These self-driving concept vehicles are like nothing you've seen before BMW has unveiled its vision for a self-driving electric crossover SUV and, if it actually ends up being a lot like the concept version, it should be a pretty cozy place to hang out. The BMW Vision iNext is a concept vehicle for now. But BMW executives said they plan to put something like it into production in 2021. The car was unveiled inside the belly of a cargo plane at John F. Kennedy International Airport in New York. Inside, BMW's executives displayed working versions of some of the technologies being considered for the car, including touch sensitive fabrics and interactive projections.

The British installation at the London Design Biennale is an international project that demonstrates how victims of human rights violations around the world can gather proof of their own experiences. Plastic bottles, digital cameras and kites, just some of the low-cost items in the exhibition, are being used in the Sinjar region of northern Iraq to gather the remaining evidence of Isis's 2014 treatment of the Yazidi ethnic minority, treatment that survivors and their supporters have called genocide and hope to prosecute in the international courts. Not only do they say thousands were killed by the terrorist group and thousands more displaced, but Yazidi cultural and religious heritage sites were destroyed and their temples were used as mass graves. Four years later, the region is still dangerous, littered with landmines and booby-traps left by the militants as they retreated. So when Yazda, a global rights organisation established by the Yazidi diaspora, sought help in supplementing their documentation efforts from Forensic Architecture, an independent research agency based at Goldsmiths, University of London, its team of architects, photographers, software developers, lawyers and archaeologists adapted their investigative methods to provide ways for Yazidis to gather video and data without entering the most hazardous areas.

Rapid developments in technology require professionals to upgrade their skills for technology-centered jobs of tomorrow. Srikanth Vidapanakal, who has been into data for more than 18 years, was inquisitive to learn about new technologies. He did a Self-Driving Car Engineer Nanodegree that helped him acquire advanced skills and landed him with a job in automation sector. Srikanth is an example of lifelong learning where staying relevant in the age of rapidly changing technologies is the need of the hour. In 2017, research suggested that AI and robotics could collectively take over 800 million jobs worldwide by 2030.

Last year, Bipin Preet Singh let go of his personal assistant. "A traditional secretary is just a messaging medium and not incredibly efficient," says the CEO and founder of MobiKwik. "Plus, there are some things that I wouldn't schedule through a secretary." For people who directly report to him, a secretary represented a layer of bureaucracy. We encourage interaction and informal conversations," Singh adds. Times have never been tougher for the secretary. Competing with virtual assistants, Google calender and mobile apps that can make bookings, take down minutes of meetings, store records and even send reminders to drink water, the secretary has lost some of his/her swag and salary. For an earlier generation of corporate leaders, having a secretary used to be a symbol of status, a measure of their professional success. But new-age managers scoff at the idea of having one. "Communication has evolved so much.

BOSTON and BANGALORE, India, September 15, 2018 /PRNewswire-PRWeb/ -- Independent analyst firm, rpa2ai has released RPA50, the first in a series of in-depth research reports on Robotic Process Automation (RPA). RPA50 is the industry's most comprehensive listing of RPA vendors to-date. The RPA50 infographic lists the top 50 global vendors within the Robotic Process Automation (RPA) marketplace. It also identifies eight different vendor categories and provides guidance on when to consider which category of vendors. In addition to vendor summaries, the research analyzes the RPA ecosystem and marketplace development, examines the impact of RPA, highlights implementation challenges and the role of professional services.

A polygonal mesh representation provides an efficient approximation for 3D shapes. It explicitly captures both shape surface and topology, and leverages non-uniformity to represent large flat regions as well as sharp, intricate features. This non-uniformity and irregularity, however, inhibits mesh analysis efforts using neural networks that combine convolution and pooling operations. In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes. Analogous to classic CNNs, MeshCNN combines specialized convolution and pooling layers that operate on the mesh edges, by leveraging their intrinsic geodesic connections. Convolutions are applied on edges and the four edges of their incident triangles, and pooling is applied via an edge collapse operation that retains surface topology, thereby, generating new mesh connectivity for the subsequent convolutions. MeshCNN learns which edges to collapse, thus forming a task-driven process where the network exposes and expands the important features while discarding the redundant ones. We demonstrate the effectiveness of our task-driven pooling on various learning tasks applied to 3D meshes.

Variational Auto-Encoders enforce their learned intermediate latent-space data distribution to be a simple distribution, such as an isotropic Gaussian. However, this causes the posterior collapse problem and loses manifold structure which can be important for datasets such as facial images. A GAN can transform a simple distribution to a latent-space data distribution and thus preserve the manifold structure, but optimizing a GAN involves solving a Min-Max optimization problem, which is difficult and not well understood so far. Therefore, we propose a GAN-like method to transform a simple distribution to a data distribution in the latent space by solving only a minimization problem. This minimization problem comes from training a discriminator between a simple distribution and a latent-space data distribution. Then, we can explicitly formulate an Optimal Transport (OT) problem that computes the desired mapping between the two distributions. This means that we can transform a distribution without solving the difficult Min-Max optimization problem. Experimental results on an eight-Gaussian dataset show that the proposed OT can handle multi-cluster distributions. Results on the MNIST and the CelebA datasets validate the effectiveness of the proposed method.

Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers' decision. Most existing methods rely on conventional features to represent an image, such as the visual features extracted by convolutional neural networks (CNN features) and the scale-invariant feature transform algorithm (SIFT features), color histograms, and so on. Nevertheless, one important type of features, the \emph{aesthetic features}, is seldom considered. It plays a vital role in clothing recommendation since a users' decision depends largely on whether the clothing is in line with her aesthetics, however the conventional image features cannot portray this directly. To bridge this gap, we propose to introduce the aesthetic information, which is highly relevant with user preference, into clothing recommender systems. To achieve this, we first present the aesthetic features extracted by a pre-trained neural network, which is a brain-inspired deep structure trained for the aesthetic assessment task. Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner. We conduct extensive experiments on real-world datasets, which demonstrate that our approach can capture the aesthetic preference of users and significantly outperform several state-of-the-art recommendation methods.

Process Mining consists of techniques where logs created by operative systems are transformed into process models. In process mining tools it is often desired to be able to classify ongoing process instances, e.g., to predict how long the process will still require to complete, or to classify process instances to different classes based only on the activities that have occurred in the process instance thus far. Recurrent neural networks and its subclasses, such as Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), have been demonstrated to be able to learn relevant temporal features for subsequent classification tasks. In this paper we apply recurrent neural networks to classifying process instances. The proposed model is trained in a supervised fashion using labeled process instances extracted from event log traces. This is the first time we know of GRU having been used in classifying business process instances. Our main experimental results shows that GRU outperforms LSTM remarkably in training time while giving almost identical accuracies to LSTM models. Additional contributions of our paper are improving the classification model training time by filtering infrequent activities, which is a technique commonly used, e.g., in Natural Language Processing (NLP).

Class labels are often imperfectly observed, due to mistakes and to genuine ambiguity among classes. We propose a new semi-supervised deep generative model that explicitly models noisy labels, called the Mislabeled VAE (M-VAE). The M-VAE can perform better than existing deep generative models which do not account for label noise. Additionally, the derivation of M-VAE gives new theoretical insights into the popular M1+M2 semi-supervised model.