This is your brain on stories. By tracking the blood flow in people's brains as they listened to a storytelling radio show, scientists at UC Berkeley have mapped out where the meanings associated with basic words are encoded in the cortex, creating the first semantic atlas of the brain. The findings, described in the journal Nature, provide an unprecedented view of language and meaning as it plays out on our neural terrain, and could potentially offer a road map for those looking to help patients with certain types of aphasia or other neurological disorders. For a long time, researchers thought about language as a primarily left-hemisphere function that took place in specific spots of the brain, such as Broca's area and Wernicke's area. But those areas aren't associated with understanding language but producing it – speech, in short.

Last month we heard about DRU, the Domino's delivery robot that's getting a trial run in Australia. The idea may seem silly, but some new restaurant industry numbers highlight the growing importance of food delivery in an age when consumers expect online ordering and rapid to-their-door service. As consumers get more comfortable with autonomous delivery (which is on the way, despite lots of skepticism), a restaurant industry that already uses state of the art logistics services could begin adding delivery robots to their operations in the next decade. According to information provided by 1010data, our hunger for the pies is growing. Domino's, Pizza Hut, and Papa John's combined to account for 45.1% of total food delivery sales, up from 40.3% in 2014.

Artificial intelligence has revolutionized information technology. The new economy of information technology has shaped the way we are living. Google led the way, showing the power of data-driven artificial intelligence delivered over the cloud, not only in search but also in tasks like language translation and computer vision. Artificial intelligence run through the cloud is now the dominant approach used by researchers at technology companies, universities and government labs. We're seeing a rebirth of artificial intelligence driven by the cloud, huge amounts of data and the learning algorithms of software.

Hyundai Mobis (KRX:012330) is increasing opportunities for technical exchange to secure original future car technologies, such as environmentally-friendly cars and intelligent cars, which emerged as new growth engines of the automotive industry. On April 28th, Hyundai Mobis announced that it would invite dozens of local and overseas experts consisting of university professors and researchers from institutions and organizations, and hold the industry-academia'Technology Forum' from May to November. This'Technology Forum' was first held in 2010, and this year marks its 7th anniversary. The forum began with the aim of actively embracing the knowledge and ideas of external experts from various fields, and thus improving the R&D capabilities of Hyundai Mobis. As part of the forum, the company will hold professional technical seminars and workshops, and receive feedback from experts in various fields and explore the direction of developing advanced environmentally-friendly and intelligent vehicle technologies.

When it comes to robots replacing humans, we might think we have the upper hand since we're the ones who build and program them but that's not neccesarily the case anymore. Google is taking a different approach to training its robots – it's letting them teach each other. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May. Researchers at Google have released a report showing how they connected 14 robotic arms together and used convolutional neural networks to let them teach themselves how to pick things up. The approach mimics how young children learn between the ages of one and four years old, and is essentially helping the robots to develop reliable hand-eye coordination.

Visualization of high-dimensional data, such as text documents, is useful to map out the similarities among various data points. In the high-dimensional space, documents are commonly represented as bags of words, with dimensionality equal to the vocabulary size. Classical approaches to document visualization directly reduce this into visualizable two or three dimensions. Recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. While aiming for a good fit between the model parameters and the observed data, previous approaches have not considered the local consistency among data instances. We consider the problem of semantic visualization by jointly modeling topics and visualization on the intrinsic document manifold, modeled using a neighborhood graph. Each document has both a topic distribution and visualization coordinate. Specifically, we propose an unsupervised probabilistic model, called Semafore, which aims to preserve the manifold in the lower-dimensional spaces through a neighborhood regularization framework designed for the semantic visualization task. To validate the efficacy of Semafore, our comprehensive experiments on a number of real-life text datasets of news articles and Web pages show that the proposed methods outperform the state-of-the-art baselines on objective evaluation metrics.

Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and noisy observations. This can be a hard problem in complex processes with large state spaces. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the process. We consider a specific type of causal relation, called passivity, which pertains to how state variables cause changes in other variables. We present the Passivity-based Selective Belief Filtering (PSBF) method, which maintains a factored belief representation and exploits passivity to perform selective updates over the belief factors. PSBF produces exact belief states under certain assumptions and approximate belief states otherwise, where the approximation error is bounded by the degree of uncertainty in the process. We show empirically, in synthetic processes with varying sizes and degrees of passivity, that PSBF is faster than several alternative methods while achieving competitive accuracy. Furthermore, we demonstrate how passivity occurs naturally in a complex system such as a multi-robot warehouse, and how PSBF can exploit this to accelerate the filtering task.

Face recall is a basic human cognitive process performed routinely, e.g., when meeting someone and determining if we have met that person before. Assisting a subject during face recall by suggesting candidate faces can be challenging. One of the reasons is that the search space - the face space - is quite large and lacks structure. A commercial application of face recall is facial composite systems - such as Identikit, PhotoFIT, and CD-FIT - where a witness searches for an image of a face that resembles his memory of a particular offender. The inherent uncertainty and cost in the evaluation of the objective function, the large size and lack of structure of the search space, and the unavailability of the gradient concept makes this problem inappropriate for traditional optimization methods. In this paper we propose a novel evolutionary approach for searching the face space that can be used as a facial composite system. The approach is inspired by methods of Bayesian optimization and differs from other applications in the use of the skew-normal distribution as its acquisition function. This choice of acquisition function provides greater granularity, with regularized, conservative, and realistic results.

The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not account for future effects. This paper introduces new strategies for the optimal design of sequential experiments. First, we rigorously formulate the general sequential optimal experimental design (sOED) problem as a dynamic program. Batch and greedy designs are shown to result from special cases of this formulation. We then focus on sOED for parameter inference, adopting a Bayesian formulation with an information theoretic design objective. To make the problem tractable, we develop new numerical approaches for nonlinear design with continuous parameter, design, and observation spaces. We approximate the optimal policy by using backward induction with regression to construct and refine value function approximations in the dynamic program. The proposed algorithm iteratively generates trajectories via exploration and exploitation to improve approximation accuracy in frequently visited regions of the state space. Numerical results are verified against analytical solutions in a linear-Gaussian setting. Advantages over batch and greedy design are then demonstrated on a nonlinear source inversion problem where we seek an optimal policy for sequential sensing.

This paper considers the problem of robust subspace recovery: given a set of $N$ points in $\mathbb{R}^D$, if many lie in a $d$-dimensional subspace, then can we recover the underlying subspace? We show that Tyler's M-estimator can be used to recover the underlying subspace, if the percentage of the inliers is larger than $d/D$ and the data points lie in general position. Empirically, Tyler's M-estimator compares favorably with other convex subspace recovery algorithms in both simulations and experiments on real data sets.