SpaceX's new crew capsule arrived at the International Space Station on Sunday, acing its second milestone in just over a day. No one was aboard the Dragon capsule launched Saturday on its first test flight, only an instrumented dummy. But the three station astronauts had front-row seats as the sleek, white vessel neatly docked and became the first American-made, designed-for-crew spacecraft to pull up in eight years. TV cameras on Dragon as well as the space station provided stunning views of one another throughout the rendezvous. If the six-day demo goes well, SpaceX could launch two astronauts this summer under NASA's commercial crew program.

Are you consciously or unconsciously aware of the fact that Artificial Intelligence is omnipresent? It is finely weaved in our day to day routine from phones to computer and tablets, every device embraces the technology in our surrounding. Even the rising trending craze for Netflix is also a gift of Artificial Intelligence, for sure. Therefore, it would not be an element surprise if we monitor the involvement of the Indian government in this sector. The Government of India is set to regulate Artificial Intelligence with a transparent set of guidelines for the procedure to develop and implement the AI technology, as confirmed by Minister of Commerce & Industry and Civil Aviation Suresh Prabhu.

In likelihood-free settings where likelihood evaluations are intractable, approximate Bayesian computation (ABC) addresses the formidable inference task to discover plausible parameters of simulation programs that explain the observations. However, they demand large quantities of simulation calls. Critically, hyperparameters that determine measures of simulation discrepancy crucially balance inference accuracy and sample efficiency, yet are difficult to tune. In this paper, we present kernel embedding likelihood-free inference (KELFI), a holistic framework that automatically learns model hyperparameters to improve inference accuracy given limited simulation budget. By leveraging likelihood smoothness with conditional mean embeddings, we nonparametrically approximate likelihoods and posteriors as surrogate densities and sample from closed-form posterior mean embeddings, whose hyperparameters are learned under its approximate marginal likelihood. Our modular framework demonstrates improved accuracy and efficiency on challenging inference problems in ecology.

As data continues to pile up, enterprises that maintain flexible approaches to managing and mining that data are the ones most likely to achieve competitive success, according to Gartner, which recently released its top 10 analytics technologies and trends for 2019. The Global Datashere currently measures 33 zettabytes, according to a recent IDC report, and is predicted to grow to 175 zettabytes by 2025. Navigating this data deluge is no simple matter, as the volume and velocity exceeds the capabilities of existing data analytics rigs running atop legacy architectures. "The size, complexity, distributed nature of data, speed of action, and the continuous intelligence required by digital business means that rigid and centralized architectures and tools break down," explains Donald Feinberg, vice president and distinguished analyst at Gartner. "The continued survival of any business will depend upon an agile, data-centric architecture that responds to the constant rate of change."

It's obviously important to Australians to make sure their koala population is closely tracked -- but how can you do so when the suckers live in forests and climb trees all the time? A new project from Queensland University of Technology combines some well-known techniques in a new way to help keep an eye on wild populations of the famous and soft marsupials. They used a drone equipped with a heat-sensing camera, then ran the footage through a deep learning model trained to look for koala-like heat signatures. It's similar in some ways to an earlier project from QUT in which dugongs -- endangered sea cows -- were counted along the shore via aerial imagery and machine learning. But this is considerably harder.

StarCraft II provides an extremely challenging platform for reinforcement learning due to its huge state-space and game length. The previous fastest method requires days to train a full-length game policy in a single commercial machine. In this paper, we introduce the mind-game to facilitate the reinforcement learning, which is an abstract task model. With the mind-game, the policy is firstly trained in the mind-game fastly and is then mapped to the real game for the second phase training. In our experiments, the trained agent can achieve a 100% win-rate on the map Simple64 against the most difficult non-cheating built-in bot (level-7), and the training is 100 times faster than the previous ones under the same computational resource. To test the generalization performance of the agent, a Golden level of StarCraft II Ladder human player has competed with the agent. With restricted strategy, the agent wins the human player by 4 out of 5 games. The mind-game approach might shed some light for further studies of efficient reinforcement learning. The codes are publicly available (https://github.com/mindgameSC2/mind-SC2).

The Palmerston North-based Health Hub Project in New Zealand is aiming to reduce health inequities and increase access to care with the help of artificial intelligence, machine learning and blockchain. Project co-founder David Hill is a GP at the Health Hub Project in Palmerston North, which runs four general practices with around 9000 patients. Hill says clinically trained people are a diminishing resource in healthcare and the system cannot rely on that to ensure its sustainability in the future, therefore technology needs to be used to "balance that inequity of supply and demand". "The whole point of what we are doing is trying to make sure that we use IT in a way that allows or permits greater equity of access to patients and starts to reduce the reliance on the ever-dwindling resource of healthcare workers," he says. "Also, to advance the value proposition that we give to patients."

Variational Bayes (VB) is a recent approximate method for Bayesian inference. It has the merit of being a fast and scalable alternative to Markov Chain Monte Carlo (MCMC) but its approximation error is often unknown. In this paper, we derive the approximation error of VB in terms of mean, mode, variance, predictive density and KL divergence for the linear Gaussian multi-equation regression. Our results indicate that VB approximates the posterior mean perfectly. Factors affecting the magnitude of underestimation in posterior variance and mode are revealed. Importantly, We demonstrate that VB estimates predictive densities accurately.

Non-linear source separation is a challenging open problem with many applications. We extend a recently proposed Adversarial Non-linear ICA (ANICA) model, and introduce Cramer-Wold ICA (CW-ICA). In contrast to ANICA we use a simple, closed--form optimization target instead of a discriminator--based independence measure. Our results show that CW-ICA achieves comparable results to ANICA, while foregoing the need for adversarial training.

Collective intelligence is manifested when multiple agents coherently work in observation, interaction, decision-making and action. In this paper, we define and quantify the intelligence level of heterogeneous agents group with the improved Anytime Universal Intelligence Test(AUIT), based on an extension of the existing evaluation of homogeneous agents group. The relationship of intelligence level with agents composition, group size, spatial complexity and testing time is analyzed. The intelligence level of heterogeneous agents groups is compared with the homogeneous ones to analyze the effects of heterogeneity on collective intelligence. Our work will help to understand the essence of collective intelligence more deeply and reveal the effect of various key factors on group intelligence level.