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<p>Dear colleagues,<br>
<br>
you are cordially invited to attend the 3rd lecture in the seminar
series called Artificial Intelligence at the Jozef Stefan
Institute (AI@JSI). This lecture is simultaneously part of the JSI
Colloquium series
(<a class="moz-txt-link-freetext"
href="https://kolokviji.ijs.si/umetna-inteligenca-in-znanstvena-odkritja-paradigme-napredek-in-potencial/">https://kolokviji.ijs.si/umetna-inteligenca-in-znanstvena-odkritja-paradigme-napredek-in-potencial/</a>),
where an announcement in Slovenian can be found. The lecture will
be in English and will be held on Wednesday, June 26th, at 13:00
CEST<b> </b>in the main lecture hall of the Jozef Stefan
Institute (velika predavalnica IJS), but remote attendance is also
possible via JSI TV: <a class="moz-txt-link-freetext"
href="http://tv.ijs.si" style="white-space: pre-wrap;">http://tv.ijs.si</a>.</p>
<p> </p>
<p>The details on the lecture and lecturer are given below. We are
very much looking forward to meeting you at the seminar.</p>
<p>---<br>
</p>
<pre class="moz-quote-pre" wrap=""><b>Title: </b>Artificial Intelligence and Scientific Discovery:
Paradigms, Progress, and Potential
<b>Lecturer</b>: Pat Langley, Institute for the Study of Learning and Expertise, Palo Alto, California
<a class="moz-txt-link-freetext" href="http://www.isle.org/~langley/">http://www.isle.org/~langley/</a>
<b>
Abstract: </b>Within the general excitement about artificial intelligence, there has been special interest in the technology's application to discovery of scientific knowledge. Like AI itself, this subfield has a long history and many successes, but also outstanding challenges. In this talk, I focus on two problems that have received considerable attention: discovery of numeric equations and construction of qualitative process models. In each case, I define the computational task, review basic approaches, and report successes that led to scientific insights. After this, I turn to a third paradigm - inductive process modeling - that moves beyond earlier efforts to generate quantitative explanations of scientific data in terms of domain knowledge. I present multiple approaches to this problem, present encouraging results, and discuss open issues that merit further attention from the AI community.
<b>Short info on the lecturer: </b>Dr. Pat Langley serves as Director of the Institute for the Study of Learning and Expertise. He has contributed to AI and cognitive science for more than 40 years, publishing over 300 papers and five books on these topics. Dr. Langley developed some of the first computational systems for scientific knowledge discovery, and he was an early champion of experimental studies of machine learning and its application to real-world problems. He is the founding editor of two journals, Machine Learning in 1986 and Advances in Cognitive Systems in 2012, and he is a Fellow of both AAAI and the Cognitive Science Society. Dr. Langley's current research focuses on architectures for embodied agents and the discovery of explanatory process models in science.</pre>
<p> ---</p>
<p>Recordings of previous AI@JSI seminars can be found here: <a
href="https://video.arnes.si/?hashtag=AI_at_JSI">Arnes Video</a></p>
<p><br>
</p>
<p> With best wishes,</p>
Nina Omejc and Saso Dzeroski
<p></p>
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