Confidentiality of patient information is an essential part of Electronic Health Record System. Patient information, if exposed, can cause a serious damage to the privacy of individuals receiving healthcare. Hence it is important to remove such details from physician notes. A system is proposed which consists of a deep learning model where a de-convolutional neural network and bi-directional LSTM-CNN is used along with regular expressions to recognize and eliminate the individually identifiable information. This information is then removed from a medical practitioner's data which further allows the fair usage of such information among researchers and in clinical trials.

Multi-Task Gaussian processes (MTGPs) have shown a significant progress both in expressiveness and interpretation of the relatedness between different tasks: from linear combinations of independent single-output Gaussian processes (GPs), through the direct modeling of the cross-covariances such as spectral mixture kernels with phase shift, to the design of multivariate covariance functions based on spectral mixture kernels which model delays among tasks in addition to phase differences, and which provide a parametric interpretation of the relatedness across tasks. In this paper we further extend expressiveness and interpretability of MTGPs models and introduce a new family of kernels capable to model nonlinear correlations between tasks as well as dependencies between spectral mixtures, including time and phase delay. Specifically, we use generalized convolution spectral mixture kernels for modeling dependencies at spectral mixture level, and coupling coregionalization for discovering task level correlations. The proposed kernels for MTGP are validated on artificial data and compared with existing MTGPs methods on three real-world experiments. Results indicate the benefits of our more expressive representation with respect to performance and interpretability.

We show that the Brussels operational-realistic approach to quantum physics and quantum cognition offers a fundamental strategy for modeling the meaning associated with collections of documental entities. To do so, we take the World Wide Web as a paradigmatic example and emphasize the importance of distinguishing the Web, made of printed documents, from a more abstract meaning entity, which we call the Quantum Web, or QWeb, where the former is considered to be the collection of traces that can be left by the latter, in specific measurements, similarly to how a non-spatial quantum entity, like an electron, can leave localized traces of impact on a detection screen. The double-slit experiment is extensively used to illustrate the rationale of the modeling, which is guided by how physicists constructed quantum theory to describe the behavior of the microscopic entities. We also emphasize that the superposition principle and the associated interference effects are not sufficient to model all experimental probabilistic data, like those obtained by counting the relative number of documents containing certain words and co-occurrences of words. For this, additional effects, like context effects, must also be taken into consideration.

This paper proposes a multichannel source separation method called the multichannel variational autoencoder (MVAE), which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture. By training the CVAE using the spectrograms of training examples with source-class labels, we can use the trained decoder distribution as a universal generative model that is able to generate spectrograms conditioned on a specified class label. By treating the latent space variables and the class label as the unknown parameters of this generative model, we can develop a convergence-guaranteed semi-blind source separation algorithm that consists of iteratively estimating the power spectrograms of the underlying sources as well as the separation matrices. Through experimental evaluations, our MVAE showed higher separation performance than a baseline method.

Prudential Asia, a business unit of British insurer Prudential plc., has signed a licensing deal with digital health startup Babylon Health to exclusively use its AI-powered software for its own apps across 12 countries in Asia. Prudential is paying approximately $100 million over the course of several years, according to sources close to the deal, to access proprietary software that includes an inference engine, simulation software and a medical-knowledge graph that over time aims to replicate and automate consultations with human doctors. Babylon declined to comment on the deal pricing, and spokespeople for Prudential Asia could not be reached for comment on pricing. Babylon Health is best known for providing a virtual-doctor service in the U.K., where more than 26,000 NHS patients in London can get appointments with doctors via video calls and thousands more use its private service for $80 a year. Babylon won't provide remote doctors to Prudential; it'll instead provide the software that powers the medical chatbot on its app.

Google Assistant could finally arrive on Sonos speakers in time for this year's holiday shopping season, the company's CEO said. "We're working as hard as we can and so is Google to get it ready for that time," Patrick Spence told The Verge. Additionally, he noted Sonos is considering a partnership with the likes of Tencent or Baidu to bring a Chinese-language voice assistant to the speakers. Spence also discussed the potential effect of US trade tariffs, as its speakers are built in China. He noted that if tariffs come into play, consumers will likely have to pay a little more to offset those costs.

Smart devices, or IoT-enabled devices such as fitness bands that monitor your daily activity and speed trackers in vehicles, could soon play a part in deciding your insurance premium. A report by a working group set up by the Insurance Regulatory and Development Authority of India (IRDAI) has batted for the use of wearable devices for life insurance and health insurance policies as they can provide a regular stream of data about the policy holder. "Insurers may develop frameworks/models using wearable data throughout the life cycle of the insured and help in building attractive product propositions, and also monitor experience throughout the policy term," the report noted, adding that for health insurance such devices can play an equally crucial role as insurers, at present, only have a point in time data through medical tests or self disclosures. The working group to examine innovations in insurance involving wearable or portable devices was set up in January this year, and has examined a range of devices, including fitness bands, skin patch sensors, smart contact lenses, medical e-textiles and even implantable devices. Similarly for non-life insurance, such as motor cover, the report noted that the use of IoT devices and Artificial Intelligence would not only help improve assessment of underwriting risk by tracking the driving habits of the insured, but can also lower fraud risk and improve claim handling.

The U.K.-based research firm noted that "several barriers" could keep AI from truly hitting the mainstream in medical imaging. Those factors include the "challenging" regulatory process, the need for additional large-scale validation studies and the need for full AI integration into current radiology workflows. "Up to now, the market has mainly been driven by the many start-ups and specialist companies who are applying machine learning to medical imaging, but the major medical imaging vendors are now ramping-up their AI activities," Harris said in the same statement. "In the last year or so, we've also seen several of the world's technology giants apply their AI expertise to medical imaging, most notably China's Tencent and Alibaba. Over the coming years, the combined R&D firepower of the expanding ecosystem will knock down the remaining barriers and radiologists will have a rapidly expanding array of AI-powered workflow and diagnostic tools at their disposal."

South Korean consumer electronics giant LG Electronics Inc. is turning to the University of Toronto to build its artificial-intelligence muscle, as the university hopes to build a better reputation in applying its sought-after research in the real world. As first reported by The Globe and Mail in June, LG has been planning to develop an AI presence in Toronto since at least last year, posting job listings for a "Toronto AI lab" and sending a delegation to Canada's largest university. In an interview, LG's global president and chief technology officer, I.P. Park, confirmed the multinational manufacturer will establish both a five-year, multimillion-dollar research partnership with U of T and build an AI lab on its campus's southern border as soon as this fall. LG is the latest in a string of global giants, among them Fujitsu Laboratories and Huawei Technologies, to partner with the university for research. Mr. Park said the company is thrilled to tap U of T's foundational expertise in AI – in particular its prowess in the discipline and development of deep learning, led by professor emeritus Geoffrey Hinton.

Artificial intelligence is being called the "new electricity" as it revolutionizes business, employment, and society. Through fields such as machine learning, deep learning, and neural networks, AI is transforming everything from health care, agriculture, and construction to financial and professional services. Research and advisory company Gartner predicted that almost every new software product would implement AI by 2020. "AI is bigger than the internet in many ways," said Karthik Ramakrishnan, Montreal-based Element AI's vice-president of industry solutions, in an interview. "This is now the age of intelligence."