Leela Chess Zero (abbreviated as LCZero, lc0) is a free, open-source chess engine and volunteer computing project based on Google's AlphaZero engine. It was spearheaded by Gary Linscott, a developer for the Stockfish chess engine, and adapted from the Leela Zero Go engine. Like Leela Zero and AlphaGo Zero, early iterations of Leela Chess Zero started with no intrinsic chess-specific knowledge other than the basic rules of the game. It learned how to play chess through reinforcement learning from repeated self-play, using a distributed computing network coordinated at the Leela Chess Zero website. However, as of November 2024 most models used by the engine are trained through supervised learning on data generated by previous reinforcement learning runs. As of June 2025, Leela Chess Zero has played over 2.5 billion games against itself, playing around 1 million games every day, and is capable of play at a level that is comparable with Stockfish, the leading conventional chess program. == History == The Leela Chess Zero project was first announced on TalkChess.com on January 9, 2018, as an open-source, self-learning chess engine attempting to recreate the success of AlphaZero. Within the first few months of training, Leela Chess Zero had already reached the Grandmaster level, surpassing the strength of early releases of Rybka, Stockfish, and Komodo, despite evaluating orders of magnitude fewer positions due to the size of the deep neural network it uses as its evaluation function. In December 2018, the AlphaZero team published a paper in Science magazine revealing previously undisclosed details of the architecture and training parameters used for AlphaZero. These changes were soon incorporated into Leela Chess Zero and increased both its strength and training efficiency. Work on Leela Chess Zero has informed the AobaZero project for shogi. The engine has been rewritten and carefully iterated upon since its inception, and since 2019 has run on multiple backends, allowing it to run on both CPU and GPU. The engine can be configured to use different weights, including even different architectures. This same mechanism of substitutable weights can also be used for alternative chess rules, such as for the Fischer Random Chess variant, which was done in 2019. == Neural network == Like AlphaZero, Leela Chess Zero employs neural networks which output both a policy vector, a distribution over subsequent moves used to guide search, and a position evaluation. These neural networks are designed to run on GPU, unlike traditional engines. It originally used residual neural networks, but in 2022 switched to using a transformer-based architecture designed by Daniel Monroe and Philip Chalmers. These models represent a chessboard as a sequence of 64 tokens and apply a trunk consisting of a stack of Post-LN encoder layers, outputting a sequence of 64 encoded tokens which is used to generate a position evaluation and a distribution over subsequent moves. They use a custom domain-specific position encoding called smolgen to improve the self-attention layer. As of November 2024, the models used by the engine are significantly larger and more efficient than the residual network used by AlphaZero, reportedly achieving grandmaster-level strength at one position evaluation per move. These models are able to detect and exploit positional features like trapped pieces and fortresses to outmaneuver traditional engines, giving Leela a unique playstyle. There is also evidence that they are able to perform look-ahead. == Program and use == Like AlphaZero, Leela Chess Zero learns through reinforcement learning, continually training on data generated through self-play. However, unlike AlphaZero, Leela Chess Zero decentralizes its data generation through distributed computing, with volunteers generating self-play data on local hardware which is fed to the reinforcement algorithm. In order to contribute training games, volunteers must download the latest non-release candidate (non-rc) version of the engine and the client. The client connects to the Leela Chess Zero server and iteratively receives the latest neural network version and produces self-play games which are sent back to the server and use to train the network. In order to run the Leela Chess Zero engine, two components are needed: the engine binary used to perform search, and a network used to evaluate positions. The client, which is used to contribute training data to the project, is not needed for this purpose. Older networks can also be downloaded and used by placing those networks in the folder with the Lc0 binary. == Spinoffs == In season 15 of the Top Chess Engine Championship, the engine AllieStein competed alongside Leela. AllieStein is a combination of two different spinoffs from Leela: Allie, which uses the same neural network as Leela, but has a unique search algorithm for exploring different lines of play, and Stein, a network which was trained using supervised learning on existing game data from games between other engines. While neither of these projects were admitted to TCEC separately due to their similarity to Leela, the combination of Allie's search algorithm with the Stein network, called AllieStein, was deemed unique enough to warrant its inclusion in the competition. In early 2021, the LcZero blog announced Ceres, a transliteration of the engine to C# which introduced several algorithmic improvements. The engine has performed competitively in tournaments, achieving third place in the TCEC Swiss 7 and fourth place in the TCEC Cup 14. In 2024, the CeresTrain framework was announced to support training deep neural networks for chess in PyTorch. == Competition results == In April 2018, Leela Chess Zero became the first engine using a deep neural network to enter the Top Chess Engine Championship (TCEC), during Season 12 in the lowest division, Division 4. Out of 28 games, it won one, drew two, and lost the remainder; its sole victory came from a position in which its opponent, Scorpio 2.82, crashed in three moves. However, it improved quickly. In July 2018, Leela placed seventh out of eight competitors at the 2018 World Computer Chess Championship. In August 2018, it won division 4 of TCEC season 13 with a record of 14 wins, 12 draws, and 2 losses. In Division 3, Leela scored 16/28 points, finishing third behind Ethereal, which scored 22.5/28 points, and Arasan on tiebreak. By September 2018, Leela had become competitive with the strongest engines in the world. In the 2018 Chess.com Computer Chess Championship (CCCC), Leela placed fifth out of 24 entrants. The top eight engines advanced to round 2, where Leela placed fourth. Leela then won the 30-game match against Komodo to secure third place in the tournament. Leela participated in the "TCEC Cup", an event in which engines from different TCEC divisions can play matches against one another. Leela defeated higher-division engines Laser, Ethereal and Fire before finally being eliminated by Stockfish in the semi-finals. In December 2018, Leela participated in Season 14 of the Top Chess Engine Championship. Leela dominated divisions 3, 2, and 1, easily finishing first in all of them. In the premier division, Stockfish dominated while Houdini, Komodo and Leela competed for second place. It came down to a final-round game where Leela needed to hold Stockfish to a draw with black to finish second ahead of Komodo. Leela managed this and therefore met Stockfish in the superfinal. In a back and forth match, first Stockfish and then Leela took three game leads before Stockfish won by the narrow margin of 50.5–49.5. In February 2019, Leela scored its first major tournament win when it defeated Houdini in the final of the second TCEC cup. Leela did not lose a game the entire tournament. In April 2019, Leela won the Chess.com Computer Chess Championship 7: Blitz Bonanza, becoming the first neural-network project to take the title. In the season 15 of the Top Chess Engine Championship (May 2019), Leela defended its TCEC Cup title, this time defeating Stockfish with a score of 5.5–4.5 (+2 =7 −1) in the final after Stockfish blundered a seven-man tablebase draw. Leela also won the Superfinal for the first time, scoring 53.5–46.5 (+14 −7 =79) versus Stockfish, including winning as both white and black in the same predetermined opening in games 61 and 62. Season 16 of TCEC saw Leela finish in third place in premier division, missing qualification for the Superfinal to Stockfish and the new deep neural network engine AllieStein. Leela was the only engine not to suffer any losses in the Premier division, and defeated Stockfish in one of the six games they played. However, Leela only managed to score nine wins, while AllieStein and Stockfish both scored 14 wins. This inability to defeat weaker engines led to Leela finishing third, half a point behind AllieStein and a point behind Stockfish. In the fourth TCEC Cup, Leela was seeded first as the defending champion,
Synthesia (company)
Synthesia Limited is a British multinational artificial intelligence company based in London, United Kingdom. It is a synthetic media-generation software developer and creator of AI-generated video content, including audio-visual agents and cloned avatars. Britain's largest generative-AI firm, it is used by 70% of FTSE 100 and over 90% of Fortune 100 companies. == Overview == Synthesia is most often used by corporations for localized communication, orientation, employee training videos, advertising campaigns, reporting, product demonstrations, customer service, and to create chatbots. Its software algorithm mimics speech and facial movements based on video recordings of an individual’s speech and facial expressions. From this, a text-to-speech video is created to look and sound like the individual. Swiss bank UBS incorporated Synthesia AI-powered avatars of their human financial experts, for instance, in 2025. Users create content via the platform's pre-generated AI presenters or by creating digital representations of themselves, or personal avatars, using the platform's AI video editing tool. These avatars can be used to narrate videos generated from text. As of August 2021, Synthesia's voice database included multiple gender options in over 60 languages. Its free voice library doubled by 2025, to 140 languages and accents, and its Express-Voice technology can clone a user's own voice, or generate a synthetic one. === Deepfakes === The platform prohibits use of its software to create non-consensual clones, including of celebrities or political figures for satirical purposes. Explicit consent must be provided in addition to a strict pre-screening regimen for use of an individual's likeness to avoid “deepfaking”. While the company prohibits use of its technology for misinformation or "news-like content", an October 2023 Freedom House report stated that Synthesia tools had been used by governments in Venezuela, China, Burkina Faso, and Russia to create videos of fake TV news outlets with AI-generated avatars in order to spread propaganda. Actor Dan Dewhirst signed a contract with the company in 2021, becoming one of the first actors whose likeness would be made into an AI avatar, finding his likeness used in the Venezuelan generated-videos. The company stated, in February 2024, that it had improved its misuse detection systems, and, in April 2024, that new users of its technology are screened by the company, and content employing it is further vetted by Synthesia moderators. == History == Synthesia's software utilizes deep learning architecture developed by Lourdes Agapito and Matthias Niessner. The company was co-founded in 2017 by Agapito, Niessner, Victor Riparbelli, and Steffen Tjerrild. In 2018, the company first demonstrated the software's capabilities on the BBC programme Click when it presented a digitization of Matthew Amroliwala speaking Spanish, Mandarin, and Hindi. Through Synthesia's first two years of existence, it employed 10 people and struggled to make sales, leading to an expansion of the company's focus. It moved on from just targeting entertainment studios to a variety of businesses. In 2020, Synthesia users were reported to include Amazon, Tiffany & Co. and IHG Hotels & Resorts. In January 2024, the company introduced its AI video assistant, which turns text-to-video. That April, with a reported 55,000 customers, including half of the Fortune 100, Synthesia launched "expressive avatars". That September, an enhanced dubbing feature was launched, to translate video in 30 languages with naturalized lip-syncing. Peter Hill joined Synthesia as CTO in January 2025, following 25 years at Amazon, and two years as CEO and CPO of Wildfire Studios. That March, a million dollar base of shares was formed to furnish human actors, employed to generate digital avatars, with company stock, which all of its employees hold. By June of that year, 150,000 individuals from among Synthesia's 65,000 customers had created AI-generated avatars of themselves. In July 2025, the company's new global headquarters at Regent’s Place was opened by London mayor Sadiq Khan, who described Britain's largest generative-AI company, then valued at over $2 billion, as a "London success story". By that October, its technology was employed by 90% of the Fortune 100, and Synthesia 3.0 was launched, with hyper-realistic digital avatars equipped with AI-powered dubbing and translation, and a built-in video assistant. In January 2026, it reached a $4 billion valuation, with 70% of FTSE 100 companies noted among its customers. === Funding === The company raised $3.1 million in seed funding in 2019. In April 2021, the company raised $12.5 million in Series A funding. In December 2021, it raised $50 million in a Series B funding round led by Kleiner Perkins and GV (then Google Ventures). Synthesia gained a total valuation of $1 billion, and achieved unicorn status, when it raised $90 million from Accel and Nvidia partnership NVentures, in June 2023, during its Series C funding round. Counting 60,000 customers by January 2025, including over 60% of Fortune 100 companies; the company raised $180 million in a Series D round led by NEA, with new investors World Innovation Lab (WiL), Atlassian Ventures and PSP Growth, as well as existing investors GV, MMC Ventures and FirstMark, doubling Synthesia's valuation to $2.1 billion. Capital raised by 2025 had reached $330 million, with investments slated to further product innovation, talent growth, and company expansion in North America, Europe, Japan and Australia. In April 2025, Adobe Inc. invested £10 million in the company for a strategic partnership. Synthesia subsequently rejected a $3 billion acquisition offer from Adobe, choosing to remain independent. With a revenue stream then exceeding $100 million annually; GV led a Series E funding round in October 2025, resulting in Synthesia's $4 billion valuation, raising $200 million from GV, Nvidia and Accel to develop, in 2026, interactive audio-visual avatar "agents" that converse on topic, for automated sales training and corporate communications, such as recruiting. == Recognition == In 2021, Synthesia partnered with Lay's to create the Messi Messages campaign featuring Argentine footballer Lionel Messi. Users created personalized messages with Synthesia's software and sent custom artificial reality video messages from Messi based on their text input. The campaign received a Cannes Lion Award under the Bronze category. In February 2025, UK Science and Technology Minister Peter Kyle commended Synthesia's "pioneering generative AI innovations."
Software-defined mobile network
Software-defined mobile networking (SDMN) is an approach to the design of mobile networks where all protocol-specific features are implemented in software, maximizing the use of generic and commodity hardware and software in both the core network and radio access network (RAN). == History == Through the 20th century, telecommunications technology was driven by hardware development, with most functions implemented in special-purpose equipment. In the early 2000s, generally available CPUs became cheap enough to enable commercial software-defined radio (SDR) technology and softswitches. SDMN extends these trends into the design of mobile networks, moving nearly all network functions into software. The term "software-defined mobile network" first appeared in public literature in early 2014, used independently by Lime Microsystems and researchers from University of Oulu, Finland. == Limitations of hardware-based mobile networks == Mobile networks based on special-purpose hardware suffer from the following limitations: They have limited provisions for upgrades and usually must be replaced entirely when new standards are introduced. The individual components are not scalable in terms of performance and capacity, because the capacity of a component is fixed by the hardware implementation. Specialized equipment and its associated specialized software require vendor-specific training for the mobile operator's staff. Specialized hardware systems are usually supported and serviced by a single vendor, resulting in vendor lock-in. == Characteristics of SDMN designs == === Use of software-defined radio === SDR is an important element of SDMN, because it replaces protocol-specific radio hardware with protocol-agnostic digital transceivers. While many earlier digital radio systems used field-programmable gate arrays (FPGAs) or special-purposed digital signal processors (DSPs) for calculations on baseband radio waveforms, the SDMN approach moves all of the baseband processing into general-purpose CPUs. SDMN radio systems also use hardware with publicly-documented interfaces that is designed to be readily reproducible by multiple manufacturers. === Commodity components === SDMN designs avoid the use of components that are specialized as to their functions or that are available from only a single vendor. This is true of both the hardware and software elements of the network. === Software switching and transcoding === The telephony switches of SDMN networks are software-based, including software transcoding for speech codecs. === Centralized, distributed, or hybrid? === A new SDN architecture for wireless distribution systems (WDSs) is explored that eliminates the need for multi-hop flooding of route information and therefore enables WDNs to easily expand. The key idea is to split network control and data forwarding by using two separate frequency bands. The forwarding nodes and the SDN controller exchange link-state information and other network control signaling in one of the bands, while actual data forwarding takes place in the other band. == Advantages of SDMN == The SDMN approach has many advantages over hardware-based mobile network designs. Because SDMN hardware is protocol-agnostic, upgrades are software-only, even across technology generations. In the radio network, these changes can even be made on a site-by-site basis. Because SDMN hardware is designed to be easily sourced and reproduced: SDMN equipment can be serviced by a wider range of vendors, lowering maintenance costs. SDMN equipment can be manufactured anywhere in the world, lowering production costs. Because SDMN software is based on commodity operating systems and development tools: Support staff can be trained more quickly because they are already familiar with the underlying software systems. Many aspects of the SDMN can be monitored and managed with pre-existing tools, because they are already available in the commodity operating systems. Because SDMN network components run on general purpose computers, the network components can be scaled up in capacity by adding more computing power.
Telecommunications
Telecommunication, often used in its plural form or abbreviated as telecom, is the transmission of information over a distance using electrical or electronic means, typically through cables, radio waves, or other communication technologies. These means of transmission may be divided into communication channels for multiplexing, allowing for a single medium to transmit several concurrent communication sessions. Long-distance technologies invented during the 19th, 20th and 21st centuries generally use electric power, and include the electrical telegraph, telephone, television, and radio. Early telecommunication networks used metal wires as the medium for transmitting signals. These networks were used for telegraphy and telephony for many decades. In the first decade of the 20th century, a revolution in wireless communication began with breakthroughs including those made in radio communications by Guglielmo Marconi, who won the 1909 Nobel Prize in Physics. Other early pioneers in electrical and electronic telecommunications include co-inventors of the telegraph Charles Wheatstone and Samuel Morse, numerous inventors and developers of the telephone including Antonio Meucci, Philipp Reis, Elisha Gray and Alexander Graham Bell, inventors of radio Edwin Armstrong and Lee de Forest, as well as inventors of television like Vladimir K. Zworykin, John Logie Baird and Philo Farnsworth. Since the 1960s, the proliferation of digital technologies has meant that voice communications have gradually been supplemented by data. The physical limitations of metallic media prompted the development of optical fibre. The Internet, a technology independent of any given medium, has provided global access to services for individual users and further reduced location and time limitations on communications. == Definition == At the 1932 Plenipotentiary Telegraph Conference and the International Radiotelegraph Conference in Madrid, the two organizations merged to form the International Telecommunication Union (ITU). They defined telecommunication as "any telegraphic or telephonic communication of signs, signals, writing, facsimiles and sounds of any kind, by wire, wireless or other systems or processes of electric signaling or visual signaling (semaphores)." The definition was later reconfirmed, according to Article 1.3 of the ITU Radio Regulations, which defined it as "Any transmission, emission or reception of signs, signals, writings, images and sounds or intelligence of any nature by wire, radio, optical, or other electromagnetic systems". As such, slow communications technologies like postal mail and pneumatic tubes are excluded from the telecommunication's definition. The term telecommunication was coined in 1904 by the French engineer and novelist Édouard Estaunié, who defined it as "remote transmission of thought through electricity". Telecommunication is a compound noun formed from the Greek prefix tele- (τῆλε), meaning distant, far off, or afar, and the Latin verb communicare, meaning to share. Communication was first used as an English word in the late 14th century. It comes from Old French comunicacion (14c., Modern French communication), from Latin communicationem (nominative communication), noun of action from past participle stem of communicare, "to share, divide out; communicate, impart, inform; join, unite, participate in," literally, "to make common", from communis. == History == Many transmission media have been used for long-distance communication throughout history, from smoke signals, beacons, semaphore telegraphs, signal flags, and optical heliographs to wires and empty space made to carry electromagnetic signals. === Before the electrical and electronic era === Long-distance communication was used long before the discovery of electricity and electromagnetism enabled the invention of telecommunications. A few of the many ingenious methods for communicating over distances prior to that are described here. Homing pigeons have been used throughout history by different cultures. Pigeon post had Persian roots and was later used by the Romans to aid their military. Frontinus claimed Julius Caesar used pigeons as messengers in his conquest of Gaul. The Greeks also conveyed the names of the victors at the Olympic Games to various cities using homing pigeons. In the early 19th century, the Dutch government used the system in Java and Sumatra. And in 1849, Paul Julius Reuter started a pigeon service to fly stock prices between Aachen and Brussels, a service that operated for a year until the gap in the telegraph link was closed. In the Middle Ages, chains of beacons were commonly used on hilltops as a means of relaying a signal. Beacon chains suffered the drawback that they could only pass a single bit of information, so the meaning of the message, such as "the enemy has been sighted" had to be agreed upon in advance. One notable instance of their use was during the Spanish Armada, when a beacon chain relayed a signal from Plymouth to London. In 1792, Claude Chappe, a French engineer, built the first fixed visual telegraphy system (or semaphore line) between Lille and Paris. However semaphore suffered from the need for skilled operators and expensive towers at intervals of ten to thirty kilometres (six to nineteen miles). As a result of competition from the electrical telegraph, the last commercial line was abandoned in 1880. === Telegraph and telephone === On July 25, 1837, the first commercial electrical telegraph was demonstrated by English inventor Sir William Fothergill Cooke and English scientist Sir Charles Wheatstone. Both inventors viewed their device as "an improvement to the [existing] electromagnetic telegraph" and not as a new device. Samuel Morse independently developed a version of the electrical telegraph that he unsuccessfully demonstrated on September 2, 1837. His code was an important advance over Wheatstone's signaling method. The first transatlantic telegraph cable was successfully completed on July 27, 1866, allowing transatlantic telecommunication for the first time. After early attempts to develop a talking telegraph by Antonio Meucci and a telefon by Johann Philipp Reis, a patent for the conventional telephone was filed by Alexander Bell in February 1876 (just a few hours before Elisha Gray filed a patent caveat for a similar device). The first commercial telephone services were set up by the Bell Telephone Company in 1878 and 1879 on both sides of the Atlantic in the cities of New Haven and London. === Radio and television === In 1894, Italian inventor Guglielmo Marconi began developing wireless communication using the then-newly discovered phenomenon of radio waves, demonstrating, by 1901, that they could be transmitted across the Atlantic Ocean. This was the start of wireless telegraphy by radio. On 17 December 1902, a transmission from the Marconi station in Glace Bay, Nova Scotia, Canada, became the world's first radio message to cross the Atlantic from North America. In 1904, a commercial service was established to transmit nightly news summaries to subscribing ships, which incorporated them into their onboard newspapers. World War I accelerated the development of radio for military communications. After the war, commercial radio AM broadcasting began in the 1920s and became an important mass medium for entertainment and news. World War II again accelerated the development of radio for the wartime purposes of aircraft and land communication, radio navigation, and radar. Development of stereo FM broadcasting of radio began in the 1930s in the United States and the 1940s in the United Kingdom, displacing AM as the dominant commercial standard in the 1970s. On March 25, 1925, John Logie Baird demonstrated the transmission of moving pictures at the London department store Selfridges. Baird's device relied upon the Nipkow disk by Paul Nipkow and thus became known as the mechanical television. It formed the basis of experimental broadcasts done by the British Broadcasting Corporation beginning on 30 September 1929. === Vacuum tubes === Vacuum tubes use thermionic emission of electrons from a heated cathode for a number of fundamental electronic functions such as signal amplification and current rectification. The simplest vacuum tube, the diode invented in 1904 by John Ambrose Fleming, contains only a heated electron-emitting cathode and an anode. Electrons can only flow in one direction through the device—from the cathode to the anode. Adding one or more control grids within the tube enables the current between the cathode and anode to be controlled by the voltage on the grid or grids. These devices became a key component of electronic circuits for the first half of the 20th century and were crucial to the development of radio, television, radar, sound recording and reproduction, long-distance telephone networks, and analogue and early digital computers. While some applications had used earlier technologies such as the sp
Gnowit
Gnowit (pronounced "know it") is a Canadian software company that provides automated, near-real-time monitoring of legislative, regulatory, and political activity across Canada. Its platform aggregates and analyzes information from government publications, parliamentary debates, committee, and proceedings to provide searchable alerts and reports for organizations monitoring public policy and regulatory developments. The system uses natural-language processing and machine learning techniques to organize and filter large volumes of public information.; the company reports that new publication documents are captured and millions of items are added to its repository daily. == History, Founders and Leadership == Gnowit was co-founded in Ottawa in 2010 by Shahzad Khan and Mohammad Al-Azzouni; Khan serves as chief executive officer. Khan holds a PhD in Computer Science from the University of Cambridge, has more than two decades of experience in AI/ML and computational linguistics, and has authored or co-authored 37 peer-reviewed publications and five patents. Traditionally, companies performed this analysis manually; Gnowit has delivered efficiencies achieved through AI innovations. The company has participated in several Canadian startup and accelerator programs, including Carleton University's Lead To Win initiative, the University of Ottawa's Startup Garage, the Invest Ottawa incubator, and the League of Innovators' BOOST program. === Kubernetes validation (2019–2020) === As part of a Canada's Centre of Excellence in Next Generation Networks (CENGN) project, Gnowit validated a containerized version of its web-intelligence software on Kubernetes. Between 2019 and 2020, Gnowit participated in a project with Canada’s Centre of Excellence in Next Generation Networks (CENGN) to test and scale its platform using containerized infrastructure based on Kubernetes. The initiative focused on improving scalability and supporting the company’s transition from a monolithic software architecture to a cloud-native deployment model. == Products and services == Gnowit markets several modules for public-affairs, compliance, and market-intelligence teams. Legislative & Regulatory Monitoring (vAnalyst). vAnalyst is a monitoring platform that tracks legislative and regulatory activity across Canadian federal, provincial, and territorial jurisdictions. The system aggregates parliamentary debates, bills, committee proceedings, and regulatory publications and provides searchable alerts and reporting tools. The product monitors more than two million web sources to surface relevant items quickly. Parliamentary Live (vAnalyst). Monitors live video feeds from parliamentary sessions and committees with same-day transcripts, AI-generated summaries, witness summaries, and motion detection; municipal coverage is offered as an option. Gnowit can avail transcripts up to two weeks before official releases. These transcripts enable users to navigate and review lengthy parliamentary sittings and committee discussions through searchable text. Municipal Monitoring (vAnalyst). The platform also tracks council meetings, agendas, bylaws, and other municipal government publications from hundreds of Canadian municipalities. The platform aggregates these sources into a single searchable interface for reviewing local government decisions. Curation Edge (analyst service). Curation Edge is an add-on service in which expert analysts work and collaborate with clients to develop a tailored curation guide and deliver daily newsletters or briefs on legislation and media. These reports provide concise summaries, relevant links, and optional metadata, prioritizing key updates with additional context and analysis. The service is customizable, including branding and formatting for executive audiences, and is intended to reduce information overload, support decision-making, and streamline the synthesis and distribution of information. === Coverage and sources === Gnowit monitors sources span Canadian government materials across federal, provincial, and territorial jurisdictions Hansard transcripts (All Jurisdictions, including committees), order papers, committee transcripts, gazettes, bills, acts and regulations, consultations, regulatory-agency publications, and global news media as well as press releases and council-meeting materials from hundreds of municipalities. == Partnerships and support == Gnowit reports collaborations with Canadian academic and ecosystem partners, including: Algonquin College Carleton University McGill University University of Ottawa Université du Québec en Outaouais (UQO) Queen's University The company also participated in the accelerator program at Invest Ottawa and has received support from Canadian research and innovation programs, including: NRC Industrial Research Assistance Program (NRC-IRAP) Mitacs Ontario Centre of Innovation (OCI) (formerly OCE) Gnowit has also referenced membership in the Southern Ontario Smart Computing Innovation Platform (Government of Canada profile: FedDev Ontario – SOSCIP overview). == Technology == Gnowit develops technology intended to support timely decision-making by delivering updates from monitored web sources as they are published. The platform applies artificial intelligence (AI) and machine learning (ML) techniques to monitor, capture, clean, analyze, filter, and organize text, and to generate concise briefs. Its technical approach combines Boolean queries, shallow language processing techniques, and machine learning classifiers within a self-service interface. The company has described its longer-term development framework in relation to a belief–desire–intention (BDI) model of intelligent agents on the web. Gnowit and its founder are listed as inventors/assignees on patents concerning multi-document clustering, salient-content extraction, and sentiment analysis methods that are consistent with these features: US 9,600,470 – Method and system relating to re-labelling multi-document clusters (assignee: Whyz Technologies Ltd.). US 9,336,202 – Method and system relating to salient content extraction for information retrieval (assignee: Whyz Technologies Ltd.). CA 2,865,184 C – Method and system relating to re-labelling multi-document clusters. CA 2,865,186 C – Procédé et système concernant l'analyse de sentiment d'un contenu (sentiment analysis; French record). CA 2,865,187 C – Method and system relating to salient content extraction for information retrieval. == Research and community == In January 2025, Gnowit personnel contributed to regulatory NLP by co-authoring a peer-reviewed paper at the 1st Regulatory NLP Workshop (RegNLP 2025), co-located with COLING in Abu Dhabi. Titled Unifying Large Language Models and Knowledge Graphs for Efficient Regulatory Information Retrieval and Answer Generation, the work introduces PolicyInsight, a framework that joins a dynamic policy data model and knowledge graph with LLMs to monitor policy texts, detect changes, and support retrieval and answer generation; the author list includes Shahzad Khan (CEO, Gnowit Inc.). (ACL Anthology, aclweb.org). Similar information-retrieval technologies are widely used for competitive intelligence, policy monitoring, and media analysis. == White paper == Gnowit has published a practical guide, Automated Government Information Monitoring, which outlines how GR and regulatory teams can design a monitoring and briefing workflow and describes Gnowit's automation features and export options (PDF, email, dashboards, CSV/JSON/XML/API).
Hierarchical RBF
In computer graphics, hierarchical RBF is an interpolation method based on radial basis functions (RBFs). Hierarchical RBF interpolation has applications in treatment of results from a 3D scanner, terrain reconstruction, and the construction of shape models in 3D computer graphics (such as the Stanford bunny, a popular 3D model). This problem is informally named as "large scattered data point set interpolation." == Method == The steps of the interpolation method (in three dimensions) are as follows: Let the scattered points be presented as set P = { c i = ( x i , y i , z i ) | i = 1 N ⊂ R 3 } {\displaystyle \mathbf {P} =\{\mathbf {c} _{i}=(\mathbf {x} _{i},\mathbf {y} _{i},\mathbf {z} _{i})\vert _{i=1}^{N}\subset \mathbb {R} ^{3}\}} Let there exist a set of values of some function in scattered points H = { h i | i = 1 N ⊂ R } {\displaystyle \mathbf {H} =\{\mathbf {h} _{i}\vert _{i=1}^{N}\subset \mathbb {R} \}} Find a function f ( x ) {\displaystyle \mathbf {f} (\mathbf {x} )} that will meet the condition f ( x ) = 1 {\displaystyle \mathbf {f} (\mathbf {x} )=1} for points lying on the shape and f ( x ) ≠ 1 {\displaystyle \mathbf {f} (\mathbf {x} )\neq 1} for points not lying on the shape As J. C. Carr et al. showed, this function takes the form f ( x ) = ∑ i = 1 N λ i φ ( x , c i ) {\displaystyle \mathbf {f} (\mathbf {x} )=\sum _{i=1}^{N}\lambda _{i}\varphi (\mathbf {x} ,\mathbf {c} _{i})} where φ {\displaystyle \varphi } is a radial basis function and λ {\displaystyle \lambda } are the coefficients that are the solution of the following linear system of equations: [ φ ( c 1 , c 1 ) φ ( c 1 , c 2 ) . . . φ ( c 1 , c N ) φ ( c 2 , c 1 ) φ ( c 2 , c 2 ) . . . φ ( c 2 , c N ) . . . . . . . . . . . . φ ( c N , c 1 ) φ ( c N , c 2 ) . . . φ ( c N , c N ) ] ∗ [ λ 1 λ 2 . . . λ N ] = [ h 1 h 2 . . . h N ] {\displaystyle {\begin{bmatrix}\varphi (c_{1},c_{1})&\varphi (c_{1},c_{2})&...&\varphi (c_{1},c_{N})\\\varphi (c_{2},c_{1})&\varphi (c_{2},c_{2})&...&\varphi (c_{2},c_{N})\\...&...&...&...\\\varphi (c_{N},c_{1})&\varphi (c_{N},c_{2})&...&\varphi (c_{N},c_{N})\end{bmatrix}}{\begin{bmatrix}\lambda _{1}\\\lambda _{2}\\...\\\lambda _{N}\end{bmatrix}}={\begin{bmatrix}h_{1}\\h_{2}\\...\\h_{N}\end{bmatrix}}} For determination of surface, it is necessary to estimate the value of function f ( x ) {\displaystyle \mathbf {f} (\mathbf {x} )} in specific points x. A lack of such method is a considerable complication on the order of O ( n 2 ) {\displaystyle \mathbf {O} (\mathbf {n} ^{2})} to calculate RBF, solve system, and determine surface. == Other methods == Reduce interpolation centers ( O ( n 2 ) {\displaystyle \mathbf {O} (\mathbf {n} ^{2})} to calculate RBF and solve system, O ( m n ) {\displaystyle \mathbf {O} (\mathbf {m} \mathbf {n} )} to determine surface) Compactly support RBF ( O ( n log n ) {\displaystyle \mathbf {O} (\mathbf {n} \log {\mathbf {n} })} to calculate RBF, O ( n 1.2..1.5 ) {\displaystyle \mathbf {O} (\mathbf {n} ^{1.2..1.5})} to solve system, O ( m log n ) {\displaystyle \mathbf {O} (\mathbf {m} \log {\mathbf {n} })} to determine surface) FMM ( O ( n 2 ) {\displaystyle \mathbf {O} (\mathbf {n} ^{2})} to calculate RBF, O ( n log n ) {\displaystyle \mathbf {O} (\mathbf {n} \log {\mathbf {n} })} to solve system, O ( m + n log n ) {\displaystyle \mathbf {O} (\mathbf {m} +\mathbf {n} \log {\mathbf {n} })} to determine surface) == Hierarchical algorithm == A hierarchical algorithm allows for an acceleration of calculations due to decomposition of intricate problems on the great number of simple (see picture). In this case, hierarchical division of space contains points on elementary parts, and the system of small dimension solves for each. The calculation of surface in this case is taken to the hierarchical (on the basis of tree-structure) calculation of interpolant. A method for a 2D case is offered by Pouderoux J. et al. For a 3D case, a method is used in the tasks of 3D graphics by W. Qiang et al. and modified by Babkov V.
NexDock
NexDock is a series of lapdock devices (containing a laptop screen, keyboard, trackpad, and battery connected to a phone or other device) sold by Nex Computer LLC. The product can be used with mobile desktop environments, including Samsung DeX and the former Windows Continuum. Critical reception for the series has been mixed, with reviewers praising the concept's utility for mobile productivity while noting hardware limitations and its niche appeal. == History == The first NexDock was introduced in 2016 through a successful Indiegogo campaign. Its development coincided with interest in smartphone-powered desktop interfaces, and it was marketed as a companion for Windows 10 Mobile's Continuum feature. Subsequent models, often launched via Kickstarter, added features like higher-resolution displays, touchscreens, and convertible hinges to adapt to the growing capabilities of smartphones. == Models == === NexDock (Original, 2016) === The first model featured a 14.1-inch 1366x768 display and connected primarily via a mini HDMI port. === NexDock 2 (2019) === This model introduced a 13.3-inch 1080p IPS display and a USB-C port, improvements aimed at better supporting platforms like Samsung DeX. === NexDock Touch (2020) === A touchscreen was added to the 13.3-inch display, allowing for more direct interaction with the connected device's operating system. === NexDock 360 (2021) === This version incorporated a 360-degree hinge, allowing the device to be used in laptop, tablet, tent, or stand modes. === NexDock Wireless (2023) === Wireless display connectivity was the key feature of this model, offering a cable-free connection to compatible phones and computers. === NexDock XL (2023) === The screen size was increased to 15.6 inches. It retained the 360-degree hinge and also offered a version with wireless charging for a connected phone. == Reception == Reviews of NexDock products have been mixed, generally praising the concept while pointing out execution flaws. The devices are often lauded for their utility with Samsung DeX, turning a high-end Samsung phone into a viable portable workstation. A review of the NexDock 2 from ZDNet concluded it was a "great companion for the modern road warrior," and Digital Trends called the original a "no-brainer shell" for expanding a phone's capability. However, reviewers have consistently highlighted hardware limitations. In its review of the NexDock Touch, TechRadar stated that while it was a "compelling package for a very specific niche," the "trackpad and keyboard are a bit of a letdown and the screen could be brighter." This sentiment was echoed in other reviews, with criticism often aimed at the trackpad's performance and feel. A review of the NexDock 2 from Android Authority described the experience as being "janky at times," concluding that the device "delivers on its promise — sort of." A common point across many reviews is that the overall performance is entirely dependent on the power of the connected phone, and the experience is often best suited for light productivity tasks rather than replacing a dedicated laptop.