Misguided though it may have been to cleave "Kill Bill" in two, the decision yielded more than just a maddening exercise in delayed gratification and ruptured narrative. It separated the individual flavors, genres and tonal extremities of Quentin Tarantino's sprawling revenge epic into two distinct yin-and-yang halves, capable of being savored in isolation even when screened back-to-back. The two are connected less by narrative than by the feverish intensity of Tarantino's genre-straddling cinephilia. This Saturday's Cinespia screening at Hollywood Forever Cemetery may not be "The Whole Bloody Affair," the unofficial title of a combined single-film version (complete with extended 30-minute anime sequence) that has made the rounds in recent years. But any time there's a chance to see Uma Thurman shred every enemy in her path, well, all is right in the jungle. When: July 16, gates open at 7:15 p.m., movie starts at 9 p.m. Robot' is back, and it looks and feels like nothing else on TV Under pressure to turn his struggling studio around, will Paramount's Brad Grey survive the turmoil at Viacom?

The Andhra Pradesh Government and SAP have signed a MoU here on Thursday to set up a start-up accelerator here. The MoU was signed by SAP Director (Start-up Focus Programme) Mayank Mathur and IT Adviser to AP Government J.A. Chowdary. Mathur said that SAP would strive to create the right kind of eco system in Vizag for the flourishing of start-ups and to develop entrepreneurial spirit among the young. "We will launch the operations initially under our experts based at Bengaluru within a month. They will be visiting Vizag periodically," he said.

In the event of a c-section, if a robot suggested who should perform it, would you listen? Ninety percent of the time, nurses and physicians did. A robot programmed by MIT's Computer Science and Artificial Intelligence Lab (CSAIL) suggests where to move patients and who should perform caesarian sections. The team from CSAIL thinks robots are most effective in helping with one of the most "complex" tasks in the labor ward of the hospital: scheduling. "The aim of the work was to develop artificial intelligence that can learn from people about how the labor and delivery unit works, so that robots can better anticipate how to be helpful or when to stay out of the way--and maybe even help by collaborating in making challenging decisions," says MIT professor Julie Shah, senior author on two papers based on CSAIL's research.

A panel of Venture Capitalists from throughout the Bay Area come together to discuss how Artificial Intelligence is catching the eyes of investors across the globe. Panelists discuss the growth of the field and the recent shift in investment from different types of disruptive technologies to AI specifically.

An anonymous reader quotes a report from Reuters: A former Google engineer, whose speech recognition software is used in more than a billion Android smartphones, has launched a company that uses blockchain technology to build a new operating system for banks. Paul Taylor, a Cambridge University academic with an expertise in artificial intelligence, speech synthesis and machine learning, started working on the system, called Vault OS, two years ago in a basement in London's Shoreditch district, known for being a tech start-up hub. The technology, which underpins the digital currency bitcoin, creates a shared database in which participants can trace every transaction ever made. The ledger is tamper-proof and transparent, meaning that transactions can be processed without the need for third-party verification. The system also negates the need for costly in-house data centers, as it uses cloud-based systems, which banks can use on a "pay-as-you-go" basis, which means that there is no single point of failure.

Diagnosing cancer is about to get more accurate, with the help of artificial intelligence. Pathologists have diagnosed diseases in more or less the same way for the past 100 years, by laboring over a microscope reviewing biopsy samples on little glass slides. Working almost robotically, they sift through millions of normal cells to identify just a few diseased ones. The task is tedious and prone to human error. But now, scientists and engineers have created a technique that uses artificial intelligence (AI) and can differentiate cancer cells from normal cells almost as well as a top-notch pathologist.

A 300-pound security robot at at the Stanford Shopping Center knocked down and ran over a 16-month-old boy. The parents of the injured boy are understandably pissed, claiming the autonomous machine is dangerous. "The robot hit my son's head and he fell down facing down on the floor and the robot did not stop and it kept moving forward," noted the boy's mom, Tiffany Teng, in an ABC7 News report. "He was crying like crazy and he never cries. The toddler, named Harwin, was allegedly assaulted by the shopping mall's security robot, which stands five-feet-tall and weighs 300 pounds.

Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call dynamic question ordering (DQO), where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with an example of providing energy estimates to prospective tenants. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.

Causal modeling has long been an attractive topic for many researchers and in recent decades there has seen a surge in theoretical development and discovery algorithms. Generally discovery algorithms can be divided into two approaches: constraint-based and score-based. The constraint-based approach is able to detect common causes of the observed variables but the use of independence tests makes it less reliable. The score-based approach produces a result that is easier to interpret as it also measures the reliability of the inferred causal relationships, but it is unable to detect common confounders of the observed variables. A drawback of both score-based and constrained-based approaches is the inherent instability in structure estimation. With finite samples small changes in the data can lead to completely different optimal structures. The present work introduces a new hypothesis-free score-based causal discovery algorithm, called stable specification search, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Structure search is performed over Structural Equation Models. Our approach uses exploratory search but allows incorporation of prior background knowledge. We validated our approach on one simulated data set, which we compare to the known ground truth, and two real-world data sets for Chronic Fatigue Syndrome and Attention Deficit Hyperactivity Disorder, which we compare to earlier medical studies. The results on the simulated data set show significant improvement over alternative approaches and the results on the real-word data sets show consistency with the hypothesis driven models constructed by medical experts.

OBJECTIVE: We aim to extract and denoise the attended speaker in a noisy, two-speaker acoustic scenario, relying on microphone array recordings from a binaural hearing aid, which are complemented with electroencephalography (EEG) recordings to infer the speaker of interest. METHODS: In this study, we propose a modular processing flow that first extracts the two speech envelopes from the microphone recordings, then selects the attended speech envelope based on the EEG, and finally uses this envelope to inform a multi-channel speech separation and denoising algorithm. RESULTS: Strong suppression of interfering (unattended) speech and background noise is achieved, while the attended speech is preserved. Furthermore, EEG-based auditory attention detection (AAD) is shown to be robust to the use of noisy speech signals. CONCLUSIONS: Our results show that AAD-based speaker extraction from microphone array recordings is feasible and robust, even in noisy acoustic environments, and without access to the clean speech signals to perform EEG-based AAD. SIGNIFICANCE: Current research on AAD always assumes the availability of the clean speech signals, which limits the applicability in real settings. We have extended this research to detect the attended speaker even when only microphone recordings with noisy speech mixtures are available. This is an enabling ingredient for new brain-computer interfaces and effective filtering schemes in neuro-steered hearing prostheses. Here, we provide a first proof of concept for EEG-informed attended speaker extraction and denoising.