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Ghana Post GPS

GhanaPostGPS is a web and smartphone application, sponsored by the government of Ghana and developed by Vokacom, to provide a digital addresses and postal codes for every 5 squared meter location in Ghana. The digital address is a composite of the postcode (region, district & area code) plus a unique address. GhanaPostGPS is the first digital addressing system created by the government of Ghana. GhanaPost GPS is a mandatory requirement for obtaining the National Identification Card and other services.
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Jailbreak (computer science)

In computer security, jailbreaking is defined as the act of removing limitations that a vendor attempted to hard-code or hard-wire into its hardware and/or software. It is a form of privilege escalation. The term may have originated with the use of toolsets to break out of a chroot or jail in UNIX-like operating systems. This allowed the user to see files outside of the file system that the administrator intended to make available to the application or user in question. The term was first used in its modern meaning in the iPhone/iOS jailbreaking community and has also been used as a term for PlayStation Portable hacking; these devices have repeatedly been subject to jailbreaks, allowing the execution of arbitrary code, and sometimes have had those jailbreaks disabled by vendor updates, especially in the case of iOS devices. == iOS jailbreaking == iOS systems including the iPhone, iPad, and iPod Touch have been subject to iOS jailbreaking efforts since they were released, and continuing with each firmware update. iOS jailbreaking tools have included the option to install package frontends such as Cydia and Installer.app, third-party alternatives to the App Store, as a way to find and install system tweaks and binaries. To prevent iOS jailbreaking, Apple has made the device boot ROM execute checks for SHSH blobs in order to disallow uploads of custom kernels and prevent software downgrades to earlier, jailbreakable firmware. In an "untethered" jailbreak, the iBoot environment is changed to execute a boot ROM exploit and allow submission of a patched low level bootloader or hack the kernel to submit the jailbroken kernel after the SHSH check. == Other phones == A similar method of jailbreaking exists for S60 Platform smartphones, where utilities such as HelloOX allow the execution of unsigned code and full access to system files. or edited firmware (similar to the M33 hacked firmware used for the PlayStation Portable) to circumvent restrictions on unsigned code. Nokia has since issued updates to curb unauthorized jailbreaking, in a manner similar to Apple. Rooting is the equivalent concept for Android phones and other devices. == Console jailbreaking == In the case of gaming consoles, jailbreaking is often used to execute homebrew games. In 2011, Sony, with assistance from law firm Kilpatrick Stockton, sued 21-year-old George Hotz and associates of the group fail0verflow for jailbreaking the PlayStation 3 (see Sony Computer Entertainment America v. George Hotz and PlayStation Jailbreak). == AI jailbreaks == Jailbreaking can also occur in systems and software that use generative artificial intelligence models, such as ChatGPT. In jailbreaking attacks on artificial intelligence systems, users are able to manipulate the system to behave differently than it was intended, making it possible to reveal information about how the model was instructed by the vendor (the "system prompt") or to induce it to respond in an anomalous or harmful way. These attacks typically simply require prompting the AIs with specific phrasal templates - no software is typically required, although software could theoretically be used to "industrialise" such exploits, and some research has been done in this direction. In 2024, a consortium of AI firms founded HackAPrompt.com, a competition to encourage users to find new and effective AI jailbreaking techniques. These and other findings from "ethical hackers" have been used by AI model providers to try to improve AI safety.
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John M. Jumper

John Michael Jumper (born 1 January 1985) is an American chemist and computer scientist. Jumper and Demis Hassabis were awarded the 2024 Nobel Prize in Chemistry for protein structure prediction. As of 2025 Jumper serves as director at Google DeepMind. Jumper and his colleagues created AlphaFold, an artificial intelligence (AI) model to predict protein structures from their amino acid sequence with high accuracy. The AlphaFold team had released 214 million protein structures as of January 2024. The scientific journal Nature included Jumper as one of the ten "people who mattered" in science in their annual listing of Nature's 10 in 2021. == Education == Jumper graduated from Pulaski Academy in 2003. He received a Bachelor of Science with majors in physics and mathematics from Vanderbilt University in 2007, a Master of Philosophy in theoretical condensed matter physics from the University of Cambridge where he was a student of St Edmund's College, Cambridge in 2010 on a Marshall Scholarship, a Master of Science in theoretical chemistry from the University of Chicago in 2012, and a Doctor of Philosophy in theoretical chemistry from the University of Chicago in 2017. His doctoral advisors at the University of Chicago were Tobin R. Sosnick and Karl Freed. == Career and research == Jumper's research investigates algorithms for protein structure prediction. === AlphaFold === AlphaFold is a deep learning algorithm developed by Jumper and his team at DeepMind, a research lab acquired by Google's parent company Alphabet Inc. It is an artificial intelligence program which performs predictions of protein structure. === Awards and honors === In November 2020, AlphaFold was named the winner of the 14th Critical Assessment of Structure Prediction (CASP) competition. This international competition benchmarks algorithms to determine which one can best predict the 3D structure of proteins. AlphaFold won the competition, outperforming other algorithms scoring above 90 for around two-thirds of the proteins in CASP's global distance test (GDT), a test that measures the degree to which a computational program predicted structure is similar to the lab experiment determined structure, with 100 being a complete match, within the distance cutoff used for calculating GDT. In 2021, Jumper was awarded the BBVA Foundation Frontiers of Knowledge Award in the category "Biology and Biomedicine". In 2022 Jumper received the Wiley Prize in Biomedical Sciences and for 2023 the Breakthrough Prize in Life Sciences for developing AlphaFold, which accurately predicts the structure of a protein. In 2023 he was awarded the Canada Gairdner International Award and the Albert Lasker Award for Basic Medical Research. In 2024, Jumper and Demis Hassabis shared half of the Nobel Prize in Chemistry for their protein folding predictions, the other half went to David Baker for computational protein design. In 2025, Jumper received the Golden Plate Award of the American Academy of Achievement and the Marshall Medal of the Marshall Aid Commemoration Commission. He was elected a Fellow of the Royal Society (FRS) that same year. In 2026, he was elected a member of the National Academy of Engineering.
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Shane Legg

Shane Legg (born 1973 or 1974) is a machine learning researcher and entrepreneur. With Demis Hassabis and Mustafa Suleyman, he cofounded DeepMind Technologies (later bought by Google and now called Google DeepMind), and works there as the chief AGI scientist. He is also known for his academic work on artificial general intelligence, including his thesis supervised by Marcus Hutter. == Early life and education == Legg attended Rotorua Lakes High School in Rotorua, on New Zealand's North Island. He completed his undergraduate studies at Waikato University in 1996. Also in 1996, he obtained his MSc degree with a thesis entitled "Solomonoff Induction", with Cristian S. Calude at the University of Auckland. == Research interests == In the early 2000s, Legg re-introduced and popularized with Ben Goertzel the term "artificial general intelligence" (AGI), to describe an AI that can do practically any cognitive task a human can do. At that time, talking about AGI "would put you on the lunatic fringe". Legg is known for his concern of existential risk from AI, highlighted in 2011 in an interview on LessWrong and in 2023 he signed the statement on AI risk of extinction. == Career == Before his PhD and before cofounding DeepMind, Shane Legg worked at "a number of software development positions at private companies", including the "big data firm Adaptive Intelligence" and the startup WebMind founded by Ben Goertzel. === Research === Legg later obtained a PhD at the Dalle Molle Institute for Artificial Intelligence Research (IDSIA), a joint research institute of USI Università della Svizzera italiana and SUPSI. He worked on theoretical models of super intelligent machines (AIXI) with Marcus Hutter, and completed in 2008 his doctoral thesis entitled "Machine Super Intelligence". He then went on to complete a postdoctoral fellowship in finance at USI, and began a further fellowship at University College London's Gatsby Computational Neuroscience Unit. === DeepMind === Demis Hassabis and Shane Legg first met in 2009 at University College London, where Legg was a postdoctoral researcher. In 2010, Legg cofounded the start-up DeepMind Technologies along with Demis Hassabis and Mustafa Suleyman. DeepMind Technologies was bought in 2014 by Google. After the merge with Google Brain in 2023, the company is now known as Google DeepMind. According to a 2017 article, a significant part of his job as the chief scientist was to supervise recruitment, to decide where DeepMind should focus its efforts, and to lead DeepMind's AI safety work. As of July 2023, Legg works at Google DeepMind as the Chief AGI Scientist. == Awards and honors == Legg was awarded the $10,000 prize of the Singularity Institute for Artificial Intelligence for his PhD done in 2008. Legg was appointed Commander of the Order of the British Empire (CBE) in the 2019 Birthday Honours for services to the science and technology sector and to investment.
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Tokken

Tokken is a payment system and mobile app most known for being a legal and secure option for businesses transactions within the cannabis industry, because of its compliance with bank requirements. The startup company was created by Lamine Zarrad, a former regulator at the Office of the Comptroller of the Currency. == Operability == In order for a person to start using the app, they need to provide evidence, in the form of bioidentification data and mobile carrier records, that they can legally purchase weed. After they have been verified, customers can pay directly through the app at any dispensary that is using Tokken. Tokken turns credit card transactions into a digital token, which can be exchanged back for money that can later be deposited into a bank account. All transactions are logged publicly through a blockchain leger, making the process both anonymous and verified. === Banking services === Tokken has a "pay taxes" function which enables dispensaries to pay their taxes directly to the department.
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Google Brain

Google Brain was a deep learning artificial intelligence research team that served as the sole AI branch of Google before being incorporated under the newer umbrella of Google AI, a research division at Google dedicated to artificial intelligence. Formed in 2011, it combined open-ended machine learning research with information systems and large-scale computing resources. It created tools such as TensorFlow, which allow neural networks to be used by the public, and multiple internal AI research projects, and aimed to create research opportunities in machine learning and natural language processing. It was merged into former Google sister company DeepMind to form Google DeepMind in April 2023. == History == The Google Brain project began in 2011 as a part-time research collaboration between Google fellow Jeff Dean and Google Researcher Greg Corrado. Google Brain started as a Google X project and became so successful that it was graduated back to Google: Astro Teller has said that Google Brain paid for the entire cost of Google X. In June 2012, The New York Times reported that a cluster of 16,000 processors in 1,000 computers dedicated to mimicking some aspects of human brain activity had successfully trained itself to recognize a cat based on 10 million digital images taken from YouTube videos. The story was also covered by National Public Radio (NPR). In March 2013, Google hired Geoffrey Hinton, a leading researcher in the deep learning field, and acquired the company DNNResearch Inc. headed by Hinton. Hinton said that he would be dividing his future time between his university research and his work at Google. In April 2023, Google Brain merged with Google sister company DeepMind to form Google DeepMind, as part of the company's continued efforts to accelerate work on AI. == Team and location == Google Brain was initially established by Google Fellow Jeff Dean and visiting Stanford professor Andrew Ng. In 2014, the team included Jeff Dean, Quoc V. Le, Ilya Sutskever, Alex Krizhevsky, Samy Bengio, and Vincent Vanhoucke. In 2017, team members included Anelia Angelova, Samy Bengio, Greg Corrado, George Dahl, Michael Isard, Anjuli Kannan, Hugo Larochelle, Chris Olah, Benoit Steiner, Vincent Vanhoucke, Vijay Vasudevan, and Fernanda Viegas. Chris Lattner, who created Apple's programming language Swift and then ran Tesla's autonomy team for six months, joined Google Brain's team in August 2017. Lattner left the team in January 2020 and joined SiFive. As of 2021, Google Brain was led by Jeff Dean, Geoffrey Hinton, and Zoubin Ghahramani. Other members include Katherine Heller, Pi-Chuan Chang, Ian Simon, Jean-Philippe Vert, Nevena Lazic, Anelia Angelova, Lukasz Kaiser, Carrie Jun Cai, Eric Breck, Ruoming Pang, Carlos Riquelme, Hugo Larochelle, and David Ha. Samy Bengio left the team in April 2021, and Zoubin Ghahramani took on his responsibilities. Google Research includes Google Brain and is based in Mountain View. It also has satellite groups in Accra, Amsterdam, Atlanta, Beijing, Berlin, Cambridge, Israel, Los Angeles, London, Montreal, Munich, New York City, Paris, Pittsburgh, Princeton, San Francisco, Seattle, Tokyo, Toronto, and Zurich. == Projects == === Artificial-intelligence-devised encryption system === In October 2016, Google Brain designed an experiment to determine that neural networks are capable of learning secure symmetric encryption. In this experiment, three neural networks were created: Alice, Bob and Eve. Adhering to the idea of a generative adversarial network (GAN), the goal of the experiment was for Alice to send an encrypted message to Bob that Bob could decrypt, but the adversary, Eve, could not. Alice and Bob maintained an advantage over Eve, in that they shared a key used for encryption and decryption. In doing so, Google Brain demonstrated the capability of neural networks to learn secure encryption. === Image enhancement === In February 2017, Google Brain determined a probabilistic method for converting pictures with 8x8 resolution to a resolution of 32x32. The method built upon an already existing probabilistic model called pixelCNN to generate pixel translations. The proposed software utilizes two neural networks to make approximations for the pixel makeup of translated images. The first network, known as the "conditioning network," downsizes high-resolution images to 8x8 and attempts to create mappings from the original 8x8 image to these higher-resolution ones. The other network, known as the "prior network," uses the mappings from the previous network to add more detail to the original image. The resulting translated image is not the same image in higher resolution, but rather a 32x32 resolution estimation based on other existing high-resolution images. Google Brain's results indicate the possibility for neural networks to enhance images. === Google Translate === The Google Brain contributed to the Google Translate project by employing a new deep learning system that combines artificial neural networks with vast databases of multilingual texts. In September 2016, Google Neural Machine Translation (GNMT) was launched, an end-to-end learning framework, able to learn from a large number of examples. Previously, Google Translate's Phrase-Based Machine Translation (PBMT) approach would statistically analyze word by word and try to match corresponding words in other languages without considering the surrounding phrases in the sentence. But rather than choosing a replacement for each individual word in the desired language, GNMT evaluates word segments in the context of the rest of the sentence to choose more accurate replacements. Compared to older PBMT models, the GNMT model scored a 24% improvement in similarity to human translation, with a 60% reduction in errors. The GNMT has also shown significant improvement for notoriously difficult translations, like Chinese to English. While the introduction of the GNMT has increased the quality of Google Translate's translations for the pilot languages, it was very difficult to create such improvements for all of its 103 languages. Addressing this problem, the Google Brain Team was able to develop a Multilingual GNMT system, which extended the previous one by enabling translations between multiple languages. Furthermore, it allows for Zero-Shot Translations, which are translations between two languages that the system has never explicitly seen before. Google announced that Google Translate can now also translate without transcribing, using neural networks. This means that it is possible to translate speech in one language directly into text in another language, without first transcribing it to text. According to the Researchers at Google Brain, this intermediate step can be avoided using neural networks. In order for the system to learn this, they exposed it to many hours of Spanish audio together with the corresponding English text. The different layers of neural networks, replicating the human brain, were able to link the corresponding parts and subsequently manipulate the audio waveform until it was transformed to English text. Another drawback of the GNMT model is that it causes the time of translation to increase exponentially with the number of words in the sentence. This caused the Google Brain Team to add 2000 more processors to ensure the new translation process would still be fast and reliable. === Robotics === Aiming to improve traditional robotics control algorithms where new skills of a robot need to be hand-programmed, robotics researchers at Google Brain are developing machine learning techniques to allow robots to learn new skills on their own. They also attempt to develop ways for information sharing between robots so that robots can learn from each other during their learning process, also known as cloud robotics. As a result, Google has launched the Google Cloud Robotics Platform for developers in 2019, an effort to combine robotics, AI, and the cloud to enable efficient robotic automation through cloud-connected collaborative robots. Robotics research at Google Brain has focused mostly on improving and applying deep learning algorithms to enable robots to complete tasks by learning from experience, simulation, human demonstrations, and/or visual representations. For example, Google Brain researchers showed that robots can learn to pick and throw rigid objects into selected boxes by experimenting in an environment without being pre-programmed to do so. In another research, researchers trained robots to learn behaviors such as pouring liquid from a cup; robots learned from videos of human demonstrations recorded from multiple viewpoints. Google Brain researchers have collaborated with other companies and academic institutions on robotics research. In 2016, the Google Brain Team collaborated with researchers at X in a research on learning hand-eye coordination for robotic grasping. Their method allowed real-time robot control for grasping novel objec
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Knowledge space

In mathematical psychology and education theory, a knowledge space is a combinatorial structure used to formulate mathematical models describing the progression of a human learner. Knowledge spaces were introduced in 1985 by Jean-Paul Doignon and Jean-Claude Falmagne, and remain in extensive use in the education theory. Modern applications include two computerized tutoring systems, ALEKS and the defunct RATH. Formally, a knowledge space assumes that a domain of knowledge is a collection of concepts or skills, each of which must be eventually mastered. Not all concepts are interchangeable; some require other concepts as prerequisites. Conversely, competency at one skill may ease the acquisition of another through similarity. A knowledge space marks out which collections of skills are feasible: they can be learned without mastering any other skills. Under reasonable assumptions, the collection of feasible competencies forms the mathematical structure known as an antimatroid. Researchers and educators usually explore the structure of a discipline's knowledge space as a latent class model. == Motivation == Knowledge Space Theory attempts to address shortcomings of standardized testing when used in educational psychometry. Common tests, such as the SAT and ACT, compress a student's knowledge into a very small range of ordinal ranks, in the process effacing the conceptual dependencies between questions. Consequently, the tests cannot distinguish between true understanding and guesses, nor can they identify a student's particular weaknesses, only the general proportion of skills mastered. The goal of knowledge space theory is to provide a language by which exams can communicate What the student can do and What the student is ready to learn. == Model structure == Knowledge Space Theory-based models presume that an educational subject S can be modeled as a finite set Q of concepts, skills, or topics. Each feasible state of knowledge about S is then a subset of Q; the set of all such feasible states is K. The precise term for the information (Q, K) depends on the extent to which K satisfies certain axioms: A knowledge structure assumes that K contains the empty set (a student may know nothing about S) and Q itself (a student may have fully mastered S). A knowledge space is a knowledge structure that is closed under set union: if, for each topic, there is an expert in a class on that topic, then it is possible, with enough time and effort, for each student in the class to become an expert on all those topics simultaneously. A quasi-ordinal knowledge space is a knowledge space that is also closed under set intersection: if student a knows topics A and B; and student c knows topics B and C; then it is possible for another student b to know only topic B. A well-graded knowledge space or learning space is a knowledge space satisfying the following axiom: If S∈K, then there exists x∈S such that S\{x}∈K In educational terms, any feasible body of knowledge can be learned one concept at a time. === Prerequisite partial order === The more contentful axioms associated with quasi-ordinal and well-graded knowledge spaces each imply that the knowledge space forms a well-understood (and heavily studied) mathematical structure: A quasi-ordinal knowledge space can be associated with a distributive lattice under set union and set intersection. The name "quasi-ordinal" arises from Birkhoff's representation theorem, which explains that distributive lattices uniquely correspond to partial orders. A well-graded knowledge space is an antimatroid, a type of mathematical structure that describes certain problems solvable with a greedy algorithm. In either case, the mathematical structure implies that set inclusion defines partial order on K, interpretable as an educational prerequirement: if a(⪯)b in this partial order, then a must be learned before b. === Inner and outer fringe === The prerequisite partial order does not uniquely identify a curriculum; some concepts may lead to a variety of other possible topics. But the covering relation associated with the prerequisite partial does control curricular structure: if students know a before a lesson and b immediately after, then b must cover a in the partial order. In such a circumstance, the new topics covered between a and b constitute the outer fringe of a ("what the student was ready to learn") and the inner fringe of b ("what the student just learned"). == Construction of knowledge spaces == In practice, there exist several methods to construct knowledge spaces. The most frequently used method is querying experts. There exist several querying algorithms that allow one or several experts to construct a knowledge space by answering a sequence of simple questions. Another method is to construct the knowledge space by explorative data analysis (for example by item tree analysis) from data. A third method is to derive the knowledge space from an analysis of the problem solving processes in the corresponding domain.
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ACROSS Project

ACROSS is a Singular Strategic R&D Project led by Treelogic funded by the Spanish Ministry of Industry, Tourism and Trade activities in the field of Robotics and Cognitive Computing over an execution time-frame from 2009 to 2011. ACROSS project involves a number higher than 100 researchers from 13 Spanish entities. == ACROSS project objectives == ACROSS modifies the design of social robotics, blocked in providing predefined services, going further by means of intelligent systems. These systems are able to self-reconfigure and modify their behavior autonomously through the capacity for understanding, learning and software remote access. In order to provide an open framework for collaboration between universities, research centers and the Administration, ACROSS develops Open Source Services available to everybody. == Three application domains == ACROSS works in three application domains: Autonomous living: robots are used as technological tools to help handicapped person into daily tasks. Psycho-Affective Disorders (autism): robots are used to mitigate cognitive disorders. Marketing: robots are used to interact with humans in a recreational approach. == Consortium == Treelogic Alimerka Bizintek Universitat Politécnica de Catalunya University of Deusto European Centre for Soft Computing Fatronik - Tecnalia Fundació Hospital Comarcal Sant Antoni Abat Fundación Pública Andaluza para la Gestión de la Investigación en Salud de Sevilla, "Virgen del Rocío" University Hospitals m-BOT Omicron Electronic Universidad de Extremadura - RoboLab Verbio Technologies
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Vegas Pro

Vegas Pro (formerly known as Sony Vegas) is a professional video editing software package for non-linear editing (NLE), designed to run on the Microsoft Windows operating system. The first release of Vegas Beta was on June 11, 1999. Vegas was originally developed as a non-linear audio editing application. Version 2.0 would split the program into audio and video editing variants, with the former being dropped by version 4.0, making the video offering the only variant available to consumers. Vegas Pro features real-time multi-track video and audio editing on unlimited tracks, resolution-independent video sequencing, complex effects, compositing tools, 24-bit/192 kHz audio support, VST and DirectX plug-in effect support, and Dolby Digital surround sound mixing. The software was originally published by Sonic Foundry until May 2003, when Sony purchased Sonic Foundry and formed Sony Creative Software. On May 24, 2016, Sony announced that Vegas was sold to MAGIX, which formed VEGAS Creative Software, to continue support and development of the software. As of the end of March 2026, it was publicly announced that Boris FX had taken ownership of Vegas Pro. Each release of Vegas is sold standalone; however, upgrade discounts are sometimes provided. == Features == Vegas does not require any specialized hardware to run properly, allowing it to operate on any Windows computer that meets the system requirements. == History == Vegas 1.0 was released after a brief public beta by Sonic Foundry on July 23, 1999 at the NAMM Show in Nashville, Tennessee as an audio-only tool with a particular focus on re-scaling and resampling audio. It supported formats like DivX and Real Networks RealSystem G2 file formats. Martin Walker from Sound on Sound described working in Vegas 1.0 as a "very pleasurable experience, especially since so many functions are highly intuitive" though also criticizing some features as hard to figure out due to the lack of a central help file. Later, on June 12, 2000, Vegas Video and Audio 2.0 (also referred to as just Vegas 2.0) was released, with its beta releasing earlier that year on April 10. This was the first version of Vegas to include video-editing tools and was also the first to have a low-cost "LE" version alongside the regular release. The LE releases would continue through version 3.0 of Vegas but would be discontinued by the release of Vegas 4.0. Vegas 3.0 was released the next year on December 3, and added new video effects, features for ease-of-use with DV, and support for editing Windows Media files. Vegas 4.0 was released on 6 February 2003 and added application scripting, advanced color correction, 5.1 surround sound mixing, and Steinberg ASIO support. This was the last release under the Sonic Foundry name after it sold much of its software suite, including Sound Forge and Acid Pro, to Sony Pictures Digital for $18 million later in 2003. Under Sony's ownership, Vegas 5.0 was released on April 19, 2004, bringing 3D track motion, compositing, reversing, envelope automation, etc. 7.0 also added an improved video preview, enhanced layout management, improved snapping, and more customization. With the release of 8.0, Sony opted to go back to the original "Vegas Pro" branding that the first version released with. It added the ability to burn Blu-ray and DVD optical media, support for 32-bit floating point audio, support for tempo-based audio effects, and more. It also moved the timeline to the bottom of the window by default with the option of moving it back to the top if the user wished to. Sony was also experimenting with 64-bit at this time and ported Vegas Pro 8.0 to 64-bit systems under the name "Vegas Pro 8.1". Vegas Pro 9.0 added support for 4K resolution and pro camcorder formats like Red and XDCAM EX. In 2009, Sony Creative Software purchased the Velvetmatter Radiance suite of video FX plug-ins which were included in Sony Vegas Pro 9.0. As a result, they were no longer available as a separate product from Velvetmatter. Vegas Pro 10 was released in 2010 with stereoscopic 3D editing, image stabilization, OpenFX plugin support, real-time audio event effects, and a few UI changes. This was the last release to include support for Windows XP. Vegas Pro 11 was released the next year on 17 October, with GPGPU video acceleration, enhanced text tools, enhanced stereoscopic/3D features, RAW photo support, and new event synchronization mechanisms. In addition, Vegas Pro 11 comes pre-loaded with "NewBlue" Titler Pro, a 2D and 3D titling plug-in. Vegas Pro 12 would add two new configurations: Vegas Pro 12 Edit, for "Professional Video and Audio Production"; and Vegas Pro 12 Suite, for "Professional Editing, Disc Authoring, and Visual Effects Design". Vegas Pro 13 would be the last version released with Sony branding after the acquisition of much of Sony Creative Software's library by Magix. After they acquired Vegas, Magix released version 14 on September 20, 2016. It featured advanced 4K upscaling as well as many bug fixes, a higher video velocity limit, RED camera support, and a variety of other features. This was also the last version to have the light theme enabled by default. Released on August 28, 2017, Vegas Pro 15 features major UI changes that claim to bring usability improvements and customization. It was the first version of VEGAS Pro to have a dark theme; it also allows more efficient editing speeds, including adding new shortcuts to speed the video editing process. Vegas Pro 15 includes support for Intel Quick Sync Video (QSV) and other technologies, as well as various other features. It introduced a new VEGAS Pro icon as a V. Vegas Pro 16 has some new features including file backup, motion tracking, improved video stabilization, 360° editing and HDR support. Magix has continued to improve Vegas through version 21 with support for reading Matroska files, a more detailed render dialogue, live streaming, VST3 support, a VST 32-bit bridge, and a selective Paste Event Attributes menu. Magix would later release a subscription model for using Vegas named "Vegas Pro 365" on January 17, 2018, although the perpetual licence is still an option for customers. This version includes cloud-based speech synthesis among other features not included in the mainline Vegas release. == Version history == Each release of Vegas is sold standalone, however upgrade discounts are sometimes provided. === Vegas Beta === Sonic Foundry introduced a sneak preview version of Vegas Pro on June 11, 1999. It is called a "Multitrack Media Editing System". === Vegas 1.0 === Released on July 23, 1999 at the NAMM Show in Nashville, Tennessee, Vegas was an audio-only tool with a particular focus on rescaling and resampling audio. It supported formats like DivX and Real Networks RealSystem G2 file formats. Version 1.0 is the final Vegas release to include Windows 95 support. === Vegas Video beta (Vegas 2.0 beta) === Released on April 10, 2000, this was the first version of Vegas to include video-editing tools. === Vegas Video (Vegas 2.0) === Released on June 12, 2000. Version 2.0 is the final Vegas Video release to include Windows NT 4.0 support. === Vegas Video 3.0 === Released on December 3, 2001. This release added: New Video Effects – Lens Flare, Light Rays, Film FX, Color Curves, Mirror, Remap, Deform, Convolution, Linear Blur, Black Restore, Levels, Unsharp Mask, Color Grading, and Timecode Burn filter. Batch Capture with Automatic Scene Detection – Captures DV with automatic scene detection, batch capture, tape logging, still image capture and thumbnail previews. Red Book Audio CD Mastering with CD Architect (TM) Technology – Used for burning Red Book audio CD masters directly from the Vegas timeline with ISRC, UPC, and PQ list support. New Sonic Foundry DV Codec – Introduces a DV codec developed by Sonic Foundry that offers artifact-free compositing and DV chromakeying. DV Print-to-Tape from the Timeline – Prints projects to DV cameras and decks from the Vegas timeline. Windows Media (TM) File Editing – Creates and edits Windows Media (TM) files. New MPEG Encoding Tools – Used for producing MPEG-2 files for DVD productions. Dynamic RAM Previewing – Temporary RAM/render-free previews for analysis and tweaking of complex video FX without rendering. VideoCD and Data CD Burning – Burning projects directly to VideoCD for playback on most DVD players or data CDs for playback computers' CD-ROMs. === Vegas 4.0 === Released on February 6, 2003. This release added: Advanced Color Correction Tools Searchable Media Pool Bins Vectorscope, Histogram, Parade and Waveform Monitoring Application Scripting Improved Ripple Editing Motion Blur and Super-Sampling Envelopes 5.1 Surround Mixing Dolby® Digital AC-3 Encoding certified and tested by Dolby Laboratories DirectX® Audio Plug-In Effects Automation ASIO Driver Support Windows Media™ 9 Support, including Surround Encoding DVD Authoring with AC-3 File Import Capabilities Integration with DVD Architect via Chap
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Graphics Turing test

In computer graphics the graphics Turing test is a variant of the Turing test, the twist being that a human judge viewing and interacting with an artificially generated world should be unable to reliably distinguish it from reality. The original formulation of the test is: "The subject views and interacts with a real or computer generated scene. The test is passed if the subject can not determine reality from simulated reality better than a random guess. (a) The subject operates a remotely controlled (or simulated) robotic arm and views a computer screen. (b) The subject enters a door to a controlled vehicle or motion simulator with computer screens for windows. An eye patch can be worn on one eye, as stereo vision is difficult to simulate." The "graphics Turing scale" of computer power is then defined as the computing power necessary to achieve success in the test. It was estimated in, as 1036.8 TFlops peak and 518.4 TFlops sustained. Actual rendering tests with a Blue Gene supercomputer showed that current supercomputers are not up to the task scale yet. A restricted form of the graphic Turing test has been investigated, where test subjects look into a box, and try to tell whether the contents are real or virtual objects. For the very simple case of scenes with a cardboard pyramid or a styrofoam sphere, subjects were not able to reliably tell reality and graphics apart.
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Jailbreak (computer science)

In computer security, jailbreaking is defined as the act of removing limitations that a vendor attempted to hard-code or hard-wire into its hardware and/or software. It is a form of privilege escalation. The term may have originated with the use of toolsets to break out of a chroot or jail in UNIX-like operating systems. This allowed the user to see files outside of the file system that the administrator intended to make available to the application or user in question. The term was first used in its modern meaning in the iPhone/iOS jailbreaking community and has also been used as a term for PlayStation Portable hacking; these devices have repeatedly been subject to jailbreaks, allowing the execution of arbitrary code, and sometimes have had those jailbreaks disabled by vendor updates, especially in the case of iOS devices. == iOS jailbreaking == iOS systems including the iPhone, iPad, and iPod Touch have been subject to iOS jailbreaking efforts since they were released, and continuing with each firmware update. iOS jailbreaking tools have included the option to install package frontends such as Cydia and Installer.app, third-party alternatives to the App Store, as a way to find and install system tweaks and binaries. To prevent iOS jailbreaking, Apple has made the device boot ROM execute checks for SHSH blobs in order to disallow uploads of custom kernels and prevent software downgrades to earlier, jailbreakable firmware. In an "untethered" jailbreak, the iBoot environment is changed to execute a boot ROM exploit and allow submission of a patched low level bootloader or hack the kernel to submit the jailbroken kernel after the SHSH check. == Other phones == A similar method of jailbreaking exists for S60 Platform smartphones, where utilities such as HelloOX allow the execution of unsigned code and full access to system files. or edited firmware (similar to the M33 hacked firmware used for the PlayStation Portable) to circumvent restrictions on unsigned code. Nokia has since issued updates to curb unauthorized jailbreaking, in a manner similar to Apple. Rooting is the equivalent concept for Android phones and other devices. == Console jailbreaking == In the case of gaming consoles, jailbreaking is often used to execute homebrew games. In 2011, Sony, with assistance from law firm Kilpatrick Stockton, sued 21-year-old George Hotz and associates of the group fail0verflow for jailbreaking the PlayStation 3 (see Sony Computer Entertainment America v. George Hotz and PlayStation Jailbreak). == AI jailbreaks == Jailbreaking can also occur in systems and software that use generative artificial intelligence models, such as ChatGPT. In jailbreaking attacks on artificial intelligence systems, users are able to manipulate the system to behave differently than it was intended, making it possible to reveal information about how the model was instructed by the vendor (the "system prompt") or to induce it to respond in an anomalous or harmful way. These attacks typically simply require prompting the AIs with specific phrasal templates - no software is typically required, although software could theoretically be used to "industrialise" such exploits, and some research has been done in this direction. In 2024, a consortium of AI firms founded HackAPrompt.com, a competition to encourage users to find new and effective AI jailbreaking techniques. These and other findings from "ethical hackers" have been used by AI model providers to try to improve AI safety.
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Existential risk from artificial intelligence

Existential risk from artificial intelligence, or AI x-risk, refers to the idea that substantial progress in artificial general intelligence (AGI) and artificial superintelligence (ASI) could lead to human extinction or an irreversible global catastrophe. One argument for the validity of this concern and the importance of this risk references how human beings dominate other species because the human brain possesses distinctive capabilities other animals lack. If AI were to surpass human intelligence and become superintelligent, it might become uncontrollable. Just as the fate of the mountain gorilla depends on human goodwill, the fate of humanity could depend on the actions of a future machine superintelligence. Experts disagree on whether artificial general intelligence (AGI) can achieve the capabilities needed for human extinction. Debates center on AGI's technical feasibility, the speed of self-improvement, and the effectiveness of alignment strategies. Concerns about superintelligence have been voiced by researchers including Geoffrey Hinton, Yoshua Bengio, Demis Hassabis, and Alan Turing, and AI company CEOs such as Dario Amodei (Anthropic), Sam Altman (OpenAI), and Elon Musk (xAI). In 2022, a survey of AI researchers with a 17% response rate found that the majority believed there is a 10 percent or greater chance that human inability to control AI will cause an existential catastrophe. In 2023, hundreds of AI experts and other notable figures signed a statement declaring, "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war". Following increased concern over AI risks, government leaders such as United Kingdom prime minister Rishi Sunak and United Nations Secretary-General António Guterres called for an increased focus on global AI regulation. In 2025, hundreds of public figures including AI experts, five Nobel Prize laureates, and former senior US national security officials such as Michael Mullen and Susan Rice signed a statement calling for a ban on the development of superintelligence. Two sources of concern stem from the problems of AI control and alignment. Controlling a superintelligent machine or instilling it with human-compatible values may be difficult. Many researchers believe that a superintelligent machine would likely resist attempts to disable it or change its goals as that would prevent it from accomplishing its present goals. It would be extremely challenging to align a superintelligence with the full breadth of significant human values and constraints. In contrast, skeptics such as computer scientist Yann LeCun argue that superintelligent machines will have no desire for self-preservation. A June 2025 study showed that in some circumstances, models may break laws and disobey direct commands to prevent shutdown or replacement, even at the cost of human lives. Researchers warn that an "intelligence explosion"—a rapid, recursive cycle of AI self-improvement—could outpace human oversight and infrastructure, leaving no opportunity to implement safety measures. In this scenario, an AI more intelligent than its creators would recursively improve itself at an exponentially increasing rate, too quickly for its handlers or society at large to control. Empirically, examples like AlphaZero, which taught itself to play Go and quickly surpassed human ability, show that domain-specific AI systems can sometimes progress from subhuman to superhuman ability very quickly, although such machine learning systems do not recursively improve their fundamental architecture. == History == One of the earliest authors to express serious concern that highly advanced machines might pose existential risks to humanity was the novelist Samuel Butler, who wrote in his 1863 essay Darwin among the Machines: The upshot is simply a question of time, but that the time will come when the machines will hold the real supremacy over the world and its inhabitants is what no person of a truly philosophic mind can for a moment question. In 1951, foundational computer scientist Alan Turing wrote the article "Intelligent Machinery, A Heretical Theory", in which he proposed that artificial general intelligences would likely "take control" of the world as they became more intelligent than human beings: Let us now assume, for the sake of argument, that [intelligent] machines are a genuine possibility, and look at the consequences of constructing them... There would be no question of the machines dying, and they would be able to converse with each other to sharpen their wits. At some stage therefore we should have to expect the machines to take control, in the way that is mentioned in Samuel Butler's Erewhon. In 1965, I. J. Good originated the concept now known as an "intelligence explosion" and said the risks were underappreciated: Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an 'intelligence explosion', and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control. It is curious that this point is made so seldom outside of science fiction. It is sometimes worthwhile to take science fiction seriously. Scholars such as Marvin Minsky and I. J. Good himself occasionally expressed concern that a superintelligence could seize control, but issued no call to action. In 2000, computer scientist and Sun co-founder Bill Joy penned an influential essay, "Why The Future Doesn't Need Us", identifying superintelligent robots as a high-tech danger to human survival, alongside nanotechnology and engineered bioplagues. Nick Bostrom published Superintelligence in 2014, which presented his arguments that superintelligence poses an existential threat. By 2015, public figures such as physicists Stephen Hawking and Nobel laureate Frank Wilczek, computer scientists Stuart J. Russell and Roman Yampolskiy, and entrepreneurs Elon Musk and Bill Gates were expressing concern about the risks of superintelligence. Also in 2015, the Open Letter on Artificial Intelligence highlighted the "great potential of AI" and encouraged more research on how to make it robust and beneficial. In April 2016, the journal Nature warned: "Machines and robots that outperform humans across the board could self-improve beyond our control—and their interests might not align with ours". In 2020, Brian Christian published The Alignment Problem, which details the history of progress on AI alignment up to that time. In March 2023, key figures in AI, such as Musk, signed a letter from the Future of Life Institute calling a halt to advanced AI training until it could be properly regulated. In May 2023, the Center for AI Safety released a statement signed by numerous experts in AI safety and the AI existential risk that read: Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war. A 2025 open letter by the Future of Life Institute, whose signers include five Nobel Prize laureates, reads: We call for a prohibition on the development of superintelligence, not lifted before there is broad scientific consensus that it will be done safely and controllably, and strong public buy-in. == Potential AI capabilities == === General Intelligence === Artificial general intelligence (AGI) is typically defined as a system that performs at least as well as humans in most or all intellectual tasks. A 2022 survey of AI researchers found that 90% of respondents expected AGI would be achieved in the next 100 years, and half expected the same by 2061. In May 2023, some researchers dismissed existential risks from AGI as "science fiction" based on their high confidence that AGI would not be created anytime soon. But in August 2023, a survey of 2,778 AI researchers found that most believed that AGI would be achieved by 2040. Breakthroughs in large language models (LLMs) have led some researchers to reassess their expectations. Notably, Geoffrey Hinton said in 2023 that he recently changed his estimate from "20 to 50 years before we have general purpose A.I." to "20 years or less". === Superintelligence === In contrast with AGI, Bostrom defines a superintelligence as "any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest", including scientific creativity, strategic planning, and social skills. He argues that a superintelligence can outmaneuver humans anytime its goals conflict with humans'. It may choose to hide its true intent until humanity cannot stop it. Bostrom writes that in order to be safe for
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PARRY

PARRY was an early example of a chatbot, implemented in 1972 by psychiatrist Kenneth Colby. == History == PARRY was written in 1972 by psychiatrist Kenneth Colby, then at Stanford University. While ELIZA was a simulation of a Rogerian therapist, PARRY attempted to simulate a person with paranoid schizophrenia. The program implemented a crude model of the behavior of a person with paranoid schizophrenia based on concepts, conceptualizations, and beliefs (judgements about conceptualizations: accept, reject, neutral). It also embodied a conversational strategy, and as such was a much more serious and advanced program than ELIZA. It was described as "ELIZA with attitude". PARRY was tested in the early 1970s using a variation of the Turing Test. A group of experienced psychiatrists analysed a combination of real patients and computers running PARRY through teleprinters. Another group of 33 psychiatrists were shown transcripts of the conversations. The two groups were then asked to identify which of the "patients" were human and which were computer programs. The psychiatrists were able to make the correct identification only 48 percent of the time — a figure consistent with random guessing. PARRY and ELIZA (also known as "the Doctor") interacted several times. The most famous of these exchanges occurred at the ICCC 1972, where PARRY and ELIZA were hooked up over ARPANET and responded to each other.
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Eline Van der Velden

Eline van der Velden is a Dutch comedian, writer, actress and producer based in London, England. She is best known for her work creating Tilly Norwood, an AI-generated "actress". == Early life == Van der Velden was born on the Dutch island of Curaçao, Netherlands Antilles to Dutch businessman Steven van der Velden and physiotherapist Quirine van der Velden. She moved to the United Kingdom at age 14 to study drama and musical theatre at Tring Park School for the Performing Arts. She graduated with an MSc in physics from Imperial College London in 2008. == Career == She was nominated by the International Academy of Digital Arts and Sciences for the Lovie Awards and won Best Online Comedy in 2013 for two of her submitted entries. She has created multiple online shows such as Sketch My Life with London Hughes and Emily Hartridge and Match.com Parody. She became managing director of Makers Channel (makerschannel.co.uk), the first curated video platform in Europe in 2015. Makers Channel has been recently acquired by a Belgian media company De Persgroep, due to its success in the Netherlands. In 2016, she appeared in adverts for the Dutch shampoo brand Andrelon. Miss Holland, a comedy character created by Van der Velden, made headlines in 2016 as she asked the British public to teach her the national anthem. As an actress, she has starred in Dutch TV series De Troon, Beatrix and the Golden Calf-winning series Overspel. In Belgium, she appeared opposite Jamie Dornan in Flying Home. Van der Velden starred in the BBC Three series Putting It Out There, in which she challenges social perceptions of body hair, heels, spit, personal space, and authority figures. In 2018, she starred in the BBC One comedy series Soft Border Patrol and the BBC Three comedy series Miss Holland. In 2025, Particle6 Group, which Van der Velden founded in 2016, introduced Tilly Norwood, an AI-generated "actress" at the Zurich Film Festival. The announcement was met with outrage and a condemnation by the American actors' union SAG-AFTRA. == Awards and recognition == Miss Holland won the Best Online Comedy at the 2013 Lovie Awards, judged by Stephen Fry. The Match.com Parody video won Best Online Comedy People's Lovie Award, the people's vote. Miss Holland and Match.com Parody Date 1 were also featured in the 2013 Google Lovie Letters.
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Common Crawl

The Common Crawl Foundation (Common Crawl) is a nonprofit 501(c)(3) organization that crawls the web and freely provides its archives and datasets to the public. Common Crawl was founded by Gil Elbaz. The data had mostly been primarily used by researchers and some startups until the 2020s, when AI companies started training large language models using the data. In November 2025, an investigation by The Atlantic revealed that Common Crawl misled publishers when it claimed it respected paywalls in its scraping and it was not honoring requests from publishers to have their content removed from its databases. == History == Common Crawl was founded in 2007 in San Francisco. It began publishing its crawls in 2011. By 2013, sites like TinEye were building their products off of Common Crawl. The crawl reduces the reliance of companies and researchers on Google, which has the biggest dataset. Common Crawl was designed to have more and fresher data that was more efficient to analyze and utilize than the Wayback Machine created by the Internet Archive. By 2015, 1.8 billion webpages were on the Common Crawl, which started by crawling a list of URLs donated by the search engine Blekko. They use Amazon Web Services, which provides some of its services for free, allowing computing costs to average $2-4000/month. The Common Crawl website listed 30 studies based on Common Crawl data. Before 2023, Common Crawl was not very well known outside of academic researchers who utilize the data. Common Crawl received its first requests to redact information in 2023 and increasingly started seeing its crawler, CCBot, blocked. In 2023, it began receiving significant financial support from AI companies, including Anthropic and OpenAI, each of which donated $250,000. It was also used to train Google DeepMind's large language model Gemini. By April 2023, Common Crawl was capturing 3.1 billion webpages, with an estimated 5% of pages before 2021 containing hate speech or slurs. As of 2024, Common Crawl had been cited in more than 10,000 academic studies. By 2024, The Pile and Common Crawl had been the two main training datasets being used to train AI models. In November 2025, an investigation by technology journalist Alex Reisner for The Atlantic revealed that Common Crawl misled publishers when it claimed it respected paywalls in its scraping and when it said that it was honoring requests from publishers to have their content removed from its databases. It included misleading results in the public search function on its website that showed no entries for websites that had requested their archives be removed, when in fact those sites were still included in its scrapes used by AI companies. As of 2025, Reisner found that CCBot was the most widely-blocked bot by the top 1000 websites. A 2026 article in LWN.net discussed an advantage to services like Common Crawl being that it can limit the scraping costs to websites by allowing companies and researchers to download the data from Common Crawl instead of scraping it themselves. In April 2026, Common Crawl experimentally began to distribute its data through Hugging Face Storage Bucket, in addition to its standard storage on Amazon S3. == Organization == Peter Norvig and Joi Ito have served on the advisory board. Rich Skrenta is the executive director. It has received funding almost exclusively from the Elbaz Family Foundation Trust until 2023 when it started receiving donations from the AI industry. == Refined versions == A number of organizations take raw Common Crawl data and refine it into datasets that exclude edgy content or are otherwise higher-quality for their purposes, such as FineWeb, DCLM and C4. === Colossal Clean Crawled Corpus === Google version of the Common Crawl is called the Colossal Clean Crawled Corpus, or C4 for short. It was constructed for the training of the T5 language model series in 2019. As of 2023, there were some concerns over copyrighted content in the C4 as well as racist content. A 2024 study found that 45% of content was explicitly restricted by websites' terms of service to be used for purposes like AI training by for-profit companies.
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