In telecommunications and data communication systems, an errored second is an interval of a second during which any error whatsoever has occurred, regardless of whether that error was a single bit error or a complete loss of communication for that entire second. The type of error is not important for the purpose of counting errored seconds. In communication systems with very low uncorrected bit error rates, such as modern fiber-optic transmission systems, or systems with higher low-level error rates that are corrected using large amounts of forward error correction, errored seconds are often a better measure of the effective user-visible error rate than the raw bit error rate. For many modern packet-switched communication systems, even a single uncorrected bit error is enough to cause the loss of a data packet by causing its CRC check to fail; whether that packet loss was caused by a single bit error or a hundred-bit-long error burst is irrelevant. For systems using large amounts of forward error correction, the reverse applies; a single low-level bit error will almost never occur, since any small errors will almost always be corrected, but any error sufficiently large to cause the forward error correction to fail will almost always result in a large burst error. More specialist and precise definitions of errored seconds exist in standards such as the T1 and DS1 transport systems.
Inferential theory of learning
Inferential Theory of Learning (ITL) is an area of machine learning which describes inferential processes performed by learning agents. ITL has been continuously developed by Ryszard S. Michalski, starting in the 1980s. The first known publication of ITL was in 1983. In the ITL learning process is viewed as a search (inference) through hypotheses space guided by a specific goal. The results of learning need to be stored. Stored information will later be used by the learner for future inferences. Inferences are split into multiple categories including conclusive, deduction, and induction. In order for an inference to be considered complete it was required that all categories must be taken into account. This is how the ITL varies from other machine learning theories like Computational Learning Theory and Statistical Learning Theory; which both use singular forms of inference. == Usage == The most relevant published usage of ITL was in scientific journal published in 2012 and used ITL as a way to describe how agent-based learning works. According to the journal "The Inferential Theory of Learning (ITL) provides an elegant way of describing learning processes by agents".
Open-source robotics
Open-source robotics is a branch of robotics where robots are developed with open-source hardware and free and open-source software, publicly sharing blueprints, schematics, and source code. It is thus closely related to the open design movement, the maker movement and open science. == Requirements == Open source robotics means that information about the hardware is easily discerned, so that others can easily rebuild it. In turn, this requires design to use only easily available standard subcomponents and tools, and for the build process to be documented in detail including a bill of materials and detailed ('Ikea style') step-by-step building and testing instructions. (A CAD file alone is not sufficient, as it does not show the steps for performing or testing the build). These requirements are standard to open source hardware in general, and are formalised by various licences, certifications, especially those defined by the peer-reviewed journals Journal of Open Hardware and HardwareX. Licensing requirements for software are the same as for any open source software. But in addition, for software components to be of practical use in real robot systems, they need to be compatible with other software, usually as defined by some robotics middleware community standard. == Hardware systems == Applications to date include: Robot arms, e.g. PARA or Thor Wheeled mobile robots. e.g. OpenScout Four-legged robots such as the Open Dynamic Robot Initiative UAV quadcopters (drones) such as Agilicious Humanoid robots, e.g. iCub, Berkeley Humanoid Lite Self-driving cars, e.g. OpenPodcar (→ Personal rapid transit) Submersible robots, eg. OpenFish Laboratory robotics such as chemical liquid handling Vertical farming Swarm robots, e.g. HeRoSwarm Domestic tasks: vacuum cleaning, floor washing and grass mowing Robot sports including robot combat and autonomous racing Education == Hardware subcomponents == Most open source hardware definitions allow non-open subcomponents to be used in modular design, as long as they are easily available. However many designs try to push openness down into as many subcomponents as possible, with the aim of ultimately reaching fully open designs. Open hardware manual-drive vehicles and their subcomponents, such as from Open Source Ecology, are often used as starting points and extended with automation systems. Open subcomponents can include open-source computing hardware as subcomponents, such as Arduino and RISC-V, as well as open source motors and drivers such as the Open Source Motor Controller and ODrive. Open hardware robotics interface boards can simplify interfacing between middleware software and physical hardware. == Software subcomponents == === Middleware === Robotics middleware is software which links multiple other software components together. In robotics, this specifically means real-time communication systems with standardized message passing protocols. The predominant open source middleware is ROS2, the robot operating system, now as version 2. Other alternatives include ROS1, YARP — used in the iCub, URBI, and Orca. Open source middleware is usually run on an open source operating system, especially the Ubuntu distribution of Linux. === Driver software === Most robot sensors and actuators require software drivers. There is little standardization of open source software at this level, because each hardware device is different. Creating open drivers for closed hardware is difficult as it requires both low level programming and reverse engineering. === Simulation software === Open source robotics simulators include Gazebo, MuJoCo and Webots. Open source 3D game engines such as Godot are also sometimes used as simulators, when equipped with suitable middleware interfaces. === Automation software === At the level of AI, many standard algorithms have open source software implementations, mostly in ROS2. Major components include: Machine vision systems such as the YOLO object detector. 3D photogrammetry Navigation including SLAM and planning such as nav2 Arm inverse kinematics such as moveIt2 == Community == The first signs of the increasing popularity of building and sharing robot designs were found with the maker culture community. What began with small competitions for remote operated vehicles (e.g. Robot combat), soon developed to the building of autonomous telepresence robots such as Sparky and then true robots (being able to take decisions themselves) as the Open Automaton Project. Several commercial companies now also produce kits for making simple robots. The community has adopted open source hardware licenses, certifications, and peer-reviewed publications, which check that source has been made correctly and permanently available under community definitions, and which validate that this has been done. These processes have become critically important due to many historical projects claiming to be open source but them reverting on the promise due to commercialisation or other pressures. As with other forms of open source hardware, the community continues to debate precise criteria for 'ease of build'. A common standard is that designs should be buildable by a technical university student, in a few days, using typical fablab tools, but definitions of all of these subterms can also be debated. Compared to other forms of open source hardware, open source robotics typically includes a large software element, so involves software as well as hardware engineers. Open source concepts are more established in open source software than hardware, so robotics is a field in which those concepts can be shared and transferred from software to hardware. While the community in open source robotics is multi-faceted with a wide range of backgrounds, a sizable sub-community uses the ROS middleware and meets at the ROSCon conferences to discuss development of ROS itself and automation components built on it.
Passenger drone
A passenger drone is an autonomous aircraft that is designed to carry a small number of passengers to a destination. In 2021, Ehang, a technology company based in Guangzhou, China, developed the Ehang 184, the world's first passenger drone. == History == Unmanned aerial vehicles were first introduced in World War 1, when Britain first developed the Aerial Target, an aircraft controlled remotely through radio signals. A year later in the United States, testing of Kettering Bug, a 12-foot long biplane attached with a bomb and that launched via a “slingshot-like rail”, was also under progress. Both of their unreliable test results and their possibility of endangering friendly troops in deployment caused neither aircraft to be used during the war. Production of UAVs continued after World War I and into World War II and the Vietnam War, where they would be invaluable in assisting with training as well as reconnaissance. Late 20th century also saw the proposition and development of unique methods of travel, including personal jetpacks and even flying cars. While the previously mentioned are not drones, they serve as a precursor and foundation for the passenger drones of today. The first passenger drone was unveiled on January 6 of 2016 at the international Consumer Electronics Show (CES) in Las Vegas. Produced by Ehang, a Chinese company based in Guangzhou, the 184 was a one passenger drone equipped with four propellers that could fly for approximately 23 minutes at a top speed of 63 mph. Since then, many new companies have entered the market, but none yet have been accessible by the public. == Technological development == Since 2013, improvements in designs to wing structures have contributed to the economic feasibility of passenger drones. New structural advancements, such as the flapping-wing propulsion system based on the mechanisms of birds’ wings, are more available as they have proven their capabilities in laboratory testing. As of September 29th, 2015, most market-ready drones are delivery drones with a carrying capacity limited to small packages - with a typical max capacity of under 5 pounds. However, while the technology exists for drones with larger carrying capacities, specifically those capable of carrying multiple humans, the execution of this technology is not yet market accessible. This capacity limit must be addressed for passenger drones; given current designs strive to carry a maximum of 5 people. However, some estimates believe that passengers drones could become a reality, specifically for paid transportation and emergency purposes, as early as 2026. With implementation of this technology, there could be significant effects on ground traffic including reducing gridlock in heavily congested areas and conserving up to 15% of the fuel currently used in heavy traffic patterns. However, extensive growth of the passenger drone market also risks clouding the low-altitude airspace and causing new safety risks. However, this concern is being addressed by recent advancements in the Internet of Drones (IoD) which links drones together to ensure appropriate pathing and reduce mid-air collisions. While this brings additional security issues, including maintaining reliable communication channels in the case of technological failure, researchers hope that this will help reduce crashes that can result in damage to passengers, buildings, and people in and around the airspace. == Notable companies == Ehang is a Chinese company that has developed numerous drones including passenger plane Ehang 184. EHang 184 was their first model, developed as an eight dual rotor wing blade drone that can carry two passengers. The model was retired in 2020 and is replaced by the Ehang 216. Ehang also released a one passenger drone, Ehang 116. Ehang in 2021 unveiled the model VT-30. VT-30 is designed to have eight dual rotor wing blades to complement its fixed wing platform. Flyastro, a Texas-based drone company, developed the Astro ALTA, with two and four person passenger models. The company is known for being the first to develop a solar-powered airplane. The development team initially began with the model, Elroy. It was a two passenger drone with similar design to the ALTA. Once flight was achieved, the model Astro ALTA began development. Joby Aviation is a California based company that has developed a five passenger drone, with one seat for the pilot. The company expects to complete its FAA certification process 2022. Joby in 2020 acquired a 75 million dollar investment from service provider Uber Technologies Inc., leading to Uber Elevate and Expands partnership. Archer Aviation is a California-based company that has developed a two passenger model called Maker. It has fixed wings with twelve rotor wings. Archer is developing five person model. United Airlines has partnered with Archer for commercial sale of the model, Maker. Maker is expected to be released within Los Angeles and Miami by 2024. CityAirbus is a drone project developed by Airbus, a European multinational aerospace company, based in the Netherlands. CityAirbus has developed a four- person passenger drone with fixed wings that include rotor wing blades. Its expected certification for public flight is in 2025. Boeing, an American multinational aviation corporation is developing a passenger drone model called the Passenger Air Vehicle (PAV). The model is a fixed wing with eight rotor blade wings attached onto a platform underneath the base structure. This model can hold two passengers and still is in development. Volocopter is a German aircraft manufacturer that is developing a passenger drone called Volocity. The model consist of eighteen rotor wings above the cockpit on a circular ring. Japan Airlines, an investor of Volocopter plans to have public test in Japan as early as 2023. == Future use == === Potential benefits === Passenger drones can greatly reduce the time for travel. As passenger drones flight paths are not restricted by conventional roads, the travel distance is shortened. Current ventures such as Joby Aviation, after acquiring Uber Air, plan to take advantage of this technology in the form of air taxis. Other potential benefits include the use of passenger drones by emergency services such as search and rescue missions and the delivery of life saving goods. Companies like Ehang have already begun using passenger drones as emergency vehicles as a response to the potential river collapses during the flood season in China. === Concerns === Passenger and air traffic safety remains at the forefront of concerns. Regulations for air traffic centered around passenger drones are still underway and would continue to develop with increasing use cases for passenger drones. Remote security threats on commercial drones such as Man-In-The-Middle (MITM) attack have also exposed the vulnerabilities in current drone systems. Among American adults, 54 percent say that they would feel unsafe flying inside a passenger drone. Passenger drones can be very noisy; a single passenger drone such as Joby Aviation’s all-electric vertical take-off and landing (“eVTOL”) aircraft has an estimated noise production of 70 decibels (dB), a noise level equating to “loud traffic”.
Agent-assisted automation
Agent-assisted automation is a type of call center technology that automates elements of what the call center agent 1) does with his/her desktop tools and/or 2) says to customers during the call using pre-recorded audio. It is a relatively new category of call center technology that shows promise in improving call center productivity and compliance. == Types of agent-assisted automation == === Pre-recorded audio === Pre-recorded audio (sometimes referred to as soundboard (computer program) or as soundboard technology) is another form of agent-assisted automation. The purpose of using pre-recorded messages is to increase the probability (and in some cases error-proof the process so) that the right information is provided to customers at the right time. The required disclosures are pre-recorded to ensure accuracy and understandability. By integrating the recordings with the customer relationship management software, the right combination of disclosures can be played based on the combination of goods and services the customer purchased. The integration with the customer relationship management software also ensures that the order cannot be submitted until the disclosures are played, essentially error-proofing (poka-yoke) the process of ensuring the customer gets all the required consumer protection information. Phone surveys are ideal applications of this technology. Whether surveying market preferences or political views, the pre-recorded audio with an agent listening allows the questions to be asked in the same way every time, uninfluenced by the agents' fatigue levels, accents, or their own views. === Fraud prevention === Fraud prevention is a specialized type of agent-assisted automation focused on reducing ID theft and credit card fraud. ID theft and credit card fraud are huge threats for call centers and their customers and few good solutions exist, but new agent-assisted automation solutions are producing promising results. The technology allows the agents to remain on the phone while the customers use their phone key pads to enter the information. The tones are masked and the information passes directly into the customer relationship management system or payment gateway in the case of credit card transactions. The automation essentially makes it impossible for call center agents and also call center personnel that might be monitoring the calls to steal the credit card number, social security number, or other personally identifiable information. === Outbound telemarketing === Another specialized application space of agent-assisted automation is in outbound telemarketing, which goes under numerous headings including outbound prospecting, cold calling, solicitation, fund-raising, etc. Turnover is high among agents engaged in this kind of work because the task is tedious and emotionally difficult. It is tedious because the agent spends the bulk of their day, not talking to qualified leads, but in getting wrong numbers and answering machines. == Benefits == Just as automation has benefited manufacturing by reducing the mental and physical effort required of workers while simultaneously improving throughput, quality, and safety, agent-assisted automation is improving call center results while reducing the tiring aspects of the job for agents. In some cases, the agent-assisted automation streamlines the process and allows calls to be handled more quickly. By eliminating cutting and pasting from one application to another, by auto-navigating applications, and by providing a single view of the customer, agent-assisted automation can reduce call handle time and increase agent productivity. Second, in theory, the more steps that can be automated and the more logic that can be built into the call flow (e.g., if the customer buys items 2 and 9, then disclosures a, c, and f are read by the pre-recorded audio), then companies may be able to reduce the amount of training that is required of the agents while at the same time ensuring more consistency and accuracy. However, no published studies have reported this result yet. But an even larger problem in call centers is between-agent variation in behavior and results. Agents differ in the amount of training and coaching they receive, they differ in the amount of experience they have, their jobs are repetitious and tiring, and the process and procedures the agents are supposed to follow constantly change. Moreover, there are significant individual differences between agents in their intelligence, personality, motivations, etc. which all affect performance. Despite the large amount of money call centers have spent over decades trying to reduce between-agent variation, the problem is still so prevalent that one large study of customer interactions with call centers found that a customer's experience was completely a function of the quality of the agent who happened to answer the phone. Therefore, the most significant benefit of agent-assisted automation may prove to be in how the automation error-proofs or poka-yoke the process and ensures that something that needs to be done or said happens every time. Properly implemented, the between-agent variation for whatever step of the process the automation is applied to may be able to be reduced to near zero. This is especially important in a collection agency whose processes and procedures are closely regulated by the Fair Debt Collection Practices Act.
Scripped
Scripped was an online screenplay services company offering three services: script writing, script registration, and script coverage. Scripped did not facilitate collaboration among screenwriters. It combined with Zhura in 2010. According to Techcrunch, Scripped had more than 60,000 writers as of March 2010. Scripped was administered by Sunil Rajaraman, Ryan Buckley and Zak Freer. Actor, writer, and director Edward Burns and screenwriter Steven E. de Souza joined Scripped's Board of Advisers in May 2008. In 2008, the company formed a partnership with Write Brothers, makers of Movie Magic Screenwriter software. On March 29, 2010, Scripped announced that it closed $250,000 in private investment and merged with competitor Zhura. Scripped's CEO, Sunil Rajaraman, remains the merged company's Chief Executive Officer. On April 1, 2015, citing a serious technical failure, Scripped shuttered its service. As part of the announcement, it was disclosed that their backup servers had failed as well, losing all of its users' stored scripts. The website URL currently redirects to WriterDuet's website, another online scriptwriting service; Scripped had advertised WriterDuet in Scripped's shutdown open letter. == Features == The Scripped Writer provided a built-in screenplay template which formatted the document to a standard for scripts as recommended by the AMPAS. The screenplay document was composed of seven elements: scene, action, character, dialog, parenthetical, transition and general. Each element had a specific style to which the Scripped Writer conformed as text was entered. Like other client-side screenplay software, Scripped offered Tab-Enter toggling between screenplay elements, making the writing process much faster. Text files could be imported into the Scripped Writer and automatically conformed to the screenplay template. Completed scripts could be exported as PDF files. In May 2011 the administrators of Scripped launched Scripted.com - a sister site focused on freelance writing jobs. Subsequent to the service's launch, the company was renamed to Scripted, Inc.
Cups (app)
Cups (stylized as CUPS) was a mobile app launched in New York City in April 2014. It was a mobile payment and discovery platform for independent coffee shops nearby. The app was active in more than 400 cafes in New York, San Francisco, Philadelphia, Nashville, Minneapolis and Saint Paul, and other U.S. cities. == History == Cups was founded in Israel in 2012 by Gilad Rotem and four other co-founders, who were all high school friends. The company ran a limited beta pilot in Tel Aviv and Jerusalem, featuring 80 locations, from September 2012 until September 2014. Customers received all-you-can-drink coffee at certain coffee shops in Tel Aviv for approximately $45 a month. In October 2013, the founders relocated to New York. Cups participated in the Entrepreneur's Roundtable Accelerator program and went live in New York in 2014, initially working with 50 small coffee shops in Manhattan and Brooklyn. In early 2016, the company launched 30 locations in Philadelphia in February, followed by 40 more locations in San Francisco in March. == Functionality == The Cups app gave the user a list of the nearest participating coffee shops to their current location. The app user can order a drink using the app and pay the cashier with their phone. The cashier would enter a code that entered the purchase into the app's system. The app also allowed for onboard tipping and food purchases. The company reimbursed the coffee shop and kept a portion of their sales. In early 2016, the Cups Café Network was launched, using bulk purchasing power to land discounts with service providers which would normally be reserved for larger chains. In this way, the company aimed to help its café partners compete with the larger coffee chains.