Allan Costa

Allan Costa, Benjamin Jenett, Irina Kostitsyna et al.

Assembly of large scale structural systems in space is understood as critical to serving applications that cannot be deployed from a single launch. Recent literature proposes the use of discrete modular structures for in-space assembly and relatively small scale robotics that are able to modify and traverse the structure. This paper addresses the algorithmic problems in scaling reconfigurable space structures built through robotic construction, where reconfiguration is defined as the problem of transforming an initial structure into a different goal configuration. We analyze different algorithmic paradigms and present corresponding abstractions and graph formulations, examining specialized algorithms that consider discretized space and time steps. We then discuss fundamental design trades for different computational architectures, such as centralized versus distributed, and present two representative algorithms as concrete examples for comparison. We analyze how those algorithms achieve different objective functions and goals, such as minimization of total distance traveled, maximization of fault-tolerance, or minimization of total time spent in assembly. This is meant to offer an impression of algorithmic constraints on scalability of corresponding structural and robotic design. From this study, a set of recommendations is developed on where and when to use each paradigm, as well as implications for physical robotic and structural system design.

Owen Dugan, Rumen Dangovski, Allan Costa et al.

Neural networks' expressiveness comes at the cost of complex, black-box models that often extrapolate poorly beyond the domain of the training dataset, conflicting with the goal of finding compact analytic expressions to describe scientific data. We introduce OccamNet, a neural network model that finds interpretable, compact, and sparse symbolic fits to data, à la Occam's razor. Our model defines a probability distribution over functions with efficient sampling and function evaluation. We train by sampling functions and biasing the probability mass toward better fitting solutions, backpropagating using cross-entropy matching in a reinforcement-learning loss. OccamNet can identify symbolic fits for a variety of problems, including analytic and non-analytic functions, implicit functions, and simple image classification, and can outperform state-of-the-art symbolic regression methods on real-world regression datasets. Our method requires a minimal memory footprint, fits complicated functions in minutes on a single CPU, and scales on a GPU.

Max Langenkamp, Allan Costa, Chris Cheung

Widespread developments in automation have reduced the need for human input. However, despite the increased power of machine learning, in many contexts these programs make decisions that are problematic. Biases within data and opaque models have amplified human prejudices, giving rise to such tools as Amazon's (now defunct) experimental hiring algorithm, which was found to consistently downgrade resumes when the word "women's" was added before an activity. This article critically surveys the existing legal and technological landscape surrounding algorithmic hiring. We argue that the negative impact of hiring algorithms can be mitigated by greater transparency from the employers to the public, which would enable civil advocate groups to hold employers accountable, as well as allow the U.S. Department of Justice to litigate. Our main contribution is a framework for automated hiring transparency, algorithmic transparency reports, which employers using automated hiring software would be required to publish by law. We also explain how existing regulations in employment and trade secret law can be extended by the Equal Employment Opportunity Commission and Congress to accommodate these reports.