South Korean tech Major LG Electronics has announced that Google Assistant is coming to its 2018 line-up of artificial intelligence (AI)-enabled ThinQ TVs in seven new countries. The company's ThinQ TVs came with integrated Google's digital assistant when they were introduced in the US and added support for Amazon's virtual assistant Alexa's commands soon after. "Google Assistant will be available in Canada, Australia and the UK, with support coming to South Korea, Spain, France and Germany by the end of the year," The Verge reported late on Friday. "The built-in ThinQ AI, which runs on LG's own'WebOS' can be used for TV-specific commands, such as'search for the soundtrack of this movie', while Google Assistant and Alexa can be used as a smart home hub," it said. The company was also planning on bringing Amazon Alexa support to Australia and Canada in the future.

According to the World Economic Forum and Deloitte report, released today, AI will dramatically upend the traditional dynamics of the financial services system, and this is good news for customers. "Banks today may have customers who aren't willing to change banks because of the high costs associated and the effort involved with shifting mortgages," Deloitte Australia digital partner Joel Lipman said. "But the future will see these costs removed as AI developments, such as personal banking assistants, are able to identify the best deal for customers and move them without the current high dependency on humans." He said this will be the "new battlefield for customer loyalty" as past barriers to switching, like cost, speed and access are eroded. At the same time, consumers can expect tailored banking solutions, which will also shift the existing dynamics. "As past methods of differentiation erode, AI presents an opportunity for institutions to escape a'race to the bottom' in price competition by introducing new ways to distinguish themselves to customers," the report said.

TAMPA, FLORIDA – Two years after launching from Florida, a NASA spacecraft is closing in on an ancient asteroid, Bennu, for a sample of space dust that could reveal clues to the start of life in the solar system. The spacecraft, OSIRIS-REx, has even snapped its first, blurry pic of the cosmic body, which is about the size of a small mountain, about 500 yards (meters) in diameter. The spacecraft is designed to circle Bennu, and reach out with a robotic arm to "high-five" its surface, then return the sample it collects to Earth in 2023. The first images of Bennu were taken on Aug. 17 at a distance of 1.4 million miles (2.3 million km) from the $800 million spacecraft. "This is the closest we have even been to Bennu," said Dante Lauretta, OSIRIS-REx principal investigator at the University of Arizona, Tucson.

An autumn classic matching the unbeaten Tigers, with star tailback Dick Kazmaier--a gifted passer, runner, and punter who would capture a record number of votes to win the Heisman Trophy--against rival Dartmouth. Princeton prevailed over Big Green in the penalty-plagued game, but not without cost: Nearly a dozen players were injured, and Kazmaier himself sustained a broken nose and a concussion (yet still played a "token part"). It was a "rough game," The New York Times described, somewhat mildly, "that led to some recrimination from both camps." Each said the other played dirty. The game not only made the sports pages, it made the Journal of Abnormal and Social Psychology.

Prudential Corporation Asia and UK-based Babylon Health (Babylon) have partnered for AI-backed digital health services in Asia. The partnership will see Babylon's AI expertise made available to existing and new customers of Prudential across Asia. Prudential has more than five million medical insurance customers in Asia and premium income exceeded £800 million in 2017. Based in the UK, Babylon offers AI-powered health services, including personal health assessment and treatment information. The Prudential-Babylon proposition will offer customers in up to 12 markets in Asia 24/7 access to a comprehensive set of digital health tools, complementing Prudential's existing suite of insurance products.

Banking firms have many of the same IT challenges of any other industry: infrastructure scalabity requirements, the need for application modernization and a pressure to use data to build better customer experiences. At the same time, banking firms also face some of the most stringent security and compliance standards of any industry. Cloud technology can be a powerful tool for meeting these demands simultaneously. In France, approximately 50 percent of corporate fraud attempts involve tricking companies into diverting payments into a criminal's bank account instead of paying suppliers. With help from IBM, SiS, a French company that specializes in fraud protection, built a blockchain in the IBM Cloud that acts as a tamper-proof repository of verified bank information, and developed a service that helps clients check transactions and detect anomalies in seconds.

Welcome to the AI era. We missed the official announcement too, but it's obvious that's what we're in. This new paradigm requires the acceptance or denial of a new brand of faith: Artificial general intelligence (AGI). Or, sentient machines, if you prefer. Either way, let's talk about killer robots.

We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allowing for language specific parameterization, and that can also be used for domain adaptation. Our approach requires no changes to the model architecture of a standard NMT system, but instead introduces a new component, the contextual parameter generator (CPG), that generates the parameters of the system (e.g., weights in a neural network). This parameter generator accepts source and target language embeddings as input, and generates the parameters for the encoder and the decoder, respectively. The rest of the model remains unchanged and is shared across all languages. We show how this simple modification enables the system to use monolingual data for training and also perform zero-shot translation. We further show it is able to surpass state-of-the-art performance for both the IWSLT-15 and IWSLT-17 datasets and that the learned language embeddings are able to uncover interesting relationships between languages.

Heterogeneous treatment effects are the center of gravity in many modern causal inference applications. In this paper, we investigate the estimation and inference of heterogeneous treatment effects with precision in a general nonparametric setting. To this end, we enhance the classical $k$-nearest neighbor method with a simple algorithm, extend it to a distributional setting, and suggest the two-scale distributional nearest neighbors (DNN) estimator with reduced finite-sample bias. Our recipe is first to subsample the data and average the 1-nearest neighbor estimators from each subsample. With appropriately chosen subsampling scale, the resulting DNN estimator is proved to be asymptotically unbiased and normal under mild regularity conditions. We then proceed with combining DNN estimators with different subsampling scales to further reduce bias. Our theoretical results on the advantages of the new two-scale DNN framework are well supported by several Monte Carlo simulations. The newly suggested method is also applied to a real-life data set to study the heterogeneity of treatment effects of smoking on children's birth weights across mothers' ages.

With the rising influence of machine learning algorithms on many important aspects of our daily lives, there are growing concerns that biases inherent in data can lead the behavior of these algorithms to discriminate against certain populations [1, 2, 4, 6, 8, 28, 29, 15]. In recent years, substantial research effort has been devoted to the development of mathematical definitions of bias, or its opposite, fairness, in algorithms and in data [15, 18, 26, 23, 19, 32]. In this work, we focus on the fairness scenario where there are multiple protected attributes that we aim to ensure fairness for, and which may potentially overlap with each other, such as gender, race, and sexual orientation. Our guiding principle is intersectionality, the core theoretical framework underlying the thirdwave feminist movement [13]. The principle of intersectionality states that racism, sexism, and other social systems which harm marginalized groups are interlocking in their effects, such that the lived experience of, e.g., black women, is very different than that of, e.g., white women. Intersectionality was defined by Kimberlé Crenshaw in the 1980's [13] and popularized in the 1990's, e.g. by Patricia Hill Collins [10], although the ideas are much older [11, 35]. In the context of machine learning and fairness, intersectionality was recently considered by [7], who studied the impact of the intersection of gender and skin color on computer vision performance, and by [23, 19], who aimed to protect certain subgroups in order to prevent "fairness gerrymandering."