AI Headshot Apk

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Automated restaurant

An automated restaurant or robotic restaurant is a restaurant that uses robots to do tasks such as delivering food and drink to the tables or cooking the food. Restaurant automation means the use of a restaurant management system to automate some or occasionally all of the major operations of a restaurant establishment. More recently, restaurants are opening that have completely or partially automated their services. These may include: taking orders, preparing food, serving, and billing. A few fully automated restaurants operate without any human intervention whatsoever. Robots are designed to help and sometimes replace human labour (such as waiters and chefs). The automation of restaurants may also allow for the option for greater customization of an order. == History == === Vending machines === In the late 19th and early 20th century a number of restaurants served food solely through vending machines. These restaurants were called automats or, in Japan, shokkenki. Customers ordered their food directly through the machines. === Sushi conveyors === Yoshiaki Shiraishi is a Japanese innovator who is known for the creation of conveyor belt sushi. He had the idea following difficulty staffing his small sushi restaurant and managing the restaurant on his own. He was inspired seeing beer bottles on a conveyor belt in an Asahi brewery. Yoshiaki's restaurants are an early example of restaurant automation; they used a conveyor belt to distribute dishes around the restaurant, eliminating the need for waiters. This example of automation dates back to the Japanese economic miracle; the first of Yoshiaki's conveyor belt sushi restaurants was opened under the name Mawaru Genroku Sushi in 1958, in Osaka. === Partial automation === As of 2011, across Europe, McDonald's had already begun implementing 7,000 touch screen kiosks that could handle cashiering duties. From 2015 to 2020, Zume had an automated pizza parlor. Later companies would try to produce smaller, less ambitious devices, with one robotics company producing a machine that could automate the slowest and most repetitive parts of assembling a pizza, such as spreading pizza sauce or placing slices of pepperoni, while leaving other customizations to employees. In 2020, a restaurant in the Netherlands began trialling the use of a robot to serve guests. In September 2021, Karakuri's 'Semblr' food service robot served personalised lunches for the 4,000 employees of grocery technology solutions provider ocado Group's head offices in Hatfield, UK. 2,700 different combinations of dishes were on offer. Customers could specify in grams what hot and cold items, proteins, sauces and fresh toppings they wanted. In 2021, Columbia University School of Engineering and Applied Science engineers developed a method of cooking 3D printed chicken with software-controlled robotic lasers. The “Digital Food” team exposed raw 3D printed chicken structures to both blue and infrared light. They then assessed the cooking depth, colour development, moisture retention and flavour differences of the laser-cooked 3D printed samples in comparison to stove-cooked meat. In June 2022 a California nonprofit chain of residential communities, Front Porch, experimented with robots in dining rooms at two locations to supplement wait staff by carrying plated food and drink to tables, and removing dishes. 65% of residents found the robots helpful, with 51% saying they let the staff spend more quality time with diners. 51% of staff were "excited" and 58% said they enabled more quality time with diners. The chain has 19 senior living communities (and 35 affordable housing communities), so it has potential to expand robots to more dining rooms. It is shifting to memory care, which may affect plans. == Rationales == === Advantages === Efficiency: Automated restaurants can significantly enhance operational efficiency by minimizing human error and reducing service time. With automated ordering, payment, and food preparation systems, customers can enjoy faster service and reduced waiting times. Cost savings: By reducing the need for human staff, automated restaurants can potentially lower labor costs. This can be particularly beneficial in areas with high labor expenses, as it allows for better resource allocation and cost management. Consistency: Automation ensures consistency in food quality and presentation. With precise portion control and standardized cooking methods, customers can expect the same quality and taste in their meals every time they visit. Enhanced customer experience: Self-service kiosks and automated systems provide customers with control and convenience. They can customize their orders, browse through menu options, and pay seamlessly, creating a more interactive and satisfying dining experience. === Disadvantages === Lack of personal touch: Automated restaurants may lack the personal interaction and warmth that traditional restaurants provide. Some customers prefer the human touch, personalized recommendations, and the social aspect of dining out. Technical issues: Reliance on technology means that technical glitches and malfunctions can occur, resulting in service disruptions or delays. Maintenance and technical support become critical in ensuring smooth operations. Limited menu complexity: The automation process may be better suited for standardized menu items rather than complex or customized dishes. The ability to cater to unique dietary preferences or accommodate special requests may be limited. Employment implications: Automated restaurants may result in job losses for traditional restaurant staff, potentially impacting the local workforce. It is important to consider the social and economic implications of adopting such technology. == Locations == Automated restaurants have been opening in many countries. Examples include: Nala Restaurant in Naperville, Illinois Fritz's Railroad Restaurant in Kansas City, Kansas Výtopna, a Railway Restaurant using model trains: franchise of various restaurants and coffeehouses in the Czech Republic Bagger's Restaurant in Nuremberg, Germany FuA-Men Restaurant, a ramen restaurant located in Nagoya, Japan Fōster Nutrition in Buenos Aires, Argentina Dalu Robot Restaurant in Jinan, China Haohai Robot Restaurant in Harbin, China Robot Kitchen Restaurant in Hong Kong Robo-Chef restaurant in Tehran, Iran, started in 2017, is the first robotic and "waiterless" restaurant of the Middle East. MIT graduates opened Spyce Kitchens in downtown Boston, Massachusetts, in 2018 Foodom, under Country Garden Holdings, opened January 12, 2020, in Guangzhou, China Robot Chacha, the first robot restaurant of India, is planning to open in the capital city of New Delhi. Kura Revolving Sushi Bar, with a number of locations in the United States, uses a tablets at tables for ordering, a conveyor belt to deliver food, and robots to deliver drinks and condiments. Chipotle Mexican Grill is beginning to deploy the Hyphen Makeline, which assembles up to 350 bowls and salads automatically per hour, and Chippy, an automatic tortilla chip fryer made by Miso Robotics. Serious Dumplings in Boca Raton, Florida
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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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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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HTK (software)

HTK (Hidden Markov Model Toolkit) is a proprietary software toolkit for handling HMMs. It is mainly intended for speech recognition, but has been used in many other pattern recognition applications that employ HMMs, including speech synthesis, character recognition and DNA sequencing. Originally developed at the Machine Intelligence Laboratory (formerly known as the Speech Vision and Robotics Group) of the Cambridge University Engineering Department (CUED), HTK is now being widely used among researchers who are working on HMMs.
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Hooked (app)

Hooked is a mobile application where users can write or read chat fiction, short pieces of fiction told in the format of text messages between fictional characters. The app was released in September 2015 and was developed by Telepathic Inc. == Features == Hooked is a freemium smartphone app that allows users to write or read short stories made up of text messages between characters. CEO Prerna Gupta described the app as "books for the Snapchat generation" or "Twitter for fiction." As of March 2019, the app had more than 40 million active users. The stories are written by a mix of professional authors and crowd-sourced participants. The most popular genres are suspense and horror. The stories usually lack literary elements like character arcs, are simply written and are intended to be suspenseful or addicting. Each piece of fiction on the app is approximately 1,000 to 1,300 words long and can be read in about five minutes. Some longer stories are told in "chapters" and a 32,000-word thriller called Dark Matter was released in 2018. The app provides a certain number of text messages for free, then delays the next text message by 15 minutes unless the user pays for a subscription. Prior to 2020, the app offered a three-day free trial and then required users to pay. According to Gupta, the app was intended to get the younger generation to read more without getting distracted. Most users of the app are between 13 and 24 years-old. == History == The Hooked app was first released in September 2015. Initially, Hooked featured about 200 stories that were written by professional authors selected by the app developers. The following year, Telepathic Inc. released Hooked 2.0, which allowed users of the app to create and share their own short stories. By mid-2016, the app had 700 stories written by professional authors and 9,000 stories written by users. Hooked had 1.8 million downloads by 2016 and 20 million download as of 2017, which generated $6.5 million in revenue. The response to Hooked prompted others to create similar text-message based short story apps, like Yarn and Tap. Sensor Tower reported that the Hooked app received 2.22 million downloads during the period from October 2016 to March 2017. Starting in 2020, longer stories divided into chapters debuted on the app. In March, the company launched Hooked TV, an app to showcase video pilots based on a number of scripts themed around the app's content. Out of 50 pilots, those that were most popular among users of the app and social media were expanded into original series as Hooked TV evolved into a streaming platform in the second half of 2021. == Background == The idea for Hooked was conceived when Gupta was working on writing a book of her own. Prerna Gupta and her husband Parag Chordia tested short stories with 15,000 people and found that readers were five times more likely to read a story to its end if the story was presented in a text message format. They created Telepathic Inc., which developed Hooked. According to Celebrity Secret when they first started out, the stories were basically as if two people were texting each other and some sort of drama unfolds. Some of their most popular initial stories were actually horror stories, where a mom gets a text from her daughter and something creepy is happening to her. Over time, they started to turn those into podcasts, which then led to making their own movies and TV shows. As of 2017, the Telepathic has raised $6 million in funding to develop and support the Hooked app. From the main website itself the Hooked investors include Sound Ventures, The Chernin Group, WME/Endeavor, MACRO, Greg Silverman, Steph Curry, Kevin Durant, LeBron James, Mariah Carey, Jamie Foxx, Joe Montana, Aasif Mandvi, Max Martin, Anjula Acharia, Savan Kotecha, Cyan Banister, Eric Ries, A Capital, SV Angel, Cowboy Ventures, Founders Fund and Greylock, among many others.
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Early-exit network

Early-exit networks are a class of dynamic neural networks designed for efficient inference by allowing models to make confident predictions at intermediate layers, rather than processing the full network. Early-exit mechanisms are methods for deep neural networks that add intermediate classifiers, allowing inference to stop at earlier layers for inputs assessed as low uncertainty. Decisions to exit are typically based on confidence measures such as softmax-derived scores, classification margins, or entropy-based criteria, with the goal of reducing computational cost. These approaches are commonly paired with specialized training procedures and system-level optimizations to improve efficiency while preserving accuracy. The main idea behind the technology is to stop excessive calculations when a good answer can already be given with a high degree of probability, which can save both computation and time. Early-exit networks have also been extended with expert-based exit criteria, where intermediate classifiers are treated as multiple “experts” whose predictions and confidence scores can be aggregated to decide whether to stop computation early. Hardware implementations are also being developed.
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Global Partnership on Artificial Intelligence

The Global Partnership on Artificial Intelligence (GPAI, pronounced "gee-pay") is an international initiative established to guide the responsible development and use of artificial intelligence (AI) in a manner that respects human rights and the shared democratic values of its members. The partnership was first proposed by Canada and France at the 2018 44th G7 summit, and officially launched in June 2020. GPAI is hosted by the Organisation for Economic Co-operation and Development (OECD). GPAI seeks to bridge the gap between theory and practice by supporting research and applied activities in areas that are directly relevant to policymakers in the realm of AI. It brings together experts from industry, civil society, governments, and academia to collaborate on the challenges and opportunities presented by artificial intelligence. == History == The Global Partnership on Artificial Intelligence was announced on the margins of the 2018 G7 Summit by Canadian Prime Minister Justin Trudeau and French President Emmanuel Macron. It officially launched on June 15, 2020 with fifteen founding members: Australia, Canada, France, Germany, India, Italy, Japan, Mexico, New Zealand, the Republic of Korea, Singapore, Slovenia, the United Kingdom, the United States, and the European Union. The Organisation for Economic Co-operation and Development (OECD) hosts a dedicated secretariat to support GPAI's governing bodies and activities. UNESCO joined the partnership in December 2020 as an observer. On November 11, 2021, Czechia, Israel and few more EU countries also joined the GPAI, bringing the total membership to 25 countries. Since the November 2022 summit, the list of members stands at 29. Austria, Chile, Finland, Malaysia, Norway, Slovakia and Switzerland were invited. The seven, however, are pending membership approval. == Membership == The following 29 members of the GPAI are: Argentina Australia Belgium Brazil Canada Czech Republic Denmark France Germany India Ireland Israel Italy Japan Mexico Netherlands New Zealand Poland Republic of Korea Senegal Serbia Singapore Slovenia Spain Sweden Turkey United Kingdom United States European Union Invited members: Austria (pending membership approval) Chile (pending membership approval) Finland (pending membership approval) Malaysia (pending membership approval) Norway (pending membership approval) Slovakia (pending membership approval) Switzerland (pending membership approval) == Organization == GPAI's experts collaborate across several Working Groups themes: Responsible AI (including an ad-hoc subgroup on AI and Pandemic Response), Data Governance, Future of Work, and Innovation & Commercialization. GPAI's Working Groups are supported by two Centres of Expertise: one in Montreal that supports the first two Working Groups, and one in Paris that supports the latter two. It also has a Steering Committee, the elected chair of which has also been to date elected chair of the Multi Stakeholder Group (MEG). These chairs have been: Jordan Zed and Baroness Joanna Shields (Shields, MEG chair; 2020-2021), Joanna Shields and Renaud Vedel (Shields, MEG chair; 2021-2022), Yoichi Iida and Inma Martinez (Martinez, MEG chair; 2023-2024) GPAI has a rotating presidency and host (much like the G7). The presidencies to date have been: Canada (2020) France (2021) Japan (2022) India (2023)
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Goal node (computer science)

In computer science, a goal node is a node in a graph that meets defined criteria for success or termination. Heuristical artificial intelligence algorithms, like A and B, attempt to reach such nodes in optimal time by defining the distance to the goal node. When the goal node is reached, A defines the distance to the goal node as 0 and all other nodes' distances as positive values.
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AIXI

AIXI is a theoretical mathematical formalism for artificial general intelligence. It combines Solomonoff induction with sequential decision theory. AIXI was first proposed by Marcus Hutter in 2000 and several results regarding AIXI are proved in Hutter's 2005 book Universal Artificial Intelligence. AIXI is a reinforcement learning (RL) agent. It maximizes the expected total rewards received from the environment. Intuitively, it simultaneously considers every computable hypothesis (or environment). In each time step, it looks at every possible program and evaluates how many rewards that program generates depending on the next action taken. The promised rewards are then weighted by the subjective belief that this program constitutes the true environment. This belief is computed from the length of the program: longer programs are considered less likely, in line with Occam's razor. AIXI then selects the action that has the highest expected total reward in the weighted sum of all these programs. == Etymology == According to Hutter, the word "AIXI" can have several interpretations. AIXI can stand for AI based on Solomonoff's distribution, denoted by ξ {\displaystyle \xi } (which is the Greek letter xi), or e.g. it can stand for AI "crossed" (X) with induction (I). There are other interpretations. == Definition == AIXI is a reinforcement learning agent that interacts with some stochastic and unknown but computable environment μ {\displaystyle \mu } . The interaction proceeds in time steps, from t = 1 {\displaystyle t=1} to t = m {\displaystyle t=m} , where m ∈ N {\displaystyle m\in \mathbb {N} } is the lifespan of the AIXI agent. At time step t, the agent chooses an action a t ∈ A {\displaystyle a_{t}\in {\mathcal {A}}} (e.g. a limb movement) and executes it in the environment, and the environment responds with a "percept" e t ∈ E = O × R {\displaystyle e_{t}\in {\mathcal {E}}={\mathcal {O}}\times \mathbb {R} } , which consists of an "observation" o t ∈ O {\displaystyle o_{t}\in {\mathcal {O}}} (e.g., a camera image) and a reward r t ∈ R {\displaystyle r_{t}\in \mathbb {R} } , distributed according to the conditional probability μ ( o t r t | a 1 o 1 r 1 . . . a t − 1 o t − 1 r t − 1 a t ) {\displaystyle \mu (o_{t}r_{t}|a_{1}o_{1}r_{1}...a_{t-1}o_{t-1}r_{t-1}a_{t})} , where a 1 o 1 r 1 . . . a t − 1 o t − 1 r t − 1 a t {\displaystyle a_{1}o_{1}r_{1}...a_{t-1}o_{t-1}r_{t-1}a_{t}} is the "history" of actions, observations and rewards. The environment μ {\displaystyle \mu } is thus mathematically represented as a probability distribution over "percepts" (observations and rewards) which depend on the full history, so there is no Markov assumption (as opposed to other RL algorithms). Note again that this probability distribution is unknown to the AIXI agent. Furthermore, note again that μ {\displaystyle \mu } is computable, that is, the observations and rewards received by the agent from the environment μ {\displaystyle \mu } can be computed by some program (which runs on a Turing machine), given the past actions of the AIXI agent. The only goal of the AIXI agent is to maximize ∑ t = 1 m r t {\displaystyle \sum _{t=1}^{m}r_{t}} , that is, the sum of rewards from time step 1 to m. The AIXI agent is associated with a stochastic policy π : ( A × E ) ∗ → A {\displaystyle \pi :({\mathcal {A}}\times {\mathcal {E}})^{}\rightarrow {\mathcal {A}}} , which is the function it uses to choose actions at every time step, where A {\displaystyle {\mathcal {A}}} is the space of all possible actions that AIXI can take and E {\displaystyle {\mathcal {E}}} is the space of all possible "percepts" that can be produced by the environment. The environment (or probability distribution) μ {\displaystyle \mu } can also be thought of as a stochastic policy (which is a function): μ : ( A × E ) ∗ × A → E {\displaystyle \mu :({\mathcal {A}}\times {\mathcal {E}})^{}\times {\mathcal {A}}\rightarrow {\mathcal {E}}} , where the ∗ {\displaystyle } is the Kleene star operation. In general, at time step t {\displaystyle t} (which ranges from 1 to m), AIXI, having previously executed actions a 1 … a t − 1 {\displaystyle a_{1}\dots a_{t-1}} (which is often abbreviated in the literature as a < t {\displaystyle a_{ Read more →
The Machine Question

The Machine Question: Critical Perspectives on AI, Robots, and Ethics is a 2012 nonfiction book by David J. Gunkel that discusses the evolution of the theory of human ethical responsibilities toward non-human things and to what extent intelligent, autonomous machines can be considered to have legitimate moral responsibilities and what legitimate claims to moral consideration they can hold. The book was awarded as the 2012 Best Single Authored Book by the Communication Ethics Division of the National Communication Association. == Content == The book is spread across three chapters, with the first two chapters focusing on an overall review of the history of philosophy and its discussion of moral agency, moral rights, human rights, and animal rights and the third chapter focusing on what defines "thingness" and why machines have been excluded from moral and ethical consideration due to a misuse of the patient/agent binary. The first chapter, titled Moral Agency, breaks down the history of said agency based on what it included and excluded in various parts of history. Gunkel also raises the conflict between discussing the morality of humans toward objects and the theory of the philosophy of technology that "technology is merely a tool: a means to an end". The main issue, he explains, in defining what constitutes an appropriate moral agent is that there will be things left outside of what is included, as the definition is based on a set of characteristics that will inherently not be all-encompassing. The subject of consciousness is broached and subsequently derided by Gunkel because of it being one of the main arguments against machine rights, while Gunkel points out that no "settled definition" of the term exists and that he considers it no better than a synonym used for "the occultish soul". In addition, the issue of the other minds problem entails that no proper understanding of consciousness can come to pass due to the inability to properly understand the mind of a being that is not oneself. The second chapter, titled Moral Patiency, focuses on the patient end of the topic and discusses the expansion of the fields of animal studies and environmental studies. Gunkel describes moral patients as the ones that are to be the object of moral consideration and deserve such consideration even if they lack their own agency, such as animals, thus allowing moral consideration itself to be broader and more inclusive. The topic of other minds is discussed again when examining the question of whether animals can suffer, a question that Gunkel ultimately abandons because it encounters the same problems that the topic of consciousness does. Especially because the subject of animal rights is often only afforded for the animals deemed to be "cute", but often not including "reptiles, insects, or microbes". Gunkel continues on to examine environmental ethics and information ethics, but finds them to be too anthropocentric, just as all the other examined models have been. The third chapter, titled Thinking Otherwise, proposes a combination of Heideggerian ontology and Levinasian ethics to properly discuss the otherness of technology and machines, but finds that the patient/agent binary is unable to be properly extended to confine the extent of "the machine question". In discussing the land ethic philosophy espoused by Aldo Leopold, Gunkel proposes that it is the entire relationship between agent and patient that should have moral consideration and not a specific definition based on either side, as each part contributes to the relationship as a whole and cannot be removed without breaking that relationship. == Critical reception == Choice: Current Reviews for Academic Libraries writer R. S. Stansbury explained that the book is able to use simple examples to discuss difficult topics and separate ideas and that it would be "useful for philosophy students, and for engineering students interested in exploring the ethical implications of their work". Dominika Dzwonkowska, writing for International Philosophical Quarterly, stated that the "unprecedented value of the book is that Gunkel not only analyzes important aspects of the immediate problem but also that he places his discussion in the context of philosophical discussions on such related issues as rights discourse." Mark Coeckelbergh in Ethics and Information Technology noted that focusing on the question itself of the machine question allows further exploration of machine ethics and the expansion of general ethics and that the book's questions point out that "good, critical philosophical reflection on machines is not only about how we should cope with machines, but also about how we (should) think and what role technology plays (and should play) in this thinking." A review in Notre Dame Philosophical Reviews by Colin Allen criticized some of Gunkel's methodology and the indecisiveness of his ultimate answer to the machine question, but also acknowledged that the book "succeeded in connecting the ethics of robots and AI to a much broader ethical discussion than has been represented in the literature on machine ethics to date". Blay Whitby, in a review for AISB Quarterly, lauded The Machine Question for its "clear exposition" and wide range of references to other works, concluding that the book is "essential reading for philosophers interested in AI, robot ethics, or animal ethics". In a twin review of The Machine Question and Robot Ethics: The Ethical and Social Implications of Robots by Patrick Lin, Keith Abney, and George A. Bekey, Techné: Research in Philosophy and Technology reviewer Jeff Shaw called Gunkel's book a good introduction to the "complex field of robot ethics" and that both books are "highly recommended to both the general reader as well as to experts in the field of robotics, philosophy, and ethics." In a 2017 paper for Ethics and Information Technology, Katharyn Hogan investigated whether the machine question presented by Gunkel in the book is any different from the longstanding animal question. She concludes that the real question that is revealed from this discussion is whether humans deserve any moral preference over artificial life in the first place.
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Sense Networks

Sense Networks is a New York City based company with a focus on applications that analyze big data from mobile phones, carrier networks, and taxicabs, particularly by using machine learning technology to make sense of large amounts of location (latitude/longitude) data. In 2009, Sense was named one of "The 25 Most Intriguing Startups in the World" by Bloomberg Businessweek and was called "The Next Google" on the cover of Newsweek. In 2014, Sense Networks was acquired by YP, "the local search and advertising company owned by Cerberus Capital Management and AT&T." It was subsequently sold off to Verve in 2017 == History == Sense Networks was founded by Greg Skibiski in February 2006 (2003?) near his home in Northampton, Massachusetts. After establishing an office in NoHo, New York City near Silicon Alley, Skibiski recruited Alex Pentland, Director of Human Dynamics Research and former Academic Head of the MIT Media Lab, Tony Jebara, Associate Professor and Head of the Machine Learning Laboratory at Columbia University, and Christine Lemke, who would later become co-founders. Sense Networks investors include Intel Capital, Javelin Venture Partners, and Kenan Altunis. Founder Greg Skibiski was pushed out by lead investor Intel Capital in November 2009 following the company's B round of financing. During the same week, the company won the Emerging Communications Conference "Company to Watch" Award. The company has three published patent applications for analyzing sensor data streams: System and Method of Performing Location Analytics (US 20090307263), Comparing Spatial-Temporal Trails in Location Analytics (US 20100079336), and Anomaly Detection in Sensor Analytics (US 20100082301). The company was acquired by the Yellow Pages in 2014. This is a marketing conglomerate under AT&T and Cerberus Capital Management. == Products and services == The Citysense consumer application that shows hotspots of human activity in real-time from mobile phone location and taxicab GPS data was named by ReadWriteWeb (in The New York Times) as "Top 10 Internet of Things Products of 2009". The Cabsense consumer application that shows the best place to catch a New York City taxicab based on GPS data from the vehicle was launched in March 2010. The Macrosense platform is for mobile application providers and mobile phone carriers to analyze billions of customer location data points for predictive analytics in advertising and churn management applications. == Privacy and data ownership == The company allows users to opt-out of their service through their website, and users may monitor their profile through their application. The company does not collect identifiable data (such as phone numbers or names); it collects data received from cellphone to construct anonymous profiles of consumers. This anonymous data/profiles may then be sold to third parties. The company's privacy and data ownership policies are based on The New Deal on Data, as advocated by Alex "Sandy" Pentland, head of the Human Dynamics group at MIT.
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Dendral

Dendral was a project in artificial intelligence (AI) of the 1960s, and the computer software expert system that it produced. Its primary aim was to study hypothesis formation and discovery in science. For that, a specific task in science was chosen: help organic chemists in identifying unknown organic molecules, by analyzing their mass spectra and using knowledge of chemistry. It was done at Stanford University by Edward Feigenbaum, Bruce G. Buchanan, Joshua Lederberg, and Carl Djerassi, along with a team of highly creative research associates and students. It began in 1964 and spans approximately half the history of AI research. The software program Dendral is considered the first expert system because it automated the decision-making process and problem-solving behavior of organic chemists. The project consisted of research on two main programs Heuristic Dendral and Meta-Dendral, and several sub-programs. It was written in the Lisp programming language, which was considered the language of AI because of its flexibility. Many systems were derived from Dendral, including MYCIN, MOLGEN, PROSPECTOR, XCON, and STEAMER. There are many other programs today for solving the mass spectrometry inverse problem, see List of mass spectrometry software, but they are no longer described as 'artificial intelligence', just as structure searchers. The name Dendral is an acronym of the term "Dendritic Algorithm". == Heuristic Dendral == Heuristic Dendral is a program that uses mass spectra or other experimental data together with a knowledge base of chemistry to produce a set of possible chemical structures that may be responsible for producing the data. A mass spectrum of a compound is produced by a mass spectrometer, and is used to determine its molecular weight, the sum of the masses of its atomic constituents. For example, the compound water (H2O), has a molecular weight of 18 since hydrogen has a mass of 1.01 and oxygen 16.00, and its mass spectrum has a peak at 18 units. Heuristic Dendral would use this input mass and the knowledge of atomic mass numbers and valence rules, to determine the possible combinations of atomic constituents whose mass would add up to 18. As the weight increases and the molecules become more complex, the number of possible compounds increases drastically. Thus, a program that is able to reduce this number of candidate solutions through the process of hypothesis formation is essential. New graph-theoretic algorithms were invented by Lederberg, Harold Brown, and others that generate all graphs with a specified set of nodes and connection-types (chemical atoms and bonds) -- with or without cycles. Moreover, the team was able to prove mathematically that the generator is complete, in that it produces all graphs with the specified nodes and edges, and that it is non-redundant, in that the output contains no equivalent graphs (e.g., mirror images). The CONGEN program, as it became known, was developed largely by computational chemists Ray Carhart, Jim Nourse, and Dennis Smith. It was useful to chemists as a stand-alone program to generate chemical graphs showing a complete list of structures that satisfy the constraints specified by a user. == Meta-Dendral == Meta-Dendral is a machine learning system that receives the set of possible chemical structures and corresponding mass spectra as input, and proposes a set of rules of mass spectrometry that correlate structural features with processes that produce the mass spectrum. These rules would be fed back to Heuristic Dendral (in the planning and testing programs described below) to test their applicability. Thus, "Heuristic Dendral is a performance system and Meta-Dendral is a learning system". The program is based on two important features: the plan-generate-test paradigm and knowledge engineering. === Plan-generate-test paradigm === The plan-generate-test paradigm is the basic organization of the problem-solving method, and is a common paradigm used by both Heuristic Dendral and Meta-Dendral systems. The generator (later named CONGEN) generates potential solutions for a particular problem, which are then expressed as chemical graphs in Dendral. However, this is feasible only when the number of candidate solutions is minimal. When there are large numbers of possible solutions, Dendral has to find a way to put constraints that rules out large sets of candidate solutions. This is the primary aim of Dendral planner, which is a “hypothesis-formation” program that employs “task-specific knowledge to find constraints for the generator”. Last but not least, the tester analyzes each proposed candidate solution and discards those that fail to fulfill certain criteria. This mechanism of plan-generate-test paradigm is what holds Dendral together. === Knowledge Engineering === The primary aim of knowledge engineering is to attain a productive interaction between the available knowledge base and problem solving techniques. This is possible through development of a procedure in which large amounts of task-specific information is encoded into heuristic programs. Thus, the first essential component of knowledge engineering is a large “knowledge base.” Dendral has specific knowledge about the mass spectrometry technique, a large amount of information that forms the basis of chemistry and graph theory, and information that might be helpful in finding the solution of a particular chemical structure elucidation problem. This “knowledge base” is used both to search for possible chemical structures that match the input data, and to learn new “general rules” that help prune searches. The benefit Dendral provides the end user, even a non-expert, is a minimized set of possible solutions to check manually. == Heuristics == A heuristic is a rule of thumb, an algorithm that does not guarantee a solution, but reduces the number of possible solutions by discarding unlikely and irrelevant solutions. The use of heuristics to solve problems is called "heuristics programming", and was used in Dendral to allow it to replicate in machines the process through which human experts induce the solution to problems via rules of thumb and specific information. Heuristics programming was a major approach and a giant step forward in artificial intelligence, as it allowed scientists to finally automate certain traits of human intelligence. It became prominent among scientists in the late 1940s through George Polya’s book, How to Solve It: A New Aspect of Mathematical Method. As Herbert A. Simon said in The Sciences of the Artificial, "if you take a heuristic conclusion as certain, you may be fooled and disappointed; but if you neglect heuristic conclusions altogether you will make no progress at all." == History == During the mid 20th century, the question "can machines think?" became intriguing and popular among scientists, primarily to add humanistic characteristics to machine behavior. John McCarthy, who was one of the prime researchers of this field, termed this concept of machine intelligence as "artificial intelligence" (AI) during the Dartmouth summer in 1956. AI is usually defined as the capacity of a machine to perform operations that are analogous to human cognitive capabilities. Much research to create AI was done during the 20th century. Also around the mid 20th century, science, especially biology, faced a fast-increasing need to develop a "man-computer symbiosis", to aid scientists in solving problems. For example, the structural analysis of myoglobin, hemoglobin, and other proteins relentlessly needed instrumentation development due to its complexity. In the early 1960s, Joshua Lederberg started working with computers and quickly became tremendously interested in creating interactive computers to help him in his exobiology research. Specifically, he was interested in designing computing systems to help him study alien organic compounds. Lederberg had been heading a team designing instruments for the Mars Viking lander to search for precursor molecules of life in samples of the Mars surface, using a mass spectrometer coupled with a minicomputer. As he was not an expert in either chemistry or computer programming, he collaborated with Stanford chemist Carl Djerassi to help him with chemistry, and Edward Feigenbaum with programming, to automate the process of determining chemical structures from raw mass spectrometry data. Feigenbaum was an expert in programming languages and heuristics, and helped Lederberg design a system that replicated the way Djerassi solved structure elucidation problems. They devised a system called Dendritic Algorithm (Dendral) that was able to generate possible chemical structures corresponding to the mass spectrometry data as an output. Dendral then was still very inaccurate in assessing spectra of ketones, alcohols, and isomers of chemical compounds. Thus, Djerassi "taught" general rules to Dendral that could help eliminate most of the "chemically implausible" structures, and p
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Wrike

Wrike, Inc. is an American project management application service provider based in San Jose, California. Wrike also has offices in India, Dallas, Tallinn, Nicosia, Dublin, Tokyo, Melbourne, and Prague. == History == Wrike was founded in 2006 by Andrew Filev. Currently CEO at Wrike is Thomas Scott. Filev initially self-funded the company before later obtaining investor funding. Wrike released the beta version of its software (also called Wrike) in December 2006. The company then launched a new "Enterprise" platform in December 2013. In June 2015, Wrike announced the opening of an office in Dublin, Ireland and in 2016, Wrike launched a datacenter there to host data in compliance with local privacy regulations. In July 2016, Wrike announced the launch of Wrike for Marketers. That same year, Wrike's headquarters moved from Mountain View to San Jose, California. In January 2021, Citrix Systems announced its intention to acquire Wrike for $2.25 billion. The acquisition closed in March 2021. On January 31, 2022, it was announced that Citrix had been acquired in a $16.5 billion deal by affiliates of Vista Equity Partners and Evergreen Coast Capital. Citrix would merge with TIBCO Software, a Vista portfolio company to form Cloud Software Group (CSG). In September 2022, Wrike separated from Citrix Systems. In July 2023, Vista transferred ownership to Symphony Technology Group. == Investments == Wrike received $1 million in Angel funding in 2012 from TMT Investments. In October, 2013, Wrike secured $10 million in investment funding from Bain Capital. In May 2015, the company secured $15 million in a new round of funding. Investors included Scale Venture Partners, DCM Ventures, and Bain Capital. At that time, Wrike had 8,000 customers, 200 employees, and 30,000 new users each month. On November 29, 2018, Wrike signed a definitive agreement to receive a majority investment by Vista Equity Partners (“Vista”), a firm focused on software, data and technology-enabled businesses. == Software == The Wrike project management software is a Software-as-a-Service (SaaS) product with tools for managing projects, deadlines, schedules, and workflow processes. It includes collaboration features. The application is available in English, French, Spanish, German, Portuguese, Italian, Japanese and Russian. Wrike has triggers for task automation in workflow management. === Features === Wrike features a multi-pane UI and consists of features in two categories: project management, and team collaboration. According to Wrike, project management features are designed to help teams track dates and dependencies associated with projects, manage assignments and resources, and track time. These include an interactive Gantt chart, a workload view, and a sortable table that can be customized to store project data. The software includes a co-editing tool, discussion threads on tasks, and tools for attaching documents, editing them, and tracking their changes. Wrike uses an "inbox" feature and browser notifications to alert users of updates from their colleagues and dashboards for quick overviews of pending tasks. These updates are also available in Wrike's mobile apps on iOS and Android. Wrike has an optional feature set called "Wrike for Marketers" which has several tools for managing marketing workflows. In May 2012, Wrike announced the launch of a freemium version of its software for teams of up to 5 users. That year also saw the integration of a live text coeditor into its workspace to unify collaboration and task management. In late 2013 Wrike released a new feature set called Wrike Enterprise which included advanced analytics and other tools targeted at large business customers. Since then it has released several major updates to Wrike Enterprise, including a customizable spreadsheet called "Dynamic Platform" in late 2014 and custom workflows for teams in 2015. In July 2016, Wrike was updated with a set of add-on features under the name "Wrike for Marketers," which includes integrations with Adobe Photoshop, a tool for submitting requests, and proofing and approval tools for creative assets like videos and images. Wrike is available as native Android and iOS apps. Mobile apps include an interactive Gantt chart that syncs across devices. The apps are available offline, and sync when connection is restored. === Criticism === Critics said new users may have a learning curve with complex features. Wrike has 2,710 customers for an estimated 0.04% market share. Competitors include Google Workspace, Slack (software), and Quip (software).
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User profile

A user profile is a collection of settings and information associated with a user. It contains critical information that is used to identify an individual, such as their name, age, portrait photograph and individual characteristics such as knowledge or expertise. User profiles are most commonly present on social media websites such as Facebook, Instagram, and LinkedIn; and serve as voluntary digital identity of an individual, highlighting their key features and traits. In personal computing and operating systems, user profiles serve to categorise files, settings, and documents by individual user environments, known as 'accounts', allowing the operating system to be more friendly and catered to the user. Physical user profiles serve as identity documents such as passports, driving licenses and legal documents that are used to identify an individual under the legal system. A user profile can also be considered as the computer representation of a user model. A user model is a (data) structure that is used to capture certain characteristics about an individual user, and the process of obtaining the user profile is called user modeling or profiling. == Origin == The origin of user profiles can be traced to the origin of the passport, an identity document (ID) made mandatory in 1920, after World War I following negotiations at the League of Nations. The passport served as an official government record of an individual. Consequently, Immigration Act of 1924 was established to identify an individual's country of origin. In the 21st century, passports have now become a highly sought-after commodity as it is widely accepted as a source of verifying an individual's identity under the legal system. With the advent of digital revolution and social media websites, user profiles have transitioned to an organised group of data describing the interaction between a user and a system. Social media sites like Instagram allow individuals to create profiles that are representative of their desired personality and image. Filling all fields of profile information may not be necessary to create a meaningful self-presentation, which grants individual more control over of the identity they wish to present by displaying the most meaningful attributes. A personal user profile is a key aspect of an individual's social networking experience, around which his/her public identity is built. == Types of user profiles == A user profile can be of any format if it contains information, settings and/or characteristics specific to an individual. Most popular user profiles include those on photo and video sharing websites such as Facebook and Instagram, accounts on operating systems, such as those on Windows and MacOS and physical documents such as passports and driving licenses. === Social media === Effectively structured user profiles on social media channels such as Instagram and Facebook offer a way for people to form impressions about someone that is predictive or similarly meeting them offline. The condensed format of social media profiles allows for quick filtering of millions of profiles by matching individuals by similar characteristics and interests; information provided upon sign up. A research conducted highlights that only a "thin slice" of information is required to form an impression about an individual online (Stecher and Counts 2008). Online user profiles eliminate the complexity of interaction that is present in 'face-to-face' meetings such as behavioural, facial, and environmental information, resulting in increased predictiveness of user personality. Dating apps and websites solely rely on an individual's user profile and the information provided to form interactions and communication with others on the platform. Despite having control over presented information, lying is minimal in online dating contexts (Hancock, Toma and Ellison, 2007). Apps such as Bumble allow users to 'match' with other individuals based on their characteristics and selected filters that allow users to narrow the spectrum of search to their preference. Information for a user's profile is voluntarily specified by the user and includes information such as height, interests, photographs, gender or education. The requirement of information varies respective to each platform, and there surrounds little consensus to an appropriate amount of information for a condensed user profile. Universally, all social networking platforms display an individual's profile picture and an "about me" page that allows for self-expression. === Influencers === Influencer user profiles are third party endorsers who shape audience attitudes and decisions through social media content such as photos, blogs and tweets. Social Media Influencers (SMI) often hold a significant following on a social media platform which enables them to be recognised as opinion leaders to shape an information influence to their audience. 'Influencer marketing' industry gained prominence in 2018, when the photo sharing app Instagram crossed 1 billion users, subsequently with approximately 60,000 google search queries for 'influencer marketing' the same year. Influencer user profiles hold a unique selling point, or public personality that is unique and charismatic to the needs and wants of their target audience. SMI profiles advertise product information, latest promotions and regularly engage with their followers to maintain their online persona. Messages endorsed by social media influencers are often perceived as reliable and compelling, as a study conducted found 82% of followers were more inclined to follow the suggestions of their favorite influencer. This allows advertisers to leverage online user profiles and their audience rapport to target younger and niche audiences. According to a market survey, influencer marketing through social media profiles yields a return 11 times higher than traditional marketing, as they are more capable of communicating to a niche segment. Most popular influencers include sport starts such as Cristiano Ronaldo and Hollywood personalities such as Dwayne Johnson and Kylie Jenner each with over 200 million followers respectively. === Ecommerce === Online shopping or Ecommerce websites such as Amazon use information from a customer's user profile and interests to generate a list of recommended items to shop. Recommendation algorithms analyse user demographic data, history, and favourite artists to compile suggestions. The store rapidly adapts to changing user needs and preferences, with generation of real time results required within half of a second. New profiles naturally have limited information for algorithms to analyse, and customer data of each interaction provides valuable information which is stored as a database linked with each individual profile. User profiles on ecommerce websites also serve to improve sales of sellers as individuals are recommend products that other "customers who bought this item also bought" to widen the selection of the buyer. A study conducted found that user profiles and recommendation algorithms have significant impact on related product sales and overall spending of an individual. A process known as "collaborative filtering" tries to analyse common products of interest for an individual on the basis of views expressed by other similar behaving profiles. Features such as product ratings, seller ratings and comments allow individual user profiles to contribute to recommendation algorithms, eliminate adverse selection and contribute to shaping an online marketplace adhering to Amazons zero tolerance policy for misleading products. == Digital user profiles == Modern software and applications account for user profiles as a foundation on which a usable application is built. The structure and layout of an application such as its menus, features and controls are often derived from user's selected settings and preferences. The origin of digital user profiles in computer systems was first initiated by Windows NT that held user settings and information in a separate environment variable named %USERPROFILE% and held the framework to a user's profile root. Consequently, operating systems such as MacOS further accelerated prominence of user profiles in Mac OS X 10.0. Iterations since have been made with each operating system release with the aim to maximise user friendliness with the system. Features such as keyboard layouts, time zones, measurement units, synchronisation of different services and privacy preferences are made available during the setup of a user account on the computer === Types of accounts === ==== Administrator ==== Administrator user profiles have complete access to the system and its permissions. It is often the first user profile on a system by design, and is what allows other accounts to be created. However, since the administrator account has no restrictions, they are highly vulnerable to malware and viruses, with potential to impact all other accounts.
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Generative AI Copyright Disclosure Act

The Generative AI Copyright Disclosure Act is a piece of legislation introduced by California Representative Adam Schiff in the United States Congress on April 9, 2024. It concerns the transparency of companies regarding their use of copyrighted work to train their generative artificial intelligence (AI) models. The legislation requires the submission of a notice regarding the identity and the uniform resource locator (URL) address of the copyrighted works used in the training data to the Register of Copyrights at least 30 days before the public release of the new or updated version of the AI model; it does not ban the use of copyrighted works for AI training. The bill's requirements would apply retroactively to prior AI models. Violation penalties would start at US$5,000. The legislation does not have a maximum penalty assessment that can be charged. The bill by Schiff was introduced a few days after The New York Times published an article regarding the business activities of major tech firms, including Google and Meta, in the training of their generative AI platforms on April 6, 2024. The legislation is supported by the Professional Photographers of America (PPA), SAG-AFTRA, the Writers Guild of America, the International Alliance of Theatrical Stage Employees (IATSE), the Recording Industry Association of America (RIAA), and others.
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