guest lectures and l3 l4 slides

Author: avaamini

index.html

@@ -1482,7 +1482,7 @@ <h4>Social Media</h4>
               </div>
               <div class="col-md-8 v-center">
                 <div class="card card-lecture card-modal">
-                  <h4 align="left">Large Language Models</h4>
+                  <h4 align="left">Introduction to Language Modeling</h4>
 									<h5 align="left">Peter Grabowski, Lead of Gemini Applied Research, Google</h5>
                 </div>
               </div>
@@ -1492,13 +1492,13 @@ <h5 align="left">Peter Grabowski, Lead of Gemini Applied Research, Google</h5>
               <div class="col-md-12 v-center">
                 <h6>Talk Abstract</h6>
                 <p>
-									Coming soon!
+									Want to get started with LLMs? This lecture will cover an introduction to language modeling and prompt engineering, example use cases and applications, and a discussion of common considerations for LLM usage (cost, efficiency, accuracy, bias).
                 </p>
 
-                <!-- <h6>Speaker Bio</h6>
+                <h6>Speaker Bio</h6>
                 <p>
-									Doug is leading Google DeepMind’s research efforts in Generative Media, including image, video, 3D, music and audio generation. He also leads a broader group active in areas including including Fundamental Learning Algorithms, Natural Language Processing, Multimodal Learning, Reinforcement Learning, Computer Vision and Generative Models. His own research lies at the intersection of machine learning and human-computer interaction (HCI). Doug created <a href="https://magenta.tensorflow.org/">Magenta</a>, an ongoing research project exploring the role of AI in art and music creation. He is also an advocate for <a href="https://pair.withgoogle.com/">PAIR</a>, a multidisciplinary team that explores the human side of AI through fundamental research, building tools, creating design frameworks, and working with diverse communities. In the past, Doug worked on music perception, aspects of music performance, machine learning for large audio datasets and music recommendation. He completed his PhD in Computer Science and Cognitive Science at Indiana University in 2000 and went on to a postdoctoral fellowship with Juergen Schmidhuber at <a href="http://www.idsia.ch/">IDSIA</a> in Lugano Switzerland. Before joining Google in 2010, Doug was faculty in Computer Science in the University of Montreal machine learning group (now the <a href="https://mila.quebec/en/">MILA machine learning lab</a>) where he became Associate Professor.  For more information see <a href="http://g.co/research/douglaseck">http://g.co/research/douglaseck</a>.	</p>
-								</p> -->
+                  Peter leads the Gemini Applied Research group, focused on developing fast, efficient, and scalable models in partnership with DeepMind, Search, Ads, Cloud, and other teams across Google. Prior to that, he led a group focused on Google's Enterprise AI, worked on making the Google Assistant better for Kids, and led the data integration / machine learning team at Nest. Peter loves to teach, and is a member of the faculty at UC Berkeley's School of Information, where he teaches courses focused on Deep Learning and Natural Language Processing.
+								</p>
               </div>
             </div>
           </div>
@@ -1528,8 +1528,8 @@ <h6>Talk Abstract</h6>
               </div>
               <div class="col-md-8 v-center">
                 <div class="card card-lecture card-modal">
-                  <h4 align="left">Large Language Models</h4>
-									<h5 align="left">Maxime Labonne, Head of Post-training, Liquid AI</h5>
+                  <h4 align="left">Introduction to LLM Post-Training</h4>
+									<h5 align="left">Maxime Labonne, Head of Post-Training, Liquid AI</h5>
                 </div>
               </div>
             </div>
@@ -1538,13 +1538,13 @@ <h5 align="left">Maxime Labonne, Head of Post-training, Liquid AI</h5>
               <div class="col-md-12 v-center">
                 <h6>Talk Abstract</h6>
                 <p>
-									Coming soon!
+                  In this talk, we will cover the fundamentals of modern LLM post-training at various scales with concrete examples. High-quality data generation is at the core of this process, focusing on the accuracy, diversity, and complexity of the training samples. We will explore key training techniques, including supervised fine-tuning, preference alignment, and model merging. The lecture will delve into evaluation frameworks with their pros and cons for measuring model performance. We will conclude with an overview of emerging trends in post-training methodologies and their implications for the future of LLM development.
                 </p>
 
-                <!-- <h6>Speaker Bio</h6>
+                <h6>Speaker Bio</h6>
                 <p>
-									Doug is leading Google DeepMind’s research efforts in Generative Media, including image, video, 3D, music and audio generation. He also leads a broader group active in areas including including Fundamental Learning Algorithms, Natural Language Processing, Multimodal Learning, Reinforcement Learning, Computer Vision and Generative Models. His own research lies at the intersection of machine learning and human-computer interaction (HCI). Doug created <a href="https://magenta.tensorflow.org/">Magenta</a>, an ongoing research project exploring the role of AI in art and music creation. He is also an advocate for <a href="https://pair.withgoogle.com/">PAIR</a>, a multidisciplinary team that explores the human side of AI through fundamental research, building tools, creating design frameworks, and working with diverse communities. In the past, Doug worked on music perception, aspects of music performance, machine learning for large audio datasets and music recommendation. He completed his PhD in Computer Science and Cognitive Science at Indiana University in 2000 and went on to a postdoctoral fellowship with Juergen Schmidhuber at <a href="http://www.idsia.ch/">IDSIA</a> in Lugano Switzerland. Before joining Google in 2010, Doug was faculty in Computer Science in the University of Montreal machine learning group (now the <a href="https://mila.quebec/en/">MILA machine learning lab</a>) where he became Associate Professor.  For more information see <a href="http://g.co/research/douglaseck">http://g.co/research/douglaseck</a>.	</p>
-								</p> -->
+                  Maxime Labonne is Head of Post-Training at Liquid AI. He holds a Ph.D. in Machine Learning from the Polytechnic Institute of Paris and is a Google Developer Expert in AI/ML. He has made significant contributions to the open-source community, including the LLM Course, tutorials on fine-tuning, tools such as LLM AutoEval, and several state-of-the-art models like NeuralDaredevil. He is the author of the best-selling books “LLM Engineer’s Handbook” and “Hands-On Graph Neural Networks Using Python”.
+                </p>
               </div>
             </div>
           </div>
@@ -1574,7 +1574,7 @@ <h6>Talk Abstract</h6>
               </div>
               <div class="col-md-8 v-center">
                 <div class="card card-lecture card-modal">
-                  <h4 align="left">ML for Biology</h4>
+                  <h4 align="left">AI to Optimize Biology</h4>
 									<h5 align="left">Ava Amini, Senior Research Scientist, Microsoft</h5>
                 </div>
               </div>
@@ -1584,13 +1584,13 @@ <h5 align="left">Ava Amini, Senior Research Scientist, Microsoft</h5>
               <div class="col-md-12 v-center">
                 <h6>Talk Abstract</h6>
                 <p>
-									Coming soon!
+                  The potential of AI in biology is immense, yet its success is contingent on interfacing effectively with wet-lab experimentation and remaining grounded in the system, structure, and physics of biology. I will share how, at Microsoft Research, we are developing new AI systems that help us better understand and design biology via generative design and interactive discovery. I will focus on Generative AI models for the design of novel and useful biomolecules, expanding our ability to engineer new proteins for therapeutic, biological, and industrial applications and beyond.
                 </p>
 
-                <!-- <h6>Speaker Bio</h6>
+                <h6>Speaker Bio</h6>
                 <p>
-									Doug is leading Google DeepMind’s research efforts in Generative Media, including image, video, 3D, music and audio generation. He also leads a broader group active in areas including including Fundamental Learning Algorithms, Natural Language Processing, Multimodal Learning, Reinforcement Learning, Computer Vision and Generative Models. His own research lies at the intersection of machine learning and human-computer interaction (HCI). Doug created <a href="https://magenta.tensorflow.org/">Magenta</a>, an ongoing research project exploring the role of AI in art and music creation. He is also an advocate for <a href="https://pair.withgoogle.com/">PAIR</a>, a multidisciplinary team that explores the human side of AI through fundamental research, building tools, creating design frameworks, and working with diverse communities. In the past, Doug worked on music perception, aspects of music performance, machine learning for large audio datasets and music recommendation. He completed his PhD in Computer Science and Cognitive Science at Indiana University in 2000 and went on to a postdoctoral fellowship with Juergen Schmidhuber at <a href="http://www.idsia.ch/">IDSIA</a> in Lugano Switzerland. Before joining Google in 2010, Doug was faculty in Computer Science in the University of Montreal machine learning group (now the <a href="https://mila.quebec/en/">MILA machine learning lab</a>) where he became Associate Professor.  For more information see <a href="http://g.co/research/douglaseck">http://g.co/research/douglaseck</a>.	</p>
-								</p> -->
+                  Ava Amini is a Senior Researcher at Microsoft, where she develops new AI technologies for precision biology and medicine. She completed her PhD in Biophysics at Harvard University and her BS in Computer Science and Molecular Biology at MIT and has been recognized by the National Academy of Engineering, the National Science Foundation, TEDx, Venture Beats, and the Association of MIT Alumnae, among others, for her research. Ava is passionate about AI education and outreach -- she is a lead organizer and instructor for MIT Introduction to Deep Learning, where she has taught AI to 1000s of students in-person and over 100,000 globally registered students online, garnering more than 11 million online lecture views, and served as a co-founder and director of MomentumAI, which taught all-expenses-paid education programs for high schoolers to learn AI.
+								</p>
               </div>
             </div>
           </div>
@@ -1623,7 +1623,7 @@ <h6>Talk Abstract</h6>
               </div>
               <div class="col-md-8 v-center">
                 <div class="card card-lecture card-modal">
-                  <h4 align="left">Endless Experimentation: Stories from Models in the Wild</h4>
+                  <h4 align="left">A Hippocratic Oath, for your AI</h4>
                   <!-- <h5 align="left">Nikolas Laskaris, VP, Comet ML</h5> -->
                   <h5 align="left">Douglas Blank, Head of Research, Comet ML</h5>
                 </div>
@@ -1634,7 +1634,7 @@ <h5 align="left">Douglas Blank, Head of Research, Comet ML</h5>
               <div class="col-md-12 v-center">
                 <h6>Talk Abstract</h6>
                 <p>
-									While ML model development is a challenging process, the management of these models becomes even more complex once they're in production. Shifting data distributions, upstream pipeline failures, segmentation challenges, model hallucination and much more can create thorny feedback loops between development and production. In this talk, Niko will: (1) examine naive ML workflows that don’t take the development-production feedback loop into account and explore why they break down; (2) share industry case studies where teams have applied these principles to their production ML systems; (3) showcase generalizable system design principles that help manage these feedback loops more effectively.
+                  While Deep Learning has achieved remarkable advancements, I believe its deployment requires a shift in perspective. Just as the Hippocratic Oath guides medical practice, a fundamental ethical framework is crucial for responsible AI deployment. This talk delves into the critical question: Can your AI system adhere to the principle of 'Do no Harm'? We will explore the risks associated with releasing your AI project into the wild, while considering the ethical implications alongside technical advancements.
                 </p>
 
                 <h6>Speaker Bio</h6>