Tommi S. Jaakkola: Modeling with Machine Learning: Challenges and Some Solutions скачать в хорошем качестве

Tommi S. Jaakkola: Modeling with Machine Learning: Challenges and Some Solutions

Machine learning has become an integral part of engineering and sciences. It is rapidly extending the horizon of modeling, offering to accelerate / bypass detailed simulations, create statistical (and causal) linkages and complex inferences about entities whose underlying relations may not yet be well-understood. This broad integration with other disciplines also brings back new machine learning challenges, ushering advances in core capabilities. For example, in the context of molecular design, we need to be able to learn to modify, combine and synthesize highly structured objects (molecules). I will illustrate this with our recent efforts to transform chemistry, especially in terms of forward synthesis (reaction outcome) and targeted molecular optimization (drug design). Another challenge that comes on the heels of complex machine learning approaches is that the resulting solutions can be opaque, difficult to understand and/or verify. Attempts to explain the behavior of complex models after the fact, i.e., after they have been trained, can be unstable and unsatisfactory. I will highlight some of our recent work on self-explaining models where transparency is forced upon the models already as they are being trained. The two parts of the talk mimic some of the overall challenges of delivering broadly applicable, explainable AI. About the Speaker Tommi S. Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems and Society (IDSS) at MIT. He received M.Sc. in theoretical physics from Helsinki University of Technology, Ph.D. from MIT in computational neuroscience, and joined the MIT faculty 1998. His research focuses on both foundational theory as well as applications of machine learning with the goal of delivering algorithms that operate at scale in an efficient, principled, and interpretable manner. The applied side of his work involves multi-faceted recommender, retrieval, or inferential tasks (e.g., biomedical), design and optimization of molecules or reactions for the purpose of drug design, and modeling strategic, game theoretic interactions. He has received many awards for his publications.