AI researchers from Nvidia and King's College London have used federated learning to train a neural network for brain tumor segmentation, a milestone Nvidia claims is a first for medical image analysis. The technique can allow data-sharing between hospitals and researchers while preserving patient privacy. Federated learning is an approach to machine learning that -- when using a client-server approach -- can eliminate the need to create a single data lake in order to train models. Instead, models are trained locally on devices that then transfer insights from multiple machines to a central model. "You need to get to these innovations, and I believe there's kind of two ways. One, which we released last August, is create the best generalizable model that you have today and just send it to each one of these hospitals, where they can localize it for their own patients," Nvidia director of healthcare Abdul Halabi told VentureBeat in a phone interview.

Asia's highest health burdens are fertile ground for public-private sector collaboration. In several Asian countries there are examples of different stakeholders collaborating to tackle a pernicious medical challenge while leveraging local skills and talent, or data resources. Aging is rapidly becoming one of Asia's leading health-care crises; Japan is currently in the lead for its share of the population aged over 65 (nearly a third), but several of the region's other economies are close behind and carefully watching the emerging innovations in elder care. Other health-care burdens where AI is playing a leading role are infant mortality in India and hyperglycemia, hyperlipidemia, and hypertension in Singapore. Over the coming decades, policymakers and AI developers will increasingly collaborate to improve public health.

Business-software maker SAP SE this month plans to start offering a partner company's artificial-intelligence software to help customers comply with data-privacy regulations, including the sweeping California law that takes effect in January. Germany-based SAP, which makes software that supports core corporate functions such as accounting, supply chains and human resources, said Monday that it would resell privacy-management software from BigID Inc., a startup with headquarters in New York and Tel Aviv.

Reliance Jio Infocomm (Jio) debuted its patent-filed innovation – an artificial intelligence (AI) based video call assistant (bot) that can be accessed via a 4G phone call, without the need for installing any other application. Aimed at improving customer, the bot will solve the problems of endless call-hold music or seemingly never ending IVR waittimes can become things of the past. The bot was rolled out in India Mobile Congress on Monday . This video assistant facility was developed by Jio along with US based Radisys, a Reliance Industries subsidiary . The company said that this video call bot can be adapted by brands to give it a unique avatar.

Mansour asked Vorhies about Google's business engagements in China. "Google has gotten in trouble in the past for doing business with the Communist government of China," Mansour said. "And I wanted to ask you were those efforts ongoing when you were with the company? Can you give us any insight into that? Because it was quite troubling."

In this post I will go over the technical aspects of automatic forecast generation, as well as some of the organizational considerations that will arise when deciding to go with an automatic forecast generating system. As I said earlier, in many fields, including my field of retail demand forecasting, most commercial forecasting packages do perform automatic forecast generation. Several open source packages do so as well, most notably Rob Hyndman's auto.arima() Both the commercial products and the open source packages that I mentioned work based on the idea of using information criteria to choose the best forecasting model: You fit a bunch of models, and then select the model with the lowest AIC, BIC, AICc, etc….(typically this is done in lieu of out of sample validation -- see this presentation for details). There is however a major caveat: all of these methods work within a single family of models.

While the debate regarding how much screen time is appropriate for children rages on among educators, psychologists, and parents, it's another emerging technology in the form of artificial intelligence and machine learning that is beginning to alter education tools and institutions and changing what the future might look like in education. It is expected that artificial intelligence in U.S. education will grow by 47.5% from 2017-2021 according to the Artificial Intelligence Market in the US Education Sector report. Even though most experts believe the critical presence of teachers is irreplaceable, there will be many changes to a teacher's job and to educational best practices. AI has already been applied to education primarily in some tools that help develop skills and testing systems. As AI educational solutions continue to mature, the hope is that AI can help fill needs gaps in learning and teaching and allow schools and teachers to do more than ever before.

Three new life science industries should be established, including genomics and digital health, to keep the UK at the forefront of the field, a professor of medicine has said. Speaking at the annual lecture of the Medicines and Healthcare products Regulatory Agency (MHRA), John Bell, Regius Professor of Medicine at the University of Oxford, said the new industries would be critical to the UK continuing to lead in the field of life sciences. The three industries, genomics; digital health; and early diagnosis will come with their own regulatory challenges, Bell told 200 healthcare leaders at the event. "Innovation in regulation fundamentally underpins the entire sector and is vital for economic growth," he said. "As the largest and most innovative regulator in Europe, the MHRA is crucial to the UK's strategy."

Visual 1st, the executive conference focused on promoting innovation and partnerships in the photo and video ecosystem, will bring AI, AR, computational photography, and the future of digital cameras to the center stage, Oct. 3-4, at the Golden Gate Club, San Francisco, Calif. AI is already everywhere in imaging, from recognition to enhancement to auto-editing – and of course, there's much more to come. In parallel, AR solutions are proliferating at a rapid pace, serving use cases ranging from having lots of fun to being highly productive. As these two technologies evolve in mutually reinforcing ways, we, as an industry, must take the imaging solutions they enable to the next level of value and profitability, while also keeping things safe, secure and private for our customers – but how? Alexander Schiffhauer recently left his role as Technical Advisor to Google's CEO Sundar Pichai to take product management responsibility for the company's computational photography teams. Under his leadership, these teams have pioneered innovation on Pixel Camera, leveraging AI and computer vision techniques to create photos unimaginable only a few years ago.

The promise of AI is easily understood by anyone with an imagination, and for 40 years, venture capitalists have been enthusiastically investing in that promise. However, it's been significantly harder for founders to turn that investment into sustainable business models. Today we are going to look at why that is, and go over what might be a blueprint for startups to create business models around artificial intelligence. Tatsuo Nakamura founded Valuenex in 2006 with the goal of using artificial intelligence to supplement the work being done by patent attorneys, and their software was instrumental in the resolution of one of Japan's most famous, and most valuable, lawsuits. We also talk about how to sell to large companies as a small startup, the challenges in trying to make product strategy based on technology, why staying private longer is not always a good thing for startups, and how Valuenex technology accidentally discovered a secret collaboration between Honda and Google. It's a great discussion with the founder of one of Japan's most successful AI companies, and I think you will really enjoy it. Welcome to Disrupting Japan, straight talk from Japan's most successful entrepreneurs. Today, we're going to be talking about something that's frankly difficult to talk about on an audio podcast. Tatsuo Nakamura founded Valuenex in 2006 to use Artificial Intelligence and modern visualization techniques to help clients make sense of their patent portfolios and to keep an eye on what the competition is doing. In fact, this technology uncovered some of the core evidence that decided the famous blue LED case. It's highly effective but highly visual, so let me try to explain it. Valuenex creates a kind of topographical map that shows companies where in the market, their IP is strong and where it's weak.