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seminars.html
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<li><a href="projects.html#4">Identifying Active Extravasation on Arteriograms using Machine
Learning</a></li>
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Carcinoma</a></li>
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<div class="head"><h1>Seminars</h1>
<h2>AI@UW hosts various seminars througout the semester. Learn about the speakers and abstracts of their respective talks!</h2>
</div>
<!-- main content -->
<div class="seminarMain">
<h3>Seminar Category Color Key:</h3>
<div class="key">
<div class="keyBox" id="research"><h4>AI Research</h4></div>
<div class="keyBox" id="society"><h4>AI & Society</h4></div>
<div class="keyBox" id="applied"><h4>Applied AI</h4></div>
</div>
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<header class="seminarDate">
<h2>October 23rd</h2>
<h4>6:30-7:30pm <i>in</i> CS 1240</h4>
</header>
<article class="seminarDetails">
<h3>LARISSA ALBANTAKIS- Agency and Autonomy within the causal framework of integrated information theory</h3>
<p>By definition, an agent must be an open system that dynamically and informationally interacts with an
environment. This
simple requirement, however, immediately poses a methodological problem: When subsystems within a larger
system are
characterized by physical, but also biological, or informational properties, their boundaries are typically
taken for
granted and assumed as given. How can we distinguish the agent from its environment in objective terms? In
addition,
agents are supposed to act upon their environment in an autonomous manner. But can we formally distinguish
autonomous
actions from mere reflexes? Here, I will address these two issues in light of the causal framework of
integrated
information theory, which provides the tools to reveal causal borders within larger systems and to identify
the causes
of an agent's actions (or, e.g., the output of an image classifier). Finally, we will discuss the notion of
functional
equivalence (e.g., between a human and a computer simulating the human's behavior) and argue that
implementation matters
for determining agency and autonomy.</p>
</article>
<aside class="seminarCategory" id="research"></aside>
</div>
<br><br>
<button class="collapsible">Past Seminars:</button>
<div class="seminarContent">
<div class="seminarContainer">
<header class="seminarDate">
<h2>February 20th</h2>
<h4>6:30-7:30pm <i>in</i> CS 1240</h4>
</header>
<article class="seminarDetails">
<h3>Matthew Banks- Great Expectations: The Neurobiology of Predictive Processing</h3>
<p>We bring expectations to every moment of experience, and those expectations color how that experience
unfolds. How does that happen in the brain? Evidence suggests that brains implement predictive processing
schemes, making use of neural circuitry to implement predictive coding in the service of efficiency. Yet the
evidence is largely circumstantial, and fundamental questions remain open. I will present some of the
evidence from my lab and others' for predictive processing in the brain, and show how we are exploring the
relationship between predictive processing and consciousness.</p>
</article>
<aside class="seminarCategory" id="research"></aside>
<aside class="seminarSpeakerImage">
<img src="images/seminarSpeakers/matthewBanks.jpg"></img>
</aside>
</div>
<div class="seminarContainer">
<header class="seminarDate">
<h2>March 13th</h2>
<h4>6:30-7:30pm <i>in</i> CS 1240</h4>
</header>
<article class="seminarDetails">
<h3>Bill Hibbard- Coming of Age with AI</h3>
<p>Technology changes constantly and rapidly. A technology career requires lifelong learning. Constant change
enables new uses of technology and career opportunities for those who develop those uses. Recently computers
have become powerful enough to make artificial neural networks useful, enabling many new applications. AI
and automation are transforming labor markets, AI is transforming medicine, warfare and other fields, and AI
connected via the Internet to billions of people is transforming society. There is a good chance of reaching
human-level AI, and beyond, within the next few decades, which will greatly accelerate these
transformations. AI students in 2019 will be part of these transformations and will be at the center of the
political storm they create. AI developers will need a clear understanding of what they value to weather the
storm. This will be a short talk followed by a longer open discussion.</p>
</article>
<aside class="seminarCategory" id="society"></aside>
<aside class="seminarSpeakerImage">
<img src="images/seminarSpeakers/billHibbard.jpg"></img>
</aside>
</div>
<div class="seminarContainer">
<header class="seminarDate">
<h2>April 10th</h2>
<h4>6:30-7:30pm <i>in</i> CS 1240</h4>
</header>
<article class="seminarDetails">
<h3>Joe Austerweil- Do people teach others as if they are reinforcement learners?</h3>
<p>Humans teach others in a variety of ways. Although people often teach others through verbal description,
people also
teach using nonverbal means. For example, a parent might punish their child for not taking off her muddy
shoes before
walking on the carpet. Or, a baseball pitching coach might teach a new type of pitch by demonstrating the
sequence of
arm and leg movements to produce it. In this talk, I will discuss three different forms of nonverbal
teaching: teaching
by evaluative feedback (or reinforcement), teaching by demonstration, and teaching by intervention. In all
three cases,
I will present evidence that teachers expect learners to interpret nonverbal teaching as communication. I
will discuss
implications for artificial intelligence and machine learning research, where reward-maximizing is often an
assumption
of the model.</p>
</article>
<aside class="seminarCategory" id="research"></aside>
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