What is JavaScript and what does it do? Before you start learning something new, it’s important to understand exactly what it is and what it does. This is especially useful when it comes to mastering a new programming language. In simple terms, JavaScript is a programming language used to make websites interactive. If you think about the basic makeup of a website, you have HTML, which describes and defines the basic content and structure of the website, then you have CSS, which tells the browser how this HTML content should be displayed—determining things like color and font. With just HTML and CSS, you have a website that looks good but doesn’t actually do much. JavaScript brings the website to life by adding functionality. JavaScript is responsible for elements that the user can interact with, such as drop-down menus, modal windows, and contact forms. It is also used to create things like animations, video players, and interactive maps. Nowadays, JavaScript is an all-purpose programming language—meaning it runs across the entire software stack. The most popular application of JavaScript is on the client side (aka frontend), but since Node.js came on the scene, many people run JavaScript on the server side (aka backend) as well. When used on the client side, JavaScript code is read, interpreted, and executed in the user’s web browser. When used on the server side, it is run on a remote computer. You can learn more about the difference between frontend and backend programming here. JavaScript isn’t only used to create websites. It can also be used to build browser-based games and, with the help of certain frameworks, mobile apps for different operating systems. The creation of new libraries and frameworks is also making it possible to build backend programs with JavaScript, such as web apps and server apps. Is it still worth learning JavaScript in 2021? The world of web development is constantly moving. With so many new tools popping up all the time, it can be extremely difficult to know where you should focus your efforts. As an aspiring developer, you’ll want to make sure that what you’re learning is still relevant in today’s industry. If you’re having doubts about JavaScript, it’s important to know that, since its creation in 1995, JavaScript is pretty much everywhere on the web—and that’s not likely to change any time soon. According to the 2020 StackOverflow developer survey, JavaScript is the most commonly used programming language for the eighth year in a row. It is currently used by 94.5% of all websites and, despite originally being designed as a client-side language, JavaScript has now made its way to the server-side of websites (thanks to Node.js), mobile devices (thanks to React Native and Ionic) and desktop (courtesy of Electron). As long as people are interacting with the web, you can assume that JavaScript is highly relevant—there’s no doubt that this is a language worth knowing! With that in mind, let’s look at some of the key benefits of becoming a JavaScript expert. Why learn JavaScript? The most obvious reason for learning JavaScript is if you have hopes of becoming a web developer. Even if you haven’t got your heart set on a tech career, being proficient in JavaScript will enable you to build websites from scratch—a pretty useful skill to have in today’s job market! If you do want to become a web developer, here are some of the main reasons why you should learn JavaScript: JavaScript experts are versatile JavaScript is an extremely versatile language. Once you’ve mastered it, the possibilities are endless: you can code on the client-side (frontend) using Angular and on the server-side (backend) using Node.js. You can also develop web, mobile, and desktop apps using React, React Native, and Electron, and you can even get involved in machine learning. If you want to become a frontend developer, JavaScript is a prerequisite. However, that’s not the only career path open to you as a JavaScript expert. Mastering this key programming language could see you go on to work in full-stack development, games development, information security software engineering, machine learning, and artificial intelligence—to name just a few! Ultimately, if you want any kind of development or engineering career, proficiency in JavaScript is a must. JavaScript experts are in-demand (and well-paid) JavaScript is the most popular programming language in the world, so it’s no wonder that JavaScript is one of the most sought-after skills in the web development industry today. According to the Devskiller IT Skills and Hiring Report 2020, 72% of companies are looking to hire JavaScript experts. Enter the search term “JavaScript” on job site Indeed and you’ll find over 40,000 jobs requiring this skill (in the US). Run the same search on LinkedIn and the results are in excess of 125,000. At the same time, the global demand for JavaScript seems to outweigh the expertise available on the market. According to this 2018 HackerRank report, 48% of employers worldwide need developers with JavaScript skills, while only 42% of student developers claim to be proficient in JavaScript. And, in their most recent report for 2020, HackerRank once again reports that JavaScript is the most popular language that hiring mangers look for in a web developer candidate. Not only are JavaScript experts in demand—they are also well-paid. In the United States, JavaScript developers earn an average yearly salary of $111,953 per year. We’ve covered this topic in more detail in our JavaScript salary guide, but as you can see, learning JavaScript can really boost your earning potential as a developer. JavaScript is beginner-friendly Compared to many other programming languages, JavaScript offers one of the more beginner-friendly entry points into the world of coding. The great thing about JavaScript is that it comes installed on every modern web browser—there’s no need to set up any kind of development environment, which means you can start coding with JavaScript right away! Another advantage of learning JavaScript as your first programming language is that you get instant feedback; with a minimal amount of JavaScript code, you’ll immediately see visible results. There’s also a huge JavaScript community on sites like Stack Overflow, so you’ll find plenty of support as you learn. Not only is JavaScript beginner-friendly; it will also set you up with some extremely valuable transferable skills. JavaScript supports object-oriented, functional, and imperative styles of programming—skills which can be transferred to any new language you might learn later on, such as Python, Java, or C++. JavaScript provides a crucial introduction to key principles and practices that you’ll take with you throughout your career as a developer. Should you learn plain JavaScript first or can you skip to frameworks and libraries? When deciding whether or not to learn JavaScript, what you’re really asking is whether or not you should learn “vanilla” JavaScript. Vanilla JavaScript just means plain JavaScript without any libraries or frameworks. Let’s explore what this means in more detail now. What is meant by vanilla JavaScript, libraries, and frameworks? If you research the term “vanilla JavaScript”, you might run into some confusion; however, all you need to know is that vanilla JavaScript is used to refer to native, standards-based, non-extended JavaScript. There is no difference between vanilla JavaScript and JavaScript—it’s just there to emphasize the usage of plain JavaScript without the use of libraries and frameworks. So what are libraries and frameworks? JavaScript libraries and frameworks both contain sets of prewritten, ready-to-use JavaScript code—but they’re not the same thing. You can think of a framework as your blueprint for building a website: it gives you a structure to work from, and contains ready-made components and tools that help you to build certain elements much quicker than if you were to code them from scratch. Some popular JavaScript frameworks include Angular, React, Vue, and Node.js. Frameworks also contain libraries. Libraries are smaller than frameworks, and tend to be used for more specific cases. A JavaScript library contains sets of JavaScript code which can be called upon to implement certain functions and features. Let’s imagine you want to code a particular element into your website. You could write, say, ten lines of JavaScript from scratch—or you could take the condensed, ready-made version from your chosen JavaScript library. Some examples of JavaScript libraries include jQuery, Lodash, and Underscore. The easiest way to understand how frameworks and libraries work together is to imagine you are building a house. The framework provides the foundation and the structure, while the library enables you to add in ready-made components (like furniture) rather than building your own from scratch. You can learn more about the relationship between languages and libraries in this post explaining the main differences between JavaScript and jQuery. For now, let’s go back to our original question: How important is it to learn vanilla JavaScript? Should you learn vanilla JavaScript first? When it comes to learning JavaScript, it can be tempting to skip ahead to those time-saving frameworks and libraries we just talked about—and many developers do. However, there are many compelling arguments for learning plain JavaScript first. While JavaScript frameworks may help you get the job done quicker, there’s only so far you can go if you don’t understand the core concepts behind these frameworks. Frontend developer Abhishek Nagekar describes how not learning vanilla JavaScript came back to bite him when he started learning the JavaScript frameworks Node and Express: “As I went to write more and more code in Node and Express, I began to get stuck at even the tiniest problems. Suddenly, I was surrounded with words like callbacks, closures, event loop and prototype. It felt like I got a reintroduction to JavaScript, but this time, it was not a toddler playing in its cradle, it was something of a mysterious monster, challenging me on every other step for not having taken it seriously.” The above Tweet references a long-running joke within the developer community, and although it dates way back to 2015, it’s still highly relevant today. If you want to become a developer who can innovate, not just execute, you need to understand the underlying principles of the web—not just the shortcuts. This means learning vanilla JavaScript before you move on to frameworks. In fact, understanding plain JavaScript will help you later on when it comes to deciding whether to use a framework for a certain project, and if so, which framework to use. Why Study JavaScript? JavaScript is one of the 3 languages all web developers must learn: 1. HTML to define the content of web pages 2. CSS to specify the layout of web pages 3. JavaScript to program the behavior of web pages Learning Speed In this tutorial, the learning speed is your choice. Everything is up to you. If you are struggling, take a break, or re-read the material. Always make sure you understand all the "Try-it-Yourself" examples. The only way to become a clever programmer is to: Practice. Practice. Practice. Code. Code. Code ! Commonly Asked Questions How do I get JavaScript? Where can I download JavaScript? Is JavaScript Free? You don't have to get or download JavaScript. JavaScript is already running in your browser on your computer, on your tablet, and on your smart-phone. JavaScript is free to use for everyone.

Poole, Mackworth & Goebel 1998, p. 1. Russell & Norvig 2003, p. 55. Definition of AI as the study of intelligent agents: Poole, Mackworth & Goebel (1998), which provides the version that is used in this article. These authors use the term "computational intelligence" as a synonym for artificial intelligence.[1] Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field".[2] Nilsson 1998 Legg & Hutter 2007 Russell & Norvig 2009, p. 2. McCorduck 2004, p. 204 Maloof, Mark. "Artificial Intelligence: An Introduction, p. 37" (PDF). georgetown.edu. Archived (PDF) from the original on 25 August 2018. "How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech". Hackernoon. Archived from the original on 11 September 2019. Retrieved 14 February 2020. Schank, Roger C. (1991). "Where's the AI". AI magazine. Vol. 12 no. 4. p. 38. Russell & Norvig 2009. "AlphaGo – Google DeepMind". Archived from the original on 10 March 2016. Allen, Gregory (April 2020). "Department of Defense Joint AI Center - Understanding AI Technology" (PDF). AI.mil - The official site of the Department of Defense Joint Artificial Intelligence Center. Archived (PDF) from the original on 21 April 2020. Retrieved 25 April 2020. Optimism of early AI: * Herbert Simon quote: Simon 1965, p. 96 quoted in Crevier 1993, p. 109. * Marvin Minsky quote: Minsky 1967, p. 2 quoted in Crevier 1993, p. 109. Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI: * McCorduck 2004, pp. 426–441 * Crevier 1993, pp. 161–162,197–203, 211, 240 * Russell & Norvig 2003, p. 24 * NRC 1999, pp. 210–211 * Newquist 1994, pp. 235–248 First AI Winter, Mansfield Amendment, Lighthill report * Crevier 1993, pp. 115–117 * Russell & Norvig 2003, p. 22 * NRC 1999, pp. 212–213 * Howe 1994 * Newquist 1994, pp. 189–201 Second AI winter: * McCorduck 2004, pp. 430–435 * Crevier 1993, pp. 209–210 * NRC 1999, pp. 214–216 * Newquist 1994, pp. 301–318 AI becomes hugely successful in the early 21st century * Clark 2015 Pamela McCorduck (2004, p. 424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other." This list of intelligent traits is based on the topics covered by the major AI textbooks, including: * Russell & Norvig 2003 * Luger & Stubblefield 2004 * Poole, Mackworth & Goebel 1998 * Nilsson 1998 Kolata 1982. Maker 2006. Biological intelligence vs. intelligence in general: Russell & Norvig 2003, pp. 2–3, who make the analogy with aeronautical engineering. McCorduck 2004, pp. 100–101, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones." Kolata 1982, a paper in Science, which describes McCarthy's indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real".[19] McCarthy recently reiterated his position at the AI@50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence".[20]. Neats vs. scruffies: * McCorduck 2004, pp. 421–424, 486–489 * Crevier 1993, p. 168 * Nilsson 1983, pp. 10–11 Symbolic vs. sub-symbolic AI: * Nilsson (1998, p. 7), who uses the term "sub-symbolic". General intelligence (strong AI) is discussed in popular introductions to AI: * Kurzweil 1999 and Kurzweil 2005 See the Dartmouth proposal, under Philosophy, below. McCorduck 2004, p. 34. McCorduck 2004, p. xviii. McCorduck 2004, p. 3. McCorduck 2004, pp. 340–400. This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition."[26] "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized."[27] "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction."[28] She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods."[29] "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews. 21 October 2016. Archived from the original on 28 August 2017. Lombardo P, Boehm I, Nairz K (2020). "RadioComics – Santa Claus and the future of radiology". Eur J Radiol. 122 (1): 108771. doi:10.1016/j.ejrad.2019.108771. PMID 31835078. Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian. Archived from the original on 16 June 2018. Retrieved 13 January 2018. AI applications widely used behind the scenes: * Russell & Norvig 2003, p. 28 * Kurzweil 2005, p. 265 * NRC 1999, pp. 216–222 * Newquist 1994, pp. 189–201 AI in myth: * McCorduck 2004, pp. 4–5 * Russell & Norvig 2003, p. 939 AI in early science fiction. * McCorduck 2004, pp. 17–25 Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Turing, Alan (1948), "Machine Intelligence", in Copeland, B. Jack (ed.), The Essential Turing: The ideas that gave birth to the computer age, Oxford: Oxford University Press, p. 412, ISBN 978-0-19-825080-7 Russell & Norvig 2009, p. 16. Dartmouth conference: * McCorduck 2004, pp. 111–136 * Crevier 1993, pp. 47–49, who writes "the conference is generally recognized as the official birthdate of the new science." * Russell & Norvig 2003, p. 17, who call the conference "the birth of artificial intelligence." * NRC 1999, pp. 200–201 McCarthy, John (1988). "Review of The Question of Artificial Intelligence". Annals of the History of Computing. 10 (3): 224–229., collected in McCarthy, John (1996). "10. Review of The Question of Artificial Intelligence". Defending AI Research: A Collection of Essays and Reviews. CSLI., p. 73, "[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with "cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert (not Robert) Wiener as a guru or having to argue with him." Hegemony of the Dartmouth conference attendees: * Russell & Norvig 2003, p. 17, who write "for the next 20 years the field would be dominated by these people and their students." * McCorduck 2004, pp. 129–130 Russell & Norvig 2003, p. 18. Schaeffer J. (2009) Didn't Samuel Solve That Game?. In: One Jump Ahead. Springer, Boston, MA Samuel, A. L. (July 1959). "Some Studies in Machine Learning Using the Game of Checkers". IBM Journal of Research and Development. 3 (3): 210–229. CiteSeerX 10.1.1.368.2254. doi:10.1147/rd.33.0210. "Golden years" of AI (successful symbolic reasoning programs 1956–1973): * McCorduck 2004, pp. 243–252 * Crevier 1993, pp. 52–107 * Moravec 1988, p. 9 * Russell & Norvig 2003, pp. 18–21 The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU. DARPA pours money into undirected pure research into AI during the 1960s: * McCorduck 2004, p. 131 * Crevier 1993, pp. 51, 64–65 * NRC 1999, pp. 204–205 AI in England: * Howe 1994 Lighthill 1973. Expert systems: * ACM 1998, I.2.1 * Russell & Norvig 2003, pp. 22–24 * Luger & Stubblefield 2004, pp. 227–331 * Nilsson 1998, chpt. 17.4 * McCorduck 2004, pp. 327–335, 434–435 * Crevier 1993, pp. 145–62, 197–203 * Newquist 1994, pp. 155–183 Mead, Carver A.; Ismail, Mohammed (8 May 1989). Analog VLSI Implementation of Neural Systems (PDF). The Kluwer International Series in Engineering and Computer Science. 80. Norwell, MA: Kluwer Academic Publishers. doi:10.1007/978-1-4613-1639-8. ISBN 978-1-4613-1639-8. Archived from the original (PDF) on 6 November 2019. Retrieved 24 January 2020. Formal methods are now preferred ("Victory of the neats"): * Russell & Norvig 2003, pp. 25–26 * McCorduck 2004, pp. 486–487 McCorduck 2004, pp. 480–483. Markoff 2011. 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Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Problem solving, puzzle solving, game playing and deduction: * Russell & Norvig 2003, chpt. 3–9, * Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9, * Luger & Stubblefield 2004, chpt. 3,4,6,8, * Nilsson 1998, chpt. 7–12 Uncertain reasoning: * Russell & Norvig 2003, pp. 452–644, * Poole, Mackworth & Goebel 1998, pp. 345–395, * Luger & Stubblefield 2004, pp. 333–381, * Nilsson 1998, chpt. 19 Psychological evidence of sub-symbolic reasoning: * Wason & Shapiro (1966) showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive social intelligence, performance dramatically improves. (See Wason selection task) * Kahneman, Slovic & Tversky (1982) have shown that people are terrible at elementary problems that involve uncertain reasoning. (See list of cognitive biases for several examples). * Lakoff & Núñez (2000) have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See Where Mathematics Comes From) Knowledge representation: * ACM 1998, I.2.4, * Russell & Norvig 2003, pp. 320–363, * Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345, * Luger & Stubblefield 2004, pp. 227–243, * Nilsson 1998, chpt. 18 Knowledge engineering: * Russell & Norvig 2003, pp. 260–266, * Poole, Mackworth & Goebel 1998, pp. 199–233, * Nilsson 1998, chpt. ≈17.1–17.4 Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts): * Russell & Norvig 2003, pp. 349–354, * Poole, Mackworth & Goebel 1998, pp. 174–177, * Luger & Stubblefield 2004, pp. 248–258, * Nilsson 1998, chpt. 18.3 Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): * Russell & Norvig 2003, pp. 328–341, * Poole, Mackworth & Goebel 1998, pp. 281–298, * Nilsson 1998, chpt. 18.2 Causal calculus: * Poole, Mackworth & Goebel 1998, pp. 335–337 Representing knowledge about knowledge: Belief calculus, modal logics: * Russell & Norvig 2003, pp. 341–344, * Poole, Mackworth & Goebel 1998, pp. 275–277 Sikos, Leslie F. (June 2017). Description Logics in Multimedia Reasoning. Cham: Springer. doi:10.1007/978-3-319-54066-5. ISBN 978-3-319-54066-5. S2CID 3180114. Archived from the original on 29 August 2017. Ontology: * Russell & Norvig 2003, pp. 320–328 Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval". IEEE Multimedia. 1 (2): 62–72. doi:10.1109/93.311653. S2CID 32710913. Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics". Image and Vision Computing. 26 (1): 82–101. doi:10.1016/j.imavis.2007.08.013. Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371. doi:10.1197/jamia.M2055. PMC 1513681. PMID 16622160. MCGARRY, KEN (1 December 2005). "A survey of interestingness measures for knowledge discovery". The Knowledge Engineering Review. 20 (1): 39–61. doi:10.1017/S0269888905000408. S2CID 14987656. Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM '06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682. Qualification problem: * McCarthy & Hayes 1969 * Russell & Norvig 2003[page needed] While McCarthy was primarily concerned with issues in the logical representation of actions, Russell & Norvig 2003 apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge. Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"): * Russell & Norvig 2003, pp. 354–360, * Poole, Mackworth & Goebel 1998, pp. 248–256, 323–335, * Luger & Stubblefield 2004, pp. 335–363, * Nilsson 1998, ~18.3.3 Breadth of commonsense knowledge: * Russell & Norvig 2003, p. 21, * Crevier 1993, pp. 113–114, * Moravec 1988, p. 13, * Lenat & Guha 1989 (Introduction) Dreyfus & Dreyfus 1986. Gladwell 2005. Expert knowledge as embodied intuition: * Dreyfus & Dreyfus 1986 (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI) * Gladwell 2005 (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.) * Hawkins & Blakeslee 2005 (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.) Planning: * ACM 1998, ~I.2.8, * Russell & Norvig 2003, pp. 375–459, * Poole, Mackworth & Goebel 1998, pp. 281–316, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Information value theory: * Russell & Norvig 2003, pp. 600–604 Classical planning: * Russell & Norvig 2003, pp. 375–430, * Poole, Mackworth & Goebel 1998, pp. 281–315, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: * Russell & Norvig 2003, pp. 430–449 Multi-agent planning and emergent behavior: * Russell & Norvig 2003, pp. 449–455 Turing 1950. Solomonoff 1956. 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Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about."[163] AI's immediate precursors: * McCorduck 2004, pp. 51–107 * Crevier 1993, pp. 27–32 * Russell & Norvig 2003, pp. 15, 940 * Moravec 1988, p. 3 Haugeland 1985, pp. 112–117 The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt. Cognitive simulation, Newell and Simon, AI at CMU (then called Carnegie Tech): * McCorduck 2004, pp. 139–179, 245–250, 322–323 (EPAM) * Crevier 1993, pp. 145–149 Soar (history): * McCorduck 2004, pp. 450–451 * Crevier 1993, pp. 258–263 McCarthy and AI research at SAIL and SRI International: * McCorduck 2004, pp. 251–259 * Crevier 1993 AI research at Edinburgh and in France, birth of Prolog: * Crevier 1993, pp. 193–196 * Howe 1994 AI at MIT under Marvin Minsky in the 1960s : * McCorduck 2004, pp. 259–305 * Crevier 1993, pp. 83–102, 163–176 * Russell & Norvig 2003, p. 19 Cyc: * McCorduck 2004, p. 489, who calls it "a determinedly scruffy enterprise" * Crevier 1993, pp. 239–243 * Russell & Norvig 2003, p. 363−365 * Lenat & Guha 1989 Knowledge revolution: * McCorduck 2004, pp. 266–276, 298–300, 314, 421 * Russell & Norvig 2003, pp. 22–23 Frederick, Hayes-Roth; William, Murray; Leonard, Adelman. "Expert systems". AccessScience. doi:10.1036/1097-8542.248550. 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Archived (PDF) from the original on 4 September 2013. Retrieved 4 June 2013 – via msu.edu. "Applications of AI". www-formal.stanford.edu. Archived from the original on 28 August 2016. Retrieved 25 September 2016. Further reading DH Author, 'Why Are There Still So Many Jobs? The History and Future of Workplace Automation' (2015) 29(3) Journal of Economic Perspectives 3. Boden, Margaret, Mind As Machine, Oxford University Press, 2006. Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192–98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple enough to be understandable will not be complicated enough to behave intelligently, while any system complicated enough to behave intelligently will be too complicated to understand." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple stupid. They work, but they work by brute force." (p. 198.) Domingos, Pedro, "Our Digital Doubles: AI will serve our species, not control it", Scientific American, vol. 319, no. 3 (September 2018), pp. 88–93. Gopnik, Alison, "Making AI More Human: Artificial intelligence has staged a revival by starting to incorporate what we know about how children learn", Scientific American, vol. 316, no. 6 (June 2017), pp. 60–65. Johnston, John (2008) The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI, MIT Press. Koch, Christof, "Proust among the Machines", Scientific American, vol. 321, no. 6 (December 2019), pp. 46–49. Christof Koch doubts the possibility of "intelligent" machines attaining consciousness, because "[e]ven the most sophisticated brain simulations are unlikely to produce conscious feelings." (p. 48.) According to Koch, "Whether machines can become sentient [is important] for ethical reasons. If computers experience life through their own senses, they cease to be purely a means to an end determined by their usefulness to... humans. Per GNW [the Global Neuronal Workspace theory], they turn from mere objects into subjects... with a point of view.... Once computers' cognitive abilities rival those of humanity, their impulse to push for legal and political rights will become irresistible – the right not to be deleted, not to have their memories wiped clean, not to suffer pain and degradation. The alternative, embodied by IIT [Integrated Information Theory], is that computers will remain only supersophisticated machinery, ghostlike empty shells, devoid of what we value most: the feeling of life itself." (p. 49.) Marcus, Gary, "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", Scientific American, vol. 316, no. 3 (March 2017), pp. 58–63. A stumbling block to AI has been an incapacity for reliable disambiguation. An example is the "pronoun disambiguation problem": a machine has no way of determining to whom or what a pronoun in a sentence refers. (p. 61.) E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018) SSRN, part 2(3) Archived 24 May 2018 at the Wayback Machine. George Musser, "Artificial Imagination: How machines could learn creativity and common sense, among other human qualities", Scientific American, vol. 320, no. 5 (May 2019), pp. 58–63. Myers, Courtney Boyd ed. (2009). "The AI Report" Archived 29 July 2017 at the Wayback Machine. Forbes June 2009 Raphael, Bertram (1976). The Thinking Computer. W.H.Freeman and Company. ISBN 978-0-7167-0723-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135–44. "Today's AI technologies are powerful but unreliable. Rules-based systems cannot deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in cars, although they perform well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate and hack an AI system, the risks are even greater." (p. 140.) Serenko, Alexander (2010). "The development of an AI journal ranking based on the revealed preference approach" (PDF). Journal of Informetrics. 4 (4): 447–459. doi:10.1016/j.joi.2010.04.001. Archived (PDF) from the original on 4 October 2013. Retrieved 24 August 2013. Serenko, Alexander; Michael Dohan (2011). "Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence" (PDF). Journal of Informetrics. 5 (4): 629–649. doi:10.1016/j.joi.2011.06.002. Archived (PDF) from the original on 4 October 2013. Retrieved 12 September 2013. Sun, R. & Bookman, L. (eds.), Computational Architectures: Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994. Tom Simonite (29 December 2014). "2014 in Computing: Breakthroughs in Artificial Intelligence". MIT Technology Review. Tooze, Adam, "Democracy and Its Discontents", The New York Review of Books, vol. LXVI, no. 10 (6 June 2019), pp. 52–53, 56–57. "Democracy has no clear answer for the mindless operation of bureaucratic and technological power. We may indeed be witnessing its extension in the form of artificial intelligence and robotics. Likewise, after decades of dire warning, the environmental problem remains fundamentally unaddressed.... Bureaucratic overreach and environmental catastrophe are precisely the kinds of slow-moving existential challenges that democracies deal with very badly.... Finally, there is the threat du jour: corporations and the technologies they promote." (pp. 56–57.)

The field of Artificial Intelligence (AI) is a frontier of computer science that focuses on creating systems capable of performing tasks that would typically require human intelligence. This encompasses a wide range of capabilities such as visual perception, speech recognition, decision-making, and language translation.

Sri Venkateshwara University (SVU) strives to create professionals who are not only adept in academics but also in application for the benefit of humanity. We foster a culture of learning by doing. We believe in nurturing students who are at the forefront of innovation by offering an environment of research & development to make us Best University in Uttar Pradesh (UP). SVU believes in experiential learning. To facilitate this, we have an ultra-modern infrastructure that motivates students to experiment & excel in their area of interest. The Best University of Moradabad has laboratories & workshops that signify our commitment to core research, thus enabling innovation. SVU is the only institution to have set up labs in collaboration with the industry. This way we can train our students on the latest skills & make them employable. Students sharpen their practical skills under the watch full eyes of trainers & become competent professionals. For the overall development of the students, we organize cultural programs. Students take part in these programs & exhibit their talent to become confident professionals. The annual fest attracts students from all over the country & showcase their talent to make us the Top University in India. We equipped the computing labs with the latest software & hardware to augment the technical skills of the students. SVU’s library is an epitome of knowledge. It has over 3000 books & journals that ensure the students are never short on intellectual input. The team of industry trainers educate them on the key skills so crucial for employment & make us the Best University in Gajraula. The specially created engineering labs assist engineers to refine their technical acumen so much needed for the country. The Chairman Dr. Sudhir Giri believes in removing all the economic & social barriers that can hinder education. Hence, SVU provides many scholarships & grants to meritorious students. Up till now, the college has enabled over 500000 students to attain their academic desires to make us the Best Private University in Uttar Pradesh (UP). The group is running a dozen educational institutions that include medical colleges in India & abroad. Our commitment towards education & healthcare has enabled Dr Sudhir Giri to win the International Glory Man of the year Award 2021. The Best Private University in Moradabad is on the Delhi Moradabad highway, well connected with rail & road. The green surroundings provide peace of mind that enables research based learning. The carefully recruited faculty is the pride of the university. They have years of industrial & academic experience so vital for the students. They transfer key skills & make us the Best Private University in Gajraula. The faculty encourages students to undertake research & sharpen their skills that will enable them to get jobs. Majority of the faculty members are doctorates who educate the students to become competent professionals. The faculty takes part in FDP in order to develop a culture of research. The specialty of SVU is the internship. We have partnered with leading industries for providing internship to the students. We believe that education without applicability is incomplete. Students gain hands on exposure through internship & become job ready. We place most of the students during internship to make us the Top University in India. SVU, the Best University in Uttar Pradesh (UP), adopts a futuristic teaching pedagogy. We strive for experiential learning of our students through role plays, projects & presentation. The students take part in the learning activity & imbibe concepts that enable their placements. The AC seminar & conference halls allow knowledge dispersion for the development of the students. The University is running over 150 undergraduate (UG), postgraduate (PG) courses, (Ph.D.), diploma and certificate courses in various fields of Applied Sciences, Medical Science, Humanities & Social Sciences. We also run courses in Languages, Design, Agriculture, Engineering & Technology, Nursing, Pharmacy, Paramedical, Commerce & Management, Law, Library & information Sciences, Mass Comm. & Journalism to enhance the employability of the youth. SVU has a culture of project based learning. Students do projects in each semester under the guidance of faculty. They complete these projects in earmarked industries to garner hands-on skills. Through these projects, we train students on the hot skills so crucial for employment to make us the Best University in Moradabad. SVU’s Research & Development (R&D) wing encourages students to work on research areas important for the country. We have partnered with leading research institutions to undertake research. The breath-taking infrastructure of the best university in Gajraula motivates researchers to achieve their goals for research. Owing to our dedication, SVU has received grants from GOI for research on areas of national importance. The faculty members provide guidance to the scholars until they achieve their aim. We have set up the incubation center to provide fillip to new ideas that foster entrepreneurship. We want to be an institution that supports the ‘Make in India’ vision of the government. The center supports new ideas that enable the young entrepreneurs to create startups & become successful. Under the strong leadership of Dr. Sudhir Giri, till date we have successfully incubated 150 start-ups. This speaks of our exemplary education & make us the Best Private University in Uttar Pradesh (UP). These startups are not only creating wealth but also providing employment to the needy. The industrialists have lamented that the epicenter for entrepreneurship will be the educational institutions. We need to provide them with the support & infrastructure for this. The annual hackathon attracts individuals who showcase their business acumen to make us the Best Private University in Moradabad. SVU has a dedicated International Research & collaboration Cell (IRCC) that collaborates with universities abroad. Faculty & students who want to pursue studies abroad the IRCC starts admission formalities for them. We have partnered with reputed institutions for providing excellent research collaborations. Those who wish to do P. HD abroad the IRCC help them gain admission & make us the Top University in India. A lot of our faculty members are pursuing their research internationally & contributing to the welfare of humanity. SVU strives to make our students feel comfortable at the campus. Separate hostel for boys & girls with 24 hour security is available at SVU. The cafeteria serves nutritious food to the students. Gym, recreation hall & the sports ground help to relax our students & make us the Best University in Uttar Pradesh (UP). The campus has an in house ATM & convenience store for the benefit of the students. SVU enables placement through exemplary training. We train on communication & interpersonal skills in order to refine the personality of the students. We make them practice mock interviews & group discussion that help to clear placement tests. Ninety percent of the students get placed before their last semester to make us the best university in Moradabad. We have hired industrial trainers in order to provide training on block chain, machine learning, artificial intelligence (AI), and python & data science. These trainers have years of experience that enables them in training the students. The students gain key insights on these technologies & sharpen their acumen to make us the Best University in Gajraula.

Python is a popular and versatile programming language that is widely used in various industries, including web development, data science, artificial intelligence, and more Top 40 Python Projects. Project Includes Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

An open source Artificial Intelligence research and development project approached from the psychology of the human mind.

Introduction Metaverse is the next evolution of digital technologies. It includes 3D virtualization and will transform digital technologies in the next 5-10 years. Elements of a Metaverse are considered very important related to industry 4.0. Metaverse will have numerous technologies comprising the below: Digital currency Online shopping Workplace automation Social media Digital Humans Natural Language Processing Infrastructure Device independence In this article, we would understand what Metaverse is and what are its different elements? Description Metaverse is a combined virtual space. It is made by the convergence of virtually improved physical and digital reality. We can also say that it is device-free and is not owned by a single seller. Metaverse is known as an independent virtual economy. It is allowed by digital currencies and non-fungible tokens (NFTs). It characterizes a combinatorial innovation because it needs many technologies and trends to function. The following are contributing tech capabilities: Augmented reality (AR) Flexible work styles Head-mounted displays (HMDs) An AR cloud The Internet of Things (IoT) 5G Artificial intelligence (AI) Spatial technologies For better understanding, the concepts of a Metaverse, consider it as the next version of the Internet. That begins as separate bulletin boards and independent online destinations. In the long run, these destinations developed sites on a virtual shared space same as how a Metaverse would develop. Importance of the Metaverse There is a lot of exhilaration around Metaverse. Greatly of it driven by technology companies tactically claiming to be Metaverse companies. Similarly, Metaverse creates to improve or augment the digital and physical realities of people. Furthermore, deeds that now happen in siloed locations will finally occur in a single Metaverse, for example: Buying clothes and fittings for online avatars Business digital land and building virtual homes Take part in a virtual social experience Supermarkets run in virtual malls through immersive commerce With virtual classrooms to practice immersive learning Purchasing digital art, breakables, and assets (NFTs) Networking with digital humans for onboarding business connections It is likely that a Metaverse will make available determined, decentralized, joint, and interoperable opportunities. It will create the business models, which will allow organizations to extend digital business. Elements of a Metaverse Gartner is a technology research and consulting company. It described the elements of a Metaverse in the below diagram. Elements of a Metaverse Applications Virtual reality The social network company Facebook launched a social VR world named Facebook Horizon in 2019. Facebook chairman Mark Zuckerberg confirmed in 2021, a company pledged to develop a metaverse. Several VR technologies promoted by Meta Platforms remain to be developed. Microsoft developed the VR Company AltspaceVR in 2017. Microsoft has since applied metaverse features for example virtual avatars and meetings thought in virtual reality into Microsoft Teams. Future implementations for metaverse technology comprise refining work output, shared learning environments, e-commerce, real estate, and fashion. Video games Many works of metaverse technologies have by now been developed inside modern internet-enabled video games. The Second Life is combined several features of social media into a determined three-dimensional world with the user signified as an avatar. Social functions are repeatedly an integral story in many hugely multiplayer online games. Social-based gameplay of Minecraft characterizes an innovative analog of a metaverse. Hardware Technology Entrance points for metaverse comprise general-purpose computers and smartphones. Also, included augmented reality (AR), mixed reality, virtual reality (VR), and virtual world technologies. Need on VR technology has limited metaverse growth and wide-scale acceptance. Limits of moveable hardware and the requirement to balance cost and design have produced a deficiency of high-quality graphics and mobility. Lightweight wireless headsets have fought to attain the retina display pixel density required for visual immersion. Present hardware development is dedicated to choking limitations of VR headsets, sensors, and growing immersion with haptic technology. Software Technology There has been nothing for wide-scale acceptance of a uniform technical requirement for metaverse applications. Current applications depend chiefly on proprietary technology. Interoperability is the main anxiety in metaverse development. There have been a number of virtual environment standardization projects. Metaverse is known as a three-dimensional Internet that is occupied with live people. The technology company NVIDIA declared in 2021 they will accept USD for their metaverse development tools. The OpenXR is an open standard for entree into virtual and augmented reality plans and involvements. It has been accepted by Microsoft for HoloLens 2, Meta Platforms for the Oculus Quest, and Valve for SteamVR. For more details visit: https://www.technologiesinindustry4.com

🎓 Portfolio of projects developed during my BS in Artificial Intelligence at UET Peshawar , featuring work in AI and Data Science, databases, OOP, full-stack development, Machine Learning, Deep Learning, NLP, Computer Vision, Gen Ai and research-driven applications.

Efficient and accurate object grasping and placement in open hybrid scenes is an important frontier research hotspot in the field of robotics and artificial intelligence in recent years. It has extensive and important application requirements in the fields of industrial manufacturing, home services, etc. Our project takes the intelligent completion of desktop sorting by robots as the typical scene to carry out relevant research. It is proposed to realize the complete perception of objects through independent exploration of the environment, and then reasonably plan the motion trajectory as the research idea. The main contents are as follows: 1. Perceptual recognition and pose estimation of target object combined with autonomous environment exploration; 2. Optimal trajectory planning of manipulator considering object state and collision; 3. Development and demonstration verification of manipulator desktop finishing integrated system.

The garbage classification is a social issue related to people’s livelihood and sustainable development, so letting service robots autonomously perform intelligent garbage classification has important research significance. In this project we proposed an artificial intelligence-based machine learning algorithm to classify commercial and domestic waste in an efficient way to resolve this serious issue. In order to build this algorithm, we used Py-torch which is an open-source machine learning framework based on Python, we also used convolutional neural network-based architecture Resnet50 model pretrained on ImageNet dataset. Finally, we tested the model with random images and achieve the remarkable results having the actual accuracy of 95% on the testing images and 60% accuracy on the external images.

Introduction Metaverse is the next evolution of digital technologies. It includes 3D virtualization and will transform digital technologies in the next 5-10 years. Elements of a Metaverse are considered very important related to industry 4.0. Metaverse will have numerous technologies comprising the below: Digital currency Online shopping Workplace automation Social media Digital Humans Natural Language Processing Infrastructure Device independence In this article, we would understand what Metaverse is and what are its different elements? Description Metaverse is a combined virtual space. It is made by the convergence of virtually improved physical and digital reality. We can also say that it is device-free and is not owned by a single seller. Metaverse is known as an independent virtual economy. It is allowed by digital currencies and non-fungible tokens (NFTs). It characterizes a combinatorial innovation because it needs many technologies and trends to function. The following are contributing tech capabilities: Augmented reality (AR) Flexible work styles Head-mounted displays (HMDs) An AR cloud The Internet of Things (IoT) 5G Artificial intelligence (AI) Spatial technologies For better understanding, the concepts of a Metaverse, consider it as the next version of the Internet. That begins as separate bulletin boards and independent online destinations. In the long run, these destinations developed sites on a virtual shared space same as how a Metaverse would develop. Importance of the Metaverse There is a lot of exhilaration around Metaverse. Greatly of it driven by technology companies tactically claiming to be Metaverse companies. Similarly, Metaverse creates to improve or augment the digital and physical realities of people. Furthermore, deeds that now happen in siloed locations will finally occur in a single Metaverse, for example: Buying clothes and fittings for online avatars Business digital land and building virtual homes Take part in a virtual social experience Supermarkets run in virtual malls through immersive commerce With virtual classrooms to practice immersive learning Purchasing digital art, breakables, and assets (NFTs) Networking with digital humans for onboarding business connections It is likely that a Metaverse will make available determined, decentralized, joint, and interoperable opportunities. It will create the business models, which will allow organizations to extend digital business. Elements of a Metaverse Gartner is a technology research and consulting company. It described the elements of a Metaverse in the below diagram. Elements of a Metaverse Applications Virtual reality The social network company Facebook launched a social VR world named Facebook Horizon in 2019. Facebook chairman Mark Zuckerberg confirmed in 2021, a company pledged to develop a metaverse. Several VR technologies promoted by Meta Platforms remain to be developed. Microsoft developed the VR Company AltspaceVR in 2017. Microsoft has since applied metaverse features for example virtual avatars and meetings thought in virtual reality into Microsoft Teams. Future implementations for metaverse technology comprise refining work output, shared learning environments, e-commerce, real estate, and fashion. Video games Many works of metaverse technologies have by now been developed inside modern internet-enabled video games. The Second Life is combined several features of social media into a determined three-dimensional world with the user signified as an avatar. Social functions are repeatedly an integral story in many hugely multiplayer online games. Social-based gameplay of Minecraft characterizes an innovative analog of a metaverse. Hardware Technology Entrance points for metaverse comprise general-purpose computers and smartphones. Also, included augmented reality (AR), mixed reality, virtual reality (VR), and virtual world technologies. Need on VR technology has limited metaverse growth and wide-scale acceptance. Limits of moveable hardware and the requirement to balance cost and design have produced a deficiency of high-quality graphics and mobility. Lightweight wireless headsets have fought to attain the retina display pixel density required for visual immersion. Present hardware development is dedicated to choking limitations of VR headsets, sensors, and growing immersion with haptic technology. Software Technology There has been nothing for wide-scale acceptance of a uniform technical requirement for metaverse applications. Current applications depend chiefly on proprietary technology. Interoperability is the main anxiety in metaverse development. There have been a number of virtual environment standardization projects. Metaverse is known as a three-dimensional Internet that is occupied with live people. The technology company NVIDIA declared in 2021 they will accept USD for their metaverse development tools. The OpenXR is an open standard for entree into virtual and augmented reality plans and involvements. It has been accepted by Microsoft for HoloLens 2, Meta Platforms for the Oculus Quest, and Valve for SteamVR.

This workshop is part of the AI4Science Series, an initiative within the Artificial Intelligence in Sustainable Development Goals project. The series aims to empower researchers across scientific disciplines with practical AI tools to accelerate discovery and innovation.

AI-Graph-Project a multi-phase research and development effort focused on graph-based machine learning and AI applications. This is final project for Artificial intelligence course (Bachelor's degree) at KNTU, taught by Dr. Koohzadi.

A partnership project between UFAC, FUNDAPE, and FOXCONN aims to disseminate Artificial Intelligence through activities focused on human resources development, research, and development. The primary objective is to enhance the professional training of higher education students in the IT field.

code and documentation of my senior research project for my final year at Shippensburg University. The project in question featured the development of an artificial intelligence for use in Fighting Games. This project was largely accomplished using the fightingICE software and frame work

Research project consisting on the development of a visual interface for enhancing Machine Learning's accessibility and transparency, exploring the possibilities of XAI (Explanaible Artificial Intelligence). The project was built using Django and Scikit. Available at: https://wizardml.herokuapp.com/ml/

I’m working on several areas in the field of artificial intelligence, focusing on research, development, and building practical projects: 1. Data science and preprocessing 2. Machine learning and deep learning 3. Image processing 4. Reinforcement learning and image processing All projects here will be shared on GitHub and updated regularly

In field of prosthetic, humanoid used as tool for research and development. There are many humanoid robots are available as per their tasks. Generally humanoid robots should able to perform most of the tasks of human.So,its essential to design humanoid as per work of human body parts.So study and research of "How works different parts like legs,arms,torso etc. and it's muscles and bones of human body?" is first step of our project. We designed different mechanisms related fingers,palm and arms of humanoid and after we did it for all parts of humanoid.Then we did wiring these different parts with micro-controller and its programming.We made these parts on 3-D printers for easy making. Than we tested it's all these parts and then assembled then gave to it artificial intelligence or manual control.

With the rapid development of computer vision technology, human action recognition technology has occupied an important position in this field. It has important practical value and research value in security protection, advanced human-computer interaction, video search analysis and sports analysis. Due to the non rigid body characteristics of the human body, the change of illumination, and the influence of the changeable surrounding environment, human action recognition is more challenging. Technology powered by Artificial intelligence is used in various HAR(Human Action recognition) systems. Our project focuses on building a model that recognises human actions accurately from a given video. This can be used in entertainment, athlete performance analysis, etc.

Future Development Trend of Underwater Drone 1. Technological innovation will be the eternal theme As a highly technology-intensive and systematic project, underwater vehicle mainly includes six technologies: simulation, intelligent control, underwater target detection and recognition, underwater navigation (positioning), communication and energy system. Compared with developed countries, there is still a big gap in the research of some key components and materials of underwater vehicles in our country. To realize industrialization, the emphasis is the research and development of basic materials, important components, high-precision detection equipment and sensors. Therefore, technological innovation will be a long-term work. 2. Intelligence and endurance of AUV is the key Since 1953, especially after 1974, with the rapid development of offshore oil and gas industry, AUV has also been developing rapidly. Compared with ROV, AUV is not limited by cables, and has the characteristics of wide range of activities and good mobility, so it is suitable for a wider range of fields. On the one hand, the level of intelligence has become a key factor in the development of AUV technology. Intelligent control technology aims at improving the autonomy of underwater vehicle. Its architecture is the integration of artificial intelligence technology and various control technologies, which is equivalent to human brain and nervous system. On the other hand, the requirements of AUV for energy system are small size, light weight, high energy density, repeated use, safety and low cost. From this point of view, the underwater UAV still has a lot of room for improvement. 4. Prospects for the consumer sector are promising In terms of market size, the global experiential diving service market has grown at an average rate of about 15% in the past five years and nearly $25 billion in 2015. At present, the global scale of diving enthusiasts has reached 200 million, the scale of consumer-grade underwater image market has reached 10 billion US dollars, and some mature enterprises have emerged. The average growth rate of experiential diving service market in China in the past five years is about 36.5%, which is obviously higher than the global growth rate, exceeding $2.1 billion in 2015. Consumption prospects are considerable. In summary, under the principle of government guidance and enterprise leadership, underwater vehicles have great potential in marine environment monitoring, marine resources development, disaster prevention and mitigation, and marine environment security.

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Coding/Artificial Intelligence Research and Development Blog where i share my current projects

This research and development project focuses on integrating Human-Computer Interaction (HCI) with Artificial Intelligence (AI) to optimize solar energy generation, storage, and distribution.

A sleek, animated, and mobile-responsive personal portfolio built with React, Framer Motion, and TailwindCSS. Showcasing projects, skills, and research interests in Civil Engineering, Software Development, and Artificial Intelligence.

Welcome to the Quantum Computing and Artificial Intelligence Research Project! This repository houses our ongoing research and development efforts in the exciting and rapidly evolving fields of quantum computing and artificial intelligence (AI). Our mission is to explore the intersection of these two groundbreaking technologies and drive innovation

A research project analyzing how artificial intelligence enhances threat detection and response in cybersecurity, with a focus on intrusion detection, SOAR, social engineering, and future developments.

Global AI: A comprehensive repository dedicated to advancing artificial intelligence research and development. This repository includes resources, tools, and projects aimed at promoting responsible, ethical, and innovative AI solutions worldwide

A collection of my academic and personal projects in Artificial Intelligence, Machine Learning, Deep Learning, Full-Stack Development, and Software Engineering. Each project showcases practical implementations, research insights, and creative problem-solving.

I am a professional development enthusiast. My main research areas include linux c++ server, network, web development, artificial intelligence, computer vision and other fields, which mainly include the contents of my own projects.