 2 allowed copyrighted content by default unless copyright holders contacted OpenAI to restrict the generation of their content on the platform. On October 3, 2025, OpenAI stated that a future update to Sora 2 would give copyright holders "more granular control" over the generation of copyrighted content, but the company did not state whether existing content would be removed. On October 6, the chairman of the MPA criticized OpenAI's approach to copyright with Sora 2. On December 11, 2025, the Walt Disney Company announced that it would invest $1 billion in OpenAI to allow users to generate more than 200 of its copyrighted characters on Sora 2. These characters include those from Disney Animation, Pixar, Marvel Studios, and Star Wars. == Capabilities and limitations == The technology behind Sora is an adaptation of the technology behind DALL-E 3. According to OpenAI, Sora is a diffusion transformer, a denoising latent diffusion model with one transformer as its denoiser. A video is generated in latent space by denoising 3D "patches", then transformed to standard space by a video decompressor. Recaptioning is employed to augment training data by using a video-to-text model to create detailed captions for videos. OpenAI trained the model using publicly available videos as well as copyrighted videos licensed for the purpose, but did not reveal the number or the exact source of the videos. Upon its release, OpenAI acknowledged some of Sora's shortcomings, including its limited capacity to simulate complex physics, to understand causality and to differentiate left from right. OpenAI also stated that, in adherence to the company's existing safety practices, Sora will restrict text prompts for sexual, violent, hateful or celebrity imagery, as well as content featuring existing intellectual property. Sora researcher Tim Brooks stated that the model learned how to create 3D graphics from its dataset alone, while fellow Sora researcher Bill Peebles said that the model automatically created different video angles without being prompted. According to OpenAI, Sora-generated videos are also tagged with C2PA metadata to indicate that they are AI-processed. === Comparison with other models === The Artificial Analysis have placed Sora 2 pro lower than other text-to-video AI generators in the market on its leaderboard. Other models, such as Seedance 2.0 from ByteDance, Runaway 4.5 from Runaway, and Kling 3.0 from KlingAI, have ranked higher than Sora 2.0. == Reception == === Positive === In 2024, Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", but noted that they must have been cherry-picked and may not be representative of Sora's typical output. Lisa Lacy of CNET called its example videos "remarkably realistic – except perhaps when a human face appears close up or when sea creatures are swimming". In October 2025, The New York Times remarked that the release of the Sora 2 app in September 2025 was "jaw-dropping (for better and worse)" though also remarked that the app was a "social network in disguise" and "the type of product that companies like Meta and X have sought to build: a way to bring A.I. to the masses that people can share." The article expressed concern regarding the product's potential impact on society and its potential use to promote misinformation, disinformation, and scams. A 2025 study in Science Advances found that generative AI tools can lower barriers to entry in creative work. It enables users with diverse skill sets, including people with less formal artistic training and technical skills, to act on their creative and imaginative ideas. The lower barrier to entry allows such users previously locked out of the creative industry to produce content and easily act on their creative ideas. === Negative === Some internet users and online content creators, such as Hank Green, called the mobile app "SlopTok," a reference to both the mobile app TikTok and the term AI slop. Filmmaker Tyler Perry announced he would be putting a planned

    Read more →
  • Trustworthy computing

    Trustworthy computing

    The term trustworthy computing (TwC) has been applied to computing systems that are inherently secure, available, and reliable. It is particularly associated with the Microsoft initiative of the same name, launched in 2002. == History == Until 1995, there were restrictions on commercial traffic over the Internet. On, May 26, 1995, Bill Gates sent the "Internet Tidal Wave" memorandum to Microsoft executives assigning "...the Internet this highest level of importance..." but Microsoft's Windows 95 was released without a web browser as Microsoft had not yet developed one. The success of the web had caught them by surprise but by mid 1995, they were testing their own web server, and on August 24, 1995, launched a major online service, The Microsoft Network (MSN). The National Research Council recognized that the rise of the Internet simultaneously increased societal reliance on computer systems while increasing the vulnerability of such systems to failure and produced an important report in 1999, "Trust in Cyberspace". This report reviews the cost of un-trustworthy systems and identifies actions required for improvement. == Microsoft and Trustworthy Computing == Bill Gates launched Microsoft's "Trustworthy Computing" initiative with a January 15, 2002 memo, referencing an internal whitepaper by Microsoft CTO and Senior Vice President Craig Mundie. The move was reportedly prompted by the fact that they "...had been under fire from some of its larger customers–government agencies, financial companies and others–about the security problems in Windows, issues that were being brought front and center by a series of self-replicating worms and embarrassing attacks." such as Code Red, Nimda, Klez and Slammer. Four areas were identified as the initiative's key areas: Security, Privacy, Reliability, and Business Integrity, and despite some initial scepticism, at its 10-year anniversary it was generally accepted as having "...made a positive impact on the industry...". The Trustworthy Computing campaign was the main reason why Easter eggs disappeared from Windows, Office and other Microsoft products.

    Read more →
  • SmartAction

    SmartAction

    SmartAction Company LLC is a U.S.-based software company that develops artificial intelligence–driven virtual agents for customer service applications, including voice-based interactive voice response (IVR) systems, chat, and SMS. The company was founded in 2009 by inventor and entrepreneur Peter Voss and is headquartered in Fort Worth, Texas. == History == In 2001, Peter Voss founded Adaptive AI, Inc., a research and development company focused on artificial intelligence concepts. In 2009, Voss founded SmartAction Company, LLC to commercialize customer-service automation software derived from this work. The company’s initial products focused on automating inbound and outbound calls for contact center environments. In November 2022, Kyle Johnson was appointed chief executive officer, succeeding Gary Davis, who had served as CEO since 2020. In 2024, SmartAction was acquired by Capacity, an AI-powered customer support automation company based in St. Louis, Missouri. == Technology == SmartAction develops cloud-based voice automation software that integrates speech recognition and natural language processing to support automated customer interactions in contact center environments. The platform supports automated handling of common customer service tasks and is designed to integrate with enterprise systems.

    Read more →
  • Fuzzy markup language

    Fuzzy markup language

    Fuzzy Markup Language (FML) is a specific purpose markup language based on XML, used for describing the structure and behavior of a fuzzy system independently of the hardware architecture devoted to host and run it. == Overview == FML was designed and developed by Giovanni Acampora during his Ph.D. course in Computer Science, at University of Salerno, Italy, in 2004. The original idea inspired Giovanni Acampora to create FML was the necessity of creating a cooperative fuzzy-based framework aimed at automatically controlling a living environment characterized by a plethora of heterogeneous devices whose interactions were devoted to maximize the human comfort under energy saving constraints. This framework represented one of the first concrete examples of Ambient Intelligence. Beyond this pioneering application, the major advantage of using XML to describe a fuzzy system is hardware/software interoperability. Indeed, all that is needed to read an FML file is the appropriate schema for that file, and an FML parser. This markup approach makes it much easier to exchange fuzzy systems between software: for example, a machine learning application could extract fuzzy rules which could then be read directly into a fuzzy inference engine or uploaded into a fuzzy controller. Also, with technologies like XSLT, it is possible to compile the FML into the programming language of your choice, ready for embedding into whatever application you please. As stated by Mike Watts on his popular Computational Intelligence blog: "Although Acampora's motivation for developing FML seems to be to develop embedded fuzzy controllers for ambient intelligence applications, FML could be a real boon for developers of fuzzy rule extraction algorithms: from my own experience during my PhD, I know that having to design a file format and implement the appropriate parsers for rule extraction and fuzzy inference engines can be a real pain, taking as much time as implementing the rule extraction algorithm itself. I would much rather have used something like FML for my work." A complete overview of FML and related applications can be found in the book titled On the power of Fuzzy Markup Language edited by Giovanni Acampora, Chang-Shing Lee, Vincenzo Loia and Mei-Hui Wang, and published by Springer in the series Studies on Fuzziness and Soft Computing. == Syntax, grammar and hardware synthesis == FML allows fuzzy systems to be coded through a collection of correlated semantic tags capable of modeling the different components of a classical fuzzy controller such as knowledge base, rule base, fuzzy variables and fuzzy rules. Therefore, the FML tags used to build a fuzzy controller represent the set of lexemes used to create fuzzy expressions. In order to design a well-formed XML-based language, an FML context-free grammar is defined by means of a XML schema which defines name, type and attributes characterized each XML element. However, since an FML program represents only a static view of a fuzzy logic controller, XSLT is provided to change this static view to a computable version. Indeed, XSLTs modules are able to convert the FML-based fuzzy controller in a general purpose computer language using an XSL file containing the translation description. At this level, the control is executable for the hardware. In short, FML is essentially composed by three layers: XML in order to create a new markup language for fuzzy logic control; a XML Schema in order to define the legal building blocks; eXtensible Stylesheet Language Transformations (XSLT) in order to convert a fuzzy controller description into a specific programming language. === Syntax === FML syntax is composed of XML tags and attributes which describe the different components of a fuzzy logic controller listed below: fuzzy knowledge base; fuzzy rule base; inference engine fuzzification subsystem; defuzzification subsystem. In detail, the opening tag of each FML program is which represents the fuzzy controller under modeling. This tag has two attributes: name and ip. The first attribute permits to specify the name of fuzzy controller and ip is used to define the location of controller in a computer network. The fuzzy knowledge base is defined by means of the tag which maintains the set of fuzzy concepts used to model the fuzzy rule base. In order to define the fuzzy concept related controlled system, tag uses a set of nested tags: defines the fuzzy concept; defines a linguistic term describing the fuzzy concept; a set of tags defining a shape of fuzzy sets are related to fuzzy terms. The attributes of tag are: name, scale, domainLeft, domainRight, type and, for only an output, accumulation, defuzzifier and defaultValue. The name attribute defines the name of fuzzy concept, for instance, temperature; scale is used to define the scale used to measure the fuzzy concept, for instance, Celsius degree; domainLeft and domainRight are used to model the universe of discourse of fuzzy concept, that is, the set of real values related to fuzzy concept, for instance [0°,40°] in the case of Celsius degree; the position of fuzzy concept into rule (consequent part or antecedent part) is defined by type attribute (input/output); accumulation attribute defines the method of accumulation that is a method that permits the combination of results of a variable of each rule in a final result; defuzzifier attribute defines the method used to execute the conversion from a fuzzy set, obtained after aggregation process, into a numerical value to give it in output to system; defaultValue attribute defines a real value used only when no rule has fired for the variable at issue. As for tag , it uses two attributes: name used to identify the linguistic value associate with fuzzy concept and complement, a boolean attribute that defines, if it is true, it is necessary to consider the complement of membership function defined by given parameters. Fuzzy shape tags, used to complete the definition of fuzzy concept, are: Every shaping tag uses a set of attributes which defines the real outline of corresponding fuzzy set. The number of these attributes depends on the chosen fuzzy set shape. In order to make an example, consider the Tipper Inference System described in Mathworks Matlab Fuzzy Logic Toolbox Tutorial. This Mamdani system is used to regulate the tipping in, for example, a restaurant. It has got two variables in input (food and service) and one in output (tip). FML code for modeling part of knowledge base of this fuzzy system containing variables food and tip is shown below. A special tag that can furthermore be used to define a fuzzy shape is . This tag is used to customize fuzzy shape (custom shape). The custom shape modeling is performed via a set of tags that lists the extreme points of geometric area defining the custom fuzzy shape. Obviously, the attributes used in tag are x and y coordinates. As for rule base component, FML allows to define a set of rule bases, each one of them describes a different behavior of system. The root of each rule base is modeled by tag which defines a fuzzy rule set. The tag uses five attributes: name, type, activationMethod, andMethod and orMethod. Obviously, the name attribute uniquely identifies the rule base. The type attribute permits to specify the kind of fuzzy controller (Mamdani or TSK) respect to the rule base at issue. The activationMethod attribute defines the method used to implication process; the andMethod and orMethod attribute define, respectively, the and and or algorithm to use by default. In order to define the single rule the tag is used. The attributes used by the tag are: name, connector, operator and weight. The name attribute permits to identify the rule; connector is used to define the logical operator used to connect the different clauses in antecedent part (and/or); operator defines the algorithm to use for chosen connector; weight defines the importance of rule during inference engine step. The definition of antecedent and consequent rule part is obtained by using and tags. tag is used to model the fuzzy clauses in antecedent and consequent part. This tag use the attribute modifier to describe a modification to term used in the clause. The possible values for this attribute are: above, below, extremely, intensify, more or less, norm, not, plus, slightly, somewhat, very, none. To complete the definition of fuzzy clause the nested and tags have to be used. A sequence of tags realizes a fuzzy rule base. As example, consider a Mamdani rule composed by (food is rancid) OR (servi

    Read more →
  • Evolutionary acquisition of neural topologies

    Evolutionary acquisition of neural topologies

    Evolutionary acquisition of neural topologies (EANT/EANT2) is an evolutionary reinforcement learning method that evolves both the topology and weights of artificial neural networks. It is closely related to the works of Angeline et al. and Stanley and Miikkulainen. Like the work of Angeline et al., the method uses a type of parametric mutation that comes from evolution strategies and evolutionary programming (now using the most advanced form of the evolution strategies CMA-ES in EANT2), in which adaptive step sizes are used for optimizing the weights of the neural networks. Similar to the work of Stanley (NEAT), the method starts with minimal structures which gain complexity along the evolution path. == Contribution of EANT to neuroevolution == Despite sharing these two properties, the method has the following important features which distinguish it from previous works in neuroevolution. It introduces a genetic encoding called common genetic encoding (CGE) that handles both direct and indirect encoding of neural networks within the same theoretical framework. The encoding has important properties that makes it suitable for evolving neural networks: It is complete in that it is able to represent all types of valid phenotype networks. It is closed, i.e. every valid genotype represents a valid phenotype. (Similarly, the encoding is closed under genetic operators such as structural mutation and crossover.) These properties have been formally proven. For evolving the structure and weights of neural networks, an evolutionary process is used, where the exploration of structures is executed at a larger timescale (structural exploration), and the exploitation of existing structures is done at a smaller timescale (structural exploitation). In the structural exploration phase, new neural structures are developed by gradually adding new structures to an initially minimal network that is used as a starting point. In the structural exploitation phase, the weights of the currently available structures are optimized using an evolution strategy. == Performance == EANT has been tested on some benchmark problems such as the double-pole balancing problem, and the RoboCup keepaway benchmark. In all the tests, EANT was found to perform very well. Moreover, a newer version of EANT, called EANT2, was tested on a visual servoing task and found to outperform NEAT and the traditional iterative Gauss–Newton method. Further experiments include results on a classification problem.

    Read more →
  • Computer Law & Security Review

    Computer Law & Security Review

    The Computer Law & Security Review is an international peer-reviewed journal published by Elsevier. It has been published six times a year since 1985 and is indexed in Scopus and SSCI. It is accessible to a wide range of professional legal and IT practitioners, businesses, academics, researchers, libraries and organisations in both the public and private sectors. The journal regularly covers: CLSR Briefing with special emphasis on UK/US developments European Union update National news from 10 European jurisdictions Pacific rim news column Refereed practitioner and academic papers on topics such as Web 2.0, IT security, Identity management, ID cards, RFID, interference with privacy, Internet law, telecoms regulation, online broadcasting, intellectual property, software law, e-commerce, outsourcing, data protection and freedom of information and many other topics. The Journal's Correspondent Panel includes more than 40 specialists in IT law and security. Each issue contains articles, case law analysis and current news on information and communications technology. Special Features High quality peer reviewed papers from internationally renowned practitioner and academic experts Latest developments reported in situ by more than 20 leading law firms from around the world Highly experienced and respected editor and correspondents panel Online access to all 23 volumes of CLSR with embedded web links to primary sources Contact details of all authors A pool of expertise that can collectively identify the key topics that need to be examined.

    Read more →
  • Roborace

    Roborace

    Roborace was a competition with autonomously driving, electrically powered vehicles. Founded in 2015 by Denis Sverdlov, it aimed to be the first global championship for autonomous cars. From 2017 to 2019, the official CEO was 2016–17 Formula E champion, Lucas Di Grassi, who later became a member of Roborace’s supervisory board. The series tested their technology and race formats at FIA Formula E Championship events during 2016–2018. In 2019 Roborace organized Season Alpha, which consisted of 4 trial racing events with several independent teams competing against each other for the first time. In 2020–21 Roborace held Season Beta with 7 competing teams. All teams utilized the same chassis and powertrain, but they had to develop their own real-time computing algorithms and artificial intelligence technologies. In May 2022, Arrival, the owner of Roborace, confirmed that they were no longer continuing the Roborace programme, but that they were hoping to find alternative funding. In February 2024, after getting its stock delisted from the Nasdaq, Arrival's UK division entered administration, with future plans of a sale of Arrival and all of its affiliated assets. == Cars == === Robocar === The world's first purpose-built autonomous racing car, Robocar, was designed by Daniel Simon, who previously worked on vehicles for movies such as Tron: Legacy and Oblivion, as well as designing the livery for the 2011 HRT Formula One car. Michelin is the official tyre supplier, and the internal computing processors (Drive PX 2) are Nvidia. The chassis itself is shaped like a teardrop, improving aerodynamic efficiency. The car weighs around 1350 kg and is 4.8 metres (16 ft) long and 2 metres (6.6 ft) wide. It has four electric motors, each with a power of 135 kW producing over 500 hp combined, and utilizes a 840V battery. For navigation, it relies on a mixture of optical systems, radars, lidars and ultrasonic sensors. The vehicle has been demonstrated at speeds of almost 300 km/h (190 mph). === DevBot === Development of the Robocar started in early 2016, with a first outing of a test vehicle, the so-called DevBot, following in the summer of the same year. The test car consisted of the same internal units (battery, motor, electronics) used in the Robocar, but were placed in the chassis of a Ginetta LMP3 car without an engine cover in order to provide better cooling and access. DevBot saw its first public outing at the Formula E pre-season tests in Donington Park in August 2016. After battery issues in Hong Kong caused the development team to abandon their demonstration run, the DevBot successfully drove twelve laps around the Moulay El Hassan Formula E circuit in Marrakesh. Other test tracks included Michelin's testing ground in Ladoux and the Silverstone Stowe Circuit. During testing ahead of the 2017 Buenos Aires ePrix, two DevBot cars raced against each other autonomously, resulting in one of the vehicles crashing on a corner. During the 2017–18 Formula E season, Roborace pitched pro-drifter Ryan Tuerck against a DevBot at the Rome ePrix. At the Berlin ePrix, Roborace held the Human + Machine Challenge, the first race for combined teams of human drivers and AIs using a pair of Devbots. === DevBot 2.0 === An upgraded version of DevBot was announced in late 2018, and after private testing made its public debut in 2019 at the inaugural Season Alpha event. DevBot 2.0 uses the same technology as both Robocar and DevBot, with the main changes being a conversion to being driven on the rear axle only, a lower position for the driver for safety reasons and a bespoke composite bodywork. == Seasons == === Testing === ==== 2016–17 Formula E season ==== Roborace appeared at a number of Formula E events during the 2016–17 Formula E season. However, in this period only test drives with two different DevBots took place. Within the framework of the 2017 Buenos Aires ePrix both DevBot vehicles drove against each other on a race track for the first time. There were also DevBot demonstrations at the 2016 Marrakesh ePrix, 2017 Berlin ePrix, 2017 New York City ePrix and 2017 Montreal ePrix. At the 2017 Paris ePrix, the developers also let a Robocar onto the track for the first time, even though the vehicle only drove the track at walking speed. ==== 2017–18 Formula E season ==== At the start of the 2017/18 Formula E season, the Roborace developers once again tested the DevBot during a public time trial between the Roborace CI and the TV presenter Nicki Shields at the 2017 Hong Kong ePrix. As part of a similar time trial at the 2018 Rome ePrix, drift professional Ryan Tuerck also tested the DevBot. The Human + Machine Challenge was created for the Formula E race on the Berlin ePrix. A team of doctoral students from the Technical University of Munich (TUM) and the University of Pisa programmed the software for the Devbot to drive autonomously around the circuit in Berlin. Afterwards both teams in combination with a human driver competed in a public time trial. The vehicle of the team of the Technical University of Munich finished the Human + Machine Challenge with an average lap time of 91.59 seconds, almost four seconds faster than that of the University of Pisa with 95.36 seconds and thus won the Challenge. At the Goodwood Festival of Speed, Robocar became the first ever fully autonomous race car to complete the Goodwood Hill Climb. The vehicle completed the first official autonomous run on 13 July 2018 within the framework of the event. === Season Alpha (2019) === Season Alpha took place at various locations in Europe and North America with the aim of testing several competition formats using the new DevBot 2.0. The first event was held at the Circuito Monteblanco in Spain, and featured the first race between two fully autonomous cars. The events were not broadcast live, instead short clips on YouTube were released. Two teams were competing: Arrival and the Technical University of Munich. On 7 July 2019, the Roborace DevBot 2.0 car set the first ever autonomous official timed run at Goodwood Festival of Speed, with a time of 66.96 s and a top speed of 162.8 km/h (101.2 mph). This is currently the record for autonomous vehicles. Roborace also set the Guinness World Record for having the fastest autonomous car in the world. The Robocar reached a speed of 282.42 km/h (175.49 mph). === Season Beta (2020–21) === The second testing season took place at various locations between September 2020 and October 2021, featuring 16 races and involving mixed reality elements dubbed "Roborace Metaverse", which is based on Roborace's patented technology. The program of Season Beta competitions has gradually complicating rules arranged in a progression of so-called missions. Each mission consists of two racing rounds — one round per day. A mission plan issued by Roborace for each mission defines its objectives, rules, and point-scoring system. The key objective of Season Beta is to come to the point when the majority of competing teams have developed sufficient capability for wheel-to-wheel racing in Season 1. There were 7 teams competing in Season Beta: Arrival Racing (UK/Russia), Autonomous Racing Graz (Austria), MIT Driverless (United States), Acronis SIT (Switzerland), University of Pisa (Italy), PoliMOVE (Italy), CMU (United States).

    Read more →
  • DARPA Grand Challenge

    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

    Read more →