Course curriculum
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1
Tackling Climate Change with Machine Learning
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Abstract and Bio
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Tackling Climate Change with Machine Learning
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2
Emerging Approaches to AI Governance: Tech-Led vs Policy-Led
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Abstract and Bio
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Emerging Approaches to AI Governance: Tech-Led vs Policy-Led
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3
AI-driven Healthcare Navigation
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Abstract and Bio
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AI-driven Healthcare Navigation
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Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges that society faces today, requiring rapid action from all corners. In this talk, I will describe how machine learning can be a potentially powerful tool for addressing climate change, when applied in coordination with policy, engineering, and other areas of action. From energy to agriculture to disaster response, I will describe high impact problems where machine learning can help through avenues such as distilling decision-relevant information, optimizing complex systems, and accelerating scientific experimentation. I will then dive into some of my own work in this area, which merges data-driven approaches with physical knowledge to facilitate the transition to low-carbon electric power grids.
Priya Donti, PhD, Co-founder and Chair @ Climate Change AI
Emerging Approaches to AI Governance: Tech-Led vs Policy-Led
Over the past few years, many have become more familiar with the potential risks posed to the improper deployment and usage of AI/ML systems. Companies of almost all sizes and across almost all sectors have seen examples of major AI failures, leading into significant decay in trust of these systems. As a result, stakeholders across organizations have emerged as interested in remediating these risks and getting a handle on AI -- in owning AI governance. Some are drawn to technical capabilities which promise solutions to ethical problems and enable quality. Others rely on existing compliance and policy methods to enforce standards. In this session, we will describe what these different approaches look like, the pros and cons of each, and considerations to build a robust framework around AI governance that engages technical, business, and compliance teams.
Ilana Golbin, Director @ PwC Emerging Technologies and Responsible AI Lead
AI-driven Healthcare Navigation
Our world faces increasingly complex challenges: we destabilized the climate, haven’t beaten all diseases, and haven’t spread the values of democracy and freedom to large parts of the globe, where violence and riots reign supreme. The world must be fixed in our generation - everyone would agree. But in order to take action, build a plan, we need to see the complete picture, and empower decision makers with tools to make those changes. We have finally reached a critical amount of data to facilitate the creation of such tools. The algorithms I developed deal with the complexity of discovering such patterns. Large-scale digital histories, social and real-time media, and human web behavior are harvested and augmented with human knowledge mined from the web to afford real-time estimations of likelihoods of future events.
Kira Radinsky, PhD, Chairwoman & Chief Technology Officer | Visiting Professor @ Diagnostic Robotics | Technion - Israel Institute of Technology
Guy Elad, VP of Data Science @ Diagnostic Robotics