Recognize Technology Categories: Basic, Standard, Advanced, Potentiate and Scientific — (Leve 1–5)
Technology is the sum of techniques, skills, methods, and processes used in the production of goods or services or in the accomplishment of objectives, such as scientific investigation. Technology can be the knowledge of techniques, processes, and the like, or it can be embedded in machines to allow for operation without detailed knowledge of their workings. Systems applying technology by taking an input, changing it according to the system’s use, and then producing an outcome are referred to as technology systems or technological systems. Information and communications technology is an extensional term for information technology that stresses the role of unified communications and the integration of telecommunications (telephone lines and wireless signals) and computers, as well as necessary enterprise software, middleware, storage and audiovisual systems, that enable users to access, store, transmit, and manipulate information.
The term Information and Communications Technology is also used to refer to the convergence of audiovisual and telephone networks with computer networks through a single cabling or link system. There are large economic incentives to merge the telephone network with the computer network system using a single unified system of cabling, signal distribution, and management. Information and Communications Technology is an umbrella term that includes any communication device, encompassing radio, television, cell phones, computer and network hardware, satellite systems and so on, as well as the various services and appliances with them such as video conferencing and distance learning. Information and Communications Technology is a broad subject and the concepts are evolving. It covers any product that will store, retrieve, manipulate, transmit, or receive information electronically in a digital form (e.g., personal computers, digital television, email, or robots). Theoretical differences between interpersonal communication technologies and mass communication technologies skills Framework for the Information Age is one of many models for describing and managing competencies for Information and Communications Technology professionals for the 21st century. The simplest form of technology is the development and use of basic tools. The prehistoric discovery of how to control fire and the later Neolithic Revolution increased the available sources of food, and the invention of the wheel helped humans to travel in and control their environment. Developments in historic times, including the printing press, the telephone, and the Internet, have lessened physical barriers to communication and allowed humans to interact freely on a global scale.
Levels of Technology
The phrase “information and communication technologies” has been used by academic researchers since the 1980s. The Information and Communications Technology became popular after it was used in a report to the UK government by Dennis Stevenson in 1997, and then in the revised National Curriculum for England, Wales and Northern Ireland in 2000. However, in 2012, the Royal Society recommended that the use of the term The Information and Communications Technology should be discontinued in British schools “as it has attracted too many negative connotations”. From 2014 the National Curriculum has used the word computing, which reflects the addition of computer programming into the curriculum. Variations of the phrase have spread worldwide.
The United Nations has created a “United Nations Information and Communication Technologies Task Force” and an internal “Office of Information and Communications Technology”. Technology can be viewed as an activity that forms or changes culture. Additionally, technology is the application of math, science, and the arts for the benefit of life as it is known. A modern example is the rise of communication technology, which has lessened barriers to human interaction and as a result has helped spawn new subcultures; the rise of cyberculture has at its basis the development of the Internet and the computer. Not all technology enhances culture in a creative way; technology can also help facilitate political oppression and war via tools such as guns. As a cultural activity, technology predates both science and engineering, each of which formalize some aspects of technological endeavor.
Computer Science Information Technology and Communication has Monetary Patrimony Rate
The money spent on IT worldwide has been estimated as US$3.8 trillion in 2017 and has been growing at less than 5% per year since 2009. The estimate 2018 growth of the entire ICT is 5%. The biggest growth of 16% is expected in the area of new technologies (IoT, Robotics, AR/VR, and AI).
The 2014 IT budget of the US federal government was nearly $82 billion. IT costs, as a percentage of corporate revenue, have grown 50% since 2002, putting a strain on IT budgets.
When looking at current companies’ IT budgets, 75% are recurrent costs, used to “keep the lights on” in the IT department, and 25% are the cost of new initiatives for technology development. The average IT budget has the following breakdown:
- 31% personnel costs (internal) ;
- 29% software costs (external/purchasing category) ;
- 26% hardware costs (external/purchasing category) ;
- 14% costs of external service providers (external/services) ;
- The estimate of money to be spent in 2022 is just over US$6 trillions.
In many countries, mobile phones are used to provide mobile banking services, which may include the ability to transfer cash payments by secure SMS text message. Kenya’s M-PESA mobile banking service, for example, allows customers of the mobile phone operator Safaricom to hold cash balances which are recorded on their SIM cards.
Cash can be deposited or withdrawn from M-PESA accounts at Safaricom retail outlets located throughout the country and can be transferred electronically from person to person and used to pay bills to companies. Branchless banking has been successful in South Africa and the Philippines.
A pilot project in Bali was launched in 2011 by the International Finance Corporation and an Indonesian bank, Bank Mandiri. Another application of mobile banking technology is Zidisha, a US-based nonprofit micro-lending platform that allows residents of developing countries to raise small business loans from Web users worldwide. Zidisha uses mobile banking for loan disbursements and repayments, transferring funds from lenders in the United States to borrowers in rural Africa who have mobile phones and can use the Internet.
Mobile payments were first trialled in Finland in 1998 when two Coca-Cola vending machines in Espoo were enabled to work with SMS payments. Eventually, the idea spread and in 1999, the Philippines launched the country’s first commercial mobile payments systems with mobile operators Globe and Smart. Some mobile phones can make mobile payments via direct mobile billing schemes, or through contactless payments if the phone and the point of sale support near field communication (NFC).
Enabling contactless payments through NFC-equipped mobile phones requires the co-operation of manufacturers, network operators, and retail merchants. Some apps allows for sending and receiving facsimile (Fax), over a smartphone, including facsimile data (composed of raster bi-level graphics) generated directly and digitally from document and image file formats.
The rise in popularity of touchscreen smartphones and mobile apps distributed via app stores along with rapidly advancing network, mobile processor, and storage technologies led to a convergence where separate mobile phones, organizers, and portable media players were replaced by a smartphone as the single device most people carried.
Advances in digital camera sensors and on-device image processing software more gradually led to smartphones replacing simpler cameras for photographs and video recording. The built-in GPS capabilities and mapping apps on smartphones largely replaced stand-alone satellite navigation devices, and paper maps became less common.
Mobile gaming on smartphones greatly grew in popularity, allowing many people to use them in place of handheld game consoles, and some companies tried creating game console/phone hybrids based on phone hardware and software. People frequently have chosen not to get fixed-line telephone service in favor of smartphones.
Music streaming apps and services have grown rapidly in popularity, serving the same use as listening to music stations on a terrestrial or satellite radio. Streaming video services are easily accessed via smartphone apps and can be used in place of watching television. People have often stopped wearing wristwatches in favor of checking the time on their smartphones, and many use the clock features on their phones in place of alarm clocks.
Additionally, in many lesser technologically developed regions smartphones are people’s first and only means of Internet access due to their portability, with personal computers being relatively uncommon outside of business use. The cameras on smartphones can be used to photograph documents and send them via email or messaging in place of using fax (facsimile) machines.
Payment apps and services on smartphones allow people to make less use of wallets, purses, credit and debit cards, and cash. Mobile banking apps can allow people to deposit checks simply by photographing them, eliminating the need to take the physical check to an ATM or teller.
Guide book apps can take the place of paper travel and restaurant/business guides, museum brochures, and dedicated audio guide equipment. The use of mobile phones in schools by students has become a controversial topic debated by students, parents, teachers and authorities.
Technology Levels are applied as measurement of character of development, usage and performances. People who support the use of cell phones believe that these phones are essential for safety by allowing children to communicate with their parents and guardians, could simplify many school matters, and it is important in today’s world that children learn how to deal with new media properly as early as possible.
Many persons also think that you should take advantage of the fact that nowadays, there is no need to memorize every fact anymore, as cell phones can be used to access all human knowledge virtually anywhere, allowing schools to shift their focus from imparting knowledge to understanding how certain things work together and promoting the development of personality, teamwork, creativity, social skills etc.
Opponents of students using mobile phones during school believe that mobile phones cause disruption and may be used inappropriately such as by cheating on tests, taking inappropriate photographs, and playing mobile games. Rather than paying attention to teachers, students are spending more time distracted by their phones.
To prevent distractions caused by mobile phones, some schools have implemented policies that restrict students from using their phones during school hours.
Some administrators have attempted cell phone jamming, but this practice is illegal in certain jurisdictions. The software can be used in order to monitor and restrict phone usage to reduce distractions and prevent unproductive use.
However, these methods of regulation raise concerns about privacy violation and abuse of power.
Basic (Level 1)
The simplest form of using technology, making a phone call, downloading a mobile app, using a social media, using a search engine, buying something on a webs-shop, easy cyber security tasks, downloading and installing antivirus, installing firewalls, and much more kind of tasks that anyone can accomplish following a easy guide or just guessing.
The low number of scientific studies may be indicative of a general assumption that if talking on a mobile phone increases risk, then texting also increases risk, and probably more so. Market research by Pinger, a company selling a voice-based alternative to texting reported that 89% of US adults think that text messaging while driving is “distracting, dangerous and should be outlawed.” The AAA Foundation for Traffic Safety released polling data in 2009 that showed 87% of people consider texting and e-mailing while driving a “very serious” safety threat, almost equivalent to the 90% of those polled who consider drunk driving a threat. Despite the acknowledgement of the dangers of texting behind the wheel, about half of drivers 16 to 24 say they have texted while driving, compared with 22% of drivers 35 to 44.
A study conducted by the University of Vienna using the theory of planned behavior identified two key determinants of high-level mobile phone use. Those two factors, subjective norm (i.e., perceived social norms) and self-identity (i.e., the degree to which individuals see mobile phones as a part of their self), might be promising targets for the development of persuasive strategies and other interventions aimed at reducing inappropriate and problematic use of mobile phones, such as using mobile phones while driving.
Standard (Level 2)
Level 2 Commercial Standards license includes in test extra functionalities: 100 widgets and controls, plus 300 locales. The following features are included: additional DataTable functionality, saving and restoring application state, extra styling and customization, ability to use complex widgets: Pivot, Kanban, Spreadsheet, File Manager, and Scheduler. Complex widgets are available at extra cost.
Advanced (Level 3)
The complicate form of usage of development tools, development of technologies for developers, web developers that really understand programming languages, advanced design for softwares UI/UX designers, corporate marketing tasks on the search engine, development of databases, development of cyber security softwares, Big Data and Artificial Intelligence, automation and Robotics, kind of tasks that you cannot really make it without universities preparation of heavy reading books and mathematics knowledge.
It consist in match advanced development process of machine learning stack system, this complex widget acts as a ready-made app for data management. Pivot allows extracting information from huge datasets, exporting tables to various formats, flexible UI customization, and creating functions. The widget offers all the features necessary for effortless data management: filtering, sorting, and cell highlighting, on-the-fly configuring, structure presets, etc.
Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
SpreadSheet is a functional data table. It’s possible to export Excel documents to it and import data in several formats: Excel, PNG, and PDF. A user can change the number of columns and rows, create functions, and customize UI. The widget allows setting up localization and adding other components. SpreadSheet can be integrated with such back-end and front-end platforms like PHP, Node.js, .NET, jQuery, AngularJS, Vue.js, React, and other libraries.
Pivot chart is an effective tool for information visualization. Users can export charts in several formats: PDF, PNG, CSV, and Excel. UI customization is also available. It’s possible to tune the settings, filters, configuration window, etc. Pivot Chart can be integrated with several front-end and back-end platforms. The widget offers such features as filtering and sorting, clickable chart legend, on-the-fly configuring, and structure presets.
Java models allows compact visualization of the software development team’s workflow. Users can create, modify, and delete task cards. The number of tasks and columns is unlimited. This widget offers features like drag-n-drop of cards, swimlanes, filtering, single or multiple card selection, context menu, highlighting, and custom arrangement for cards. Like other complex widgets by java models can be integrated with various front-end and back-end platforms.
Document Manager is an extension of File Manager. The user can edit text files and xls/xlsx sheets, view edit history and restore any version, remove files and folders into Trash, mark files as Favorite, view the list of recent files, share files with other users, tag files and folders and manage tags.
User Manager is a rich complex widget for managing user access rights and roles. The user can add and edit user information, assign rights to other users and group rights into roles. There are two view modes: role matrix and audit.
This widget is a scheduler with a touch-oriented design for mobile devices. It’s possible to add several types of events: one-day, long-lasting, or recurring. A user can switch different view modes, e.g., a schedule for a day, week, month, or year. Localization is also available. Mobile Scheduler offers features like custom date formats, recurring, creating, editing, and highlighting events. Like other complex widgets, Mobile Scheduler supports all modern browsers. It can be integrated with several other libraries and frameworks. This widget also follows Section 508 and WAI-ARIA standards and supports keyboard navigation.
Potentiate (Level 4)
Potentiate are those Technologies on sale and non-learning availability, those technologies on sale or not on sale that there is no way to learn without developing it or dealing with the developer or development team/corporation.
Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer’s part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step.
The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset of handwritten digits has often been used.
Example, Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.” This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing’s proposal in his paper “Computing Machinery and Intelligence”, in which the question “Can machines think?” is replaced with the question “Can machines do what we (as thinking entities) can do?”.
Modern day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. Where as, a machine learning algoritihim for stock trading may inform the trader of future potential predictions.
The study of mathematical modeling of the dynamic behavior of systems and designing them, usually using feedback signals, so that their dynamic behavior is desirable (stable, without large excursions, with minimum error). This applies to the dynamic behavior of aircraft, propulsion systems, and subsystems that exist on military vehicles.
Scientific (Level 5)
Scientific are those technologies with development or destination and usage are outside of the earth planet, which, Aerospace engineering is the primary field of engineering concerned with the development of aircraft and spacecraft in necessary. It has two major and overlapping branches: aeronautical engineering and astronautical engineering. Avionics engineering is similar, but deals with the electronics side of aerospace engineering.
Example, Ball Aerospace began building pointing controls for military rockets in 1956, and later won a contract to build one of NASA’s first spacecraft, the Orbiting Solar Observatory. Over the years, the company has been responsible for numerous technological and scientific projects and continues to provide aerospace technology to NASA and related industries.
Ball Aerospace also has many other products and services for the aerospace industry, including lubricants, optical systems, star trackers and antennas. As a wholly owned subsidiary of the Ball Corporation, Ball Aerospace was cited in 2014 as the 88th largest defense contractor in the world. Both parent and subsidiary headquarters are co-located in Broomfield, Colorado.
As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what was then termed “neural networks”; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis.
However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation, expert systems had come to dominate AI, and statistics was out of favor. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval. Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as “connectionism”, by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.
Machine learning (ML), reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory.
The origin of aerospace engineering can be traced back to the aviation pioneers around the late 19th to early 20th centuries, although the work of Sir George Cayley dates from the last decade of the 18th to mid-19th century. One of the most important people in the history of aeronautics and a pioneer in aeronautical engineering, Cayley is credited as the first person to separate the forces of lift and drag, which affect any atmospheric flight vehicle.
Early knowledge of aeronautical engineering was largely empirical, with some concepts and skills imported from other branches of engineering. Some key elements, like fluid dynamics, were understood by 18th-century scientists.
The scientific literature is mixed on the dangers of talking on a cell phone versus those of talking with a passenger. The common conception is that passengers are able to better regulate conversation based on the perceived level of danger, therefore the risk is negligible. A study by a University of South Carolina psychology researcher featured in the journal, Experimental Psychology, found that planning to speak and speaking put far more demands on the brain’s resources than listening.
Measurement of attention levels showed that subjects were four times more distracted while preparing to speak or speaking than when they were listening. The Accident Research Unit at the University of Nottingham found that the number of utterances was usually higher for mobile calls when compared to blindfolded and non-blindfolded passengers across various driving conditions. The number of questions asked averaged slightly higher for mobile phone conversations, although results were not constant across road types and largely influenced by a large number of questions on the urban roads.
A 2004 simulation study that compared passenger and cell-phone conversations concluded that the driver performs better when conversing with a passenger because the traffic and driving task become part of the conversation.
Drivers holding conversations on cell phones were four times more likely to miss the highway exit than those with passengers, and drivers conversing with passengers showed no statistically significant difference from lone drivers in the simulator.
A study led by Andrew Parkes at the Transport Research Laboratory, also with a driving simulator, concluded that hands-free phone conversations impair driving performance more than other common in-vehicle distractions such as passenger conversations.
However, some have criticized the use of simulation studies to measure the risk of cell-phone use while driving since the studies may be impacted by the Hawthorne effect.
In contrast, the University of Illinois meta-analysis concluded that passenger conversations were just as costly to driving performance as cell phone ones. AAA ranks passengers as the third most reported cause of distraction-related crashes at 11%, compared to 1.5% for cellular telephones.
A simulation study funded by the American Transportation Research Board concluded that driving events that require urgent responses may be influenced by in-vehicle conversations, and that there is little practical evidence that passengers adjusted their conversations to changes in the traffic. It concluded that drivers’ training should address the hazards of both mobile phone and passenger conversations.