AI For Students Copilot

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Singularity studies

Singularity studies is an interdisciplinary academic field which examines the idea of technological singularity — the hypothesised point at which artificial intelligence may surpass human intelligence, might be attained by artificial intelligence (AI), robotics, and other technologies and sciences, and its social impacts. In this academic field, the study and research are conducted across a broad array of terrains such as information science, robotics, social informatics, economics, philosophy, and ethics. The primary aim of singularity studies is to gain an integrative understanding of the transformation of social systems occurring in tandem with the explosive evolution of AI and also the changes to be effected by such transformation in the view of humans, ethics, and legal systems. == History == An academic work on technological singurality has appeared in computer science, philosophy, sociology, and law since the early 1990s. Early discussions of an intelligence explosion were popularised by science-fiction writer Vernor Vinge in 1993 and later systematised by futurist Ray Kurzweil. Since the 2010s, universities such as Oxford, Stanford, and Keio have established dedicated programmes, while peer-reviewed journals have begun to publish scenario analyses and policy studies. Ongoing debates question the predictive value of singularity scenarios and warn against a deterministic view of technology. == Characteristics of research == Singularity studies extends beyond mere future predictions and offer an intellectual foundation for proactively designing and creating a desirable future. Principal research themes in this realm include: Ethics of AI; Social implications of technologies; Possibility of harmonious coexistence of humans and AI; Communication with AI; and Redesign of social systems. == Technologists and academics == Vernor Vinge: Propounded the concept of singularity in 1993, making a massive impact on the academic and science-fiction spheres. Ray Kurzweil: Predicted the advent around 2045 of the technological singularity in his 2005 book The Singularity Is Near. Nick Bostrom: Offered philosophical reflections on superintelligence and the risks posed by AI. He is the founding director of the now-dissolved Future of Humanity Institute at the University of Oxford. === Japan === Kento Sasano: A social informatician, AI educator, and inventor. He is the president of the Japan Society of Singularity Studies. == Challenges and outlook == Singularity studies is still evolving as an academic field, and quite a few challenges remain unresolved in regard to the systematization of their theories, research methods, and educational curricula. That said, in this day and age of accelerating technological and societal shifts, interdisciplinary approaches have gained in importance and are drawing much attention in the arenas of scholarly research, intercorporate collaboration, and policy planning.
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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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Google Nest

Google Nest, formerly branded Google Home, is a line of smart home products including smart speakers, smart displays, streaming devices, thermostats, smoke detectors, routers and security systems including smart doorbells, cameras and smart locks. The Nest brand name was originally owned by Nest Labs, co-founded by former Apple engineers Tony Fadell and Matt Rogers in 2010. Its flagship product, which was the company's first offering, is the Nest Learning Thermostat, introduced in 2011. The product is programmable, self-learning, sensor-driven, and Wi-Fi-enabled: features that are often found in other Nest products. It was followed by the Nest Protect smoke and carbon monoxide detectors in October 2013. After its acquisition of Dropcam in 2014, the company introduced its Nest Cam branding of security cameras beginning in June 2015. The company quickly expanded to more than 130 employees by the end of 2012. Google acquired Nest Labs for US$3.2 billion in January 2014, when the company employed 280. As of late 2015, Nest employs more than 1,100 and added a primary engineering center in Seattle. After Google reorganized itself under the holding company Alphabet Inc., Nest operated independently of Google from 2015 to 2018. However, in 2018, Nest was merged into Google's home-devices unit led by Rishi Chandra, effectively ceasing to exist as a separate business. In July 2018, it was announced that all Google Home electronics products will henceforth be marketed under the brand Google Nest. == History == === Nest Labs before acquisition by Google === Nest Labs was founded in 2010 by former Apple engineers Tony Fadell and Matt Rogers. The idea came when Fadell was building a vacation home and found all of the available thermostats on the market to be inadequate, motivated to bring something better on the market. Early investors in Nest Labs included Shasta Ventures and Kleiner Perkins. === Acquisition by Google of Nest Labs, Dropcam, and Revolv === On January 13, 2014, Google announced plans to acquire Nest Labs for $3.2 billion in cash. Google completed the acquisition the next day, on January 14, 2014. The company would operate independently from Google's other businesses. In June 2014, it was announced that Nest would buy camera startup Dropcam for $555 million. With the purchase, Dropcam became integrated with other Nest products; if the Protect alarm is triggered, the Dropcam can automatically start recording, and the Thermostat can use Dropcam to sense for motion. In September 2014, the Nest Thermostat and Nest Protect (a smoke alarm) became available in Belgium, France, Ireland, and the Netherlands. Initially, they were sold in approximately 400 stores across Europe, with another 150 stores to be added by the end of the year. In June 2015, the new Nest Cam, replacing the Dropcam, was announced, together with the second generation of the Nest Protect; there were internal reports that sales of the rebranded camera fell. On October 24, 2014, Nest both acquired the hub service Revolv, and discontinued its product line, gaining the expertise of Revolv's staff. === Nest as a subsidiary of Alphabet Inc. === In August 2015, Google announced that it would restructure its operations under a new parent company, Alphabet Inc., with Nest being separated from Google as a subsidiary of the new holding company. In January 2016, some Nest thermostats stopped working, a fault attributed to a software update from two weeks earlier. There were no lawsuits, individual or class-action, due to an arbitration clause in the contract. All Revolv smart hubs, costing several hundred dollars, were deliberately remotely bricked on May 15, 2016; notice was posted on the company's website in February. The story became news on April 4. The "lifetime subscription" to Revolv's online service, which had been sold with the hub, was defined by Nest to be the lifetime of the device, which ended May 15. Nest's decision to brick the hubs, and its "acerbic" corporate culture, faced substantial criticism from within Google/Alphabet and in press coverage. Many of Nest's staffers came from Dropcam and Revolv, and by November 2015, about 70 of about 1000 staffers had quit, causing management concern. Some countermeasures had been taken in takeover deals, to financially discourage senior people from leaving before set dates. Of the ~100 Dropcam staffers, about half had left by March 2016, when former Dropcam CEO Greg Duffy (who left 8 months after the takeover) wrote a post openly regretting selling his company to Nest. He stated that about 500 people had left (of a 1200-person staff). On June 6, 2016, Tony Fadell, the Nest CEO, announced in a blog post that he was leaving the company he founded with Matt Rogers and stepping into an "advisory" role. At this point the Nest acquisition was described by some press as a "disaster" for Google. As of mid-June 2016, Nest's problems were considered symptomatic of the limited market for home automation. According to Frank Gillet of Forrester Research, only 6% of American households possessed internet-connected devices such as appliances, home-monitoring systems, speakers, or lighting. He also predicted this percentage would grow to only 15% by 2021. Furthermore, 72% of respondents in a 2016 British survey conducted by Pricewaterhouse Coopers did not foresee adopting smart-home technology over the next two to five years. === Nest as a part of Google hardware division === On February 7, 2018, it was announced by hardware head Rick Osterloh that Nest had been merged into Google's hardware division, directly alongside units such as Google Home and Chromecast. It would retain its separate Palo Alto headquarters, but Nest CEO Marwan Fawaz would now report to Osterloh, and there were plans for tighter integration with Google platforms and software such as Google Assistant in future products. Shortly after the announcement, co-founder and chief product officer Matt Rogers announced his plans to leave the company. On July 18, 2018, Nest CEO Marwan Fawaz stepped down. Nest was merged with Google's home devices team, led by Rishi Chandra. During the Google I/O keynote on May 7, 2019, it was announced that Google Nest will now serve as the blanket branding for all of Google's home products. The Google Home Hub was retroactively renamed Google Nest Hub, while a new and larger version of the product is now available called the Nest Hub Max with both a larger screen and an amplified speaker, for a greater low-end audio experience. Also, product lines such as Chromecast, Google Home, and Google Wifi will now be marketed under the Google Nest brand. In addition, Nest began to deprecate its own internal platforms, announcing the discontinuation of the existing "Works with Nest" program in favor of Google Assistant going forward, and pushing users to migrate themselves from Nest's account system to Google accounts. Google published Nest-specific privacy information outlining a commitment to transparency, not selling personal information, and giving users control of their data. In February 2019, a privacy incident affecting the Google Nest Guard system came about. The controversy stemmed from the fact that Nest Guard, a security device that was part of the Nest Secure system, contained a hidden microphone that was not disclosed in any product specifications. It resulted in a public relations failure. === Partnership with ADT === In August 2020 Google announced intent to invest $450 million in ADT Inc. for a 6.6% stake in the company. The companies intend to integrate Nest devices with ADT's security monitoring services and eventually make them the “cornerstone of ADT’s smart home offering”, according to Nest. Upon the announcement, the shares of ADT doubled in value and hit all-time high of $17.21. === Use with Amazon Alexa === As of mid-2022, Google's newer Nest cameras will now work with Amazon Alexa devices such as Amazon Echo Show, Fire TV, and Fire Tablet to view captured security camera footage. === End of support policies === On October 25, 2025, software support was ended for the 1st and 2nd generation Nest Learning Thermostats. In addition, most of the smart functionality including the Home Away features, notifications, and carbon monoxide sensor became inoperative as they were dependent on connection with Google servers. By mid-November, third-party software solutions became available to restore functionality to affected thermostats. == Products == === Nest Learning Thermostat === The Nest Learning Thermostat is an electronic, programmable, and self-learning Wi-Fi-enabled thermostat that optimizes heating and cooling of homes and businesses to conserve energy. It is based on a machine-learning algorithm: for the first weeks users have to regulate the thermostat in order to provide the reference data set. Nest can then learn people's schedules, at which temperature they are used to and when. Using built-in sensors and phones' locations it can
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Journal of Experimental and Theoretical Artificial Intelligence

The Journal of Experimental and Theoretical Artificial Intelligence is a quarterly peer-reviewed scientific journal published by Taylor and Francis. It covers all aspects of artificial intelligence and was established in 1989. The editor-in-chief is Eric Dietrich (Binghamton University), the deputy editors-in-chief are Li Pheng Khoo (School of Mechanical & Aerospace Engineering, Nanyang Technological University) and Antonio Lieto (Department of Computer Science, University of Turin). == Abstracting and indexing == The journal is abstracted and indexed in: According to the Journal Citation Reports, the journal has a 2020/2021 impact factor of 2.340 .
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Linux Trace Toolkit

The Linux Trace Toolkit (LTT) is a set of tools that is designed to log program execution details from a patched Linux kernel and then perform various analyses on them, using console-based and graphical tools. LTT has been mostly superseded by its successor LTTng (Linux Trace Toolkit Next Generation). LTT allows the user to see in-depth information about the processes that were running during the trace period, including when context switches occurred, how long the processes were blocked for, and how much time the processes spent executing vs. how much time the processes were blocked. The data is logged to a text file and various console-based and graphical (GTK+) tools are provided for interpreting that data. In order to do data collection, LTT requires a patched Linux kernel. The authors of LTT claim that the performance hit for a patched kernel compared to a regular kernel is minimal; Their testing has reportedly shown that this is less than 2.5% on a "normal use" system (measured using batches of kernel makes) and less than 5% on a file I/O intensive system (measured using batches of tar). == Usage == === Collecting trace data === Data collection is Started by: trace 15 foo This command will cause the LTT tracedaemon to do a trace that lasts for 15 seconds, writing trace data to foo.trace and process information from the /proc filesystem to foo.proc. The trace command is actually a script which runs the program tracedaemon with some common options. It is possible to run tracedaemon directly and in that case, the user can use a number of command-line options to control the data which is collected. For the complete list of options supported by tracedaemon, see the online manual page for tracedaemon. === Viewing the results === Viewing the results of a trace can be accomplished with: traceview foo This command will launch a graphical (GTK+) traceview tool that will read from foo.trace and foo.proc. This tool can show information in various interesting ways, including Event Graph, Process Analysis, and Raw Trace. The Event Graph is perhaps the most interesting view, showing the exact timing of events like page faults, interrupts, and context switches, in a simple graphical way. The traceview command is a wrapper for a program called tracevisualizer. For the complete list of options supported by tracevisualizer, see the online manual page for tracevisualizer.
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Partial-order planning

Partial-order planning is an approach to automated planning that maintains a partial ordering between actions and only commits ordering between actions when forced to, that is, ordering of actions is partial. Also this planning doesn't specify which action will come out first when two actions are processed. By contrast, total-order planning maintains a total ordering between all actions at every stage of planning. Given a problem in which some sequence of actions is needed to achieve a goal, a partial-order plan specifies all actions that must be taken, but specifies an ordering between actions only where needed. Consider the following situation: a person must travel from the start to the end of an obstacle course. The course is composed of a bridge, a see-saw, and a swing-set. The bridge must be traversed before the see-saw and swing-set are reachable. Once reachable, the see-saw and swing-set can be traversed in any order, after which the end is reachable. In a partial-order plan, ordering between these obstacles is specified only when needed. The bridge must be traversed first. Second, either the see-saw or swing-set can be traversed. Third, the remaining obstacle can be traversed. Then the end can be traversed. Partial-order planning relies upon the principle of least commitment for its efficiency. == Partial-order plan == A partial-order plan or partial plan is a plan which specifies all actions that must be taken, but only specifies the order between actions when needed. It is the result of a partial-order planner. A partial-order plan consists of four components: A set of actions (also known as operators). A partial order for the actions. It specifies the conditions about the order of some actions. A set of causal links. It specifies which actions meet which preconditions of other actions. Alternatively, a set of bindings between the variables in actions. A set of open preconditions. It specifies which preconditions are not fulfilled by any action in the partial-order plan. To keep the possible orders of the actions as open as possible, the set of order conditions and causal links must be as small as possible. A plan is a solution if the set of open preconditions is empty. A linearization of a partial order plan is a total order plan derived from the particular partial order plan; in other words, both order plans consist of the same actions, with the order in the linearization being a linear extension of the partial order in the original partial order plan. === Example === For example, a plan for baking a cake might start: go to the store get eggs; get flour; get milk pay for all goods go to the kitchen This is a partial plan because the order for finding eggs, flour and milk is not specified, the agent can wander around the store reactively accumulating all the items on its shopping list until the list is complete. == Partial-order planner == A partial-order planner is an algorithm or program which will construct a partial-order plan and search for a solution. The input is the problem description, consisting of descriptions of the initial state, the goal and possible actions. The problem can be interpreted as a search problem where the set of possible partial-order plans is the search space. The initial state would be the plan with the open preconditions equal to the goal conditions. The final state would be any plan with no open preconditions, i.e. a solution. The initial state is the starting conditions, and can be thought of as the preconditions to the task at hand. For a task of setting the table, the initial state could be a clear table. The goal is simply the final action that needs to be accomplished, for example setting the table. The operators of the algorithm are the actions by which the task is accomplished. For this example there may be two operators: lay (tablecloth), and place (glasses, plates, and silverware). === Plan space === The plan space of the algorithm is constrained between its start and finish. The algorithm starts, producing the initial state and finishes when all parts of the goal have been achieved. In the setting a table example, two types of actions exist that must be addressed: the put-out and lay operators. Four unsolved operators also exist: Action 1, lay-tablecloth, Action 2, Put-out (plates), Action 3, Put-out (silverware), and Action 4, Put-out (glasses). However, a threat arises if Action 2, 3, or 4 comes before Action 1. This threat is that the precondition to the start of the algorithm will be unsatisfied as the table will no longer be clear. Thus, constraints exist that must be added to the algorithm that force Actions 2, 3, and 4 to come after Action 1. Once these steps are completed, the algorithm will finish and the goal will have been completed. === Threats === As seen in the algorithm presented above, partial-order planning can encounter certain threats, meaning orderings that threaten to break connected actions, thus potentially destroying the entire plan. There are two ways to resolve threats: Promotion Demotion Promotion orders the possible threat after the connection it threatens. Demotion orders the possible threat before the connection it threatens. Partial-order planning algorithms are known for being both sound and complete, with sound being defined as the total ordering of the algorithm, and complete being defined as the capability to find a solution, given that a solution does in fact exist. == Partial-order vs. total-order planning == Partial-order planning is the opposite of total-order planning, in which actions are sequenced all at once and for the entirety of the task at hand. The question arises when one has two competing processes, which one is better? Anthony Barret and Daniel Weld have argued in their 1993 book, that partial-order planning is superior to total-order planning, as it is faster and thus more efficient. They tested this theory using Korf’s taxonomy of subgoal collections, in which they found that partial-order planning performs better because it produces more trivial serializability than total-order planning. Trivial serializability facilitates a planner’s ability to perform quickly when dealing with goals that contain subgoals. Planners perform more slowly when dealing with laboriously serializable or nonserializable subgoals. The determining factor that makes a subgoal trivially or laboriously serializable is the search space of different plans. They found that partial-order planning is more adept at finding the quickest path, and is therefore the more efficient of these two main types of planning. == The Sussman anomaly == Partial-order plans are known to easily and optimally solve the Sussman anomaly. Using this type of incremental planning system solves this problem quickly and efficiently. This was a result of partial-order planning that solidified its place as an efficient planning system. == Disadvantages to partial-order planning == One drawback of this type of planning system is that it requires a lot more computational power for each node. This higher per-node cost occurs because the algorithm for partial-order planning is more complex than others. This has important artificial intelligence implications. When coding a robot to do a certain task, the creator needs to take into account how much energy is needed. Though a partial-order plan may be quicker it may not be worth the energy cost for the robot. The creator must be aware of and weigh these two options to build an efficient robot.
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Versata

Versata is a privately held software company, one of several business units under the ESW Capital umbrella. Versata acquires underperforming or financially struggling enterprise software companies, integrates them into their portfolio, and makes operational changes to improve the viability and performance of the companies. == History == === Early years (1991–2000) === This company was founded in 1991 with the name Image Innovations; Naren Bakshi was co-founder and president, Kevin Fletcher Tweedy was vice president of technology, and they sold a development tool set named Image Application WorkBench that worked with Plexus Software's imaging platform. In 1997, the company name changed to Vision Software. They sold a small suite of software: Vision Builder for accelerated coding; and Vision StoryBoard Pro for creating software documentation. In 1998, their flagship product was a Java development tool named Vision JADE. In January 2000, the company changed names again, this time to Versata, and their e-business automation system, Versata Logic Suite, had three components: Versata Logic Server to host business rules written in Java, Versata Studio for developing the business rules, and Versata Connectors for connecting the logic server to IBM database servers. === Public company (2000–2006) === They went public in March 2000 during the dot-com bubble, raising about $94 million and reaching a market capitalization of over $2.5 billion despite reporting just $13 million in revenue and a $21 million loss in the prior year. In November 2000, Versata expanded into the business workflow area with the acquisition of Verve, Inc. and its workflow management system by the same name. From early 2001 through mid-2003, Versata's revenues were in quarter-over-quarter decline until Alan Baratz took over as CEO. Five consecutive quarters of growth followed until early 2005, when revenues once again took a downward plunge. In mid-2005, the company was notified by NASDAQ that it no longer met NASDAQ's requirements for continued listing, related to maintenance of a minimum amount of shareholder's equity, market value, or net income. In July 2005, Versata was delisted from NASDAQ and publicly traded on the OTC (also known as the Pink Sheets). == Versata, a business unit of ESW Capital == In January 2006, Austin-based Trilogy, Inc. acquired the company and took it private. Trilogy then proceeded to merge portions of Trilogy, specifically, Trilogy Technology Group, into Versata and began acquiring further companies, reorganizing dramatically and offshoring most technical positions to its office in Bangalore, India. From 2006 to 2008, Versata continued to make acquisitions mostly in US. Most of the employees in the acquired companies were laid -off with the majority work being offshored to its India office in Bangalore. In early 2009, Versata made another major overhaul of its business model when it asked all its employees in India to work as contractors through oDesk for a gDev which is an entity incorporated by Trilogy to manage its outsourcing activities. The only employees left in Versata were the ones in US. == Acquisitions == a Corizon was acquired by Metatomix, while Metatomix was part of Versata. b Infopia was acquired by Everest Software, while Everest Software was part of Versata. c Symphony Commerce was acquired by Quantum Retail, while Quantum Retail was part of Versata. == Legal disputes == === Patent infringement and "poison pill" lawsuits with Selectica === The legal disputes with Selectica began in 2004 (before Trilogy acquired Versata in January 2006) and lasted until 2010. While there were many suits and counter-suits, they largely centered around three issues: 2004–2006: Patent infringement in configure, price, and quote (CPQ) software 2005–2007: Patent infringement in contract lifecycle management (CLM) software 2008–2010: The "poison pill" lawsuit In 2004, Selectica and Trilogy had competing CPQ software: Selectica sold Solutions Advisor and Deal Optimization, while Trilogy sold Selling Chain. In April of that year, Trilogy Software sued Selectica for patent infringement. In 2005, before the court ruling, Trilogy made several offers to buy Selectica, but the board rejected them. In January 2006, the court ordered Selectica to pay Trilogy $7.5 million in damages. Four days after the January 2006 judgment in the first lawsuit, Trilogy announced its acquisition of Versata for an undisclosed amount. In 2005, Selectica had acquired the Determine CLM software platform, which included features that overlapped with some offered by Versata. In October 2006, Versata filed a second patent infringement lawsuit. The case was settled in 2007, with Selectica agreeing to pay Trilogy and Versata $10 million, plus up to $7.5 million in additional contingent payments. In 2008, Versata began acquiring Selectica stock. By December, Selectica's board amended its shareholder rights plan to adopt a "poison pill" with an unusually low trigger threshold: if any shareholder acquired more than 4.99% of company stock, their ownership would be diluted. The board explained that the move was meant to protect Selectica's net operating losses (NOLs), which were tax-deductible if the company returned to profitability. Under IRS Section 382, a significant change in stock ownership could cause those NOLs to be disqualified. Versata intentionally triggered the poison pill and also offered to sell back the stocks at a profit (greenmailing them), which prompted a legal dispute over whether Selectica's board had the authority to set such a low threshold and whether defending NOLs justified triggering shareholder dilution. The case ultimately reached the Delaware Supreme Court, which upheld the poison pill in October 2010, ruling in favor of Selectica. === Intellectual property lawsuit over joint development with Sun Microsystems === In 1998, Sun Microsystems hired Trilogy to help Sun's developers in California create a software configurator (later named the WC5 Configurator) that Sun's customers could use to modify products they wanted to buy, customizing products to have the features they wanted. Trilogy worked on the WC5 Configurator for several years, then Sun transferred the work to Oracle to finish. Trilogy believed that they owned the copyright to the work they'd done for Sun, and in 2006 after the merger with Versata they sued Sun for more than $100 million in damages. In April 2009, a jury ruled in favor of Sun and rejected Versata's claims. === Patent lawsuit and ruling on patents of abstract ideas with SAP === SAP developed Pricing Engine, a component in their enterprise resource planning (ERP) system. It competed with an older Trilogy product called Pricer, which was part of Trilogy's Selling Chain platform in the mid-1990s before they merged with Versata. In April 2007—the year after Trilogy acquired Versata—Versata filed a lawsuit against SAP for patent infringement. In August 2009, the jury agreed with Versata and awarded them $139 million. The court granted a new trial on damages and in September 2011, in the retrial, the jury awarded Versata $345 million. This then went to the US Court of Appeals, which in May 2013 affirmed the $345 million damages award, plus interest that had accumulated. In October 2014, Versata and SAP settled their litigation for an undisclosed amount of money. With the dispute between Versata and SAP settled, in June 2013 the Patent Trial and Appeal Board (PTAB) reviewed the validity of the patent itself, and issued a decision in a Covered Business Method (CBM) review, stating that the disputed items were abstract ideas and thus under the US patent law not patentable. In July 2015, the Federal Circuit agreed with PTAB's decision that the challenged items were not patentable. === Trade secrets and damages dispute with Internet Brands === Internet Brands was formerly known as CarsDirect and AutoData Solutions. Like Trilogy, they made software for automakers that helped customers compare vehicles online. In the late 1990s, Trilogy and Internet Brands tried to combine their products but failed to do so, and after a December 1999 lawsuit they made a settlement agreement in May 2001. In 2008, Versata sued Internet Brands claiming they had violated the settlement agreement by making presentations to potential clients stating they had a license from Versata to use and sell Versata technical solutions; and doing so had cost Versata business with Chrysler. Internet Brands' countersuit argued that Versata had misappropriated trade secrets and asked the jury to use Versata's business relationship with Toyota—including revenue from Toyota contracts—as a benchmark to calculate damages. The jury agreed and used that data to determine a $2 million damages award in favor of Internet Brands’ subsidiary, AutoData Solutions. Versata appealed the decision, and in January 2014 the court upheld the $2 million award to Internet Brands. === Patent challenges a
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Hyper basis function network

In machine learning, a Hyper basis function network, or HyperBF network, is a generalization of radial basis function (RBF) networks concept, where the Mahalanobis-like distance is used instead of the Euclidean distance measure. Hyper basis function networks were first introduced by Poggio and Girosi in the 1990 paper “Networks for Approximation and Learning”. == Network Architecture == The typical HyperBF network structure consists of a real input vector x ∈ R n {\displaystyle x\in \mathbb {R} ^{n}} , a hidden layer of activation functions and a linear output layer. The output of the network is a scalar function of the input vector, ϕ : R n → R {\displaystyle \phi :\mathbb {R} ^{n}\to \mathbb {R} } , is given by where N {\displaystyle N} is a number of neurons in the hidden layer, μ j {\displaystyle \mu _{j}} and a j {\displaystyle a_{j}} are the center and weight of neuron j {\displaystyle j} . The activation function ρ j ( | | x − μ j | | ) {\displaystyle \rho _{j}(||x-\mu _{j}||)} at the HyperBF network takes the following form where R j {\displaystyle R_{j}} is a positive definite d × d {\displaystyle d\times d} matrix. Depending on the application, the following types of matrices R j {\displaystyle R_{j}} are usually considered R j = 1 2 σ 2 I d × d {\displaystyle R_{j}={\frac {1}{2\sigma ^{2}}}\mathbb {I} _{d\times d}} , where σ > 0 {\displaystyle \sigma >0} . This case corresponds to the regular RBF network. R j = 1 2 σ j 2 I d × d {\displaystyle R_{j}={\frac {1}{2\sigma _{j}^{2}}}\mathbb {I} _{d\times d}} , where σ j > 0 {\displaystyle \sigma _{j}>0} . In this case, the basis functions are radially symmetric, but are scaled with different width. R j = d i a g ( 1 2 σ j 1 2 , . . . , 1 2 σ j z 2 ) I d × d {\displaystyle R_{j}=diag\left({\frac {1}{2\sigma _{j1}^{2}}},...,{\frac {1}{2\sigma _{jz}^{2}}}\right)\mathbb {I} _{d\times d}} , where σ j i > 0 {\displaystyle \sigma _{ji}>0} . Every neuron has an elliptic shape with a varying size. Positive definite matrix, but not diagonal. == Training == Training HyperBF networks involves estimation of weights a j {\displaystyle a_{j}} , shape and centers of neurons R j {\displaystyle R_{j}} and μ j {\displaystyle \mu _{j}} . Poggio and Girosi (1990) describe the training method with moving centers and adaptable neuron shapes. The outline of the method is provided below. Consider the quadratic loss of the network H [ ϕ ∗ ] = ∑ i = 1 N ( y i − ϕ ∗ ( x i ) ) 2 {\displaystyle H[\phi ^{}]=\sum _{i=1}^{N}(y_{i}-\phi ^{}(x_{i}))^{2}} . The following conditions must be satisfied at the optimum: where R j = W T W {\displaystyle R_{j}=W^{T}W} . Then in the gradient descent method the values of a j , μ j , W {\displaystyle a_{j},\mu _{j},W} that minimize H [ ϕ ∗ ] {\displaystyle H[\phi ^{}]} can be found as a stable fixed point of the following dynamic system: where ω {\displaystyle \omega } determines the rate of convergence. Overall, training HyperBF networks can be computationally challenging. Moreover, the high degree of freedom of HyperBF leads to overfitting and poor generalization. However, HyperBF networks have an important advantage that a small number of neurons is enough for learning complex functions.
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AI agent

In the context of generative artificial intelligence, AI agents (also referred to as compound AI systems or agentic AI) are a class of intelligent agents that can pursue goals, use tools, and take actions with varying degrees of autonomy. In practice, they usually operate within human-defined objectives, constraints, and available tools. == Overview == AI agents possess several key attributes, including goal-directed behavior, natural language interfaces, the capacity to use external tools, and the ability to perform multi-step tasks. Their control flow is frequently driven by large language models (LLMs). Agent systems may also include memory components, planning logic, tool interfaces, and orchestration software for coordinating agent components. AI agents do not have a standard definition. NIST describes agentic AI as an emerging area requiring standards for secure operation, interoperability, and reliable interaction with external systems. A common application of AI agents is task automation: for example, booking travel plans based on a user's prompted request. Companies such as Google, Microsoft and Amazon Web Services have offered platforms for deploying pre-built AI agents. Several protocols have been proposed for standardizing inter-agent communication, with examples including the Model Context Protocol, Gibberlink, and many others. Some of these protocols are also used for connecting agents to external applications. In December 2025, Linux Foundation announced the formation of the Agentic AI Foundation (AAIF), with the goal of ensuring agentic AI evolves transparently and collaboratively. == History == AI agents have been traced back to research from the 1990s, with Harvard professor Milind Tambe noting that the definition of an AI agent was not clear at the time. Researcher Andrew Ng has been credited with spreading the term "agentic" to a wider audience in 2024. == Training and testing == Researchers have attempted to build world models and reinforcement learning environments to train or evaluate AI agents. For example, video games such as Minecraft and No Man's Sky as well as replicas of company websites, have also been used for training such agents. == Autonomous capabilities == The Financial Times compared the autonomy of AI agents to the SAE classification of self-driving cars, likening most applications to level 2 or level 3, with some achieving level 4 in highly specialized circumstances, and level 5 being theoretical. == Cognitive architecture == The following are some internal design options for reasoning within an agent: Retrieval-augmented generation ReAct (Reason + Act) pattern is an iterative process in which an AI agent alternates between reasoning and taking actions, receives observations from the environment or external tools, and integrates these observations into subsequent reasoning steps. Reflexion, which uses an LLM to create feedback on the agent's plan of action and stores that feedback in a memory cache. A tool/agent registry, for organizing software functions or other agents that the agent can use. One-shot model querying, which queries the model once to create the plan of action. === Reference architecture === Ken Huang proposed an AI agent reference architecture, which consists of seven interconnected layers, with each layer building on the functionality of the layers beneath it: Layer 1: Foundation models - provide the core AI engines to power agent capabilities. Layer 2: Data operations - manage the complex data infrastructure required for AI agent operations, including Vector database, data loaders, RAG. Layer 3: Agent frameworks - sophisticated software and tools that simplify the development and management of the AI agents. Layer 4: Deployment and infrastructure - provide the robust technical foundation for running AI agents. Layer 5: Evaluation and observability - focus on assessing the safety and performance of AI agents. Layer 6: Security and compliance - a crucial protective framework ensuring AI agents operate safely, securely, and conform to regulatory boundaries. At this layer security and compliance features embedded into all the AI agent stack layers are integrated together. Layer 7: Agent ecosystem - represents the AI agents' interface with real-world applications and users. == Orchestration patterns == To execute complex tasks, autonomous agents are often integrated with other agents or specialized tools. These configurations, known as orchestration patterns or workflows, include the following: Prompt chaining: A sequence where the output of one step serves as the input for the next. Routing: The classification of an input to direct it to a specialized downstream task or tool. Parallelization: The simultaneous execution of multiple tasks. Sequential processing: A fixed, linear progression of tasks through a predefined pipeline. Planner-critic: An iterative pattern where one agent generates a proposal and another evaluates it to provide feedback for refinement. == Multimodal AI agents == In addition to large language models (LLMs), vision-language models (VLMs) and multimodal foundation models can be used as the basis for agents. In September 2024, Allen Institute for AI released an open-source vision-language model. Nvidia released a framework for developers to use VLMs, LLMs and retrieval-augmented generation for building AI agents that can analyze images and videos, including video search and video summarization. Microsoft released a multimodal agent model – trained on images, video, software user interface interactions, and robotics data – that the company claimed can manipulate software and robots. == Applications == As of April 2025, per the Associated Press, there are few real-world applications of AI agents. As of June 2025, per Fortune, many companies are primarily experimenting with AI agents. The Information divided AI agents into seven archetypes: business-task agents, for acting within enterprise software; conversational agents, which act as chatbots for customer support; research agents, for querying and analyzing information (such as OpenAI Deep Research); analytics agents, for analyzing data to create reports; software developer or coding agents (such as Cursor); domain-specific agents, which include specific subject matter knowledge; and web browser agents (such as OpenAI Operator). By mid-2025, AI agents have been used in video game development, gambling (including sports betting), cryptocurrency wallets (including cryptocurrency trading and meme coins) and social media. In August 2025, New York Magazine described software development as the most definitive use case of AI agents. Likewise, by October 2025, noting a decline in expectations, The Information noted AI coding agents and customer support as the primary use cases by businesses. In November 2025, The Wall Street Journal reported that few companies that deployed AI agents have received a return on investment. === Applications in government === Several government bodies in the United States and United Kingdom have deployed or announced the deployment of agents, at the local and national level. The city of Kyle, Texas deployed an AI agent from Salesforce in March 2025 for 311 customer service. In November 2025, the Internal Revenue Service stated that it would use Agentforce, AI agents from Salesforce, for the Office of Chief Counsel, Taxpayer Advocate Services and the Office of Appeals. That same month, Staffordshire Police announced that they would trial Agentforce agents for handling non-emergency 101 calls in the United Kingdom starting in 2026. In December 2025, the Department of Neighborhoods in Detroit, Michigan, in partnership with a local business, deployed a pilot project in two Detroit districts for an AI agent to be used for customer service calls. In February 2025, Thomas Shedd, the director of the Technology Transformation Services, proposed using AI coding agents across the United States federal government. A recruiter for the Department of Government Efficiency proposed in April 2025 to use AI agents to automate the work of about 70,000 United States federal government employees, as part of a startup with funding from OpenAI and a partnership agreement with Palantir. This proposal was criticized by experts for its impracticality, if not impossibility, and the lack of corresponding widespread adoption by businesses. In December 2025, the Food and Drug Administration announced that it would offer "agentic AI capabilities" to its staff for "meeting management, pre-market reviews, review validation, post-market surveillance, inspections and compliance and administrative functions." That same month, the United States Department of Defense launched GenAI.mil, an internal platform for American military personnel to use generative AI-based applications based on Google Gemini, including "intelligent agentic workflows". Defense Secretary Pete Hegseth listed applications such as "[conducting] deep r
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TasteDive

TasteDive (formerly named TasteKid) is an entertainment recommendation engine for films, TV shows, music, video games, books, people, places, and brands. It also has elements of a social media site; it allows users to connect with "tastebuds", people with like minded interests. == History == TasteDive was founded in 2008 as TasteKid by brothers Andrei Oghina and Felix Oghina. In 2019, it was acquired by Qloo headquartered in NYC. "Qloo has built for developers and enterprises what TasteDive has built for individuals". == Description == When a user types in the title of a film or TV show, the site's algorithm provides a list of similar content. It provides recommendations for TV shows to watch based on films liked by the user, and vice versa. It also provides recommendations for music, video games, and books, and includes film and TV trailers and music videos. An account is free and is not required to receive recommendations, but recommendations are more accurate for those with an account. The more a user explores the site, the more the site learns about the user's preferences and the better the results become. The site also has a social media aspect where one can see activity and gain recommendations from other users, how many others in the community like or dislike any recommendation, and how popular their tastes are within the TasteDive community. The main competitors of TasteDive are Taste App, Trakt.tv and Tastoid.
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Zvi Mowshowitz

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

AlphaGo Zero is a version of DeepMind's Go software AlphaGo. AlphaGo's team published an article in Nature in October 2017 introducing AlphaGo Zero, a version created without using data from human games, and stronger than any previous version. By playing games against itself, AlphaGo Zero: surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0; reached the level of AlphaGo Master in 21 days; and exceeded all previous versions in 40 days. Training artificial intelligence (AI) without datasets derived from human experts has significant implications for the development of AI with superhuman skills, as expert data is "often expensive, unreliable, or simply unavailable." Demis Hassabis, the co-founder and CEO of DeepMind, said that AlphaGo Zero was so powerful because it was "no longer constrained by the limits of human knowledge". Furthermore, AlphaGo Zero performed better than standard deep reinforcement learning models (such as Deep Q-Network implementations) due to its integration of Monte Carlo tree search. David Silver, one of the first authors of DeepMind's papers published in Nature on AlphaGo, said that it is possible to have generalized AI algorithms by removing the need to learn from humans. Google later developed AlphaZero, a generalized version of AlphaGo Zero that could play chess and shōgi in addition to Go. In December 2017, AlphaZero beat the 3-day version of AlphaGo Zero by winning 60 games to 40, and with 8 hours of training it outperformed AlphaGo Lee on an Elo scale. AlphaZero also defeated a top chess program (Stockfish) and a top Shōgi program (Elmo). == Architecture == The network in AlphaGo Zero is a ResNet with two heads. The stem of the network takes as input a 17x19x19 tensor representation of the Go board. 8 channels are the positions of the current player's stones from the last eight time steps. (1 if there is a stone, 0 otherwise. If the time step go before the beginning of the game, then 0 in all positions.) 8 channels are the positions of the other player's stones from the last eight time steps. 1 channel is all 1 if black is to move, and 0 otherwise. The body is a ResNet with either 20 or 40 residual blocks and 256 channels. There are two heads, a policy head and a value head. Policy head outputs a logit array of size 19 × 19 + 1 {\displaystyle 19\times 19+1} , representing the logit of making a move in one of the points, plus the logit of passing. Value head outputs a number in the range ( − 1 , + 1 ) {\displaystyle (-1,+1)} , representing the expected score for the current player. -1 represents current player losing, and +1 winning. == Training == AlphaGo Zero's neural network was trained using TensorFlow, with 64 GPU workers and 19 CPU parameter servers. Only four TPUs were used for inference. The neural network initially knew nothing about Go beyond the rules. Unlike earlier versions of AlphaGo, Zero only perceived the board's stones, rather than having some rare human-programmed edge cases to help recognize unusual Go board positions. The AI engaged in reinforcement learning, playing against itself until it could anticipate its own moves and how those moves would affect the game's outcome. In the first three days AlphaGo Zero played 4.9 million games against itself in quick succession. It appeared to develop the skills required to beat top humans within just a few days, whereas the earlier AlphaGo took months of training to achieve the same level. According to Epoch.ai, training cost 3e23 FLOPs. For comparison, the researchers also trained a version of AlphaGo Zero using human games, AlphaGo Master, and found that it learned more quickly, but actually performed more poorly in the long run. DeepMind submitted its initial findings in a paper to Nature in April 2017, which was then published in October 2017. == Hardware cost == The hardware cost for a single AlphaGo Zero system in 2017, including the four TPUs, has been quoted as around $25 million. == Applications == According to Hassabis, AlphaGo's algorithms are likely to be of the most benefit to domains that require an intelligent search through an enormous space of possibilities, such as protein folding (see AlphaFold) or accurately simulating chemical reactions. AlphaGo's techniques are probably less useful in domains that are difficult to simulate, such as learning how to drive a car. DeepMind stated in October 2017 that it had already started active work on attempting to use AlphaGo Zero technology for protein folding, and stated it would soon publish new findings. == Reception == AlphaGo Zero was widely regarded as a significant advance, even when compared with its groundbreaking predecessor, AlphaGo. Oren Etzioni of the Allen Institute for Artificial Intelligence called AlphaGo Zero "a very impressive technical result" in "both their ability to do it—and their ability to train the system in 40 days, on four TPUs". The Guardian called it a "major breakthrough for artificial intelligence", citing Eleni Vasilaki of Sheffield University and Tom Mitchell of Carnegie Mellon University, who called it an impressive feat and an “outstanding engineering accomplishment" respectively. Mark Pesce of the University of Sydney called AlphaGo Zero "a big technological advance" taking us into "undiscovered territory". Gary Marcus, a psychologist at New York University, has cautioned that for all we know, AlphaGo may contain "implicit knowledge that the programmers have about how to construct machines to play problems like Go" and will need to be tested in other domains before being sure that its base architecture is effective at much more than playing Go. In contrast, DeepMind is "confident that this approach is generalisable to a large number of domains". In response to the reports, South Korean Go professional Lee Sedol said, "The previous version of AlphaGo wasn’t perfect, and I believe that’s why AlphaGo Zero was made." On the potential for AlphaGo's development, Lee said he will have to wait and see but also said it will affect young Go players. Mok Jin-seok, who directs the South Korean national Go team, said the Go world has already been imitating the playing styles of previous versions of AlphaGo and creating new ideas from them, and he is hopeful that new ideas will come out from AlphaGo Zero. Mok also added that general trends in the Go world are now being influenced by AlphaGo's playing style. "At first, it was hard to understand and I almost felt like I was playing against an alien. However, having had a great amount of experience, I’ve become used to it," Mok said. "We are now past the point where we debate the gap between the capability of AlphaGo and humans. It’s now between computers." Mok has reportedly already begun analyzing the playing style of AlphaGo Zero along with players from the national team. "Though having watched only a few matches, we received the impression that AlphaGo Zero plays more like a human than its predecessors," Mok said. Chinese Go professional Ke Jie commented on the remarkable accomplishments of the new program: "A pure self-learning AlphaGo is the strongest. Humans seem redundant in front of its self-improvement." == Comparison with predecessors == == AlphaZero == On 5 December 2017, DeepMind team released a preprint on arXiv, introducing AlphaZero, a program using generalized AlphaGo Zero's approach, which achieved within 24 hours a superhuman level of play in chess, shogi, and Go, defeating world-champion programs, Stockfish, Elmo, and 3-day version of AlphaGo Zero in each case. AlphaZero (AZ) is a more generalized variant of the AlphaGo Zero (AGZ) algorithm, and is able to play shogi and chess as well as Go. Differences between AZ and AGZ include: AZ has hard-coded rules for setting search hyperparameters. The neural network is now updated continually. Chess (unlike Go) can end in a tie; therefore AZ can take into account the possibility of a tie game. An open source program, Leela Zero, based on the ideas from the AlphaGo papers is available. It uses a GPU instead of the TPUs recent versions of AlphaGo rely on.
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Wunderlist

Wunderlist is a discontinued cloud-based task management application. It allowed users to create lists to manage their tasks from a smartphone, tablet, computer and smartwatch. Wunderlist was free; additional collaboration features were available in a paid version known as Wunderlist Pro, released April 2013. Wunderlist was created in 2011 by Berlin-based startup 6Wunderkinder (Engl.: 6Prodigies). The company was acquired by Microsoft in June 2015, at which time the app had over 13 million users. In April 2017, Microsoft announced that Wunderlist would eventually be discontinued in favor of Microsoft To Do, a new multi-platform app developed by the Wunderlist team that has direct integration with the company's Office 365 service. On December 6, 2019, Microsoft announced that it would shut down Wunderlist on May 6, 2020. After this date, the application would no longer sync but users could still import their content into Microsoft To Do. == History == In 2009, Wunderlist's CEO Christian Reber called on the social network platform XING for business partners to create a new to-do app. Frank Thelen responded and together Reber and Thelen developed first concepts for Wunderlist. The necessary seed funding was granted by High-Tech Gründerfonds and e42 GmbH. The first version of Wunderlist was launched on November 9, 2010. Initially, the program was created for desktop PCs and platforms such as Windows, Linux and Mac OS X. In December 2011, the app received approval for the iPhone. Subsequently, the developers released a version prepared for the iPad with the name Wunderlist HD. In September 2012, the developers announced a shutdown of their service Wunderkit. Instead they wanted to focus on creating a new version of Wunderlist, which was later on released in December 2012 under the name Wunderlist 2. In September 2013, the company announced it had over 5 million users. In July 2014, a new major update was released under the name of Wunderlist 3, with a new real-time sync architecture. Wunderlist reached 10 million users in December 2014. On June 1, 2015, it was announced that Microsoft had acquired 6Wunderkinder, makers of Wunderlist, for between US$100 million and US$200 million (~$258 million in 2024). Following its acquisition of the app, Microsoft announced in April 2017 a preview of To-Do, a multi-platform task management app developed by the Wunderlist team that was intended to eventually replace Wunderlist and incorporate most of its features. As of January 2019, To-Do had not yet reached feature parity with Wunderlist, with its team citing that the service had to be completely re-written to use Microsoft Azure instead of Amazon Web Services. Frustrated by the perceived lack of roadmap, in September 2019, Reber began to publicly ask Microsoft-related accounts on Twitter whether he could buy Wunderlist back. Shortly afterward, however, Microsoft unveiled updates to To-Do that make it more closely resemble Wunderlist. In December 2019, Microsoft announced that it would fully shut down Wunderlist as of May 6, 2020. The team responsible for creating Wunderlist, led by co-founder Christian Reber, created that Superlist app in early 2024. == Finances == In its initial round of funding, 100,000 euro was invested in 6Wunderkinder by Frank Thelen and others. In December 2010, High-Tech Gründerfonds invested 500,000 euro (approximately US$660,000) in the company. T-Venture also invested an undisclosed amount in the startup. In its Series A round of funding in November 2011, Atomico invested $4.2 million (~$5.76 million in 2024) while High-Tech Gründerfonds invested an undisclosed additional amount. In May 2012, High-Tech Gründerfonds sold off its stake in 6Wunderkinder to Earlybird Venture Capital. In November 2013, $19 million (~$25.2 million in 2024) was raised in a Series B round led by Sequoia Capital with participation from Earlybird and Atomico. == Awards == In 2013, Wunderlist for Mac was named App of the Year. Wunderlist was selected as a Google Play Top Developer in 2013. In 2014, Wunderlist won the "Golden Mi" award from Xiaomi, and also named as one of its Best Apps of 2014 was given a "Google Play Editor's Choice" award, and was named in Google Play's Best Apps of 2014 as well as Apple's Best of 2014.
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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.
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Philosophy of information

The philosophy of information (PI) is a branch of philosophy that studies topics relevant to information processing, representational system and consciousness, cognitive science, computer science, information science and information technology. It includes: the critical investigation of the conceptual nature and basic principles of information, including its dynamics, utilisation and sciences the elaboration and application of information-theoretic and computational methodologies to philosophical problems. == History == The philosophy of information (PI) has evolved from the philosophy of artificial intelligence, logic of information, cybernetics, social theory, ethics and the study of language and information. === Logic of information === The logic of information, also known as the logical theory of information, considers the information content of logical signs and expressions along the lines initially developed by Charles Sanders Peirce. === Study of language and information === Later contributions to the field were made by Fred Dretske, Jon Barwise, Brian Cantwell Smith, and others. The Center for the Study of Language and Information (CSLI) was founded at Stanford University in 1983 by philosophers, computer scientists, linguists, and psychologists, under the direction of John Perry and Jon Barwise. === P.I. === More recently this field has become known as the philosophy of information. The expression was coined in the 1990s by Luciano Floridi, who has published prolifically in this area with the intention of elaborating a unified and coherent, conceptual frame for the whole subject. == Definitions of "information" == The concept information has been defined by several theorists. Charles S. Peirce's theory of information was embedded in his wider theory of symbolic communication he called the semiotic, now a major part of semiotics. For Peirce, information integrates the aspects of signs and expressions separately covered by the concepts of denotation and extension, on the one hand, and by connotation and comprehension on the other. Donald M. MacKay says that information is a distinction that makes a difference. According to Luciano Floridi, four kinds of mutually compatible phenomena are commonly referred to as "information": Information about something (e.g. a train timetable) Information as something (e.g. DNA, or fingerprints) Information for something (e.g. algorithms or instructions) Information in something (e.g. a pattern or a constraint). == Philosophical directions == === Computing and philosophy === Recent creative advances and efforts in computing, such as semantic web, ontology engineering, knowledge engineering, and modern artificial intelligence provide philosophy with fertile ideas, new and evolving subject matters, methodologies, and models for philosophical inquiry. While computer science brings new opportunities and challenges to traditional philosophical studies, and changes the ways philosophers understand foundational concepts in philosophy, further major progress in computer science would only be feasible when philosophy provides sound foundations for areas such as bioinformatics, software engineering, knowledge engineering, and ontologies. Classical topics in philosophy, namely, mind, consciousness, experience, reasoning, knowledge, truth, morality and creativity are rapidly becoming common concerns and foci of investigation in computer science, e.g., in areas such as agent computing, software agents, and intelligent mobile agent technologies. According to Luciano Floridi " one can think of several ways for applying computational methods towards philosophical matters: Conceptual experiments in silico: As an innovative extension of an ancient tradition of thought experiment, a trend has begun in philosophy to apply computational modeling schemes to questions in logic, epistemology, philosophy of science, philosophy of biology, philosophy of mind, and so on. Pancomputationalism: On this view, computational and informational concepts are considered to be so powerful that given the right level of abstraction, anything in the world could be modeled and represented as a computational system, and any process could be simulated computationally. Then, however, pancomputationalists have the hard task of providing credible answers to the following two questions: how can one avoid blurring all differences among systems? what would it mean for the system under investigation not to be an informational system (or a computational system, if computation is the same as information processing)?
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