Mitsubishi Electric Corp. launched a research and development open house event in Tokyo this week to showcase their latest advanced research products. The main focus of the event was the company's new array of AI technology. The firm spent a record ¥212 billion on research and development in 2017. With research centers in Japan, China, the U.S. and Europe, and a growing number of open innovation ties with universities and research organizations both inside and outside of Japan, the company appears poised to maintain its large research and development budget well into the future. Worldwide spending on AI systems totaled $12 billion last year, 59 percent more than in 2016.

According to a survey by online dating app "Coffee Meets Bagel," the perception of Valentine's Day appears to have changed dramatically. Singles are loud and proud, feeling empowered and less pressured to have a date for the hallmark occasion -- and this isn't the best news for dating apps. As a result, some operators are trying to head off dating-app fatigue with tech solutions: from the creation of in-person authentic experiences such as concerts and cultural experiences, to leveraging blockchain and artificial intelligence for courtship advice. Others may even accept cryptocurrencies for payment to match-making platforms. With men twice as active on relationship sites, according to numerous surveys, a slew of dating apps have emerged catering to women already, allowing ladies to call the shots.

According to an expert in Artificial Intelligence, machines will become billions of times smarter than humans, and mankind needs to fuse with machines in order to survive. Artificial intelligence could evolve to become "billions of times smarter" than humans according to Ian Pearson, a futurist at Futurizon who says people will need to merge with machines to survive. The fact is that AI can go further than humans, it could be billions of times smarter than humans at this point," said Pearson during a panel hosted by CNBC at the World Government Summit in Dubai. "So we really do need to make sure that we have some means of keeping up." Pearson continued, "The way to protect against that is to link that AI to your brain so you have the same IQ… as the computer.

SpaceX is now a step closer to launching broadband-powering satellites into orbit. Editor's Remarks: The Federal Communications Commission (FCC) announced that the agency would approve SpaceX's application to leverage satellite technology to provide broadband to the US and wider world. FCC chairman Ajit Pai said that the opportunity would help remove the US' digital divide and bring online rural parts of the country that are still without reliable internet. SpaceX declined to give an immediate statement regarding the progress but previous releases show that the company wants to launch 4,425 satellites that will form a constellation 800 miles above the Earth. Apple's latest offering costs $216 to build, giving it a lower margin than the company's other goods.

Artificial intelligence (AI) has finally caught the Indian government's attention. On Feb. 01, delivering his budget speech, finance minister Arun Jaitley told parliament that the government think-tank, Niti Aayog, will spearhead a national programme on AI, including research and development. The intent showed in the numbers: Budget allocation for Digital India, the government's umbrella initiative to promote AI, machine learning, 3D printing, and other technologies, was almost doubled to Rs3,073 crore ($477 million) this year. "It's extremely encouraging to see the government recognise the need for research in cutting-edge technologies," Subrat Kar, CEO and co-founder of Noida-based video intelligence platform Vidooly, told Quartz. Niti Aayog's support will "allow us to indigenously develop technologies on par with our Silicon Valley counterparts, and reduce dependency on them," Kar said. Niti Aayog, led by CEO Amitabh Kant, has been a key promoter of various digital campaigns in the country, including the massive biometric programme, Aadhaar, and the India chain project, which is creating blockchain infrastructure to support IndiaStack, a set of codes developed around Aadhaar.

Artificial intelligence (AI) continues to capture the imagination and interest of entrepreneurs, investors, big business, and consumers alike. Across sectors and industries, innovators are developing and implementing AI – enabled technologies that streamline processes, improve operational efficiency, and aim to identify actionable solutions to complex problems. As AI technology continues to advance in sophistication; systems, machines, and processes designed to effectively analyze data, provide insights, and make decisions will become commonplace in our world. Whether it is the automation of tasks previously relegated to you and your colleagues at work, the software and hardware that increases your vehicle's safety and will one day power self-driving vehicles, or the algorithms guiding how products are marketed to you, chances are AI is already having a major impact on your day-to-day life. Over the next few years and coming decades, global entrepreneurs will continue to develop the Artificial Intelligence applications that will transform our future.

Welcome to Digital Health Briefing, a new email providing the latest news, data, and insight on how digital technology is disrupting the healthcare ecosystem, produced by BI Intelligence. Sign up and receive Digital Health Briefing free to your inbox. We'd like to hear from you. RESEARCHERS TAP DEEP LEARNING TO PREDICT IN-HOSPITAL PATIENT MORTALITY AND READMISSION RATES: Newly published collaborative research from Google, Stanford, the University of Chicago, and the University of California, suggests that artificial intelligence (AI) can be used in combination with electronic health record data to predict mortality, readmission, and other events that have an adverse impact on healthcare in the US. The study adds considerable weight to the growing body of research in the field of big data and health analytics.

– Blockchain is a mystery story or provides the foundation for cryptocurrencies like Bitcoin. What's different about blockchains compared to traditional big-data distributed databases like MongoDB. Its like featuring a product that contains small blocks of brain in form of dust but consider that the innovation efforts of several publicly traded asset managers and banks are also on this brain block dust quest. Computers start simulating the brain's sensation, action, interaction, perception and cognition abilities. Disclaimer – all credits if remains on the original contributor only.

A generalized additive model (GAM, Hastie and Tibshirani (1987)) is a nonparametric model by the sum of univariate functions with respect to each explanatory variable, i.e., $f({\mathbf x}) = \sum f_j(x_j)$, where $x_j\in\mathbb{R}$ is $j$-th component of a sample ${\mathbf x}\in \mathbb{R}^p$. In this paper, we introduce the total variation (TV) of a function as a measure of the complexity of functions in $L^1_{\rm c}(\mathbb{R})$-space. Our analysis shows that a GAM based on TV-regularization exhibits a Rademacher complexity of $O(\sqrt{\frac{\log p}{m}})$, which is tight in terms of both $m$ and $p$ in the agnostic case of the classification problem. In result, we obtain generalization error bounds for finite samples according to work by Bartlett and Mandelson (2002).

Machine learning models are vulnerable to Adversarial Examples: minor perturbations to input samples intended to deliberately cause misclassification. Current defenses against adversarial examples, especially for Deep Neural Networks (DNN), are primarily derived from empirical developments, and their security guarantees are often only justified retroactively. Many defenses therefore rely on hidden assumptions that are subsequently subverted by increasingly elaborate attacks. This is not surprising: deep learning notoriously lacks a comprehensive mathematical framework to provide meaningful guarantees. In this paper, we leverage Gaussian Processes to investigate adversarial examples in the framework of Bayesian inference. Across different models and datasets, we find deviating levels of uncertainty reflect the perturbation introduced to benign samples by state-of-the-art attacks, including novel white-box attacks on Gaussian Processes. Our experiments demonstrate that even unoptimized uncertainty thresholds already reject adversarial examples in many scenarios.