Apple unveiled a smarter Siri personal assistant on Monday that's picked up some features already offered by Google, but emphasized that it's improving Siri without compromising the company's commitment to user privacy. An update to the iPhone and iPad operating system coming this fall will deliver a Siri that's able to search through more apps than ever and offer users' information based on what it thinks they might want to know. That includes automatically adding event invitations to the Calendar app, telling iPhone holders who might be calling based on an unknown number matching one in an email and launching the Music app when someone plugs in headphones in the morning because that's become their routine. Apple made the announcement to kick off its weeklong Worldwide Developers Conference, a gathering for appmakers to learn about Apple products. "We think these kind of intelligence features make a huge difference in iOS 9," Craig Federighi, Apple's senior vice president of software engineering, told an audience of media and software developers at the Moscone Center in San Francisco.

Apple's voice assistant, Siri, has a new look as well as a host of enhancements. At its Worldwide Developers Conference on Monday, Apple unveiled some key changes to its voice assistant. Siri boasts a new interface so it looks a bit more appealing. But perhaps most notably, Siri will be able to remind you of appointments and other events. That's a key enhancement as it's been one of the most unique features to Microsoft's Cortana voice assistant.

The place to talk about games and other things that matter Monday 8 June 2015 03.13 EDT Last modified on Monday 8 June 2015 03.15 EDT

A linear function submission-based double-auction (LFS-DA) mechanism for a regional electricity network is proposed in this paper. Each agent in the network is equipped with a battery and a generator. Each agent simultaneously becomes a producer and consumer of electricity, i.e., a prosumer and trades electricity in the regional market at a variable price. In the LFS-DA, each agent uses linear demand and supply functions when they submit bids and asks to an auctioneer in the regional market.The LFS-DA can achieve an exact balance between electricity demand and supply for each time slot throughout the learning phase and was shown capable of solving the primal problem of maximizing the social welfare of the network without any central price setter, e.g., a utility or a large electricity company, in contrast with conventional real-time pricing (RTP). This paper presents a clarification of the relationship between the RTP algorithm derived on the basis of a dual decomposition framework and LFS-DA. Specifically, we proved that the changes in the price profile of the LFS-DA mechanism are equal to those achieved by the RTP mechanism derived from the dual decomposition framework except for a constant factor.

We consider the problem of designing locality sensitive hashes (LSH) for inner product similarity, and of the power of asymmetric hashes in this context. Shrivastava and Li argue that there is no symmetric LSH for the problem and propose an asymmetric LSH based on different mappings for query and database points. However, we show there does exist a simple symmetric LSH that enjoys stronger guarantees and better empirical performance than the asymmetric LSH they suggest. We also show a variant of the settings where asymmetry is in-fact needed, but there a different asymmetric LSH is required.

We propose a clustering algorithm which, for input, takes data assumed to be sampled from a uniform distribution supported on a metric space $X$, and outputs a clustering of the data based on a topological estimate of the connected components of $X$. The algorithm works by choosing a weighted graph on the samples from a natural one-parameter family of graphs using an error based on the heat operator on the graphs. The estimated connected components of $X$ are identified as the support of the eigenfunctions of the heat operator with eigenvalue $1$, which allows the algorithm to work without requiring the number of expected clusters as input.

Maximum pseudolikelihood method has been among the most important methods for learning parameters of statistical physics models, such as Ising models. In this paper, we study how pseudolikelihood can be derived for learning parameters of a mixture of Ising models. The performance of the proposed approach is demonstrated for Ising and Potts models on both synthetic and real data.

We revisit the choice of SGD for training deep neural networks by reconsidering the appropriate geometry in which to optimize the weights. We argue for a geometry invariant to rescaling of weights that does not affect the output of the network, and suggest Path-SGD, which is an approximate steepest descent method with respect to a path-wise regularizer related to max-norm regularization. Path-SGD is easy and efficient to implement and leads to empirical gains over SGD and AdaGrad.

Several authors have recently developed risk-sensitive policy gradient methods that augment the standard expected cost minimization problem with a measure of variability in cost. These studies have focused on specific risk-measures, such as the variance or conditional value at risk (CVaR). In this work, we extend the policy gradient method to the whole class of coherent risk measures, which is widely accepted in finance and operations research, among other fields. We consider both static and time-consistent dynamic risk measures. For static risk measures, our approach is in the spirit of policy gradient algorithms and combines a standard sampling approach with convex programming. For dynamic risk measures, our approach is actor-critic style and involves explicit approximation of value function. Most importantly, our contribution presents a unified approach to risk-sensitive reinforcement learning that generalizes and extends previous results.

We propose LOCO, an algorithm for large-scale ridge regression which distributes the features across workers on a cluster. Important dependencies between variables are preserved using structured random projections which are cheap to compute and must only be communicated once. We show that LOCO obtains a solution which is close to the exact ridge regression solution in the fixed design setting. We verify this experimentally in a simulation study as well as an application to climate prediction. Furthermore, we show that LOCO achieves significant speedups compared with a state-of-the-art distributed algorithm on a large-scale regression problem.