{"id":16196,"date":"2021-12-13T13:02:27","date_gmt":"2021-12-13T13:02:27","guid":{"rendered":"https:\/\/clinlabint.com\/?p=16196"},"modified":"2021-12-15T09:42:10","modified_gmt":"2021-12-15T09:42:10","slug":"what-is-ai-and-how-can-be-applied-to-scientific-instruments","status":"publish","type":"post","link":"https:\/\/clinlabint.com\/what-is-ai-and-how-can-be-applied-to-scientific-instruments\/","title":{"rendered":"What is AI and how can it be applied to scientific instruments?"},"content":{"rendered":"<h3><img decoding=\"async\" class=\"alignnone size-medium wp-image-16198\" src=\"https:\/\/clinlabint.com\/wp-content\/uploads\/sites\/2\/2021\/12\/ziath-267x300.png\" alt=\"Ziath\" width=\"267\" height=\"300\" title=\"\" srcset=\"https:\/\/clinlabint.com\/wp-content\/uploads\/sites\/2\/2021\/12\/ziath-267x300.png 267w, https:\/\/clinlabint.com\/wp-content\/uploads\/sites\/2\/2021\/12\/ziath.png 542w\" sizes=\"(max-width: 267px) 100vw, 267px\" \/><\/h3>\n<h3><strong>Abstract: <\/strong><\/h3>\n<p>Senior Electrical Design Engineer Dr Alex Beaseley looks at current applications of Artificial Intelligence as applied to real problems in scientific instruments. He demonstrates how a Neural Network approach can be deployed to analyse real-world images and determine key properties from the data with far more accuracy and far faster than traditional detection techniques.<\/p>\n<p>&nbsp;<\/p>\n<h3><strong>Introduction: <\/strong><\/h3>\n<p>In recent years \u2013 the scientific community has increasingly looked at Artificial Intelligence (AI) as a future tool that can deliver great benefits when applied to operations and measurements that instruments undertake. But what exactly is AI?<\/p>\n<p>Firstly, let\u2019s define a few terms you may have heard of: <strong>Machine Learning <\/strong>is the process to create <strong>Artificial Intelligence (AI)<\/strong>. Machine learning can be applied through several different mechanisms which include \u201cfuzzy logic\u201d, \u201cdiscriminant analysis\u201d and \u201c<strong>neural networks<\/strong>\u201d. Due to their ability to process computationally intensive problems, neural networks are the basis of most commercially-viable AI that could be applied to the type of problems that scientific instruments try to solve.<\/p>\n<p>The limit to implementing a neural network is simply the number of \u201cnodes\u201d, or possible connections, in the processor being used. The number of nodes in the human brain is orders of magnitudes greater than even the most powerful processors can muster. The much-vaunted \u201csingularity\u201d where a human consciousness could be uploaded <em>in silico <\/em>is still only the stuff of science fiction. However, in the real world, neural networks have an important role to play.<strong>\u00a0<\/strong><\/p>\n<p><a href=\"https:\/\/clinlabint.com\/wp-content\/uploads\/sites\/2\/2021\/12\/Ziath-AI-applied-to-scientific-instruments.pdf\" target=\"_blank\" rel=\"noopener\"><strong>Download the full article as a PDF.<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: Senior Electrical Design Engineer Dr Alex Beaseley looks at current applications of Artificial Intelligence as applied to real problems in scientific instruments. He demonstrates how a Neural Network approach can be deployed to analyse real-world images and determine key properties from the data with far more accuracy and far faster than traditional detection techniques. [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":16198,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[37],"tags":[],"_links":{"self":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/posts\/16196"}],"collection":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/comments?post=16196"}],"version-history":[{"count":3,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/posts\/16196\/revisions"}],"predecessor-version":[{"id":16201,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/posts\/16196\/revisions\/16201"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/media\/16198"}],"wp:attachment":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/media?parent=16196"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/categories?post=16196"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/tags?post=16196"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}