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‎series2.md‎

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<tr>
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<td>August 29th</td>
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<td>
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<strong>Governance: LLM + AI Agents</strong><br>
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Sabina (Yudi) Nong, Stanford<br><br>
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<strong>Key Points:</strong><br>
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<ul>
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<li>AI Governance & its Stakes</li>
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<li>Why AI Agents Require a Distinct Governance Lens (Procedural vs. Predictive)</li>
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<li>Developer Governance</li>
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<li>Regulatory Governance</li>
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<li>What does a good governance structure look like?</li>
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</ul>
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<a href="https://docs.google.com/presentation/d/145F-UnXUBswnz03WvZkliK7eLiLyhGY0bTrDJo8VUAk/mobilepresent?slide=id.g376427fc988_0_361" target="_blank">Slides (Website)</a> ·
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<a href="https://www.youtube.com/watch?v=MpbzMipStXk&pp=0gcJCU0KAYcqIYzv" target="_blank">Recording</a>
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</td>
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<td>
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<em>To be updated</em>
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</td>
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</tr>
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<tr>
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<td>September 3rd</td>
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<td>
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<strong>Interactive symbolic regression with co-design mechanism</strong><br>
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Yuan Tian, ETH Zurich<br><br>
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<strong>Key Topics:</strong><br>
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<ul>
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<li>Introduction to symbolic regression and its applications in scientific discovery</li>
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<li>Challenges in traditional symbolic regression approaches</li>
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<li>Co-design mechanism: integrating human expertise with machine learning</li>
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<li>Interactive frameworks for collaborative model discovery</li>
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<li>Case studies and practical applications in scientific computing</li>
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</ul>
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<a href="https://www.youtube.com/watch?v=Q-l6pVR3z-Y" target="_blank">Recording</a>
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</td>
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<td>
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<em>To be updated</em>
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</td>
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</tr>
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<tr>
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<td>October 3rd</td>
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<td>
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<strong>Rewiring the reward pathways</strong><br>
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Kumar Shridhar, ETH Zürich<br><br>
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<strong>Key Topics:</strong><br>
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<ul>
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<li>Reward models in ML pipeline</li>
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<li>Brittleness of current reward models</li>
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<li>What to do when there is no clear reward (non verifiable rewards)</li>
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<li>How to rewire reward pathways</li>
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<li>A common reward model for both verifiable and non verifiable rewards</li>
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</ul>
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<a href="https://www.youtube.com/watch?v=x7wvhD28EMU" target="_blank">Recording</a>
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</td>
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<td>
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<em>To be updated</em>
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</td>
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</tr>
103+
<tr>
104+
<td>August 29th</td>
105+
<td>
106+
<strong>Governance: LLM + AI Agents</strong><br>
107+
Sabina (Yudi) Nong, Stanford<br><br>
108+
109+
<strong>Key Points:</strong><br>
110+
<ul>
111+
<li>AI Governance & its Stakes</li>
112+
<li>Why AI Agents Require a Distinct Governance Lens (Procedural vs. Predictive)</li>
113+
<li>Developer Governance</li>
114+
<li>Regulatory Governance</li>
115+
<li>What does a good governance structure look like?</li>
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</ul>
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<a href="https://docs.google.com/presentation/d/145F-UnXUBswnz03WvZkliK7eLiLyhGY0bTrDJo8VUAk/mobilepresent?slide=id.g376427fc988_0_361" target="_blank">Slides (Website)</a> ·
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<a href="https://www.youtube.com/watch?v=MpbzMipStXk&pp=0gcJCU0KAYcqIYzv" target="_blank">Recording</a>
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</td>
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<td>
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<em>To be updated</em>
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</td>
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</tr>
125+
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<tr>
127+
<td>September 3rd</td>
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<td>
129+
<strong>Interactive symbolic regression with co-design mechanism</strong><br>
130+
Yuan Tian, ETH Zurich<br><br>
131+
132+
<strong>Key Topics:</strong><br>
133+
<ul>
134+
<li>Introduction to symbolic regression and its applications in scientific discovery</li>
135+
<li>Challenges in traditional symbolic regression approaches</li>
136+
<li>Co-design mechanism: integrating human expertise with machine learning</li>
137+
<li>Interactive frameworks for collaborative model discovery</li>
138+
<li>Case studies and practical applications in scientific computing</li>
139+
</ul>
140+
141+
<a href="https://www.youtube.com/watch?v=Q-l6pVR3z-Y" target="_blank">Recording</a>
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</td>
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<td>
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<em>To be updated</em>
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</td>
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</tr>
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<tr>
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<td>October 3rd</td>
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<td>
151+
<strong>Rewiring the reward pathways</strong><br>
152+
Kumar Shridhar, ETH Zürich<br><br>
153+
154+
<strong>Key Topics:</strong><br>
155+
<ul>
156+
<li>Reward models in ML pipeline</li>
157+
<li>Brittleness of current reward models</li>
158+
<li>What to do when there is no clear reward (non verifiable rewards)</li>
159+
<li>How to rewire reward pathways</li>
160+
<li>A common reward model for both verifiable and non verifiable rewards</li>
161+
</ul>
162+
163+
<a href="https://www.youtube.com/watch?v=x7wvhD28EMU" target="_blank">Recording</a>
164+
</td>
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<td>
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<em>To be updated</em>
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</td>
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</tr>
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</tbody>
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</table>

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