BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250228T221132Z UID:48DC3F67-7A61-4D8A-8E25-743118818692 DTSTART;TZID=America/Los_Angeles:20250228T120000 DTEND;TZID=America/Los_Angeles:20250228T140000 DESCRIPTION:We are in the midst of an artificial intelligence (AI) revoluti on. It may seem that AI development is recent\, but the term AI was coined (1955) not too long after the invention of computing devices. However\, t he idea that a machine can behave like a human being is even older. The te rm “automaton” was used for it. Please see:\n\n- [https://www.tableau. com/data-insights/ai/historyLinks to an external site.](https://www.tablea u.com/data-insights/ai/history)\n- [https://en.wikipedia.org/wiki/Logic_Th eoristLinks to an external site.](https://en.wikipedia.org/wiki/Logic_Theo rist)\n\nWhat has changed\, though\, in recent times\, is the amount of co mputation that can be performed. GPUs have made tremendous progress\, and it is now possible to do heavy computation with very large amount of memor y in real time.\n\nThe terms AI and machine learning (ML) are normally use d interchangeably though there are differences. We will discuss what is th e difference between them. While AI tries to mimic the human behavior\, ML is a set of statistical tools to look at the data to find patterns withou t direct instructions. In some ways\, it is a subset of AI. We will dig a little deeper into how AI works. This will give us a better understanding of what kind of problems it can solve effectively and where it should be a voided\, or one needs enhanced or use better tools.\n\nNeural networks are normally used to perform AI calculations: Deep Neural Networks (DNN)\, Co nvolutional Neural Networks (CNN)\, Recurrent Neural Networks (RNN) are so me examples. They have their origin in the way the human brain works.\n\nW e will discuss the biological basis of neural networks and discuss how the se networks are implemented. We will focus on concepts and will develop in tuition about how they are trained. We will also see how and when a certai n type of network can be used.\n\nWe will go over some caution around data analysis and what to watch out for when using AI.\n\nSpeaker(s): Dr Md Us man\n\nAgenda: \n- 12:10 to 12:20 PM : Introduction by Gora Datta\, FHL7\n - 12:20 to 1::55 PM: Expert Lecture by Dr Md Usman\n- The difference betwe en AI and ML\,\n- Biological basis of some AI networks\,\n- Basics of how an AI model is trained and what does it mean\,\n- Advantages and disadvant ages of using AI – why should we use it\, and what does it measure anywa ys?\n- What are large language models (LLM)\, and their usefulness in medi cine\,\n- Will understand some examples of using AI\, and\n- Know when to use AI for health measurements.\n\n- 1:55 to 2 PM : Wrap Up\n\nRoom: Shark Cove\, Bldg: Bealle Applied Innovation\, 5270 California Ave\, Irvine\, C alifornia\, United States\, 92617\, Virtual: https://events.vtools.ieee.or g/m/471821 LOCATION:Room: Shark Cove\, Bldg: Bealle Applied Innovation\, 5270 Californ ia Ave\, Irvine\, California\, United States\, 92617\, Virtual: https://ev ents.vtools.ieee.org/m/471821 ORGANIZER:goradatta@ieee.org SEQUENCE:25 SUMMARY:MEDICAL DEVICES - AI/ML/LLM\, its application in Public Health URL;VALUE=URI:https://events.vtools.ieee.org/m/471821 X-ALT-DESC:Description: <br /><p>We are in the midst of an artificial intel ligence (AI) revolution. It may seem that AI development is recent\, but t he term AI was coined (1955) not too long after the invention of computing devices. However\, the idea that a machine can behave like a human being is even older. The term &ldquo\;automaton&rdquo\; was used for it. Please see:</p>\n<ul>\n<li><a class="external" href="https://www.tableau.com/data -insights/ai/history" target="_blank" rel="noreferrer noopener">https://ww w.tableau.com/data-insights/ai/history<span class="external_link_icon" rol e="presentation"><span class="screenreader-only">Links to an external site .</span></span></a></li>\n<li><a class="external" href="https://en.wikiped ia.org/wiki/Logic_Theorist" target="_blank" rel="noreferrer noopener">http s://en.wikipedia.org/wiki/Logic_Theorist<span class="external_link_icon" r ole="presentation"><span class="screenreader-only">Links to an external si te.</span></span></a></li>\n</ul>\n<p>What has changed\, though\, in recen t times\, is the amount of computation that can be performed. GPUs have ma de tremendous progress\, and it is now possible to do heavy computation wi th very large amount of memory in real time.</p>\n<p>The terms AI and mach ine learning (ML) are normally used interchangeably though there are diffe rences. We will discuss what is the difference between them.&nbsp\; While AI tries to mimic the human behavior\, ML is a set of statistical tools to look at the data to find patterns without direct instructions. In some wa ys\, it is a subset of AI. We will dig a little deeper into how AI works. This will give us a better understanding of what kind of problems it can s olve effectively and where it should be avoided\, or one needs enhanced or use better tools.</p>\n<p>Neural networks are normally used to perform AI calculations: Deep Neural Networks (DNN)\, Convolutional Neural Networks (CNN)\, Recurrent Neural Networks (RNN) are some examples. They have their origin in the way the human brain works.</p>\n<p>We will discuss the biol ogical basis of neural networks and discuss how these networks are impleme nted. We will focus on concepts and will develop intuition about how they are trained. We will also see how and when a certain type of network can b e used.</p>\n<p>We will go over some caution around data analysis and what to watch out for when using AI.</p>\n<h1>&nbsp\;</h1>\n<p class="MsoNorma l" style="margin: 6.0pt 0in 0in 0in\;">&nbsp\;</p><br /><br />Agenda: <br /><ul>\n<li>12:10 to 12:20 PM : Introduction by Gora Datta\, FHL7</li>\n<l i aria-level="1">12:20 to 1::55 PM: Expert Lecture by Dr Md Usman\n<ul>\n< li>The difference between AI and ML\,</li>\n<li>Biological basis of some A I networks\,</li>\n<li>Basics of how an AI model is trained and what does it mean\,</li>\n<li>Advantages and disadvantages of using AI &ndash\; why should we use it\, and what does it measure anyways?</li>\n<li>What are la rge language models (LLM)\, and their usefulness in medicine\,</li>\n<li>W ill understand some examples of using AI\, and</li>\n<li>Know when to use AI for health measurements.</li>\n</ul>\n</li>\n<li aria-level="1">1:55 to 2 PM : Wrap Up</li>\n</ul> END:VEVENT END:VCALENDAR