The importance of algorithmic fairness grows with the increasing impact machine learning has on people's lives. Recent work on fairness metrics shows the need for causal reasoning in fairness constraints. In this work, a practical method named FairTrade is proposed for creating flexible prediction models which integrate fairness constraints on sensitive causal paths. The method uses recent advances in variational inference in order to account for unobserved confounders. Further, a method outline is proposed which uses the causal mechanism estimates to audit black box models. Experiments are conducted on simulated data and on a real dataset in the context of detecting unlawful social welfare. This research aims to contribute to machine learning techniques which honour our ethical and legal boundaries.

'These cool worlds offer the opportunity for new insights into the formation and atmospheres of planets beyond the Solar System,' said paper author and astronomer Aaron Meisner of the National Science Foundation (NSF)'s NOIRLab. 'This collection of cool brown dwarfs also allows us to accurately estimate the number of free-floating worlds roaming interstellar space near the Sun.' Brown dwarves are the'cooling embers' of space -- to small to support the nuclear reactions that power stars, they are faint and challenging to spot, which is why astronomers have been hunting for them close by, in our galactic neighbourhood. Experts believe that brown dwarves cool as they age, starting at near-stellar temperatures but cooling until they are on a par with planets like Earth -- a hypothesis which the recent findings have provided evidence to support. The Backyard Worlds project recruited more than 100,000 citizen scientists to study trillions of pixels of telescope images looking for the subtle signs of planets and brown dwarves moving out in space. According to the astronomers, there is still no substitute for the human eye when it comes to scouring telescope images for subtle evidence of moving objects -- despite recent advances in machine learning and supercomputer hardware. The astronomical data studied was collected by the Nicholas U. Mayall 4-meter Telescope at the Kitt Peak National Observatory in Arizona and the Victor M. Blanco 4-meter Telescope at the Cerro Tololo Inter-American Observatory in Chile. Although the researchers have only published data on the coldest 95 of the finds, the volunteers have identified more than 1,500 brown dwarves in the astronomical data -- a record-breaker for any citizen science program by a factor of 20. 'It's awesome to know that our discoveries are now counted among the Sun's neighbour and will be targets of further research,' said paper co-author and citizen scientist Jim Walla added. The discoveries were part of'Backyard Worlds: Planet 9', a project which recruited more than 100,000 people to scour astronomical data for new'nearby' objects.

"F1 and Amazon Machine Learning Solutions Lab took a full year to build the algorithm that led to the fastest driver." Formula 1 has been working with Amazon Web Services (AWS) to rank their racers. After a year of algorithmic heavy lifting, the results are out now. Ayrton Senna, the three-time world champion from Brazil came out on top, followed by the seven-time champion, Michael Schumacher with a time differential of 0.114 second. Whereas current World Champion Lewis Hamilton featured at 3rd position with a relative time of 0.275 seconds.

This paper proposes to model chaos in the ATM cash withdrawal time series of a big Indian bank and forecast the withdrawals using deep learning methods. It also considers the importance of day-of-the-week and includes it as a dummy exogenous variable. We first modelled the chaos present in the withdrawal time series by reconstructing the state space of each series using the lag, and embedding dimension found using an auto-correlation function and Cao's method. This process converts the uni-variate time series into multi variate time series. The "day-of-the-week" is converted into seven features with the help of one-hot encoding. Then these seven features are augmented to the multivariate time series. For forecasting the future cash withdrawals, using algorithms namely ARIMA, random forest (RF), support vector regressor (SVR), multi-layer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN), long short term memory neural network and 1-dimensional convolutional neural network. We considered a daily cash withdrawals data set from an Indian commercial bank. After modelling chaos and adding exogenous features to the data set, we observed improvements in the forecasting for all models. Even though the random forest (RF) yielded better Symmetric Mean Absolute Percentage Error (SMAPE) value, deep learning algorithms, namely LSTM and 1D CNN, showed similar performance compared to RF, based on t-test.

3D object detection is a core perceptual challenge for robotics and autonomous driving. However, the class-taxonomies in modern autonomous driving datasets are significantly smaller than many influential 2D detection datasets. In this work, we address the long-tail problem by leveraging both the large class-taxonomies of modern 2D datasets and the robustness of state-of-the-art 2D detection methods. We proceed to mine a large, unlabeled dataset of images and LiDAR, and estimate 3D object bounding cuboids, seeded from an off-the-shelf 2D instance segmentation model. Critically, we constrain this ill-posed 2D-to-3D mapping by using high-definition maps and object size priors. The result of the mining process is 3D cuboids with varying confidence. This mining process is itself a 3D object detector, although not especially accurate when evaluated as such. However, we then train a 3D object detection model on these cuboids, consistent with other recent observations in the deep learning literature, we find that the resulting model is fairly robust to the noisy supervision that our mining process provides. We mine a collection of 1151 unlabeled, multimodal driving logs from an autonomous vehicle and use the discovered objects to train a LiDAR-based object detector. We show that detector performance increases as we mine more unlabeled data. With our full, unlabeled dataset, our method performs competitively with fully supervised methods, even exceeding the performance for certain object categories, without any human 3D annotations.

We propose a general semantics for strategic abilities of agents in asynchronous systems, with and without perfect information. Based on the semantics, we show some general complexity results for verification of strategic abilities in asynchronous interaction. More importantly, we develop a methodology for partial order reduction in verification of agents with imperfect information. We show that the reduction preserves an important subset of strategic properties, with as well as without the fairness assumption. We also demonstrate the effectiveness of the reduction on a number of benchmarks. Interestingly, the reduction does not work for strategic abilities under perfect information.

Recent work has shown that deep learning models can be used to classify land-use data from geospatial satellite imagery. We show that when these deep learning models are trained on data from specific continents/seasons, there is a high degree of variability in model performance on out-of-sample continents/seasons. This suggests that just because a model accurately predicts land-use classes in one continent or season does not mean that the model will accurately predict land-use classes in a different continent or season. We then use clustering techniques on satellite imagery from different continents to visualize the differences in landscapes that make geospatial generalization particularly difficult, and summarize our takeaways for future satellite imagery-related applications.

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Artificial intelligence, like other forms of technology, reflects the culture and values of the people who create it and those who provide the data frameworks upon which it is built. AI technology developed in different countries or organizations may thus offer different answers to the same problem. On June 25, the National Security Commission on Artificial Intelligence, an independent U.S. government body, released, "The Role of AI Technology in Pandemic Response and Preparedness: Recommended Investments and Initiatives." The report makes 10 recommendations, including calls for the creation of a federal "Pandemic Preparedness Dataset" and investment in "the digital modernization of state and local health infrastructure required for effective disease surveillance." It was the commission's third report published since May 6 on AI and the government's response to the COVID-19 pandemic.