"What I eat in a day" videos are a trend on several social media platforms where a person describes all the meals and snacks that they eat during a given day, often as part of a given diet. The videos, shared on platforms including Twitter, TikTok and YouTube, become increasingly popular in 2020, with some of them accumulating millions of views, and they are considered a profitable industry for the people making them. Some have raised concerns that the videos may promote an unrealistic standard for healthy eating and contribute to the development of eating disorders. == Format == These videos often feature a montage of the food that the creator eats over the course of the day, sometimes with the associated calorie count of the foods that they describe. Unlike related mukbang videos, however, in which participants eat large amounts of food, the diets described are often restrictive. However, other videos are labeled as "unhealthy" and depict large portion sizes and higher amounts of processed food. == Popularity == "What I eat in a day" videos have existed for a long time, especially on YouTube, but they have become much more widespread in recent years. This phenomenon is self-reinforcing because when social media users watch or like these videos they are likely to see more of them in the future. Indeed, some of the most successful videos have tens of millions of view each. == Criticism and controversy == Several dieticians and mental health professionals over the impacts that these videos can have, as they can advocate a restrictive style of eating and not "promote body diversity." They have also raised concerns that this trend could contribute to a rise in disordered eating, especially since use of social media is known to increase feelings of negative body image. This trend is particularly prevalent among young adults, which are also the group with the highest vulnerability to eating disorders. More recently, a portion of these videos have begun to challenge diets and depict more realistic ways of eating in order to reduce the potential consequences of the trend.
MegaHAL
MegaHAL is a computer conversation simulator, or "chatterbot", created by Jason Hutchens. == Background == In 1996, Jason Hutchens entered the Loebner Prize Contest with HeX, a chatterbot based on ELIZA. HeX won the competition that year and took the $2000 prize for having the highest overall score. In 1998, Hutchens again entered the Loebner Prize Contest with his new program, MegaHAL. MegaHAL made its debut in the 1998 Loebner Prize Contest. Like many chatterbots, the intent is for MegaHAL to appear as a human fluent in a natural language. As a user types sentences into MegaHAL, MegaHAL will respond with sentences that are sometimes coherent and at other times complete gibberish. MegaHAL learns as the conversation progresses, remembering new words and sentence structures. It will even learn new ways to substitute words or phrases for other words or phrases. Many would consider conversation simulators like MegaHAL to be a primitive form of artificial intelligence. However, MegaHAL doesn't understand the conversation or even the sentence structure. It generates its conversation based on sequential and mathematical relationships. In the world of conversation simulators, MegaHAL is based on relatively old technology and could be considered primitive. However, its popularity has grown due to its humorous nature; it has been known to respond with twisted or nonsensical statements that are often amusing. == Theory of Operation == MegaHal is based at least in part on a so-called "hidden Markov Model", so that the first thing that Megahal does when it "trains" on a script or text is to build a database of text fragments encompassing every possible subset of perhaps 4, 5, or even 6 consecutive words, so that for example - if MegaHal trains on the Declaration of Independence, then MegaHal will build a database containing text fragments such as "When in the course", "in the course of", "the course of human", "course of human events", "of human events, one", "human events, one people", and so on. Then if Megahal is fed another text, such has "Superman, Yes! It's Superman - he can change the course of mighty rivers, bend steel with his bare hands - and who disguised at Clark Kent …" IT MIGHT induce Megahal to apparently bemuse itself to proffer whether Superman can change the course of human events, or something else altogether - such as some rambling about "when in the course of mighty rivers", and so on. Thus likewise - if a phrase like "the White house said" comes up a lot in some text; then Megahal's ability to switch randomly between different contexts which otherwise share some similarity can result at times in some surprising lucidity, or else it might otherwise seem quite bizarre. == Examples == There are some sentences that MegaHAL generated: CHESS IS A FUN SPORT, WHEN PLAYED WITH SHOT GUNS. and COWS FLY LIKE CLOUDS BUT THEY ARE NEVER COMPLETELY SUCCESSFUL. == Distribution == MegaHAL is distributed under the Unlicense. Its source code can be downloaded from the Github repository.
List of artificial intelligence projects
The following is a list of current and past, non-classified notable artificial intelligence projects. == Specialized projects == === Brain-inspired === Blue Brain Project, an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level. Google Brain, a deep learning project part of Google X attempting to have intelligence similar or equal to human-level. Human Brain Project, ten-year scientific research project, based on exascale supercomputers. === Cognitive architectures === 4CAPS, developed at Carnegie Mellon University under Marcel A. Just ACT-R, developed at Carnegie Mellon University under John R. Anderson. AIXI, Universal Artificial Intelligence developed by Marcus Hutter at IDSIA and ANU. CALO, a DARPA-funded, 25-institution effort to integrate many artificial intelligence approaches (natural language processing, speech recognition, machine vision, probabilistic logic, planning, reasoning, many forms of machine learning) into an AI assistant that learns to help manage your office environment. CHREST, developed under Fernand Gobet at Brunel University and Peter C. Lane at the University of Hertfordshire. CLARION, developed under Ron Sun at Rensselaer Polytechnic Institute and University of Missouri. CoJACK, an ACT-R inspired extension to the JACK multi-agent system that adds a cognitive architecture to the agents for eliciting more realistic (human-like) behaviors in virtual environments. Copycat, by Douglas Hofstadter and Melanie Mitchell at the Indiana University. DUAL, developed at the New Bulgarian University under Boicho Kokinov. FORR developed by Susan L. Epstein at The City University of New York. IDA and LIDA, implementing Global Workspace Theory, developed under Stan Franklin at the University of Memphis. OpenCog Prime, developed using the OpenCog Framework. Procedural Reasoning System (PRS), developed by Michael Georgeff and Amy L. Lansky at SRI International. Psi-Theory developed under Dietrich Dörner at the Otto-Friedrich University in Bamberg, Germany. Soar, developed under Allen Newell and John Laird at Carnegie Mellon University and the University of Michigan. Society of Mind and its successor The Emotion Machine proposed by Marvin Minsky. Subsumption architectures, developed e.g. by Rodney Brooks (though it could be argued whether they are cognitive). === Games === AlphaGo, software developed by Google that plays the Chinese board game Go. Chinook, a computer program that plays English draughts; the first to win the world champion title in the competition against humans. Deep Blue, a chess-playing computer developed by IBM which beat Garry Kasparov in 1997. Halite, an artificial intelligence programming competition created by Two Sigma in 2016. Libratus, a poker AI that beat world-class poker players in 2017, intended to be generalisable to other applications. The Matchbox Educable Noughts and Crosses Engine (sometimes called the Machine Educable Noughts and Crosses Engine or MENACE) was a mechanical computer made from 304 matchboxes designed and built by artificial intelligence researcher Donald Michie in 1961. Quick, Draw!, an online game developed by Google that challenges players to draw a picture of an object or idea and then uses a neural network to guess what the drawing is. The Samuel Checkers-playing Program (1959) was among the world's first successful self-learning programs, and as such a very early demonstration of the fundamental concept of artificial intelligence (AI). Stockfish AI, an open source chess engine currently ranked the highest in many computer chess rankings. TD-Gammon, a program that learned to play world-class backgammon partly by playing against itself (temporal difference learning with neural networks). === Internet activism === Serenata de Amor, project for the analysis of public expenditures and detect discrepancies. === Knowledge and reasoning === Alice (Microsoft), a project from Microsoft Research Lab aimed at improving decision-making in Economics Braina, an intelligent personal assistant application with a voice interface for Windows OS. Cyc, an attempt to assemble an ontology and database of everyday knowledge, enabling human-like reasoning. Eurisko, a language by Douglas Lenat for solving problems which consists of heuristics, including some for how to use and change its heuristics. Google Now, an intelligent personal assistant with a voice interface in Google's Android and Apple Inc.'s iOS, as well as Google Chrome web browser on personal computers. Holmes a new AI created by Wipro. Microsoft Cortana, an intelligent personal assistant with a voice interface in Microsoft's various Windows 10 editions. MindsDB, is an AI automation platform for building AI/ML powered features and applications. Mycin, an early medical expert system. Open Mind Common Sense, a project based at the MIT Media Lab to build a large common sense knowledge base from online contributions. Siri, an intelligent personal assistant and knowledge navigator with a voice-interface in Apple Inc.'s iOS and macOS. SNePS, simultaneously a logic-based, frame-based, and network-based knowledge representation, reasoning, and acting system. Viv (software), a new AI by the creators of Siri. Wolfram Alpha, an online service that answers queries by computing the answer from structured data. === Motion and manipulation === AIBO, the robot pet for the home, grew out of Sony's Computer Science Laboratory (CSL). Cog, a robot developed by MIT to study theories of cognitive science and artificial intelligence, now discontinued. === Music === Melomics, a bioinspired technology for music composition and synthesization of music, where computers develop their own style, rather than mimic musicians. === Natural language processing === AIML, an XML dialect for creating natural language software agents. Apache Lucene, a high-performance, full-featured text search engine library written entirely in Java. Apache OpenNLP, a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking and parsing. Artificial Linguistic Internet Computer Entity (A.L.I.C.E.), a natural language processing chatterbot. ChatGPT, a chatbot built on top of OpenAI's GPT-3.5 and GPT-4 family of large language models. Claude, a family of large language models developed by Anthropic and launched in 2023. Claude LLMs achieved high coding scores in several recognized LLM benchmarks. Cleverbot, successor to Jabberwacky, now with 170m lines of conversation, Deep Context, fuzziness and parallel processing. Cleverbot learns from around 2 million user interactions per month. DeepSeek: Chinese chatbot funded by hedge fund High-Flyer. DBRX, 136 billion parameter open sourced large language model developed by Mosaic ML and Databricks. ELIZA, a famous 1966 computer program by Joseph Weizenbaum, which parodied person-centered therapy. FreeHAL, a self-learning conversation simulator (chatterbot) which uses semantic nets to organize its knowledge to imitate a very close human behavior within conversations. Gemini, a family of multimodal large language model developed by Google's DeepMind. Drives the Gemini chatbot, formerly known as Bard. GigaChat, a chatbot by Russian Sberbank. GPT-3, a 2020 language model developed by OpenAI that can produce text difficult to distinguish from that written by a human. Jabberwacky, a chatbot by Rollo Carpenter, aiming to simulate natural human chat. LaMDA, a family of conversational neural language models developed by Google. LLaMA, a 2023 language model family developed by Meta that includes 7, 13, 33 and 65 billion parameter models.[1] Mycroft, a free and open-source intelligent personal assistant that uses a natural language user interface. PARRY, another early chatterbot, written in 1972 by Kenneth Colby, attempting to simulate a paranoid schizophrenic. SHRDLU, an early natural language processing computer program developed by Terry Winograd at MIT from 1968 to 1970. SYSTRAN, a machine translation technology by the company of the same name, used by Yahoo!, AltaVista and Google, among others. === Speech recognition === CMU Sphinx, a group of speech recognition systems developed at Carnegie Mellon University. DeepSpeech, an open-source Speech-To-Text engine based on Baidu's deep speech research paper. Whisper, an open-source speech recognition system developed at OpenAI. === Speech synthesis === 15.ai, a real-time artificial intelligence text-to-speech tool developed by an anonymous researcher from MIT. Amazon Polly, a speech synthesis software by Amazon. Festival Speech Synthesis System, a general multi-lingual speech synthesis system developed at the Centre for Speech Technology Research (CSTR) at the University of Edinburgh. WaveNet, a deep neural network for generating raw audio. === Video === CapCut is a video editor tool, developed
Information explosion
Information explosion is the rapid increase in the amount of published information or data and the effects of this abundance. As the amount of available data grows, the problem of managing the information becomes more difficult, which can lead to information overload. The Online Oxford English Dictionary indicates use of the phrase in a March 1964 New Statesman article. The New York Times first used the phrase in its editorial content in an article by Walter Sullivan on June 7, 1964, in which he described the phrase as "much discussed". The earliest known use of the phrase was in a speech about television by NBC president Pat Weaver at the Institute of Practitioners of Advertising in London on September 27, 1955. The speech was rebroadcast on radio station WSUI in Iowa City and excerpted in the Daily Iowan newspaper two months later. Many sectors are seeing this rapid increase in the amount of information available such as healthcare, supermarkets, and governments. Another sector that is being affected by this phenomenon is journalism. Such a profession, which in the past was responsible for the dissemination of information, may be suppressed by the overabundance of information today. Techniques to gather knowledge from an overabundance of electronic information (e.g., data fusion may help in data mining) have existed since the 1970s. Another common technique to deal with such amount of information is qualitative research. Such approaches aim to organize the information, synthesizing, categorizing and systematizing in order to be more usable and easier to search. == Growth patterns == The world's technological capacity to store information grew from, optimally compressed, 2.6 exabytes in 1986 to 15.7 in 1993, over 54.5 in 2000, and to 295 exabytes in 2007. The world's technological capacity to receive information through one-way broadcast networks was 432 exabytes of (optimally compressed) information in 1986, 715 (optimally compressed) exabytes in 1993, 1,200 (optimally compressed) exabytes in 2000, and 1,900 in 2007. The world's effective capacity to exchange information through two-way telecommunications networks was 0.281 exabytes of (optimally compressed) information in 1986, 0.471 in 1993, 2.2 in 2000, and 65 (optimally compressed) exabytes in 2007. A new metric that is being used in an attempt to characterize the growth in person-specific information, is the disk storage per person (DSP), which is measured in megabytes/person (where megabytes is 106 bytes and is abbreviated MB). Global DSP (GDSP) is the total rigid disk drive space (in MB) of new units sold in a year divided by the world population in that year. The GDSP metric is a crude measure of how much disk storage could possibly be used to collect person-specific data on the world population. In 1983, one million fixed drives with an estimated total of 90 terabytes were sold worldwide; 30MB drives had the largest market segment. In 1996, 105 million drives, totaling 160,623 terabytes were sold with 1 and 2 gigabyte drives leading the industry. By the year 2000, with 20GB drive leading the industry, rigid drives sold for the year are projected to total 2,829,288 terabytes Rigid disk drive sales to top $34 billion in 1997. According to Latanya Sweeney, there are three trends in data gathering today: Type 1. Expansion of the number of fields being collected, known as the “collect more” trend. Type 2. Replace an existing aggregate data collection with a person-specific one, known as the “collect specifically” trend. Type 3. Gather information by starting a new person-specific data collection, known as the “collect it if you can” trend. == Related terms == Since "information" in electronic media is often used synonymously with "data", the term information explosion is closely related to the concept of data flood (also dubbed data deluge). Sometimes the term information flood is used as well. All of those basically boil down to the ever-increasing amount of electronic data exchanged per time unit. A term that covers the potential negative effects of information explosion is information inflation. The awareness about non-manageable amounts of data grew along with the advent of ever more powerful data processing since the mid-1960s. == Challenges == Even though the abundance of information can be beneficial in several levels, some problems may be of concern such as privacy, legal and ethical guidelines, filtering and data accuracy. Filtering refers to finding useful information in the middle of so much data, which relates to the job of data scientists. A typical example of a necessity of data filtering (data mining) is in healthcare since in the next years is due to have EHRs (Electronic Health Records) of patients available. With so much information available, the doctors will need to be able to identify patterns and select important data for the diagnosis of the patient. On the other hand, according to some experts, having so much public data available makes it difficult to provide data that is actually anonymous. Another point to take into account is the legal and ethical guidelines, which relates to who will be the owner of the data and how frequently he/she is obliged to the release this and for how long. With so many sources of data, another problem will be accuracy of such. An untrusted source may be challenged by others, by ordering a new set of data, causing a repetition in the information. According to Edward Huth, another concern is the accessibility and cost of such information. The accessibility rate could be improved by either reducing the costs or increasing the utility of the information. The reduction of costs according to the author, could be done by associations, which should assess which information was relevant and gather it in a more organized fashion. == Web servers == As of August 2005, there were over 70 million web servers. As of September 2007 there were over 135 million web servers. == Blogs == According to Technorati, the number of blogs doubles about every 6 months with a total of 35.3 million blogs as of April 2006. This is an example of the early stages of logistic growth, where growth is approximately exponential, since blogs are a recent innovation. As the number of blogs approaches the number of possible producers (humans), saturation occurs, growth declines, and the number of blogs eventually stabilizes.
Ontology merging
Ontology merging defines the act of bringing together two conceptually divergent ontologies or the instance data associated to two ontologies. This is similar to work in database merging (schema matching). This merging process can be performed in a number of ways, manually, semi automatically, or automatically. Manual ontology merging although ideal is extremely labour-intensive and current research attempts to find semi or entirely automated techniques to merge ontologies. These techniques are statistically driven often taking into account similarity of concepts and raw similarity of instances through textual string metrics and semantic knowledge. These techniques are similar to those used in information integration employing string metrics from open source similarity libraries.
Podium (company)
Podium is a private technology company headquartered in Lehi, Utah that develops cloud-based software related to messaging, customer feedback, online reviews, selling products, and requesting payments. == History == Podium was founded in 2014 by Eric Rea and Dennis Steele, who developed a tool to help small businesses "build their online reputation" through online reviews. Podium was initially known as RepDrive before rebranding as Podium in 2015. In 2015, Podium moved from a spare bedroom to a new location above a Provo bike shop. In March 2020, Podium added payments technology to its product suite. In November 2021, Podium raised $201 million in Series D funding and was valued at $3 billion. == Product == Podium is a software-as-a-service platform designed to improve business online reputation. It helps users manage business interactions in one tool. Users can communicate reviews, texts, chats, and post payment directly within the app.
Small data
Small data is data that is 'small' enough for human comprehension. It is data in a volume and format that makes it accessible, informative and actionable. The term "big data" is about machines and "small data" is about people. This is to say that eyewitness observations or five pieces of related data could be small data. Small data is what we used to think of as data. The only way to comprehend Big data is to reduce the data into small, visually-appealing objects representing various aspects of large data sets (such as histogram, charts, and scatter plots). Big Data is all about finding correlations, but Small Data is all about finding the causation, the reason why. A formal definition of small data has been proposed by Allen Bonde, former vice-president of Innovation at Actuate - now part of OpenText: "Small data connects people with timely, meaningful insights (derived from big data and/or “local” sources), organized and packaged – often visually – to be accessible, understandable, and actionable for everyday tasks." Another definition of small data is: The small set of specific attributes produced by the Internet of Things. These are typically a small set of sensor data such as temperature, wind speed, vibration and status. It was estimated (2016) that “If one takes the top 100 biggest innovations of our time, perhaps around 60% to 65% percent are really based on Small Data.” as Martin Lindstrom puts it. Small data includes everything from Snapchat to simple objects such as the post-it note. Lindstrom believes we become so focused on Big-Data that we tend to forget about more basic concepts and creativity. Lindstrom defines Small Data "as seemingly insignificant observations you identify in consumers’ homes, is everything from how you place your shoes on how you hang your paintings". He thus considers that one should perfectly master the basic (Small Data) in order to mine and find correlations. == Academic Recognition and Methodology == The growing significance of "small data" as a distinct field of inquiry was highlighted by the 2024 Thematic Einstein Semester (TES) on Small Data Analysis, hosted by the Berlin Mathematics Research Center MATH+. A central focus of this semester was the transition from theoretical analysis to practical decision-making. Because small data sets are primarily used to drive specific actions, the presentation of results becomes an essential methodological step. The semester’s findings emphasized that while small data may lack volume, it often contains a high density of "many possible interpretations." Consequently, the final conference of the TES was structured around the pillars of interpretation, explanation, and knowledge gain. Participants sought to develop new mathematical and methodical representations that could accurately depict this wealth of interpretative possibilities. This work underscores that analyzing small data is not purely a computational task; it requires a robust interface between mathematics and diverse disciplines to ensure that insights are both contextually grounded and scientifically rigorous. == Uses in business == === Marketing === Bonde has written about the topic for Forbes, Direct Marketing News, CMO.com and other publications. According to Martin Lindstrom, in his book, Small Data: "{In customer research, small data is} Seemingly insignificant behavioural observations containing very specific attributes pointing towards an unmet customer need. Small data is the foundation for breakthrough ideas or completely new ways to turnaround brands." His approach is based on the combination of the observation of small samples with intuition. Marketers can obtain market insights from gathering Small Data by engaging with and observing people in their own environments. In comparison to Big Data, Small Data has the power to trigger emotions and to provide insights into the reasons behind the behaviours of customers. It may uncover detailed information on a person's extroversion or introversion, self-confidence, whether one is having problems in his/her relationship, etc. According to Lindstrom, relationships among people and customer segments are organized around four criteria: Climate: It reveals for example how a person's environment affects their diet. Rulership: The power or government in charge Religion: The prevalence of religion in a country, depending on its influence, indicates whether a person's decision making process is impacted by their belief system. Tradition: Cultural norms influence people's behaviors and interactions. Many companies underestimate the power of Small Data, using samples of millions of consumers instead of recognizing the value of closely observing small samples in their market research. In his book, Lindstrom defines "7Cs", which companies should consider in the attempt to derive meaningful customer insights and market trends through small data from their customers: Collecting: Understanding the manner in which observations are translated inside a home. Clues: Uncovering other distinctive emotional reflections that can be observed. Connecting: Identifying the consequences of emotional behaviour. Causation: Understanding what emotions are being evoked. Correlation: Identifying the initial date of appearance of the behaviour or emotion. Compensation: Identifying the unmet or unfulfilled desire. Concept: Defining the “big idea” compensation for the identified consumer need. Some of Lindstrom's clients such as Lowes Foods looked at data in a different way and actually chose to live with the customer. “As you enter their store, they have now created an amazing community where every staff member acts in a character mood, based on Small Data”. The supermarket made everything it can to make the customer feel at home. All the behaviours of employees are inspired by customer feedbacks gathered from interviews directly done at customer’s home. === Healthcare === Researchers at Cornell University started developing applications to monitor health problems in patients, based on small data. This is an initiative of Cornell's Small Data Lab, in close cooperation with Weill Cornell Medicine College, led by Deborah Estrin. The Small Data Lab developed a series of apps, focusing not only on gathering data from patients' pain but also tracking habits in areas such as grocery shopping. In the case of patients with rheumatoid arthritis for example, which has flares and remissions that do not follow a particular cycle, the app gathers information passively, thus allowing to forecast when a flare might be coming up based on small changes in behaviour. Other apps developed also include monitoring online grocery shopping, to use this information from every user to adapt their groceries to the recommendations of nutritionists, or monitoring email language to identify patterns that might indicate "fluctuations in cognitive performance, fatigue, side effects of medication or poor sleep, and other conditions and treatments that are typically self-reported and self-medicated". === Postal Service === The United States Postal Service (USPS) used optical character recognition (OCR) to automatically read and process 98% of all hand-addressed mail and 99.5% of machine-printed mail. By combining this technology with its small data sample of US zip codes, the USPS can now process more than 36,000 pieces of mail per hour. === Aerospace === In 2015, Boeing established the analytics lab for aerospace data in cooperation with the Carnegie Mellon University to leverage the university's leadership in machine learning, language technologies and data analytics. One of the initiatives projects aims to by standardize maintenance logs using AI to dramatically reduce costs. Currently, there is no standardized procedure to document maintenance logs leading to small but highly unstructured data sets. As a result, it becomes highly difficult for maintenance workers to translate these variations in maintenance logs within a short period of time. However, with AI and a narrow data set of common aircraft maintenance terminology, it becomes possible to dynamically translate these logs in real time. By using AI to enhance the speed and accuracy of the airline maintenance workflow, airlines stand to save billions according to the Harvard Business Review.