Invited Talks

Dexterous manipulation of objects is robotics’ 21st century primary goal. It envisions robots capable of sorting objects and packaging them, of chopping vegetables and folding clothes, and this, at high speed. To manipulate these objects cannot be done with traditional control approaches, for lack of accurate models of objects and contact dynamics. Robotics leverages, hence, the immense progress in machine learning to encapsulate models of uncertainty and to support further advances on adaptive and robust control.

I will present applications of machine learning for controlling robots to: a) learn non-linear control laws in closed-form, which enables fast retrieval and adaptation at run time – and have robots catch flying objects; b) model complex deformations of objects – to peel and grate vegetables; c) learn manifolds, as embedding of feasible solutions and extract latent spaces in which stability of control laws can be more easily ensured.

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The Fundamental Law of Information Reconstruction, a.k.a. the Database Reconstruction Theorem, exposes a vulnerability in the way statistical agencies have traditionally published data. But it also exposes the same vulnerability for the way Amazon, Apple, Facebook, Google, Microsoft, Netflix, and other Internet giants publish data. We are all in this data-rich world together. And we all need to find solutions to the problem of how to publish information from these data while still providing meaningful privacy and confidentiality protections to the providers. Fortunately for the American public, the Census Bureau's curation of their data is already regulated by a very strict law that mandates publication for statistical purposes only and in a manner that does not expose the data of any respondent--person, household or business--in a way that identifies that respondent as the source of specific data items. The Census Bureau has consistently interpreted that stricture on publishing identifiable data as governed by the laws of probability. An external user of Census Bureau publications should not be able to assert with reasonable certainty that particular data values were directly supplied by an identified respondent. Traditional methods of disclosure avoidance now fail because they are not able to formalize and quantify that risk. Moreover, when traditional methods are assessed using current tools, the relative certainty with which specific values can be associated with identifiable individuals turns out to be orders of magnitude greater than anticipated at the time the data were released. In light of these developments, the Census Bureau has committed to an open and transparent modernization of its data publishing systems using formal methods like differential privacy. The intention is to demonstrate that statistical data, fit for their intended uses, can be produced when the entire publication system is subject to a formal privacy-loss budget. To date, the team developing these systems has demonstrated that differential privacy can be implemented for the data publications from the 2020 Census used to re-draw every legislative district in the nation (PL94-171 tables). That team has also developed methods for quantifying and displaying the system-wide trade-offs between the accuracy of those data and the privacy-loss budget assigned to the tabulations. Considering that work began in mid-2016 and that no organization anywhere in the world has yet deployed a full, central differential privacy system, this is already a monumental achievement. But it is only the tip of the iceberg in terms of the statistical products historically produced from a decennial census. Demographic profiles, based on the detailed tables traditionally published in summary files following the publication of redistricting data, have far more diverse uses than the redistricting data. Summarizing those use cases in a set of queries that can be answered with a reasonable privacy-loss budget is the next challenge. Internet giants, businesses and statistical agencies around the world should also step-up to these challenges. We can learn from, and help, each other enormously.

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The last few years has seen dramatic progress in artificial intelligence, particularly in machine learning, most notably in new work in the connectionist tradition, such as deep learning, but also in work on inferring structured generative models from data. Nevertheless, this new work still is limited to relatively narrow and well-defined spaces of hypotheses. In contrast, human beings and human children, in particular, characteristically generate new, uninstructed and unexpected, yet relevant and plausible hypotheses. I will present several studies showing a surprising pattern. Not only can preschoolers learn abstract higher-order principles from data, but younger learners are actually better at inferring unusual or unlikely principles than older learners and adults. I relate this pattern to computational ideas about search and sampling, to evolutionary ideas about human life history, and to neuroscience findings about the negative effects of frontal control on wide exploration. I uggest that children solve these problems through model-building, exploration and social learning. My hypothesis is that the evolution of our distinctively long, protected human childhood allows an early period of broad hypothesis search, exploration and creativity, before the demands of goal-directed action set in. This evolutionary solution to the search problem may have implications for AI solutions.

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Test of Time Award