A Thai customer receiving food served by a homemade robotic automated guided vehicle called UBOT-01 at a diner in Hang Dong, Chiang Mai province, northern Thailand yesterday. The owner of an eatery in Thailand built an automated guided vehicle robot to work as a waiter serving food to customers, mainly aimed at saving hiring costs and attracting customers. The diner's UBOT-01 is a portable robot that operates by following a guided magnetic tape marked on the floor for navigation with safety sensors to detect obstacles.

This story was produced by the WIRED Brand Lab for Deloitte. Click here for the expanded article, including footnotes, on Deloitte.com. The Internet of Things is getting smarter. Companies are incorporating artificial intelligence--in particular, machine learning--into their IoT applications. With a wave of investment, a raft of new products, and a rising tide of enterprise deployments, artificial intelligence is making a splash in the Internet of Things (IoT).

LONDON – For many U.S. allies, Secretary of Defense Jim Mattis is the last of the Trump administration's so-called grown-ups in the room. So at Asia's main annual security forum he got a warm reception for his firm defense of the rules-based order the U.S. helped to build after World War II. Increasingly, though, Mattis' reassurance is not enough. The U.S. -- as much as China -- is seen as a threat to that system, undermining the very solutions the retired Marine Corps general offered to counter Beijing's rule breaking in the South China Sea. On Sunday, tiny Singapore, one of the United States' most like-minded partners in the region, drew a direct equivalence between the U.S. and China.

LAHORE - An exhibition titled "2nd Machine Learning Projects" featuring projects of International Technology University students on Artificial Intelligence opened at the Punjab Signal Processing and Information Decoding Research Laboratory on Sunday. The exhibition opened after four-month training of MS and PhD students of ITU who presented their Machine Learning course projects, geared towards solving interesting and locally relevant problems. The projects included a project on grocery stores who always find difficult to forecast sales and purchase of items. The project Walmart Data is aimed at predicting unit sales quantities of sales items across 54 grocery stores using the technique of rolling means and LSTM neural. This project will help managers in warehouse management, manpower estimation and effective sales promotions.

Tencent is a Chinese tech company founded in 1998 and based in Shenzhen that hosts 55% of China's mobile internet usage on its platforms. Its mission is to "become the most respected internet enterprise." The company is China's biggest social network company with 1 billion users on its app WeChat and 632 million monthly user accounts on social networking platform Qzone, is worth more than Facebook and has extended beyond instant messaging (its product is QQ) and social networking to gaming, digital assistants, mobile payments, cloud storage, education, live streaming, sports, movies and artificial intelligence. The company's dedication to artificial intelligence is evident in one of its slogans, "AI in all." In 2016, Tencent opened an AI lab in Schenzhen with a vision to "Make AI Everywhere." Its focus is on research in machine learning, speech recognition, natural language processing and computer vision and to develop practical AI applications for business in the areas of content, online games, social and cloud services.

Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region of the Central and Eastern North America (CENA). This paper outlines a framework for using Artificial Neural Networks (ANNs) to develop attenuation models from the ground motion recordings in this region. While attenuation models exist for the CENA, concerns over the increased rate of seismicity in this region necessitate investigation of ground motions prediction models particular to these states. To do so, an ANN-based framework is proposed to predict peak ground acceleration (PGA) and peak ground velocity (PGV) given magnitude, earthquake source-to-site distance, and shear wave velocity. In this framework, approximately 4,500 ground motions with magnitude greater than 3.0 recorded in these three states (Texas, Oklahoma, and Kansas) since 2005 are considered. Results from this study suggest that existing ground motion prediction models developed for CENA do not accurately predict the ground motion intensity measures for earthquakes in this region, especially for those with low source-to-site distances or on very soft soil conditions. The proposed ANN models provide much more accurate prediction of the ground motion intensity measures at all distances and magnitudes. The proposed ANN models are also converted to relatively simple mathematical equations so that engineers can easily use them to predict the ground motion intensity measures for future events. Finally, through a sensitivity analysis, the contributions of the predictive parameters to the prediction of the considered intensity measures are investigated.

Proportional representation (PR) is often discussed in voting settings as a major desideratum. For the past century or so, it is common both in practice and in the academic literature to jump to single transferable vote (STV) as the solution for achieving PR. Some of the most prominent electoral reform movements around the globe are pushing for the adoption of STV. It has been termed a major open problem to design a voting rule that satisfies the same PR properties as STV and better monotonicity properties. In this paper, we first present a taxonomy of proportional representation axioms for general weak order preferences, some of which generalise and strengthen previously introduced concepts. We then present a rule called Expanding Approvals Rule (EAR) that satisfies properties stronger than the central PR axiom satisfied by STV, can handle indifferences in a convenient and computationally efficient manner, and also satisfies better candidate monotonicity properties. In view of this, our proposed rule seems to be a compelling solution for achieving proportional representation in voting settings.

Speech is produced when time varying vocal tract system is excited with time varying excitation source. Therefore, the information present in a speech such as message, emotion, language, speaker is due to the combined effect of both excitation source and vocal tract system. However, there is very less utilization of excitation source features to recognize emotion. In our earlier work, we have proposed a novel method to extract glottal closure instants (GCIs) known as epochs. In this paper, we have explored epoch features namely instantaneous pitch, phase and strength of epochs for discriminating emotions. We have combined the excitation source features and the well known Male-frequency cepstral coefficient (MFCC) features to develop an emotion recognition system with improved performance. DNN-HMM speaker adaptive models have been developed using MFCC, epoch and combined features. IEMOCAP emotional database has been used to evaluate the models. The average accuracy for emotion recognition system when using MFCC and epoch features separately is 59.25% and 54.52% respectively. The recognition performance improves to 64.2% when MFCC and epoch features are combined.

We initiate the study of mechanism design without money for common goods. Our model captures a variation of the well-known one-dimensional facility location problem if the facility is assumed to have a capacity constraint $k

We theoretically find novel statistics of the FIM, which are universal among a wide class of deep networks with any number of layers and various activation functions. Although most of the FIM's eigenvalues are close to zero, the maximum eigenvalue takes on a huge value and the eigenvalue distribution has an extremely long tail. These statistics suggest that the shape of a loss landscape is locally flat in most dimensions, but strongly distorted in the other dimensions. Moreover, our theory of the FIM leads to quantitative evaluation of learning in deep networks. First, the maximum eigenvalue enables us to estimate an appropriate size of a learning rate for steepest gradient methods to converge. Second, the flatness induced by the small eigenvalues is connected to generalization ability through a norm-based capacity measure.