AI Essay No Plagiarism

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Security.txt

security.txt is an accepted standard for website security information that allows security researchers to report security vulnerabilities easily. The standard prescribes a text file named security.txt in the well known location, similar in syntax to robots.txt but intended to be machine and human readable, for those wishing to contact a website's owner about security issues. security.txt files have been adopted by Google, GitHub, LinkedIn, and Facebook. == History == The Internet Draft was first submitted by Edwin Foudil in September 2017. At that time it covered four directives, "Contact", "Encryption", "Disclosure" and "Acknowledgement". Foudil expected to add further directives based on feedback. In addition, web security expert Scott Helme said he had seen positive feedback from the security community while use among the top 1 million websites was "as low as expected right now". In 2019, the Cybersecurity and Infrastructure Security Agency (CISA) published a draft binding operational directive that requires all US federal agencies to publish a security.txt file within 180 days. The Internet Engineering Steering Group (IESG) issued a Last Call for security.txt in December 2019 which ended on January 6, 2020. A study in 2021 found that over ten percent of top-100 websites published a security.txt file, with the percentage of sites publishing the file decreasing as more websites were considered. The study also noted a number of discrepancies between the standard and the content of the file. In April 2022 the security.txt file has been accepted by Internet Engineering Task Force (IETF) as RFC 9116. == File format == security.txt files can be served under the /.well-known/ directory (i.e. /.well-known/security.txt) or the top-level directory (i.e. /security.txt) of a website. The file must be served over HTTPS and in plaintext format.
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Guideline execution engine

A guideline execution engine is a computer program which can interpret a clinical guideline represented in a computerized format and perform actions towards the user of an electronic medical record. A guideline execution engine needs to communicate with a host clinical information system. Virtual Medical Record (vMR) is one possible interface which can be used. The engine's main function is to manage instances of executed guidelines of individual patients. == Architecture == The following modules are generally needed for any engine: interface to clinical information system new guidelines loading module guideline interpreter module clinical events parser alert/recommendations dispatch == Guideline Interchange Format == The Guideline Interchange Format (GLIF) is a computer representation format for clinical guidelines. Represented guidelines can be executed using a guideline execution engine. The format has several versions as it has been improved. In 2003 GLIF3 was introduced. == Use of third party workflow engine as a guideline execution engine == Some commercial electronic health record systems use a workflow engine to execute clinical guidelines. RetroGuide and HealthFlow are examples of such an approach.
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Norm (artificial intelligence)

Norms can be considered from different perspectives in artificial intelligence to create computers and computer software that are capable of intelligent behaviour. In artificial intelligence and law, legal norms are considered in computational tools to automatically reason upon them. In multi-agent systems (MAS), a branch of artificial intelligence (AI), a norm is a guide for the common conduct of agents, thereby easing their decision-making, coordination and organization. Since most problems concerning regulation of the interaction of autonomous agents are linked to issues traditionally addressed by legal studies, and since law is the most pervasive and developed normative system, efforts to account for norms in artificial intelligence and law and in normative multi-agent systems often overlap. == Artificial intelligence and law == With the arrival of computer applications into the legal domain, and especially artificial intelligence applied to it, logic has been used as the major tool to formalize legal reasoning and has been developed in many directions, ranging from deontic logics to formal systems of argumentation. The knowledge base of legal reasoning systems usually includes legal norms (such as governmental regulations and contracts), and as a consequence, legal rules are the focus of knowledge representation and reasoning approaches to automatize and solve complex legal tasks. Legal norms are typically represented into a logic-based formalism, such as deontic logic. Artificial intelligence and law applications using an explicit representation of norms range from checking the compliance of business processes and the automatic execution of smart contracts to legal expert systems advising people on legal matters. == Multi-agent systems == Norms in multi-agent systems may appear with different degrees of explicitness ranging from fully unambiguous written prescriptions to implicit unwritten norms or tacit emerging patterns. Computer scientists’ studies mirror this polarity. Explicit norms are typically investigated in formal logics (e.g. deontic logics and argumentation) to represent and reason upon them, leading eventually to architecture for cognitive agents, while implicit norms are accounted as patterns emerging from repeated interactions amongst agents (typically reinforced learning agents). Explicit and implicit norms can be used together to coordinate agents. Explicit norms are typically represented as a deontic statement that aims at regulating the life of software agents and the interactions among them. It can be an obligation, a permission or a prohibition, and is often represented with some dialect or extension of Deontic logic. At the opposite, implicit norms are social norms that are not written, and they usually emerge from the repetitive interactions of agents.
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Jakub Pachocki

Jakub Pachocki (born 1991) is a Polish computer scientist and former competitive programmer. He is best known as OpenAI's chief scientist and for his role in overseeing development of GPT-4. == Background == Pachocki was born in 1991 in Gdańsk, Poland. In high school, he was a six-time finalist of the Polish Olympiad in Informatics. In 2009, he qualified for the International Olympiad in Informatics, winning a silver medal. Pachocki obtained his undergraduate degree in Computer Science from the University of Warsaw. He represented his university at the International Collegiate Programming Contest with his team winning a gold medal and coming second place overall in 2012. In the same year he was also the champion of the Google Code Jam. From 2011 to 2012, Pachocki worked at Facebook as a software engineering intern. Pachocki attended graduate school at Carnegie Mellon University, where he obtained his PhD under the supervision of Gary Miller. == Career == After graduation, Pachocki did postdoc work at Harvard University and Simons Institute for the Theory of Computing. === OpenAI === In 2017, Pachocki joined OpenAI. In 2021, he became OpenAI's research director where he led the development of GPT-4 and OpenAI Five. In May 2024, he became chief scientist after his mentor Ilya Sutskever left the company. OpenAI CEO Sam Altman has called Pachocki "easily one of the greatest minds of our generation". == Competitive programming achievements == International Olympiad in Informatics: Silver medal (2009) International Collegiate Programming Contest World Finals: Gold medal (second place overall in 2012) Google Code Jam: Champion (2012), Third place (2011) Facebook Hacker Cup: Second place (2013) TopCoder Open Algorithm: Second place (2012) A more comprehensive list of achievements can be found at the Competitive Programming Hall Of Fame website.
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Color clock

The color clock, or color timer, is a part of the video circuitry of computer graphics hardware that works with analog color television systems. The clock is timed to match the timing of the color standard it works with, typically NTSC or PAL, ensuring that the data being read from the computer memory to create the image on-screen is in sync with the display. Depending on the speed of the color clock, the product of the resolution and number of colors is defined. Slow color clocks of many early games consoles and home computers resulted in limited color palettes at the highest resolutions.
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Karen Hao

Karen Hao (born in the United States c. 1993) is an American journalist and author. Currently a freelancer for publications like The Atlantic and previously a foreign correspondent based in Hong Kong for The Wall Street Journal and senior artificial intelligence editor at the MIT Technology Review, she is best known for her coverage on AI research, technology ethics and the social impact of AI. Hao also co-produced the podcast In Machines We Trust and wrote the newsletter The Algorithm. Previously, she worked at Quartz as a tech reporter and data scientist and was an application engineer at the first startup to spin out of X Development. Hao's writing has also appeared in Mother Jones, Sierra Magazine, The New Republic, and other publications. == Early life and education == Hao is the daughter of Chinese immigrant parents, and grew up in New Jersey. She is a native speaker of both English and Mandarin Chinese. She graduated from The Lawrenceville School in 2011. She then studied at the Massachusetts Institute of Technology (MIT), graduating with a B.S. in mechanical engineering and a minor in energy studies in 2015. == Career == Hao is known in the technology world for her coverage of new AI research findings and their societal and ethical impacts. Her writing has spanned research and issues regarding big tech data privacy, misinformation, deepfakes, facial recognition, and AI healthcare tools. In March 2021, Hao published a piece that uncovered previously unknown information about how attempts to combat misinformation by different teams at Facebook using machine learning were impeded and constantly at odds with Facebook's drive to grow user engagement. Upon its release, leaders at Facebook including Mike Schroepfer and Yann LeCun immediately criticized the piece through Twitter responses. AI researchers and AI ethics experts Timnit Gebru and Margaret Mitchell responded in support of Hao's writing and advocated for more change and improvement for all. Hao also co-produced the podcast In Machines We Trust, which discusses the rise of AI with people developing, researching, and using AI technologies. The podcast won the 2020 Front Page Award in investigative reporting. Hao has occasionally created data visualizations that have been featured in her work at the MIT Technology Review and elsewhere. In 2018, her "What is AI?" flowchart visualization was exhibited as an installation at the Museum of Applied Arts in Vienna. She has been an invited speaker at TEDxGateway, the United Nations Foundation, EmTech, WNPR, and many other conferences and podcasts. Her TEDx talk discussed the importance of democratizing how AI is built. In March 2022, she was hired by The Wall Street Journal to cover China technology and society, while being based in Hong Kong. She left the WSJ in 2023. In May 2025, Hao released the book Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI. The book became a New York Times Bestseller and was named a Book of the Year by the Financial Times. In December 2025, after criticism from readers, Hao issued a correction to her book where she had previously overestimated the water consumption of a data center in Chile compared to the community's water consumption by factor of 1,000, due to an error in a government document. In April 2026 the book won the New York Public Library's Helen Bernstein Book Award for Excellence in Journalism. === Selected awards and honors === 2019 Webby Award nominee for best newsletter, as a writer of The Algorithm 2021 Front Page Award in investigative reporting, as a co-producer for In Machines We Trust 2021 Ambies Award nominee for best knowledge and science podcast, as a co-producer for In Machines We Trust 2021 Webby Award nominee for best technology podcast, as a co-producer for In Machines We Trust 2024 American Humanist Media Award 2025 TIME100 AI, named by TIME magazine as one of the 100 most influential people in artificial intelligence 2026 New York Public Library's Helen Bernstein Book Award for Excellence in Journalism 2026 Whiting Award in Non-fiction
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Minimum information standard

Minimum information standards are sets of guidelines and formats for reporting data derived by specific high-throughput methods. Their purpose is to ensure the data generated by these methods can be easily verified, analysed and interpreted by the wider scientific community. Ultimately, they facilitate the transfer of data from journal articles (unstructured data) into databases (structured data) in a form that enables data to be mined across multiple data sets. Minimal information standards are available for a vast variety of experiment types including microarray (MIAME), RNAseq (MINSEQE), metabolomics (MSI) and proteomics (MIAPE). Minimum information standards typically have two parts. Firstly, there is a set of reporting requirements – typically presented as a table or a checklist. Secondly, there is a data format. Information about an experiment needs to be converted into the appropriate data format for it to be submitted to the relevant database. In the case of MIAME, the data format is provided in spreadsheet format (MAGE-TAB). Some of the communities that maintain minimum information standards also provide tools to help experimental researchers to annotate their data. == MI Standards == The individual minimum information standards are brought by the communities of cross-disciplinary specialists focused on the problematic of the specific method used in experimental biology. The standards then provide specifications what information about the experiments (metadata) is crucial and important to be reported together with the resultant data to make it comprehensive. The need for this standardization is largely driven by the development of high-throughput experimental methods that provide tremendous amounts of data. The development of minimum information standards of different methods is since 2008 being harmonized by "Minimum Information about a Biomedical or Biological Investigation" (MIBBI) project. === MIAPPE, Minimum Information About a Plant Phenotyping Experiment === MIAPPE is an open, community driven project to harmonize data from plant phenotyping experiments. MIAPPE comprises both a conceptual checklist of metadata required to adequately describe a plant phenotyping experiment. === MIQE, Minimum Information for Publication of Quantitative Real-Time PCR Experiments === Published in 2009 these guidelines for the basis of requirements by many journals when submitting QPCR data, sadly they are not adhered to enough. === MIAME, gene expression microarray === Minimum Information About a Microarray Experiment (MIAME) describes the Minimum Information About a Microarray Experiment that is needed to enable the interpretation of the results of the experiment unambiguously and potentially to reproduce the experiment and is aimed at facilitating the dissemination of data from microarray experiments. It was published by the FGED Society in 2001 and was the first published minimum information standard for high-throughput experiments in the life sciences. MIAME contains a number of extensions to cover specific biological domains, including MIAME-env, MIAME-nut and MIAME-tox, covering environmental genomics, nutritional genomics and toxogenomics, respectively. === MINI: Minimum Information about a Neuroscience Investigation === ==== MINI: Electrophysiology ==== Electrophysiology is a technology used to study the electrical properties of biological cells and tissues. Electrophysiology typically involves the measurements of voltage change or electric current flow on a wide variety of scales from single ion channel proteins to whole tissues. This document is a single module, as part of the Minimum Information about a Neuroscience investigation (MINI) family of reporting guideline documents, produced by community consultation and continually available for public comment. A MINI module represents the minimum information that should be reported about a dataset to facilitate computational access and analysis to allow a reader to interpret and critically evaluate the processes performed and the conclusions reached, and to support their experimental corroboration. In practice a MINI module comprises a checklist of information that should be provided (for example about the protocols employed) when a data set is described for publication. The full specification of the MINI module can be found here. === MIARE, RNAi experiment === Minimum Information About an RNAi Experiment (MIARE) is a data reporting guideline which describes the minimum information that should be reported about an RNAi experiment to enable the unambiguous interpretation and reproduction of the results. === MIACA, cell based assay === Advances in genomics and functional genomics have enabled large-scale analyses of gene and protein function by means of high-throughput cell biological analyses. Thereby, cells in culture can be perturbed in vitro and the induced effects recorded and analyzed. Perturbations can be triggered in several ways, for instance with molecules (siRNAs, expression constructs, small chemical compounds, ligands for receptors, etc.), through environmental stresses (such as temperature shift, serum starvation, oxygen deprivation, etc.), or combinations thereof. The cellular responses to such perturbations are analyzed in order to identify molecular events in the biological processes addressed and understand biological principles. We propose the Minimum Information About a Cellular Assay (MIACA) for reporting a cellular assay, and CA-OM, the modular cellular assay object model, to facilitate exchange of data and accompanying information, and to compare and integrate data that originate from different, albeit complementary approaches, and to elucidate higher order principles. Documents describing MIACA are available and provide further information as well as the checklist of terms that should be reported. === MIAPE, proteomic experiments === The Minimum Information About a Proteomic Experiment documents describe information which should be given along with a proteomic experiment. The parent document describes the processes and principles underpinning the development of a series of domain specific documents which now cover all aspects of a MS-based proteomics workflow. === MIMIx, molecular interactions === This document has been developed and maintained by the Molecular Interaction worktrack of the HUPO-PSI (www.psidev.info) and describes the Minimum Information about a Molecular Interaction experiment. === MIAPAR, protein affinity reagents === The Minimum Information About a Protein Affinity Reagent has been developed and maintained by the Molecular Interaction worktrack of the HUPO-PSI (www.psidev.info)in conjunction with the HUPO Antibody Initiative and a European consortium of binder producers and seeks to encourage users to improve their description of binding reagents, such as antibodies, used in the process of protein identification. === MIABE, bioactive entities === The Minimum Information About a Bioactive Entity was produced by representatives from both large pharma and academia who are looking to improve the description of usually small molecules which bind to, and potentially modulate the activity of, specific targets in a living organism. This document encompasses drug-like molecules as well as herbicides, pesticides and food additives. It is primarily maintained through the EMBL-EBI Industry program (www.ebi.ac.uk/industry). === MIGS/MIMS, genome/metagenome sequences === This specification is being developed by the Genomic Standards Consortium === MIFlowCyt, flow cytometry === === Minimum Information about a Flow Cytometry Experiment === The Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) is a standard related to flow cytometry which establishes criteria to record information on experimental overview, samples, instrumentation and data analysis. It promotes consistent annotation of clinical, biological and technical issues surrounding a flow cytometry experiment. === MINDR, dual gene expression reporters === Requires (1) reporting absolute values of reporter readouts, (2) list of positive and negative controls, and (3) sequences of all reporter constructs. === MISFISHIE, In Situ Hybridization and Immunohistochemistry Experiments === === MIAPA, Phylogenetic Analysis === Criteria for Minimum Information About a Phylogenetic Analysis were described in 2006. === MIRAGE, Glycomics === The MIRAGE project is supported and coordinated by the Beilstein-Institut to establish guidelines for data handling and processing in glycomics research [1] === MIAO, ORF === === MIAMET, METabolomics experiment === === MIAFGE, Functional Genomics Experiment === === MIRIAM, Minimum Information Required in the Annotation of Models === The Minimal Information Required In the Annotation of Models (MIRIAM), is a set of rules for the curation and annotation of quantitative models of biological systems. === MIASE, Minimum Information About a Simulation Experiment =
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Cerebellar model articulation controller

The cerebellar model arithmetic computer (CMAC) is a type of neural network based on a model of the mammalian cerebellum. It is also known as the cerebellar model articulation controller. It is a type of associative memory. The CMAC was first proposed as a function modeler for robotic controllers by James Albus in 1975 (hence the name), but has been extensively used in reinforcement learning and also as for automated classification in the machine learning community. The CMAC is an extension of the perceptron model. It computes a function for n {\displaystyle n} input dimensions. The input space is divided up into hyper-rectangles, each of which is associated with a memory cell. The contents of the memory cells are the weights, which are adjusted during training. Usually, more than one quantisation of input space is used, so that any point in input space is associated with a number of hyper-rectangles, and therefore with a number of memory cells. The output of a CMAC is the algebraic sum of the weights in all the memory cells activated by the input point. A change of value of the input point results in a change in the set of activated hyper-rectangles, and therefore a change in the set of memory cells participating in the CMAC output. The CMAC output is therefore stored in a distributed fashion, such that the output corresponding to any point in input space is derived from the value stored in a number of memory cells (hence the name associative memory). This provides generalisation. == Building blocks == In the adjacent image, there are two inputs to the CMAC, represented as a 2D space. Two quantising functions have been used to divide this space with two overlapping grids (one shown in heavier lines). A single input is shown near the middle, and this has activated two memory cells, corresponding to the shaded area. If another point occurs close to the one shown, it will share some of the same memory cells, providing generalisation. The CMAC is trained by presenting pairs of input points and output values, and adjusting the weights in the activated cells by a proportion of the error observed at the output. This simple training algorithm has a proof of convergence. It is normal to add a kernel function to the hyper-rectangle, so that points falling towards the edge of a hyper-rectangle have a smaller activation than those falling near the centre. One of the major problems cited in practical use of CMAC is the memory size required, which is directly related to the number of cells used. This is usually ameliorated by using a hash function, and only providing memory storage for the actual cells that are activated by inputs. == One-step convergent algorithm == Initially least mean square (LMS) method is employed to update the weights of CMAC. The convergence of using LMS for training CMAC is sensitive to the learning rate and could lead to divergence. In 2004, a recursive least squares (RLS) algorithm was introduced to train CMAC online. It does not need to tune a learning rate. Its convergence has been proved theoretically and can be guaranteed to converge in one step. The computational complexity of this RLS algorithm is O(N3). == Hardware implementation infrastructure == Based on QR decomposition, an algorithm (QRLS) has been further simplified to have an O(N) complexity. Consequently, this reduces memory usage and time cost significantly. A parallel pipeline array structure on implementing this algorithm has been introduced. Overall by utilizing QRLS algorithm, the CMAC neural network convergence can be guaranteed, and the weights of the nodes can be updated using one step of training. Its parallel pipeline array structure offers its great potential to be implemented in hardware for large-scale industry usage. == Continuous CMAC == Since the rectangular shape of CMAC receptive field functions produce discontinuous staircase function approximation, by integrating CMAC with B-splines functions, continuous CMAC offers the capability of obtaining any order of derivatives of the approximate functions. == Deep CMAC == In recent years, numerous studies have confirmed that by stacking several shallow structures into a single deep structure, the overall system could achieve better data representation, and, thus, more effectively deal with nonlinear and high complexity tasks. In 2018, a deep CMAC (DCMAC) framework was proposed and a backpropagation algorithm was derived to estimate the DCMAC parameters. Experimental results of an adaptive noise cancellation task showed that the proposed DCMAC can achieve better noise cancellation performance when compared with that from the conventional single-layer CMAC. == Summary ==
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Cybernetic Serendipity

Cybernetic Serendipity was an exhibition of cybernetic art curated by Jasia Reichardt, shown at the Institute of Contemporary Arts, London, England, from 2 August to 20 October 1968, and then toured across the United States. Two stops in the United States were the Corcoran Annex (Corcoran Gallery of Art), Washington, D.C., from 16 July to 31 August 1969, and the newly opened Exploratorium in San Francisco, from 1 November to 18 December 1969. == Content == One part of the exhibition was concerned with algorithms and devices for generating music. Some exhibits were pamphlets describing the algorithms, whilst others showed musical notation produced by computers. Devices made musical effects and played tapes of sounds made by computers. Peter Zinovieff lent part of his studio equipment - visitors could sing or whistle a tune into a microphone and his equipment would improvise a piece of music based on the tune. Another part described computer projects such as Gustav Metzger's self-destructive Five Screens With Computer, a design for a new hospital, a computer programmed structure, and dance choreography. The machines and installations were a very noticeable part of the exhibition. Gordon Pask produced a collection of large mobiles (Colloquy of Mobiles (1968)) with interacting parts that let the viewers join in the conversation. Many machines formed kinetic environments or displayed moving images. Bruce Lacey contributed his radio-controlled robots and a light-sensitive owl. Nam June Paik was represented by Robot K-456 and televisions with distorted images. Jean Tinguely provided two of his painting machines. Edward Ihnatowicz's biomorphic hydraulic ear (Sound Activated Mobile (SAM, 1968)) turned toward sounds and John Billingsley's Albert 1967 turned to face light. Wen-Ying Tsai presented his interactive cybernetic sculptures of vibrating stainless-steel rods, stroboscopic light, and audio feedback control. Several artists exhibited machines that drew patterns that the visitor could take away, or involved visitors in games. Cartoonist Rowland Emett designed the mechanical computer Forget-me-not, which was commissioned by Honeywell. Another section explored the computer's ability to produce text - both essays and poetry. Different programs produced Haiku, children's stories, and essays. One of the first computer-generated poems, by Alison Knowles and James Tenney, was included in the exhibition and catalogue. Computer-generated movies were represented by John Whitney's Permutations and a Bell Labs movie on their technology for producing movies. Some samples included images of tesseracts rotating in four dimensions, a satellite orbiting the Earth, and an animated data structure. Computer graphics were also represented, including pictures produced on cathode ray oscilloscopes and digital plotters. There was a variety of posters and graphics demonstrating the power of computers to do complex (and apparently random) calculations. Other graphics showed a simulated Mondrian and the iconic decreasing squares spiral that appeared on the exhibition's poster and book. The Boeing Company exhibited their use of wireframe graphics. The innovative computer-generated sculpture, Quad 1, was displayed at the Cybernetic Serendipity exhibit. Created by the American abstract expressionist sculptor, Robert Mallary, in 1968, Quad 1 is widely believed to be the world's first Computer Aided Design sculpture. Keith Albarn & Partners contributed to the design of the exhibition. Reflecting the prominence of music in the show, a ten-track album Cybernetic Serendipity Music was released by the ICA to accompany the show. Artists featured included Iannis Xenakis, John Cage, and Peter Zinovieff, a detail of whose graphic score for 'Four Sacred April Rounds’ (1968) was used as the cover artwork. == Attendance == Time magazine noted that there had been 40,000 visitors to the London exhibition. Other reports suggested visitor numbers were as high as 44,000 to 60,000. However, the ICA did not accurately count visitors. == After-effects == The exhibition provided the energy for the formation of British Computer Arts Society which continued to explore the interaction between science, technology and art, and put on exhibitions (for example Event One at the Royal College of Art). Several pieces were purchased by the Exploratorium in 1971, some of which are on display to this day. In 2014 the ICA held a retrospective exhibition Cybernetic Serendipity: A Documentation which included documents, installation photographs, press reviews and publications and a series of discussions in one of which Peter Zinovieff took part. To coincide with the exhibition, Cybernetic Serendipity Music was re-released as a limited-edition vinyl LP by The Vinyl Factory. The Victoria and Albert Museum marked the 50th anniversary with an exhibition in 2018 entitled "Chance and Control: Art in the Age of Computers". The V&A exhibition included many works by artists who featured in the original ICA show, plus related ephemera. "Chance and Control" subsequently toured to Chester Visual Arts and Firstsite, Colchester. In 2020, The Centre Pompidou exhibited the replica of Gordon Pask's 1968 Colloquy of Mobiles, reproduced by Paul Pangaro and TJ McLeish in 2018. In 2022 the Australian National University's School of Cybernetics launched the school by presenting an exhibition Australian Cybernetic: a point through time. The exhibition included works from Cybernetic Serendipity (1968), Australia ‘75: Festival of Creative Arts and Science (1975), and contemporary pieces curated by the School of Cybernetics. In describing Reichardt's Cybernetic Serendipity exhibition the school stated that it "represented points of expanding the cybernetic imagination" and was a "ground-breaking" "glimpse of a future in which computers were entangled with people and cultures, and through this she fashioned a blueprint for the future of computing that has since inspired generations".
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GuideGeek

GuideGeek is an AI-powered travel assistant that was launched by travel publisher Matador Network in April 2023 and is accessed by users through Instagram, WhatsApp and Facebook Messenger to plan itineraries or provide travel tips and recommendations. It uses generative artificial intelligence technology from OpenAI. Matador Network is a San Francisco-based digital media company and online travel publication with millions of monthly visitors and social media followers. == Features == Users message GuideGeek questions about travel and receive customized answers and itineraries that are pulled from ChatGPT in addition to over 1,000 additional travel-specific integrations such as live flight, hotel and vacation rental data. Travelers can specify their budget and needs to generate custom itineraries. GuideGeek is not an app and does not require the user to download anything, instead relying on messaging apps such as Instagram to connect users with the AI. GuideGeek is free to use, doesn't include ads, and doesn't sell user data. Matador Network has a team of staff members monitoring conversations to correct them if the AI makes a false statement; for example, one user incorrectly inputted “Crete Freeze” instead of “Crete, Greece”, and the AI made up a fictional soft serve company. Using a technique known as reinforcement learning from human feedback (RLHF), the accuracy of GuideGeek increased to 98%, according to Matador Network CEO, Ross Borden. == Destination partnerships == Matador Network is monetizing GuideGeek via white-label partnerships with tourism bureaus and destination marketing organizations (DMOs). As of March 2024, it had over a dozen such clients. Estes Park, Colorado, was one of the first DMOs to partner with Matador for a custom version of GuideGeek called “Rocky Mountain Roamer.” For Discover Greece, Matador created Pythia, a custom AI named after the high priestess of the Temple of Apollo at Delphi. As Borden explained to Travel + Leisure, “Visitors to the Discover Greece website will find Pythia in the bottom right corner, and they can converse with the AI like a friend who knows everything about Greece.” Other DMOs who have partnerships with GuideGeek include the Aruba Tourism Authority, Visit Reno Tahoe, Illinois Office of Tourism, and Tourism Richmond. == Awards == In recognition of GuideGeek, Fast Company named Matador Network to its 2024 list of Most Innovative Companies. Following growth driven by the launch of GuideGeek, Matador Network was ranked on the 2024 Inc. 5000 list of fastest-growing private companies in America. The 2024 Skift IDEA Awards recognized Matador Network as a finalist in the category of Best Use of AI for GuideGeek's customized AI for the travel industry. == Michael Motamedi experiment == Travel influencer and chef Michael Motamedi traveled the world with his wife Vanessa Salas and their 2-year-old daughter on a six-month trip (which was later extended to a full year) led by GuideGeek. The family started off in Morocco before heading to Spain and continuing east. The experiment became the basis of a web series called “No Fixed Address.” Motamedi used GuideGeek's AI to select countries the family visited, where they ate, and what sites they saw. Motamedi and Salas first tested out the technology in April 2023 while using the chatbot to plan a date night in Mexico City. GuideGeek provided speakeasy and drink recommendations as well as local history facts.
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Knowledge Engineering Environment

Knowledge Engineering Environment (KEE) is a frame-based development tool for expert systems. It was developed and sold by IntelliCorp, and was first released in 1983. It ran on Lisp machines, and was later ported to Lucid Common Lisp with the CLX library, an X Window System (X11) interface for Common Lisp. This version was available on several different UNIX workstations. On KEE, several extensions were offered: Simkit, a frame-based simulation library KEEconnection, database connection between the frame system and relational databases In KEE, frames are called units. Units are used for both individual instances and classes. Frames have slots and slots have facets. Facets can describe, for example, a slot's expected values, its working value, or its inheritance rule. Slots can have multiple values. Behavior can be implemented using a message passing model. KEE provides an extensive graphical user interface (GUI) to create, browse, and manipulate frames. KEE also includes a frame-based rule system. In the KEE knowledge base, rules are frames. Both forward chaining and backward chaining inference are available. KEE supports non-monotonic reasoning through the concepts of worlds. Worlds allow providing alternative slot-values of frames. Through an assumption-based truth or reason maintenance system, inconsistencies can be detected and analyzed. ActiveImages allows graphical displays to be attached to slots of Units. Typical examples are buttons, dials, graphs, and histograms. The graphics are also implemented as Units via KEEPictures, a frame-based graphics library.
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Visual hierarchy

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

Visual servoing, also known as vision-based robot control and abbreviated VS, is a technique which uses feedback information extracted from a vision sensor (visual feedback) to control the motion of a robot. One of the earliest papers that talks about visual servoing was from the SRI International Labs in 1979. == Visual servoing taxonomy == There are two fundamental configurations of the robot end-effector (hand) and the camera: Eye-in-hand, or end-point open-loop control, where the camera is attached to the moving hand and observing the relative position of the target. Eye-to-hand, or end-point closed-loop control, where the camera is fixed in the world and observing the target and the motion of the hand. Visual Servoing control techniques are broadly classified into the following types: Image-based (IBVS) Position/pose-based (PBVS) Hybrid approach IBVS was proposed by Weiss and Sanderson. The control law is based on the error between current and desired features on the image plane, and does not involve any estimate of the pose of the target. The features may be the coordinates of visual features, lines or moments of regions. IBVS has difficulties with motions very large rotations, which has come to be called camera retreat. PBVS is a model-based technique (with a single camera). This is because the pose of the object of interest is estimated with respect to the camera and then a command is issued to the robot controller, which in turn controls the robot. In this case the image features are extracted as well, but are additionally used to estimate 3D information (pose of the object in Cartesian space), hence it is servoing in 3D. Hybrid approaches use some combination of the 2D and 3D servoing. There have been a few different approaches to hybrid servoing 2-1/2-D Servoing Motion partition-based Partitioned DOF Based == Survey == The following description of the prior work is divided into 3 parts Survey of existing visual servoing methods. Various features used and their impacts on visual servoing. Error and stability analysis of visual servoing schemes. === Survey of existing visual servoing methods === Visual servo systems, also called servoing, have been around since the early 1980s , although the term visual servo itself was only coined in 1987. Visual Servoing is, in essence, a method for robot control where the sensor used is a camera (visual sensor). Servoing consists primarily of two techniques, one involves using information from the image to directly control the degrees of freedom (DOF) of the robot, thus referred to as Image Based Visual Servoing (IBVS). While the other involves the geometric interpretation of the information extracted from the camera, such as estimating the pose of the target and parameters of the camera (assuming some basic model of the target is known). Other servoing classifications exist based on the variations in each component of a servoing system , e.g. the location of the camera, the two kinds are eye-in-hand and hand–eye configurations. Based on the control loop, the two kinds are end-point-open-loop and end-point-closed-loop. Based on whether the control is applied to the joints (or DOF) directly or as a position command to a robot controller the two types are direct servoing and dynamic look-and-move. Being one of the earliest works the authors proposed a hierarchical visual servo scheme applied to image-based servoing. The technique relies on the assumption that a good set of features can be extracted from the object of interest (e.g. edges, corners and centroids) and used as a partial model along with global models of the scene and robot. The control strategy is applied to a simulation of a two and three DOF robot arm. Feddema et al. introduced the idea of generating task trajectory with respect to the feature velocity. This is to ensure that the sensors are not rendered ineffective (stopping the feedback) for any the robot motions. The authors assume that the objects are known a priori (e.g. CAD model) and all the features can be extracted from the object. The work by Espiau et al. discusses some of the basic questions in visual servoing. The discussions concentrate on modeling of the interaction matrix, camera, visual features (points, lines, etc..). In an adaptive servoing system was proposed with a look-and-move servoing architecture. The method used optical flow along with SSD to provide a confidence metric and a stochastic controller with Kalman filtering for the control scheme. The system assumes (in the examples) that the plane of the camera and the plane of the features are parallel., discusses an approach of velocity control using the Jacobian relationship s˙ = Jv˙ . In addition the author uses Kalman filtering, assuming that the extracted position of the target have inherent errors (sensor errors). A model of the target velocity is developed and used as a feed-forward input in the control loop. Also, mentions the importance of looking into kinematic discrepancy, dynamic effects, repeatability, settling time oscillations and lag in response. Corke poses a set of very critical questions on visual servoing and tries to elaborate on their implications. The paper primarily focuses the dynamics of visual servoing. The author tries to address problems like lag and stability, while also talking about feed-forward paths in the control loop. The paper also, tries to seek justification for trajectory generation, methodology of axis control and development of performance metrics. Chaumette in provides good insight into the two major problems with IBVS. One, servoing to a local minima and second, reaching a Jacobian singularity. The author show that image points alone do not make good features due to the occurrence of singularities. The paper continues, by discussing the possible additional checks to prevent singularities namely, condition numbers of J_s and Jˆ+_s, to check the null space of ˆ J_s and J^T_s . One main point that the author highlights is the relation between local minima and unrealizable image feature motions. Over the years many hybrid techniques have been developed. These involve computing partial/complete pose from Epipolar Geometry using multiple views or multiple cameras. The values are obtained by direct estimation or through a learning or a statistical scheme. While others have used a switching approach that changes between image-based and position-based on a Lyapnov function. The early hybrid techniques that used a combination of image-based and pose-based (2D and 3D information) approaches for servoing required either a full or partial model of the object in order to extract the pose information and used a variety of techniques to extract the motion information from the image. used an affine motion model from the image motion in addition to a rough polyhedral CAD model to extract the object pose with respect to the camera to be able to servo onto the object (on the lines of PBVS). 2-1/2-D visual servoing developed by Malis et al. is a well known technique that breaks down the information required for servoing into an organized fashion which decouples rotations and translations. The papers assume that the desired pose is known a priori. The rotational information is obtained from partial pose estimation, a homography, (essentially 3D information) giving an axis of rotation and the angle (by computing the eigenvalues and eigenvectors of the homography). The translational information is obtained from the image directly by tracking a set of feature points. The only conditions being that the feature points being tracked never leave the field of view and that a depth estimate be predetermined by some off-line technique. 2-1/2-D servoing has been shown to be more stable than the techniques that preceded it. Another interesting observation with this formulation is that the authors claim that the visual Jacobian will have no singularities during the motions. The hybrid technique developed by Corke and Hutchinson, popularly called portioned approach partitions the visual (or image) Jacobian into motions (both rotations and translations) relating X and Y axes and motions related to the Z axis. outlines the technique, to break out columns of the visual Jacobian that correspond to the Z axis translation and rotation (namely, the third and sixth columns). The partitioned approach is shown to handle the Chaumette Conundrum discussed in. This technique requires a good depth estimate in order to function properly. outlines a hybrid approach where the servoing task is split into two, namely main and secondary. The main task is keep the features of interest within the field of view. While the secondary task is to mark a fixation point and use it as a reference to bring the camera to the desired pose. The technique does need a depth estimate from an off-line procedure. The paper discusses two examples for which depth estimates are obtained from robot odometry and by assuming that all
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Spark NLP

Spark NLP is an open-source text processing library for advanced natural language processing for the Python, Java and Scala programming languages. The library is built on top of Apache Spark and its Spark ML library. Its purpose is to provide an API for natural language processing pipelines that implement recent academic research results as production-grade, scalable, and trainable software. The library offers pre-trained neural network models, pipelines, and embeddings, as well as support for training custom models. == Features == The design of the library makes use of the concept of a pipeline which is an ordered set of text annotators. Out of the box annotators include, tokenizer, normalizer, stemming, lemmatizer, regular expression, TextMatcher, chunker, DateMatcher, SentenceDetector, DeepSentenceDetector, POS tagger, ViveknSentimentDetector, sentiment analysis, named entity recognition, conditional random field annotator, deep learning annotator, spell checking and correction, dependency parser, typed dependency parser, document classification, and language detection. The Models Hub is a platform for sharing open-source as well as licensed pre-trained models and pipelines. It includes pre-trained pipelines with tokenization, lemmatization, part-of-speech tagging, and named entity recognition that exist for more than thirteen languages; word embeddings including GloVe, ELMo, BERT, ALBERT, XLNet, Small BERT, and ELECTRA; sentence embeddings including Universal Sentence Embeddings (USE) and Language Agnostic BERT Sentence Embeddings (LaBSE). It also includes resources and pre-trained models for more than two hundred languages. Spark NLP base code includes support for East Asian languages such as tokenizers for Chinese, Japanese, Korean; for right-to-left languages such as Urdu, Farsi, Arabic, Hebrew and pre-trained multilingual word and sentence embeddings such as LaUSE and a translation annotator. == Usage in healthcare == Spark NLP for Healthcare is a commercial extension of Spark NLP for clinical and biomedical text mining. It provides healthcare-specific annotators, pipelines, models, and embeddings for clinical entity recognition, clinical entity linking, entity normalization, assertion status detection, de-identification, relation extraction, and spell checking and correction. The library offers access to several clinical and biomedical transformers: JSL-BERT-Clinical, BioBERT, ClinicalBERT, GloVe-Med, GloVe-ICD-O. It also includes over 50 pre-trained healthcare models, that can recognize the entities such as clinical, drugs, risk factors, anatomy, demographics, and sensitive data. == Spark OCR == Spark OCR is another commercial extension of Spark NLP for optical character recognition (OCR) from images, scanned PDF documents, and DICOM files. It is a software library built on top of Apache Spark. It provides several image pre-processing features for improving text recognition results such as adaptive thresholding and denoising, skew detection & correction, adaptive scaling, layout analysis and region detection, image cropping, removing background objects. Due to the tight coupling between Spark OCR and Spark NLP, users can combine NLP and OCR pipelines for tasks such as extracting text from images, extracting data from tables, recognizing and highlighting named entities in PDF documents or masking sensitive text in order to de-identify images. Several output formats are supported by Spark OCR such as PDF, images, or DICOM files with annotated or masked entities, digital text for downstream processing in Spark NLP or other libraries, structured data formats (JSON and CSV), as files or Spark data frames. Users can also distribute the OCR jobs across multiple nodes in a Spark cluster. == License and availability == Spark NLP is licensed under the Apache 2.0 license. The source code is publicly available on GitHub as well as documentation and a tutorial. Prebuilt versions of Spark NLP are available in PyPi and Anaconda Repository for Python development, in Maven Central for Java & Scala development, and in Spark Packages for Spark development. == Award == In March 2019, Spark NLP received Open Source Award for its contributions in natural language processing in Python, Java, and Scala.
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Blockhead (thought experiment)

Blockhead is a theoretical computer system invented as part of a thought experiment by philosopher Ned Block, which appeared in a paper titled "Psychologism and Behaviorism". Block did not personally name the computer in the paper. == Overview == In "Psychologism and Behaviorism", Block argues that the internal mechanism of a system is important in determining whether that system is intelligent and claims to show that a non-intelligent system could pass the Turing test. Block asks the reader to imagine a conversation lasting any given amount of time. He states that given the nature of language, there are a finite number of syntactically and grammatically correct sentences that can be used to start a conversation. Consequently, there is a limit to how many "sensible" responses can be made to the first sentence, then to the second sentence, and so on until the conversation ends. Block then asks the reader to imagine a computer which had been programmed with all the sentences in theory, if not in practice. Block argues that such a machine could continue a conversation with a person on any topic because the computer would be programmed with every sentence that it was possible to use so the computer would be able to pass the Turing test despite the fact that—according to Block—it was not intelligent. Block says that this does not show that there is only one correct internal structure for generating intelligence but simply that some internal structures do not generate intelligence. The argument is related to John Searle's Chinese room.
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