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DTSTAMP:20260924T110423Z
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DTSTART;TZID=America/New_York:20260921T100000
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DESCRIPTION:Speaker: Dr. Ram D. Sriram\, Senior Science Advisor\, Informati
 on Technology Laboratory (ITL)\, National Institute of Standards and Techn
 ology\n\nGenerative AI is transforming the landscape of scientific discove
 ry across three distinct operational pathways: combinatorial space search\
 , data-driven hypothesis generation\, and paradigmatic reframing. However\
 , each pathway introduces unique evaluation challenges that require a form
 al discipline of AI metrology—a systematic framework for quantifying sys
 tem reliability\, operational scale\, and human oversight requirements.\n\
 nThis talk presents a structured metrological roadmap mapping established 
 cognitive theories (such as the Cattell-Horn-Carroll hierarchy) and stages
  of AI capability—Artificial Limited Intelligence (ALI)\, Artificial Gen
 eral Intelligence (AGI)\, and Artificial Superintelligence (ASI)—to thes
 e three modes of discovery:\n\n- ALI &amp; Combinatorial Search: Focuses on op
 timizing search bounds\, parameter scaling\, and uncertainty quantificatio
 n\, with humans acting as ground-truth oracles to measure correction rates
  and time-to-override.\n- AGI &amp; Hypothesis Generation: Addresses explore-e
 xploit cycles via dual-layer neuro-symbolic governance\, formal ontology m
 apping\, and the Garland Test (evaluating perceived problem awareness)\, w
 hile humans serve as symbolic arbiters to calibrate appropriate reliance.\
 n- ASI &amp; Paradigmatic Reframing: Reconceptualizes fundamental laws through
  asynchronous multi-agent scaffolding (&quot;A-Teams&quot;)\, placing humans in an o
 n-the-loop auditing role measured by spot-audit coverage and escalation la
 tency.\n\nFinally\, the talk outlines actionable tracks for institutions t
 o standardize evaluation protocols across this spectrum\, emphasizing that
  AI metrology must evolve qualitatively—transitioning from output verifi
 cation to trust calibration and system-level auditing—to ensure scientif
 ic claims remain trustworthy and reproducible.\n\nREGISTER: https://fau-ed
 u.zoom.us/meeting/register/kj39AvFAQViGa39JrgefUQ\n\nCo-sponsored by: Dept
  of Electrical Engineering and Computer Science\, Florida Atlantic Univers
 ity\n\nVirtual: https://events.vtools.ieee.org/m/577624
LOCATION:Virtual: https://events.vtools.ieee.org/m/577624
ORGANIZER:hkalva@fau.edu
SEQUENCE:17
SUMMARY:AI Metrology for Scientific Discovery: Measuring Systems from Bound
 ed Search to Paradigmatic Reframing 
URL;VALUE=URI:https://events.vtools.ieee.org/m/577624
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-bottom: 0i
 n\; line-height: normal\;&quot;&gt;&lt;span style=&quot;font-size: 14pt\;&quot;&gt;&lt;strong&gt;&lt;span s
 tyle=&quot;font-family: &#39;Calibri&#39;\,sans-serif\;&quot;&gt;Speaker:&lt;/span&gt;&lt;/strong&gt;&lt;/span
 &gt;&lt;span style=&quot;font-family: &#39;Calibri&#39;\,sans-serif\;&quot;&gt;&lt;span style=&quot;font-size
 : 14pt\;&quot;&gt; Dr. Ram D. Sriram\, Senior Science Advisor\, Information Techno
 logy Laboratory (ITL)\,&amp;nbsp\;&lt;span style=&quot;line-height: 115%\; font-family
 : Calibri\, sans-serif\;&quot;&gt;National Institute of Standards and Technology&lt;/
 span&gt;&lt;/span&gt;&lt;br&gt;&lt;br&gt;&lt;/span&gt;&lt;span style=&quot;font-family: Calibri\, sans-serif\
 ; font-size: 14pt\;&quot;&gt;Generative AI is transforming the landscape of scient
 ific discovery across three distinct operational pathways: combinatorial s
 pace search\, data-driven hypothesis generation\, and paradigmatic reframi
 ng. However\, each pathway introduces unique evaluation challenges that re
 quire a formal discipline of AI metrology&amp;mdash\;a systematic framework fo
 r quantifying system reliability\, operational scale\, and human oversight
  requirements.&amp;nbsp\;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-botto
 m: 0in\; line-height: normal\;&quot;&gt;&lt;span style=&quot;font-family: Calibri\, sans-s
 erif\; font-size: 14pt\;&quot;&gt;This talk presents a structured metrological roa
 dmap mapping established cognitive theories (such as the Cattell-Horn-Carr
 oll hierarchy) and stages of AI capability&amp;mdash\;Artificial Limited Intel
 ligence (ALI)\, Artificial General Intelligence (AGI)\, and Artificial Sup
 erintelligence (ASI)&amp;mdash\;to these three modes of discovery: &lt;/span&gt;&lt;/p&gt;
 \n&lt;ul style=&quot;margin-top: 0in\;&quot; type=&quot;disc&quot;&gt;\n&lt;li class=&quot;MsoNormal&quot; style=
 &quot;margin-bottom: 0in\; line-height: normal\; font-size: 14pt\;&quot;&gt;&lt;span style
 =&quot;font-size: 14pt\;&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-family: &#39;Calibri&#39;\,sans-ser
 if\;&quot;&gt;ALI &amp;amp\; Combinatorial Search:&lt;/span&gt;&lt;/strong&gt;&lt;span style=&quot;font-fa
 mily: &#39;Calibri&#39;\,sans-serif\;&quot;&gt; Focuses on optimizing search bounds\, para
 meter scaling\, and uncertainty quantification\, with humans acting as gro
 und-truth oracles to measure correction rates and time-to-override. &lt;/span
 &gt;&lt;/span&gt;&lt;/li&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;margin-bottom: 0in\; line-heig
 ht: normal\; font-size: 14pt\;&quot;&gt;&lt;span style=&quot;font-size: 14pt\;&quot;&gt;&lt;strong&gt;&lt;s
 pan style=&quot;font-family: &#39;Calibri&#39;\,sans-serif\;&quot;&gt;AGI &amp;amp\; Hypothesis Gen
 eration:&lt;/span&gt;&lt;/strong&gt;&lt;span style=&quot;font-family: &#39;Calibri&#39;\,sans-serif\;&quot;
 &gt; Addresses explore-exploit cycles via dual-layer neuro-symbolic governanc
 e\, formal ontology mapping\, and the Garland Test (evaluating perceived p
 roblem awareness)\, while humans serve as symbolic arbiters to calibrate a
 ppropriate reliance. &lt;/span&gt;&lt;/span&gt;&lt;/li&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;mar
 gin-bottom: 0in\; line-height: normal\; font-size: 14pt\;&quot;&gt;&lt;span style=&quot;fo
 nt-size: 14pt\;&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-family: &#39;Calibri&#39;\,sans-serif\;
 &quot;&gt;ASI &amp;amp\; Paradigmatic Reframing:&lt;/span&gt;&lt;/strong&gt;&lt;span style=&quot;font-fami
 ly: &#39;Calibri&#39;\,sans-serif\;&quot;&gt; Reconceptualizes fundamental laws through as
 ynchronous multi-agent scaffolding (&quot;A-Teams&quot;)\, placing humans in an on-t
 he-loop auditing role measured by spot-audit coverage and escalation laten
 cy. &lt;/span&gt;&lt;/span&gt;&lt;/li&gt;\n&lt;/ul&gt;\n&lt;p class=&quot;FirstParagraph&quot; style=&quot;text-alig
 n: justify\; text-justify: inter-ideograph\;&quot;&gt;&lt;span style=&quot;font-size: 14pt
 \; line-height: 115%\; font-family: Calibri\, sans-serif\;&quot;&gt;Finally\, the 
 talk outlines actionable tracks for institutions to standardize evaluation
  protocols across this spectrum\, emphasizing that AI metrology must evolv
 e qualitatively&amp;mdash\;transitioning from output verification to trust cal
 ibration and system-level auditing&amp;mdash\;to ensure scientific claims rema
 in trustworthy and reproducible. &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;
 background: white\; vertical-align: baseline\; text-align: center\;&quot;&gt;&lt;span
  style=&quot;font-size: 11.0pt\; line-height: 115%\; font-family: &#39;Times New Ro
 man&#39;\,serif\; mso-fareast-font-family: Calibri\; mso-fareast-theme-font: m
 inor-latin\; mso-ansi-language: EN-US\; mso-fareast-language: EN-US\; mso-
 bidi-language: AR-SA\;&quot;&gt;&lt;span style=&quot;background-color: rgb(45\, 194\, 107)
 \; font-size: 14pt\;&quot;&gt;&lt;strong&gt;REGISTER&lt;/strong&gt;&lt;/span&gt;&lt;span style=&quot;font-si
 ze: 12pt\;&quot;&gt;: &lt;a href=&quot;https://fau-edu.zoom.us/meeting/register/kj39AvFAQV
 iGa39JrgefUQ&quot;&gt;https://fau-edu.zoom.us/meeting/register/kj39AvFAQViGa39Jrge
 fUQ&lt;/a&gt;&amp;nbsp\;&lt;/span&gt;&lt;br&gt;&lt;br&gt;&lt;/span&gt;&lt;/p&gt;
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