Another weak spot was Nvidia, which fell 7.8 percent after the chipmaker temporarily suspended self-driving tests across the globe after an Uber Technologies Inc autonomous vehicle killed a woman. Investors rotated out of the tech sector, which had long outperformed the market on hopes of new technologies such as artificial intelligence (AI) and internet of things (IoT). "There is a sense that there will be more regulations on Facebook or FANG and that the cost of compliance will increase," said Nobuhiko Kuramochi, chief strategist at Mizuho Securities. The so-called FANG, a quartet of tech stocks that include Facebook, Amazon.com, Netflix and Alphabet, have been a darling of many investors.

Elias, the new language teacher at a Finnish primary school, has endless patience for repetition, never makes a pupil feel embarrassed for asking a question and can even do the'Gangnam Style' dance. Elias is also a robot. The language-teaching machine comprises a humanoid robot and mobile application, one of four robots in a pilot programme at primary schools in the southern city of Tampere. Pictured is Elias, a robot teaching children in a Finnish school. The robot is able to understand and speak 23 languages and is equipped with software that allows it to understand students' requirements and helps it to encourage learning.

The driver of a Tesla Model S crashed into a fire truck while driving down a California highway. In this Friday March 23, 2018 photo provided by TV station KTVU, emergency personnel work a the scene where a Tesla electric SUV crashed into a barrier on U.S. Highway 101 in Mountain View, Calif. SAN FRANCISCO -- Tesla shares dropped 8% Tuesday as federal investigators announced they would be looking into a deadly California crash and concerns about the company's production of Model 3 cars knocked its credit rating. The National Transportation Safety Board tweeted that two investigators were conducting field research into the March 23 accident, in which a Model X SUV struck a highway median near Mountain View, Calif. and flipped into oncoming lanes, where it was struck by two vehicles. NTSB officials said it remained "unclear if automated control system was active at time of crash," a reference to Tesla's Autopilot system. Driving the Tesla Model 3 reveals a vehicle whose characteristics are in keeping with sedans in this $35,000 and up price range, and whose looks suggest that electric vehicles need not be boxy or oddly futuristic affairs.

The recent successes of AI have captured the wildest imagination of both the scientific communities and the general public. Robotics and AI amplify human potentials, increase productivity and are moving from simple reasoning towards human-like cognitive abilities. Current AI technologies are used in a set area of applications, ranging from healthcare, manufacturing, transport, energy, to financial services, banking, advertising, management consulting and government agencies. The global AI market is around 260 billion USD in 2016 and it is estimated to exceed 3 trillion by 2024. To understand the impact of AI, it is important to draw lessons from it's past successes and failures and this white paper provides a comprehensive explanation of the evolution of AI, its current status and future directions.

For highly sensitive real-world predictive analytic applications such as healthcare and medicine, having good prediction accuracy alone is often not enough. These kinds of applications require a decision making process which uses uncertainty estimation as input whenever possible. Quality of uncertainty estimation is a subject of over or under confident prediction, which is often not addressed in many models. In this paper we show several extensions to the Gaussian Conditional Random Fields model, which aim to provide higher quality uncertainty estimation. These extensions are applied to the temporal disease graph built from the State Inpatient Database (SID) of California, acquired from the HCUP. Our experiments demonstrate benefits of using graph information in modeling temporal disease properties as well as improvements in uncertainty estimation provided by given extensions of the Gaussian Conditional Random Fields method.

In the past five years, the areas of adversarial attacks (Szegedy et al., 2013) on deep learning models, as well as defenses against such attacks, have received significant attention in the deep learning research community (Yuan et al., 2017; Akhtar & Mian, 2018). Defenses against adversarial attacks can be categorized into two main types. Approaches of the first type modify the net training procedures or architectures, usually in order to make the net compute a smooth function; see, for example (Shaham et al., 2015; Gu & Rigazio, 2014; Cisse et al., 2017; Papernot et al., 2016b). Defenses of the second type leave the training procedure and architecture unchanged, but rather modify the data, aiming to detect or remove adversarial perturbations often by smoothing the input data. For example, Guo et al. (2017) applied image transformations, such as total variance minimization and quilting to smooth input images.

Causal influence measures for machine learnt classifiers shed light on the reasons behind classification, and aid in identifying influential input features and revealing their biases. However, such analyses involve evaluating the classifier using datapoints that may be atypical of its training distribution. Standard methods for training classifiers that minimize empirical risk do not constrain the behavior of the classifier on such datapoints. As a result, training to minimize empirical risk does not distinguish among classifiers that agree on predictions in the training distribution but have wildly different causal influences. We term this problem covariate shift in causal testing and formally characterize conditions under which it arises. As a solution to this problem, we propose a novel active learning algorithm that constrains the influence measures of the trained model. We prove that any two predictors whose errors are close on both the original training distribution and the distribution of atypical points are guaranteed to have causal influences that are also close. Further, we empirically demonstrate with synthetic labelers that our algorithm trains models that (i) have similar causal influences as the labeler's model, and (ii) generalize better to out-of-distribution points while (iii) retaining their accuracy on in-distribution points.

Conditional probabilistic graphical models provide a powerful framework for structured regression in spatio-temporal datasets with complex correlation patterns. However, in real-life applications a large fraction of observations is often missing, which can severely limit the representational power of these models. In this paper we propose a Marginalized Gaussian Conditional Random Fields (m-GCRF) structured regression model for dealing with missing labels in partially observed temporal attributed graphs. This method is aimed at learning with both labeled and unlabeled parts and effectively predicting future values in a graph. The method is even capable of learning from nodes for which the response variable is never observed in history, which poses problems for many state-of-the-art models that can handle missing data. The proposed model is characterized for various missingness mechanisms on 500 synthetic graphs. The benefits of the new method are also demonstrated on a challenging application for predicting precipitation based on partial observations of climate variables in a temporal graph that spans the entire continental US. We also show that the method can be useful for optimizing the costs of data collection in climate applications via active reduction of the number of weather stations to consider. In experiments on these real-world and synthetic datasets we show that the proposed model is consistently more accurate than alternative semi-supervised structured models, as well as models that either use imputation to deal with missing values or simply ignore them altogether.

Probabilistic linear discriminant analysis (PLDA) is a method used for biometric problems like speaker or face recognition that models the variability of the samples using two latent variables, one that depends on the class of the sample and another one that is assumed independent across samples and models the within-class variability. In this work, we propose a generalization of PLDA that enables joint modeling of two sample-dependent factors: the class of interest and a nuisance condition. The approach does not change the basic form of PLDA but rather modifies the training procedure to consider the dependency across samples of the latent variable that models within-class variability. While the identity of the nuisance condition is needed during training, it is not needed during testing since we propose a scoring procedure that marginalizes over the corresponding latent variable. We show results on a multilingual speaker-verification task, where the language spoken is considered a nuisance condition. We show that the proposed joint PLDA approach leads to significant performance gains in this task for two different datasets, in particular when the training data contains mostly or only monolingual speakers.

Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data. In this paper, we compare the standard "fully supervised" approach (that relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training. This so-called "weakly supervised" technique has access to less information than the fully supervised method and yet is still able to yield impressive discriminating power. In addition, weak supervision seems particularly well suited to particle physics since quantum mechanics is incompatible with the notion of mapping an individual event onto any single Feynman diagram. We examine the technique in detail -- both analytically and numerically -- with a focus on the robustness to issues of mischaracterizing the training samples. Weakly supervised networks turn out to be remarkably insensitive to systematic mismodeling. Furthermore, we demonstrate that the event level outputs for weakly versus fully supervised networks are probing different kinematics, even though the numerical quality metrics are essentially identical. This implies that it should be possible to improve the overall classification ability by combining the output from the two types of networks. For concreteness, we apply this technology to a signature of beyond the Standard Model physics to demonstrate that all these impressive features continue to hold in a scenario of relevance to the LHC.