AI Chatbot Miles

AI Chatbot Miles — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Stripe, Inc.

Stripe, Inc. is an Irish and American multinational financial services and software as a service (SaaS) company dual-headquartered in South San Francisco, California, United States, and Dublin, Ireland. The company primarily offers payment-processing software and application programming interfaces for e-commerce websites and mobile applications. Stripe is the largest privately owned financial technology company with a valuation of about $159 billion and over $1.9 trillion in payment volume processed in 2025, processing transactions for 5 million businesses in that year. == History == Irish entrepreneur brothers John and Patrick Collison founded Stripe in Palo Alto, California, in 2010, and serve as the company's president and CEO, respectively. In 2011 the company received a $2 million investment, including contributions from Elon Musk, PayPal founder Peter Thiel, Irish entrepreneur Liam Casey, and venture capital firms Sequoia Capital, Andreessen Horowitz, and SV Angel. In March 2013, Stripe made its first acquisition, Kickoff, a chat and task-management application. In 2012 the company moved from Palo Alto to San Francisco. In October 2019, the company announced that it would be moving from the South of Market area to Oyster Point in the neighbouring city of South San Francisco in 2021. In February 2021, Mark Carney, former governor of the Bank of Canada and of the Bank of England, was appointed to the company's board. Carney stepped down from his role with the company in 2025 in order to run for the leadership of the Liberal Party. Stripe acquired accountancy platform Recko in October 2021 whose solution was to be added to Stripe's existing suite of financial tools. In January 2022, Stripe entered a five-year partnership with Ford Motor Company. Through the deal, Stripe would handle transactions for consumer vehicle orders and reservations. That same month, Stripe partnered with Spotify to help the company monetize subscriptions. In April 2022, Twitter announced that it would partner with Stripe, Inc. (digital payments processor) for piloting cryptocurrency pay-outs for limited users in the platform. In April 2022, Stripe announced its strategic partnership with UK-based financial technology company ION. The Wall Street Journal reported in July 2022 that the company's internal share price had fallen, causing its implied valuation to drop from $95 billion to $74 billion. In November 2022, the company announced it intended to initiate layoffs, terminating some 14% of its workforce. Throughout 2022 and 2023, the company announced a number of large enterprise customers, including Airbnb, Amazon, Microsoft, Uber, BMW, Maersk, Zara, Lotus, Alaska Airlines, Le Monde, and Toyota. The company also announced in March 2023 that OpenAI is working with Stripe to commercialize its generative AI technology. In January 2025, Stripe sent layoff notices to nearly 300 workers, primarily affecting roles in Product, Operations and Engineering. The company experienced controversy when the company sent a cartoon picture of a duck to the laid-off employees. Stripe's Chief People Officer Rob McIntosh later apologized for the mistake. After re-enabling cryptocurrency pay-ins in April 2024, starting with USDC, Stripe completed the acquisition of Bridge in February 2025. The acquisition of the two-year-old stablecoin platform company is valued at $1.1 billion. In June 2025, the company acquired Privy, which powers crypto wallets. In September 2025, Stripe announced it was powering Instant Checkout in ChatGPT and released Agentic Commerce Protocol for agentic commerce, which was co-developed with OpenAI. In October 2025, the company opened its second headquarters in Dublin, Ireland. In February 2026, Stripe was valued at $159 billion in a tender offer posted for employees and shareholders. The tender offer was about a 70% increase from Stripe's previous valuation published in February 2025, where it was valued at $91.5 billion. Stripe also announced that its total volume increased to $1.9 trillion USD in 2025, a 34% increase from 2024. == Technology company == === Payment processing === Stripe provides application programming interfaces that web developers can use to integrate payment processing into their websites and mobile applications. The company introduced Stripe Connect in 2012, a multiparty payments solution that lets software developers embed payments natively into their products. In April 2018, Stripe released antifraud tools, branded "Radar", that block fraudulent transactions. The same year, it expanded its services to include a billing product for online businesses, allowing businesses to manage subscription recurring revenue and invoicing. Stripe's point-of-sale service called Terminal was made available to US users on 11 June 2019. Terminal had previously been invitation-only. Terminal is currently available in Australia, Canada, France, Germany, Ireland, the Netherlands, New Zealand, Singapore, and the United Kingdom. The service offers physical credit-card readers designed to work with Stripe. On 5 September 2019, Stripe launched a merchant cash-advance scheme called Stripe Capital. The scheme allows Stripe merchants to request an advance on future payments they expect to process through their Stripe merchant account. In June 2021, the company launched Stripe Tax, a service to allow businesses to automatically calculate and collect sales tax, VAT, and GST, initially rolling out to 30 countries and all US states. As of 2025, it has been made available in 102 countries. In May that year, Stripe introduced Payment Links, a no-code product allowing businesses to create a link to a checkout page and begin accepting payments on social platforms or direct channels. In January 2022, Stripe agreed to acquire Terminal manufacturing partner BBPOS, allowing the company to bring the hardware development of Terminal readers in-house. In February, it was announced as Apple's first partner on in-person Tap to Pay, which enables businesses to accept contactless payments using an iPhone and a partner-enabled iOS app. In May, Stripe announced Data Pipeline, a tool for Stripe users who store data with Amazon Redshift or Snowflake Data Cloud. Data Pipeline syncs Stripe data and reports with Amazon Redshift or Snowflake Data Cloud, where they can be queried in combination with other business information. That month, the company also introduced Stripe Financial Connections, enabling businesses to establish direct connections with their customers’ bank accounts to verify accounts for payments and pay-outs, check balances to reduce payment failures, and cut fraud by confirming bank account ownership. In September 2023, Stripe announced that its optimized checkout suite allowed businesses to offer their customers more than 100 payment methods. In May 2025, Stripe announced a new AI foundational model for payments, and introduced stablecoin powered accounts. === Corporate finance === In July 2018, Stripe introduced Stripe Issuing, a product that allows online businesses and platforms to create their own physical and digital credit and debit cards. === Atlas === On 14 February 2016, the company launched the Atlas platform to help start-ups register as US corporations, targeting foreign entrepreneurs. The platform was originally invitation-only. In March 2016, Cuba was added to the list of countries covered under the program. Originally, companies registered using Atlas were set up as Delaware-based C corporations. As of 30 April 2018, the option to be registered as limited liability companies was added. Companies set up using Atlas automatically had a business bank account and Stripe merchant account set up. === Link === In May 2021, Stripe launched Link, a service for saving and auto-filling payment details when paying via Stripe. The service supported payments in over 185 countries and Stripe reported plans to make it available to platform businesses through its API. In September 2025, Patrick Collison announced that Link had surpassed 200 million users. === Other === In 2018, Stripe started a publishing company named Stripe Press to promote ideas that support businesses. In 2019, Stripe began offering loans and credit cards to businesses in the United States. The company stated that loans are approved automatically using machine-learning models, with no human intervention. The following year, the company introduced Stripe Treasury, which provides its platform users APIs to embed financial services, allowing their customers to send, receive, and store funds. In October 2020, Stripe announced Stripe Climate, a service for businesses to fund atmospheric carbon research and capture. In 2022, Stripe started a new subsidiary called Frontier that would direct spending on carbon removal. It announced $925 million in funding from major Silicon Valley companies to fund start up companies performing carbon capture to kick-start the industry. Stripe Identity, launched in Ju
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Logic Programming Associates

Logic Programming Associates (LPA) is a company specializing in logic programming and artificial intelligence software. LPA was founded in 1980 and is widely known for its range of Prolog compilers, the Flex expert system toolkit and most recently, VisiRule. LPA was established to exploit research at the Department of Computing and Control at Imperial College London into logic programming carried out under the supervision of Prof Robert Kowalski. == History of LPA Prolog == One of the first Prolog implementations made available by LPA was micro-PROLOG which ran on popular 8-bit home computers such as the Sinclair ZX Spectrum and Apple II. The 8-bit micro-PROLOG interpreter was soon followed by micro-PROLOG Professional one of the first Prolog implementations for the IBM PC running MS-DOS. micro-PROLOG Professional could access all of the 640K memory available under MS-DOS and therefore manage much larger programs In 1985, LPA released LPA MacProlog which ran on the MacPlus and Mac II computers which could access up to 4 Mb memory. MacProlog was later licensed to Quintus for re-distribution in the USA. In 1989, LPA started work on a new 32-bit Prolog compiler which could use DOS-extender technology to access up to 4GB memory. This became the basis for LPA Prolog for Windows, aka WIN-PROLOG, which was then released for Windows 3.0 in 1990. LPA's core Prolog product is LPA Prolog for Windows, a compiler and development system for the Microsoft Windows platform. The current LPA software range comprises an integrated AI toolset which covers various aspects of Artificial Intelligence including Logic Programming, Expert Systems, Knowledge-based Systems, Data Mining, Agents and Case-based reasoning etc. As well as continuing with Prolog compiler technology development, LPA has a track record of creating innovative associated tools and products to address specific challenges and opportunities. == Flex Expert System toolkit == In 1989, in response to the rise of interest in Expert Systems and the emergence of products such as Crystal, GoldWorks, NExpert, LPA developed the Flex expert system toolkit, which incorporated frame-based reasoning with inheritance, rule-based programming and data-driven procedures. Flex has its own English-like Knowledge Specification Language (KSL) which means that knowledge and rules are defined in an easy-to-read and understand way. LPA supported Flex on Windows, DOS and Macintosh PCs, as an add-on toolkit to its various LPA Prolog systems and eanbled LPA to enter the then quick vibrant Expert Systems rules-market. Flex was quickly established as the leading Prolog-based expert system toolkit and was licensed to other Prolog providors on other hardware platforms including Telecomputing Plc to supplement Top One on IBM and ICL mainframes. Other implementations included Quintec-Flex, Quintus Flex, Poplog Flex and BIM Flex which were all running on Unix and/or Vax/VMS platforms. POPLOG-Flex was used to build BRAND EVALUATOR - an expert system to assist brand specialists in evaluating the worth of branded products Quintec-Flex was used to build a hybrid system for the non-linear dynamic analysis/design of coupled shear walls Flex was adopted by the Open University as part of its course T396, "Artificial intelligence for technology" which was designed by Prof Adrian Hopgood. Some of the teaching material is now available on his AI tookit website. Flex was also used by David A Ferrucci and Selmer Bringsjord in their storytelling machine, BRUTUS. == PVG == In 1992, LPA helped set up the Prolog Vendors Group, a not-for-profit organization whose aim was to help promote Prolog by making people aware of its usage in industry. == Business Integrity Ltd and Contract Express == Between 1996 and 1998, based on work co-funded through a DTI Smart award, LPA developed ScaffoldIT, a tool for building dynamic documents and intelligent web sites. This technology, built using the LPA Prolog engine and associated ProWeb Server, was able to generate complex, personalised documents such as insurance policy schedules, legal contracts, and complex sales proposals, over the Web. In 1999/2000, LPA helped set up Business Integrity Ltd, as a Joint Venture with Tarlo-Lyons, to bring the above document assembly technology to market. This product eventually became Contract Express. Contract Express became very popular amongst large law firms and was sold worldwide for both internal and external use. Partners and GCs liked Contract Express because lawyers were able to quickly and accurately automate and update their legal templates in Word without requiring IT specialists to convert them into programs. As a result of the commercial success of Contract Express, BIL was acquired by Thomson Reuters in 2015. The very early days of BIL are described by Clive Spenser here. == VisiRule == In 2004, LPA launched VisiRule a graphical tool for developing knowledge-based and decision support systems. VisiRule was described in IEEE Potentials in 2007 (see Drawing on your knowledge with VisiRule): VisiRule has been used in various sectors, to build legal expert systems, machine diagnostic programs, medical and financial advice systems, etc. In 2013, VisiRule was incorporated into Ecosystem Management Decision Support (EMDS) where it has been used to provide enhanced decision support capabilities. EMDS integrates state-of-the-art geographic information system (GIS) as well as logic programming and decision modeling technologies on multiple platforms (Windows, Linux, Mac OS X) to provide decision support for a substantial portion of the adaptive management process of ecosystem management. EMDS is actively used, extended, supported and maintained by Mountain View Business Group (for an in-depth reprise of EMDS see the article in Frontiers in Environmental Science). In 2023, VisiRule was listed as one of the 5 best decision support software for large enterprises in 2024. == Customers == For many years, LPA has worked closely with Valdis Krebs, an American-Latvian researcher, author, and consultant in the field of social and organizational network analysis. Valdis is the founder and chief scientist of Orgnet, and the creator of the popular Inflow software package. LPA Prolog and Flex were used to create Allergenius, an expert system for the interpretation of allergen microarray results. Rules representing the knowledge base (KB) were derived from the literature and specialized databases. The input data included the patient's ID and disease(s), the results of either a skin prick test or specific IgE assays and ISAC results. The output was a medical report.
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Hive (artificial intelligence company)

Hive is an American artificial intelligence company offering machine learning models via APIs to enterprise customers. Hive uses around 700,000 gig workers to train data for its models through its Hive Work app. One of Hive's major offerings is to provide automated content moderation services. == Products == Hive is reported to have been engaged to provide content moderation services to social news aggregator Reddit, Giphy, BeReal, Donald Trump-affiliated social network Truth Social, and on online chat website Chatroulette. Parler, after its shutdown by content service providers in early 2021 due to a lack of content moderation, integrated with Hive and was allowed back in the App Store. Hive's content moderation models have been leveraged widely in the livestreaming industry, where the cost of human moderation is high. Hive's models have also been used in events such as the Super Bowl and March Madness, and its contextual advertising models used by NBC Universal and Vevo. Hive provides APIs to detect deepfakes and AI-generated artwork. In early 2023, Hive released a free demo text classifier intended to detect AI-generated text. Mark Hachman at PC World rated Hive's classifier favorably and found it more reliable than OpenAI's AI text classifier. == History == Hive was founded by Kevin Guo and Dmitriy Karpman, and in April 2021, announced $85M in new capital at a valuation of $2 billion.
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Visual hierarchy

Visual hierarchy, in Gestalt psychology, describes how particular elements in a visual field stand out more than others in a pattern, creating a perceived order of importance. Although it can occur naturally, the term is most often used in design—especially graphic design and cartography—where elements are arranged to appear more important than others. This order is created by the visual contrast between forms in a field of perception. Objects with highest contrast to their surroundings are recognized first by the human mind. == Evidence == There is some scientific evidence for visual hierarchy using eye tracking. For example, one study found that when people agree that a graphic design is good, they exhibit more similar eye movements; measured by the Fréchet distance. == Theory == The concept of visual hierarchy is based in Gestalt psychological theory, an early 20th-century German theory that proposes that the human brain has innate organizing tendencies that “structure individual elements, shapes or forms into a coherent, organized whole,” especially when processing visual information. The German word Gestalt translates into “form,” “pattern,” or “shape” in English. When an element in a visual field disconnects from the ‘whole’ created by the brain's perceptual organization, it “stands out” to the viewer. The shapes that disconnect most severely from their surroundings stand out the most. This is commonly encapsulated as the Von Restorff effect, which states that isolation attracts attention. === Physical characteristics === The brain distinguishes objects based on differences in their physical appearances. These characteristics fall into four categories: color, size, alignment, and character. Each type of contrast can be used to construct a visual hierarchy. The same characteristics are also sometimes categorized (especially among cartographers) according to the visual variables of Jacques Bertin. Color encompasses the hue, saturation, value, and perceived texture of forms. Dark figures will stand out on a light background, light figures will stand out on a dark background, brightly colored figures will stand out on a muted background, and so on. The fluorescent colors used for tennis balls and other sports equipment is intended to make them instantly stand out against almost any natural visual field. Size has a strong influence on visual hierarchy. Large elements typically attract attention, provided that they can be recognized as figures. Alignment is the arrangement of forms relative to one another. For example, items in the upper left corner of a page are often seen first (at least for those readers accustomed to western languages), the center of the field has prominence. Negative space can also be employed: a figure isolated among large amounts of white space will stand out more than one amid other figures. Character includes several kinds of contrasts based on shape. For example, complex patterns attract more attention than simple or predictable patterns, intricate shapes attract more attention than generalized ones. Even large-scale patterns can attract attention if they contrast with the pattern in the remainder of the visual field. Camouflage is an example of eliminating contrast in character in color and/or character specifically to reduce visual hierarchy. The "squint test" is often suggested as a simple, if unscientific, method to evaluate the visual hierarchy of a graphical product like a map or web page. When viewed out of focus (or from a great distance), the viewer is not distracted by details, but can only see overall (gestalt) patterns such as visual hierarchy. All of the above patterns, except some aspects of character, are recognizable by this method. == Application == Visual hierarchy is an important concept in the field of graphic design, a field that specializes in visual organization. Designers attempt to control visual hierarchy to guide the eye to information in a specific order for a specific purpose. One could compare visual hierarchy in graphic design to grammatical structure in writing in terms of the importance of each principle to these fields. === Cartography === In cartographic design, visual hierarchy is used to emphasize certain important features on a map over less important features. Typically, a map has a purpose that dictates a conceptual hierarchy of what should be more or less important, so one of the goals of the choice of map symbols is to match the visual hierarchy to the conceptual hierarchy. The Visual hierarchy of a map may apply to individual geographic features (such as making a single country stand out), to map layers of related features (e.g., making lakes stand out more than roads), and to the entire layout of map and non-map elements (e.g., making the title look more important than the scale bar). Like the main map elements, such features have weight, and the properties that apply to visual hierarchy of map layers also apply to other elements on the page. Size and alignment are the two main determinants of the visual hierarchy for these features. Cartographers often utilize principles of negative space and figure-ground contrast to design an appropriate visual hierarchy by employing contrast between unused space and layout features. === User experience design and behavioral design === In user experience design and behavioural design, such as web design, visual hierarchy is used to prioritize navigational structures and content, so that audiences focus on elements that facilitate system usage, or increases the chance that they notice content that contains psychological nudges. Color is one of many factors used in the design of a visual hierarchy, and a key factor due to the high salience of color perception.
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Fragment (computer graphics)

In computer graphics, a fragment is the data necessary to generate a single pixel's worth of a drawing primitive in the frame buffer. These data may include, but are not limited to: raster position depth interpolated attributes (color, texture coordinates, etc.) stencil alpha window ID As a scene is drawn, drawing primitives (the basic elements of graphics output, such as points, lines, circles, text etc.) are rasterized into fragments which are textured and combined with the existing frame buffer. How a fragment is combined with the data already in the frame buffer depends on various settings. In a typical case, a fragment may be discarded if it is further away than the pixel which is already at that location (according to the depth buffer). If it is nearer than the existing pixel, it may replace what is already there, or, if alpha blending is in use, the pixel's color may be replaced with a mixture of the fragment's color and the pixel's existing color, as in the case of drawing a translucent object. In general, a fragment can be thought of as the data needed to shade the pixel, plus the data needed to test whether the fragment survives to become a pixel (depth, alpha, stencil, scissor, window ID, etc.). Shading a fragment is done through a fragment shader (or pixel shaders in Direct3D). In computer graphics, a fragment is not necessarily opaque, and could contain an alpha value specifying its degree of transparency. The alpha is typically normalized to the range of [0, 1], with 0 denotes totally transparent and 1 denotes totally opaque. If the fragment is not totally opaque, then part of its background object could show through, which is known as alpha blending.
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Chris Olah

Christopher Olah (born 1992 or 1993) is a Canadian machine learning researcher and a co-founder of Anthropic. He is known for his work on neural network interpretability, particularly mechanistic interpretability, and for research and tools that visualise internal representations in neural networks. In 2025, Forbes reported he had become a billionaire due to his ownership in Anthropic. == Early life and education == Olah was born in Canada. According to Wired, he left university at age 18 without earning a degree and later received a Thiel Fellowship, which supported him in pursuing independent work. == Career == Olah has worked on interpretability research at Google Brain, OpenAI, and Anthropic. Time called him one of the pioneers of mechanistic interpretability and noted that he pursued this research line first at Google, then at OpenAI, and later at Anthropic, which he co-founded. Wired reported that Olah was involved in neural network visualisation work including DeepDream in 2015, as part of efforts to better understand what neural networks learn. Later coverage linked him to more structured interpretability approaches such as "activation atlases". The Verge covered activation atlases as a collaboration between Google and OpenAI researchers to help inspect neural network representations. At Anthropic, Olah has been identified in major press coverage as leading interpretability work aimed at mapping internal "features" in large language models and relating interpretability findings to AI safety. Quanta Magazine has also quoted Olah in reporting on interpretability and the internal structure of modern language models. Time included Olah in its TIME100 AI list in 2024. === Vatican address on AI ethics === On May 25, 2026, Olah spoke at the Vatican during the official presentation of Magnifica Humanitas, the first encyclical of Pope Leo XIV, which addresses artificial intelligence and human dignity. Olah said AI could lead to large-scale displacement of human labor and exacerbate global inequality. He said the commercial and geopolitical incentives driving frontier AI labs often conflict with the public good, and described AI systems as "grown" rather than strictly engineered. Olah called for external moral oversight from religious institutions, scholars, and civil society to hold the technology sector accountable.
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Semantic parameterization

Semantic parameterization is a conceptual modeling process for expressing natural language descriptions of a domain in first-order predicate logic. The process yields a formalization of natural language sentences in Description Logic to answer the who, what and where questions in the Inquiry-Cycle Model (ICM) developed by Colin Potts and his colleagues at the Georgia Institute of Technology. The parameterization process complements the Knowledge Acquisition and autOmated Specification (KAOS) method, which formalizes answers to the when, why and how ICM questions in Temporal Logic, to complete the ICM formalization. The artifacts used in the parameterization process include a dictionary that aligns the domain lexicon with unique concepts, distinguishing between synonyms and polysemes, and several natural language patterns that aid in mapping common domain descriptions to formal specifications. == Relationship to other theories == Semantic Parameterization defines a meta-model consisting of eight roles that are domain-independent and reusable. Seven of these roles correspond to Jeffrey Gruber's thematic relations and case roles in Charles Fillmore's case grammar: The Inquiry-Cycle Model (ICM) was introduced to drive elicitation between engineers and stakeholders in requirements engineering. The ICM consists of who, what, where, why, how and when questions. All but the when questions, which require a Temporal Logic to represent such phenomena, have been aligned with the meta-model in semantic parameterization using Description Logic (DL). == Introduction with Example == The semantic parameterization process is based on Description Logic, wherein the TBox is composed of words in a dictionary, including nouns, verbs, and adjectives, and the ABox is partitioned into two sets of assertions: 1) those assertions that come from words in the natural language statement, called the grounding, and 2) those assertions that are inferred by the (human) modeler, called the meta-model. Consider the following unstructured natural language statement (UNLS) (see Breaux et al. for an extended discussion): UNLS1.0 The customer1,1 must not share2,2 the access-code3,3 of the customer1,1 with someone4,4 who is not the provider5,4. The modeler first identifies intensional and extensional polysemes and synonyms, denoted by the subscripts: the first subscript uniquely refers to the intensional index, i.e., the same first index in two or more words refer to the same concept in the TBox; the second subscript uniquely refers to the extensional index, i.e., two same second index in two or more words refer to the same individual in the ABox. This indexing step aligns words in the statement and concepts in the dictionary. Next, the modeler identifies concepts from the dictionary to compose the meta-model. The following table illustrates the complete DL expression that results from applying semantic parameterization.
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StepFun

Shanghai Jieyue Xingchen Intelligent Technology Co., Ltd, known as StepFun, is an artificial intelligence (AI) company based in Shanghai, China. It has been dubbed one of China's "AI Tiger" companies by investors. == Background == StepFun was founded in April 2023 by former Microsoft employees. Investors include Tencent, Qiming Venture Partners and Shanghai State-owned Capital Investment. In July 2025 at the World Artificial Intelligence Conference, StepFun announced the "Model-Chip Ecosystem Innovation Alliance" which consisted of Chinese developers of large language models (LLMs) and AI chip manufacturers. This included companies such as Huawei, Biren Technology, Moore Threads and Enflame. Another second alliance named the "Shanghai General Chamber of Commerce AI Committee" was also established that included StepFun, SenseTime, MiniMax, MetaX and Iluvatar CoreX. On 25 February 2026, it was reported that StepFun was seeking an initial public offering on the Hong Kong Stock Exchange. StepFun focuses on multimodal models which are designed to understand multiple types of input data such as text, video and audio. == Products == In July 2024 at the World Artificial Intelligence Conference, StepFun officially launched Step-2, a trillion-parameter LLM, along with the Step-1.5V multimodal model and the Step-1X image generation model. In February 2025, StepFun and Geely jointly announced the open-sourcing of two multimodal large models to global developers. They were Step-Video-T2V and Step-Audio. In July 2025, StepFun released Step 3. The Model-Chip Ecosystem Innovation Alliance aimed to optimize Step 3 for domestic chips. In April 2025, Step-R1-V-Mini was released. It is a multimodal reasoning model designed for visual interpretation and image understanding. In February 2026, Step-3.5-Flash, a mixture-of-experts model with 196 billion parameters and 11 billion active parameters was released under the free and open-source Apache 2.0 license. It supports tool use and a 256k token context window. == Models ==
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CineAsset

CineAsset was a complete mastering software suite by Doremi Labs that could create and playback encrypted (Pro version) and unencrypted DCI compliant packages from virtually any source. CineAsset included a separate "Editor" application for generating Digital Cinema Packages (DCPs). CineAsset Pro added the ability to generate encrypted DCPs and Key Delivery Messages (KDMs) for any encrypted content in the database. It has since been discontinued, along with CineAsset Player. == Features == == Supported formats == === Input === Source: ==== Containers ==== AVI MOV MXF MPG TS WMV M2TS MTS MP4 MKV ==== Video Codecs ==== JPEG2000 ProRes 422 DNxHD® YUV Uncompressed 8-10 bits DIVX® XVID® MPEG4 AVC / H-264 VC-1 MPEG2 ==== Image Sequences ==== BMP TIFF TGA DPX JPG J2C ==== Audio Files ==== WAV MP3 WMA MP2 === Output === Source: ==== JPEG2000 ==== 2D and 3D at up to 4K resolution Bit Rate: 50–250 Mbit/s (500 Mbit/s for frame rates above 30 fps) Speed: Faster than real-time processing when using optional render nodes ==== MPEG2 ==== I-Only or Long GOP 1080p up to 80 Mbit/s ==== H264 ==== 1080p up to 50 Mbit/s ==== VC1 ==== DCP wrapping only (no transcode)
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International Journal on Artificial Intelligence Tools

The International Journal on Artificial Intelligence Tools was founded in 1992 and is published by World Scientific. It covers research on artificial intelligence (AI) tools, including new architectures, languages and algorithms. Topics include AI in Bioinformatics, Cognitive Informatics, Knowledge-Based/Expert Systems and Object-Oriented Programming for AI. == Abstracting and indexing == The journal is abstracted and indexed in: Inspec Science Citation Index Expanded ISI Alerting Services CompuMath Citation Index Current Contents/Engineering, Computing, and Technology
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Layer (deep learning)

A layer in a deep learning model is a structure or network topology in the model's architecture, which takes information from the previous layers and then passes it to the next layer. == Layer types == The first type of layer is the Dense layer, also called the fully-connected layer, and is used for abstract representations of input data. In this layer, neurons connect to every neuron in the preceding layer. In multilayer perceptron networks, these layers are stacked together. The Convolutional layer is typically used for image analysis tasks. In this layer, the network detects edges, textures, and patterns. The outputs from this layer are then fed into a fully-connected layer for further processing. See also: CNN model. The Pooling layer is used to reduce the size of data input. The Recurrent layer is used for text processing with a memory function. Similar to the Convolutional layer, the output of recurrent layers are usually fed into a fully-connected layer for further processing. See also: RNN model. The Normalization layer adjusts the output data from previous layers to achieve a regular distribution. This results in improved scalability and model training. A Hidden layer is any of the layers in a Neural Network that aren't the input or output layers. == Differences with layers of the neocortex == There is an intrinsic difference between deep learning layering and neocortical layering: deep learning layering depends on network topology, while neocortical layering depends on intra-layers homogeneity.
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Ashish Vaswani

Ashish Vaswani is an Indian computer scientist and entrepreneur. He conducted research at Google Brain, co-founded Adept AI, and, as of 2025, was co-founder and chief executive officer of Essential AI. Vaswani is a co-author of the 2017 paper "Attention Is All You Need", which introduced the Transformer neural network architecture. The Transformer model has been used in the development of subsequent NLP models BERT, ChatGPT, and their successors. == Career == Vaswani completed his engineering in Computer Science from Birla Institute of Technology, Mesra (BIT Mesra) in 2002. In 2004, he enrolled at the University of Southern California for graduate studies. He earned his PhD in Computer Science at the University of Southern California supervised by David Chiang. During his research career at Google, Vaswani was part of the Google Brain team, where he conducted the work leading to the 'Attention Is All You Need' publication. Prior to joining Google, he was affiliated with the Information Sciences Institute at the University of Southern California. After Google, Vaswani co-founded Adept AI, a machine learning-focused startup that developed AI agents and tools for software automation. He has since left the company. He later co-founded Essential AI with Niki Parmar. As of 2025, he was chief executive officer of Essential AI. == Notable works == Vaswani's most notable paper, "Attention Is All You Need", was published in 2017. The paper introduced the Transformer model, which uses self-attention mechanisms instead of recurrence for sequence-to-sequence tasks. The Transformer architecture has become foundational to modern language models and NLP systems, including BERT (2018), GPT-2, GPT-3 (2019–2020) and many more recent models. The "Attention Is All You Need" paper is among the most cited papers in machine learning.
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Open-source software security

Open-source software security is the measure of assurance or guarantee in the freedom from danger and risk inherent to an open-source software system. == Implementation debate == === Benefits === Proprietary software forces the user to accept the level of security that the software vendor is willing to deliver and to accept the rate that patches and updates are released. It is assumed that any compiler that is used creates code that can be trusted, but it has been demonstrated by Ken Thompson that a compiler can be subverted using a compiler backdoor to create faulty executables that are unwittingly produced by a well-intentioned developer. With access to the source code for the compiler, the developer has at least the ability to discover if there is any mal-intention. Kerckhoffs' principle is based on the idea that an enemy can steal a secure military system and not be able to compromise the information. His ideas were the basis for many modern security practices, and followed that security through obscurity is a bad practice. === Drawbacks === Simply making source code available does not guarantee review. An example of this occurring is when Marcus Ranum, an expert on security system design and implementation, released his first public firewall toolkit. At one time, there were over 2,000 sites using his toolkit, but only 10 people gave him any feedback or patches. Having a large amount of eyes reviewing code can "lull a user into a false sense of security". Having many users look at source code does not guarantee that security flaws will be found and fixed. == Metrics and models == There are a variety of models and metrics to measure the security of a system. These are a few methods that can be used to measure the security of software systems. === Number of days between vulnerabilities === It is argued that a system is most vulnerable after a potential vulnerability is discovered, but before a patch is created. By measuring the number of days between the vulnerability and when the vulnerability is fixed, a basis can be determined on the security of the system. There are a few caveats to such an approach: not every vulnerability is equally bad, and fixing a lot of bugs quickly might not be better than only finding a few and taking a little bit longer to fix them, taking into account the operating system, or the effectiveness of the fix. === Poisson process === The Poisson process can be used to measure the rates at which different people find security flaws between open and closed source software. The process can be broken down by the number of volunteers Nv and paid reviewers Np. The rates at which volunteers find a flaw is measured by λv and the rate that paid reviewers find a flaw is measured by λp. The expected time that a volunteer group is expected to find a flaw is 1/(Nv λv) and the expected time that a paid group is expected to find a flaw is 1/(Np λp). === Morningstar model === By comparing a large variety of open source and closed source projects a star system could be used to analyze the security of the project similar to how Morningstar, Inc. rates mutual funds. With a large enough data set, statistics could be used to measure the overall effectiveness of one group over the other. An example of such as system is as follows: 1 Star: Many security vulnerabilities. 2 Stars: Reliability issues. 3 Stars: Follows best security practices. 4 Stars: Documented secure development process. 5 Stars: Passed independent security review. === Coverity scan === Coverity in collaboration with Stanford University has established a new baseline for open-source quality and security. The development is being completed through a contract with the Department of Homeland Security. They are utilizing innovations in automated defect detection to identify critical types of bugs found in software. The level of quality and security is measured in rungs. Rungs do not have a definitive meaning, and can change as Coverity releases new tools. Rungs are based on the progress of fixing issues found by the Coverity Analysis results and the degree of collaboration with Coverity. They start with Rung 0 and currently go up to Rung 2. Rung 0 The project has been analyzed by Coverity's Scan infrastructure, but no representatives from the open-source software have come forward for the results. Rung 1 At rung 1, there is collaboration between Coverity and the development team. The software is analyzed with a subset of the scanning features to prevent the development team from being overwhelmed. Rung 2 There are 11 projects that have been analyzed and upgraded to the status of Rung 2 by reaching zero defects in the first year of the scan. These projects include: AMANDA, ntp, OpenPAM, OpenVPN, Overdose, Perl, PHP, Postfix, Python, Samba, and Tcl.
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Sasha Stiles

Sasha Stiles (born 1980) is an American artist and poet. After discovering natural language processing, she created the 2021 poetry collection Technelegy through an eponymous AI model, before presenting the 2025–2026 installation A Living Poem at the Museum of Modern Art. In addition to artificial intelligence, binary code and non-fungible tokens have been important aspects of her work. == Biography == Stiles was born in 1980 in Pasadena, California, to documentary filmmaker parents whose work includes Cosmos: A Personal Voyage. She was interested in science fiction during her youth, particularly how they addressed human-machine collaboration and posthumanism. She graduated magna cum laude from Harvard University with a Bachelor of Arts in 2002 and she graduated with high honors from the University of Oxford with a Master of Studies in 2004. Originally, Stiles's poetry focused on technology. In 2017, she discovered natural language processing, piquing her interest in its ability to process thoughts and words comparably to its human counterparts. Despite lacking a technological background, she managed to channel people like Gwern Branwen, Ross Goodwin, and Allison Parrish as inspirations for her AI work, and in 2019, she started training an AI model named Technelegy. In 2021, Black Spring Press published her poetry collection Technelegy, where she combines AI-generated content produced by the titular AI model with her own traditionally-created work; the AI-generated content was produced by processing Stiles's own poetry onto GPT-2 and GPT-3. She and Technelegy later co-created A Living Poem, which ran at the Museum of Modern Art's Hyundai Card Digital Wall from September 2025 to March 2026. Stiles also has used non-fungible tokens as a platform for her poetry, having been inspired to go into blockchain by her experiences working with a metaverse exhibition curated by Jess Conatser. She has used Christie's and SuperRare to sell several of her poems as tokenized real-world assets, including Daughter of E.V.E. (Ex-Vivo Uterine Environment), a 2021 single-channel video using freeze-frame shots to hide poetry. In 2021, she co-founded TheVerseVerse (stylized as theVERSEverse), a non-fungible token gallery specializing in poetry. She later created Four Core Texts: Humanifesto and Other Poems, involving four NFT videos of poetry written in looping handwriting and powered by Technelegy. Stiles uses binary code as an inspiration for her work, citing in part its "quite antagonistic system of a binary 'EITHER / OR'", which she connected to several dichotomies pitting humanity and the present against technology and the future. In 2018, she started Analog Binary Code, where she creates sculptures by arranging objects in binary code ciphers. She also created Cursive Binary, where she combines binary with cursive handwriting, after writing zeros and ones on a steamed wall while showering. Stiles and the robot BINA48 co-created the 2020 ArtYard exhibition A Valentine for the Future. She was part of the 2021 group exhibition Computational Poetics at the Beall Center for Art and Technology. From February 24 to March 18, 2023, she held her solo show Binary Odes (stylized as B1NARY 0DES) at Annka Kultys Gallery. By 2024, her work had appeared in places such as Gucci storefronts and Times Square billboards. She designed Words Beyond Words, the official poster for Art Basel in Basel 2025. Stiles is based in Milford, New Jersey, where she lives with her husband, musician Kris Bones. She has also lived in Jersey City and Bucks County, Pennsylvania. She is Kalmyk-American on her mother's side, and she has also announced plans to create a version of Technelegy in her ancestral language Kalmyk.
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Mustafa Suleyman

Mustafa Suleyman (born in August 1984) is a British artificial intelligence (AI) entrepreneur. He is the CEO of Microsoft AI, and the co-founder and former head of applied AI at DeepMind, an AI company which was acquired by Google. After leaving DeepMind, he co-founded Inflection AI, a machine learning and generative AI company, in 2022. == Early life and education == Suleyman's Syrian father worked as a taxi driver and his English mother was a nurse. He grew up off Caledonian Road, London, where he lived with his parents and his two younger brothers. Suleyman went to Thornhill Primary School, a state school in Islington, followed by Queen Elizabeth's School, Barnet, a boys' grammar school. Around that time, he met his DeepMind co-founder, Demis Hassabis, through his best friend, who was Demis's younger brother. Suleyman shared that he and Hassabis often discussed how they could make a positive impact on the world. Suleyman enrolled to study philosophy and theology at the University of Oxford where he was an undergraduate student at Mansfield College, Oxford, before dropping out at 19. == Career == In August 2001, while still a teenager and a "strong atheist", Suleyman helped Mohammed Mamdani establish a telephone counselling service called the Muslim Youth Helpline. The organization would later become one of the largest mental health support services. Suleyman subsequently worked as a policy officer on human rights for Ken Livingstone, the Mayor of London, before going on to start Reos Partners, a "systemic change" consultancy that uses methods from conflict resolution to navigate social problems. As a negotiator and facilitator, Mustafa worked for a wide range of clients such as the United Nations, the Dutch government, and the World Wide Fund for Nature. === DeepMind and Google === In 2010 Suleyman co-founded DeepMind Technologies, an artificial intelligence (AI) and machine learning company, and became its chief product officer. The company quickly established itself as one of the leaders in the AI sector. In 2014 DeepMind was acquired by Google for a reported £400 million, the company's largest acquisition in Europe at that time. Following the acquisition, Suleyman became head of applied AI at DeepMind, taking on responsibility for integrating the company's technology across a wide range of Google products. In February 2016 Suleyman launched DeepMind Health at the Royal Society of Medicine. DeepMind Health builds clinician-led technology for the National Health Service (NHS) and other partners to improve frontline healthcare services. Under Suleyman, DeepMind also developed research collaborations with healthcare organizations in the United Kingdom, including Moorfields Eye Hospital NHS foundation trust. In 2016, Suleyman led an effort to apply DeepMind's machine learning algorithms to help reduce the energy required to cool Google's data centres. The system evaluated the billions of possible combinations of actions that the data centre operators could take, and came up with recommendations based on the predicted power usage. The system discovered novel methods of cooling, leading to a reduction of up to 40% of the amount of energy used for cooling, and a 15% improvement in the buildings' overall energy efficiency. Since June 2019, Suleyman has served on the board of The Economist Group, which publishes The Economist newspaper. In August 2019, Suleyman was placed on administrative leave following allegations of bullying employees. The company hired an external lawyer to investigate, and shortly thereafter Suleyman left to take a VP role at parent company Google. An email circulated by DeepMind's leadership to staff after the story broke, as well as additional details published by Business Insider, said Suleyman's "management style fell short" of expected standards. In December 2019, Suleyman announced he would be leaving DeepMind to join Google, working in a policy role. === Inflection AI === Suleyman left Google in January 2022 and joined Greylock Partners as a venture partner and in March 2022, Suleyman co-founded Inflection AI, a new AI lab venture with Greylock's Reid Hoffman. The company was founded with the goal of leveraging "AI to help humans 'talk' to computers," recruited former staff from companies such as Google and Meta and raised $225 million in its first funding round. In 2023, Inflection AI launched a chatbot named “Pi” for Personal Intelligence. The bot “remembers” past conversations and seems to get to know its users over time. According to Suleyman, the long-term goal for Pi is to be a digital “Chief of Staff”, with the initial design focused on maintaining conversational dialogue with users, asking questions, and offering emotional support. === Microsoft AI === In March 2024, Microsoft appointed Suleyman as Executive Vice President (EVP) and CEO of its newly created consumer AI unit, Microsoft AI. Several members of Inflection AI's team were also appointed to the division, including co-founder Karen Simonyan. === Awards and honours === Suleyman was appointed a Commander of the British Empire (CBE) in the 2019 New Year Honours. Suleyman was named by Time as one of the 100 most influential people in artificial intelligence in 2023 and in 2024. === Views on AI ethics === Suleyman is prominent in the debate over the ethics of AI and has spoken widely about the need for companies, governments and civil society to join in holding technologists accountable for the impacts of their work. He has advocated redesigning incentives in the technology industry to steer business leaders toward prioritising social responsibility alongside their fiduciary duties. Within DeepMind he set up a research unit called DeepMind Ethics & Society to study the real-world impacts of AI and help technologists put ethics into practice. Suleyman is also a founding co-chair of the Partnership on AI – an organisation that includes representatives from companies such as Amazon, Apple, DeepMind, Meta, Google, IBM, and Microsoft. The organisation studies and formulates best practices for AI technologies, advances the public's understanding of AI, and serves as an open platform for discussion and engagement about AI and how it affects people and society. Its board of directors has equal representation from non-profit and for profit entities. In September 2023, Suleyman, in collaboration with researcher Michael Bhaskar, published The Coming Wave, Technology, Power and the 21st Century's Greatest Dilemma, a book that examines the transformative and potentially perilous impact of advanced technologies, particularly AI and synthetic biology. According to Suleyman, AI notably has the potential to bring "radical abundance", address climate change and empower people with its cheap problem-solving capabilities. But it may also improve its own design and manufacturing processes, leading to a period of dangerously rapid AI progress. And it could enable catastrophic misuse, from bioengineered pathogens to autonomous weapons, making global oversight and containment essential to avoid unintended consequences. It was shortlisted for the 2023 Financial Times Business Book of the Year Award. In June 2024, in an interview with Andrew Ross Sorkin at the Aspen Ideas Festival, Suleyman expressed the view that unless a website explicitly specifies otherwise, for "content that is already on the open web, the social contract of that content since the 90s has been that it is fair use. Anyone can copy it, recreate with it, reproduce with it. That has been freeware, if you like. That's been the understanding." The statement sparked controversy over the use of Internet data for training AI models. == Personal life == A Business Insider profile in 2017 described Suleyman as being liberal.
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