This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where restaurants have latent characteristics (whose distribution may depend on restaurant observables, such as star ratings, food category, and price range), each user has preferences for these latent characteristics, and these preferences are heterogeneous across users. Similarly, each item has latent characteristics that describe users' willingness to travel to the restaurant, and each user has individual-specific preferences for those latent characteristics. Thus, both users' willingness to travel and their base utility for each restaurant vary across user-restaurant pairs. We use a Bayesian approach to estimation. To make the estimation computationally feasible, we rely on variational inference to approximate the posterior distribution, as well as stochastic gradient descent as a computational approach. Our model performs better than more standard competing models such as multinomial logit and nested logit models, in part due to the personalization of the estimates. We analyze how consumers re-allocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants with similar characteristics, and we compare our predictions to actual outcomes. Finally, we show how the model can be used to analyze counterfactual questions such as what type of restaurant would attract the most consumers in a given location.
AT&T has announced that it will be bringing 5G to five more cities by the end of 2018, with mobile services to launch in Houston, New Orleans, San Antonio, Jacksonville, and Louisville. The carrier also announced that it is planning to launch mobile 5G services in parts of Las Vegas, Los Angeles, Nashville, Orlando, San Diego, San Francisco, and San Jose in early 2019. The 12 new cities slated to receive 5G from AT&T join the previously announced Dallas, Atlanta, Waco, Charlotte, Raleigh, and Oklahoma City. Across its 19 5G deployments, AT&T said it has selected Ericsson, Nokia, and Samsung as its vendors. "Working with these three suppliers, we've already started deploying 3GPP Release 15 compliant equipment in a handful of our early 5G cities," AT&T said.
Robots with truly humanlike dexterity are far from becoming reality, but progress accelerated by AI has brought us closer to achieving this vision than ever before. In a research paper published in September, a team of scientists at Google detailed their tests with a robotic hand that enabled it to rotate Baoding balls with minimal training data. And at a computer vision conference in June, MIT researchers presented their work on an AI model capable of predicting the tactility of physical things from snippets of visual data alone. Now, OpenAI -- the San Francisco-based AI research firm cofounded by Elon Musk and others, with backing from luminaries like LinkedIn cofounder Reid Hoffman and former Y Combinator president Sam Altman -- says it's on the cusp of solving something of a grand challenge in robotics and AI systems: solving a Rubik's cube. Unlike breakthroughs achieved by teams at the University of California, Irvine and elsewhere, which leveraged machines tailor-built to manipulate Rubik's cubes with speed, the approach devised by OpenAI researchers uses a five-fingered humanoid hand guided by an AI model with 13,000 years of cumulative experience -- on the same order of magnitude as the 40,000 years used by OpenAI's Dota-playing bot.
E LASTIC-I NFOGAN: U NSUPERVISEDD ISENTANGLED R EPRESENTATIONL EARNING IN I MBALANCEDD ATA Utkarsh Ojha 1, Krishna Kumar Singh 1, Cho-Jui Hsieh 2, and Y ong Jae Lee 1 1 University of California, Davis 2 University of California, Los Angeles A BSTRACT We propose a novel unsupervised generative model, Elastic-InfoGAN, that learns to disentangle object identity from other low-level aspects in class-imbalanced datasets. We first investigate the issues surrounding the assumptions about uniformity made by InfoGAN (Chen et al. (2016)), and demonstrate its ineffectiveness to properly disentangle object identity in imbalanced data. Our key idea is to make the discovery of the discrete latent factor of variation invariant to identity-preserving transformations in real images, and use that as the signal to learn the latent distribution's parameters. Experiments on both artificial (MNIST) and real-world (Y ouTube-Faces) datasets demonstrate the effectiveness of our approach in imbalanced data by: (i) better disentanglement of object identity as a latent factor of variation; and (ii) better approximation of class imbalance in the data, as reflected in the learned parameters of the latent distribution. Recent deep neural network based models such as Generative Adversarial Networks (Goodfellow et al. (2014); Salimans et al. (2016); Radford et al. (2016)) and V ariational Autoen-coders (Kingma & Welling (2014); Higgins et al. (2017)) have led to promising results in generating realistic samples for high-dimensional and complex data such as images. More advanced models show how to discover disentangled representations (Y an et al. (2016); Chen et al. (2016); Tran et al. (2017); Hu et al. (2018); Singh et al. (2019)), in which different latent dimensions can be made to represent independent factors of variation (e.g., pose, identity) in the data (e.g., human faces). InfoGAN (Chen et al. (2016)) in particular, tries to learn an unsupervised disentangled representation by maximizing the mutual information between the discrete or continuous latent variables and the corresponding generated samples. For discrete latent factors (e.g., digit identities), it assumes that they are uniformly distributed in the data, and approximates them accordingly using a fixed uniform categorical distribution. Although this assumption holds true for many existing benchmark datasets (e.g., MNIST LeCun (1998)), real-word data often follows a long-tailed distribution and rarely exhibits perfect balance between the categories.
Artificial intelligence is all the rage in healthcare as companies look for tech-driven ways to cut costs and promote patient health. Tech giants like Intel, Google, Amazon, Microsoft and Apple have swooped in to assist payers and providers with their efforts to join the fast-paced environment. Santa Clara, California-based Intel boasts partnerships across myriad sectors in healthcare. For example, earlier this year, not-for-profit integrated health system Sharp HealthCare, which is based in San Diego, used Intel's predictive analytics capabilities to alert its rapid-response team to identify high-risk patients before a health crisis occurred. And currently, Intel is working with pharmaceutical company Novartis on deep neural networks to accelerate content screening in drug discovery.