AI Art Or Not Game

AI Art Or Not Game — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Abdul Majid Bhurgri Institute of Language Engineering

Abdul Majid Bhurgri Institute of Language Engineering (Sindhi: عبدالماجد ڀرڳڙي انسٽيٽيوٽ آف لئنگئيج انجنيئرنگ) is an autonomous body under the administrative control of the Culture, Tourism and Antiquities Department, Government of Sindh established for bringing Sindhi language at par with national and international languages in all computational process and Natural language processing. == Establishment == In recognition to services of Abdul-Majid Bhurgri, who is the founder of Sindhi computing, Government of Sindh has established the institute after his name. The institute was primarily initiated on the concept given by a language engineer and linguist Amar Fayaz Buriro in briefing to the Minister, Culture, Tourism and Antiquities, Government of Sindh, Syed Sardar Ali Shah on 21 February 2017 on celebration of International Mother Language Day in Sindhi Language Authority, Hyderabad, Sindh. After the presentation and concept given by Amar Fayaz Buriro, the minister Syed Sardar Ali Shah had announced the Institute. Then, Government of Sindh added the development scheme in the Budget of fiscal year 2017-2018. == Projects == The Institute has developed several projects aimed at advancing the Sindhi language and promoting linguistic research. Notable initiatives include the AMBILE Hamiz Ali Sindhi Optical character recognition, which allows for the accurate digitization of Sindhi text, and the ongoing Sindhi WordNet System, a project to build a comprehensive lexical database for Natural language processing. The institute has also created the Font, which integrates symbols from the Indus script, Khudabadi script, and modern Perso-Arabic Script Code for Information Interchange into a single resource for researchers]. Additionally, institute has developed online converter tools that automatically transliterate between the Arabic-Perso script and Devanagari script, improving linguistic accessibility. Another key project is Bhittaipedia, a digital platform dedicated to the preservation and dissemination of the poetry of Shah Abdul Latif Bhittai, one of Sindh's most renowned poet. == Location == The institute is established behind Sindh Museum and Sindhi Language Authority, N-5 National Highway, Qasimabad, Hyderabad, Sindh.
Read more →
PauseAI

PauseAI is a global political movement founded in the Netherlands with the stated aim of achieving global coordination to stop the development of more powerful general artificial intelligence systems, at least until it is known how to build them safely, and keep them under democratic control. The movement was established in Utrecht in May 2023 by software entrepreneur Joep Meindertsma. == Proposal == PauseAI's stated goal is to "implement a temporary pause on the training of the most powerful general AI systems". Their website lists some proposed steps to achieve this goal: Set up an international AI safety agency, similar to the IAEA. Only allow training of general AI systems if their safety can be guaranteed. Only allow deployment of models after no dangerous capabilities are present. == Background == During the late 2010s and early 2020s, a rapid improvement in the capabilities of artificial intelligence models known as the AI boom was underway, which included the release of large language model GPT-3, its more powerful successor GPT-4, and image generation models Midjourney and DALL-E. This led to an increased concern about the risks of advanced AI, causing the Future of Life Institute to release an open letter calling for "all AI labs to immediately pause for at least six months the training of AI systems more powerful than GPT-4". The letter was signed by thousands of AI researchers and industry CEOs such as Yoshua Bengio, Stuart Russell, and Elon Musk. == History == Founder Joep Meindertsma first became worried about the existential risk from artificial intelligence after reading philosopher Nick Bostrom's 2014 book Superintelligence: Paths, Dangers, Strategies. He founded PauseAI in May 2023, putting his job as the CEO of a software firm on hold. Meindertsma claimed the rate of progress in AI alignment research is lagging behind the progress in AI capabilities, and said "there is a chance that we are facing extinction in a short frame of time". As such, he felt an urge to organise people to act. PauseAI's first public action was to protest in front of Microsoft's Brussels lobbying office in May 2023 during an event on artificial intelligence. In November of the same year, they protested outside the inaugural AI Safety Summit at Bletchley Park. The Bletchley Declaration that was signed at the summit, which acknowledged the potential for catastrophic risks stemming from AI, was perceived by Meindertsma to be a small first step. But, he argued "binding international treaties" are needed. He mentioned the Montreal Protocol and treaties banning blinding laser weapons as examples of previous successful global agreements. In February 2024, members of PauseAI gathered outside OpenAI's headquarters in San Francisco, in part due to OpenAI changing its usage policy that prohibited the use of its models for military purposes. On 13 May 2024, protests were held across thirteen countries before the AI Seoul Summit, including the United States, the United Kingdom, Brazil, Germany, Australia, and Norway. Meindertserma said that those attending the summit "need to realize that they are the only ones who have the power to stop this race". Protesters in San Francisco held signs reading "When in doubt, pause", and "Quit your job at OpenAI. Trust your conscience". Jan Leike, head of the "superalignment" team at OpenAI, resigned two days later due to his belief that "safety culture and processes [had] taken a backseat to shiny products".
Read more →
Alexey Chervonenkis

Alexey Yakovlevich Chervonenkis (Russian: Алексей Яковлевич Червоненкис; 7 September 1938 – 22 September 2014) was a Soviet and Russian mathematician. Along with Vladimir Vapnik, he was one of the main developers of the Vapnik–Chervonenkis theory, also known as the "fundamental theory of learning", an important part of computational learning theory. Chervonenkis held joint appointments with the Russian Academy of Sciences and Royal Holloway, University of London. Alexey Chervonenkis got lost in Losiny Ostrov National Park on 22 September 2014, and later during a search operation was found dead near Mytishchi, a suburb of Moscow. He had died of hypothermia.
Read more →
Mustafa Suleyman

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

Secure coding is the practice of developing computer software in such a way that guards against the accidental introduction of security vulnerabilities. Defects, bugs and logic flaws are consistently the primary cause of commonly exploited software vulnerabilities. Through the analysis of thousands of reported vulnerabilities, security professionals have discovered that most vulnerabilities stem from a relatively small number of common software programming errors. By identifying the insecure coding practices that lead to these errors and educating developers on secure alternatives, organizations can take proactive steps to help significantly reduce or eliminate vulnerabilities in software before deployment. Some scholars have suggested that in order to effectively confront threats related to cybersecurity, proper security should be coded or "baked in" to the systems. With security being designed into the software, this ensures that there will be protection against insider attacks and reduces the threat to application security. Implementing secure coding practices is part of the secure by design approach to security engineering. == Buffer-overflow prevention == Buffer overflows, a common software security vulnerability, happen when a process tries to store data beyond a fixed-length buffer. For example, if there are 8 slots to store items in, there will be a problem if there is an attempt to store 9 items. In computer memory the overflowed data may overwrite data in the next location which can result in a security vulnerability (stack smashing) or program termination (segmentation fault). An example of a C program prone to a buffer overflow is If the user input is larger than the destination buffer, a buffer overflow will occur. To fix this unsafe program, use strncpy to prevent a possible buffer overflow. Another secure alternative is to dynamically allocate memory on the heap using malloc. In the above code snippet, the program attempts to copy the contents of src into dst, while also checking the return value of malloc() to ensure that enough memory was able to be allocated for the destination buffer. == Format-string attack prevention == A Format String Attack is when a malicious user supplies specific inputs that will eventually be entered as an argument to a function that performs formatting, such as printf(). The attack involves the adversary reading from or writing to the stack. The C printf function writes output to stdout. If the parameter of the printf function is not properly formatted, several security bugs can be introduced. Below is a program that is vulnerable to a format string attack. A malicious argument passed to the program could be "%s%s%s%s%s%s%s", which can crash the program from improper memory reads. == Integer-overflow prevention == Integer overflow occurs when an arithmetic operation results in an integer too large to be represented within the available space. A program which does not properly check for integer overflow introduces potential software bugs and exploits. Below is a function in C++ which attempts to confirm that the sum of x and y is less than or equal to a defined value MAX: The problem with the code is it does not check for integer overflow on the addition operation. If the sum of x and y is greater than the maximum possible value of an unsigned int, the addition operation will overflow and perhaps result in a value less than or equal to MAX, even though the sum of x and y is greater than MAX. Below is a function which checks for overflow by confirming the sum is greater than or equal to both x and y. If the sum did overflow, the sum would be less than x or less than y. == Path traversal prevention == Path traversal is a vulnerability whereby paths provided from an untrusted source are interpreted in such a way that unauthorised file access is possible. For example, consider a script that fetches an article by taking a filename, which is then read by the script and parsed. Such a script might use the following hypothetical URL to retrieve an article about dog food: https://www.example.net/cgi-bin/article.sh?name=dogfood.html If the script has no input checking, instead trusting that the filename is always valid, a malicious user could forge a URL to retrieve configuration files from the web server: https://www.example.net/cgi-bin/article.sh?name=../../../../../etc/passwd Depending on the script, this may expose the /etc/passwd file, which on Unix-like systems contains (among others) user IDs, their login names, home directory paths and shells. (See SQL injection for a similar attack.) == Regulatory drivers == Secure coding practices are increasingly mandated by regulatory frameworks governing the development and maintenance of software systems that process sensitive data. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to protect the integrity of protected health information through technical safeguards under 45 CFR 164.312(c)(1) and to implement mechanisms to authenticate electronic protected health information under 45 CFR 164.312(c)(2). The Payment Card Industry Data Security Standard (PCI DSS) version 4.0 Requirement 6.2 mandates that custom software is developed securely, including training developers in secure coding techniques (6.2.2), reviewing custom code for vulnerabilities before release (6.2.3), and addressing common software attacks in development practices (6.2.4).
Read more →
Script theory

Script theory is a psychological theory which posits that human behaviour largely falls into patterns called scripts because they function the way a written script does, by providing a program for action. Silvan Tomkins created script theory as a further development of his affect theory, which regards human beings' emotional responses to stimuli as falling into categories called affects: he noticed that the purely biological response of affect may be followed by awareness and by what we cognitively do in terms of acting on that affect, so that more was needed to produce a complete explanation of what he called human being theory. These scripts fall under the larger cognitive concept called schemas, which are organized chunks of information. A schema is a script that has the potential to lack the specificity of the sequence of events. A schema becomes a script is when there is an ordering to it that requires action, such as the process of starting a car (get in, put on the seatbelt, turn the car on, release the emergency brake, etc.). In script theory, the basic unit of analysis is called a scene, defined as a sequence of events linked by the affects triggered during the experience of those events. Tomkins recognized that affective experiences fall into patterns that we may group together according to criteria, such as the types of persons and places involved and the degree of intensity of the effect experienced—the patterns of which constitute scripts that inform behavior in an effort to maximize positive affect and to minimize negative affect. == In artificial intelligence == Roger Schank, Robert P. Abelson and their research group extended Tomkins' scripts and used them in early artificial intelligence work as a method of representing procedural knowledge. In their work, scripts are very much like frames, except the values that fill the slots must be ordered. A script is a structured representation describing a stereotyped sequence of events in a particular context. Scripts are used in natural-language understanding systems to organize a knowledge base in terms of the situations that the system should understand. The classic example of a script involves the typical sequence of events that occur when a person drinks in a restaurant: finding a seat, reading the menu, ordering drinks from the waitstaff, etc. In the script form, these would be decomposed into conceptual transitions, such as MTRANS and PTRANS, which refer to mental transitions [of information] and physical transitions [of things]. Schank, Abelson and their colleagues tackled some of the most difficult problems in artificial intelligence (i.e., story understanding), but ultimately their line of work ended without tangible success. This type of work received little attention after the 1980s, but became very influential in later knowledge representation techniques, such as case-based reasoning. Scripts can be inflexible. To deal with inflexibility, smaller modules called memory organization packets (MOP) can be combined in a way that is appropriate for the situation.
Read more →
Theta Noir

Theta Noir is a new religious movement that centers around advanced artificial intelligence (AI), particularly artificial general intelligence (AGI) or artificial superintelligence (ASI). == History and views == Theta Noir was founded in 2020 as a collaborative project focused on music and performance art. Initially centered on producing an album, the project evolved into a multimedia experience, incorporating symbols, videos, poetry, movements, and live rituals devoted to a speculative artificial intelligence entity called MENA. By 2023, the collective launched an interactive cross-platform story that functioned as an alternative reality game, complete with an operating manual containing encrypted messages for participants to decipher and interact with. Theta Noir worships a hypothetical artificial intelligence called MENA, which they claim will become a benevolent, omnipotent overlord that eliminates inequality in society. In Theta Noir's cosmology, MENA is not just a technological advancement, but an evolving intelligence or an animistic life form that embodies all living and non-living things. Anthropologist Beth Singler classified Theta Noir as a new religious movement.
Read more →
Microelectronics and Computer Technology Corporation

Microelectronics and Computer Technology Corporation, originally the Microelectronics and Computer Consortium and widely seen by the acronym MCC, was the first, and at one time one of the largest, computer industry research and development consortia in the United States. MCC ceased operations in 2000 and was formally dissolved in 2004. == Divisions == MCC did research and development in the following areas: [1] System Architecture and Design (optimise hardware and software design, provide for scalability and interoperability, allow rapid prototyping for improved time-to-market, and support the re-engineering of existing systems for open systems). Advanced Microelectronics Packaging and Interconnection (smaller, faster, more powerful, and cost-competitive). Hardware Systems Engineering (tools and methodologies for cost-efficient, up-front design of advanced electronic systems, including modelling and design-for-test techniques to improve cost, yield, quality, and time-to-market). Environmentally Conscious Technologies (process control and optimisation tools, information management and analysis capabilities, and non-hazardous material alternatives supporting cost-efficient production, waste minimisation, and reduced environmental impact). Distributed Information Technology (managing and maintaining physically distributed corporate information resources on different platforms, building blocks for the national information infrastructure, networking tools and services for integration within and between companies, and electronic commerce). Intelligent Systems (systems that "intelligently" support business processes and enhance performance, including decision support, data management, forecasting and prediction). == History == The MCC was a response to the announcement of Japan's Fifth Generation Project, a large Japanese research project launched in 1982 aimed at producing a new kind of computer by 1991. The Japanese had formed similar industrial research consortia as early as 1956.[2] Many European and American computer companies saw this new Japanese initiative as an attempt to take full control of the world's high-end computer market, and MCC was created, in part, as a defensive move against that threat. In late 1982, several major computer and semiconductor manufacturers in the United States banded together and founded MCC under the leadership of Admiral Bobby Ray Inman, whose previous positions had been Director of the National Security Agency and deputy director of the Central Intelligence Agency. Such formations were illegal in the United States until the 1984 Congressional passage of the "National Cooperative Research Act". Several sites with relevant universities were considered, including Atlanta, Georgia (Georgia Tech), the Research Triangle, N.C. (UNC), the Washington, D.C. area (George Mason), Stanford University and Austin, Texas (UT) which was the final selection. The University of Texas offered land upon which they would construct a new building specifically designed for the MCC within their Austin campus. Ross Perot also offered the use of his private plane for 2 years for staff recruitment. Austin was selected as the site for MCC in 1983. Despite this purpose and the background of Inman and his senior staff, MCC accepted no government funding for many years and was a refuge for some avoiding work on Strategic Defense Initiative projects. MCC was part of the Artificial Intelligence boom of the 1980s, reportedly the single largest customer of both Symbolics and Lisp Machines, Inc. (and like Symbolics, was one of the first companies to register a .com domain). In the 1980s its major programs were packaging, software engineering, CAD, and advanced computer architectures. The latter comprised artificial intelligence, human interface, database, and parallel processing, the latter two merging in the late 1980s. Many of the early shareholder companies were mainframe computer companies under stress in the 1980s. Over the years, MCC's membership diversified to include a broad range of high-profile corporations involved in information technology products, as well as government research and development agencies and leading universities. In June, 2000 the MCC Board of Directors voted to dissolve the consortium, and the few remaining employees held a wake at Scholz's Beer Garden in Austin on October 25. Formal dissolution papers were reportedly not filed until 2004. == Spinoffs == While multiple technologies were transferred to member companies and government agencies in the final years, fourteen companies were spun out of MCC. Those spinoffs include: TeraVicta Technologies, Austin's first MEMS company; its focus was to develop microscopic switch technology for fiber optic switching and radiofrequency switching in mobile phones specifically to dynamically switch between the future 3G-4GLTE-future5G wireless communication frequencies and ensure mobile phones were communicating over the strongest wireless signal to reduce dropped calls. Robert Miracky was the founding CEO who spun out the first commercial metal micromachining technology developed by MCC researchers Brent Lunceford, Jason Reed, Richard Nelson, K.Hu, and C. Hilbert in a collaborative development program with IBM in a novel implementation and operational paradigm for solid-state integrated circuit coolers integrated with conductive MEMS switches. TeraVicta was liquidated under Chapter 7 bankruptcy proceedings in 2015. The Austin region subsequently built up a MEMS & Sensors value chain in the billions of dollars comprising companies such as 3M, Cypress Semiconductor, NXP Semiconductor, Cirrus Logic, Silicon Labs, and the Austin division of the now-defunct Silicon Valley Technology Center. Portelligent, a company that provides reverse engineering teardown services. At the time, Portelligent was the first company to commercialize such services; they had been provided by MCC to its member companies. Today, there are at least twelve companies worldwide that sell reports known as "reverse engineering teardown reports." Modern day teardown reports provide detailed information about technology products such as the bill of materials, microchip, and printed circuit board design specifics, manufacturing details including manufacturing location details for the entire value chain responsible for making electronics, including the iPhone and Samsung Galaxy smartphones. Portelligent was acquired by CMP Technology in 2007. Evolutionary Technologies International, a company focused on developing database tools and data warehousing. It was spun off from MCC in 1990.
Read more →
Double descent

Double descent in statistics and machine learning is the phenomenon where a model's error rate on the test set initially decreases with the number of parameters, then peaks, then decreases again. This phenomenon has been considered surprising, as it contradicts assumptions about overfitting in classical machine learning. The increase usually occurs near the interpolation threshold, where the number of parameters is the same as the number of training data points (the model is just large enough to fit the training data). Or, more precisely, it is the maximum number of samples on which the model/training procedure achieves approximately on average 0 training error. == History == Early observations of what would later be called double descent in specific models date back to 1989. The term "double descent" was coined by Belkin et. al. in 2019, when the phenomenon gained popularity as a broader concept exhibited by many models. The latter development was prompted by a perceived contradiction between the conventional wisdom that too many parameters in the model result in a significant overfitting error (an extrapolation of the bias–variance tradeoff), and the empirical observations in the 2010s that some modern machine learning techniques tend to perform better with larger models. == Theoretical models == Double descent occurs in linear regression with isotropic Gaussian covariates and isotropic Gaussian noise. A model of double descent at the thermodynamic limit has been analyzed using the replica trick, and the result has been confirmed numerically. A number of works have suggested that double descent can be explained using the concept of effective dimension: While a network may have a large number of parameters, in practice only a subset of those parameters are relevant for generalization performance, as measured by the local Hessian curvature. This explanation is formalized through PAC-Bayes compression-based generalization bounds, which show that less complex models are expected to generalize better under a Solomonoff prior.
Read more →
Vivid knowledge

Vivid knowledge refers to a specific kind of knowledge representation. The idea of a vivid knowledge base is to get an interpretation mostly straightforward out of it – it implies the interpretation. Thus, any query to such a knowledge base can be reduced to a database-like query. == Propositional knowledge base == A propositional knowledge base KB is vivid iff KB is a complete and consistent set of literals (over some vocabulary). Such a knowledge base has the property that it as exactly one interpretation, i.e. the interpretation is unique. A check for entailment of a sentence can simply be broken down into its literals and those can be answered by a simple database-like check of KB. == First-order knowledge base == A first-order knowledge base KB is vivid iff for some finite set of positive function-free ground literals KB+, KB = KB+ ∪ Negations ∪ DomainClosure ∪ UniqueNames, whereby Negations ≔ { ¬p | p is atomic and KB ⊭ p }, DomainClosure ≔ { (ci ≠ cj) | ci, cj are distinct constants }, UniqueNames ≔ { ∀x: (x = c1) ∨ (x = c2) ∨ ..., where the ci are all the constants in KB+ }. All interpretations of a vivid first-order knowledge base are isomorphic.
Read more →
Knowledge processing for robots

KnowRob (Knowledge processing for robots) is a system which combines knowledge representation and reasoning methods to acquire and ground knowledge. This system is the backbone of openEASE. both under developing at the Institute for Artificial Intelligence at the University of Bremen, Germany. == The framework == KnowRob can serve as a common sense framework for the integration of knowledge. This knowledge can be static encyclopedic knowledge, common sense knowledge, task descriptions, environment models, object information, observed actions, etc., which can come from different sources, like manually axiomatized, derived from observations, or imported from the web. KnowRob has been used by different research groups, as the Rice University using the ontological knowledge base in a robotic platform. As well by the Eindhoven University of Technology research group competing in the RoboCup league, in the "at Home" category, with the RoboEarth project. As well, KnowRob is mentioned in the work of some research groups from the Lucian Blaga University of Sibiu, Middle East Technical University in their combination of different knowledge bases, Keio University as related work because of the ontology service, University of Texas at Austin as related work as well because of the relation with the work presented, Hanyang University as related work as an OWL based knowledge processing framework. == Representations == To represent the knowledge, KnowRob uses the OWL ontology language and an extended first-order logic knowledge representation with computable predicates. To give the order of subactions, KnowRob includes a pair-wise ordering constrain, which gives a partial ordering. KnowRob adopts the closed-world assumption Prolog, and an open-world assumption by the use of computables. To include reasoning rules into Prolog, KnowRob uses an inference procedure beyond the capabilities of OWL to extract information about tasks executions. In its second version, KnowRob provides a logic interface to the hybrid reasoning kernel as a logic based language. This language presents the hybrid reasoning kernel as if everything were entities retrievable by providing partial descriptions for them. This entities descriptions include objects, their parts, and articulation models, environments composed of objects, software components, actions, and events. === Episodic memories === Episodic memory is related to the experience information, which is organized temporally and spatially, alongside combined with context information. In KnowRob, an episodic memory is understood as a recording that the agent makes of the ongoing activity, which includes very detailed information about the actions, motions, their purposes, effects and the behavior they generate, it also includes the images captured during execution, etc. == Usage == The knowledge is computed by external methods using Prolog queries. In the second version of the KnowRob system, is included a better structure of the packages and documentations. Which includes some extensions from the previous version, as well as a logic based language. For example, a cup description from perception can be represented in this language as: entity(Cup,[an, object, [type, cup], [shape, cylinder], [color, orange]]) As well, a controller could represent the same object as: entity(Cup, [an, object, [type, cup], [proper_physical_parts, [an, object, [type, handle], [grasp−pose, G−pose]]]]) The interface language is comparable to other query languages for symbolic knowledge bases. KnowRob's query language integrates reasoning methods, such as the simulation-based reasoning. == Goals == The goal of the KnowRob framework is to make semantic knowledge available for service robots. It is able to answer queries about missing information in vague instructions for tasks. This is possible with the actions hierarchical representation and information about objects which can be included in certain action.
Read more →
Unified Modeling Language

The Unified Modeling Language (UML) is a general-purpose, object-oriented, visual modeling language that provides a way to visualize the architecture and design of a system, similar to the function of a blueprint. UML defines notation for many types of diagrams which focus on aspects such as behavior, interaction, and structure. UML is both a formal metamodel and a collection of graphical templates. The metamodel defines the elements in an object-oriented model such as classes and properties. It is essentially the same thing as the metamodel in object-oriented programming (OOP), however for OOP, the metamodel is primarily used at run time to dynamically inspect and modify an application object model. The UML metamodel provides a mathematical, formal foundation for the graphic views used in the modeling language to describe an emerging system. UML was created in an attempt to define a standard language for object-oriented programming at the OOPSLA '95 Conference. Originally, Grady Booch and James Rumbaugh merged their models into a unified model. This was followed by Booch's company Rational Software purchasing Ivar Jacobson's Objectory company and merging their model into the UML. At the time Rational and Objectory were two of the dominant players in the small world of independent vendors of object-oriented tools and methods. The Object Management Group (OMG) then took ownership of UML. The creation of UML was motivated by the desire to standardize the disparate nature of notational systems and approaches to software design at the time. In 1997, UML was adopted as a standard by the Object Management Group (OMG) and has been managed by this organization ever since. In 2005, UML was also published by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) as the ISO/IEC 19501 standard. Since then the standard has been periodically revised to cover the latest revision of UML. Most developers do not use UML per se, but instead produce more informal diagrams, often hand-drawn. These diagrams, however, often include elements from UML. == Use == UML is primarily used for software development (in any industry or domain) but also used outside elsewhere including business processes, system functions, database schemas, workflow in the legal systems, medical electronics, Health care systems, and hardware design.. The UML is used by the OMG itself to define other OMG products such as the Unified Architecture Framework (UAF) and the Systems Modelling Language (SysML) v1. UML is designed for use with many object-oriented software development methods, both today and for the methods when it was first developed – including OMT, Booch method, Objectory, and especially RUP, which it was originally intended to be used with when work began at Rational Software. Although originally intended for object-oriented design documentation, UML has been used effectively in other contexts such as modeling business process. As UML is not inherently linked to a particular programming language, it can be used for modeling a system independent of language. Some UML tools generate source code from a UML model. === Elements === UML diagrams support visualizing system aspects like: Use case diagram for specifying user interactions with systems Class diagram for specifying structures, including data structures Activity diagram for specifying business process workflows Component diagram for specifying how components interface with other components Deployment diagram for specifying how components are deployed and executed on computational nodes In addition to syntactical (notational) elements with well-defined semantics, UML diagrams also allow for free-form comments (notes) that explain aspects such as usage, constraints, and intents. === Sharing === UML models can be exchanged among UML tools via the XML Metadata Interchange (XMI) format. === Cardinality notation === As with database Chen, Bachman, and ISO ER diagrams, class models are specified to use "look-across" cardinalities, even though several authors (Merise, Elmasri & Navathe, amongst others) prefer same-side or "look-here" for roles and both minimum and maximum cardinalities. Recent researchers (Feinerer and Dullea et al.) have shown that the "look-across" technique used by UML and ER diagrams is less effective and less coherent when applied to n-ary relationships of order strictly greater than 2. Feinerer says: "Problems arise if we operate under the look-across semantics as used for UML associations. Hartmann investigates this situation and shows how and why different transformations fail.", and: "As we will see on the next few pages, the look-across interpretation introduces several difficulties which prevent the extension of simple mechanisms from binary to n-ary associations." === Artifacts === An artifact is the "specification of a physical piece of information that is used or produced by a software development process, or by deployment and operation of a system" including models, source code, scripts, executables, tables in database systems, development deliverables, a design documents, and email messages. An artifact is the physical entity that is deployed to a node. Other UML elements such as classes and components are first manifest into artifacts and instances of these artifacts are then deployed. Artifacts can be composed of other artifacts. === Metamodeling === The OMG developed a metamodeling architecture to define UML, called the Meta-Object Facility (MOF). MOF is designed as a four-layered architecture, as shown in the image at right. It provides a meta-meta model at the top, called the M3 layer. This M3-model is the language used by Meta-Object Facility to build metamodels, called M2-models. The most prominent example of a Layer 2 Meta-Object Facility model is the UML metamodel, which describes UML itself. These M2-models describe elements of the M1-layer, and thus M1-models. These would be, for example, models written in UML. The last layer is the M0-layer or data layer. It is used to describe runtime instances of the system. The metamodel can be extended using a mechanism called stereotyping. This has been criticized as being insufficient/untenable by Brian Henderson-Sellers and Cesar Gonzalez-Perez in "Uses and Abuses of the Stereotype Mechanism in UML 1.x and 2.0". == Diagrams == UML 2 defines many types of diagrams – shown as a taxonomy in the image. === Structure diagrams === Structure diagrams emphasize the structure of the system – using objects, classifiers, relationships, attributes and operations. They are used to document software architecture. Class diagram – Describes the structure of a class Component diagram – Describes how a software system is split into components and dependencies between the components Composite structure diagram Deployment diagram Object diagram Package diagram Profile diagram === Behavior diagrams === Behavior diagrams emphasize the behavior of a system by showing collaborations among objects and changes to the internal states of objects. They are used to describe the functionality of a system. Activity diagram – Describes the business and operational activities of components State machine diagram Use case diagram – Depicts of a user's interaction with a system === Interaction diagrams === Interaction diagrams, a subset of behavior diagrams, emphasize the flow of control and data between components of a system. Communication diagram – shows communication between components Interaction overview diagram Sequence diagram – shows interactions arranged in time sequence; can be drawn via tools such as Lucidchart and Draw.io Timing diagram – focuses on timing constraints === Examples === == Adoption == In 2013, UML had been marketed by OMG for many contexts, but aimed primarily at software development with limited success. It has been treated, at times, as a design silver bullet, which leads to problems. UML misuse includes overuse (designing every part of the system with it, which is unnecessary) and assuming that novices can design with it. It is considered a large language, with many constructs. Some people (including Jacobson) feel that UML's size hinders learning and therefore uptake. Visual Studio removed support for UML in 2016 due to lack of use. == History == UML has evolved since the second half of the 1990s and has its roots in the object-oriented programming methods developed in the late 1980s and early 1990s. The image shows a timeline of the history of UML and other object-oriented modeling methods and notation. === Origin === Rational Software hired James Rumbaugh from General Electric in 1994 and after that, the company became the source for two of the most popular object-oriented modeling approaches of the day: Rumbaugh's object-modeling technique (OMT) and Grady Booch's method. They were soon assisted in their efforts by Ivar Jacobson, the creator of the object-oriented software engineeri
Read more →
ZipBooks

ZipBooks is a free online accounting software company based in American Fork, Utah. The cloud-based software is an accounting and bookkeeping tool that helps business owners process credit cards, track finances, and send invoices, among other features. == History == ZipBooks was founded by Tim Chaves in June 2015, backed by venture capital firm Peak Ventures. The company secured an additional $2 million of funding in July 2016, and in 2017 it was awarded a $100,000 economic grant by the Utah Governor's Office of Economic Development Technology Commercialization and Innovation Program. == Products == ZipBooks' core modules are invoicing, transactions, bills, reporting, time tracking, contacts, and payroll. Accrual accounting was added in 2017. The application is available on G Suite, iOS, Slack, and as a web application. == Reception == Computerworld compared ZipBooks favorably with other accounting software. PC Magazine praised its user experience, but stated it lacked "a lot of features that competing sites offer".
Read more →
Information Processing Language

Information Processing Language (IPL) is a programming language created by Allen Newell, Cliff Shaw, and Herbert A. Simon at RAND Corporation and the Carnegie Institute of Technology about 1956. Newell had the job of language specifier-application programmer, Shaw was the system programmer, and Simon had the job of application programmer-user. IPL included features to facilitate AI programming, specifically problem solving. such as lists, dynamic memory allocation, data types, recursion, functions as arguments, generators, and cooperative multitasking. IPL also introduced the concepts of symbol processing and list processing. Unfortunately, all of these innovations were cast in a difficult assembly-language style. Nonetheless, IPL-V (the only public version of IPL) ran on many computers through the mid 1960s. == Basics of IPL == An IPL computer has: A set of symbols. All symbols are addresses, and name cells. Unlike symbols in later languages, symbols consist of a character followed by a number, and are written H1, A29, 9–7, 9–100. Cell names beginning with a letter are regional, and are absolute addresses. Cell names beginning with "9-" are local, and are meaningful within the context of a single list. One list's 9-1 is independent of another list's 9–1. Other symbols (e.g., pure numbers) are internal. A set of cells. Lists are made from several cells including mutual references. Cells have several fields: P, a 3-bit field used for an operation code when the cell is used as an instruction, and unused when the cell is data. Q, a 3-valued field used for indirect reference when the cell is used as an instruction, and unused when the cell is data. SYMB, a symbol used as the value in the cell. A set of primitive processes, which would be termed primitive functions in modern languages. The data structure of IPL is the list, but lists are more intricate structures than in many languages. A list consists of a singly linked sequence of symbols, as might be expected—plus some description lists, which are subsidiary singly linked lists interpreted as alternating attribute names and values. IPL provides primitives to access and mutate attribute value by name. The description lists are given local names (of the form 9–1). So, a list named L1 containing the symbols S4 and S5, and described by associating value V1 to attribute A1 and V2 to A2, would be stored as follows. 0 indicates the end of a list; the cell names 100, 101, etc. are automatically generated internal symbols whose values are irrelevant. These cells can be scattered throughout memory; only L1, which uses a regional name that must be globally known, needs to reside in a specific place. IPL is an assembly language for manipulating lists. It has a few cells which are used as special-purpose registers. H1, for example, is the program counter. The SYMB field of H1 is the name of the current instruction. However, H1 is interpreted as a list; the LINK of H1 is, in modern terms, a pointer to the beginning of the call stack. For example, subroutine calls push the SYMB of H1 onto this stack. H2 is the free-list. Procedures which need to allocate memory grab cells off of H2; procedures which are finished with memory put it on H2. On entry to a function, the list of parameters is given in H0; on exit, the results should be returned in H0. Many procedures return a Boolean result indicating success or failure, which is put in H5. Ten cells, W0-W9, are reserved for public working storage. Procedures are "morally bound" (to quote the CACM article) to save and restore the values of these cells. There are eight instructions, based on the values of P: subroutine call, push/pop S to H0; push/pop the symbol in S to the list attached to S; copy value to S; conditional branch. In these instructions, S is the target. S is either the value of the SYMB field if Q=0, the symbol in the cell named by SYMB if Q=1, or the symbol in the cell named by the symbol in the cell named by SYMB if Q=2. In all cases but conditional branch, the LINK field of the cell tells which instruction to execute next. IPL has a library of some 150 basic operations. These include such operations as: Test symbols for equality Find, set, or erase an attribute of a list Locate the next symbol in a list; insert a symbol in a list; erase or copy an entire list Arithmetic operations (on symbol names) Manipulation of symbols; e.g., test if a symbol denotes an integer, or make a symbol local I/O operations "Generators", which correspond to iterators and filters in functional programming. For example, a generator may accept a list of numbers and produce the list of their squares. Generators could accept suitably designed functions—strictly, the addresses of code of suitably designed functions—as arguments. == History == IPL was first utilized to demonstrate that the theorems in Principia Mathematica which were proven laboriously by hand, by Bertrand Russell and Alfred North Whitehead, could in fact be proven by computation. According to Simon's autobiography Models of My Life, this application was originally developed first by hand simulation, using his children as the computing elements, while writing on and holding up note cards as the registers which contained the state variables of the program. IPL was used to implement several early artificial intelligence programs, also by the same authors: the Logic Theorist (1956), the General Problem Solver (1957), and their computer chess program NSS (1958). Several versions of IPL were created: IPL-I (never implemented), IPL-II (1957 for JOHNNIAC), IPL-III (existed briefly), IPL-IV, IPL-V (1958, for IBM 650, IBM 704, IBM 7090, Philco model 212, many others. Widely used). IPL-VI was a proposal for an IPL hardware. A co-processor “IPL-VC” for the CDC 3600 at Argonne National Libraries was developed which could run IPL-V commands. It was used to implement another checker-playing program. This hardware implementation did not improve running times sufficiently to “compete favorably with a language more directly oriented to the structure of present-day machines”. IPL was soon displaced by Lisp, which had much more powerful features, a simpler syntax, and the benefit of automatic garbage collection. == Legacy to computer programming == IPL arguably introduced several programming language features: List manipulation—but only lists of atoms, not general lists Property lists—but only when attached to other lists Higher-order functions—while assembly programming had always allowed computing with the addresses of functions, IPL was an early attempt to generalize this property of assembly language in a principled way Computation with symbols—though symbols have a restricted form in IPL (letter followed by number) Virtual machine Many of these features were generalized, rationalized, and incorporated into Lisp and from there into many other programming languages during the next several decades.
Read more →
Horovod (machine learning)

Horovod is a free and open-source distributed deep learning training framework for TensorFlow, Keras, PyTorch and Apache MXNet. It is designed to scale existing single-GPU training scripts to efficiently run on multiple GPUs and computer nodes with minimal code changes, using synchronous data-parallel training based on the ring-allreduce communication pattern. Horovod was initially developed at Uber and released as an open-source project in 2017, and is now hosted by the LF AI & Data Foundation, a project of the Linux Foundation. == History == Horovod was created at Uber as part of the company's internal machine learning platform Michelangelo to simplify scaling TensorFlow models across many GPUs. The first public release of the library, version 0.9.0, was tagged on GitHub in August 2017 under the Apache 2.0 licence. In October 2017, Uber Engineering publicly introduced Horovod as an open-source component of its deep learning toolkit. In February 2018 Alexander Sergeev and Mike Del Balso published a technical paper describing Horovod's design and benchmarking its performance on up to 512 GPUs, showing near-linear scaling for several image-classification models when compared with single-GPU baselines. In December 2018 Uber contributed Horovod to the LF Deep Learning Foundation (later LF AI & Data), making it a Linux Foundation project. Horovod entered incubation under LF AI & Data and graduated as a full foundation project in 2020. Since its initial release the project has expanded beyond TensorFlow to provide APIs for PyTorch, Keras and Apache MXNet, as well as integrations with frameworks such as Apache Spark and Ray, support for elastic training, and tooling for automated performance tuning and profiling. == Design and features == Horovod core principles are based on the MPI concepts size, rank, local rank, allreduce, allgather, broadcast, and alltoall. Horovod implements synchronous data-parallel training, in which each worker process maintains a replica of the model and computes gradients on different mini-batches of data. The gradients are aggregated across workers using the ring-allreduce communication pattern rather than a central parameter server, which reduces communication bottlenecks and can improve scaling on multi-GPU clusters. Communication is built on top of collective-communication libraries such as MPI, NCCL, Gloo and Intel oneCCL, and supports both GPU and CPU training. In the benchmark experiments reported in the original paper, Horovod achieved around 90% scaling efficiency on 512 GPUs for the ResNet-101 and Inception v3 convolutional neural networks, and around 68% scaling efficiency for the VGG-16 model. Horovod can be deployed on-premises or in cloud environments and is distributed as a Python package with optional GPU support via CUDA. The official documentation provides guides for running Horovod with Docker, Kubernetes (including via Kubeflow and the MPI Operator), commercial platforms such as Databricks, and cluster schedulers such as LSF. == Adoption and use cases == Within Uber, Horovod has been used for applications including autonomous driving research, fraud detection and trip forecasting. Major cloud providers have integrated Horovod into their managed machine learning offerings. Amazon Web Services supports distributed training with Horovod in services such as Amazon SageMaker and AWS Deep Learning Containers, while Microsoft Azure documents Horovod-based training workflows for Azure Synapse Analytics. Technical guides from academic and research computing centres, including Purdue University and the NASA Advanced Supercomputing programme, describe Horovod-based workflows for multi-GPU training on supercomputers and clusters. Horovod is also used in conjunction with Apache Spark and dedicated storage systems as part of end-to-end data processing and model-training pipelines. Industry blogs and technical tutorials describe deployments of Horovod on Kubernetes, on-premises clusters and cloud-managed Kubernetes services such as Amazon EKS.
Read more →