Artificial intelligence might seem more the preserve of computer nerds and tech giants than pharma companies. But according to Boehringer Ingelheim's global chief data scientist, Philipp Diesinger, "the entire industry is looking at data science and AI". This increased focus on data could drastically change the way drugs are developed and paid for. For example, AI will be vital if outcomes-based healthcare is to be successfully implemented, pointed out Philips' chief innovation & strategy officer, Jeroen Tas, who also stressed that AI really signaled a new way of handling data. He described AI as "the way you interpret data. You constantly stream the data and add that data to the body of knowledge," he told EP Vantage during the AI Summit in London in May.

Elon Musk has hired a new director of AI research at Tesla, and it may signal a plan to rethink the way its automated driving works. This week, Musk poached Andrej Karpathy, an expert on vision, deep learning, and reinforcement learning, from OpenAI, a nonprofit that Musk and others are funding that's dedicated to "discovering and enacting the path to safe artificial general intelligence." Karpathy, who will apparently report directly to Musk, is a rising star in the world of AI, having studied at Stanford with Fei-Fei Li, a leading AI expert who is now the chief scientist of Google Cloud. Li is famous in tech circles for having developed a data set of images that helped inspire a breakthrough in machine vision. Many have pointed to Karpathy's expertise in computer vision as a key asset for Tesla, and that's true.

Newswire) Research and Markets has announced the addition of the "Innovations in IoT-, Machine Learning-, and Artificial Intelligence-based Security Solutions" report to their offering. This edition of Network Security TOE provides a snapshot of the emerging security solutions based on artificial intelligence, machine learning, and other new technologies that help companies mitigate threats and defend against modern attacks. TechVision Information & Communication Technology cluster provides global industry analysis, technology competitive analysis, and insights into game-changing technologies in the wireless communication and computing space. Innovations in ICT have deeply permeated various applications and markets. These innovations have profound impact on a range of business functions for computing, communications, business intelligence, data processing, information security, workflow automation, quality of service (QoS) measurements, simulations, customer relationship management, knowledge management functions and many more.

How can we anticipate and manage trends in AI, and will creative and analytical skills really be the key to job success in an AI world? The progression and extensive testing of these vehicles presents a problem that cannot be easily fixed, meaning there'll be a gap between the smart cruise control phase and the introduction of a truly autonomous vehicle. That's not to say that autonomous vehicles won't be amazing. This next generation will become amazing at controlling and training the bots that manage their life.

Suppose, we are given a set of $n$ elements to be clustered into $k$ (unknown) clusters, and an oracle/expert labeler that can interactively answer pair-wise queries of the form, "do two elements $u$ and $v$ belong to the same cluster?". The goal is to recover the optimum clustering by asking the minimum number of queries. In this paper, we initiate a rigorous theoretical study of this basic problem of query complexity of interactive clustering, and provide strong information theoretic lower bounds, as well as nearly matching upper bounds. Most clustering problems come with a similarity matrix, which is used by an automated process to cluster similar points together. Our main contribution in this paper is to show the dramatic power of side information aka similarity matrix on reducing the query complexity of clustering. A similarity matrix represents noisy pair-wise relationships such as one computed by some function on attributes of the elements. A natural noisy model is where similarity values are drawn independently from some arbitrary probability distribution $f_+$ when the underlying pair of elements belong to the same cluster, and from some $f_-$ otherwise. We show that given such a similarity matrix, the query complexity reduces drastically from $\Theta(nk)$ (no similarity matrix) to $O(\frac{k^2\log{n}}{\cH^2(f_+\|f_-)})$ where $\cH^2$ denotes the squared Hellinger divergence. Moreover, this is also information-theoretic optimal within an $O(\log{n})$ factor. Our algorithms are all efficient, and parameter free, i.e., they work without any knowledge of $k, f_+$ and $f_-$, and only depend logarithmically with $n$. Along the way, our work also reveals intriguing connection to popular community detection models such as the {\em stochastic block model}, significantly generalizes them, and opens up many venues for interesting future research.

Reservoir Computing (RC) refers to a Recurrent Neural Networks (RNNs) framework, frequently used for sequence learning and time series prediction. The RC system consists of a random fixed-weight RNN (the input-hidden reservoir layer) and a classifier (the hidden-output readout layer). Here we focus on the sequence learning problem, and we explore a different approach to RC. More specifically, we remove the non-linear neural activation function, and we consider an orthogonal reservoir acting on normalized states on the unit hypersphere. Surprisingly, our numerical results show that the system's memory capacity exceeds the dimensionality of the reservoir, which is the upper bound for the typical RC approach based on Echo State Networks (ESNs). We also show how the proposed system can be applied to symmetric cryptography problems, and we include a numerical implementation.

Recent advances in neural networks have inspired people to design hybrid recommendation algorithms that can incorporate both (1) user-item interaction information and (2) content information including image, audio, and text. Despite their promising results, neural network-based recommendation algorithms pose extensive computational costs, making it challenging to scale and improve upon. In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing state-of-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework. We tackle this issue by first establishing a connection between the loss functions and the user-item interaction bipartite graph, where the loss function terms are defined on links while major computation burdens are located at nodes. We call this type of loss functions "graph-based" loss functions, for which varied mini-batch sampling strategies can have different computational costs. Based on the insight, three novel sampling strategies are proposed, which can significantly improve the training efficiency of the proposed framework (up to $\times 30$ times speedup in our experiments), as well as improving the recommendation performance. Theoretical analysis is also provided for both the computational cost and the convergence. We believe the study of sampling strategies have further implications on general graph-based loss functions, and would also enable more research under the neural network-based recommendation framework.

In this paper, we address the scalability of optimization algorithms for deep learning in a distributed setting. Scaling up deep learning [1] is becoming increasingly crucial for large-scale applications where the sizes of both the available data as well as the models are massive [2]. Among various algorithmic advances, many recent attempts have been made to parallelize stochastic gradient descent (SGD) based learning schemes across multiple computing agents. An early approach called Downpour SGD [3], developed within Google's disbelief software framework, primarily focuses on model parallelization (i.e., splitting the model across the agents). A different approach known as elastic averaging SGD (EASGD) [4] attempts to improve perform multiple SGDs in parallel; this method uses a central parameter server that helps in assimilating parameter updates from the computing agents. However, none of the above approaches concretely address the issue of data parallelization, which is an important issue for several learning scenarios: for example, data parallelization enables privacy-preserving learning in scenarios such as distributed learning with a network of mobile and Internet-of-Things (IoT) devices. A recent scheme called Federated Averaging SGD [5] attempts such a data parallelization in the context of deep learning with significant success; however, they still use a central parameter server. In contrast, deep learning with decentralized computation can be achieved via gossip SGD algorithms [6, 7], where agents communicate probabilistically without the aid of a parameter server.

We adopt data structure in the form of cover trees and iteratively apply approximate nearest neighbour (ANN) searches for fast compressed sensing reconstruction of signals living on discrete smooth manifolds. Levering on the recent stability results for the inexact Iterative Projected Gradient (IPG) algorithm and by using the cover tree's ANN searches, we decrease the projection cost of the IPG algorithm to be logarithmically growing with data population for low dimensional smooth manifolds. We apply our results to quantitative MRI compressed sensing and in particular within the Magnetic Resonance Fingerprinting (MRF) framework. For a similar (or sometimes better) reconstruction accuracy, we report 2-3 orders of magnitude reduction in computations compared to the standard iterative method which uses brute-force searches.

Predictive models ground many state-of-the-art developments in statistical brain image analysis: decoding, MVPA, searchlight, or extraction of biomarkers. The principled approach to establish their validity and usefulness is cross-validation, testing prediction on unseen data. Here, I would like to raise awareness on error bars of cross-validation, which are often underestimated. Simple experiments show that sample sizes of many neuroimaging studies inherently lead to large error bars, eg $\pm$10% for 100 samples. The standard error across folds strongly underestimates them. These large error bars compromise the reliability of conclusions drawn with predictive models, such as biomarkers or methods developments where, unlike with cognitive neuroimaging MVPA approaches, more samples cannot be acquired by repeating the experiment across many subjects. Solutions to increase sample size must be investigated, tackling possible increases in heterogeneity of the data.