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This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
As AI moves beyond language into systems that can perceive, understand, and act in the physical world, a new frontier is emerging: world models—AI systems that build and maintain working representations of real environments to predict how they change in response to action.
As AI moves beyond language into systems that can perceive, understand, and act in the physical world, a new frontier is emerging: world models—AI systems that build and maintain working representations of real environments to predict how they change in response to action.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
HAI Weekly Seminar
The Antarctic Ice Sheet will play a growing role in sea level rise over the next century, but models of sea level contributions from the vast ice sheet carry far larger uncertainty estimates than other major contributing sources. A number of factors contribute to this uncertainty, all of which can be traced back to a sparsity of data. The Antarctic Ice Sheet is nearly 50% larger than the United States in area and holds ice equivalent to over 60 meters of global sea level rise. Satellite observations have turned the surfaces of Earth’s ice sheets into data-rich environments, yet the subsurface environments remain sparsely observed.Multiple potential positive-feedback processes have been proposed that could dramatically alter our predictions of the future of the ice sheet. These hypotheses are difficult to test because the processes would likely not have occurred in the relatively short direct span of direct observations available to us, but, in many cases, we also lack the observational infrastructure to identify these processes beginning today. Even well-established processes still carry large uncertainties in how they will impact the ice sheet due to the poor spatial and temporal resolution of data available.Our group explores this problem from two fronts: using robotics to enable more widespread data collection and leveraging data-based approaches to maximize the value of data collected. Uncrewed aerial vehicles (UAVs) carrying ice-penetrating radar instruments hold the potential to dramatically expand sub-surface data collection by reducing the cost, logistical complexity, and safety risks associated with current approaches. At the same time, the scale of the problem is so vast that it is critical to consider how we can optimize the deployment of resources to maximize the value of the collected data. This requires a move towards data-driven approaches to understanding the behavior of the ice sheet.
Thomas TeisbergHAI Graduate Fellow 2021-22
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