Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Why Following Friends Can Hurt You: A Replication Study

This study is a methodological replication of the work originally published in Information Systems Research by Krasnova, Widjaja, Buxmann, Wenninger, and Benbasat (2015). The original work studied the effects of envy in the context of Social Network Sites (SNSs) among college-age users. We adapt the constructs and measurement items of the original survey but change the context of the SNS to Instagram instead of Facebook. We also target a sample of college-age students from the United States instead of from Germany. The results of our replication support six of the seven hypotheses from the original paper. Confirming these results reinforce the model proposed by Krasnova et al. (2015). However, our replication did not find a strong mediation effect from envy on an SNS between the intensity of social information consumption on an SNS and users’ cognitive well-being. The results suggest that the difference in population, SNS, or time has led to a change in this effect inviting further replications and new studies

Effectiveness evaluation of STOL transport operations (phase 2)

A computer simulation program which models a commercial short-haul aircraft operating in the civil air system was developed. The purpose of the program is to evaluate the effect of a given aircraft avionics capability on the ability of the aircraft to perform on-time carrier operations. The program outputs consist primarily of those quantities which can be used to determine direct operating costs. These include: (1) schedule reliability or delays, (2) repairs/replacements, (3) fuel consumption, and (4) cancellations. More comprehensive models of the terminal area environment were added and a simulation of an existing airline operation was conducted to obtain a form of model verification. The capability of the program to provide comparative results (sensitivity analysis) was then demonstrated by modifying the aircraft avionics capability for additional computer simulations

Gender Equity in Melbourne's Select Entry High School (SEHS) System

This research paper reports on findings from a comprehensive synthesis of research related to gender and Melbourne’s system of Select Entry High Schools (SEHS). The research described in this report was commissioned and funded by the Department of Education and Training, Victoria, with industry partners Portable and Huddle. This research is situated within a larger project entitled: Gender Equity - Consultation and Codesign. The report synthesises previous research in the field, alongside reflections on new data collected over 2021/2022 from students, parents and educators from both within, and in proximity to, the SEHS system, with the aim of generating original, empirically-driven recommendations for policy and practice in the area of SEHS entrance for young women. The project aimed to better understand and address the drivers of gender disparity for high performing girls at SEHS in Victoria, given the larger proportions of male-identifying students and the subsequent gender imbalance present within the system. As feminist gender scholars whose research engages with gender identities and sexuality, the authors acknowledge the complexities inherent to discussions of gender identities. Therefore, throughout the report, we use a range of socio-linguistic terms to represent gender. As the codesign workshops themselves did not explore gender identity, we cannot know how the participants understood their gender identities. To reflect this we have used the terms ‘boys’ and ‘girls’ in order to engage with gender disparity, and also the terms ‘female identifying’ and ‘male identifying’ and ‘girl identified’ and ‘boy identified’ to attempt to capture some of the nuances of contemporary gender identities (see, for example, Miller, 2016; Zimman, 2009)

Laboratory Determination of the Infrared Band Strengths of Pyrene Frozen in Water Ice: Implications for the Composition of Interstellar Ices

Broad infrared emission features (e.g., at 3.3, 6.2, 7.7, 8.6, and 11.3 microns) from the gas phase interstellar medium have long been attributed to polycyclic aromatic hydrocarbons (PAHs). A significant portion (10%-20%) of the Milky Way's carbon reservoir is locked in PAH molecules, which makes their characterization integral to our understanding of astrochemistry. In molecular clouds and the dense envelopes and disks of young stellar objects (YSOs), PAHs are expected to be frozen in the icy mantles of dust grains where they should reveal themselves through infrared absorption. To facilitate the search for frozen interstellar PAHs, laboratory experiments were conducted to determine the positions and strengths of the bands of pyrene mixed with H2O and D2O ices. The D2O mixtures are used to measure pyrene bands that are masked by the strong bands of H2O, leading to the first laboratory determination of the band strength for the CH stretching mode of pyrene in water ice near 3.25 microns. Our infrared band strengths were normalized to experimentally determined ultraviolet band strengths, and we find that they are generally ~50% larger than those reported by Bouwman et al. based on theoretical strengths. These improved band strengths were used to reexamine YSO spectra published by Boogert et al. to estimate the contribution of frozen PAHs to absorption in the 5-8 micron spectral region, taking into account the strength of the 3.25 micron CH stretching mode. It is found that frozen neutral PAHs contain 5%-9% of the cosmic carbon budget, and account for 2%-9% of the unidentified absorption in the 5-8 micron region.Comment: Accepted for publication in ApJ on 14 Feb 201

Private Multiplicative Weights Beyond Linear Queries

A wide variety of fundamental data analyses in machine learning, such as linear and logistic regression, require minimizing a convex function defined by the data. Since the data may contain sensitive information about individuals, and these analyses can leak that sensitive information, it is important to be able to solve convex minimization in a privacy-preserving way. A series of recent results show how to accurately solve a single convex minimization problem in a differentially private manner. However, the same data is often analyzed repeatedly, and little is known about solving multiple convex minimization problems with differential privacy. For simpler data analyses, such as linear queries, there are remarkable differentially private algorithms such as the private multiplicative weights mechanism (Hardt and Rothblum, FOCS 2010) that accurately answer exponentially many distinct queries. In this work, we extend these results to the case of convex minimization and show how to give accurate and differentially private solutions to *exponentially many* convex minimization problems on a sensitive dataset