<div dir="ltr">This will be of interest to those of us whose work touches reliability and trustworthiness of ML.<div><br></div><div>Artur<br><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">---------- Forwarded message ---------<br>From: <strong class="gmail_sendername" dir="auto">C3.ai Digital Transformation Institute</strong> <span dir="auto"><<a href="mailto:info@c3dti.ai">info@c3dti.ai</a>></span><br>Date: Tue, Feb 16, 2021 at 10:16 AM<br>Subject: Aleksander Madry on Why Do ML Models Fail?<br>To: <<a href="mailto:awd@cs.cmu.edu">awd@cs.cmu.edu</a>><br></div><br><br><u></u>
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<span class="m_-4497022319589195293mcnPreviewText" style="display:none;font-size:0px;line-height:0px;max-height:0px;max-width:0px;opacity:0;overflow:hidden">Aleksander Madry on Why Do ML Models Fail?</span>
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<div style="text-align:center">The Colloquium on Digital Transformation is a series of weekly online talks on how artificial intelligence, machine learning, and big data can lead to scientific breakthroughs with large-scale societal benefit. The spring 2021 series focuses largely on COVID-19 mitigation research.</div>
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<h1 style="text-align:center;display:block;margin:0;padding:0;color:#202020;font-family:Arial,'Helvetica Neue',Helvetica,sans-serif;font-size:26px;font-style:normal;font-weight:bold;line-height:125%;letter-spacing:normal"> <strong>Why Do ML Models Fail?</strong></h1>
<p style="text-align:center;font-style:normal;font-weight:normal;margin:10px 0;padding:0;color:#202020;font-family:Helvetica;font-size:16px;line-height:150%"><strong><span style="font-size:16px">February 18, 1 pm PT/4 pm ET</span></strong><br>
<br>
<strong>Aleksander Madry</strong><br>
<strong>Professor of Computer Science<br>
Massachusetts Institute of Technology</strong><br>
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<p dir="ltr" style="text-align:justify;margin:10px 0;padding:0;color:#202020;font-family:Helvetica;font-size:16px;line-height:150%">Our current machine learning (ML) models achieve impressive performance on many benchmark tasks. Yet, these models remain remarkably brittle, susceptible to manipulation and, more broadly, often behave in ways that are unpredictable to users. Why is this the case? In this talk, we identify human-ML misalignment as a chief cause of this behavior. We then take an end-to-end look at the current ML training paradigm and pinpoint some of the roots of this misalignment. We discuss how current pipelines for dataset creation, model training, and system evaluation give rise to unintuitive behavior and widespread vulnerability. Finally, we conclude by outlining possible approaches towards alleviating these deficiencies.<br>
<br>
<a href="https://c3dti.us8.list-manage.com/track/click?u=aae4610086c8b3a8646b00cbe&id=182acacc3a&e=b5ed94badf" style="color:#007c89;font-weight:normal;text-decoration:none" target="_blank">Aleksander Madry</a> is a Professor of Computer Science at MIT and leads the MIT Center for Deployable Machine Learning. His research interests span algorithms, continuous optimization, science of deep learning, and understanding machine learning from a robustness and deployability perspectives. Aleksander's work has been recognized with a number of awards, including an NSF CAREER Award, an Alfred P. Sloan Research Fellowship, an ACM Doctoral Dissertation Award Honorable Mention, and Presburger Award. He received his PhD from MIT in 2011 and, prior to joining the MIT faculty, he spent time at Microsoft Research New England and on the faculty of EPFL.</p>
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<div style="text-align:left">For those who missed last week's C3.ai DTI Colloquium, you can catch it on our YouTube channel <a href="https://c3dti.us8.list-manage.com/track/click?u=aae4610086c8b3a8646b00cbe&id=3aee0a74e5&e=b5ed94badf" style="color:#007c89;font-weight:normal;text-decoration:none" target="_blank">Colloquium Playlist</a>, where videos of all weekly talks are available.</div>
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<a href="https://c3dti.us8.list-manage.com/track/click?u=aae4610086c8b3a8646b00cbe&id=9880bf9944&e=b5ed94badf" title="Teresa Head-Gordon: Scoring Drugs: Physics-Inspired ML for Small Molecule Drug Discovery for C-19" target="_blank">
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C3.ai DTI Colloquium: February 11, 2021<br>
<br>
<strong>Scoring Drugs: Small Molecule Drug Discovery for COVID-19<br>
using Physics-Inspired Machine Learning</strong><br>
<br>
Teresa Head-Gordon<br>
Chancellor’s Professor, Department of Chemistry, Chemical<br>
and Biomolecular Engineering, and Bioengineering<br>
University of California, Berkeley
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<div style="text-align:justify">The rapid spread of SARS-CoV-2 has spurred the scientific world into action for therapeutics to help minimize fatalities from COVID-19. Molecular modeling is combating the current global pandemic through the traditional process of drug discovery, but the slow turnaround time for identifying leads for antiviral drugs, analyzing structural effects of genetic variation in the evolving virus, and targeting relevant virus-host protein interactions is still a great limitation during an acute crisis. The first component of drug discovery - the structure of potential drugs and the target proteins - has driven functional insight into biology ever since Watson, Crick, Franklin, and Wilkins solved the structure of DNA. What could we do with structural models of host and virus proteins and small molecule therapeutics? We can further enrich structure with dynamics for discovery of new surface sites exposed by fluctuations to bind drugs and peptide therapeutics not revealed by a static structural model. These “cryptic” binding sites offer new leads in drug discovery but will only yield fruit if they can be assessed rapidly for binding affinity for new small molecule drugs. We offer physics-inspired data-driven models to: 1) extend the chemical space of new drugs beyond those available; 2) create reliable scoring functions to evaluate drug binding affinities to cryptic binding sites of COVID-19 targets; 3) accelerate computation of binding affinities by training machine learning models; and 4) closing the loop of design and evaluation to bias the distribution of new drug candidates towards desired metrics enabled by the C3 AI Suite.</div>
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<div style="text-align:center"><span style="font-size:13px"><span style="font-family:arial,helvetica neue,helvetica,sans-serif"><strong>About the C3.ai Digital Transformation Institute</strong></span></span><br>
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<div style="text-align:justify"><span style="font-size:12px"><span style="font-family:arial,helvetica neue,helvetica,sans-serif">Established in March 2020 by C3 AI, Microsoft, and leading universities, the C3.ai Digital Transformation Institute is a research consortium dedicated to accelerating the socioeconomic benefits of artificial intelligence. The Institute engages the world’s leading scientists to conduct research and train practitioners in the new Science of Digital Transformation, which operates at the intersection of artificial intelligence, machine learning, cloud computing, internet of things, big data analytics, organizational behavior, public policy, and ethics. The nine C3.ai Digital Transformation Institute consortium member universities and laboratories are: University of Illinois at Urbana-Champaign; University of California, Berkeley; Carnegie Mellon University; Lawrence Berkeley National Laboratory; Massachusetts Institute of Technology; National Center for Supercomputing Applications at University of Illinois at Urbana-Champaign; Princeton University; Stanford University; and University of Chicago. Learn more at <a href="https://c3dti.us8.list-manage.com/track/click?u=aae4610086c8b3a8646b00cbe&id=6c87501aef&e=b5ed94badf" style="color:#007c89;font-weight:normal;text-decoration:none" target="_blank">C3DTI.ai</a>.</span></span></div>
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