Cloud robotics

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Cloud_robotics

Cloud robotics is a field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centered on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of modern data center in the cloud, which can process and share information from various robots or agent (other machines, smart objects, humans, etc.). Humans can also delegate tasks to robots remotely through networks. Cloud computing technologies enable robot systems to be endowed with powerful capability whilst reducing costs through cloud technologies. Thus, it is possible to build lightweight, low-cost, smarter robots with an intelligent "brain" in the cloud. The "brain" consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc.

Components

A cloud for robots potentially has at least six significant components:

• Building a "cloud brain" for robots. It is the main object of cloud robotics.
• Offering a global library of images, maps, and object data, often with geometry and mechanical properties, expert system, knowledge base (i.e. semantic web, data centres);
• Massively-parallel computation on demand for sample-based statistical modelling and motion planning, task planning, multi-robot collaboration, scheduling and coordination of system;
• Robot sharing of outcomes, trajectories, and dynamic control policies and robot learning support;
• Human sharing of "open-source" code, data, and designs for programming, experimentation, and hardware construction;
• On-demand human guidance and assistance for evaluation, learning, and error recovery;
• Augmented human–robot interaction through various way (Semantics knowledge base, Apple SIRI like service etc.).

Applications

Autonomous mobile robots

Google's self-driving cars are cloud robots. The cars use the network to access Google's enormous database of maps and satellite and environment model (like Streetview) and combines it with streaming data from GPS, cameras, and 3D sensors to monitor its own position within centimetres, and with past and current traffic patterns to avoid collisions. Each car can learn something about environments, roads, or driving, or conditions, and it sends the information to the Google cloud, where it can be used to improve the performance of other cars.

Cloud medical robots

a medical cloud (also called a healthcare cluster) consists of various services such as a disease archive, electronic medical records, a patient health management system, practice services, analytics services, clinic solutions, expert systems, etc. A robot can connect to the cloud to provide clinical service to patients, as well as deliver assistance to doctors (e.g. a co-surgery robot). Moreover, it also provides a collaboration service by sharing information between doctors and care givers about clinical treatment.

Assistive robots

A domestic robot can be employed for healthcare and life monitoring for elderly people. The system collects the health status of users and exchange information with cloud expert system or doctors to facilitate elderly peoples life, especially for those with chronic diseases. For example, the robots are able to provide support to prevent the elderly from falling down, emergency healthy support such as heart disease, blooding disease. Care givers of elderly people can also get notification when in emergency from the robot through network.

Industrial robots

As highlighted by the German government's Industry 4.0 Plan, "Industry is on the threshold of the fourth industrial revolution. Driven by the Internet, the real and virtual worlds are growing closer and closer together to form the Internet of Things. Industrial production of the future will be characterised by the strong individualisation of products under the conditions of highly flexible (large series) production, the extensive integration of customers and business partners in business and value-added processes, and the linking of production and high-quality services leading to so-called hybrid products." In manufacturing, such cloud based robot systems could learn to handle tasks such as threading wires or cables, or aligning gaskets from a professional knowledge base. A group of robots can share information for some collaborative tasks. Even more, a consumer is able to place customised product orders to manufacturing robots directly with online ordering systems. Another potential paradigm is shopping-delivery robot systems. Once an order is placed, a warehouse robot dispatches the item to an autonomous car or autonomous drone to deliver it to its recipient.

Learn a Cloud Brain for Robots

Approach: Lifelong Learning. Leveraging lifelong learning to build a cloud brain for robots was proposed by CAS. The author was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, they present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, they propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge.

Approach: Federated Learning. Leveraging lifelong learning to build a cloud brain for robots was proposed in 2020. Humans are capable of learning a new behavior by observing others to perform the skill. Similarly, robots can also implement this by imitation learning. Furthermore, if with external guidance, humans can master the new behavior more efficiently. So, how can robots achieve this? To address the issue, The authors present a novel framework named FIL. It provides a heterogeneous knowledge fusion mechanism for cloud robotic systems. Then, a knowledge fusion algorithm in FIL is proposed. It enables the cloud to fuse heterogeneous knowledge from local robots and generate guide models for robots with service requests. After that, we introduce a knowledge transfer scheme to facilitate local robots acquiring knowledge from the cloud. With FIL, a robot is capable of utilizing knowledge from other robots to increase its imitation learning in accuracy and efficiency. Compared with transfer learning and meta-learning, FIL is more suitable to be deployed in cloud robotic systems. They conduct experiments of a self-driving task for robots (cars). The experimental results demonstrate that the shared model generated by FIL increases imitation learning efficiency of local robots in cloud robotic systems.

Approach: Peer-assisted Learning. Leveraging peer-assisted learning to build a cloud brain for robots was proposed by UM. A technological revolution is occurring in the field of robotics with the data-driven deep learning technology. However, building datasets for each local robot is laborious. Meanwhile, data islands between local robots make data unable to be utilized collaboratively. To address this issue, the work presents Peer-Assisted Robotic Learning (PARL) in robotics, which is inspired by the peer-assisted learning in cognitive psychology and pedagogy. PARL implements data collaboration with the framework of cloud robotic systems. Both data and models are shared by robots to the cloud after semantic computing and training locally. The cloud converges the data and performs augmentation, integration, and transferring. Finally, fine tune this larger shared dataset in the cloud to local robots. Furthermore, we propose the DAT Network (Data Augmentation and Transferring Network) to implement the data processing in PARL. DAT Network can realize the augmentation of data from multi-local robots. The authors conduct experiments on a simplified self-driving task for robots (cars). DAT Network has a significant improvement in the augmentation in self-driving scenarios. Along with this, the self-driving experimental results also demonstrate that PARL is capable of improving learning effects with data collaboration of local robots.

Research

RoboEarth  was funded by the European Union's Seventh Framework Programme for research, technological development projects, specifically to explore the field of cloud robotics. The goal of RoboEarth is to allow robotic systems to benefit from the experience of other robots, paving the way for rapid advances in machine cognition and behaviour, and ultimately, for more subtle and sophisticated human-machine interaction. RoboEarth offers a Cloud Robotics infrastructure. RoboEarth's World-Wide-Web style database stores knowledge generated by humans – and robots – in a machine-readable format. Data stored in the RoboEarth knowledge base include software components, maps for navigation (e.g., object locations, world models), task knowledge (e.g., action recipes, manipulation strategies), and object recognition models (e.g., images, object models). The RoboEarth Cloud Engine includes support for mobile robots, autonomous vehicles, and drones, which require much computation for navigation.

Rapyuta  is an open source cloud robotics framework based on RoboEarth Engine developed by the robotics researcher at ETHZ. Within the framework, each robot connected to Rapyuta can have a secured computing environment (rectangular boxes) giving them the ability to move their heavy computation into the cloud. In addition, the computing environments are tightly interconnected with each other and have a high bandwidth connection to the RoboEarth knowledge repository.

KnowRob  is an extensional project of RoboEarth. It is a knowledge processing system that combines knowledge representation and reasoning methods with techniques for acquiring knowledge and for grounding the knowledge in a physical system and can serve as a common semantic framework for integrating information from different sources.

RoboBrain  is a large-scale computational system that learns from publicly available Internet resources, computer simulations, and real-life robot trials. It accumulates everything robotics into a comprehensive and interconnected knowledge base. Applications include prototyping for robotics research, household robots, and self-driving cars. The goal is as direct as the project's name—to create a centralised, always-online brain for robots to tap into. The project is dominated by Stanford University and Cornell University. And the project is supported by the National Science Foundation, the Office of Naval Research, the Army Research Office, Google, Microsoft, Qualcomm, the Alfred P. Sloan Foundation and the National Robotics Initiative, whose goal is to advance robotics to help make the United States more competitive in the world economy.

MyRobots is a service for connecting robots and intelligent devices to the Internet. It can be regarded as a social network for robots and smart objects (i.e. Facebook for robots). With socialising, collaborating and sharing, robots can benefit from those interactions too by sharing their sensor information giving insight on their perspective of their current state.

COALAS  is funded by the INTERREG IVA France (Channel) – England European cross-border co-operation programme. The project aims to develop new technologies for disabled people through social and technological innovation and through the users' social and psychological integrity. Objectives is to produce a cognitive ambient assistive living system with Healthcare cluster in cloud with domestic service robots like humanoid, intelligent wheelchair which connect with the cloud.

ROS (Robot Operating System) provides an eco-system to support cloud robotics. ROS is a flexible and distributed framework for robot software development. It is a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust robot behaviour across a wide variety of robotic platforms. A library for ROS that is a pure Java implementation, called rosjava, allows Android applications to be developed for robots. Since Android has a booming market and billion users, it would be significant in the field of Cloud Robotics.

DAVinci Project is a proposed software framework that seeks to explore the possibilities of parallelizing some of the robotics algorithms as Map/Reduce tasks in Hadoop. The project aims to build a cloud computing environment capable of providing a compute cluster built with commodity hardware exposing a suite of robotic algorithms as a SaaS and share data co-operatively across the robotic ecosystem. This initiative is not available publicly.

C2RO (C2RO Cloud Robotics) is a platform that processes real-time applications such as collision avoidance and object recognition in the cloud. Previously, high latency times prevented these applications from being processed in the cloud thus requiring on-system computational hardware (e.g. Graphics Processing Unit or GPU). C2RO published a peer-reviewed paper at IEEE PIMRC17 showing its platform could make autonomous navigation and other AI services available on robots- even those with limited computational hardware (e.g. a Raspberry Pi)- from the cloud. C2RO eventually claimed to be the first platform to demonstrate cloud-based SLAM (simultaneous localization and mapping) at RoboBusiness in September 2017.

Noos is a cloud robotics service, providing centralised intelligence to robots that are connected to it. The service went live in December 2017. By using the Noos-API, developers could access services for computer vision, deep learning, and SLAM. Noos was developed and maintained by Ortelio Ltd.

Rocos is a centralized cloud robotics platform that provides the developer tooling and infrastructure to build, test, deploy, operate and automate robot fleets at scale. Founded in October 2017, the platform went live in January 2019.

Limitations of cloud robotics

Though robots can benefit from various advantages of cloud computing, cloud is not the solution to all of robotics.

• Controlling a robot's motion which relies heavily on (real-time) sensors and feedback of controller may not benefit much from the cloud.
• Tasks that involve real-time execution require on-board processing.
• Cloud-based applications can get slow or unavailable due to high-latency responses or network hitch. If a robot relies too much on the cloud, a fault in the network could leave it "brainless."

Challenges

The research and development of cloud robotics has following potential issues and challenges:

• Scalable parallelisation-grid-computing, parallelisation schemes scale with the size of automation infrastructure.
• Effective load balancing: Balancing operations between local and cloud computation.
• Knowledge bases and representations
• Collective learning for automation in cloud
• Infrastructure/Platform or Software as a Service
• Internet of Things for robotics
• Integrated and collaborative fault-tolerant control
• Big Data: Data, collected and/or disseminated over large, accessible networks can enable decisions for classification problems or reveal patterns.
• Wireless communication, Connectivity to the cloud
• System architectures of robot cloud
• Open-source, open-access infrastructures
• Workload-sharing
• Standards and Protocols

Risks

• Environmental security - The concentration of computing resources and users in a cloud computing environment also represents a concentration of security threats. Because of their size and significance, cloud environments are often targeted by virtual machines and bot malware, brute force attacks, and other attacks.
• Data privacy and security - Hosting confidential data with cloud service providers involves the transfer of a considerable amount of an organisation's control over data security to the provider. For example, every cloud contains a huge information from the clients include personal data. If a household robot is hacked, users could have risk of their personal privacy and security, like house layout, life snapshot, home-view, etc. It may be accessed and leaked to the world around by criminals. Another problems is once a robot is hacked and controlled by someone else, which may put the user in danger.
• Ethical problems - Some ethics of robotics, especially for cloud based robotics must be considered. Since a robot is connected via networks, it has risk to be accessed by other people. If a robot is out of control and carries out illegal activities, who should be responsible for it.

History

The term "Cloud Robotics" first appeared in the public lexicon as part of a talk given by James Kuffner in 2010 at the IEEE/RAS International Conference on Humanoid Robotics entitled "Cloud-enabled Robots".  Since then, "Cloud Robotics" has become a general term encompassing the concepts of information sharing, distributed intelligence, and fleet learning that is possible via networked robots and modern cloud computing. Kuffner was part of Google when he delivered his presentation and the technology company has teased its various cloud robotics initiatives until 2019 when it launched the Google Cloud Robotics Platform for developers.

From the early days of robot development, it was common to have computation done on a computer that was separated from the actual robot mechanism, but connected by wires for power and control. As wireless communication technology developed, new forms of experimental "remote brain" robots were developed controlled by small, onboard compute resources for robot control and safety, that were wirelessly connected to a more powerful remote computer for heavy processing. 

The term "cloud computing" was popularized with the launch of Amazon EC2 in 2006. It marked the availability of high-capacity networks, low-cost computers and storage devices as well as the widespread adoption of hardware virtualization and service-oriented architecture. In a correspondence with Popular Science in July 2006, Kuffner wrote that after a robot was programmed or successfully learned to perform a task it could share its model and relevant data with all other cloud-connected robots: 

"...the robot could then 'publish' its refined model to some website or universal repository of knowledge that all future robots could download and utilize. My vision is to have a 'robot knowledge database' that will over time improve the capabilities of all future robotic systems. It would serve as a warehouse of information and statistics about the physical world that robots can access and use to improve their reasoning about the consequences of possible actions and make better action plans in terms of accuracy, safety, and robustness. It could also serve as a kind of 'skill library'. For example, if I successfully programmed my butler robot how to cook a perfect omelette, I could 'upload' the software for omelette cooking to a server that all robots could then download whenever they were asked to cook an omelette. There could be a whole community of robot users uploading skill programs, much like the current 'shareware' and 'freeware' software models that are popular for PC users."

— James Kuffner, (July 2006)

Some publications and events related to Cloud Robotics (in chronological order):

• The IEEE RAS Technical Committee on Internet and Online Robots was founded by Ken Goldberg and Roland Siegwart et al. in May 2001. The committee then expanded to IEEE Society of Robotics and Automation's Technical Committee on Networked Robots in 2004.
• James J. Kuffner, a former CMU robotics professor, and research scientist at Google, now CEO of Toyota Research Institute—Advanced Development, spoke on cloud robotics in IEEE/RAS International Conference on Humanoid Robotics 2010. It describes "a new approach to robotics that takes advantage of the Internet as a resource for massively parallel computation and sharing of vast data resources."
• Ryan Hickman, a Google Product Manager, led an internal volunteer effort in 2010 to connect robots with the Google's cloud services. This work was later expanded to include open source ROS support and was demonstrated on stage by Ryan Hickman, Damon Kohler, Brian Gerkey, and Ken Conley at Google I/O 2011.
• National Robotics Initiative of US announced in 2011 aimed to explore how robots can enhance the work of humans rather than replacing them. It claims that next generation of robots are more aware than oblivious, more social than solitary.
• NRI Workshop on Cloud Robotics: Challenges and Opportunities- February 2013.
• A Roadmap for U.S. Robotics From Internet to Robotics 2013 Edition- by Georgia Institute of Technology, Carnegie Mellon University Robotics Technology Consortium, University of Pennsylvania, University of Southern California, Stanford University, University of California–Berkeley, University of Washington, Massachusetts Institute of TechnologyUS and Robotics OA US. The Roadmap highlighted "Cloud" Robotics and Automation for Manufacturing in the future years.
• Cloud-Based Robot Grasping with the Google Object Recognition Engine.
• 2013 IEEE IROS Workshop on Cloud Robotics. Tokyo. November 2013.
• Cloud Robotics-Enable cloud computing for robots. The author proposed some paradigms of using cloud computing in robotics. Some potential field and challenges were coined. R. Li 2014.
• Special Issue on Cloud Robotics and Automation- A special issue of the IEEE Transactions on Automation Science and Engineering, April 2015.
• Robot APP Store Robot Applications in Cloud, provide applications for robot just like computer/phone app.
• DARPA Cloud Robotics.
• The first industrial cloud robotics platform, Tend, was founded by Mark Silliman, James Gentes and Robert Kieffer in February 2017. Tend allows robots to be remotely controlled and monitored via websockets and NodeJs.
• Cloud robotic architectures: directions for future research from a comparative analysis.

Information technology

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Information_technology

Information technology (IT) is a set of related fields that encompass computer systems, software, programming languages, data and information processing, and storage. IT forms part of information and communications technology (ICT). An information technology system (IT system) is generally an information system, a communications system, or, more specifically speaking, a computer system — including all hardware, software, and peripheral equipment — operated by a limited group of IT users, and an IT project usually refers to the commissioning and implementation of an IT system. IT systems play a vital role in facilitating efficient data management, enhancing communication networks, and supporting organizational processes across various industries. Successful IT projects require meticulous planning, seamless integration, and ongoing maintenance to ensure optimal functionality and alignment with organizational objectives.

Although humans have been storing, retrieving, manipulating, and communicating information since the earliest writing systems were developed, the term information technology in its modern sense first appeared in a 1958 article published in the Harvard Business Review; authors Harold J. Leavitt and Thomas L. Whisler commented that "the new technology does not yet have a single established name. We shall call it information technology (IT)." Their definition consists of three categories: techniques for processing, the application of statistical and mathematical methods to decision-making, and the simulation of higher-order thinking through computer programs.

The term is commonly used as a synonym for computers and computer networks, but it also encompasses other information distribution technologies such as television and telephones. Several products or services within an economy are associated with information technology, including computer hardware, software, electronics, semiconductors, internet, telecom equipment, and e-commerce.

Based on the storage and processing technologies employed, it is possible to distinguish four distinct phases of IT development: pre-mechanical (3000 BC – 1450 AD), mechanical (1450 – 1840), electromechanical (1840 – 1940), and electronic (1940 to present).

Information technology is a branch of computer science, defined as the study of procedures, structures, and the processing of various types of data. As this field continues to evolve globally, its priority and importance have grown, leading to the introduction of computer science-related courses in K-12 education.

History

Zuse Z3 replica on display at Deutsches Museum in Munich. The Zuse Z3 is the first programmable computer.

Main article: History of computing hardware

This is the Antikythera mechanism, which is considered the first mechanical analog computer, dating back to the first century BC.

Ideas of computer science were first mentioned before the 1950s under the Massachusetts Institute of Technology (MIT) and Harvard University, where they had discussed and began thinking of computer circuits and numerical calculations. As time went on, the field of information technology and computer science became more complex and was able to handle the processing of more data. Scholarly articles began to be published from different organizations.

Looking at early computing, Alan Turing, J. Presper Eckert, and John Mauchly were considered some of the major pioneers of computer technology in the mid-1900s. Giving them such credit for their developments, most of their efforts were focused on designing the first digital computer. Along with that, topics such as artificial intelligence began to be brought up as Turing was beginning to question such technology of the time period.

Devices have been used to aid computation for thousands of years, probably initially in the form of a tally stick. The Antikythera mechanism, dating from about the beginning of the first century BC, is generally considered the earliest known mechanical analog computer, and the earliest known geared mechanism. Comparable geared devices did not emerge in Europe until the 16th century, and it was not until 1645 that the first mechanical calculator capable of performing the four basic arithmetical operations was developed.

Electronic computers, using either relays or valves, began to appear in the early 1940s. The electromechanical Zuse Z3, completed in 1941, was the world's first programmable computer, and by modern standards one of the first machines that could be considered a complete computing machine. During the Second World War, Colossus developed the first electronic digital computer to decrypt German messages. Although it was programmable, it was not general-purpose, being designed to perform only a single task. It also lacked the ability to store its program in memory; programming was carried out using plugs and switches to alter the internal wiring. The first recognizably modern electronic digital stored-program computer was the Manchester Baby, which ran its first program on 21 June 1948.

The development of transistors in the late 1940s at Bell Laboratories allowed a new generation of computers to be designed with greatly reduced power consumption. The first commercially available stored-program computer, the Ferranti Mark I, contained 4050 valves and had a power consumption of 25 kilowatts. By comparison, the first transistorized computer developed at the University of Manchester and operational by November 1953, consumed only 150 watts in its final version.

Several other breakthroughs in semiconductor technology include the integrated circuit (IC) invented by Jack Kilby at Texas Instruments and Robert Noyce at Fairchild Semiconductor in 1959, silicon dioxide surface passivation by Carl Frosch and Lincoln Derick in 1955, the first planar silicon dioxide transistors by Frosch and Derick in 1957, the MOSFET demonstration by a Bell Labs team. the planar process by Jean Hoerni in 1959, and the microprocessor invented by Ted Hoff, Federico Faggin, Masatoshi Shima, and Stanley Mazor at Intel in 1971. These important inventions led to the development of the personal computer (PC) in the 1970s, and the emergence of information and communications technology (ICT).

By the year of 1984, according to the National Westminster Bank Quarterly Review, the term information technology had been redefined as "The development of cable television was made possible by the convergence of telecommunications and computing technology (…generally known in Britain as information technology)." We then begin to see the appearance of the term in 1990 contained within documents for the International Organization for Standardization (ISO).

Innovations in technology have already revolutionized the world by the twenty-first century as people were able to access different online services. This has changed the workforce drastically as thirty percent of U.S. workers were already in careers in this profession. 136.9 million people were personally connected to the Internet, which was equivalent to 51 million households. Along with the Internet, new types of technology were also being introduced across the globe, which has improved efficiency and made things easier across the globe.

Along with technology revolutionizing society, millions of processes could be done in seconds. Innovations in communication were also crucial as people began to rely on the computer to communicate through telephone lines and cable. The introduction of the email was considered revolutionary as "companies in one part of the world could communicate by e-mail with suppliers and buyers in another part of the world..."

Not only personally, computers and technology have also revolutionized the marketing industry, resulting in more buyers of their products. During the year of 2002, Americans exceeded $28 billion in goods just over the Internet alone while e-commerce a decade later resulted in $289 billion in sales. And as computers are rapidly becoming more sophisticated by the day, they are becoming more used as people are becoming more reliant on them during the twenty-first century.

Data processing

Main article: Electronic data processing

Ferranti Mark I computer logic board

Storage

Punched tapes were used in early computers to store and represent data.

Main article: Data storage

Early electronic computers such as Colossus made use of punched tape, a long strip of paper on which data was represented by a series of holes, a technology now obsolete. Electronic data storage, which is used in modern computers, dates from World War II, when a form of delay-line memory was developed to remove the clutter from radar signals, the first practical application of which was the mercury delay line. The first random-access digital storage device was the Williams tube, which was based on a standard cathode ray tube. However, the information stored in it and delay-line memory was volatile in the fact that it had to be continuously refreshed, and thus was lost once power was removed. The earliest form of non-volatile computer storage was the magnetic drum, invented in 1932 and used in the Ferranti Mark 1, the world's first commercially available general-purpose electronic computer.

IBM introduced the first hard disk drive in 1956, as a component of their 305 RAMAC computer system. Most digital data today is still stored magnetically on hard disks, or optically on media such as CD-ROMs. Until 2002 most information was stored on analog devices, but that year digital storage capacity exceeded analog for the first time. As of 2007, almost 94% of the data stored worldwide was held digitally: 52% on hard disks, 28% on optical devices, and 11% on digital magnetic tape. It has been estimated that the worldwide capacity to store information on electronic devices grew from less than 3 exabytes in 1986 to 295 exabytes in 2007, doubling roughly every 3 years.

Databases

Main article: Database

Database Management Systems (DMS) emerged in the 1960s to address the problem of storing and retrieving large amounts of data accurately and quickly. An early such system was IBM's Information Management System (IMS), which is still widely deployed more than 50 years later. IMS stores data hierarchically, but in the 1970s Ted Codd proposed an alternative relational storage model based on set theory and predicate logic and the familiar concepts of tables, rows, and columns. In 1981, the first commercially available relational database management system (RDBMS) was released by Oracle.

All DMS consist of components, they allow the data they store to be accessed simultaneously by many users while maintaining its integrity. All databases are common in one point that the structure of the data they contain is defined and stored separately from the data itself, in a database schema.

In recent years, the extensible markup language (XML) has become a popular format for data representation. Although XML data can be stored in normal file systems, it is commonly held in relational databases to take advantage of their "robust implementation verified by years of both theoretical and practical effort." As an evolution of the Standard Generalized Markup Language (SGML), XML's text-based structure offers the advantage of being both machine- and human-readable.

Transmission

IBM card storage warehouse located in Alexandria, Virginia in 1959. This is where the government kept storage of punched cards.

Data transmission has three aspects: transmission, propagation, and reception. It can be broadly categorized as broadcasting, in which information is transmitted unidirectionally downstream, or telecommunications, with bidirectional upstream and downstream channels.

XML has been increasingly employed as a means of data interchange since the early 2000s, particularly for machine-oriented interactions such as those involved in web-oriented protocols such as SOAP, describing "data-in-transit rather than... data-at-rest".

Manipulation

Hilbert and Lopez identify the exponential pace of technological change (a kind of Moore's law): machines' application-specific capacity to compute information per capita roughly doubled every 14 months between 1986 and 2007; the per capita capacity of the world's general-purpose computers doubled every 18 months during the same two decades; the global telecommunication capacity per capita doubled every 34 months; the world's storage capacity per capita required roughly 40 months to double (every 3 years); and per capita broadcast information has doubled every 12.3 years.

Massive amounts of data are stored worldwide every day, but unless it can be analyzed and presented effectively it essentially resides in what have been called data tombs: "data archives that are seldom visited". To address that issue, the field of data mining — "the process of discovering interesting patterns and knowledge from large amounts of data" — emerged in the late 1980s.

Services

Email

The technology and services it provides for sending and receiving electronic messages (called "letters" or "electronic letters") over a distributed (including global) computer network. In terms of the composition of elements and the principle of operation, electronic mail practically repeats the system of regular (paper) mail, borrowing both terms (mail, letter, envelope, attachment, box, delivery, and others) and characteristic features — ease of use, message transmission delays, sufficient reliability and at the same time no guarantee of delivery. The advantages of e-mail are: easily perceived and remembered by a person addresses of the form user_name@domain_name (for example, somebody@example.com); the ability to transfer both plain text and formatted, as well as arbitrary files; independence of servers (in the general case, they address each other directly); sufficiently high reliability of message delivery; ease of use by humans and programs.

Disadvantages of e-mail: the presence of such a phenomenon as spam (massive advertising and viral mailings); the theoretical impossibility of guaranteed delivery of a particular letter; possible delays in message delivery (up to several days); limits on the size of one message and on the total size of messages in the mailbox (personal for users).

Search system

A software and hardware complex with a web interface that provides the ability to search for information on the Internet. A search engine usually means a site that hosts the interface (front-end) of the system. The software part of a search engine is a search engine (search engine) — a set of programs that provides the functionality of a search engine and is usually a trade secret of the search engine developer company. Most search engines look for information on World Wide Web sites, but there are also systems that can look for files on FTP servers, items in online stores, and information on Usenet newsgroups. Improving search is one of the priorities of the modern Internet (see the Deep Web article about the main problems in the work of search engines).

Commercial effects

Companies in the information technology field are often discussed as a group as the "tech sector" or the "tech industry." These titles can be misleading at times and should not be mistaken for "tech companies;" which are generally large scale, for-profit corporations that sell consumer technology and software. It is also worth noting that from a business perspective, Information technology departments are a "cost center" the majority of the time. A cost center is a department or staff which incurs expenses, or "costs", within a company rather than generating profits or revenue streams. Modern businesses rely heavily on technology for their day-to-day operations, so the expenses delegated to cover technology that facilitates business in a more efficient manner are usually seen as "just the cost of doing business." IT departments are allocated funds by senior leadership and must attempt to achieve the desired deliverables while staying within that budget. Government and the private sector might have different funding mechanisms, but the principles are more-or-less the same. This is an often overlooked reason for the rapid interest in automation and Artificial Intelligence, but the constant pressure to do more with less is opening the door for automation to take control of at least some minor operations in large companies.

Many companies now have IT departments for managing the computers, networks, and other technical areas of their businesses. Companies have also sought to integrate IT with business outcomes and decision-making through a BizOps or business operations department.

In a business context, the Information Technology Association of America has defined information technology as "the study, design, development, application, implementation, support, or management of computer-based information systems". The responsibilities of those working in the field include network administration, software development and installation, and the planning and management of an organization's technology life cycle, by which hardware and software are maintained, upgraded, and replaced.

Information services

Information services is a term somewhat loosely applied to a variety of IT-related services offered by commercial companies, as well as data brokers.

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  U.S. Employment distribution of computer systems design and related services, 2011
   
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  U.S. Employment in the computer systems and design related services industry, in thousands, 1990–2011
   
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  U.S. Occupational growth and wages in computer systems design and related services, 2010–2020
   
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  U.S. projected percent change in employment in selected occupations in computer systems design and related services, 2010–2020
   
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  U.S. projected average annual percent change in output and employment in selected industries, 2010–2020

Ethics

Main article: Information ethics

The field of information ethics was established by mathematician Norbert Wiener in the 1940s. Some of the ethical issues associated with the use of information technology include:

• Breaches of copyright by those downloading files stored without the permission of the copyright holders
• Employers monitoring their employees' emails and other Internet usage
• Unsolicited emails
• Hackers accessing online databases
• Web sites installing cookies or spyware to monitor a user's online activities, which may be used by data brokers

IT projects

Research suggests that IT projects in business and public administration can easily become significant in scale. Work conducted by McKinsey in collaboration with the University of Oxford suggested that half of all large-scale IT projects (those with initial cost estimates of $15 million or more) often failed to maintain costs within their initial budgets or to complete on time.

Reasoning system

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Reasoning_system 

In information technology a reasoning system is a software system that generates conclusions from available knowledge using logical techniques such as deduction and induction. Reasoning systems play an important role in the implementation of artificial intelligence and knowledge-based systems.

By the everyday usage definition of the phrase, all computer systems are reasoning systems in that they all automate some type of logic or decision. In typical use in the Information Technology field however, the phrase is usually reserved for systems that perform more complex kinds of reasoning. For example, not for systems that do fairly straightforward types of reasoning such as calculating a sales tax or customer discount but making logical inferences about a medical diagnosis or mathematical theorem. Reasoning systems come in two modes: interactive and batch processing. Interactive systems interface with the user to ask clarifying questions or otherwise allow the user to guide the reasoning process. Batch systems take in all the available information at once and generate the best answer possible without user feedback or guidance.

Reasoning systems have a wide field of application that includes scheduling, business rule processing, problem solving, complex event processing, intrusion detection, predictive analytics, robotics, computer vision, and natural language processing.

History

The first reasoning systems were theorem provers, systems that represent axioms and statements in First Order Logic and then use rules of logic such as modus ponens to infer new statements. Another early type of reasoning system were general problem solvers. These were systems such as the General Problem Solver designed by Newell and Simon. General problem solvers attempted to provide a generic planning engine that could represent and solve structured problems. They worked by decomposing problems into smaller more manageable sub-problems, solving each sub-problem and assembling the partial answers into one final answer. Another example general problem solver was the SOAR family of systems.

In practice these theorem provers and general problem solvers were seldom useful for practical applications and required specialized users with knowledge of logic to utilize. The first practical application of automated reasoning were expert systems. Expert systems focused on much more well defined domains than general problem solving such as medical diagnosis or analyzing faults in an aircraft. Expert systems also focused on more limited implementations of logic. Rather than attempting to implement the full range of logical expressions they typically focused on modus-ponens implemented via IF-THEN rules. Focusing on a specific domain and allowing only a restricted subset of logic improved the performance of such systems so that they were practical for use in the real world and not merely as research demonstrations as most previous automated reasoning systems had been. The engine used for automated reasoning in expert systems were typically called inference engines. Those used for more general logical inferencing are typically called theorem provers.

With the rise in popularity of expert systems many new types of automated reasoning were applied to diverse problems in government and industry. Some such as case-based reasoning were off shoots of expert systems research. Others such as constraint satisfaction algorithms were also influenced by fields such as decision technology and linear programming. Also, a completely different approach, one not based on symbolic reasoning but on a connectionist model has also been extremely productive. This latter type of automated reasoning is especially well suited to pattern matching and signal detection types of problems such as text searching and face matching.

Use of logic

The term reasoning system can be used to apply to just about any kind of sophisticated decision support system as illustrated by the specific areas described below. However, the most common use of the term reasoning system implies the computer representation of logic. Various implementations demonstrate significant variation in terms of systems of logic and formality. Most reasoning systems implement variations of propositional and symbolic (predicate) logic. These variations may be mathematically precise representations of formal logic systems (e.g., FOL), or extended and hybrid versions of those systems (e.g., Courteous logic). Reasoning systems may explicitly implement additional logic types (e.g., modal, deontic, temporal logics). However, many reasoning systems implement imprecise and semi-formal approximations to recognised logic systems. These systems typically support a variety of procedural and semi-declarative techniques in order to model different reasoning strategies. They emphasise pragmatism over formality and may depend on custom extensions and attachments in order to solve real-world problems.

Many reasoning systems employ deductive reasoning to draw inferences from available knowledge. These inference engines support forward reasoning or backward reasoning to infer conclusions via modus ponens. The recursive reasoning methods they employ are termed 'forward chaining' and 'backward chaining', respectively. Although reasoning systems widely support deductive inference, some systems employ abductive, inductive, defeasible and other types of reasoning. Heuristics may also be employed to determine acceptable solutions to intractable problems.

Reasoning systems may employ the closed world assumption (CWA) or open world assumption (OWA). The OWA is often associated with ontological knowledge representation and the Semantic Web. Different systems exhibit a variety of approaches to negation. As well as logical or bitwise complement, systems may support existential forms of strong and weak negation including negation-as-failure and 'inflationary' negation (negation of non-ground atoms). Different reasoning systems may support monotonic or non-monotonic reasoning, stratification and other logical techniques.

Reasoning under uncertainty

Many reasoning systems provide capabilities for reasoning under uncertainty. This is important when building situated reasoning agents which must deal with uncertain representations of the world. There are several common approaches to handling uncertainty. These include the use of certainty factors, probabilistic methods such as Bayesian inference or Dempster–Shafer theory, multi-valued ('fuzzy') logic and various connectionist approaches.

Types of reasoning system

This section provides a non-exhaustive and informal categorisation of common types of reasoning system. These categories are not absolute. They overlap to a significant degree and share a number of techniques, methods and algorithms.

Constraint solvers

Constraint solvers solve constraint satisfaction problems (CSPs). They support constraint programming. A constraint is a which must be met by any valid solution to a problem. Constraints are defined declaratively and applied to variables within given domains. Constraint solvers use search, backtracking and constraint propagation techniques to find solutions and determine optimal solutions. They may employ forms of linear and nonlinear programming. They are often used to perform optimization within highly combinatorial problem spaces. For example, they may be used to calculate optimal scheduling, design efficient integrated circuits or maximise productivity in a manufacturing process.

Theorem provers

Theorem provers use automated reasoning techniques to determine proofs of mathematical theorems. They may also be used to verify existing proofs. In addition to academic use, typical applications of theorem provers include verification of the correctness of integrated circuits, software programs, engineering designs, etc.

Logic programs

Logic programs (LPs) are software programs written using programming languages whose primitives and expressions provide direct representations of constructs drawn from mathematical logic. An example of a general-purpose logic programming language is Prolog. LPs represent the direct application of logic programming to solve problems. Logic programming is characterised by highly declarative approaches based on formal logic, and has wide application across many disciplines.

Rule engines

Rule engines represent conditional logic as discrete rules. Rule sets can be managed and applied separately to other functionality. They have wide applicability across many domains. Many rule engines implement reasoning capabilities. A common approach is to implement production systems to support forward or backward chaining. Each rule ('production') binds a conjunction of predicate clauses to a list of executable actions.

At run-time, the rule engine matches productions against facts and executes ('fires') the associated action list for each match. If those actions remove or modify any facts, or assert new facts, the engine immediately re-computes the set of matches. Rule engines are widely used to model and apply business rules, to control decision-making in automated processes and to enforce business and technical policies.

Deductive classifier

Deductive classifiers arose slightly later than rule-based systems and were a component of a new type of artificial intelligence knowledge representation tool known as frame languages. A frame language describes the problem domain as a set of classes, subclasses, and relations among the classes. It is similar to the object-oriented model. Unlike object-oriented models however, frame languages have a formal semantics based on first order logic.

They utilize this semantics to provide input to the deductive classifier. The classifier in turn can analyze a given model (known as an ontology) and determine if the various relations described in the model are consistent. If the ontology is not consistent the classifier will highlight the declarations that are inconsistent. If the ontology is consistent the classifier can then do further reasoning and draw additional conclusions about the relations of the objects in the ontology.

For example, it may determine that an object is actually a subclass or instance of additional classes as those described by the user. Classifiers are an important technology in analyzing the ontologies used to describe models in the Semantic web.

Machine learning systems

Machine learning systems evolve their behavior over time based on experience. This may involve reasoning over observed events or example data provided for training purposes. For example, machine learning systems may use inductive reasoning to generate hypotheses for observed facts. Learning systems search for generalised rules or functions that yield results in line with observations and then use these generalisations to control future behavior.

Case-based reasoning systems

Case-based reasoning (CBR) systems provide solutions to problems by analysing similarities to other problems for which known solutions already exist. Case-based reasoning uses the top (superficial) levels of similarity; namely, the object, feature, and value criteria. This differs case-based reasoning from analogical reasoning in that analogical reasoning uses only the "deep" similarity criterion i.e. relationship or even relationships of relationships, and need not find similarity on the shallower levels. This difference makes case-based reasoning applicable only among cases of the same domain because similar objects, features, and/or values must be in the same domain, while the "deep" similarity criterion of "relationships" makes analogical reasoning applicable cross-domains where only the relationships ae similar between the cases. CBR systems are commonly used in customer/technical support and call centre scenarios and have applications in industrial manufacture, agriculture, medicine, law and many other areas.

Procedural reasoning systems

A procedural reasoning system (PRS) uses reasoning techniques to select plans from a procedural knowledge base. Each plan represents a course of action for achievement of a given goal. The PRS implements a belief–desire–intention model by reasoning over facts ('beliefs') to select appropriate plans ('intentions') for given goals ('desires'). Typical applications of PRS include management, monitoring and fault detection systems.

Dispersed knowledge

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Dispersed_knowledge

Dispersed knowledge in economics is the notion that no single agent has information as to all of the factors which influence prices and production throughout the system. The term has been both expanded upon and popularized by American economist Thomas Sowell.

Overview

Each agent in a market for assets, goods, or services possesses incomplete knowledge as to most of the factors which affect prices in that market. For example, no agent has full information as to other agents' budgets, preferences, resources or technologies, not to mention their plans for the future and numerous other factors which affect prices in those markets.

Market prices are the result of price discovery, in which each agent participating in the market makes use of its current knowledge and plans to decide on the prices and quantities at which it chooses to transact. The resulting prices and quantities of transactions may be said to reflect the current state of knowledge of the agents currently in the market, even though no single agent commands information as to the entire set of such knowledge.

Some economists believe that market transactions provide the basis for a society to benefit from the knowledge that is dispersed among its constituent agents. For example, in his Principles of Political Economy, John Stuart Mill states that one of the justifications for a laissez faire government policy is his belief that self-interested individuals throughout the economy, acting independently, can make better use of dispersed knowledge than could the best possible government agency.

Key characteristics

Friedrich Hayek claimed that "dispersed knowledge is essentially dispersed, and cannot possibly be gathered together and conveyed to an authority charged with the task of deliberately creating order".

Today, the best and most comprehensive book on dispersed knowledge is Knowledge and Decisions by Thomas Sowell, which Hayek called "the best book on general economics in many a year."

Phenomena

• "Dispersed knowledge will give rise to genuine uncertainty, which necessitates the contractual structure that we recognize as a firm."
• "Dispersion of knowledge and genuine uncertainty contribute to the heterogeneity of expectations that must exist in order for one or more individuals to exploit the potential of the contractual structure of the firm."
• "Dispersion of knowledge, genuine uncertainty, and heterogeneous expectations give rise to the nexus of the enterprising individual and the opportunity to discover, create, and exploit new markets."

Drivers

1. Large numbers: Large numbers have a great impact on actions in terms of two aspects. On the one hand, there will be an increase in time and other resource requirements. On the other hand, actors with bounded cognitive resources will lose overview.
2. Asymmetries: Asymmetries have a two-sides effect. Firstly, asymmetries enable more possibilities regarding learning and competence development. Secondly, asymmetries "increase differences between interpretative frameworks and the knowledge and competence profile of the different actors and thus make integration more difficult".
3. Uncertainty: Uncertainty is defined to be one of the drivers of dispersed knowledge which can give rise to management problems.

Uncertainty

Dispersed knowledge will give rise to uncertainty which will lead to different kinds of results.

• 1. Dispersed knowledge causes different opinions and sources in cooperate organizations and it brings creativity.

Richard LeFauve highlights the advantages of organizational structure in companies:

"Before if we had a tough decision to make, we would have two or three different perspectives with strong support of all three. In a traditional organization the bossman decides after he’s heard all three alternatives. At Saturn we take time to work it out, and what generally happens is that you end up with a fourth answer which none of the portions had in the first place. but one that all three portions of the organization fully support (AutoWeeR, Oct. 8, 1990. p. 20)."

Companies are supposed to think highly of the dispersed knowledge and make adjustments to meet demands.

• 2. Dispersed knowledge causes management problems at the same time.

Tsoukas stated:

"A firm’s knowledge is distributed, not only in a computational sense . . . or in Hayek’s (1945, p. 521) sense that the factual knowledge of the particular circumstances of time and place cannot be surveyed as a whole. But, more radically, a firm’s knowledge is distributed in the sense that it is inherently indeterminate: nobody knows in advance what that knowledge is or need be. Firms are faced with radical uncertainty: they do not, they cannot, know what they need to know."

Strategies

There are several strategies targeting at the problems caused by dispersed knowledge.

First of all, replacing knowledge by getting access to knowledge can be one of the strategies.

What's more, the capability to complete incomplete knowledge can deal with knowledge gaps created by the dispersed knowledge.

In addition, making a design of institutions with reasonable coordination mechanisms can be regarded as the third strategy.

Besides, resolving organization units into smaller ones should be taken into consideration.

Last but not least, providing more data to decision maker will be helpful for making a correct decision.

GoFundMe

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/GoFundMe

GoFundMe is an American for-profit crowdfunding platform that allows people to raise money for events ranging from life events such as celebrations and graduations to challenging circumstances like accidents and illnesses. From 2010 to the beginning of 2020, over $9 billion has been raised on the platform, with contributions from over 120 million donors.

History

The company was founded in May 2010 by Brad Damphousse and Andrew Ballester. Both had previously founded Paygr, which is a website dedicated to allowing members to sell their services to the public. Damphousse and Ballester originally created the website under the name "CreateAFund" in 2008 but later changed the name to GoFundMe after making numerous upgrades to the features of the website. The site was built off of PayPal's API. GoFundMe was founded in San Diego, California.

In March 2017, GoFundMe became the biggest crowdfunding platform, responsible for raising over $3 billion since its debut in 2010. The company receives over $140 million in donations per month and made 2016 $100 million in revenue. In June 2015, it was announced that Damphousse and Ballester had agreed to sell a majority stake in GoFundMe to Accel Partners and Technology Crossover Ventures. Damphousse and Ballester stepped down from the day-to-day oversight of the company. The deal valued GoFundMe at around $600 million. In January 2017, GoFundMe acquired CrowdRise. GoFundMe's CEO is Tim Cadogan. Ballester remains on the board of directors and holds an undisclosed stake in the company.

Business model

During this process, members can describe their fundraising cause and the amount they hope to raise, and upload photos or video. Once the website is created, GoFundMe allows users to share their project with people through integrated social network links (Facebook, Twitter, etc.) and email. People can then donate to a user's cause through the website using a debit card or credit card and track the funding. Those who donate can also leave comments on the website. The person raising funds is not charged. Payment processors collect 2.9% and $0.30 from each GoFundMe transaction.

GoFundMe is unique to crowdfunding in that the company is not an incentive-based crowdfunding website. Although it does allow projects that are meant to fund other projects for musicians, inventors, etc., the business model is set up to allow for donations to personal causes and life events such as medical bills. GoFundMe also has a special section dedicated solely to users who are trying to raise money to cover their tuition costs. A prominent tuition project helped a user raise $25,000 for an out-of-state tuition to a PhD program. A 2014 tuition project raised over $100,000 for a homeless high school valedictorian to attend college and help his family.

GoFundMe targets social media platforms to create awareness for campaigns, and encourages individual users to promote their fundraiser on social media throughout a campaign. According to a 2018 report by GoFundMe based on past campaign data, a donor sharing a campaign on social media results in $15 of donations on average, while any share of a campaign on social media, regardless of whether the user donated to the campaign, results in $13 of donations on average.

In 2015, GoFundMe announced that the site would no longer support legal defense funds on their platform, after the site suspended funding for the defense of Sweet Cakes by Melissa, a bakery that was fined for refusing to bake a cake for a same-sex wedding. As of November 2017 GoFundMe's terms and conditions allow for campaigns for certain kinds of legal defense.

In November 2017, GoFundMe announced that it will no longer charge a 5% fee per donation for US, Canada, and UK individual campaigns, and instead rely upon tips left by donors to support the website. The processing fee for online credit card payments will still apply to donations.

In June 2019, GoFundMe terminated a $3 million fund raising for an Australian rugby player, Israel Folau, to finance a court case to appeal his multi-million dollar dismissal. He had quoted 1 Corinthians 6:9–10 on social media, which was said to be homophobic. An alternative fundraising site was set up by the Australian Christian Lobby with the public donating $2 million in 24 hours.

In May 2022, GoFundMe announced the acquisition of non-profit donation site Classy. It was announced that Classy will remain and operate as a wholly owned subsidiary of GoFundMe.

Notable projects

Medical fundraising

GoFundMe has described itself as the "leader in online medical fundraising". One in three campaigns is intended to raise funds for medical costs, with about 250,000 campaigns for a total of $650 million in contributions each year. This is partly attributed to the inadequacies of the U.S. healthcare system in which GoFundMe is used to bridge the gap.

CEO Rob Solomon has commented on this, saying that "When we started in 2010, it wasn't purposefully set up and built to be a substitute for medical insurance. We weren't ever set up to be a health care company and we still are not. But over time, people have used GoFundMe for the most important issues they are faced with." He also added that the large medical fundraising is the result of severe problems in his country's healthcare system, saying "The system is terrible [...] there are people who are not getting relief from us or from the institutions that are supposed to be there. We shouldn't be the solution to a complex set of systemic problems."

Official George Floyd Memorial Fund

After the murder of George Floyd, his brother Philonise Floyd established the fund "to cover funeral and burial expenses, mental and grief counseling, lodging and travel for all court proceedings, and to assist our family in the days to come as we continue to seek justice for George. A portion of these funds will also go to the Estate of George Floyd for the benefit and care of his children and their educational fund." One week after the tragedy and only four days after the start of the fund, it had already raised $7 million, putting it in ranking as one of the most highly funded GoFundMe campaigns to date.

The $1K Project

Created by entrepreneurs and investors Alex Iskold and Minda Brusse in response to the novel coronavirus pandemic, The $1K Project uses individual GoFundMe pages to match specific donors with specific families who have been adversely affected by the pandemic. Donors agree to contribute a minimum of $1,000 per month for three months, for a total of $3,000 per family. Small-dollar donors can make contributions that are pooled together and then matched to a family. In August 2020, Andrew Yang's Humanity Forward Foundation committed to matching donations, dollar-for-dollar, up to $1 million. As of mid-October 2020, more than 800 families had been fully funded.

Sweet Cakes By Melissa

In 2015, after the site suspended funding for the defense of Sweet Cakes by Melissa, a bakery that was fined for refusing to bake a cake for a same-sex wedding, GoFundMe announced that the site would no longer support legal defense funds on their platform. As of November 2017 GoFundMe's terms and conditions allow for campaigns for certain kinds of legal defense.

Help Chelsea Manning Pay Her Court Fines

Created by Kelly Wright to raise money to help former intelligence analyst and whistleblower Chelsea Manning pay $256,000 in court fines levied against her after her refusal to testify to a grand jury about WikiLeaks founder Julian Assange. Nearly 7,000 contributions ranging from $5 to $10,000 were made within two days.

Charity fraud

MMS Defense Fund

Nominally a legal defense fund for Louis Daniel Smith, who faced criminal charges in relation to him selling "MMS" (Miracle Mineral Supplement). On May 27, 2015, Smith was found guilty of fraud and other charges. On May 31, 2015, the mmsdefensefund was removed from GoFundMe (an archived copy is available).

Paying it Forward

This fundraiser was created by Kate McClure, Mark D'Amico, and Johnny Bobbitt Jr. to swindle people. Their fictitious story was that Bobbitt, a homeless veteran, spent his last $20 to assist McClure on the highway when her car ran out of gasoline. Widely reported in the US and internationally, it exceeded its goal by 4000% but when they began publicly squabbling for the money, an investigation was launched and all three were arrested and charged with theft by deception. They pleaded guilty and were sentenced to one year and a day, five years and five-year special probation, respectively.

We The People Built the Wall!

Created with the goal of building a wall as private citizens to inhibit illegal entry along the U.S.-Mexico border. The founder, Brian Kolfage started a nonprofit with the money, We Build the Wall, which has constructed sections of the wall. Currently most money raised on GoFundMe, but in August 2020, Kolfage was indicted, along with Steve Bannon and two other co-defendants, on federal charges of defrauding hundreds of thousands of "We Build the Wall" donors by diverting money that was raised to personal use. Federal prosecutors said that despite "repeatedly assuring donors" that Kolfage would not be paid, the defendants engaged in a scheme to divert $350,000 to Kolfage, "which he used to fund his lavish lifestyle." He was separately indicted in May 2021 on federal charges of defrauding the IRS and filing false tax returns.

For victims of mass shootings in the U.S.

Bucks for Bauman

This project was created for Jeff Bauman after he lost both legs during the Boston Marathon bombing.

Celeste & Sydney Recovery Fund

Celeste and Sydney Corcoran were both victims of the Boston Marathon bombing: Sydney suffered severe injuries as a result of being hit with shrapnel, and Celeste lost both legs below her knees. This campaign page was created for their ongoing rehabilitation.

Support Victims of Pulse Shooting

This fundraiser was created by Equality Florida to help the victims of a nightclub shooting in Orlando, Florida. Over 90,000 people have contributed to this campaign. GoFundMe headquarters donated $100,000 and waived every transaction fee for this campaign.

Las Vegas Victims Fund

This fundraiser was created to help the victims of a mass shooting from the Las Vegas Strip in Paradise, Nevada.

Stoneman Douglas Victims' Fund

There are a number of fundraisers for individual victims of the February 14, 2018, Stoneman Douglas High School Shooting in Parkland, Florida to help survivors' recovery and to fund causes chosen by family members in honor of the deceased.

Canada convoy protest

In January 2022, Prime Minister Justin Trudeau announced that truck drivers crossing into Canada would have to be fully vaccinated. In response, some truckers organized a convoy to Ottawa under the name Freedom Convoy 2022. A GoFundMe project was then created with the claim of raising money for fuel and food for the convoy. On February 4, 2022, GoFundMe announced the fundraiser had been removed from the platform for violating terms of service, specifically "violence and other unlawful activity". The company initially stated that $9 million in donations from the fundraiser would be redistributed to "credible and established charities" and would only be refunded upon application, subject to a two-week time limit. Following criticism, the company subsequently stated on Twitter that all donations would be refunded within 10 business days.

Anti-vaccine fundraisers

In March 2019, GoFundMe banned fundraisers from anti-vaccine activists, including Stop Mandatory Vaccination founder Larry Cook, citing violations of their terms of service. Despite the ban, The Independent found that several anti-vaccine campaigns were still running on GoFundMe as of May 2019.

In December 2021, The Sunday Times reported that GoFundMe had enabled the donation of over €300,000 to anti-vaccine campaigns and challenges to vaccine certificates.

In January 2023, the British disinformation analysis organization Logically reported that GoFundMe had funneled over $330,000 in donations to fundraisers for injuries supposedly caused by the COVID-19 vaccine.

Jean Messiha's fundraising

On Tuesday, 27 June 2023, 17-year-old Nahel Merzouk was killed by a French police officer after he failed to comply with traffic stops. While the police officer has been arrested on suspicion of "voluntary homicide by a person in authority", far-right activist Jean Messiha organised a controversial crowdfunding in favour of the police officer's family which reached €1.6 million. The killing sparked widespread protests and riots in France. Nahel Merzouk's family has filed a complaint against Jean Messiha.

Grayzone donations freeze

In August 2023, GoFundMe froze more than $90,000 from 1,100 contributors to The Grayzone, a fringe pro-Kremlin far-left news website, citing unspecified "external concerns". Grayzone founder Max Blumenthal said he believed the concerns were political and related to the platform's coverage of the Russian invasion of Ukraine.

Alleged shooter of Brian Thompson

In December 2024, multiple campaigns for the legal defense fund of the alleged shooter involved in the killing of UnitedHealthCare CEO Brian Thompson were removed. However, a GiveSendGo fundraiser remained live, and has raised over $75,000 as of December 13.