Not many people can answer that question.

What can software designers and ICT specialists learn from maggots? Quite a lot, it would appear. Through understanding how complex learning processes in simple organisms work, EU-funded scientists hope to usher in an era of self-learning robots and predictive computing. Even with limited brain power, an organism can choose the right thing to do in response to external stimuli, which is something that current computational learning theory cannot fully account for. Learning from maggots The EU-funded MINIMAL project, launched in 2014, has focused on the learning processes in a relatively simple animal, the fruit fly larva (maggots).

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

PARIS – The Champs-Elysee was the setting of a mini-air show on Sunday as amateur drone enthusiasts flew their high-tech toys over the famed Paris avenue in the city's first festival celebrating the gadgets. Concentrating intently, punters guided their remote-controlled flying machines through a brightly colored obstacle course accompanied by commentary worthy of a Formula One race. The afternoon festival included a race and demonstrations of the remote-controlled devices that are increasingly used as toys as well as for surveillance, aerial photography and -- controversially-- in the secretive U.S. counterterror campaign. "It's really magical to be at a site like the Champs-Elysees, one of the most famous places in the world," said Dunkan Bossian, 19, one of eight pilots who competed in the race. A German entrant, 27-year-old Julia Muller, added: "Events like this are important to show people that drones are not only dangerous things but you can have fun with them as well."

We consider machine learning techniques to develop low-latency approximate solutions to a class of inverse problems. More precisely, we use a probabilistic approach for the problem of recovering sparse stochastic signals that are members of the $\ell_p$-balls. In this context, we analyze the Bayesian mean-square-error (MSE) for two types of estimators: (i) a linear estimator and (ii) a structured estimator composed of a linear operator followed by a Cartesian product of univariate nonlinear mappings. By construction, the complexity of the proposed nonlinear estimator is comparable to that of its linear counterpart since the nonlinear mapping can be implemented efficiently in hardware by means of look-up tables (LUTs). The proposed structure lends itself to neural networks and iterative shrinkage/thresholding-type algorithms restricted to a single iterate (e.g. due to imposed hardware or latency constraints). By resorting to an alternating minimization technique, we obtain a sequence of optimized linear operators and nonlinear mappings that converge in the MSE objective. The result is attractive for real-time applications where general iterative and convex optimization methods are infeasible.

We describe GTApprox -- a new tool for medium-scale surrogate modeling in industrial design. Compared to existing software, GTApprox brings several innovations: a few novel approximation algorithms, several advanced methods of automated model selection, novel options in the form of hints. We demonstrate the efficiency of GTApprox on a large collection of test problems. In addition, we describe several applications of GTApprox to real engineering problems. Keywords: 1. Introduction approximation, surrogate model, surrogate-based optimization Approximation problems (also known as regression problems) arise quite often in industrial design, and solutions of such problems are conventionally referred to as surrogate models [1]. The most common application of surrogate modeling in engineering is in connection to engineering optimization [2]. Indeed, on the one hand, design optimization plays a central role in the industrial design process; on the other hand, a single optimization step typically requires the optimizer to create or refresh a model of the response function whose optimum is sought, to be able to come up with a reasonable next design candidate. The surrogate models used in optimization range from simple local linear regression employed in the basic gradient-based optimization [3] to complex global models employed in the so-called Surrogate-Based Optimization (SBO) [4]. Aside from optimization, surrogate modeling is used in dimension reduction [5, 6], sensitivity analysis [7-10], and for visualization of response functions. Preprint submitted to February 23, 2018 Mathematically, the approximation problem can generally be described as follows. A great variety of surrogate modeling methods exist, with different assumptions on the underlying response functions, data sets, and model structure [11].

We address the task of simultaneous feature fusion and modeling of discrete ordinal outputs. We propose a novel Gaussian process(GP) auto-encoder modeling approach. In particular, we introduce GP encoders to project multiple observed features onto a latent space, while GP decoders are responsible for reconstructing the original features. Inference is performed in a novel variational framework, where the recovered latent representations are further constrained by the ordinal output labels. In this way, we seamlessly integrate the ordinal structure in the learned manifold, while attaining robust fusion of the input features. We demonstrate the representation abilities of our model on benchmark datasets from machine learning and affect analysis. We further evaluate the model on the tasks of feature fusion and joint ordinal prediction of facial action units. Our experiments demonstrate the benefits of the proposed approach compared to the state of the art.

Browse the business section of any bookshop and you'll find dozens of titles promising to share the secret to climbing the corporate ladder. But the day is not far off when such books will seem as quaint and outmoded as a housekeeping manual from the 1950s. One of the key workplace trends of the 21st century has been the collapse of the corporate ladder, whereby loyal employees climbed towards the higher echelons of management one promotion at a time. Cathy Benko, vice-chairman of Deloitte in San Francisco and co-author of The Corporate Lattice, says that the ladder model dates back to the industrial revolution, when successful businesses were built on economies of scale, standardisation and a strict hierarchy. "But we don't live in an industrial age, we live in a digital age. And if you look at all the shifts taking place, one [of the biggest] is the composition of the workforce, which is far more diverse in every way," she says.

It's a simple question: How would you sell my Dad a drone? Right now, most drone buyers are professionals, hobbyists or video enthusiasts. That leaves a pretty big number of people not currently browsing for a quadcopter. My Dad is one of those people, so if you can sell him one, you're onto something. When I asked that question to Adam Najberg, DJI's Global Director of Communications, his answer was simple: "Size is going to be an issue.

As someone who watches technology trends closely as part of my business, I have been thinking about the future impact of all the technology innovations and automation we are currently experiencing and on the cusp of achieving. Many of the headlines I read about these trends -- and even some I write -- predict some pretty negative consequences right along with the monumental achievements and improvements. While improvements in machine learning, artificial intelligence, big data, and robot automation could mean huge advances in medicine, science, commerce and human understanding, it's also undeniable that there will be consequences as well. These technological advances represent a significant challenge to capitalism. Together, they are poised to potentially create jobless growth and the paradox of an exponentially growing number of products, manufactured more and more efficiently, but with rising unemployment and underemployment, falling real wages and stagnant living standards.