When you're watching a modern blockbuster such as The Avengers, it's hard to escape the feeling that what you're seeing is almost entirely computer-generated imagery, from the effects to the sets to fantastical creatures. But if there's one thing you can rely on to be 100% real, it's the actors. We might have virtual pop stars like Hatsune Miku, but there has never been a world-famous virtual film star. Even that link with corporeal reality, though, is no longer absolute. You may have already seen examples of what's possible: Peter Cushing (or his image) appearing in Rogue One: A Star Wars Story more than 20 years after his death, or Tupac Shakur performing from beyond the grave at Coachella in 2012.

A link has been posted to your Facebook feed. Despite sending humans to Earth's orbit and the moon, the idea of humans surviving in outer space must seem like science fiction. Creating an environment that can sustain human life in the almost total absence of gravity, as well as no electrical outlets or oxygen, takes a lot of experimentation. That's been the job of teams of dedicated scientists who have facilitated some of the most unforgettable moments in space exploration. We compiled 30 common items that were invented for use in the race for space.

Tin foil is one way to keep modern car key fobs safe from creative thieves. Kim Komando explains the technology -- and how to keep them safe. In March, a Tesla Model 3 was hacked. The duo responsible for uncovering the vulnerability accessed the car's web browser, executed code on its firmware and displayed a message on the infotainment system before making off with the Model 3 and $375,000. The hackers didn't remotely take total control of the car or wreak havoc on its door locks or brakes while an innocent driver sat inside.

Teaching artificial intelligence to think like children may make them better learners. Kanishk Gandhi and Brenden Lake at New York University found that a common assumption children make while learning, called the mutual exclusivity bias, would make AIs better at learning tasks such as processing language. Mutual exclusivity bias refers to the incorrect assumption that once an object has a name or a label, it can't also have another. "A child will find it difficult to call a …

A tiny jellyfish-like robot could one day swim through the human body to deliver drugs to the right location. Metin Sitti and his colleagues at the Max Planck Institute for Intelligent Systems in Germany designed a robotic jellyfish that can swim, burrow and transport objects. It is 3 millimetres in diameter, roughly the size of a baby common jellyfish. It consists of a central body and eight bendable flaps that can beat upwards and downwards in unison. They beat roughly 150 times per minute, also similar to that of baby jellyfish, and are extended by flippers that help the robot propel through water.

YOUR employer could soon use your smartwatch to check how hard you are working. An artificially intelligent algorithm predicts whether someone has high or low workplace performance based on data from their electronic devices. However, there are concerns about how such an algorithm could be used. Andrew Campbell at Dartmouth College in New Hampshire and colleagues compared 554 people's performance at work with data from their smartphones, fitness-monitoring wristbands and location trackers.

Many people have been inspired by the football World Cup in France and now artificial intelligence is learning to play too. Karol Kurach and colleagues at Google Research in Zurich, Switzerland have made a virtual football training pitch for AIs to use to learn how to play. Because football requires a balance between short-term control and high-level strategy, it is challenging for AIs to master, says Kurach.

We present a model-based framework for robot locomotion that achieves walking based on only 4.5 minutes (45,000 control steps) of data collected on a quadruped robot. To accurately model the robot's dynamics over a long horizon, we introduce a loss function that tracks the model's prediction over multiple timesteps. We adapt model predictive control to account for planning latency, which allows the learned model to be used for real time control. Additionally, to ensure safe exploration during model learning, we embed prior knowledge of leg trajectories into the action space. The resulting system achieves fast and robust locomotion. Unlike model-free methods, which optimize for a particular task, our planner can use the same learned dynamics for various tasks, simply by changing the reward function. To the best of our knowledge, our approach is more than an order of magnitude more sample efficient than current model-free methods.

Datasets in engineering applications are often limited and contaminated, mainly due to unavoidable measurement noise and signal distortion. Thus, using conventional data-driven approaches to build a reliable discriminative model, and further applying this identified surrogate to uncertainty analysis remains to be very challenging. A deep learning approach is presented to provide predictions based on limited and noisy data. To address noise perturbation, the Bayesian learning method that naturally facilitates an automatic updating mechanism is considered to quantify and propagate model uncertainties into predictive quantities. Specifically, hierarchical Bayesian modeling (HBM) is first adopted to describe model uncertainties, which allows the prior assumption to be less subjective, while also makes the proposed surrogate more robust. Next, the Bayesian inference is seamlessly integrated into the DL framework, which in turn supports probabilistic programming by yielding a probability distribution of the quantities of interest rather than their point estimates. Variational inference (VI) is implemented for the posterior distribution analysis where the intractable marginalization of the likelihood function over parameter space is framed in an optimization format, and stochastic gradient descent method is applied to solve this optimization problem. Finally, Monte Carlo simulation is used to obtain an unbiased estimator in the predictive phase of Bayesian inference, where the proposed Bayesian deep learning (BDL) scheme is able to offer confidence bounds for the output estimation by analyzing propagated uncertainties. The effectiveness of Bayesian shrinkage is demonstrated in improving predictive performance using contaminated data, and various examples are provided to illustrate concepts, methodologies, and algorithms of this proposed BDL modeling technique.

This work demonstrates the execution of a novel process model for knowledge discovery and data mining for metabolomics (MeKDDaM). It aims to illustrate MeKDDaM process model applicability using four different real-world applications and to highlight its strengths and unique features. The demonstrated applications provide coverage for metabolite profiling, target analysis, and metabolic fingerprinting. The data analysed in these applications were captured by chromatographic separation and mass spectrometry technique (LC-MS), Fourier transform infrared spectroscopy (FT-IR), and nuclear magnetic resonance spectroscopy (NMR) and involve the analysis of plant, animal, and human samples. The process was executed using both data-driven and hypothesis-driven data mining approaches in order to perform various data mining goals and tasks by applying a number of data mining techniques. The applications were selected to achieve a range of analytical goals and research questions and to provide coverage for metabolite profiling, target analysis, and metabolic fingerprinting using datasets that were captured by NMR, LC-MS, and FT-IR using samples of a plant, animal, and human origin. The process was applied using an implementation environment which was created in order to provide a computer-aided realisation of the process model execution.