While it's easy to count our number of birthdays to figure out how long we've each been on the planet, researchers at Imperial College London have another age-related metric they think is even more important: A person's "brain age." That means taking into account the wear and tear on a person's brain to help predict individuals at greater risk of suffering poor health and dying earlier. And -- wouldn't you know it -- they've created a machine learning algorithm to help! "The study involves using brain MRI scans to look at the size of people's brain tissue -- gray matter and white matter -- across the lifespan," Dr. James Cole, a research associate who led the study, told Digital Trends. "By building a statistical model of brain volumes in N 2001 people aged 18-90, we can then compare new brain scans to get a prediction of someone's'brain age.' The idea is that if someone's brain appears'older' than their real chronological age, then this may be due to some damage or disease, or potentially indicates increased risk of future brain-health problems."

SAN FRANCISCO: Building on the success of its first-generation AI platform, Infosys Mana and its Robotic Process Automation (RPA) solution, AssistEdge, Infosys on Thursday launched'Infosys Nia', the next-generation Artificial Intelligence (AI) Platform for businesses. Infosys Nia converges the big data/analytics, machine learning, knowledge management and cognitive automation capabilities of Mana; end-to-end RPA capabilities of AssistEdge; advanced, high-performance and scalable machine learning capabilities of Skytree; and optical character recognition (OCR), natural language processing (NLP) capabilities and infrastructure management services. "Nia takes our purposeful approach to AI, one in which technology serves to amplify people and empowers them to work in new ways, to new heights," Vishal Sikka, Chief Executive Officer at Infosys, said in a statement. "When we bring this together with our unmatched ability to educate and train in AI techniques and emerging technologies, we now have the platform, the services and the skills, to deliver new unprecedented value to our clients," he added. Infosys Nia, tackles business problems such as forecasting revenues, forecasting what products need to be built, understanding customer behaviour, understanding the content of contracts and legal documents, understanding compliance, and fraud.

U.K. researchers have developed a machine learning algorithm that can look at people's brain tissue to determine their likely lifespan. While it's easy to count our number of birthdays to figure out how long we've each been on the planet, researchers at Imperial College London have another age-related metric they think is even more important: A person's "brain age." That means taking into account the wear and tear on a person's brain to help predict individuals at greater risk of suffering poor health and dying earlier. And -- wouldn't you know it -- they've created a machine learning algorithm to help! "The study involves using brain MRI scans to look at the size of people's brain tissue -- gray matter and white matter -- across the lifespan," Dr. James Cole, a research associate who led the study, told Digital Trends. "By building a statistical model of brain volumes in N 2001 people aged 18-90, we can then compare new brain scans to get a prediction of someone's'brain age.' The idea is that if someone's brain appears'older' than their real chronological age, then this may be due to some damage or disease, or potentially indicates increased risk of future brain-health problems."

Machine learning is one of the hottest new technologies to emerge into popular consciousness in the last decade, transforming fields from consumer electronics and healthcare to retail. This has led to intense curiosity about this field among many students and working professionals about the field. If you're a tech professional such as a software developer, business analyst or even a product manager, you might be curious about how machine learning can change they way you work and take your career to the next level. However, as a busy professional, you're also looking for a way to get a solid understanding of machine learning that's not only rigorous and practical, but also concise and fast. This machine learning tutorial will help you achieve your goals.

After mastering 40 human languages, a Swedish startup has turned to dolphins, hoping to use its language-analysis software to unlock the secrets of communication employed by the aquatic mammals. Using technology from artificial intelligence language-analysis company Gavagai AB, researchers from Sweden's KTH Royal Institute of Technology will begin compiling a dolphin-language dictionary. The software will monitor captive bottlenose dolphins at a wildlife park about 90 miles south of Stockholm, the company said in an emailed statement Wednesday. "We hope to be able to understand dolphins with the help of artificial intelligence technology," Jussi Karlgren, an adjunct professor of language technology at KTH and co-founder of Gavagai, said in the statement. "We know that dolphins have a complex communication system, but we don't know what they are talking about yet."

The artificial intelligence (AI) development race is heating up, and one new study shows that companies are paying upward of $650 million to their employees to advance the state of the AI art. New research from employment intelligence outfit Paysa asserted that there are more than 10,000 available AI positions across the US, with the top 20 employers setting aside an average $33,292,647 to hire available developers and talent, according to a press release from the firm. The research showed the top two companies in terms of AI development investment, Amazon and Google, are spending $227 million and $130 million, respectively, to woo talent, a sure sign that these two tech giants see AI is crucial to their success in business moving forward. "From powering the self-driving car to guiding the way consumers shop, manage our finances and even do routine tasks, AI technology plays an increasing role in every aspect of life as we know it, so it's no surprise that investment in this area is growing at a rapid pace and companies are having a hard time keeping up," said Paysa CEO Chris Bolte. The study also cited San Francisco, New York and Seattle as the top three US cities where companies are hiring the most AI experts.

At this time, all participants have been placed in a listen-only mode. And the floor will be open for your questions following the presentation. As a reminder, this conference is being recorded. And now I would like to turn the conference over to Mellanox. Leading the call today will be Eyal Waldman, President and CEO of Mellanox Technologies; and Jacob Shulman, Chief Financial Officer. By now, you've seen our press release and associated financial information that we furnished to the SEC on Form 8-K this afternoon. If not, you may access them on our website at ir.mellanox.com. As a reminder, today's discussion includes predictions, expectations, estimates and other information, all of which we consider to be forward-looking statements. Throughout today's discussion, we present important factors relating to our business that may potentially affect these forward-looking statements. These forward-looking statements are also subject to risks and uncertainties that may cause actual results to differ materially from statements made today. As a result, we caution you against placing undue reliance on these forward-looking statements. And we encourage you to review our most recent SEC reports, including our 10-K and 10-Q, for a complete discussion of these factors and other risks that may affect our future results or the market price of our ordinary shares.

With more than $5 billion in 605 deals of VC investment over last 2 years, artificial intelligence (AI) is poised to have a transformative effect on consumer, enterprise, and government markets around the world. While there are certainly obstacles to overcome, consumers believe that AI has the potential to assist in medical breakthroughs, democratize costly services, elevate poor customer service, and even free up an overburdened workforce. We dug deeper into those perceptions through an online survey of consumers and business decision makers, and an expert salon with thought leaders in the field. This original research unpacks key ways AI may impact our world, delving into its implications for society, service, and management.

Need a building customized for a particular site quickly? Researchers say a 3D printer may be the answer -- and could reduce the cost to boot. Researcher Steven Keating and colleagues write in this week's Science Robotics a 3D system could reduce construction costs while speeding the process, incorporating different materials and densities as the process moves forward to produce optimal combinations of strength and other properties. "Contemporary construction techniques are slow, labor-intensive, dangerous, expensive, and constrained to primarily rectilinear forms, often resulting in homogenous structures built using materials sourced from centralized factories," the researchers said. Instead, they propose a digital construction platform, an "automated construction system capable of customized on-site fabrication of architectural-scale structures using real-time environmental data for process control."

This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global constraint-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Finally, we also extend our approach by introducing a multi-structure scheme, which learns a set of local correspondence structures to capture the spatial correspondence sub-patterns between a camera pair, so as to handle the spatial misalignments between individual images in a more precise way. Experimental results on various datasets demonstrate the effectiveness of our approach.