Neural Information Processing Systems http://nips.cc/

Designing public spaces requires balancing the interests of diverse stakeholders within a constrained physical and institutional space. Designers usually approach these problems through participatory methods but struggle to incorporate diverse perspectives into design outputs. The growing capabilities of image-generative artificial intelligence (IGAI) could support participatory design. Prior work in leveraging IGAI's capabilities in design has focused on augmenting the experience and performance of individual creators. We study how IGAI could facilitate participatory processes when designing public spaces, a complex collaborative task. We conducted workshops and IGAI-mediated interviews in a real-world participatory process to upgrade a park in Los Angeles. We found (1) a shift from focusing on accuracy to fostering richer conversations as the desirable outcome of adopting IGAI in participatory design, (2) that IGAI promoted more space-aware conversations, and (3) that IGAI-mediated conversations are subject to the abilities of the facilitators in managing the interaction between themselves, the AI, and stakeholders. We contribute by discussing practical implications for using IGAI in participatory design, including success metrics, relevant skills, and asymmetries between designers and stakeholders. We finish by proposing a series of open research questions.

The ability to anticipate technical expertise and capability evolution trends globally is essential for national and global security, especially in safety-critical domains like nuclear nonproliferation (NN) and rapidly emerging fields like artificial intelligence (AI). In this work, we extend traditional statistical relational learning approaches (e.g., link prediction in collaboration networks) and formulate a problem of anticipating technical expertise and capability evolution using dynamic heterogeneous graph representations. We develop novel capabilities to forecast collaboration patterns, authorship behavior, and technical capability evolution at different granularities (e.g., scientist and institution levels) in two distinct research fields. We implement a dynamic graph transformer (DGT) neural architecture, which pushes the state-of-the-art graph neural network models by (a) forecasting heterogeneous (rather than homogeneous) nodes and edges, and (b) relying on both discrete -- and continuous -- time inputs. We demonstrate that our DGT models predict collaboration, partnership, and expertise patterns with 0.26, 0.73, and 0.53 mean reciprocal rank values for AI and 0.48, 0.93, and 0.22 for NN domains. DGT model performance exceeds the best-performing static graph baseline models by 30-80% across AI and NN domains. Our findings demonstrate that DGT models boost inductive task performance, when previously unseen nodes appear in the test data, for the domains with emerging collaboration patterns (e.g., AI). Specifically, models accurately predict which established scientists will collaborate with early career scientists and vice-versa in the AI domain.

Patrick Henry Winston was, by all standards, a rock star in the field of Artificial Intelligence. In 1970, Patrick wrote his Ph.D. thesis, in which he explored -- under the improvisational supervision of his advisor, Marvin Minsky -- the theoretical difficulties of learning, and wrote in Lisp a blocks-world program that could perceive blocks and block-enabled architectures (e.g. That computer program was able to learn to generalize its existing knowledge when comparing a baseline example architecture with a new example, and specialize its existing knowledge when comparing a baseline example with a near miss. That was the first effort ever in making machines learn things in ways that resemble how humans learn things. Some say that was "real" Machine Learning, much unlike statistical Machine Learning and neural-net Machine Learning, whereby programmers would program their computers to slavishly crunch through hundreds of billions of data points, which is nothing like how people learn new things, but has become popular because the theory behind them are much more understood and much easier to implement, and because this kind of big-data crunching is practically allowed for due to the tremendous computing power that we have today.

GoGuardian is a software company that makes, essentially, spyware: software that helps teachers and schools block and monitor what kids are doing online. When a student is using a school-issued Chromebook that has GoGuardian on it, the teacher can see just about everything they're doing. These technologies have been embraced by teachers and state Departments of Education alike, but students are less enthralled with having their online lives constantly surveilled. On Friday's episode of What Next: TBD, I spoke with Priya Anand, a tech reporter for Bloomberg who wrote a story on GoGuardian, about the rise of the school surveillance state and the implications of this technology for student's mental health and privacy. Lizzie O'Leary: You wrote for Bloomberg about Pekin Community High School in Illinois, which has been using GoGuardian for three years.

Past research has studied social determinants of attitudes toward foreign countries. Confounded by potential endogeneity biases due to unobserved factors or reverse causality, the causal impact of these factors on public opinion is usually difficult to establish. Using social media data, we leverage the suddenness of the COVID-19 pandemic to examine whether a major global event has causally changed American views of another country. We collate a database of more than 297 million posts on the social media platform Twitter about China or COVID-19 up to June 2020, and we treat tweeting about COVID-19 as a proxy for individual awareness of COVID-19. Using regression discontinuity and difference-in-difference estimation, we find that awareness of COVID-19 causes a sharp rise in anti-China attitudes. Our work has implications for understanding how self-interest affects policy preference and how Americans view migrant communities.

Artificial intelligence, specifically machine learning (ML), enables a new world of complex decision-making using novel relationships between data. This paradigm of a system "learning" from data instead of tedious rules-based programming on an outcome, while exciting in its possibilities, opens up a series of new challenges. Distrust, unfairness, bias and ethical ramifications of automated ML decisions are now increasingly common. The recent story about the inadvertent bias in Amazon's recruiting or face recognition software are examples of unforeseen effects of these applications of AI. They occur because, by and large, the relationships absorbed are opaque, thereby dissuading model developers in fixing it.

CaliBurger is one example of how facial recognition is beginning to make its way out of the realm of security applications--such as searching for bad guys or unlocking our phones--and into bricks-and-mortar retail and other areas of real-world commerce. Entertainment venues want to speed customers through the gate by scanning their faces. Airlines are looking to smooth out passengers' travel by letting them check bags and do other tasks by taking a selfie. Retailers want to send a salesperson over to help customers if a camera reads their expression and suggests they look annoyed. But the technology faces a big hurdle: consumer concerns.