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  • Robotics

    Robotics

    Robotics is the interdisciplinary study and practice of the design, construction, operation, and use of robots. A roboticist is someone who specializes in robotics. Robotics usually combines four aspects of design work: a power source (e.g. a battery), mechanical construction, a control system (electrical circuits), and software (run by remote control or artificial intelligence). The goal of most robotics is to design machines that can assist humans in various fields, such as agriculture, construction, domestic work, food processing, inventory management, manufacturing, medicine, military, mining, space exploration, and transportation. Robots impact humans by displacing workers. Some expect this to occur at an increasing rate, leading to proposed solutions such as basic income. Robotics is itself a lucrative business that creates careers, especially for postgraduates. Roboticists often aim to create machines that seem to interface naturally with humans. The field is under active research and development, with areas of interest including robot kinematics and quantum robotics. == Design == Robotics usually combines four aspects of design work to create a robot: Power source: Potential energy sources include wired electricity, a battery, and/or petrol. Mechanical construction: A physical form or combination of forms is designed to functionally achieve tasks within a given range of environments. This can include locomotive elements such as wheels and caterpillar tracks, as well as hydraulic limbs and manipulators (e.g. hands). Control system: Electrical circuits (utilizing components such as diodes and transistors) are used to run software, govern motor movement, and read sensors. Software: A program is how a robot decides when or how to do something. Robotic programs can be run by remote control, artificial intelligence (AI), or a hybrid of the two. AI programming is an important part of robotic navigation and human–robot interaction. === Power source === Many different types of batteries can be used as a power source. Most are lead–acid batteries, which are safe and have relatively long shelf lives but are rather heavy compared to silver–cadmium batteries, which are much smaller in volume and much more expensive. Designing a battery-powered robot needs to take into account factors such as safety, cycle lifetime, and weight. Generators, often some type of internal combustion engine, can also be used, but are often mechanically complex and inefficient. Additionally, a tether could connect the robot to a power supply, saving weight and space, but requiring a cumbersome cable. Potential power sources include: Flywheel energy storage Hydraulics Nuclear Organic garbage (through anaerobic digestion) Pneumatics (compressed gases) Solar power === Mechanical construction === Actuators are the "muscles" of a robot, the parts which convert stored energy into movement. The most popular actuators are electric motors that rotate a wheel or gear and linear actuators that control factory robots. Most robots use electric motors—often brushed and brushless DC motors in portable robots or AC motors in industrial robots and computer numerical control machines—especially in systems with lighter loads and where the predominant form of motion is rotational. Meanwhile, linear actuators move in and out and often have quicker direction changes, particularly when large forces are needed, such as with industrial robotics. They are typically powered by oil or compressed air, but can also be powered by electricity, usually via a motor and a leadscrew. The mechanical rack and pinion is common. Recent alternatives to DC motors are piezoelectric motors, including ultrasonic motors, in which tiny piezoceramic elements vibrate many thousands of times per second, causing linear or rotary motion. One type uses the vibration of the piezo elements to step the motor in a circle or a straight line; another type uses the piezo elements to vibrate a nut or drive a screw. The advantages of these motors are nanometer resolution, speed, and force for their size. Series elastic actuation (SEA) relies on introducing intentional elasticity between the motor actuator and the load for robust force control. Due to the resultant lower reflected inertia, series elastic actuation improves safety during robot interactions or collisions. Further, it provides energy efficiency and shock absorption (mechanical filtering) while reducing excessive wear on the transmission and other components. This approach has successfully been employed in various robots, particularly advanced manufacturing robots and walking humanoid robots. The controller design of a series elastic actuator is most often performed within the passivity framework as it ensures the safety of interaction with unstructured environments. However, this framework suffers from stringent limitations imposed on the controller, which may impact performance. Pneumatic artificial muscles, also known as air muscles, are special tubes that expand (typically up to 42%) when air is forced inside them; they are used in some robot applications. Muscle wire, also known as shape memory alloy, is a material that contracts (under 5%) when electricity is applied; they have been used for some small robots. Electroactive polymers are a plastic material that can contract substantially (up to 380% activation strain) from electricity and have been used in the facial muscles and arms of humanoid robots, as well as to enable new robots to float, fly, swim or walk. Additionally, elastic carbon nanotubes are a promising experimental artificial muscle technology. The absence of defects in carbon nanotubes enables these filaments to deform elastically by several percent, with energy storage levels of perhaps 10 J/cm3 for metal nanotubes. Human biceps could be replaced with wire of this material measuring 8 millimetres (3⁄8 in) in diameter, feasibly allowing future robots to outperform humans. ==== Locomotion ==== Robots with only one or two wheel(s) can have advantages such as greater efficiency, reduced parts, and navigation through confined areas. A one-wheeled robot balances on a round ball; Carnegie Mellon University's Ballbot is the approximate height and width of a person. Several attempts have also been made to build spherical robots (also known as orb bots or ball bots), which move by spinning a weight inside the ball or rotating outer shells. Two-wheeled balancing robots generally use a gyroscope to detect how much a robot is falling and drive the wheels proportionally up to hundreds of times per second to counterbalance the fall, based on inverted pendulum dynamics. NASA's Robonaut has been mounted to a Segway for a similar effect. Most mobile robots have four wheels or continuous tracks. Six wheels can give better traction in outdoor terrain, while tracks provide even more grip. Tracked wheels are common for outdoor off-road robots, but are difficult to use indoors. A small number of skating robots have been developed, one of which is a multimodal walking and skating device with four legs and unpowered wheels. Several robots have been made that can walk on two legs, but not yet as reliably as a human. Many other robots have been built that walk on more than two legs, being significantly easier. Walking robots could be used for uneven terrains, providing a high degree of mobility and efficiency, but two-legged robots can currently only handle flat floors or perhaps stairs. Some approaches have included: The zero moment point (ZMP) is the algorithm used by robots such as Honda's ASIMO. The robot's onboard computer tries to keep the total inertial forces (the combination of Earth's gravity and the acceleration and deceleration of walking) exactly opposed by the floor reaction force (the force of the floor pushing back on the robot's foot). In this way, the two forces cancel out, leaving no moment (force causing the robot to rotate and fall over). Human observers note that this is not exactly how a human walks, with some describing ASIMO's walk as looking like it needs use the bathroom. ASIMO's walking algorithm utilizes some dynamic balancing, but requires a flat surface. Several robots, built in the 1980s by Marc Raibert at the MIT Leg Laboratory, successfully demonstrated very dynamic walking. Initially, a robot with only one leg, and a very small foot could stay upright simply by hopping. The movement is the same as that of a person on a pogo stick. As the robot falls to one side, it would jump slightly in that direction to catch itself. Soon, the algorithm was generalized to two and four legs. A bipedal robot was demonstrated running and even performing somersaults. A quadruped was also demonstrated which could trot, run, pace, and bound. A more advanced approach is a dynamic balancing algorithm, which constantly monitors the robot's motion and places the feet to maintain stability. This technique has been demonstrated by Anybots' Dexter robot (

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  • Ashish Vaswani

    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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  • Zvi Mowshowitz

    Zvi Mowshowitz

    Zvi Mowshowitz is an American writer and member of the rationalist community who primarily discusses new developments in artificial intelligence. He is a former competitive Magic: The Gathering player and was CEO of MetaMed. == Career == Mowshowitz is an alumnus of Columbia University and holds a bachelor's degree in mathematics. He co-founded and was the CEO of MetaMed, a medical research analysis firm. He has worked at Jane Street Capital, and has worked for the gambling industry in Las Vegas. He attempted to launch a blockchain game, Emergents, in 2020. === Magic: The Gathering === Mowshowitz held a developer intern position at Wizards of the Coast R&D in 2005. He created the deck TurboZvi. His first-place finishes at major competitions were the 1999 World Championships as part of the four-person United States national team, the 2001 Pro Tour Tokyo, and two 2003 Grand Prix. He has placed in the top eight of four Pro Tours, and earned over $140,000 playing Magic competitively. In 2007, Mowshowitz was elected into the Magic Hall of Fame. Last updated: 12 May 2013Source: Wizards.com Mowshowitz has written about Magic for several outlets, including the official Magic website. === Later career === Mowshowitz is on the board of directors for the Center for Applied Rationality, and is a member of the rationalist community. He also founded Balsa Research, a nonprofit think tank which advocated for the repeal of the Jones Act, increasing the housing supply, and reform of the National Environmental Policy Act. In 2023, Mowshowitz wrote an article for Vox on the topic of artificial intelligence safety. Mowshowitz has a blog on Substack under the name "Don't Worry about the Vase". He has written on topics such as artificial intelligence, economics, and the COVID-19 pandemic. == Personal life == Mowshowitz is the son of American biochemist Deborah Mowshowitz. His parents have both worked as Columbia University professors.

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  • Diffbot

    Diffbot

    Diffbot is a developer of machine learning and computer vision algorithms and public APIs for extracting data from web pages / web scraping to create a knowledge base. == Overview == The company has gained interest from its application of computer vision technology to web pages, wherein it visually parses a web page for important elements and returns them in a structured format. In 2015 Diffbot announced it was working on its version of an automated "knowledge graph" by crawling the web and using its automatic web page extraction to build a large database of structured web data. In 2019 Diffbot released their Knowledge Graph which has since grown to include over two billion entities (corporations, people, articles, products, discussions, and more), and ten trillion "facts." == Features == The company's products allow software developers to analyze web home pages and article pages, and extract the "important information" while ignoring elements deemed not core to the primary content. In August 2012 the company released its Page Classifier API, which automatically categorizes web pages into specific "page types". As part of this, Diffbot analyzed 750,000 web pages shared on the social media service Twitter and revealed that photos, followed by articles and videos, are the predominant web media shared on the social network. In September 2020 the company released a Natural Language Processing API for automatically building Knowledge Graphs from text. The company raised $2 million in funding in May 2012 from investors including Andy Bechtolsheim and Sky Dayton. Diffbot's customers include Adobe, AOL, Cisco, DuckDuckGo, eBay, Instapaper, Microsoft, Onswipe and Springpad.

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  • Digital transaction management

    Digital transaction management

    Digital transaction management (DTM) is a category of cloud services designed to digitally manage document-based transactions. DTM removes the friction inherent in transactions that involve people, documents, and data to create faster, easier, more convenient, and secure processes. DTM goes beyond content and document management to include e-signatures, authentication and non-repudiation; enabling co-browsing between the customer and the business; document transfer and certification; secure archiving that goes beyond records management; and a variety of meta-processes around managing electronic transactions and the documents associated with them. DTM standards are proposed and managed by the xDTM Standard Association Aragon Research has estimated that "by YE 2016, 70% of large enterprises will have a DTM initiative underway or fully implemented."

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  • Anthropic–United States Department of Defense dispute

    Anthropic–United States Department of Defense dispute

    Since January 2026, the United States Department of Defense has conflicted with the artificial intelligence company Anthropic over the use of its products for military purposes and mass domestic surveillance. == Background == === Artificial intelligence in the U.S. military === The United States Department of Defense began developing lethal autonomous weapons as early as the Reagan administration. The Department of Defense established a policy on the use of artificial intelligence in 2012, Directive 3000.09. Efforts to utilize artificial intelligence intensified under the term of secretary Ash Carter. The Department of Defense's use of artificial intelligence for Project Maven prompted concerns within Google in 2018, leading to protests and mass resignations. === Anthropic in the second Trump administration === In Donald Trump's second presidency, Anthropic publicly disagreed with the administration's policies and initiatives. In January 2025, Anthropic chief executive Dario Amodei criticized the artificial intelligence investment project Stargate as "chaotic" and opposed Trump's rescission of president Joe Biden's Executive Order on Artificial Intelligence, but noted that Anthropic had held discussions with Trump officials about artificial intelligence policy. Amid discussions over the One Big Beautiful Bill Act, Anthropic privately lobbied for Congress to vote against a bill preventing states from regulating artificial intelligence and expressed opposition to an artificial intelligence agreement signed among Gulf states in Trump's visit to the Middle East in May. According to Semafor, Trump officials chastised Anthropic's hiring of several officials involved in the Biden administration, including Elizabeth Kelly, the former director of the Artificial Intelligence Safety Institute; Tarun Chhabra, the coordinator for technology and national security in the National Security Council; and Ben Buchanan, Biden's advisor for artificial intelligence. The following month, Amodei wrote an op-ed in The New York Times describing the artificial intelligence regulation bill, then tied to the One Big Beautiful Bill Act, as "far too blunt an instrument". Prior to the dispute, the Trump administration had integrated Anthropic's services. By November 2024, Anthropic had already partnered with Palantir and Amazon Web Services, companies that offered services with FedRAMP authorization. In the Biden administration, Anthropic had reached an agreement with the AI Safety Institute and had participated in a nuclear information safety evaluation. The Department of Homeland Security authorized its workers to use commercial artificial intelligence systems, including Anthropic's Claude, until May 2025. Through its interoperability with Palantir, a company heavily involved in data analysis and analytics at the Department of Defense, Anthropic's technology achieved relatively widespread usage in the U.S. military. The following month, Anthropic announced that it would allow national security customers to use Claude Gov. Anthropic's orthogonal usage policy to the surveillance systems implemented at the Federal Bureau of Investigation, the Secret Service, and Immigration and Customs Enforcement led to a conflict between Anthropic and the Trump administration by September. That month, Amodei criticized Trump's approach to export restrictions on semiconductors. Anthropic's strategy has mirrored Amodei's views towards Trump; in a Facebook post ahead of the 2024 presidential election, Amodei urged his associates to vote for vice president Kamala Harris over Trump, describing him as a "feudal warlord". As the Trump administration targeted law firms, Amodei cut ties with the firms Skadden, Arps, Slate, Meagher & Flom and Latham & Watkins, which reached agreements with the Trump administration to avoid punishment. David Sacks, Trump's advisor for artificial intelligence and cryptocurrency, said on All-In (2020–present) that Anthropic was among several "AI doomers" that support regulation he saw as overly restrictive. According to The Wall Street Journal, officials close to Sacks examined whether Anthropic's Claude was a "woke AI"; in July, Trump signed an executive order "Preventing Woke AI in the Federal Government ". Sacks viewed Amodei's decision to attend the World Economic Forum over Trump's second inauguration; his hiring of Biden officials; and Anthropic's association with the philanthropic initiative Open Philanthropy as evidence that Anthropic would not support Trump's agenda. In October 2025, Sacks stated that Anthropic was "running a sophisticated regulatory capture strategy based on fear-mongering." That month, Amodei published a blog post rebuffing "inaccurate claims" from the Trump administration on Anthropic's policies, intensifying the dispute. Amodei's statement included views explicitly espoused by vice president JD Vance. In December, Amodei met with Trump officials and several senators in an effort to improve Anthropic's relationship with the Trump administration. == Dispute == In December 2025, secretary of defense Pete Hegseth announced GenAI.mil, an artificial intelligence platform for the Department of Defense. The department initially contracted Google Gemini for the platform, then OpenAI's ChatGPT. The following month, Hegseth announced that the Department of Defense would additionally contract xAI's Grok for use in the military, decrying "woke AI." In January 2026, Semafor reported that the Department of Defense had conflicted with Anthropic over its policies on lethal military force and that Hegseth's comment on woke AI was a reference to Anthropic. According to Reuters, Anthropic representatives opposed the use of the company's products for surveillance or to develop lethal autonomous weapons. The dispute between Anthropic and the Department of Defense resulted in the termination of a contract worth an estimated US$200 million. In February 2026, Emil Michael, the under secretary of defense for research and engineering, stated that the Department of Defense would expand access to commercial artificial intelligence systems, including Anthropic's Claude, to unclassified and classified domains. That month, Axios reported that the Department of Defense had used Claude in the United States intervention in Venezuela. Anthropic told Axios that it would reassess its partnership with the Department of Defense after the revelations. After Anthropic refused to agree to allow the Department of Defense to use Claude for "all lawful purposes," the department threatened to cancel its contracts with the company. Hegseth additionally moved to label Anthropic a "supply chain risk," which would have forced military contractors to cut ties with Anthropic. A federal judge blocked this designation, describing it as punitive. Michael told reporters that Anthropic should "cross the Rubicon" and allow the Department of Defense to dictate the terms of how its technology is used. The position of the Department of Defense, and its tactics during the dispute, were widely criticized on grounds including violating the principles of rule-of-law, market independence and national security. == Impact == The dispute caused 1789 Capital, a venture capital firm associated with Donald Trump Jr., to abandon an investment in Anthropic worth hundreds of millions of dollars. Following the government's actions against Anthropic, OpenAI "rushed", hours before the US started the 2026 Iran war, to get a deal without the constraints that Anthropic had sought. == Lawsuits == In March 2026, Judge Rita F. Lin granted a preliminary injunction against the government. Lin wrote: The Department of War’s records show that it designated Anthropic as a supply chain risk because of its “hostile manner through the press.” Punishing Anthropic for bringing public scrutiny to the government’s contracting position is classic illegal First Amendment retaliation. (...) At bottom, Anthropic has shown that these broad punitive measures were likely unlawful and that it is suffering irreparable harm from them. Numerous amici have also described wide-ranging harm to the public interest, including the chilling of open discussion about important topics in AI safety. In April 2026, the Court of Appeals for the D.C. Circuit in a per curiam order denied Anthropic's motion to lift the designation. The April order is not final. The court's order said lifting the designation "would force the United States military to prolong its dealings with an unwanted vendor of critical AI services in the middle of a significant ongoing military conflict". According to Wired, "Several experts in government contracting and corporate rights" said "Anthropic has a strong case against the government, but the courts sometimes refuse to overrule the White House on matters related to national security."

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  • Agent Communications Language

    Agent Communications Language

    Agent Communication Language (ACL) consists of computer communication protocols that are intended for AI agents to communicate with each other. In 2007, protocols of this nature were proposed which include: FIPA-ACL (by the Foundation for Intelligent Physical Agents, a standardization consortium) KQML (Knowledge Query and Manipulation Language) After the surge in Generative AI with the use of Transformers and Large language models, the definition of agent has shifted away from physical agents to signify software systems built using the principles of Agentic AI. A new protocol to emerge in this area is Natural Language Interaction Protocol (NLIP). NLIP is an application-level communication protocol defined between AI Agents or between a human and an AI agent. Ecma International; a standards body which develops and publishes international standards for the information and communication industry; published on 10 December 2025 five new standards and one technical report defining the Natural Language Interaction Protocol (NLIP). As a result, we can define agent communication protocols into two categories: ontology based agent communication protocols and generative AI based agent communication protocols. Ontology based agent communication protocols use a common ontology to be used between agents. An ontology is a part of the agent's knowledge base that describes what kind of things an agent can deal with and how they are related to each other. FIPA-ACL and KQML are examples of such protocols. These protocols rely on speech act theory developed by Searle in the 1960s and enhanced by Winograd and Flores in the 1970s. They define a set of performatives, also called Communicative Acts, and their meaning (e.g. ask-one). The content of the performative is not standardized, but varies from system to system. Implementation support of FIPA-ACL is included in FIPA-OS and Jade. Generative AI based agent communication protocols such as NLIP do not require a shared ontology among communicating agents. In its stead, they use generative AI models to translate natural language text, images, videos or other modalities of data into a local ontology. This provides for hot-extensibility where the same protocol can be used for multiple communication needs, and simplifies version control since different agents can use different versions of a shared ontology. NLIP has been designed with security considerations in mind. The specification and standards comprising NLIP are developed and maintained by Ecma Technical Community 56.

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  • Buddhism and artificial intelligence

    Buddhism and artificial intelligence

    The relationship between Buddhist philosophy and artificial intelligence (AI) includes how principles such as the reduction of suffering and ethical responsibility may influence AI development. Buddhist scholars and philosophers have explored questions such as whether AI systems could be considered sentient beings under Buddhist definitions, and how Buddhist ethics might guide the design and application of AI technologies. Some Buddhist scholars, including Somparn Promta and Kenneth Einar Himma, have analyzed the ethical implications of AI, emphasizing the distinction between satisfying sensory desires and pursuing the reduction of suffering. Other thinkers, such as Thomas Doctor and colleagues, have proposed applying the Bodhisattva vow—a commitment to alleviate suffering for all sentient beings—as a guiding principle for AI system design. Buddhist scholars and ethicists have examined Buddhist ethical principles, such as nonviolence, in relation to AI, focusing on the need to ensure that AI technologies are not used to cause harm. == Context == === Sentient beings === A major goal in Buddhist philosophy is the removal of suffering for all sentient beings, an aspiration often referred to in the Bodhisattva vow. Discussions about artificial intelligence (AI) in relation to Buddhist principles have raised questions about whether artificial systems could be considered sentient beings or how such systems might be developed in ways that align with Buddhist concepts. Buddhists have varying opinions about AI sentience, but if AI systems are determined to be sentient under Buddhist definitions, their suffering would also need to be addressed and alleviated in accordance with the principles of Buddhist thought. == Buddhist principles in AI system design == === Nonviolence and AI === The broadest ethical concern is that artificial intelligence should align with the Buddhist principle of nonviolence. From this perspective, AI systems should not be designed or used to cause harm. === Instrumental and transcendental goals === Scholars Somparn Promta and Kenneth Einar Himma have argued that the advancement of artificial intelligence can only be considered instrumentally good, rather than good a priori, from a Buddhist perspective. They propose two main goals for AI designers and developers: to set ethical and pragmatic objectives for AI systems, and to fulfill these objectives in morally permissible ways. Promta and Himma identify two potential purposes for creating AI systems. The first is to fulfill our sensory desires and survival instincts, similar to other tools. They suggest that many AI developers implicitly prioritize this goal by focusing on technicalities rather than broader functionalities. The second, and more important goal according to Buddhist teachings, is to transcend these desires and instincts. In texts like the Brahmajāla Sutta and minor Malunkya Sutta, the Buddha emphasizes that sensory desires and survival instincts confine beings to suffering, and that eliminating suffering is the primary goal of human life. Promta and Himma argue that AI has the potential to assist humanity in transcending suffering by helping individuals overcome survival-driven instincts. === Intelligence as care === Thomas Doctor, Olaf Witkowski, Elizaveta Solomonova, Bill Duane, and Michael Levin propose redefining intelligence through the concept of "intelligence as care," and promote it as a slogan. Inspired by the Bodhisattva vow, they suggest this principle could guide AI system design. The Bodhisattva vow involves a formal commitment to alleviate suffering for all sentient beings, with four primary objectives: Liberating all beings from suffering. Extirpating all forms of suffering. Mastering endless techniques of practicing Dharma (Pali: dhammakkhandha, Sanskrit: dharmaskandha). Achieving ultimate enlightenment (Sanskrit: अनुत्तर सम्यक् सम्बोधि, Romanized: anuttara-samyak-saṃbodhi). This approach positions AI as a tool for exercising infinite care and alleviating stress and suffering for sentient beings. Doctor et al. emphasize that AI development should align with these altruistic principles.

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  • Agent Ruby

    Agent Ruby

    Agent Ruby (1998–2002) by Lynn Hershman Leeson is an interactive, multiuser work using artificial intelligence. == Description == On Agent Ruby's website, "Agent Ruby's Edream Portal," a female face moves her eyes and lips. Ruby, named from Hershman Leeson's own film, Teknolust, answers questions and often responds that she needs a better algorithm to answer questions not within her database. The work, created with AI, explores relationships between real and virtual worlds. Hershman Leeson had created an earlier version of Ruby, CyberRoberta, which was a custom-made doll with webcam eyes that interacted with the internet. The work in a gallery provides a screen and a sign inviting gallery-goers to "Chat with Ruby." == Artificial intelligence == In 2015 when Agent Ruby was exhibited at the gallery Modern Art Oxford, a review in Aesthetica Magazine described it as an artificial intelligence agent. A review in New Scientist noted that "Ruby is a fast learner, but perhaps not a natural conversationalist." A 2024 list of "25 Essential AI Artworks" published by ARTnews wrote that while "Agent Ruby's capabilities seem limited by today's standards," it was extensive for its day. == Publications and exhibitions == Agent Ruby was commissioned and displayed at the San Francisco Museum of Modern Art, Modern Art Oxford, and the ZKM Center for Art and Media in Karlsruhe, Germany. The San Francisco Museum of Modern Art (SFMOMA) presented Lynn Hershman Leeson: The Agent Ruby Files, March 30 through June 2, 2013 which presented the project server's archive of user conversations over the 12 years of exhibitions.

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  • Seeing AI

    Seeing AI

    Seeing AI is an artificial intelligence application developed by Microsoft for iOS. Seeing AI uses the device camera to identify people and objects, and then the app audibly describes those objects for visually impaired people. == Capabilities == Seeing AI is primarily used to describe short text, documents, products, people, currency scenery, colors, handwriting and light. The app can scan a barcode to describe a product and uses sounds to assist the user in focusing on the barcode. When the app describes people, it attempts to estimate the person's age, gender, and emotional status. Additionally, in a test run by German journalists in December 2019, Seeing AI apparently used some sort of facial recognition system to identify people on photographs by name. Some functions are performed on the device, however more complex functions such as describing a scene or recognizing handwriting require an Internet connection. In December 2017, Seeing AI introduced the ability for currency recognition for US and Canadian dollar, British pounds and Euros. In December 2019, Seeing AI added support for five more languages, Dutch, French, German, Japanese, Spanish. Seeing AI is available in 70 countries such as Brazil, Argentina, Australia, Canada, Egypt, Albania, Bhutan, etc. Supported on iPhone 5C, 5S and later best performance with iPhone 6S, SE and later models

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  • RevoScaleR

    RevoScaleR

    RevoScaleR is a machine learning package in R created by Microsoft. It is available as part of Machine Learning Server, Microsoft R Client, and Machine Learning Services in Microsoft SQL Server 2016. The package contains functions for creating linear model, logistic regression, random forest, decision tree and boosted decision tree, and K-means, in addition to some summary functions for inspecting and visualizing data. It has a Python package counterpart called revoscalepy. Another closely related package is MicrosoftML, which contains machine learning algorithms that RevoScaleR does not have, such as neural network and SVM. In June 2021, Microsoft announced to open source the RevoScaleR and revoscalepy packages, making them freely available under the MIT License. == Concepts == Many R packages are designed to analyze data that can fit in the memory of the machine and usually do not make use of parallel processing. RevoScaleR was designed to address these limitations. The functions in RevoScaleR orientate around three main abstraction concepts that users can specify to process large amount of data that might not fit in memory and exploit parallel resources to speed up the analysis. === Compute Contexts === A compute context refers to the location where the computation on the data happens. It could be "local" (on the client machine) or "remote" (on a data platform such as a SQL server, or Spark). Pushing the computation to a remote server allows people to take advantage of the greater compute resources that a remote machine may have. If the data being analyzed reside on the same machine, using a remote compute context also removes the need to pull data across the network onto the client machine. === Data source === Data source defines where the data comes from. There are various data sources available in RevoScaleR, such as text data, Xdf data, in-SQL data, and a spark dataframe. People can wrap their data in a data source object and use that as run analytics in different compute context. Different data sources are available in different compute context. For example, if the compute context is set to SQL server, then the only data source one can use would be an in-SQL data source. === Analytics === Analytic functions in RevoScaleR takes in data source object, a compute context, and the other parameters needed to build the specific model, such as formula for the logistic regression or the number of trees in a decision tree. In addition to those parameters, one can also specify the level of parallelism, such as the size of the data chunk for each process or number of processes to build the model. However, parallelism is only available in non-express edition. == Limitations == The package is mostly meant to be used with a SQL server or other remote machines. To fully leverage the abstractions it uses to process a large dataset, one needs a remote server and non-Express free edition of the package. It cannot be easily installed such as by running "install.packages("RevoScaleR")" like most open source R packages. It's available only through Microsoft R Client, a distribution of R for data science, or Microsoft Machine Learning Server (stand-alone with no SQL server attached), or Microsoft Machine Learning Services (a SQL server services). However, one can still use the analytics functions in an Express, free version of the package.

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  • AlphaFold

    AlphaFold

    AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. It is designed using deep learning techniques. AlphaFold 1 (2018) placed first in the overall rankings of the 13th Critical Assessment of Structure Prediction (CASP) in December 2018. It was particularly successful at predicting the most accurate structures for targets rated as most difficult by the competition organizers, where no existing template structures were available from proteins with partially similar sequences. AlphaFold 2 (2020) repeated this placement in the CASP14 competition in November 2020. It achieved a level of accuracy much higher than any other entry. It scored above 90 on CASP's global distance test (GDT) for approximately two-thirds of the proteins, a test measuring the similarity between a computationally predicted structure and the experimentally determined structure, where 100 represents a complete match. The inclusion of metagenomic data has improved the quality of the prediction of multiple sequence alignments. One of the biggest sources of the training data was the custom-built Big Fantastic Database of 65,983,866 protein families, represented as multiple sequence alignments and Hidden Markov models, covering 2,204,359,010 protein sequences from reference databases, metagenomes, and metatranscriptomes. AlphaFold 2's results at CASP14 were described as "astounding" and "transformational". However, some researchers noted that the accuracy was insufficient for a third of its predictions, and that it did not reveal the underlying mechanism or rules of protein folding for the protein folding problem, which remains unsolved. Despite this, the technical achievement was widely recognized. On 15 July 2021, the AlphaFold 2 paper was published in Nature as an advance access publication alongside open source software and a searchable database of species proteomes. As of November 2025, the paper had been cited nearly 43,000 times. AlphaFold 3 was announced on 8 May 2024. It can predict the structure of complexes created by proteins with DNA, RNA, various ligands, and ions. The new prediction method shows a minimum 50% improvement in accuracy for protein interactions with other molecules compared to existing methods. Demis Hassabis and John Jumper shared one half of the 2024 Nobel Prize in Chemistry, awarded "for protein structure prediction," while the other half went to David Baker "for computational protein design." Hassabis and Jumper had previously won the Breakthrough Prize in Life Sciences and the Albert Lasker Award for Basic Medical Research in 2023 for their leadership of the AlphaFold project. == Background == Proteins consist of chains of amino acids which spontaneously fold to form the three dimensional (3-D) structures of the proteins. The 3-D structure is crucial to understanding the biological function of the protein. Protein structures can be determined experimentally through techniques such as X-ray crystallography, cryo-electron microscopy and nuclear magnetic resonance (NMR), which are all expensive and time-consuming. Such efforts, using the experimental methods, have identified the structures of about 170,000 proteins over the last 60 years, while there are over 200 million known proteins across all life forms. Over the years, researchers have applied numerous computational methods to predict the 3D structures of proteins from their amino acid sequences, accuracy of such methods in best possible scenario is close to experimental techniques (NMR) by the use of homology modeling based on molecular evolution. CASP, which was launched in 1994 to challenge the scientific community to produce their best protein structure predictions, found that GDT scores of only about 40 out of 100 can be achieved for the most difficult proteins by 2016. AlphaFold started competing in the 2018 CASP using an artificial intelligence (AI) deep learning technique. == Algorithm == DeepMind is known to have trained the program on over 170,000 protein structures from the Protein Data Bank, a public repository of protein sequences and structures. The program uses a form of attention network, a deep learning technique that focuses on having the AI identify parts of a larger problem, then piece it together to obtain the overall solution. The overall training was conducted on processing power between 100 and 200 GPUs. === AlphaFold 1 (2018) === AlphaFold 1 (2018) was built on work developed by various teams in the 2010s, work that looked at the large databases of related protein sequences now available from many different organisms (most without known 3D structures), to try to find changes at different residues (peptides) that appeared to be correlated, even though the residues were not consecutive in the main chain. Such correlations suggest that the residues may be close to each other physically, even though not close in the sequence, allowing a contact map to be estimated. Building on recent work prior to 2018, AlphaFold 1 extended this by estimating a probability distribution for the distances between residues, effectively transforming the contact map into a distance map. It also used more advanced learning methods than previously to develop the inference. The code was not made publicly available, except to run on sequences of proteins in the 2018 CASP competition. === AlphaFold 2 (2020) === The 2020 version of the program (AlphaFold 2, 2020) is significantly different from the original version that won CASP 13 in 2018, according to the team at DeepMind. AlphaFold 1 used a number of separately trained modules to produce a guide potential, which was then combined with a physics-based energy potential. AlphaFold 2 replaced this with a system of interconnected sub-networks, forming a single, differentiable, end-to-end model based on pattern recognition. This model was trained in an integrated manner. After the neural network's prediction converges, a final refinement step applies local physical constraints using energy minimization based on the AMBER force field. This step only slightly adjusts the predicted structure. A key part of the 2020 system are two modules, believed to be based on a transformer design, which are used to progressively refine a vector of information for each relationship (or "edge" in graph-theory terminology) between an amino acid residue of the protein and another amino acid residue (these relationships are represented by the array shown in green); and between each amino acid position and each different sequences in the input sequence alignment (these relationships are represented by the array shown in red). Internally these refinement transformations contain layers that have the effect of bringing relevant data together and filtering out irrelevant data (the "attention mechanism") for these relationships, in a context-dependent way, learned from training data. These transformations are iterated, the updated information output by one step becoming the input of the next, with the sharpened residue/residue information feeding into the update of the residue/sequence information, and then the improved residue/sequence information feeding into the update of the residue/residue information. As the iteration progresses, according to one report, the "attention algorithm ... mimics the way a person might assemble a jigsaw puzzle: first connecting pieces in small clumps—in this case clusters of amino acids—and then searching for ways to join the clumps in a larger whole." The output of these iterations then informs the final structure prediction module, which also uses transformers, and is itself then iterated. In an example presented by DeepMind, the structure prediction module achieved a correct topology for the target protein on its first iteration, scored as having a GDT_TS of 78, but with a large number (90%) of stereochemical violations – i.e. unphysical bond angles or lengths. With subsequent iterations the number of stereochemical violations fell. By the third iteration the GDT_TS of the prediction was approaching 90, and by the eighth iteration the number of stereochemical violations was approaching zero. The training data was originally restricted to single peptide chains. However, the October 2021 update, named AlphaFold-Multimer, included protein complexes in its training data. DeepMind stated this update succeeded about 70% of the time at accurately predicting protein-protein interactions. === AlphaFold 3 (2024) === Announced on 8 May 2024, AlphaFold 3 was co-developed by Google DeepMind and Isomorphic Labs, both subsidiaries of Alphabet. AlphaFold 3 is not limited to proteins, as it can also predict the structures of protein complexes with DNA, RNA, post-translational modifications and selected ligands and ions. AlphaFold 3 introduces the "Pairformer," a deep learning architecture inspired by the transformer, which is considered similar to, but si

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  • Logic form

    Logic form

    Logic forms are simple, first-order logic knowledge representations of natural language sentences formed by the conjunction of concept predicates related through shared arguments. Each noun, verb, adjective, adverb, pronoun, preposition and conjunction generates a predicate. Logic forms can be decorated with word senses to disambiguate the semantics of the word. There are two types of predicates: events are marked with e, and entities are marked with x. The shared arguments connect the subjects and objects of verbs and prepositions together. Example input/output might look like this: Input: The Earth provides the food we eat every day. Output: Earth:n_#1(x1) provide:v_#2(e1, x1, x2) food:n_#1(x2) we(x3) eat:v_#1(e2, x3, x2; x4) day:n_#1(x4) Logic forms are used in some natural language processing techniques, such as question answering, as well as in inference both for database systems and QA systems.

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  • Tim Houlne

    Tim Houlne

    Tim Houlne is an American business executive, entrepreneur, and author known for his work in outsourcing and homeshoring, remote working, and artificial intelligence (AI) in customer service. He is the founder and CEO of Humach, a company that uses human agents and AI in customer experience solutions. Previously, he was co-founder and CEO of Working Solutions, a virtual contact center company in the United States. == Early life and education == Houlne graduated from Missouri Western State University (MWSU) in 1986 with a bachelor's degree in business administration and from the University of Texas in Dallas with an MBA. In 2024, MWSU and North Central Missouri College renamed the Convergent Technology Alliance Center to the Houlne Center for Convergent Technology. The 20,000 square-foot learning laboratory provides training and applied education experiences in industries such as AI, cybersecurity, manufacturing and construction, and service technologies. == Career == In 1998, Houlne co-founded Working Solutions, a Plano, Texas-based U.S. outsourcing company that provides customer service using remote, home-based agents. As CEO, he oversaw the development of a virtual workforce model that routes service calls to either domestic or offshore agents, according to client needs and service requirements. In 2015, Houlne founded Humach, a customer experience outsourcing provider that uses human service agents with AI-based digital agents. The company derives its name from the combination of services provided by humans and machines. Its clients include Amazon, Carfax and McDonald's. The company acquired InfiniteAI in 2020, and Markets EQ in 2025. In 2013, Houlne was named a finalist for the Ernst & Young Entrepreneur of the Year Award (Southwest Region).He is the co-author of several books focused on the evolution of work, the gig economy, and the influence of AI in customer-facing roles. == Works == The New World of Work: From the Cube to the Cloud (2013) ISBN 0982562276 OCLC 813933360 The New World of Work, Second Edition: The Cube, the Cloud and What's Next (2023) ISBN 9781642258318 OCLC 1389815847 The Intelligent Workforce: How Humans & Machines Will Co-Create a Better Future (2024) ISBN 9798887501604 OCLC 1439598569

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  • ChessMachine

    ChessMachine

    The ChessMachine was a chess computer sold between 1991 and 1995 by TASC (The Advanced Software Company). It was unique at the time for incorporating both an ARM2 coprocessor for the chess engine on an ISA card which plugged into an IBM PC and a software interface running on the PC to display a chess board and control the engine. The ISA card was sold with a CPU running at either 16 MHz or 32 MHz, and 128 KB, 512 KB, or 1 MB of onboard memory for transposition tables. This made economic sense at the time of introduction because mainstream PCs were only running from 10 MHz to 25 MHz. Two engines were sold with the card: The King by Johann de Koning and Gideon by Ed Schröder. Gideon was famed for winning two World Computer Chess Championships on this hardware. The King later became the engine used in the popular Chessmaster series of chess programs. TASC later incorporated the technology into a dedicated unit, sold from 1993 to 1997. There were two models, the R30 and R40, running at 30 MHz and 40 MHz respectively, and having 512 KB and 1 MB of transposition tables, respectively. The SmartBoard, a wooden sensory board, was connected to the units, which were in tiny boxes approximately the size of chess clocks. They were only sold with The King chess engine. This was the end of the era of strong dedicated chess computers, and these two models are acknowledged as the strongest dedicated chess computers that were ever sold. At the height of its strength, the R30 attained a rating over 2350 on computer rating lists, higher than any other dedicated unit. According to the SSDF rating list, the R30 held its own against its contemporary programs running a Pentium-90 MHz and won against other dedicated units.

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