AI Chat Emochi

AI Chat Emochi — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Data-centric AI

  Data-centric AI is an approach within artificial intelligence that emphasizes on improving the quality, consistency and representativeness of the data used to train machine learning models, rather than focusing primarily on optimizing model architectures or algorithms. This idea has gained traction as researchers and practitioners have come to believe that many performance limitations of machine learning systems stem from issues such as noisy labels, biased datasets, and lack of coverage in the data. Data-centric AI involves disciplined approach to data cleaning, augmentation, labeling, and governance that improves model performance and reliability in applications such as computer vision, natural language processing, and further.

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  Vismon

  Vismon was the Bell Labs system which displayed authors' faces on one of their internal e-mail systems. The name was a pun on the sysmon program used at Bell to show the load on computer systems. It can also be interpreted as "visual monitor". The system inspired Rich Burridge to develop the similar but more widespread faces system, which spread with Unix distributions in the 1980s. This in turn inspired Steve Kinzler to develop the Picons, or personal icons, which have the goal of offering symbols and other images, as well as faces, to represent individuals and institutions in email messages. Other systems such as the faces available on the LAN email functions of the NeXTSTEP platform also seem to have been influenced by the original Vismon capabilities. The faces program in Plan 9 is the direct descendant of this system. Vismon was the work of Rob Pike and Dave Presotto. It was based on some early experiments by Luca Cardelli. Many other scientists and engineers of the Computing Science Research Center of the Murray Hill facility were also involved. All had been spurred by the introduction in 1983 of the new Blit graphics terminal developed by Pike and Bart Locanthi and marketed by Teletype Corporation of Skokie, Illinois as the DMD 5620. Pike was eager, along with his colleagues, to exploit the new graphic capabilities. Pike and company went around their Center, convincing everybody, from directors and administrative assistants to engineers and scientists, to pose as they got out a 4×5 view camera with a Polaroid back and took black-and-white photos (Polaroid type 52) of their faces. Their efforts yielded nearly 100 faces, which they digitised with a scanner from graphics colleagues. They wrote several programs to transform the faces, store them and serve them on several machines at the lab. As time went by, they added faces from outside their Center and outside Bell Labs. This database also led to the pico image editor (originally named zunk) which was used for image transformations, many of them with colleagues as the preferred target. The first programs built around vismon were used to announce incoming mail in a dedicated window, using the 48 by 48 pixel faces. Later on the faces were also used to decorate line printer banners.

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  Level set (data structures)

  In computer science, a level set is a data structure designed to represent discretely sampled dynamic level sets of functions. A common use of this form of data structure is in efficient image rendering. The underlying method constructs a signed distance field that extends from the boundary, and can be used to solve the motion of the boundary in this field. == Chronological developments == The powerful level-set method is due to Osher and Sethian 1988. However, the straightforward implementation via a dense d-dimensional array of values, results in both time and storage complexity of O ( n d ) {\displaystyle O(n^{d})} , where n {\displaystyle n} is the cross sectional resolution of the spatial extents of the domain and d {\displaystyle d} is the number of spatial dimensions of the domain. === Narrow band === The narrow band level set method, introduced in 1995 by Adalsteinsson and Sethian, restricted most computations to a thin band of active voxels immediately surrounding the interface, thus reducing the time complexity in three dimensions to O ( n 2 ) {\displaystyle O(n^{2})} for most operations. Periodic updates of the narrowband structure, to rebuild the list of active voxels, were required which entailed an O ( n 3 ) {\displaystyle O(n^{3})} operation in which voxels over the entire volume were accessed. The storage complexity for this narrowband scheme was still O ( n 3 ) . {\displaystyle O(n^{3}).} Differential constructions over the narrow band domain edge require careful interpolation and domain alteration schemes to stabilise the solution. === Sparse field === This O ( n 3 ) {\displaystyle O(n^{3})} time complexity was eliminated in the approximate "sparse field" level set method introduced by Whitaker in 1998. The sparse field level set method employs a set of linked lists to track the active voxels around the interface. This allows incremental extension of the active region as needed without incurring any significant overhead. While consistently O ( n 2 ) {\displaystyle O(n^{2})} efficient in time, O ( n 3 ) {\displaystyle O(n^{3})} storage space is still required by the sparse field level set method. See for implementation details. === Sparse block grid === The sparse block grid method, introduced by Bridson in 2003, divides the entire bounding volume of size n 3 {\displaystyle n^{3}} into small cubic blocks of m 3 {\displaystyle m^{3}} voxels each. A coarse grid of size ( n / m ) 3 {\displaystyle (n/m)^{3}} then stores pointers only to those blocks that intersect the narrow band of the level set. Block allocation and deallocation occur as the surface propagates to accommodate to the deformations. This method has a suboptimal storage complexity of O ( ( n m ) 3 + m 3 n 2 ) {\displaystyle O\left((nm)3+m^{3}n^{2}\right)} , but retains the constant time access inherent to dense grids. === Octree === The octree level set method, introduced by Strain in 1999 and refined by Losasso, Gibou and Fedkiw, and more recently by Min and Gibou uses a tree of nested cubes of which the leaf nodes contain signed distance values. Octree level sets currently require uniform refinement along the interface (i.e. the narrow band) in order to obtain sufficient precision. This representation is efficient in terms of storage, O ( n 2 ) , {\displaystyle O(n^{2}),} and relatively efficient in terms of access queries, O ( log n ) . {\displaystyle O(\log \,n).} An advantage of the level method on octree data structures is that one can solve the partial differential equations associated with typical free boundary problems that use the level set method. The CASL research group has developed this line of work in computational materials, computational fluid dynamics, electrokinetics, image-guided surgery and controls. === Run-length encoded === The run-length encoding (RLE) level set method, introduced in 2004, applies the RLE scheme to compress regions away from the narrow band to just their sign representation while storing with full precision the narrow band. The sequential traversal of the narrow band is optimal and storage efficiency is further improved over the octree level set. The addition of an acceleration lookup table allows for fast O ( log ⁡ r ) {\displaystyle O(\log r)} random access, where r is the number of runs per cross section. Additional efficiency is gained by applying the RLE scheme in a dimensional recursive fashion, a technique introduced by Nielsen & Museth's similar DT-Grid. === Hash Table Local Level Set === The Hash Table Local Level Set method was introduced in 2011 by Eyiyurekli and Breen and extended in 2012 by Brun, Guittet, and Gibou, only computes the level set data in a band around the interface, as in the Narrow Band Level-Set Method, but also only stores the data in that same band. A hash table data structure is used, which provides an O ( 1 ) {\displaystyle O(1)} access to the data. However, Brun et al. conclude that their method, while being easier to implement, performs worse than a quadtree implementation. They find that as it is, [...] a quadtree data structure seems more adapted than the hash table data structure for level-set algorithms. Three main reasons for worse efficiency are listed: to obtain accurate results, a rather large band is required close to the interface, which counterbalances the absence of grid nodes far from the interface; the performances are deteriorated by extrapolation procedures on the outer edges of the local grid and the width of the band restricts the time step and slows down the method. === Point-based === Corbett in 2005 introduced the point-based level set method. Instead of using a uniform sampling of the level set, the continuous level set function is reconstructed from a set of unorganized point samples via moving least squares.

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  Enterprise mobile application

  The term enterprise mobile application is used in the context of mobile apps created/brought by individual organizations for their workers to carry out the functions required to run the organization. It is the process of building a mobile application for the requirements of an enterprise. An enterprise mobile application belonging to an organization is expected to be used by only the workers of that organization. The definition of enterprise mobile application does not include the mobile apps that an organization create for its customers or consumers of the products or services generated by the organization. == Example == An organization, whether for-profit or non-profit, may create a mobile app for its members to track inventory levels of supplies they distribute to their target communities or materials used in product manufacturing. Such a mobile app comes under the definition of enterprise mobile application. However, the same organization may also create another mobile app to sell their products to end users or spread awareness of their services to various communities, and that mobile app would not come under definition of enterprise mobile application. == Enterprise mobile solution providers == Enterprise Mobile solution providers create and develop apps for individual organizations that can buy instead of creating the apps themselves. Reasons for Organizations buying the apps include time and cost savings, technical expertise. Today Enterprise Mobility is playing track role for enterprise transformation. Today, enterprises needs productivity is a fast way. Enterprise mobility helps business owners to build their work in a progressive way by assisting enterprise mobility solutions.

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  Autoscaling

  Autoscaling, (also written as auto scaling, auto-scaling, or known as automatic scaling), is a method used in cloud computing that dynamically adjusts the amount of computational resources in a server farm - typically measured by the number of active servers - automatically based on the load on the farm. For example, the number of servers running behind a web application may be increased or decreased automatically based on the number of active users on the site. Since such metrics may change dramatically throughout the course of the day, and servers are a limited resource that cost money to run even while idle, there is often an incentive to run "just enough" servers to support the current load while still being able to support sudden and large spikes in activity. Autoscaling is helpful for such needs, as it can reduce the number of active servers when activity is low, and launch new servers when activity is high. Autoscaling is closely related to, and builds upon, the idea of load balancing. == Advantages == Autoscaling offers the following advantages: For companies running their own web server infrastructure, autoscaling typically means allowing some servers to go to sleep during times of low load, saving on electricity costs (as well as water costs if water is being used to cool the machines). For companies using infrastructure hosted in the cloud, autoscaling can mean lower bills, because most cloud providers charge based on total usage rather than maximum capacity. Even for companies that cannot reduce the total compute capacity they run or pay for at any given time, autoscaling can help by allowing the company to run less time-sensitive workloads on machines that get freed up by autoscaling during times of low traffic. Autoscaling solutions, such as the one offered by Amazon Web Services, can also take care of replacing unhealthy instances and therefore protecting somewhat against hardware, network, and application failures. Autoscaling can offer greater uptime and more availability in cases where production workloads are variable and unpredictable. Autoscaling differs from having a fixed daily, weekly, or yearly cycle of server use in that it is responsive to actual usage patterns, and thus reduces the potential downside of having too few or too many servers for the traffic load. For instance, if traffic is usually lower at midnight, then a static scaling solution might schedule some servers to sleep at night, but this might result in downtime on a night where people happen to use the Internet more (for instance, due to a viral news event). Autoscaling, on the other hand, can handle unexpected traffic spikes better. == Terminology == In the list below, we use the terminology used by Amazon Web Services (AWS). However, alternative names are noted and terminology that is specific to the names of Amazon services is not used for the names. == Practice == === Amazon Web Services (AWS) === Amazon Web Services launched the Amazon Elastic Compute Cloud (EC2) service in August 2006, that allowed developers to programmatically create and terminate instances (machines). At the time of initial launch, AWS did not offer autoscaling, but the ability to programmatically create and terminate instances gave developers the flexibility to write their own code for autoscaling. Third-party autoscaling software for AWS began appearing around April 2008. These included tools by Scalr and RightScale. RightScale was used by Animoto, which was able to handle Facebook traffic by adopting autoscaling. On May 18, 2009, Amazon launched its own autoscaling feature along with Elastic Load Balancing, as part of Amazon Elastic Compute Cloud. Autoscaling is now an integral component of Amazon's EC2 offering. Autoscaling on Amazon Web Services is done through a web browser or the command line tool. In May 2016 Autoscaling was also offered in AWS ECS Service. On-demand video provider Netflix documented their use of autoscaling with Amazon Web Services to meet their highly variable consumer needs. They found that aggressive scaling up and delayed and cautious scaling down served their goals of uptime and responsiveness best. In an article for TechCrunch, Zev Laderman, the co-founder and CEO of Newvem, a service that helps optimize AWS cloud infrastructure, recommended that startups use autoscaling in order to keep their Amazon Web Services costs low. Various best practice guides for AWS use suggest using its autoscaling feature even in cases where the load is not variable. That is because autoscaling offers two other advantages: automatic replacement of any instances that become unhealthy for any reason (such as hardware failure, network failure, or application error), and automatic replacement of spot instances that get interrupted for price or capacity reasons, making it more feasible to use spot instances for production purposes. Netflix's internal best practices require every instance to be in an autoscaling group, and its conformity monkey terminates any instance not in an autoscaling group in order to enforce this best practice. === Microsoft's Windows Azure === On June 27, 2013, Microsoft announced that it was adding autoscaling support to its Windows Azure cloud computing platform. Documentation for the feature is available on the Microsoft Developer Network. === Oracle Cloud === Oracle Cloud Platform allows server instances to automatically scale a cluster in or out by defining an auto-scaling rule. These rules are based on CPU and/or memory utilization and determine when to add or remove nodes. === Google Cloud Platform === On November 17, 2014, the Google Compute Engine announced a public beta of its autoscaling feature for use in Google Cloud Platform applications. As of March 2015, the autoscaling tool is still in Beta. === Facebook === In a blog post in August 2014, a Facebook engineer disclosed that the company had started using autoscaling to bring down its energy costs. The blog post reported a 27% decline in energy use for low traffic hours (around midnight) and a 10-15% decline in energy use over the typical 24-hour cycle. === Kubernetes Horizontal Pod Autoscaler === Kubernetes Horizontal Pod Autoscaler automatically scales the number of pods in a replication controller, deployment or replicaset based on observed CPU utilization (or, with beta support, on some other, application-provided metrics) == Alternative autoscaling decision approaches == Autoscaling by default uses reactive decision approach for dealing with traffic scaling: scaling only happens in response to real-time changes in metrics. In some cases, particularly when the changes occur very quickly, this reactive approach to scaling is insufficient. Two other kinds of autoscaling decision approaches are described below. === Scheduled autoscaling approach === This is an approach to autoscaling where changes are made to the minimum size, maximum size, or desired capacity of the autoscaling group at specific times of day. Scheduled scaling is useful, for instance, if there is a known traffic load increase or decrease at specific times of the day, but the change is too sudden for reactive approach based autoscaling to respond fast enough. AWS autoscaling groups support scheduled scaling. === Predictive autoscaling === This approach to autoscaling uses predictive analytics. The idea is to combine recent usage trends with historical usage data as well as other kinds of data to predict usage in the future, and autoscale based on these predictions. For parts of their infrastructure and specific workloads, Netflix found that Scryer, their predictive analytics engine, gave better results than Amazon's reactive autoscaling approach. In particular, it was better for: Identifying huge spikes in demand in the near future and getting capacity ready a little in advance Dealing with large-scale outages, such as failure of entire availability zones and regions Dealing with variable traffic patterns, providing more flexibility on the rate of scaling out or in based on the typical level and rate of change in demand at various times of day On November 20, 2018, AWS announced that predictive scaling would be available as part of its autoscaling offering.

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  ImageMixer

  ImageMixer is a brand name of video editing software that edits digital video and still image in camcorders and authors to VCD and DVD. It is a second-party Japanese product, distributed by Pixela Corporation, a Japanese manufacturer of PC peripheral hardware and multimedia software. == Bundling == ImageMixer is widely used for several camcorder brands, such as JVC, Hitachi and Canon. Also, Sony has chosen to package ImageMixer with its DVD and HDD Handycam. == ImageMixer series == ImageMixer has other series of software for digital camera, such as ImageMixer Label Maker and ImageMixer DVD dubbing. ImageMixer also has movie editing solution for Macintosh. == Windows Vista version of ImageMixer == A Windows Vista version of ImageMixer has been developed (ImageMixer3).

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  Computer-aided lean management

  Computer-aided lean management, in business management, is a methodology of developing and using software-controlled, lean systems integration. Its goal is to drive innovation towards cost and cycle-time savings. It attempts to create an efficient use of capital and resources through the development and use of one integrated system model to run a business's planning, engineering, design, maintenance, and operations. == Overview == Computer-Aided Lean Management (CALM) is a management philosophy that uses software to reduce risk and inefficiencies. CALM acts on uncertainties and business inefficiencies to increase profitability through the use of computational decision-making tools that enable opportunities for additional value creation. It is based on the application of software to enable continuous improvement through an Integrated System Model (ISM) of the business’s physical assets, business processes, and machine learning. This integration of software applications using lean principles was developed in the aerospace industry and has migrated to the energy industry. The creation of an ISM removes the barriers posed by the silos or stovepipes inherent in the departmentalization of most companies. Integration enables lean uses of information for the creation of actionable knowledge. CALM strives to create such a lean management approach to running the company through the rigors of software enforcement. From this software enforcement comes clear policy and procedures that are adhered to, activity-based costing, measurement of effectiveness, and the capability of using advanced algorithms for dramatic improvements in optimization of resources. CALM creates business capabilities through software to enable technology application, streamlining of processes, and a lean organizational structure. The methodology is based on a common sense approach for running a business, by measuring actions taken and using those measurements to design more efficient processes. == History == CALM was inspired by lean processes and techniques that were already dominant management technologies with a wide diversity of applications and successes. Motorola and General Electric had been known for the concepts of Six Sigma; Boeing had been managing mass (using modular and flexible assembly options), and Toyota combined elements of these methodologies to create the Toyota Production System. Boeing then took the Toyota model and added computer-aided enforcement of lean methodologies throughout the manufacturing process. One of the major sources for CALM's outgrowth was integrated definition (IDEF) modeling in aerospace manufacturing that was pioneered by the U.S. Air Force in the 1970s. IDEF is a methodology designed to model the end-to-end decisions, actions, and activities of an organization or system so that costs, performance, and cycle times can be optimized. IDEF methods have been adapted for wider use in automotive, aerospace, pharmaceuticals, and software development industries. IDEF methods serve as a starting point to understand lean management through semantic data modeling. The IDEF process begins by mapping the existing functions of an enterprise, creating a graphical model, or road map, that shows what controls each important function, who performs it, what resources are required for carrying it out, what it produces, how much it costs, and what relationships it has to other functions of the organization. IDEF simulations have been found to be efficient at streamlining and modernizing both companies and governmental agencies. Perhaps the best-developed evolution of the IDEF model beyond Toyota was at Boeing. Their project life-cycle process has grown into a rigorous software system that links people, tasks, tools, materials, and the environmental impact of any newly planned project, before any building is allowed to begin. Routinely, more than half of the time for any given project is spent building the precedence diagrams, or three-dimensional process maps, integrating with outside suppliers, and designing the implementation plan–all on the computer. Once real activity is initiated, an action tracker is used to monitor inputs and outputs versus the schedule and delivery metrics in real time throughout the organization. When the execution of a new airplane design begins, it is so well organized that it consistently cuts both costs and build time in half for each successive generation of airframe. Boeing created a complex lean management process called 'define and control airplane configuration/manufacturing resource management' (DCAC/MRM). The process was built with the help of the operations research and computer sciences departments of the University of Pittsburgh. The manufacture of the Boeing 777 was ultimately a success, and it became the precursor to succeeding generations of CALM at Boeing. The methodology of CALM has recently been applied to field orientated infrastructure based businesses with highly interdependent systems, such as electric utilities where a smart grid concept is being researched and developed. The management of infrastructure-based industries like oil, gas, electricity, water, transportation, and renewables requires massive investments in interdependent, physical infrastructure, as well as simultaneous attention to disparate market forces. In infrastructure businesses that manage field assets, uncertainty is the biggest impediment to profitability, rather than the maintenance of efficient supply chains or the management of factory assembly lines. These businesses are dominated by risk from uncertainties such as weather, market variations, transportation disruptions, government actions, logistic difficulties, geology, and asset reliability. CALM has been applied to deal with these types of infrastructure based challenges.

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  CodeCheck

  CodeCheck is a mobile app that provides consumers with information about the ingredients in cosmetic products, as well as the ingredients and nutritional values of food. Users can access this information by scanning the product’s barcode with a smartphone or by using a text-based search. The app is available for iOS and Android devices in Germany, Austria, Switzerland, the United Kingdom, the United States, and the Netherlands. == History == CodeCheck was founded in 2010 as an association, online database, and app by Roman Bleichenbacher, who was then a student in Zurich. A website of the same name had already been launched in 2002, where users could enter information about ingredients, nutritional values, and manufacturers of products. The first round of financing took place in July 2014 and raised over 1.1 million Swiss francs, which coincided with the founding of CodeCheck AG. Investors included Doodle founders Myke Näf and Paul E. Sevinç. The company subsequently expanded to Austria and Germany. In the same year, Boris Manhart became CEO. CodeCheck GmbH was established in Berlin in 2016. The app became available in the United States in 2017 and in the United Kingdom in November 2019. In 2020, it was also launched in the Netherlands. Following insolvency proceedings, the app has been owned by Producto Check GmbH since 2022. == Functions == The app can be used to scan the barcode of food and cosmetic products. It then displays information about ingredients, nutritional values, manufacturers and certification labels. For many years, users were able to enter and edit product information themselves and indicate advantages and disadvantages of individual products. Since 2020, the app has placed greater emphasis on machine text recognition. The collected data is combined with substance ratings using an algorithm. These ratings are based on scientific studies and expert assessments, including those from the Consumer Advice Centre in Hamburg, Greenpeace, the WWF and the German Association for the Environment and Nature Conservation (BUND e. V.), and cannot be modified by users or manufacturers. The app also provides information on the sugar and fat content of food products. In addition, it indicates whether a product contains hormone-active substances, microplastics, palm oil, animal-derived ingredients, lactose or gluten. Since 2020, the app has displayed a climate score for food products in cooperation with the Eaternity Institute. == Financing == CodeCheck is primarily financed through native advertising and banner ads. Since 2018, the company has also offered analysis services and survey tools directly to fast-moving consumer goods (FMCG) manufacturers. In addition, access to the API is available, enabling other companies to use the product database. With the introduction of a subscription model in 2019, the CodeCheck app can be used ad-free and in offline mode. Since 2021, CodeCheck has also offered its own “Green Label” certification for manufacturers. Products are certified if at least 90 percent of their ingredients are classified as harmless. == Awards == In May 2015, the app topped the download charts for the first time, reaching 2.3 million installations. By September 2019, the app had once again reached the top of the German app charts, surpassing five million downloads.

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  Apprenticeship learning

  In artificial intelligence, apprenticeship learning (or learning from demonstration or imitation learning) is the process of learning by observing an expert. It can be viewed as a form of supervised learning, where the training dataset consists of task executions by a demonstration teacher. == Mapping function approach == Mapping methods try to mimic the expert by forming a direct mapping either from states to actions, or from states to reward values. For example, in 2002 researchers used such an approach to teach an AIBO robot basic soccer skills. === Inverse reinforcement learning approach === Inverse reinforcement learning (IRL) is the process of deriving a reward function from observed behavior. While ordinary "reinforcement learning" involves using rewards and punishments to learn behavior, in IRL the direction is reversed, and a robot observes a person's behavior to figure out what goal that behavior seems to be trying to achieve. The IRL problem can be defined as: Given 1) measurements of an agent's behaviour over time, in a variety of circumstances; 2) measurements of the sensory inputs to that agent; 3) a model of the physical environment (including the agent's body): Determine the reward function that the agent is optimizing. IRL researcher Stuart J. Russell proposes that IRL might be used to observe humans and attempt to codify their complex "ethical values", in an effort to create "ethical robots" that might someday know "not to cook your cat" without needing to be explicitly told. The scenario can be modeled as a "cooperative inverse reinforcement learning game", where a "person" player and a "robot" player cooperate to secure the person's implicit goals, despite these goals not being explicitly known by either the person nor the robot. In 2017, OpenAI and DeepMind applied deep learning to the cooperative inverse reinforcement learning in simple domains such as Atari games and straightforward robot tasks such as backflips. The human role was limited to answering queries from the robot as to which of two different actions were preferred. The researchers found evidence that the techniques may be economically scalable to modern systems. Apprenticeship via inverse reinforcement learning (AIRP) was developed by in 2004 Pieter Abbeel, Professor in Berkeley's EECS department, and Andrew Ng, Associate Professor in Stanford University's Computer Science Department. AIRP deals with "Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we want to learn to perform". AIRP has been used to model reward functions of highly dynamic scenarios where there is no obvious reward function intuitively. Take the task of driving for example, there are many different objectives working simultaneously - such as maintaining safe following distance, a good speed, not changing lanes too often, etc. This task, may seem easy at first glance, but a trivial reward function may not converge to the policy wanted. One domain where AIRP has been used extensively is helicopter control. While simple trajectories can be intuitively derived, complicated tasks like aerobatics for shows has been successful. These include aerobatic maneuvers like - in-place flips, in-place rolls, loops, hurricanes and even auto-rotation landings. This work was developed by Pieter Abbeel, Adam Coates, and Andrew Ng - "Autonomous Helicopter Aerobatics through Apprenticeship Learning" === System model approach === System models try to mimic the expert by modeling world dynamics. == Plan approach == The system learns rules to associate preconditions and postconditions with each action. In one 1994 demonstration, a humanoid learns a generalized plan from only two demonstrations of a repetitive ball collection task. == Example == Learning from demonstration is often explained from a perspective that the working Robot-control-system is available and the human-demonstrator is using it. And indeed, if the software works, the Human operator takes the robot-arm, makes a move with it, and the robot will reproduce the action later. For example, he teaches the robot-arm how to put a cup under a coffeemaker and press the start-button. In the replay phase, the robot is imitating this behavior 1:1. But that is not how the system works internally; it is only what the audience can observe. In reality, Learning from demonstration is much more complex. One of the first works on learning by robot apprentices (anthropomorphic robots learning by imitation) was Adrian Stoica's PhD thesis in 1995. In 1997, robotics expert Stefan Schaal was working on the Sarcos robot-arm. The goal was simple: solve the pendulum swingup task. The robot itself can execute a movement, and as a result, the pendulum is moving. The problem is, that it is unclear what actions will result into which movement. It is an Optimal control-problem which can be described with mathematical formulas but is hard to solve. The idea from Schaal was, not to use a Brute-force solver but record the movements of a human-demonstration. The angle of the pendulum is logged over three seconds at the y-axis. This results into a diagram which produces a pattern. In computer animation, the principle is called spline animation. That means, on the x-axis the time is given, for example 0.5 seconds, 1.0 seconds, 1.5 seconds, while on the y-axis is the variable given. In most cases it's the position of an object. In the inverted pendulum it is the angle. The overall task consists of two parts: recording the angle over time and reproducing the recorded motion. The reproducing step is surprisingly simple. As an input we know, in which time step which angle the pendulum must have. Bringing the system to a state is called “Tracking control” or PID control. That means, we have a trajectory over time, and must find control actions to map the system to this trajectory. Other authors call the principle “steering behavior”, because the aim is to bring a robot to a given line.

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  Face Swap Live

  Face Swap Live is a mobile app created by Laan Labs that enables users to swap faces with another person in real-time using the device's camera. It was released on December 14, 2015. In addition to swapping faces with another person, the app enables users to create videos using a set of bundled live filters. The app is available on iOS and Android devices. Face Swap Live was named Apple's #2 best-selling paid app in 2016.

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  Cheekd

  Cheekd is a dating app based in New York City. It was founded in 2010 by Lori Cheek. == History == The service debuted with the name "Cheek'd". Founder Lori Cheek appeared on the television program, Shark Tank in February 2014, but did not succeed in obtaining funding from any of the five judges. She said Cheek’d only had 1000 subscribers at that time. === Business card model === Cheek'd offered two plans, paid and free. For $25, subscribers got a set of 50 business cards that could be given out once someone caught their eye. Each card had a phrase, an online code, and a URL to the subscriber's account. Recipients could look up the giver's profile. In addition to purchasing cards, there was a $9.95 monthly membership fee. === Smartphone app === In 2015, the service's name changed from "Cheek'd" to "Cheekd". The new app used Bluetooth technology to alert users whenever a compatible user was within a 30-foot radius, instead of using cards. == Patent lawsuit == The original business card-based model for Cheekd had been claimed as a patented process by Lori Cheek, as U.S. patent 8,543,465. In September 2017, a complaint was filed, alleging that the idea was not original to Lori Cheek. Cheek responded, stating that the complaint was baseless, and a complete fabrication. The lawsuit Pirri v. Cheek was dismissed in a pre-trial conference in New York's Federal Court on April 5, 2018.

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  List of 3D rendering software

  3D rendering software products are the dedicated engines used for rendering computer-generated imagery. This is not the same as 3D modeling software, which involves the creation of 3D models, for which the software listed below can produce realistically rendered visualisations.General-purpose packages which can have their own built-in rendering capabilities are not listed here; these can be found in the list of 3D computer graphics software and list of 3D animation software. See 3D computer graphics software for more discussion about the distinctions.

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  Firefox Lockwise

  Firefox Lockwise (formerly Lockbox) is a deprecated password manager for the Firefox web browser, as well as the mobile operating systems iOS and Android. On desktop, Lockwise was simply part of Firefox, whereas on iOS and Android it was available as a standalone app. If Firefox Sync was activated (with a Firefox account), then Lockwise synced passwords between Firefox installations across devices. It also featured a built-in random password generator. The application and branding have since been "phased out." == History == Developed by Mozilla, it was originally named Firefox Lockbox in 2018. It was renamed "Lockwise" in May 2019. It was introduced for iOS on 10 July 2018 as part of the Test Pilot program. On 26 March 2019, it was released for Android. On desktop, Lockwise started out as a browser addon. Alphas were released between March and August 2019. Since Firefox version 70, Lockwise has been integrated into the browser (accessible at about:logins), having replaced a basic password manager presented in a popup window. Mozilla ended support for Firefox Lockwise on December 13, 2021. As of January 2026, Lockwise is still fully functional on Android to this day.

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  Comparison of vector graphics editors

  A number of vector graphics editors exist for various platforms. Potential users of these editors will make comparisons based on factors such as the availability for the user's platform, the software license, the feature set, the merits of the user interface (UI) and the focus of the program. Some programs are more suitable for artistic work while others are better for technical drawings. Another important factor is the application's support of various vector and bitmap image formats for import and export. The tables in this article compare general and technical information for a number of vector graphics editors. See the article on each editor for further information. This article is neither all-inclusive nor necessarily up-to-date. == Some editors in detail == Adobe Fireworks (formerly Macromedia Fireworks) is a vector editor with bitmap editing capabilities with its main purpose being the creation of graphics for Web and screen. Fireworks supports RGB color scheme and has no CMYK support. This means it is mostly used for screen design. The native Fireworks file format is editable PNG (FWPNG or PNG). Adobe Fireworks has a competitive price, but its features can seem limited in comparison with other products. It is easier to learn than other products and can produce complex vector artwork. The Fireworks editable PNG file format is not supported by other Adobe products. Fireworks can manage the PSD and AI file formats which enables it to be integrated with other Adobe apps. Fireworks can also open FWPNG/PNG, PSD, AI, EPS, JPG, GIF, BMP, TIFF file formats, and save/export to FWPNG/PNG, PSD, AI (v.8), FXG (v.2.0), JPG, GIF, PDF, SWF and some others. Some support for exporting to SVG is available via a free Export extension. On May 6, 2013, Adobe announced that Fireworks would be phased out. Adobe Flash (formerly a Macromedia product) has straightforward vector editing tools that make it easier for designers and illustrators to use. The most important of these tools are vector lines and fills with bitmap-like selectable areas, simple modification of curves via the "selection" or the control points/handles through "direct selection" tools. Flash uses Actionscript for OOP, and has full XML functionality through E4X support. Adobe FreeHand (formerly Macromedia Freehand and Aldus Freehand) is mainly used by professional graphic designers. The functionality of FreeHand includes the flexibility of the application in the wide design environment, catering to the output needs of both traditional image reproduction methods and to contemporary print and digital media with its page-layout capabilities and text attribute controls. Specific functions of FreeHand include a superior image-tracing operation for vector editing, page layout features within multiple-page documents, and embedding custom print-settings (such as variable halftone-screen specifications within a single graphic, etc.) to each document independent of auxiliary printer-drivers. User-operation is considered to be more suited for designers with an artistic background compared to designers with a technical background. When being marketed, FreeHand lacked the promotional backing, development and PR support in comparison to other similar products. FreeHand was transferred to the classic print group after Macromedia was purchased by Adobe in 2005. On May 16, 2007, Adobe announced that no further updates to Freehand would be developed but continues to sell FreeHand MX as a Macromedia product. FreeHand continues to run on Mac OS X Snow Leopard (using an Adobe fix) and on Windows 7. For macOS, Affinity Designer is able to open version 10 & MX Freehand files. Adobe Illustrator is a commonly used editor because of Adobe's market dominance, but is more expensive than other similar products. It is primarily developed consistently in line with other Adobe products and is best integrated with Adobe's Creative Suite packages. The ai file format is proprietary, but some vector editors can open and save in that format. Illustrator imports over two dozen formats, including PSD, PDF and SVG, and exports AI, PDF, SVG, SVGZ, GIF, JPG, PNG, WBMP, and SWF. However, the user must be aware of unchecking the "Preserve Illustrator Editing Capabilities" option if generating interoperable SVG files is desired. Affinity Designer by Serif Europe (the successor to their previous product, DrawPlus) is non-subscription-based software that is often described as an alternative to Adobe Illustrator. The application can open Portable Document Format (PDF), Adobe Photoshop, and Adobe Illustrator files, as well as export to those formats and to the Scalable Vector Graphics (SVG) and Encapsulated PostScript (EPS) formats. It also supports import from some Adobe Freehand files (specifically versions 10 & MX). Apache OpenOffice Draw is the vector graphics editor of the Apache OpenOffice open source office suite. It supports many import and export file formats and is available for multiple desktop operating systems. Boxy SVG is a chromium-based vector graphics editor for creating illustrations, as well as logos, icons, and other elements of graphic design. It is primarily focused on editing drawings in the SVG file format. The program is available as both a web app and a desktop application for Windows, macOS, ChromeOS, and Linux-based operating systems. Collabora Online Draw is the vector graphics editor of the Collabora Online open source office suite. It supports many import and export file formats and is accessible via any modern web browser, it also supports desktop editing features, Collabora Office is available for desktop and mobile operating systems, it is the enterprise ready version of LibreOffice. ConceptDraw PRO is a business diagramming tool and vector graphics editor available for both Windows and macOS. It supports multi-page documents, and includes an integrated presentation mode. ConceptDraw PRO supports imports and exports several formats, including Microsoft Visio and Microsoft PowerPoint. Corel Designer (originally Micrografx Designer) is one of the earliest vector-based graphics editors for the Microsoft Windows platform. The product is mainly used for the creation of engineering drawings and is shipped with extensive libraries for the needs of engineers. It is also flexible enough for most vector graphics design applications. CorelDRAW is an editor used in the graphic design, sign making and fashion design industries. CorelDRAW is capable of limited interoperation by reading file formats from Adobe Illustrator. CorelDRAW has over 50 import and export filters, on-screen and dialog box editing and the ability to create multi-page documents. It can also generate TrueType and Type 1 fonts, although refined typographic control is better suited to a more specific application. Some other features of CorelDRAW include the creation and execution of VBA macros, viewing of colour separations in print preview mode and integrated professional imposing options. Dia is a free and open-source diagramming and vector graphics editor available for Windows, Linux and other Unix-based computer operating systems. Dia has a modular design and several shape packages for flowcharting, network diagrams and circuit diagrams. Its design was inspired by Microsoft Visio, although it uses a Single Document Interface similar to other GNOME software (such as GIMP). DrawPlus, first built for the Windows platform in 1993, has matured into a full featured vector graphics editor for home and professional users. Also available as a feature-limited free 'starter edition': DrawPlus SE. DrawPlus developers, Serif Europe, have now ceased its development in order to focus on its successor, Affinity Designer. Edraw Max is a cross-platform diagram software and vector graphics editor available for Windows, Mac and Linux. It supports kinds of diagram types. It supports imports and exports SVG, PDF, HTML, Multiple page TIFF, Microsoft Visio and Microsoft PowerPoint. Embroidermodder is a free machine embroidery software tool that supports a variety of formats and allows the user to add custom modifications to their embroidery designs. Fatpaint is a free, light-weight, browser-based graphic design application with built-in vector drawing tools. It can be accessed through any browser with Flash 9 installed. Its integration with Zazzle makes it particularly suitable for people who want to create graphics for custom printed products such as T-shirts, mugs, iPhone cases, flyers and other promotional products. Figma is a collaborative web-based online vector graphics editor, used primarily for UX design and prototyping. GIMP, which works mainly with raster images, offers a limited set of features to create and record SVG files. It can also load and handle SVG files created with other software like Inkscape. Inkscape is a free and open-source vector editor with the primary native format being SVG. Inkscape is available for Linux, Windows, Mac OS X, and

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  Irwin Sobel

  Irwin Sobel (born September 12, 1940) is a scientist and researcher in digital image processing. == Biography == Irwin Sobel was born in New York City. He graduated from MIT in 1961 and completed his Ph.D. research at the Stanford Artificial Intelligence Project (SAIL) with thesis Camera Models and Machine Perception. His Ph.D. advisor was Jerome A. Feldman. Starting in 1973, he spent nine years doing postdoctoral research at Columbia University. After 1982, he worked as a Senior Researcher at HP Labs. == Sobel operator == In 1968, Sobel gave a talk entitled "An Isotropic 3x3 Image Gradient Operator" at SAIL; this method became known as the Sobel operator. It was developed jointly with a colleague, Gary Feldman, also at SAIL.

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