Artificial intelligence (AI) is no longer the stuff of science fiction. While robot maids may not yet be a reality, researchers are working hard to create reasoning, problem-solving machines whose "brains" might rival our own. Seán Ó hÉigeartaigh (anglicized as Sean O'Hegarty), while enthusiastic about the benefits that AI can bring, is also wary of the technology's dark side. He holds a doctorate in genomics from Trinity College Dublin and is now executive director of the Center for the Study of Existential Risk at the University of Cambridge. He has played a central role in international research on the long-term impacts and risks of AI.

Right on the heels of Canada introducing new, stricter regulations for drone operations, the US Department of Transportation proposed a new set of rules for drones that would allow the unmanned vehicles to fly over populated areas and operate at night. The proposal also includes a pilot program for drone traffic management that would help to integrate the aircrafts into the nation's airspace. Under the proposed rules, the Federal Aviation Administration would no longer require drone operators to get waivers to operate at night. Instead, it would require drones flying after twilight to have an anti-collision light that would make it visible for at least three miles. Pilots operating a drone at night would also have to undergo knowledge testing and training before being cleared to fly.

A US federal judge has blocked new Trump administration regulations on birth control from applying across the entire country. The rules allow employers and insurers to decline to provide birth control if doing so violates their "religious beliefs" or "moral convictions". The rules were to come into effect nationwide from Monday. But the judge in Philadelphia granted an injunction requested by attorneys general in Pennsylvania and New Jersey. Judge Wendy Beetlestone ruled that the new rules would make it more difficult for many women to obtain free contraception and would be an undue burden on US states. Her decision follows a similar verdict by a judge in California on Sunday.

Improving predictive understanding of Earth system variability and change requires data-model integration. Efficient data-model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a large-scale Earth system model (ESM) with many output variables is computationally intensive because it involves a large number of expensive ESM simulations. In this effort, we propose an efficient surrogate method capable of using a few ESM runs to build an accurate and fast-to-evaluate surrogate system of model outputs over large spatial and temporal domains. We first use singular value decomposition to reduce the output dimensions, and then use Bayesian optimization techniques to generate an accurate neural network surrogate model based on limited ESM simulation samples. Our machine learning based surrogate methods can build and evaluate a large surrogate system of many variables quickly. Thus, whenever the quantities of interest change such as a different objective function, a new site, and a longer simulation time, we can simply extract the information of interest from the surrogate system without rebuilding new surrogates, which significantly saves computational efforts. We apply the proposed method to a regional ecosystem model to approximate the relationship between 8 model parameters and 42660 carbon flux outputs. Results indicate that using only 20 model simulations, we can build an accurate surrogate system of the 42660 variables, where the consistency between the surrogate prediction and actual model simulation is 0.93 and the mean squared error is 0.02. This highly-accurate and fast-to-evaluate surrogate system will greatly enhance the computational efficiency in data-model integration to improve predictions and advance our understanding of the Earth system.

Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1-dimensional (1D) convolutional neural networks (CNN): the LucasCNN, the LucasResNet which contains an identity block as residual network, and the LucasCoordConv with an additional coordinates layer. Furthermore, we modify two existing 1D CNN approaches for the presented classification task. The code of all five CNN approaches is available on GitHub (Riese, 2019). We evaluate the performance of the CNN approaches and compare them to a random forest classifier. Thereby, we rely on the freely available LUCAS topsoil dataset. The CNN approach with the least depth turns out to be the best performing classifier. The LucasCoordConv achieves the best performance regarding the average accuracy. In future work, we can further enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include additional variables of the rich LUCAS dataset.

Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human lives, such as credit lending or criminal justice. This is driven by the idea that `objective' machines base their decisions solely on facts and remain unaffected by human cognitive biases, discriminatory tendencies or emotions. Yet, there is overwhelming evidence showing that algorithms can inherit or even perpetuate human biases in their decision making when they are based on data that contains biased human decisions. This has led to a call for fairness-aware machine learning. However, fairness is a complex concept which is also reflected in the attempts to formalize fairness for algorithmic decision making. Statistical formalizations of fairness lead to a long list of criteria that are each flawed (or harmful even) in different contexts. Moreover, inherent tradeoffs in these criteria make it impossible to unify them in one general framework. Thus, fairness constraints in algorithms have to be specific to the domains to which the algorithms are applied. In the future, research in algorithmic decision making systems should be aware of data and developer biases and add a focus on transparency to facilitate regular fairness audits.

We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variational inference with implicit distributions, we put forth a statistical inference framework that enables the end-to-end training of surrogate models on paired input-output observations that may be stochastic in nature, originate from different information sources of variable fidelity, or be corrupted by complex noise processes. The resulting surrogates can accommodate high-dimensional inputs and outputs and are able to return predictions with quantified uncertainty. The effectiveness our approach is demonstrated through a series of canonical studies, including the regression of noisy data, multi-fidelity modeling of stochastic processes, and uncertainty propagation in high-dimensional dynamical systems.

U.S. District Judge Wendy Beetlestone in Philadelphia issued a nationwide injunction preventing the rules from taking effect, a day after another judge issued a more limited ruling blocking their enforcement in 13 states and the District of Columbia.

The Pentagon's research arm is looking beyond the human brain to build artificial intelligence. In a recent call for submissions, DARPA revealed that it's looking for ways to take the brains of'very small flying insects' and model their functions in AI robots. The proposal looks to pave the way for robots that are smaller, energy-efficient and easier to train. DARPA is looking beyond the human brain to build artificial intelligence. In a call for proposals, it revealed that it's looking for ways to take insect brains and model their functions in AI robots DARPA is looking for proposals that understand the sensory and nervous systems in miniature insects and can turn them into'prototype computational models.' These models would then be integrated in some type of hardware that emulates how insects think and behave, the agency explained.