Amazon's robotic Picking Challenge this past weekend demonstrated the advancement in deep learning robots and showed how they may come to rule fulfillment warehouses in the future. "The machine studied 3D scans of the stockroom items to help it decide how to manipulate items with its gripper and suction cup," Engadget explained. "That adaptive AI made a big difference, to put it mildly. The arm got a near-flawless score in the stowing half of the event, and was over three times faster at picking objects than last year's champion (100 per hour versus 30)." "The robot needs to be able to handle variety and operate in an unstructured environment," Carlos Hernández Corbato from TU Delft Robotics Institute told TechRepublic.com. "We are really happy that we have been able to develop this successful system."

Alzheimer's is a devastating chronic neurodegenerative disease that currently affects about 5.4 million people in the U.S. alone. Alzheimer's patients suffer progressive mental deterioration, which eventually impairs even basic bodily functions like walking and swallowing. While Alzheimer's can increasingly be managed, one of the big challenges of the disease is early diagnosis. MRI machines can be used to confirm advanced cases, but by the time the disease has reached this stage, brain tissue is gone and there is no way to restore it. Could machine-learning tools be used to help detect and identify Alzheimer's disease before it is currently possible to do so?

In this work we propose a game theoretic model for document clustering. Each document to be clustered is represented as a player and each cluster as a strategy. The players receive a reward interacting with other players that they try to maximize choosing their best strategies. The geometry of the data is modeled with a weighted graph that encodes the pairwise similarity among documents, so that similar players are constrained to choose similar strategies, updating their strategy preferences at each iteration of the games. We used different approaches to find the prototypical elements of the clusters and with this information we divided the players into two disjoint sets, one collecting players with a definite strategy and the other one collecting players that try to learn from others the correct strategy to play. The latter set of players can be considered as new data points that have to be clustered according to previous information. This representation is useful in scenarios in which the data are streamed continuously. The evaluation of the system was conducted on 13 document datasets using different settings. It shows that the proposed method performs well compared to different document clustering algorithms.

Bayesian inference in the presence of an intractable likelihood function is computationally challenging. When following a Markov chain Monte Carlo (MCMC) approach to approximate the posterior distribution in this context, one typically either uses MCMC schemes which target the joint posterior of the parameters and some auxiliary latent variables or pseudo-marginal Metropolis-Hastings (MH) schemes which mimic a MH algorithm targeting the marginal posterior of the parameters by approximating unbiasedly the intractable likelihood. In scenarios where the parameters and auxiliary variables are strongly correlated under the posterior and/or this posterior is multimodal, Gibbs sampling or Hamiltonian Monte Carlo (HMC) will perform poorly and the pseudo-marginal MH algorithm, as any other MH scheme, will be inefficient for high dimensional parameters. We propose here an original MCMC algorithm, termed pseudo-marginal HMC, which approximates the HMC algorithm targeting the marginal posterior of the parameters. We demonstrate through experiments that pseudo-marginal HMC can outperform significantly both standard HMC and pseudo-marginal MH schemes.

We prove statistical rates of convergence for kernel-based least squares regression from i.i.d. data using a conjugate gradient algorithm, where regularization against overfitting is obtained by early stopping. This method is related to Kernel Partial Least Squares, a regression method that combines supervised dimensionality reduction with least squares projection. Following the setting introduced in earlier related literature, we study so-called "fast convergence rates" depending on the regularity of the target regression function (measured by a source condition in terms of the kernel integral operator) and on the effective dimensionality of the data mapped into the kernel space. We obtain upper bounds, essentially matching known minimax lower bounds, for the $\mathcal{L}^2$ (prediction) norm as well as for the stronger Hilbert norm, if the true regression function belongs to the reproducing kernel Hilbert space. If the latter assumption is not fulfilled, we obtain similar convergence rates for appropriate norms, provided additional unlabeled data are available.

In this paper, we consider the problem of estimating the underlying graph associated with an Ising model given a number of independent and identically distributed samples. We adopt an \emph{approximate recovery} criterion that allows for a number of missed edges or incorrectly-included edges, in contrast with the widely-studied exact recovery problem. Our main results provide information-theoretic lower bounds on the sample complexity for graph classes imposing constraints on the number of edges, maximal degree, and other properties. We identify a broad range of scenarios where, either up to constant factors or logarithmic factors, our lower bounds match the best known lower bounds for the exact recovery criterion, several of which are known to be tight or near-tight. Hence, in these cases, approximate recovery has a similar difficulty to exact recovery in the minimax sense. Our bounds are obtained via a modification of Fano's inequality for handling the approximate recovery criterion, along with suitably-designed ensembles of graphs that can broadly be classed into two categories: (i) Those containing graphs that contain several isolated edges or cliques and are thus difficult to distinguish from the empty graph; (ii) Those containing graphs for which certain groups of nodes are highly correlated, thus making it difficult to determine precisely which edges connect them. We support our theoretical results on these ensembles with numerical experiments.

Choosing appropriate architectures and regularization strategies of deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogous problem of constructing useful priors on compositions of functions. Specifically, we study the deep Gaussian process, a type of infinitely-wide, deep neural network. We show that in standard architectures, the representational capacity of the network tends to capture fewer degrees of freedom as the number of layers increases, retaining only a single degree of freedom in the limit. We propose an alternate network architecture which does not suffer from this pathology. We also examine deep covariance functions, obtained by composing infinitely many feature transforms. Lastly, we characterize the class of models obtained by performing dropout on Gaussian processes. In this paper, we propose to study the problem of choosing neural net architectures by viewing deep neural networks as priors on functions. By viewing neural networks this way, one can analyze their properties without reference to any particular dataset, loss function, or training method.

The winners of this year's aerial photography competition run by online site Dronestagram have been announced. The winning pictures taken using drone cameras were selected from thousands of entries by the judges, including National Geographic Deputy Director Patrick Witty and Emanuela Ascoli, photo editor of National Geographic, France. Here we present the winning images from three categories.

George Burgess, the founder of Gojimo, a revision app, explores how artificial intelligence can be used within education. Artificial Intelligence (AI) has dominated tech news in 2016, from Google's ground breaking AlphaGo to Microsoft's racist Tay and Amazon's Echo. This has led to a heated debate on what it means for the human race, from socioeconomic concerns about loss of jobs in the fourth industrial revolution to moral, philosophical and even religious questions about our understanding of human consciousness. Rather than stray into these murky waters, I think it is best to concentrate on the sectors where AI can make a quantifiable and significant difference without threatening livelihoods or invoking metaphysics. One such area is education.

Scientists have taught a robot how to hunt and destroy prey in a chilling new experiment. The test comes as experts warm AI could wipe out a tenth of the global population in five years. The ability to identify and zone in on a specific target will be crucial for any useful robotic technology like driverless cars, the researchers at the University of Zurich in Switzerland believe. And despite the chilling prospect of allowing a robot to mark up a target, they believe their research will prove more useful than deadly. Scientists have taught a robot how to hunt and destroy prey in a chilling new experiment.