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In April of 2018 The Wall Street Journal published an opinion piece theorizing that “Models Will Run the World.” The authors point out that while software is advancing to “eat the world,” it is ultimately models that will drive the software. We see this playing out all around us, from credit risk models, to models that power autonomous vehicles.
Enter AI. Much of the focus of AI in recent years has been on ever more data; sorting through massive amounts of data and identifying relationships that we can use to correlate actions and responses – “When I see a big red octagon, I stop.” The power of these models is immense, especially in use cases where large amounts of data are readily available. But what happens when there is no data, or the data is dirty or sparse? AND, just because we have a lot of data, is traditional AI the best tool for creating intelligent models?
New techniques are emerging that can create powerful models when data is lacking – or even nonexistent, massively reducing the amount of time and resources required to create intelligent models, while delivering added benefits like model uncertainty and explainability. These approaches are first aimed at the industrial space, where data can be expensive and sometimes impossible to collect and where the returns of added intelligence can deliver trillions in returns through improved design and operation of smart machines and plants. Additionally, the improvements that these models drive often increase efficiency, leading to outsized sustainability returns as well. But industrial design and operations only the beginning; finance, supply chain management, and drug development are all within reach of this new model driven revolution.
In this panel our experts will discuss the problems that are ripe for these data-light solutions, and how new approaches can transform the way we think about building the crucial models we need to predict the future.
11:00 – 11:05 AM: Welcome from The Hive
11:05 – 11:15 AM: Introduction from Speakers
11:15 – 11:50 AM: Panel Discussion
11:50 – 12:00 PM: Audience Q&A
- Greg Fallon, CEO, Geminus
- Jose Celaya, Machine Learning Technical Lead, Schlumberger
- Alex Gorodetsky, Assistant Professor, University of Michical Aerospace Engineering