The Burbank Police Department has suspended officer deployments based on "predictive policing" technology hailed by top brass as the future of crime-fighting after complaints from police officers. The shift comes as police departments across the country are increasingly using computer technology to help predict crime trends and deploy officers accordingly. Some law enforcement agencies have said the system has helped crack down on crime. But in Burbank, critics said the software's algorithm couldn't beat a veteran officer's intuition and knowledge of his or her patrol area. They also said the algorithm sometimes zeroed in on obvious areas where officers already know there's crime or silly locations, such as the police station, where people often show up to report crimes.

TOKYO - This year's ad:tech Tokyo presented wide-ranging views on a variety of topics. Here are some highlights Campaign Japan picked up on. Footballing legend Hidetoshi Nakata promotes sake and other traditional Japanese offerings throughout the world. He presented some of the key issues to date in his keynote speech. "Foreign consumers cannot read Japanese labels, so they rarely choose products based on brands," he said.

After a several-month trial in which staff used the AI internally, while they dealt with business clients, RBS will let Luvo talk directly to the outside world by the end of 2016. Luvo functions as a chatbot – a program that opens when you access the bank's website – that you can ask typical customer service questions, concerning lost PINs and credit cards that need to be replaced. At first glance, this is nothing extraordinary, and chatbots have become a frequent feature for websites dealing with a large flow of individual queries. But RBS and IBM, which spent millions developing the program together, say that it is revolutionary, with a nuanced understanding of human speech, a "unique" personality, and an ability to learn on the job. "To be helpful it has to understand dialogue," the bank's managing director of digitization, Chris Popple, explained in a presentation earlier this year.

Golden parachutes can't seem to stay out of the news. Last year, Jeff Smisek, the former CEO of United Airlines, received a separation payment of 4.875 million in cash along with additional equity awards and other benefits for a total of close to 37 million after being ousted from his company. Can it also transform the nation? Hillary Clinton was campaigning for her husband in January 1992 when she learned of the race's newest flare-up: Gennifer Flowers had just released tapes of phone calls with Bill Clinton to back up her claim they had had an affair. We tend to associate salads most closely with spring and summer, when fresh produce is at its peak and when we're all in the mood for lighter, fresher-tasting meals.

This paper develops an active sensing method to estimate the relative weight (or trust) agents place on their neighbors' information in a social network. The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i.e., agents whose opinions are not influenced by their neighbors. This method can be viewed as a \emph{social RADAR}, where the stubborn agents excite the system and the latter can be estimated through the reverberation observed from the analysis of the agents' opinions. The social network sensing problem can be interpreted as a blind compressed sensing problem with a sparse measurement matrix. We prove that the network structure will be revealed when a sufficient number of stubborn agents independently influence a number of ordinary (non-stubborn) agents. We investigate the scenario with a deterministic or randomized DeGroot model and propose a consistent estimator of the steady states for the latter scenario. Simulation results on synthetic and real world networks support our findings.

Tests for Esophageal cancer can be expensive, uncomfortable and can have side effects. For many patients, we can predict non-existence of disease with 100% certainty, just using demographics, lifestyle, and medical history information. Our objective is to devise a general methodology for customizing tests using user preferences so that expensive or uncomfortable tests can be avoided. We propose to use classifiers trained from electronic health records (EHR) for selection of tests. The key idea is to design classifiers with 100% false normal rates, possibly at the cost higher false abnormals. We compare Naive Bayes classification (NB), Random Forests (RF), Support Vector Machines (SVM) and Logistic Regression (LR), and find kernel Logistic regression to be most suitable for the task. We propose an algorithm for finding the best probability threshold for kernel LR, based on test set accuracy. Using the proposed algorithm, we describe schemes for selecting tests, which appear as features in the automatic classification algorithm, using preferences on costs and discomfort of the users. We test our methodology with EHRs collected for more than 3000 patients, as a part of project carried out by a reputed hospital in Mumbai, India. Kernel SVM and kernel LR with a polynomial kernel of degree 3, yields an accuracy of 99.8% and sensitivity 100%, without the MP features, i.e. using only clinical tests. We demonstrate our test selection algorithm using two case studies, one using cost of clinical tests, and other using "discomfort" values for clinical tests. We compute the test sets corresponding to the lowest false abnormals for each criterion described above, using exhaustive enumeration of 15 clinical tests. The sets turn out to different, substantiating our claim that one can customize test sets based on user preferences.

This paper investigates the effectiveness of factorial speech processing models in noise-robust automatic speech recognition tasks. For this purpose, the paper proposes an idealistic approach for modeling state-conditional observation distribution of factorial models based on weighted stereo samples. This approach is an extension to previous single pass retraining for ideal model compensation which is extended here to support multiple audio sources. Non-stationary noises can be considered as one of these audio sources with multiple states. Experiments of this paper over the set A of the Aurora 2 dataset show that recognition performance can be improved by this consideration. The improvement is significant in low signal to noise energy conditions, up to 4% absolute word recognition accuracy. In addition to the power of the proposed method in accurate representation of state-conditional observation distribution, it has an important advantage over previous methods by providing the opportunity to independently select feature spaces for both source and corrupted features. This opens a new window for seeking better feature spaces appropriate for noisy speech, independent from clean speech features.

As recently as 2013, the [deep learning] space saw fewer than 10 deals. Computer Vision: Startups here are using deep learning for image recognition, analytics, and classification. Aerial image analytics startup Terraloupe was seed-funded this year by Germany-based Bayern Kapital. New York-based Calrifai -- backed by investors including Google Ventures, Lux Capital, and NVidia -- entered the R/GA accelerator this year, after raising 10M in Series A in Q2'15. Captricity, which extracts information from hand-written data, has raised 49M in equity funding so far from investors including Social Capital, Accomplice, White Mountains Insurance Group, and New York Life Insurance Company.

Humanoid robots could soon allow us to stream our consciousness anywhere in the world, acting as surrogate bodies that enable people to'instantly be in multiple places at once.' This is the goal of the Avatar XPrize, an ANA-sponsored concept for a 22 million contest just selected at the XPrize Visioneers 2016 Summit as'ready to launch.' The technology would make James Cameron's Avatar a reality, developing'limitless travel' avatars that can be rented and controlled remotely by a human operator, who will be able to hear, see, and feel what the robot is experiencing. Humanoid robots could soon allow us to stream our consciousness anywhere in the world, acting as surrogate bodies that enable people to'instantly be in multiple places at once.' The Avatar XPrize aims to facilitate the creation of'avatars that you – the public will be able to use to travel anywhere, anytime, instantly.'

Originally published in the ITS Ghaziabad 2nd CXO Meet Souvenir, "Digital India Mission: Transforming India for Tomorrow." Achievements in machine learning are coming at ever-increasing rapidity over the past several months. You are likely familiar with the recent accomplishments associated with machine learning, especially those of so-called deep learning, or the use of multi-layered artificial neural networks. These specific achievements include the high profile AlphaGo and Deep Dream, along with numerous others in the realms of computer vision and natural language processing. Interestingly, a number of these recent mainstream successes are primarily attributable to Google in one form or another.