In the precision medicine era, there is a growing need for precision radiotherapy where the planned radiation dose needs to be optimally determined by considering a myriad of patient-specific information in order to ensure treatment efficacy. Existing artificial-intelligence (AI) methods can recommend radiation dose prescriptions within the scope of this available information. However, treating physicians may not fully entrust the AI's recommended prescriptions due to known limitations or when the AI recommendation may go beyond physicians' current knowledge. This paper lays out a systematic method to integrate expert human knowledge with AI recommendations for optimizing clinical decision making. Towards this goal, Gaussian process (GP) models are integrated with deep neural networks (DNNs) to quantify the uncertainty of the treatment outcomes given by physicians and AI recommendations, respectively, which are further used as a guideline to educate clinical physicians and improve AI models performance. The proposed method is demonstrated in a comprehensive dataset where patient-specific information and treatment outcomes are prospectively collected during radiotherapy of $67$ non-small cell lung cancer patients and retrospectively analyzed.

Multi-model ensemble analysis integrates information from multiple climate models into a unified projection. However, existing integration approaches based on model averaging can dilute fine-scale spatial information and incur bias from rescaling low-resolution climate models. We propose a statistical approach, called NN-GPR, using Gaussian process regression (GPR) with an infinitely wide deep neural network based covariance function. NN-GPR requires no assumptions about the relationships between models, no interpolation to a common grid, no stationarity assumptions, and automatically downscales as part of its prediction algorithm. Model experiments show that NN-GPR can be highly skillful at surface temperature and precipitation forecasting by preserving geospatial signals at multiple scales and capturing inter-annual variability. Our projections particularly show improved accuracy and uncertainty quantification skill in regions of high variability, which allows us to cheaply assess tail behavior at a 0.44$^\circ$/50 km spatial resolution without a regional climate model (RCM). Evaluations on reanalysis data and SSP245 forced climate models show that NN-GPR produces similar, overall climatologies to the model ensemble while better capturing fine scale spatial patterns. Finally, we compare NN-GPR's regional predictions against two RCMs and show that NN-GPR can rival the performance of RCMs using only global model data as input.

Artificial intelligence (AI) features are increasingly being embedded in cars and are central to the operation of self-driving cars (SDC). There is little or no effort expended towards understanding and assessing the broad legal and regulatory impact of the decisions made by AI in cars. A comprehensive literature review was conducted to determine the perceived barriers, benefits and facilitating factors of SDC in order to help us understand the suitability and limitations of existing and proposed law and regulation. (1) existing and proposed laws are largely based on claimed benefits of SDV that are still mostly speculative and untested; (2) while publicly presented as issues of assigning blame and identifying who pays where the SDC is involved in an accident, the barriers broadly intersect with almost every area of society, laws and regulations; and (3) new law and regulation are most frequently identified as the primary factor for enabling SDC. Research on assessing the impact of AI in SDC needs to be broadened beyond negligence and liability to encompass barriers, benefits and facilitating factors identified in this paper. Results of this paper are significant in that they point to the need for deeper comprehension of the broad impact of all existing law and regulations on the introduction of SDC technology, with a focus on identifying only those areas truly requiring ongoing legislative attention.

Thanks to large datasets and machine learning, computers have become surprisingly adept at finding statistical relationships among many variables--and exploiting these patterns to make useful predictions. Whether the task involves recognizing objects in photographs or translating text from one language to another, much of what today's intelligent machines can accomplish stems from the computers' ability to make predictions based on statistical associations, or correlations. By and large, computers are very good at this kind of prediction. Yet for many tasks, that is not enough. "In reality, we often want to not only predict things, but we want to improve things," says Jonas Peters, a professor of statistics at the University of Copenhagen.

Recent years have seen an increased interest in the application of methods and techniques commonly associated with machine learning and artificial intelligence to spatial statistics. Here, in a celebration of the ten-year anniversary of the journal Spatial Statistics, we bring together normalizing flows, commonly used for density function estimation in machine learning, and spherical point processes, a topic of particular interest to the journal's readership, to present a new approach for modeling non-homogeneous Poisson process intensity functions on the sphere. The central idea of this framework is to build, and estimate, a flexible bijective map that transforms the underlying intensity function of interest on the sphere into a simpler, reference, intensity function, also on the sphere. Map estimation can be done efficiently using automatic differentiation and stochastic gradient descent, and uncertainty quantification can be done straightforwardly via nonparametric bootstrap. We investigate the viability of the proposed method in a simulation study, and illustrate its use in a proof-of-concept study where we model the intensity of cyclone events in the North Pacific Ocean. Our experiments reveal that normalizing flows present a flexible and straightforward way to model intensity functions on spheres, but that their potential to yield a good fit depends on the architecture of the bijective map, which can be difficult to establish in practice.

Assistant Professor Wim van Rees and his team have developed simulations of self-propelled undulatory swimmers to better understand how fish-like deformable fins could improve propulsion in underwater devices, seen here in a top-down view. MIT ocean and mechanical engineers are using advances in scientific computing to address the ocean's many challenges, and seize its opportunities. There are few environments as unforgiving as the ocean. Its unpredictable weather patterns and limitations in terms of communications have left large swaths of the ocean unexplored and shrouded in mystery. "The ocean is a fascinating environment with a number of current challenges like microplastics, algae blooms, coral bleaching, and rising temperatures," says Wim van Rees, the ABS Career Development Professor at MIT. "At the same time, the ocean holds countless opportunities -- from aquaculture to energy harvesting and exploring the many ocean creatures we haven't discovered yet."

There are few environments as unforgiving as the ocean. Its unpredictable weather patterns and limitations in terms of communications have left large swaths of the ocean unexplored and shrouded in mystery. "The ocean is a fascinating environment with a number of current challenges like microplastics, algae blooms, coral bleaching, and rising temperatures," says Wim van Rees, the ABS Career Development Professor at MIT. "At the same time, the ocean holds countless opportunities -- from aquaculture to energy harvesting and exploring the many ocean creatures we haven't discovered yet." Ocean engineers and mechanical engineers, like van Rees, are using advances in scientific computing to address the ocean's many challenges, and seize its opportunities. These researchers are developing technologies to better understand our oceans, and how both organisms and human-made vehicles can move within them, from the micro scale to the macro scale.

Reporter Chloe Veltman reacts to hearing her digital voice double, "Chloney," for the first time, with Speech Morphing chief linguist Mark Seligman. Reporter Chloe Veltman reacts to hearing her digital voice double, "Chloney," for the first time, with Speech Morphing chief linguist Mark Seligman. The science behind making machines talk just like humans is very complex, because our speech patterns are so nuanced. "The voice is not easy to grasp," says Klaus Scherer, emeritus professor of the psychology of emotion at the University of Geneva. "To analyze the voice really requires quite a lot of knowledge about acoustics, vocal mechanisms and physiological aspects. So it is necessarily interdisciplinary, and quite demanding in terms of what you need to master in order to do anything of consequence."

Reporter Chloe Veltman reacts to hearing her digital voice double, "Chloney," for the first time, with Speech Morphing chief linguist Mark Seligman. Reporter Chloe Veltman reacts to hearing her digital voice double, "Chloney," for the first time, with Speech Morphing chief linguist Mark Seligman. The science behind making machines talk just like humans is very complex, because our speech patterns are so nuanced. "The voice is not easy to grasp," says Klaus Scherer, emeritus professor of the psychology of emotion at the University of Geneva. "To analyze the voice really requires quite a lot of knowledge about acoustics, vocal mechanisms and physiological aspects. So it is necessarily interdisciplinary, and quite demanding in terms of what you need to master in order to do anything of consequence."

Japan overturned in 2020 its decision to cancel acquisition of U.S.-made reconnaissance drones due to their massive costs out of consideration to then-U.S. President Donald Trump, who was promoting American weapons exports, according to sources close to the matter. The government of then-Prime Minister Shinzo Abe had told Washington in the spring of 2020 that it would not purchase the Global Hawk drones, but reversed the decision in the summer after Tokyo scrapped in June that year its planned deployment of U.S.-developed land-based Aegis Ashore ballistic missile defense systems, they said. The about-face was prompted by concerns that cancellation of the Global Hawk acquisition would "anger Mr. Trump, who has insisted on exporting U.S.-made weapons," according to a source familiar with the matter. The policy change reflected "excessive consideration for Mr. Trump," the source said.