information-technology
senso-concept-Mcs (techInfo)

McsHitp-creation:: {2019-12-28},

overview of techInfo

description::
· info-tech is human-technology that manages brain-info.

name::
* McsEngl.McsTchInf000002.last.html//dirTchInf//dirMcs!⇒techInfo,
* McsEngl.dirMcs/dirTchInf/McsTchInf000002.last.html!⇒techInfo,
* McsEngl.IT!=info-tech!⇒techInfo,
* McsEngl.info-tech!⇒techInfo,
* McsEngl.information-technology!⇒techInfo,
* McsEngl.infotech!⇒techInfo,
* McsEngl.tchInf!⇒techInfo,
* McsEngl.techInfo!=McsTchInf000002,
* McsEngl.techInfo!=information-technology,
====== langoSinago:
* McsSngo.teko-info!=techInfo,
====== langoGreek:
* McsElln.πληροφορίας-τεχνολογία!=techInfo,

01_evaluation of techInfo

description::
·

name::
* McsEngl.techInfo'01_evaluation,
* McsEngl.techInfo'att001-evaluation,
* McsEngl.techInfo'evaluation,

02_human of techInfo

description::
· any human related with techInfo.

name::
* McsEngl.techInfo'02_human,
* McsEngl.techInfo'att002-human,
* McsEngl.techInfo'human,

specific-tree-of-techInfo'human::
* user,
* worker,
* programer,
* hacker,

03_organization of techInfo

description::
· an-ogznProduction related to techInfo.

name::
* McsEngl.ogznProduction.003-techInfo!⇒ogznTechInfo,
* McsEngl.ogznInfoTech!⇒ogznTechInfo,
* McsEngl.ogznProduction.techInfo!⇒ogznTechInfo,
* McsEngl.ogznTechInfo,
* McsEngl.techInfo'att004-ogznProduction!⇒ogznTechInfo,
* McsEngl.techInfo'ogznProduction!⇒ogznTechInfo,

ogznTechInfo.sector

description::
· the-sector of ogznTechInfo.

">information-technology sector:
The Information Technology (IT) sector is a vast and dynamic field that encompasses a wide range of technologies and services aimed at managing, processing, and communicating information. It's central to nearly every aspect of modern life, influencing how we work, communicate, and entertain ourselves. The sector is comprised of several key areas, including but not limited to:
1. **Software Development**: This includes the creation, maintenance, and implementation of computer programs and applications that help users perform tasks. Software development can range from developing simple individual applications to complex systems and operating environments.
2. **Hardware Engineering**: This involves the design, development, testing, and production of computer systems and components such as processors, circuit boards, memory devices, networks, and routers.
3. **Network Systems**: This area focuses on the communication between computer systems. It includes the design, implementation, and management of networks that connect computers and other devices, enabling communication and resource sharing across them.
4. **Data Management and Analytics**: With the exponential growth of data, this area has become crucial. It involves the storage, organization, analysis, and interpretation of large sets of data to provide actionable insights. Big data analytics and database management systems fall under this category.
5. **Cybersecurity**: Given the increasing reliance on digital systems, protecting these systems from unauthorized access, attacks, and damage is paramount. Cybersecurity involves creating secure computer systems, safeguarding data and networks, and responding to cyberattacks.
6. **Cloud Computing**: This is a model for delivering information technology services where resources are retrieved from the internet through web-based tools and applications, rather than a direct connection to a server. It allows for the on-demand availability of computer system resources, especially data storage and computing power.
7. **Artificial Intelligence and Machine Learning**: These technologies simulate human intelligence in machines. They're designed to think and learn like humans, improving over time as they are fed more data. Applications range from simple tasks like filtering spam emails to complex ones like autonomous vehicles and voice recognition systems.
8. **Internet of Things (IoT)**: IoT refers to the network of physical objects—'things'—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet.
The IT sector is characterized by rapid innovation and evolution, with new technologies and methodologies emerging constantly. This dynamism presents both opportunities and challenges, including the need for continual learning and adaptation among professionals. Additionally, as the backbone of the digital economy, the IT sector plays a crucial role in economic development, offering a wide range of job opportunities and driving innovation in other sectors."
[{2024-04-04 retrieved} https://chat.openai.com/c/e07e3ddc-2f1a-4ffb-ae56-c924f0c2650e]

">information-technology sector:
The information technology (IT) sector is a broad term that encompasses all aspects of computing and telecommunications. It includes companies that design, develop, manufacture, and sell computer hardware, software, and related services.
The IT sector is one of the fastest-growing sectors of the global economy. It has played a major role in the development of the internet, e-commerce, and other digital technologies that have transformed the way we live and work.
Here are some of the major sub-sectors of the information technology industry:
* **Software and services:** This sub-sector includes companies that develop and sell software applications, such as operating systems, enterprise software, and productivity suites. It also includes companies that provide IT services, such as consulting, systems integration, and managed services.
* **Technology hardware and equipment:** This sub-sector includes companies that design, develop, manufacture, and sell computer hardware, such as personal computers, servers, and storage devices. It also includes companies that manufacture semiconductors, which are the essential components of all electronic devices.
* **Telecommunications:** This sub-sector includes companies that provide telecommunications services, such as voice, data, and video. It also includes companies that manufacture telecommunications equipment, such as routers, switches, and cell towers.
The IT sector is a major driver of economic growth and innovation. It has created millions of jobs around the world and has helped to improve productivity and efficiency in many industries. However, the IT sector also faces a number of challenges, such as the rapid pace of technological change, the increasing threat of cybercrime, and the need for skilled workers."
[{2024-04-04 retrieved} https://gemini.google.com/app/64b08bfebfa177c3]

name::
* McsEngl.ICT-sector!⇒sectorTechInfo,
* McsEngl.IT-sector!⇒sectorTechInfo,
* McsEngl.infotech-sector!⇒sectorTechInfo,
* McsEngl.ogznTechInfo.sector!⇒sectorTechInfo,
* McsEngl.sectorICT!⇒sectorTechInfo,
* McsEngl.sectorIT!⇒sectorTechInfo,
* McsEngl.sectorTechInfo,

sectorTelecom of sectorTechInfo

description::
"The telecommunications sector is a vast and dynamic industry that encompasses a wide range of technologies, services, and applications. It is responsible for the transmission of information over long distances through various means, including wire, radio, optical fiber, and satellite.
The telecommunications sector plays a crucial role in modern society, facilitating communication, information sharing, and economic activity. It enables individuals, businesses, and governments to connect and interact with each other across geographical boundaries.
The sector is characterized by rapid technological advancements and constant innovation. New technologies and services are emerging continuously, driving growth and transforming the way people communicate and access information.
Key segments within the telecommunications sector include:
* **Fixed-line telephony:** This refers to traditional landline phone services that utilize wired connections.
* **Mobile telephony:** This includes cellular phone services that use wireless networks for communication.
**Internet service providers (ISPs):** These companies provide access to the internet through various technologies, such as DSL, cable, fiber optic, and satellite.
* **Broadband services:** This refers to high-speed internet access that enables faster data transmission and supports activities like video streaming and online gaming.
* **Data centers:** These facilities house servers and networking equipment that store and process large amounts of data.
* **Satellite communications:** This technology utilizes satellites orbiting the Earth to transmit signals for various applications, including television broadcasting, navigation, and internet connectivity.
The telecommunications sector is a major contributor to the global economy, generating significant revenue and employment opportunities. It is also a key driver of innovation and technological progress in other sectors.
However, the sector faces several challenges, including increasing competition, regulatory pressures, and cybersecurity threats. As technology continues to evolve, the telecommunications sector must adapt and innovate to remain competitive and meet the changing needs of consumers and businesses."
[{2024-06-20 retrieved} https://gemini.google.com/app/8cb6bd6245382f05]

name::
* McsEngl.sectorTelecom,
* McsEngl.sectorTelecom//sectorTechInfo,
* McsEngl.techInfo'att005-sectorTelecom!⇒sectorTelecom,
* McsEngl.techInfo'sectorTelecom!⇒sectorTelecom,
* McsEngl.telecommunications-sector!⇒sectorTelecom,

sectorMedia of sectorTechInfo

description::
">mass media vs social media:
The comparison between mass media and social media hinges on several key distinctions that reflect their unique characteristics, roles in society, and impacts on information dissemination and public discourse. Let's explore these differences:
1. **Definition and Core Characteristics**:
- **Mass Media** refers to traditional forms of media, including television, radio, newspapers, and magazines, that disseminate information to a large audience through broadcasting, publishing, and the internet. It's characterized by a one-to-many communication model, where a relatively small number of content producers reach a large audience.
- **Social Media**, on the other hand, encompasses digital platforms and technologies that enable users to create, share, or exchange information, ideas, and content in virtual communities and networks. It's defined by a many-to-many communication model, allowing for greater interaction and participation among users.
2. **Content Production and Control**:
- In **mass media**, content is typically produced by professionals and organizations with significant resources, expertise, and editorial oversight. The control over what is published or broadcasted lies with media companies and editors.
- **Social Media** allows for user-generated content, enabling anyone with internet access to create and share content. This democratizes content creation and distribution but also raises questions about accuracy, reliability, and the potential for misinformation.
3. **Interactivity and Participation**:
- **Mass Media** offers limited interactivity, with the audience primarily in the role of consumers. Feedback mechanisms exist (e.g., letters to the editor, call-in segments), but they are controlled and mediated.
- **Social Media** promotes high levels of interactivity and user engagement. Users can comment, like, share, and directly communicate with content creators and other users, fostering communities and participatory cultures.
4. **Speed and Reach**:
- **Mass Media** can rapidly disseminate information to a wide audience, particularly in the case of television and radio. However, the speed of distribution can be constrained by production schedules and editorial processes.
- **Social Media** enables real-time sharing and dissemination of information, making it possible to spread content virally across the globe in minutes. Its reach is amplified by the network effects of social platforms.
5. **Personalization and Algorithms**:
- **Mass Media** provides a more general and broad approach to content, aiming to appeal to large segments of the population. While there may be niche publications and channels, personalization is limited.
- **Social Media** utilizes algorithms to personalize user experiences, tailoring content feeds to individual preferences, behaviors, and interactions. This can create echo chambers and filter bubbles, potentially narrowing the diversity of information encountered.
6. **Impact on Society and Culture**:
- **Mass Media** has historically played a pivotal role in shaping public opinion, cultural norms, and political landscapes. It has been critical in moments of national significance, emergency broadcasting, and cultural dissemination.
- **Social Media** has transformed the way people connect, communicate, and consume information. It has empowered movements, facilitated social change, and altered personal interactions, but it also raises concerns about privacy, mental health, and the spread of false information.
In summary, while mass media and social media both serve to inform, entertain, and connect people, their approaches, capabilities, and impacts on society differ significantly. The evolution of media landscapes continues to blur these distinctions, with traditional media companies increasingly engaging with social media platforms, and social media platforms adopting more curated content strategies."
[{2024-04-05 retrieved} https://chat.openai.com/c/4afff5af-a09f-4325-8946-9d84afb26dd7]

name::
* McsEngl.mass-media,
* McsEngl.media-sector,
* McsEngl.sectorMedia,
* McsEngl.sectorMedia//sectorTechInfo,
* McsEngl.sectorTechInfo'sectorMedia,
* McsEngl.social-media,

ogznTechInfo.Google

description::
· "Google LLC (/ˈɡuːɡəl/ (listen)) is an American multinational technology company focusing on artificial intelligence,[9] online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, and consumer electronics. It has been referred to as "the most powerful company in the world"[10] and as one of the world's most valuable brands due to its market dominance, data collection, and technological advantages in the field of artificial intelligence.[11][12][13] Google's parent company Alphabet Inc. is one of the five Big Tech companies, alongside Amazon, Apple Inc., Meta Platforms, and Microsoft.
Google was founded on September 4, 1998, by computer scientists Larry Page and Sergey Brin while they were PhD students at Stanford University in California. Together they own about 14% of its publicly listed shares and control 56% of its stockholder voting power through super-voting stock. The company went public via an initial public offering (IPO) in 2004. In 2015, Google was reorganized as a wholly owned subsidiary of Alphabet Inc. Google is Alphabet's largest subsidiary and is a holding company for Alphabet's internet properties and interests. Sundar Pichai was appointed CEO of Google on October 24, 2015, replacing Larry Page, who became the CEO of Alphabet. On December 3, 2019, Pichai also became the CEO of Alphabet.[14]
The company has since rapidly grown to offer a multitude of products and services beyond Google Search, many of which hold dominant market positions. These products address a wide range of use cases, including email (Gmail), navigation (Waze & Maps), cloud computing (Cloud), web browsing (Chrome), video sharing (YouTube), productivity (Workspace), operating systems (Android), cloud storage (Drive), language translation (Translate), photo storage (Photos), video calling (Meet), smart home (Nest), smartphones (Pixel), wearable technology (Pixel Watch & Fitbit), music streaming (YouTube Music), video on demand (YouTube TV), artificial intelligence (Google Assistant), machine learning APIs (TensorFlow), AI chips (TPU), and more. Discontinued Google products include gaming (Stadia), Glass, Google+, Reader, Play Music, Nexus, Hangouts, and Inbox by Gmail.[15][16]
Google's other ventures outside of Internet services and consumer electronics include quantum computing (Sycamore), self-driving cars (Waymo, formerly the Google Self-Driving Car Project), smart cities (Sidewalk Labs), and transformer models (Google Brain).[17]
Google and YouTube are the two most visited websites worldwide followed by Facebook and Twitter. Google is also the largest search engine, mapping and navigation application, email provider, office suite, video sharing platform, photo and cloud storage provider, mobile operating system, web browser, ML framework, and AI virtual assistant provider in the world as measured by market share. On the list of most valuable brands, Google is ranked second by Forbes[18] and fourth by Interbrand.[19] It has received significant criticism involving issues such as privacy concerns, tax avoidance, censorship, search neutrality, antitrust and abuse of its monopoly position."
[{2023-08-02 retrieved} https://en.wikipedia.org/wiki/Google]

name::
* McsEngl.Google-LLC!⇒ogznGoogle,
* McsEngl.ogznGoogle,
* McsEngl.ogznTechInfo.Google!⇒ogznGoogle,

Google-Cloud of ogznGoogle

description::
· "Google Cloud is a suite of cloud services hosted on Google's infrastructure. From computing and storage, to data analytics, machine learning, and networking, Google Cloud offers a wide variety of services and APIs that can be integrated with any cloud-computing application or project, from personal to enterprise-grade."
[{2023-08-02 retrieved} https://www.cloudskillsboost.google/focuses/2794?parent=catalog]

name::
* McsEngl.Gcloud,
* McsEngl.Google-Cloud!⇒Gcloud,
* McsEngl.ogznGoogle'Google-Cloud!⇒Gcloud,

04_law of techInfo

description::
· law related to techInfo.

name::
* McsEngl.law.038-techInfo,
* McsEngl.law.techInfo,
* McsEngl.techInfo'04_law,
* McsEngl.techInfo'att003-law,
* McsEngl.techInfo'law,

05_investment of techInfo

description::
·

name::
* McsEngl.techInfo'05_investment,
* McsEngl.techInfo'investment,

info-resource of techInfo

name::
* McsEngl.techInfo'Infrsc,

addressWpg::
* http://reports.weforum.org/global-information-technology-report-2016/
* http://www.gartner.com/it-glossary/
* http://www.techterms.com/

DOING of techInfo

description::
* processing,
* communicating,
* storing,

name::
* McsEngl.techInfo'doing,

application-process of techInfo

description::
·

name::
* McsEngl.techInfo'application-process,
* McsEngl.techInfo'use,

evoluting of techInfo

description::
× search for: '{techInfo'

">info-tech evolution:
Information technology (IT) has undergone a remarkable evolution over the past century, transforming how we live, work, and communicate. This journey has been marked by groundbreaking advancements in hardware, software, and networking, leading to a world increasingly reliant on digital technologies.

Pre-computer Era (Early 20th Century):
The genesis of IT can be traced back to the early 20th century, when the first mechanical calculators and analog computers were developed. These early machines were used for specialized tasks like tabulating data and solving mathematical equations.

Mainframe Era (1940s-1970s):
The advent of digital computers in the 1940s marked a significant turning point in IT. These large, centralized mainframes were primarily employed by governments, universities, and large corporations for scientific research, data processing, and business applications.

Minicomputer Era (1960s-1980s):
The development of minicomputers in the 1960s brought computing power to a wider range of organizations and individuals. These smaller, more affordable systems were better suited for distributed computing and applications in various industries.

Personal Computing Era (1970s-1990s):
The introduction of personal computers (PCs) in the 1970s revolutionized IT, making computing accessible to everyday users. PCs became ubiquitous in homes, businesses, and educational institutions, fueling the growth of software applications and digital content.

Networked Computing Era (1980s-Present):
The rise of networked computing, spearheaded by the development of the internet in the 1980s, transformed the way people connected, communicated, and shared information. The internet enabled a global network of interconnected computers, paving the way for digital communication, e-commerce, and the rise of the World Wide Web.

Cloud Computing Era (1990s-Present):
Cloud computing, which emerged in the 1990s, further revolutionized IT by shifting computing resources from on-premises servers to remote data centers. This shift enabled businesses and individuals to access computing power and storage on-demand, eliminating the need for expensive hardware and software maintenance.

Mobile Computing Era (2000s-Present):
The rise of mobile computing, driven by the proliferation of smartphones and tablets, has transformed how we access and use information. Mobile devices have become indispensable tools for communication, navigation, entertainment, and productivity, blurring the lines between personal and professional lives.

Artificial Intelligence (AI) Era (Current):
AI, with its ability to simulate human intelligence, is emerging as a transformative force in IT. AI technologies are being applied in various fields, from healthcare and finance to transportation and manufacturing, automating tasks, improving decision-making, and enhancing user experiences.

As IT continues to evolve, we can expect even more groundbreaking advancements that will shape our future, from augmented reality (AR) and virtual reality (VR) to quantum computing and blockchain technology. The future of IT is undoubtedly bright, with limitless possibilities for innovation and transformation."
[{2023-11-20 retrieved} https://bard.google.com/chat/02dc989c648d210a?hl=en&pli=1]

">info-tech evolution:
The evolution of information technology (IT) has been a dynamic and transformative journey, shaping the way we communicate, work, and live. Here's a brief overview of key stages in the evolution of information technology:

1. **Mechanical Era (pre-1600s):** The earliest forms of information technology involved mechanical devices like the abacus for calculations and the printing press for information dissemination.

2. **Electromechanical Era (late 19th to early 20th century):** This era saw the development of early computing devices like the punched card system used in tabulating machines. The telegraph and telephone also played crucial roles in communication.

3. **First Generation Computers (1940s-1950s):** The development of electronic computers marked a significant milestone. The ENIAC (Electronic Numerical Integrator and Computer), the first general-purpose electronic digital computer, was built during this period.

4. **Second Generation Computers (1950s-1960s):** Transistors replaced vacuum tubes, leading to smaller, faster, and more reliable computers. The development of programming languages like FORTRAN and COBOL also occurred during this era.

5. **Third Generation Computers (1960s-1970s):** Integrated circuits replaced transistors, resulting in even smaller and more powerful computers. This era also saw the development of time-sharing operating systems and the beginnings of networking.

6. **Fourth Generation Computers (1970s-1980s):** Microprocessors and microcomputers emerged, making computing more accessible to individuals and businesses. The rise of personal computers like the IBM PC and Apple Macintosh occurred during this period.

7. **Networking and Internet Era (1980s-1990s):** The development of TCP/IP protocols and the World Wide Web transformed communication and information access. The internet became a global network, connecting people and businesses worldwide.

8. **Mobile and Wireless Era (1990s-Present):** The advent of mobile devices, especially smartphones and tablets, revolutionized personal computing. Wireless technologies like Wi-Fi and mobile networks enabled ubiquitous connectivity.

9. **Cloud Computing Era (2000s-Present):** Cloud computing emerged, allowing users to access computing resources and services over the internet. This shift reduced the need for extensive physical infrastructure and facilitated scalable and flexible solutions.

10. **Big Data and Analytics (2010s-Present):** The increasing volume of data led to a focus on big data technologies and analytics tools. Machine learning and artificial intelligence gained prominence for data analysis and decision-making.

11. **Internet of Things (IoT) and Industry 4.0 (2010s-Present):** The proliferation of connected devices, sensors, and smart systems marked the IoT era. In industrial settings, Industry 4.0 brought automation and connectivity to manufacturing processes.

12. **Artificial Intelligence and Automation (2010s-Present):** AI applications have become integral to various industries, including healthcare, finance, and logistics. Automation technologies, driven by AI and robotics, continue to reshape work processes.

The evolution of information technology is ongoing, with emerging technologies like quantum computing, 5G, and advanced AI poised to play significant roles in shaping the future of IT."
[{2023-11-20 retrieved} https://chat.openai.com/c/7f3b5448-9411-4058-914b-8e24cc7e568c]

name::
* McsEngl.evoluting-of-techInfo,
* McsEngl.techInfo'evoluting,

addressWpg::
* Hellenic IT Museum: https://elmp.gr/,

{2019-12-28}::
=== McsHitp-creation:
· creation of current concept.

info-revolution of techInfo

description::
"The term information revolution describes the "radical changes wrought by computer technology on the storage of and access to information since the mid-1980s"[1] or current economic, social and technological trends beyond the Industrial Revolution.
Many competing terms have been proposed that focus on different aspects of this societal development. The British polymath crystallographer J. D. Bernal introduced the term "scientific and technical revolution" in his 1939 book The Social Function of Science to describe the new role that science and technology are coming to play within society. He asserted that science is becoming a "productive force", using the Marxist Theory of Productive Forces.[2] After some controversy, the term was taken up by authors and institutions of the then-Soviet Bloc. Their aim was to show that socialism was a safe home for the scientific and technical ("technological" for some authors) revolution, referred to by the acronym STR. The book Civilization at the Crossroads, edited by the Czech philosopher Radovan Richta (1969), became a standard reference for this topic.[3]
Daniel Bell (1980) challenged this theory and advocated post-industrial society, which would lead to a service economy rather than socialism.[4] Many other authors presented their views, including Zbigniew Brzezinski (1976) with his "Technetronic Society".[5]"
[{2024-01-21 retrieved} https://en.wikipedia.org/wiki/Information_revolution]

name::
* McsEngl.info-revolution,
* McsEngl.information-revolution,
* McsEngl.knowledge-revolution,
* McsEngl.techInfo'revolution,

{2021..2030} of techInfo

description::
* {2020s} AI: is used to develop new products and services, such as self-driving cars and virtual assistants.
* {2022} ChatGPT: was released in November 2022 and quickly gained popularity due to its ability to engage in open-ended, fluent conversations. It could generate human-quality text, translate languages, write different kinds of creative content, and answer your questions in an informative way. [https://bard.google.com/]

name::
* McsEngl.techInfo'{2030i10},

{2011..2020} of techInfo

description::
* {2010s} AI: is widely adopted in a variety of industries.
* {2017} the Transformer architecture was introduced in large-language-models.
* {2016} IoT: The Internet of Things (IoT) begins to emerge, connecting billions of devices to the Internet.

name::
* McsEngl.techInfo'{2020i10},

{2001..2010} of techInfo

description::
* {2000s} Deep learning: revolutionizes AI research.
* {2009}:
- Bitcoin genesis block {2009-01-03 18:15:05}.
- OWL 2 DL: becomes a W3C Recommendation, offering full logical reasoning capabilities.
* {2006} Facebook: was launched, becoming the world's largest social networking site.
* {2005} YouTube: was launched, revolutionizing the way people share and consume video content.

name::
* McsEngl.techInfo'{2010i10},

{1991..2000} of techInfo

description::
* {1990s}: Statistical learning gained prominence, and algorithms like Support Vector Machines (SVM) and decision trees became popular. The field also saw the emergence of ensemble methods. [https://chat.openai.com/]
* {1999} RDF 1.0 was published as a W3C Recommendation in 1999.
* {1998}:
- Google Search Unveiled.
- quantum-computer: The first experimental it was built by Isaac Chuang and Neil Gershenfeld at the Massachusetts Institute of Technology.
* {1995} Cycorp, Inc., based in Austin, Texas: Founded in January 1995 by AI pioneer Doug Lenat as a spin-off from MCC

name::
* McsEngl.techInfo'{2000i10},

{1981..1990} of techInfo

description::
* {1980s}:
- Machine learning becomes a popular subfield of AI.
- The expert systems of the early 1980s proved to be difficult to bulid because of the challenge of capturing all of an expert's knowledge. They were also difficult to maintain, because their large rule bases had little organization. Most expert systems were stand-alone applications on dedicated workstations. [BYTE, JUL 1993, 107]
* {1989} WWW: Tim Berners-Lee invents the World Wide Web (WWW).
* {1984} Cyc: Lenat initiates the Cyc project at MCC.

name::
* McsEngl.techInfo'{1990i10},

{1971..1980} of techInfo

description::
* {1970s}: researchers began to develop knowledge-based systems (KBSs). KBSs are AI systems that use explicit knowledge to solve problems. This knowledge is represented in a variety of ways, such as logic rules, frames, and semantic nets. [https://bard.google.com/]
* {1972-1976} MYCIN is notable example of early expert systems.
* {1971} microprocessor: its invention led to the development of personal computers, which brought computing power to the masses.

name::
* McsEngl.techInfo'{1980i10},

{1961..1970} of techInfo

description::
* {1960s}: Development of first-order logic as a formal language for representing and reasoning about knowledge.
* {1969} ARPANET: the precursor to the Internet, is established.
* {1966} ELIZA: A language for representing and reasoning about human natural language.

name::
* McsEngl.techInfo'{1970i10},

{1951..1960} of techInfo

description::
* {1959}:
- integrated-circuit: Robert Noyce invented the first monolithic integrated circuit, from silicon.
- General Problem Solver (GPS) system developed by Allen Newell and Herbert A. Simon.
* {1957} perceptron: by Frank Rosenblatt, an American psychologist, was a simplified model of a neuron, capable of learning and recognizing patterns. [https://bard.google.com/]
* {1956}: John McCarthy coins the term "artificial intelligence".

name::
* McsEngl.techInfo'{1960i10},

{1941..1950} of techInfo

description::
* {1950}: Alan Turing proposes the Turing test, a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
* {1947} transistor: its invention revolutionized the computer industry.
* {1945} ENIAC: One of the earliest and most influential electronic computers was the ENIAC (Electronic Numerical Integrator and Computer), built in 1945 for the US Army.

name::
* McsEngl.techInfo'{1950i10},

WHOLE-PART-TREE of techInfo

name::
* McsEngl.techInfo'whole-part-tree,

whole-chain::
* human-technology
* Solar-system,
* Milky-way-galaxy,
* Sympan,

part::
*

GENERIC-SPECIFIC-TREE of techInfo

name::
* McsEngl.techInfo'generic-specific-tree,

generic-tree::
* technology
...
* entity,

techInfo.SPECIFIC

name::
* McsEngl.techInfo.specific,

specific::
* hardware-techInfo,
* software-techInfo,
* hardsoft-techInfo,
===
* char-techInfo,
* audio-techInfo,
* image-techInfo,
* video-techInfo,
===
* processing-techInfo,
* communicating-techInfo,
* storing-techInfo,
===
* data-techInfo,
* dataNo-techInfo,
** artificial-intelligence-techInfo,
** machine-learning-techInfo,
** machine-translating-techInfo,
** natural-language-processing-techInfo,
===
* computer vision,
* speech recognition,
* natural language processing,
* machine learning,
* machine translation,
* bioinformatics,
* drug design,
* medical image analysis,
* climate science,
* material inspection,
* board game programs,

techInfo.data-001

description::
· techData is techInfo that manages infoData.

name::
* McsEngl.techData,
* McsEngl.techInfo.001-data!⇒techData,
* McsEngl.techInfo.data!⇒techData,

techInfo.knowledge-002 (link)

lagConcept (link) of Cnptltech

techInfo.character-012

description::
·

name::
* McsEngl.techInfo.012-character,
* McsEngl.techInfo.character,

techInfo.audio-013

description::
· speech, music, ...
"Machine hearing, also known as machine listening or computer audition, is the ability of a computer or machine to take in and process sound data such as speech or music.[8][9] This area has a wide range of application including music recording and compression, speech synthesis, and speech recognition.[10] Moreover, this technology allows the machine to replicate the human brain's ability to selectively focus on a specific sound against many other competing sounds and background noise. This particular ability is called “auditory scene analysis”. The technology enables the machine to segment several streams occurring at the same time.[8][11][12] Many commonly used devices such as a smartphones, voice translators, and cars make use of some form of machine hearing. Present technology still occasionally struggles with speech segmentation though. This means hearing words within sentences, especially when human accents are accounted for."
[{2023-04-08 retrieved} https://en.wikipedia.org/wiki/Machine_perception#Machine_hearing]

name::
* McsEngl.computer-audition!⇒techAudio,
* McsEngl.machine-hearing!⇒techAudio,
* McsEngl.machine-listening!⇒techAudio,
* McsEngl.techAudio,
* McsEngl.techInfo.013-audio!⇒techAudio,
* McsEngl.techInfo.audio!⇒techAudio,

techInfo.image-014

description::
·

name::
* McsEngl.techInfo.014-image,
* McsEngl.techInfo.image,

techInfo.video-015

description::
· image and audio.
"Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and high-dimensional data from the real world to produce numerical or symbolic information, e.g., in the forms of decisions. Computer vision has many applications already in use today such as facial recognition, geographical modeling, and even aesthetic judgment.[7]
However, machines still struggle to interpret visual impute accurately if said impute is blurry, and if the viewpoint at which stimulus are viewed varies often. Computers also struggle to determine the proper nature of some stimulus if overlapped by or seamlessly touching another stimulus. This refers to The Principle of Good Continuation. Machines also struggle to perceive and record stimulus functioning according to the Apparent Movement principle which Gestalt psychologists researched."
[{2023-04-08 retrieved} https://en.wikipedia.org/wiki/Machine_perception#Machine_vision]

name::
* McsEngl.computer-vision!⇒techVideo,
* McsEngl.machine-vision!⇒techVideo,
* McsEngl.techInfo.015-video!⇒techVideo,
* McsEngl.techInfo.video!⇒techVideo,
* McsEngl.techVideo,

techInfo.touching-017

description::
"Machine touch is an area of machine perception where tactile information is processed by a machine or computer. Applications include tactile perception of surface properties and dexterity whereby tactile information can enable intelligent reflexes and interaction with the environment.[13] (This could possibly be done through measuring when and where friction occurs, and of what nature and intensity the friction is). Machines however still do not have any way of measuring some physical human experiences we consider ordinary, including physical pain. For example, scientists have yet to invent a mechanical substitute for the Nociceptors in the body and brain that are responsible for noticing and measuring physical human discomfort and suffering."
[{2023-04-08 retrieved} https://en.wikipedia.org/wiki/Machine_perception#Machine_touch]

name::
* McsEngl.techInfo.017-touching,
* McsEngl.techInfo.touching,
* McsEngl.machine-touching,

techInfo.smelling-018

description::
"Scientists are developing computers known as machine olfaction which can recognize and measure smells as well. Airborne chemicals are sensed and classified with a device sometimes known as an electronic nose.[14][15]"
[{2023-04-08 retrieved} https://en.wikipedia.org/wiki/Machine_perception#Machine_olfaction]

name::
* McsEngl.electronic-nose,
* McsEngl.machine-smelling,
* McsEngl.machine-olfaction,
* McsEngl.techInfo.018-smelling,
* McsEngl.techInfo.smelling,

techInfo.tasting-019

description::
"The electronic tongue is an instrument that measures and compares tastes. As per the IUPAC technical report, an “electronic tongue” as analytical instrument including an array of non-selective chemical sensors with partial specificity to different solution components and an appropriate pattern recognition instrument, capable to recognize quantitative and qualitative compositions of simple and complex solutions[16][17]
Chemical compounds responsible for taste are detected by human taste receptors. Similarly, the multi-electrode sensors of electronic instruments detect the same dissolved organic and inorganic compounds. Like human receptors, each sensor has a spectrum of reactions different from the other. The information given by each sensor is complementary, and the combination of all sensors' results generates a unique fingerprint. Most of the detection thresholds of sensors are similar to or better than human receptors.
In the biological mechanism, taste signals are transduced by nerves in the brain into electric signals. E-tongue sensors process is similar: they generate electric signals as voltammetric and potentiometric variations.
Taste quality perception and recognition are based on the building or recognition of activated sensory nerve patterns by the brain and the taste fingerprint of the product. This step is achieved by the e-tongue's statistical software, which interprets the sensor data into taste patterns."
[{2023-04-08 retrieved} https://en.wikipedia.org/wiki/Machine_perception#Machine_taste]

name::
* McsEngl.electronic-tongue,
* McsEngl.machine-tasting,
* McsEngl.techInfo.019-tasting,
* McsEngl.techInfo.tasting,

techInfo.perception-020

description::
"Machine perception is the capability of a computer system to interpret data in a manner that is similar to the way humans use their senses to relate to the world around them.[1][2][3] The basic method that the computers take in and respond to their environment is through the attached hardware. Until recently input was limited to a keyboard, or a mouse, but advances in technology, both in hardware and software, have allowed computers to take in sensory input in a way similar to humans.[1][2]
Machine perception allows the computer to use this sensory input, as well as conventional computational means of gathering information, to gather information with greater accuracy and to present it in a way that is more comfortable for the user.[1] These include computer vision, machine hearing, machine touch, and machine smelling, as artificial scents are, at a chemical compound, molecular, atomic level, indiscernible and identical.[4][5]
The end goal of machine perception is to give machines the ability to see, feel and perceive the world as humans do and therefore for them to be able to explain in a human way why they are making their decisions, to warn us when it is failing and more importantly, the reason why it is failing.[6] This purpose is very similar to the proposed purposes for artificial intelligence generally, except that machine perception would only grant machines limited sentience, rather than bestow upon machines full consciousness, self-awareness, and intentionality."
[{2023-04-08 retrieved} https://en.wikipedia.org/wiki/Machine_perception]

name::
* McsEngl.machine-perception,
* McsEngl.techInfo.020-perception,
* McsEngl.techInfo.perception,

techInfo.software-003

description::
· software is techInfo which is not hardware, ie representations of infoBrain that is-managed by the-techInfo.

name::
* McsEngl.software!⇒techInfoSoft,
* McsEngl.techInfo.003-software!⇒techInfoSoft,
* McsEngl.techInfo.software!⇒techInfoSoft,
* McsEngl.techInfoSoft,
====== langoGreek:
* McsElln.λογισμικό!=techInfoSoft,

techInfoSoft.code

description::
· Softcode is a-processing-method written in a-programing-language.

name::
* McsEngl.Softcode,
* McsEngl.soft-code!⇒Softcode,
* McsEngl.techInfoSoft.code!⇒Softcode,

techInfoSoft.data

description::
· Softdata is information we want to process.

name::
* McsEngl.Softdata,
* McsEngl.soft-data!⇒Softdata,
* McsEngl.techInfoSoft.data!⇒Softdata,

techInfoSoft.big-data

description::
">big data overview:
Big data refers to extremely large and complex datasets that cannot be easily managed, processed, or analyzed with traditional data processing tools. These datasets are characterized by their volume, velocity, variety, and sometimes veracity. Here's an overview of key aspects of big data:

1. **Volume:**
- Big data involves massive amounts of data. This could be on the order of terabytes, petabytes, or even exabytes of information.
- The sheer volume of data is a challenge for traditional database systems, necessitating new approaches to storage and processing.

2. **Velocity:**
- Data is generated and collected at unprecedented speeds. This could be real-time data streaming from various sources such as social media, sensors, or financial transactions.
- The ability to process and analyze data in near real-time is crucial for many applications.

3. **Variety:**
- Big data comes in a variety of formats and types, including structured data (e.g., relational databases), semi-structured data (e.g., XML, JSON), and unstructured data (e.g., text, images, videos).
- Dealing with this diversity requires flexible and scalable data storage and processing solutions.

4. **Veracity:**
- Veracity refers to the quality of the data. Big data sources often include incomplete, inconsistent, or inaccurate data.
- Managing and ensuring the quality of the data is a significant challenge in big data analytics.

5. **Value:**
- Extracting meaningful insights and value from big data is the ultimate goal. This involves analyzing the data to discover patterns, trends, correlations, and other valuable information.
- The value derived from big data can be used for making informed business decisions, improving processes, and gaining a competitive advantage.

6. **Challenges and Solutions:**
- **Storage:** Traditional databases may not be able to handle the volume and variety of big data. Distributed storage systems like Hadoop Distributed File System (HDFS) and cloud-based storage solutions are commonly used.
- **Processing:** Parallel processing and distributed computing frameworks, such as Apache Hadoop and Apache Spark, are used to process large datasets efficiently.
- **Analytics:** Advanced analytics techniques, including machine learning and data mining, are applied to extract meaningful insights.
- **Tools and Technologies:** A variety of tools and technologies, both open source and commercial, have been developed to handle big data. Examples include Hadoop, Spark, NoSQL databases, and data warehouses.

7. **Applications:**
- Big data is used in various industries, including finance, healthcare, retail, telecommunications, and more, to improve decision-making, enhance customer experiences, and optimize operations.

8. **Ethical and Privacy Concerns:**
- As big data involves the collection and analysis of vast amounts of personal information, ethical considerations and privacy concerns have become increasingly important. Ensuring responsible data use and adhering to data protection regulations are critical aspects.

In summary, big data presents both opportunities and challenges, requiring specialized tools, technologies, and approaches to harness its potential for extracting valuable insights and driving innovation across different domains."
[{2023-11-21 retrieved} https://chat.openai.com/c/6fcc093a-c9dd-4bcf-9b9d-61e1b190e594]

name::
* McsEngl.Softbigdata,
* McsEngl.big-data,
* McsEngl.techInfoSoft.big-data,

evoluting of Softbigdata

description::
">big data evolution:
The evolution of big data has been a dynamic and transformative journey, marked by advancements in technology, changes in data management practices, and the emergence of new tools and methodologies. Here are some key stages in the evolution of big data:

1. **Early Days: Emergence of Data Warehousing (1980s-1990s):**
- During this period, organizations began to realize the importance of collecting and storing large volumes of data for analysis.
- Data warehousing solutions emerged to consolidate and organize data from different sources.

2. **Rise of Relational Databases (1990s-2000s):**
- Relational database management systems (RDBMS) became the dominant technology for managing structured data.
- Businesses relied on SQL-based queries for data analysis.

3. **Internet and Web 2.0 (2000s):**
- The proliferation of the internet led to an explosion of data, including unstructured and semi-structured data from sources like social media, web logs, and sensors.
- The need to analyze diverse data types laid the foundation for big data technologies.

4. **Introduction of Hadoop (mid-2000s):**
- Apache Hadoop, an open-source distributed storage and processing framework, was introduced, enabling the processing of large datasets across clusters of commodity hardware.
- Hadoop's MapReduce programming model became a popular approach for distributed data processing.

5. **Diversification of Big Data Ecosystem (2010s):**
- The big data ecosystem expanded with the introduction of various tools and technologies, including Apache Spark, Apache Flink, and Apache Kafka.
- NoSQL databases emerged to handle unstructured and semi-structured data, offering more flexibility than traditional relational databases.

6. **Cloud Computing and Big Data (2010s-Present):**
- Cloud platforms, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), became popular for storing and processing big data due to their scalability and flexibility.
- Managed services like Amazon EMR and Azure HDInsight simplified the deployment and management of big data solutions.

7. **Advancements in Analytics (2010s-Present):**
- Machine learning and advanced analytics became integral to big data, allowing organizations to derive insights, make predictions, and automate decision-making processes.
- Data lakes gained popularity as a centralized repository for storing raw, unstructured data.

8. **Real-time and Edge Computing (2010s-Present):**
- The demand for real-time data processing led to the development of streaming frameworks like Apache Kafka and Apache Flink.
- Edge computing gained prominence, enabling data processing closer to the source of data generation.

9. **Focus on Data Governance and Privacy (2010s-Present):**
- With the increasing importance of data, organizations began to emphasize data governance, security, and privacy to ensure compliance with regulations and protect sensitive information.

10. **The Integration of Artificial Intelligence (AI) and Big Data:**
- The integration of AI and big data has become a key trend, with machine learning algorithms and deep learning models being applied to analyze and derive insights from massive datasets.

The evolution of big data continues, with ongoing developments in areas like federated learning, responsible AI, and the exploration of quantum computing for data processing. As technology advances, so does our ability to harness the potential of big data for innovation and decision-making across various industries."
[{2023-11-21 retrieved} https://chat.openai.com/c/642ab295-81bc-471b-8286-fe32087577e3]

name::
* McsEngl.evoluting-of-Softbigdata,
* McsEngl.Softbigdata'evoluting,

techInfo.hardware-004

description::
· the physical parts of techInfo, devices, cables, anything touchable.

name::
* McsEngl.hardware!⇒techInfoHard,
* McsEngl.techInfo.004-hardware!⇒techInfoHard,
* McsEngl.techInfo.hardware!⇒techInfoHard,
* McsEngl.techInfoHard,
====== langoGreek:
* McsElln.υλικό!=techInfoHard,

techInfo.system-005

description::
· machines or system of machines with hardware and software with specific doings.

name::
* McsEngl.ITS!⇒techInfoSys,
* McsEngl.information-technology-system!⇒techInfoSys,
* McsEngl.techInfo.005-system!⇒techInfoSys,
* McsEngl.techInfo.machine!⇒techInfoSys,
* McsEngl.techInfo.system!⇒techInfoSys,
* McsEngl.techInfoSys,

specific-tree-of-techInfoSys::
* computer,
* computer-network,

techInfo.artificial-agent

description::
">artificial-agent:
An artificial agent refers to a computer system designed to perform tasks autonomously, often by mimicking human behavior or by executing pre-defined instructions without human intervention. These agents can range from simple software programs, like chatbots or virtual assistants, that handle tasks such as answering questions or scheduling appointments, to more complex systems like autonomous vehicles, robots, or intelligent decision-making systems used in various industries.
Artificial agents operate based on algorithms and data inputs, using technologies such as machine learning, natural language processing, and robotics. They can adapt to their environment, learn from interactions, and make decisions based on the information they process. The design and capabilities of these agents vary widely, depending on their intended applications, which can span areas like customer service, healthcare, manufacturing, transportation, and entertainment.
The development of artificial agents is a multidisciplinary field, involving knowledge from computer science, artificial intelligence, cognitive science, and related areas. These agents have the potential to significantly impact society, offering opportunities for efficiency and innovation, while also raising ethical, security, and privacy concerns."
[{2024-02-21 retrieved} https://chat.openai.com/c/8a913e4e-0308-42ae-88db-564ab0ce415e]

name::
* McsEngl.AA!=artificial-agent!⇒techAA,
* McsEngl.artificial-agent!⇒techAA,
* McsEngl.techAA!=artificial-agent,
* McsEngl.techInfo.artificial-agent!⇒techAA,

techAA.SPECIFIC

description::
* robotics,

name::
* McsEngl.techAA.specific,

techInfo.artificial-intelligence-007 (link)

techInfo.phone-023

description::
·

=== diànhuà-电话!=phone:
· stxZhon: 我没有你的电话号。 :: Wǒ méiyǒu nǐde diànhuà hào. != I do not have your phone number.

name::
* McsEngl.phone,
* McsEngl.techInfo.023-phone,
* McsEngl.techInfo.phone,
====== langoChinese:
* McsZhon.diànhuà-电话!=phone,
* McsZhon.电话-diànhuà!=phone,
====== langoGreek:
* McsElln.τηλέφωνο!το!=phone,

techInfo.cellphone-021

description::
"A mobile phone (cellphone, etc.)[a] is a portable telephone that can make and receive calls over a radio frequency link while the user is moving within a telephone service area, as opposed to a fixed-location phone (landline phone). The radio frequency link establishes a connection to the switching systems of a mobile phone operator, which provides access to the public switched telephone network (PSTN). Modern mobile telephone services use a cellular network architecture and therefore mobile telephones are called cellphones (or "cell phones") in North America. In addition to telephony, digital mobile phones support a variety of other services, such as text messaging, multimedia messagIng, email, Internet access (via LTE, 5G NR or Wi-Fi), short-range wireless communications (infrared, Bluetooth), satellite access (navigation, messaging connectivity), business applications, video games and digital photography. Mobile phones offering only basic capabilities are known as feature phones; mobile phones which offer greatly advanced computing capabilities are referred to as smartphones.[1]"
[{2023-05-25 retrieved} https://en.wikipedia.org/wiki/Mobile_phone]

=== shǒujī-手机!=cellphone:
· stxZhon: 你 的 手机 多少 钱?:: _stxSbj:[Nǐ de shǒujī] _stxSbjc:[duōshao qiαn]? != How much was your cell phone?

name::
* McsEngl.cell-phone,
* McsEngl.cellphone,
* McsEngl.cellular-phone,
* McsEngl.mobile-phone,
* McsEngl.techInfo.021-cellphone,
* McsEngl.techInfo.cellphone,
====== langoChinese:
* McsZhon.shǒujī-手机!=cellphone,
* McsZhon.手机-shǒujī!=cellphone,

meta-info

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SEARCH::
· this page uses 'locator-names', names that when you find them, you find the-LOCATION of the-concept they denote.
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· clicking on the-green-BAR of a-page you have access to the-global--locator-names of my-site.
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footer::
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• email:
 
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webpage-versions::
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