{"id":2495,"date":"2022-09-08T13:13:48","date_gmt":"2022-09-08T13:13:48","guid":{"rendered":"http:\/\/boliviatypeapproval.com\/?p=2495"},"modified":"2023-03-03T13:39:55","modified_gmt":"2023-03-03T13:39:55","slug":"pdf-symbol-grounding-and-its-implications-for","status":"publish","type":"post","link":"https:\/\/boliviatypeapproval.com\/index.php\/2022\/09\/08\/pdf-symbol-grounding-and-its-implications-for\/","title":{"rendered":"PDF Symbol Grounding and its Implications for Artificial Intelligence"},"content":{"rendered":"<p>Each <a href=\"https:\/\/metadialog.com\/blog\/symbolic-ai\/\">artificial intelligence symbol<\/a>\u2014symbolic, connectionist, and behavior-based\u2014has advantages, but has been criticized by the other approaches. Symbolic AI has been criticized as disembodied, liable to the qualification problem, and poor in handling the perceptual problems where deep learning excels. In turn, connectionist AI has been criticized as poorly suited for deliberative step-by-step problem solving, incorporating knowledge, and handling planning. Finally, Nouvelle AI excels in reactive and real-world robotics domains but has been criticized for difficulties in incorporating learning and knowledge.<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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\" width=\"308px\" alt=\"robot\"\/><\/p>\n<p>The HPGe detector efficiency is measured as a function of source to detector separation using disc sources of 131I with diameter ranging from 10 to 400mm. Detector efficiencies are characterized using single photon point-like standard sources at different distances; the calculated efficiencies for disc sources were analyzed by utilizing the double point detector model and the efficiency transfer method. The axial variation and radial dependence for disc sources efficiency determination in gamma-ray spectrometry were described with both gamma ray standard sources and  measured samples as their extended sources.<\/p>\n<h2>Artificial Intelligence Symbols Vol1 by ianamoor<\/h2>\n<p>Natural language processing focuses on treating language as data to perform tasks such as identifying topics without necessarily understanding the intended meaning. Natural language understanding, in contrast, constructs a meaning representation and uses that for further processing, such as answering questions. Constraint solvers perform a more limited kind of inference than first-order logic.<\/p>\n<div itemScope itemProp=\"mainEntity\" itemType=\"https:\/\/schema.org\/Question\">\n<div itemProp=\"name\">\n<h2>What is a symbol in artificial intelligence?<\/h2>\n<\/div>\n<div itemScope itemProp=\"acceptedAnswer\" itemType=\"https:\/\/schema.org\/Answer\">\n<div itemProp=\"text\">\n<p>What is Symbolic AI? Symbolic AI is an approach that trains Artificial Intelligence (AI) the same way human brain learns. It learns to understand the world by forming internal symbolic representations of its &#x201c;world&#x201d;. Symbols play a vital role in the human thought and reasoning process.<\/p>\n<\/div><\/div>\n<\/div>\n<p>While questions remain on the limits of deep learning and large neural networks, neurons should be retained as an instrumental component in the design of artificial beings because of the utility they\u2019ve proven when it comes to storing and moving data. Agents are autonomous systems embedded in an environment they perceive and act upon in some sense. Russell and Norvig&#8217;s standard textbook on artificial intelligence is organized  to reflect agent architectures of increasing sophistication. Jos\u00e9 Mira is Professor of Computer Science and Artificial Intelligence and Head of the department of Artificial Intelligence at the National University for Distance Education in Madrid .<\/p>\n<h2>Robot-Assisted Surgery: The Application of Robotics in Healthcare<\/h2>\n<p>Sorry, a shareable link is not currently available for this article. On formally undecidable propositions of Principia Mathematica and related systems.Monatschefte f\u00fcr Mathematik und Physik, Vol. 1) Hinton, Yann LeCun and Andrew Ng have all suggested that work on unsupervised learning will lead to our next breakthroughs. Symbolic artificial intelligence, also known as Good, Old-Fashioned AI , was the dominant paradigm in the AI community from the post-War era until the late 1980s. The words sign and symbol derive from Latin and Greek words, respectively, that mean mark or token, as in \u201ctake this rose as a token of my esteem.\u201d Both words mean \u201cto stand for something else\u201d or \u201cto represent something else\u201d.<\/p>\n<div style='border: black solid 1px;padding: 13px;'>\n<h3>Flute from his femur \u2013 researcher&#8217;s personal journey with healthcare &#8230; &#8211; University of Cape Town News<\/h3>\n<p>Flute from his femur \u2013 researcher&#8217;s personal journey with healthcare &#8230;.<\/p>\n<p>Posted: Mon, 27 Feb 2023 12:21:35 GMT [<a href='https:\/\/news.google.com\/rss\/articles\/CBMieGh0dHBzOi8vd3d3Lm5ld3MudWN0LmFjLnphL2FydGljbGUvLTIwMjMtMDItMjctZmx1dGUtZnJvbS1oaXMtZmVtdXItcmVzZWFyY2hlcnMtcGVyc29uYWwtam91cm5leS13aXRoLWhlYWx0aGNhcmUtdGVjaC1hadIBAA?oc=5' rel=\"nofollow\">source<\/a>]<\/p>\n<\/div>\n<p>The CBR approach outlined in his book, Dynamic Memory, focuses first on remembering key problem-solving cases for future use and generalizing them where appropriate. When faced with a new problem, CBR retrieves the most similar previous case and adapts it to the specifics of the current problem. A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules for problem-solving.The simplest approach for an expert system knowledge base is simply a collection or network of production rules.<\/p>\n<h2>Ai Symbol royalty-free images<\/h2>\n<p>Currently, Python, a multi-paradigm programming language, is the most popular programming language, partly due to its extensive package library that supports data science, natural language processing, and deep learning. Python includes a read-eval-print loop, functional elements such as higher-order functions, and object-oriented programming that includes metaclasses. Moreover, the rise of symbolic and deep learning models has sparked an interesting debate in the AI community over which method is the better way forward.<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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\" width=\"304px\" alt=\"natural language\"\/><\/p>\n<p>A truth maintenance system tracked assumptions and justifications for all inferences. It allowed inferences to be withdrawn when assumptions were found out to be incorrect or a contradiction was derived. Explanations could be provided for an inference by explaining which rules were applied to create it and then continuing through underlying inferences and rules all the way back to root assumptions. Lofti Zadeh had introduced a different kind of extension to handle the representation of vagueness. For example, in deciding how &#8220;heavy&#8221; or &#8220;tall&#8221; a man is, there is frequently no clear &#8220;yes&#8221; or &#8220;no&#8221; answer, and a predicate for heavy or tall would instead return values between 0 and 1.<\/p>\n<h2>Sensory representation spaces in neuroscience and computation<\/h2>\n<p>The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care. Consequently, AI progress is limited by progress in modeling, formalization and programming techniques and by development in computer materials and architectures and electro-mechanic devices (\u201crobots\u201d) where the calculus is installed. Curiously, this attempt to add a spectacular nature and excessive cognitive nomenclature to our programs and robots has helped overshadow the sound results achieved by computation, robotics, artificial vision and knowledge-based systems , . Progress has also been made in formal representation techniques (logic, rules, frames, objects, agents, causal networks, etc.) and in the treatment of uncertainty and in the solution of problems for which we have more data than knowledge .<\/p>\n<ul>\n<li>Description logic ontologies enable semantic interoperability of different types and formats of information from different sources for integrated knowledge.<\/li>\n<li>These rules can be formalized in a way that captures everyday knowledge.<\/li>\n<li>The grandfather of AI, Thomas Hobbes said \u2014 Thinking is manipulation of symbols and Reasoning is computation.<\/li>\n<li>More advanced knowledge-based systems, such as Soar can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve problems and monitoring the success of problem-solving strategies.<\/li>\n<li>Forward chaining inference engines are the most common, and are seen in CLIPS and OPS5.<\/li>\n<li>Because symbolic reasoning encodes knowledge in symbols and strings of characters.<\/li>\n<\/ul>\n<p>Finally, we suggest that AI research explore social and cultural engagement as a tool to develop the cognitive machinery necessary for symbolic behaviour to emerge. This approach will allow for AI to interpret something as symbolic on its own rather than simply manipulate things that are only symbols to human onlookers, and thus will ultimately lead to AI with more human-like symbolic fluency. So much effort and investment has been put into both academia and industry, combining theoretical research and empirical data to both understand and build AI models that bear semblance to \u201cintelligent\u201d beings.<\/p>\n<!--themify_builder_content-->\n<div id=\"themify_builder_content-2495\" data-postid=\"2495\" class=\"themify_builder_content themify_builder_content-2495 themify_builder tf_clear\">\n    <\/div>\n<!--\/themify_builder_content-->\n","protected":false},"excerpt":{"rendered":"<p>Each artificial intelligence symbol\u2014symbolic, connectionist, and behavior-based\u2014has advantages, but has been criticized by the other approaches. Symbolic AI has been criticized as [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[29],"tags":[],"builder_content":"","_links":{"self":[{"href":"https:\/\/boliviatypeapproval.com\/index.php\/wp-json\/wp\/v2\/posts\/2495"}],"collection":[{"href":"https:\/\/boliviatypeapproval.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/boliviatypeapproval.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/boliviatypeapproval.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/boliviatypeapproval.com\/index.php\/wp-json\/wp\/v2\/comments?post=2495"}],"version-history":[{"count":0,"href":"https:\/\/boliviatypeapproval.com\/index.php\/wp-json\/wp\/v2\/posts\/2495\/revisions"}],"wp:attachment":[{"href":"https:\/\/boliviatypeapproval.com\/index.php\/wp-json\/wp\/v2\/media?parent=2495"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/boliviatypeapproval.com\/index.php\/wp-json\/wp\/v2\/categories?post=2495"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/boliviatypeapproval.com\/index.php\/wp-json\/wp\/v2\/tags?post=2495"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}