Dassai sake: Blending the ancient and the innovative I-CIO

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The situation was further compounded by demographic changes. Ongoing rural depopulation in Japan has seen the average age of farmers rise to 67, according to 2015 census figures. To counter the situation and encourage greater cultivation, Asahi Shuzo joined forces with Fujitsu. The ICT company's solution involved the deployment of solar-powered IoT sensors and cameras in paddy fields, with the resulting data connected to the Fujitsu Akisai food and agriculture industry cloud. The sensors measured environmental conditions, including atmospheric humidity levels, ground and air temperatures, ground moisture and electroconductivity. "The use of Fujitsu's Akisai system in Dassai production has created a win-win for both Asahi Shuzo and its farmers."


Flybits raises $35 million to personalize customer experiences with AI

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Knowing when and where a person is, was, and will be can enable magical customer experiences. Flybits today announced that it's raised $35 million in series C funding led by Point72 Ventures, with participation from Mastercard, Citi Ventures, and Reinventure, along with existing partners Portag3 Ventures, TD Bank, and Information Venture Partners. The fresh funding brings its total raised to $50 million, and it comes as Flybits notches 300% growth in 2019 and gears up to hire across sales, engineering, and business development teams and offices, including adding solutions engineers, sales executives, business development reps, and engineers. "Customers are already used to seeing content and recommendations based on their behavior," said CEO Hossein Rahnama. But Flybits leverages an unlimited amount to create far more personalized and relevant recommendations than ever before, all in an effort to help financial institutions deliver real time lifestyle banking that gets at their customers' deeper needs.



Paul Friedman on LinkedIn: "Excellent Gartner insights on all things privacy. Our partner Verint has #AI powered tools to ensure private Omni-Channel conversations stay secure. Mayday Communications Inc promotes Verint's complete portfolio of #security solutions. #datacompliance #cybersecurity #gartner #verint"

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Our partner Verint has #AI powered tools to ensure private Omni-Channel conversations stay secure. Mayday Communications Inc promotes Verint's complete portfolio of #security solutions. In this newsletter featuring Gartner's report, "Predicts 2019: The Ambiguous Future of Privacy," we dig into steps you can take now to prepare your business for the rising tide of #privacy #regulations..


The Application of Artificial Intelligence for Peacekeeping

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"The increasing amount of available data, mainly due to the proliferation of access to the internet in countries where peacekeeping missions take place, has caused a technology-driven transformation of the operational environment. This comes at a time of significant developments in the fields of artificial intelligence and particularly machine learning, most of whose applications still rely on massive amounts of data. As such these developments have produced some promising individual initiatives to exploit this new and growing potential for United Nations operations." At least as early as 1996 researchers have used machine learning (ML) to predict conflicts[1]. Today, mainly due to significantly higher amounts of available data[2], advancements in computing power and the progress made in natural language processing, several artificial intelligence (AI) tools have been added to the peacekeeping arsenal.


Emptyset turn to machine learning on new album Blossoms

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Recorded with the help of a software model programmed with 10 hours of improvised recordings using wood, metal and drum skins. Emptyset, the experimental duo comprised of James Ginzburg and Paul Purgas, have developed a machine learning system for Blossoms, their second album for Thrill Jockey. Programmed using a collection of existing material from the duo, as well as 10 hours of improvised recordings using wood, metal and drum skins, the system was designed to recognize coherent patterns within a large sonic data set. "Blossoms is a work built on hybrids and mutations", explains the label, "combining complexly synthesized audio with reverbs derived from impulses taken in architectural sites Emptyset have worked in previously." "The assembled compositions are emblematic of Emptyset's dedication to forward-looking sound and examine patterns of emergence and augmentation, fragmentation and resilience, and the convolution of biotic and abiotic agency."


This AI breast cancer diagnostic tool is the first to get FDA clearance

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QuantX recently became the first-ever computer-aided breast cancer diagnosis system cleared by the FDA for use in radiology, but it's not putting radiologists out of a job any time soon. "Radiology is the backbone of diagnosing many diseases today," said Jeffrey Aronin, chairman and CEO of Paragon Biosciences. "We believe the future is radiologists with technology." The combination of humans and machines apparently works really well. In a clinical study, QuantX helped radiologists interpret MRIs, noting the differences between cancerous and noncancerous breast lesions.


Supervised vs. Unsupervised Learning: What they are and How they work

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Unsupervised Learning is the class of algorithms that you can use that do not require labeled data to learn. This is what I've described above; there are mushrooms, and I can see the features of the mushroom (height, shape, color,) but I don't know if it's poisonous or not unless I eat it. What I can do, however, is start learning the different types of mushrooms, and combinations of features. For instance, maybe there is a common theme of red mushrooms that are taller than 3 inches, and another of green mushrooms with a very flat cap. Without knowing whether these mushrooms are poisonous or not, I'm able to make common groups of mushrooms or clusters of similar mushrooms (clustering is one of the more common types of unsupervised learning.)


Anatomy of an enterprise-scale AI strategy

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When it comes to AI, companies typically test the waters proof of concepts or small-scale use cases, taking advantage of vendor offerings, such as new features in their existing SaaS platforms. If things go well, they pursue another project, then another -- and soon they're relying on a sprawl of incompatible systems, competing data lakes, problems with cost overruns, duplication of efforts, and an inability to scale, not to mention privacy, compliance or ethics problems. Get the latest insights with our CIO Daily newsletter. At some point, the benefits of AI become obvious enough, and the pain of continuing on their present path so acute, that companies step back to develop a cohesive strategy for an enterprise-wide AI-powered transformation. "The tendency to get overwhelmed in individual technologies is not only drowning organizations in technical debt but discouraging them because they don't see a path forward to sustainable and scalable AI," said Traci Gusher, partner in data, analytics and artificial intelligence practice at KPMG.