Trying to pick the best AI voice assistant? An AI voice assistant is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI voice assistant slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
Shopify
Shopify Inc., stylized as shopify, is a Canadian multinational e-commerce company headquartered in Ottawa, Ontario that operates a platform for retail point-of-sale systems. The company has over 5 million customers and processed US$292.3 billion in transactions in 2024, of which 57% was in the United States. Major customers include Tesla, LVMH, Nestlé, PepsiCo, AB InBev, Kraft Heinz, Lindt, Whole Foods Market, Red Bull, and Hyatt. The company's software has been praised for its ease of use and reasonable fee structure. It has been described as the "go-to e-commerce platform for startups". However, the company has faced criticism for allegedly inflating their sales data and for associating with controversial sellers. == History == === 2006: Founding === Shopify was founded in 2006 by friends Tobias Lütke, Daniel Weinand and Scott Lake after launching Snowdevil, an online store for snowboarding equipment, in 2004. Dissatisfied with the existing e-commerce products on the market, Lütke, a computer programmer by trade, instead built his own. Lütke used the open source web application framework Ruby on Rails to build Snowdevil's online store and launched it after two months of development. The Snowdevil founders launched the platform as Shopify in June 2006. Shopify created an open-source template language called Liquid, which is written in Ruby and has been used since 2006. In June 2009, Shopify launched an application programming interface (API) platform and App Store. The API allows developers to create applications for Shopify online stores and then sell them on the Shopify App Store. === 2010s === In January 2010, Shopify started its Build-A-Business competition, in which participants create a business using its commerce platform. The winners of the competition received cash prizes and mentorship from entrepreneurs, such as Richard Branson, Eric Ries and others. In April of that year, Shopify launched a free mobile app on the Apple App Store. The app allows Shopify store owners to view and manage their stores from iOS mobile devices. In December 2010, Shopify raised $7 million from a series A round from Bessemer Venture Partners, FirstMark Capital, and Felicis Ventures at a $20 million pre-money valuation. At that time, the company had annualized transaction value of $132 million. In October 2011, it raised $15 million in a Series B round. In August 2013, Shopify launched Shopify Payments in partnership with Stripe. Shopify Payments allows merchants to accept payments without requiring a third-party payment gateway. The company also announced the launch of a point of sale system to enable in-person sales in addition to online. The company received $100 million in Series C funding in December 2013. Shopify earned $105 million in revenue in 2014, twice as much as it raised the previous year. In February 2014, Shopify released "Shopify Plus" for large e-commerce businesses seeking access to additional features and support. Shopify went public via an initial public offering on May 21, 2015 raising more than $131 million. In September 2015, Amazon.com closed its Amazon Webstore service for merchants and selected Shopify as the preferred migration provider; In April 2016, Shopify announced Shopify Capital, a cash advance product. Shopify Capital was initially piloted to merchants within the US and allowed merchants to receive an advance on future earnings processed through its payment gateway. Since its launch in 2016, Shopify Capital has provided more than $5.1 billion in funding to Shopify merchants, with a maximum advance of $2 million. On June 7, 2016, Shopify launched its Shopify Plus Partners Program, to help agencies connect with evolving businesses in ecommerce space. On October 3, 2016, Shopify acquired Boltmade. In November 2016, Shopify partnered with Paystack which allowed Nigerian online retailers to accept payments from customers around the world. On November 22, 2016, Shopify launched Frenzy, a mobile app that improves flash sales. In January 2017, Shopify announced integration with Amazon that would allow merchants to sell on Amazon from their Shopify stores. In April 2017, Shopify introduced its Chip & Swipe Reader, a Bluetooth enabled debit and credit card reader for brick and mortar retail purchases. The company has since released additional technology for brick and mortar retailers, including a point-of-sale system with a Dock and Retail Stand similar to that offered by Square, and a tappable chip card reader. Shopify announced a one-click accelerated checkout feature called Shopify Pay in April 2017 as an exclusive feature for merchants using Shopify Payments as their payment processor. Customers can save their shipping and payment information for future purchases from all participating Shopify stores. In November 2017 Shopify announced Arrive, a mobile application to help customers track packages from both Shopify merchants and other e-commerce websites. In September 2018, Shopify announced plans to expand its office space in Toronto's King West neighborhood in 2022 as part of "The Well" complex, jointly owned by Allied Properties REIT and RioCan REIT. In October 2018, Shopify opened its first flagship, a physical space for business owners in Los Angeles. The space offered educational classes, coworking space, a "genius bar" for companies that use Shopify software, and workshops. Online cannabis sales in Ontario, Canada, used Shopify's software when the drug was legalized in October 2018. Shopify's software is also used for in-person cannabis sales in Ontario since becoming legal in 2019. In January 2019, Shopify announced the launch of Shopify Studios, a full-service television and film content and production house. On March 22, 2019, Shopify and email marketing platform Mailchimp ended an integration agreement over disputes involving customer privacy and data collection. In April 2019, Shopify announced an integration with Snapchat to allow Shopify merchants to buy and manage Snapchat Story ads directly on the Shopify platform. The company had previously secured similar integration partnerships with Facebook and Google. On August 14, 2019, Shopify launched Shopify Chat, a new native chat function that allows merchants to have real-time conversations with customers visiting Shopify stores online. === 2020s === In January 2020, the company announced plans to hire in Vancouver, Canada. Additionally, the effects of the COVID-19 pandemic contributed to lifting stock prices. On February 21, 2020, Shopify announced plans to join the Diem Association, known as Libra Association at the time. Also that month, Shopify Pay was rebranded as Shop Pay. In April, Arrive was rebranded as Shop, combining both customer-facing features under a single brand. In May, during the COVID-19 pandemic, Shopify announced it would shift most of its global workforce to permanent remote work. It was reported that Shopify's valuation would likely rise on the back of options it had in the company Affirm that was expecting to go public shortly. In November 2020, Shopify announced a partnership with Alipay to support merchants with cross-border payments. Shopify also provided the opportunity for users to connect Alibaba and AliExpress to Shopify through a Alibaba Dropshipping app that could be purchased through the Shopify App Store. Multiple applications launched between 2021 and 2024 allowed customers to connect their Shopify store to their Alibaba account and then import and publish your products. The integration automatically syncs inventory and orders between both platforms so that Alibaba vendors can ship directly to dropshipping customers.As a result of Affirm's January 13, 2021 IPO, Shopify's 8% stake in Affirm was worth $2 billion. About half of Shopify's C-level executives left the company in early 2021. On June 29, 2021, Shopify removed the 20% revenue share for app developers that make less than US$1 million per year. On January 18, 2022, Shopify announced a partnership with JD.com to let U.S. merchants expand their operations in China, listing their products on JD's cross-border e-commerce platform JD Worldwide. On March 22, 2022, Shopify introduced Linkpop, a product to create a branded, social marketplace through which merchants can advertise and market their products via links to be added on social media channels. The following month, Shopify, Alphabet Inc., Meta Platforms, McKinsey & Company, and Stripe, Inc. announced a $925 million advance market commitment of carbon dioxide removal (CDR) from companies that are developing CDR technology over the next 9 years. In June 2022, Shopify partnered with Twitter. As a part of the deal, Twitter announced that it would launch a sales channel app for all of Shopify's U.S. merchants through its app store. Shopify also partnered with PayPal to offer Shopify Payments to merchants in France. On July 26, 2022, Lütke announced immediate layoffs totalling roughly 10 percent of its workforce. In
Library and information scientist
A library and information scientist, also known as a library scholar, is a researcher or academic who specializes in the field of library and information science and often participates in scholarly writing about and related to library and information science. A library and information scientist is neither limited to any one subfield of library and information science nor any one particular type of library. These scientists come from all information-related sectors including library and book history. == University of Chicago Graduate Library School == The University of Chicago Graduate Library School was established in 1928 to grant a graduate degree in librarianship with an emphasis on research. The program expanded the concept of librarianship, focused on scientific inquiry and established it as a domain for scientific study. In The Spirit of Inquiry: The Graduate Library School at Chicago, 1921-51 Richardson reviewed the history of the School and its impact on the discipline. == Bibliometric mappings == Bibliometric methods have been used to create maps of library and information science, thus identifying the most important researchers as well as their relative connections (or distances) and identifying emerging trends related to LIS publications within the field. White and McCain (1998) made a map of information science and Åström (2002), Chen, Ibekwe-SanJuan, and Hou (2010), Janssens, Leta, Glanzel, and De Moor (2006), and Zhao and Strotmann (2008) constructed some later maps of library and information science. Jabeen, Yun, Rafiq, and Jabeen (2015) mapped the growth and trends of LIS publications. == Notable library and information scientists == See also Beta Phi Mu Award, Award of Merit - Association for Information Science and Technology, Justin Winsor Prize (library)
Information scientist
The term information scientist developed in the latter part of the twentieth century by Wm. Hovey Smith to describe an individual, usually with a relevant subject degree (such as one in Information and Computer Science - CIS) or high level of subject knowledge, providing focused information to scientific and technical research staff in industry. It is a role quite distinct from and complementary to that of a librarian. Developments in end-user searching, together with some convergence between the roles of librarian and information scientist, have led to a diminution in its use in this context, and the term information officer or information professional (information specialist) are also now used. The term was, and is, also used for an individual carrying out research in information science. Brian C. Vickery mentions that the Institute of Information Scientists (IIS) was established in London during 1958 and lists the criteria put forward by this institute "Criteria for Information Science" (appendix 1) as well as his own "Areas of study in information science" (appendix 2). The IIS merged with the Library Association in 2002 to form the Chartered Institute of Library and Information Professionals (CILIP). == Notable Information Scientists == See also Award of Merit - Association for Information Science and Technology Marcia Bates David Blair (information technologist) Samuel C. Bradford Michael Buckland John M. Carroll Blaise Cronin Emilia Currás Brenda Dervin Eugene Garfield Paul B. Kantor Frederick Wilfrid Lancaster Calvin Mooers Tefko Saracevic Linda C. Smith Robert Saxton Taylor Brian Campbell Vickery Thomas D. Wilson == Additional reading == Ellis, David and Merete Haugan. (1997) "Modelling the information seeking patterns of engineers and research scientists in an industrial environment" (Journal of Documentation, Volume 53(4): pp. 384–403) Poole, Alex H. (2024). "'There's a big difference between going through life with the wind at your back, and going through life leaning into the wind': Feminism in Post-World War II Information Science". Proceedings of the Association for Information Science and Technology. 61: 300–313. doi:10.1002/pra2.1029. Vickery, Brian Campbell (1988) "Essays presented to B. C. Vickery" (Journal of Documentation, Volume 44, pp. 199–283). Vickery, B. & Vickery, A. (1987) Information Science in theory and practice (London: Bowker-Saur, pp. 361–369)
Applications of artificial intelligence
Artificial intelligence is the capability of computational systems to perform tasks that are typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. Artificial intelligence has been used in applications throughout industry and academia. Within the field of Artificial Intelligence, there are multiple subfields. The subfield of machine learning has been used for various scientific and commercial purposes, including language translation, image recognition, decision-making, credit scoring, and e-commerce. In recent years, massive advancements have been made in the field of generative artificial intelligence, which uses generative models to generate text, images, videos, and other forms of data. This article describes applications of AI in different sectors. == Agriculture == In agriculture, AI has been proposed as a way for farmers to identify areas that need irrigation, fertilization, or pesticide treatments to increase yields, thereby improving efficiency. AI has been used to attempt to classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and optimize irrigation. == AI-assisted software develoment == == Architecture and design == == Business == A 2023 study found that generative AI increased productivity by 15% in contact centers. Another 2023 study found it increased productivity by up to 40% in writing tasks. An August 2025 review by MIT found that of surveyed companies, 95% did not report any improvement in revenue from the use of AI. A September 2025 article by the Harvard Business Review describes how increased use of AI does not automatically lead to increases in revenue or actual productivity. Referring to "AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task" the article coins the term workslop. Per studies done in collaboration with the Stanford Social Media Lab, workslop does not improve productivity and undermines trust and collaboration among colleagues. In telehealth, agentic AI is reportedly facilitating the creation of large business models (millions in annual profit) with 1-2 employees, such as MEDVi, which as of August 2025 only had 2 employees and ~$75M in annual profit for GLP-1 weight-loss telehealth services. == Chatbots == == Computer science == === Programming assistance === ==== AI-assisted software development ==== AI can be used for real-time code completion, chat, and automated test generation. These tools are typically integrated with editors and IDEs as plugins. AI-assisted software development systems differ in functionality, quality, speed, and approach to privacy. Creating software primarily via AI is known as "vibe coding". Code created or suggested by AI can be incorrect or inefficient. The use of AI-assisted coding can potentially speed-up software development, but can also slow-down the process by creating more work when debugging and testing. The rush to prematurely adopt AI technology can also incur additional technical debt. AI also requires additional consideration and careful review for cybersecurity, since AI coding software is trained on a wide range of code of inconsistent quality and often replicates poor practices. ==== Neural network design ==== AI can be used to create other AIs. For example, around November 2017, Google's AutoML project to evolve new neural net topologies created NASNet, a system optimized for ImageNet and POCO F1. NASNet's performance exceeded all previously published performance on ImageNet. ==== Quantum computing ==== Research and development of quantum computers has been performed with machine learning algorithms. For example, there is a prototype, photonic, quantum memristive device for neuromorphic computers (NC)/artificial neural networks and NC-using quantum materials with some variety of potential neuromorphic computing-related applications. The use of quantum machine learning for quantum simulators has been proposed for solving physics and chemistry problems. === Historical contributions === AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered AI. All of the following were originally developed in AI laboratories: Time sharing Interactive interpreters Graphical user interfaces and the computer mouse Rapid application development environments The linked list data structure Automatic storage management Symbolic programming Functional programming Dynamic programming Object-oriented programming Optical character recognition Constraint satisfaction == Customer service == === Human resources === AI programs have been used in hiring processes to screen resumes and rank candidates based on their qualifications, predict a candidate's likelihood of success in a given role, and automate repetitive communication tasks using chatbots. Studies on these programs have identified tendencies for gender bias, favoring male names and male-coded characteristics, as well as bias against disabled candidates and racial minorities. === Online and telephone customer service === AI underlies avatars (automated online assistants) on web pages. It can reduce operation and training costs. Pypestream automated customer service for its mobile application to streamline communication with customers. A Google app analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately. Amazon uses a chatbot for customer service that can perform tasks like checking the status of an order, cancelling orders, offering refunds and connecting the customer with a human representative. Generative AI (GenAI), such as ChatGPT, is increasingly used in business to automate tasks and enhance decision-making. === Hospitality === In the hospitality industry, AI is used to reduce repetitive tasks, analyze trends, interact with guests, and predict customer needs. AI hotel services come in the form of a chatbot, application, virtual voice assistant and service robots. == Education == In educational institutions, AI has been used to automate routine tasks such as attendance tracking, grading, and marking. AI tools have also been used to monitor student progress and analyze learning behaviors, with the goal of facilitating timely interventions for students facing academic challenges. == Energy and environment == === Energy system === The U.S. Department of Energy wrote in an April 2024 report that AI may have applications in modeling power grids, reviewing federal permits with large language models, predicting levels of renewable energy production, and improving the planning process for electrical vehicle charging networks. Other studies have suggested that machine learning can be used for energy consumption prediction and scheduling, e.g. to help with renewable energy intermittency management (see also: smart grid and climate change mitigation in the power grid). === Environmental monitoring === Autonomous ships that monitor the ocean, AI-driven satellite data analysis, passive acoustics or remote sensing and other applications of environmental monitoring make use of machine learning. For example, "Global Plastic Watch" is an AI-based satellite monitoring-platform for analysis/tracking of plastic waste sites to help prevention of plastic pollution – primarily ocean pollution – by helping identify who and where mismanages plastic waste, dumping it into oceans. === Early-warning systems === Machine learning can be used to spot early-warning signs of disasters and environmental issues, possibly including natural pandemics, earthquakes, landslides, heavy rainfall, long-term water supply vulnerability, tipping-points of ecosystem collapse, cyanobacterial bloom outbreaks, and droughts. === Economic and social challenges === The University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address problems such as homelessness. Stanford researchers use AI to analyze satellite images to identify high poverty areas. == Entertainment and media == === Media === AI applications analyze media content such as movies, TV programs, advertisement videos or user-generated content. The solutions often involve computer vision. Typical scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for scene recognizing scenes, objects or faces. AI-based media analysis can facilitate media search, the creation of descriptive keywords for content, content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for ad placement. Motion interpolation Pixel-art scaling algorithms Image scaling Imag
HTK Limited
HTK Limited is a software-as-a-service company that provides mobile phone messaging and IVR services. Founded in 1996, HTK is headquartered in Ipswich, Suffolk, UK. HTK provide mass notification services. Specifically, the "Police Direct" messaging service to Suffolk and Norfolk Constabularies. In 2010 the HTK Horizon SaaS platform was selected by the Scottish Environment Protection Agency (SEPA) for their Floodline Warnings Direct service. == History == HTK was founded in 1996 by Marlon Bowser and Adrian Gregory and from the outset focused on what has now become commonly known as Software-as-a-Service. in 2004, according to the Deloitte Fast 50 (UK), HTK was the 17th fastest growing company in the East of England. In 2005 The Times listed HTK 65th nationally and 4th in the East of England in the Sunday Times & Microsoft "Tech Track 100" awards. In 2009 the company was approved as a supplier to UK Government under a new framework agreement. In 2010 HTK launched version 2.2 of its Horizon platform, with a feature set that signals a shift from mass notification into the customer service automation market.
Bottom-up and top-down approaches
Bottom-up and top-down are strategies of composition and decomposition in fields as diverse as information processing and ordering knowledge, software, humanistic and scientific theories (see systemics), time management, and organization. In practice they can be seen as a style of thinking, teaching, or leadership. A top-down approach (also known as stepwise design and stepwise refinement and in some cases used as a synonym of decomposition) is essentially the breaking down of a system to gain insight into its compositional subsystems in a reverse engineering fashion. In a top-down approach an overview of the system is formulated, specifying, but not detailing, any first-level subsystems. Each subsystem is then refined in yet greater detail, sometimes in many additional subsystem levels, until the entire specification is reduced to base elements. A top-down model is often specified with the assistance of black boxes, which makes it easier to manipulate. However, black boxes may fail to clarify elementary mechanisms or be detailed enough to realistically validate the model. A top-down approach starts with the big picture, then breaks down into smaller segments. A bottom-up approach is the piecing together of systems to give rise to more complex systems, thus making the original systems subsystems of the emergent system. Bottom-up processing is a type of information processing based on incoming data from the environment to form a perception. From a cognitive psychology perspective, information enters the eyes in one direction (sensory input, or the "bottom"), and is then turned into an image by the brain that can be interpreted and recognized as a perception (output that is "built up" from processing to final cognition). In a bottom-up approach the individual base elements of the system are first specified in great detail. These elements are then linked together to form larger subsystems, which then in turn are linked, sometimes in many levels, until a complete top-level system is formed. This strategy often resembles a "seed" model, by which the beginnings are small but eventually grow in complexity and completeness. But "organic strategies" may result in a tangle of elements and subsystems, developed in isolation and subject to local optimization as opposed to meeting a global purpose. == Computer science == === Software development === In the software development process, the top-down and bottom-up approaches play a key role. Top-down approaches emphasize planning and a complete understanding of the system. It is inherent that no coding can begin until a sufficient level of detail has been reached in the design of at least some part of the system. Top-down approaches are implemented by attaching the stubs in place of the module. But these delay testing of the ultimate functional units of a system until significant design is complete. Bottom-up emphasizes coding and early testing, which can begin as soon as the first module has been specified. But this approach runs the risk that modules may be coded without having a clear idea of how they link to other parts of the system, and that such linking may not be as easy as first thought. Re-usability of code is one of the main benefits of a bottom-up approach. Top-down design was promoted in the 1970s by IBM researchers Harlan Mills and Niklaus Wirth. Mills developed structured programming concepts for practical use and tested them in a 1969 project to automate the New York Times morgue index. The engineering and management success of this project led to the spread of the top-down approach through IBM and the rest of the computer industry. Among other achievements, Niklaus Wirth, the developer of Pascal programming language, wrote the influential paper Program Development by Stepwise Refinement. Since Niklaus Wirth went on to develop languages such as Modula and Oberon (where one could define a module before knowing about the entire program specification), one can infer that top-down programming was not strictly what he promoted. Top-down methods were favored in software engineering until the late 1980s, and object-oriented programming assisted in demonstrating the idea that both aspects of top-down and bottom-up programming could be used. Modern software design approaches usually combine top-down and bottom-up approaches. Although an understanding of the complete system is usually considered necessary for good design—leading theoretically to a top-down approach—most software projects attempt to make use of existing code to some degree. Pre-existing modules give designs a bottom-up flavor. === Programming === Top-down is a programming style, the mainstay of traditional procedural languages, in which design begins by specifying complex pieces and then dividing them into successively smaller pieces. The technique for writing a program using top-down methods is to write a main procedure that names all the major functions it will need. Later, the programming team looks at the requirements of each of those functions and the process is repeated. These compartmentalized subroutines eventually will perform actions so simple they can be easily and concisely coded. When all the various subroutines have been coded the program is ready for testing. By defining how the application comes together at a high level, lower-level work can be self-contained. In a bottom-up approach the individual base elements of the system are first specified in great detail. These elements are then linked together to form larger subsystems, which in turn are linked, sometimes at many levels, until a complete top-level system is formed. This strategy often resembles a "seed" model, by which the beginnings are small, but eventually grow in complexity and completeness. Object-oriented programming (OOP) is a paradigm that uses "objects" to design applications and computer programs. In mechanical engineering with software programs such as Pro/ENGINEER, Solidworks, and Autodesk Inventor users can design products as pieces not part of the whole and later add those pieces together to form assemblies like building with Lego. Engineers call this "piece part design". === Parsing === Parsing is the process of analyzing an input sequence (such as that read from a file or a keyboard) in order to determine its grammatical structure. This method is used in the analysis of both natural languages and computer languages, as in a compiler. Bottom-up parsing is parsing strategy that recognizes the text's lowest-level small details first, before its mid-level structures, and leaves the highest-level overall structure to last. In top-down parsing, on the other hand, one first looks at the highest level of the parse tree and works down the parse tree by using the rewriting rules of a formal grammar. == Natural sciences == === Nanotechnology === Top-down and bottom-up are two approaches for the manufacture of products. These terms were first applied to the field of nanotechnology by the Foresight Institute in 1989 to distinguish between molecular manufacturing (to mass-produce large atomically precise objects) and conventional manufacturing (which can mass-produce large objects that are not atomically precise). Bottom-up approaches seek to have smaller (usually molecular) components built up into more complex assemblies, while top-down approaches seek to create nanoscale devices by using larger, externally controlled ones to direct their assembly. Certain valuable nanostructures, such as Silicon nanowires, can be fabricated using either approach, with processing methods selected on the basis of targeted applications. A top-down approach often uses the traditional workshop or microfabrication methods where externally controlled tools are used to cut, mill, and shape materials into the desired shape and order. Micropatterning techniques, such as photolithography and inkjet printing belong to this category. Vapor treatment can be regarded as a new top-down secondary approaches to engineer nanostructures. Bottom-up approaches, in contrast, use the chemical properties of single molecules to cause single-molecule components to (a) self-organize or self-assemble into some useful conformation, or (b) rely on positional assembly. These approaches use the concepts of molecular self-assembly and/or molecular recognition. See also Supramolecular chemistry. Such bottom-up approaches should, broadly speaking, be able to produce devices in parallel and much cheaper than top-down methods but could potentially be overwhelmed as the size and complexity of the desired assembly increases. === Neuroscience and psychology === These terms are also employed in cognitive sciences including neuroscience, cognitive neuroscience and cognitive psychology to discuss the flow of information in processing. Typically, sensory input is considered bottom-up, and higher cognitive processes, which have more information from other sources, are considered top-down. A bottom-up proc