There are flying cars and Concorde's would-be supersonic successor, a company offering to deliver cargo to the Moon - for a mere $1.2 million per kilogram - and the latest in funky futuristic aviation ideas, both big and small. No doubt about it: the Paris Air Show is an aerospace geek's paradise. But with everything from the smallest drones to the largest passenger jets on display, it's tough to sift through it all. So here's a guide to some of the cool things that caught our eye this week. Visitors looks at the flying car Pegasus 1, built by French entrepreneur Jerome Dauffy at Paris Air Show, in Le Bourget, east of Paris, France, Tuesday, June 20, 2017 in Paris.

The US military says it has shot down an armed, Iran-made drone that had been bearing down on its forces near a garrison in Syria's southeast. In the latest sign of increasingly frequent confrontation with Damascus and its allies, Tuesday's incident closely followed Sunday's US downing of a piloted Syrian army jet in the southern Raqqa countryside after it dropped bombs near US-backed forces. The Pentagon said a US F-15 aircraft, flying over Syrian territory, fired on the drone after it displayed hostile intent and advanced on coalition forces. Pentagon spokesman Captain Jeff Davis said it had "dirty wings", meaning it was armed. "I can tell you it was an Iranian-made drone," Davis said, declining to speculate on who was operating it.

We consider the problem of approximate Bayesian parameter inference in non-linear state-space models with intractable likelihoods. Sequential Monte Carlo with approximate Bayesian computations (SMC-ABC) is one approach to approximate the likelihood in this type of models. However, such approximations can be noisy and computationally costly which hinders efficient implementations using standard methods based on optimisation and Monte Carlo methods. We propose a computationally efficient novel method based on the combination of Gaussian process optimisation and SMC-ABC to create a Laplace approximation of the intractable posterior. We exemplify the proposed algorithm for inference in stochastic volatility models with both synthetic and real-world data as well as for estimating the Value-at-Risk for two portfolios using a copula model. We document speed-ups of between one and two orders of magnitude compared to state-of-the-art algorithms for posterior inference.

Boeing just got into the autonomous aviation game, with the goal of building jetliners that fly themselves, no pilots required. "The basic building blocks of the technology clearly are available," Mike Sinnett, Boeing's vice president of product development, said ahead of the Paris Airshow. The prospect of a pilotless passenger plane may strike you as crazy, even terrifying. But developing computer systems sophisticated enough to pull it off is well under way. Autopilot technology already does most of the work once a plane is aloft, and has no trouble landing an airliner even in rough weather and limited visibility.

There is no denying the fact that Artificial Intelligence is the breakthrough technology of recent times. The machines have come a long way from assisting humans in mechanical operations to performing smarter tasks using cognitive intelligence. Every day, we are coming across interesting applications of AI. The ability of Deep Learning algorithms to learn and predict efficiently has opened the doors of possibilities. Nowadays, AI is impacting many other areas as well. In this blog post, we will discuss some niche applications of AI.

A toolkit used across Microsoft to achieve breakthroughs in artificial intelligence is generally available to the public via an open-source license, a team of researchers and software engineers announced today. "The 2.0 version of the toolkit is now in full release," said Chris Basoglu, a partner engineering manager at Microsoft. He has played a key role in developing Microsoft Cognitive Toolkit (previously known as CNTK). The full release of Microsoft Cognitive Toolkit 2.0 for use in production-grade and enterprise-grade deep learning workloads includes hundreds of new features incorporated since the beta to streamline the process of deep learning and to ensure the toolkit's seamless integration throughout the wider AI ecosystem. New with the full release today is support for Keras, a user-friendly open-source neural network library that is popular with developers working on deep learning applications.

The main task in oil and gas exploration is to gain an understanding of the distribution and nature of rocks and fluids in the subsurface. Well logs are records of petro-physical data acquired along a borehole, providing direct information about what is in the subsurface. The data collected by logging wells can have significant economic consequences, due to the costs inherent to drilling wells, and the potential return of oil deposits. In this paper, we describe preliminary work aimed at building a general framework for well log prediction. First, we perform a descriptive and exploratory analysis of the gaps in the neutron porosity logs of more than a thousand wells in the North Sea. Then, we generate artificial gaps in the neutron logs that reflect the statistics collected before. Finally, we compare Artificial Neural Networks, Random Forests, and three algorithms of Linear Regression in the prediction of missing gaps on a well-by-well basis.

Undoing man's folly is, sometimes, a robot's work. Unwittingly introduced to the Atlantic Ocean over a quarter of a century ago, the lionfish, which is native to the Pacific, is responsible for an ecological disaster of epic proportions in the Caribbean, Bermuda's, and off the shore of Florida coast, and it's spreading up the coast. A complete lack of predators, voracious appetite and ability to reproduce at an astonishing rate has resulted in a mushrooming lionfish population that is decimating ecosystems, coral reefs and the fishing business. SEE ALSO: A fish that doesn't belong is wreaking havoc on our ocean Catching and eating lionfish, which are delicious, sounds like a reasonable solution, but the fish can't be netted, and are generally fished one person and one spear at a time. Supply creates demand, which generates more demand that fisherman can supply.

When you picture laser-wielding robots, equipped with the latest machine vision algorithms, what setting do you imagine them operating in? Currently being employed in the North Sea fjords in Norway, along with a select few lochs in Scotland, a smart underwater drone developed by Stingray Marine Solutions is designed to help deal with the problem of sea lice. Didn't know that salmon had lice? Don't worry, you're not alone. "It's not a problem that's all that well known outside of the salmon farming industry in Norway," John Breivik, general manager at Stingray, told Digital Trends.

I am in an aeroplane crossing the Atlantic Ocean as I write this. We took off from Heathrow Airport more than three hours ago. By now, it's likely the plane's captain and crew are not physically in control of the aircraft. Something as complex as flying a metal tube packed with more than 300 living souls at 12,000 metres and 900kph is left to a computer and a set of algorithms. Such a device is badly needed in our hospital wards. Critical patients needing 24/7 intensive care could certainly benefit from data-based approaches that could leverage on state-of-the-art analytics and AI.