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Neo4j Announces New Version of Neo4j for Graph Data Science

#artificialintelligence

Neo4j, the leader in graph technology, announced the latest version of Neo4j for Graph Data Science, a breakthrough that democratizes advanced graph-based machine learning (ML) techniques by leveraging deep learning and graph convolutional neural networks. Until now, few companies outside of Google and Facebook have had the AI foresight and resources to leverage graph embeddings. This powerful and innovative technique calculates the shape of the surrounding network for each piece of data inside of a graph, enabling far better machine learning predictions. Neo4j for Graph Data Science version 1.4 democratizes these innovations to upend the way enterprises make predictions in diverse scenarios from fraud detection to tracking customer or patient journey, to drug discovery and knowledge graph completion. Neo4j for Graph Data Science version 1.4 is the first and only graph-native machine learning functionality commercially available for enterprises.


Rethinking stress and nutrition with smart tech

#artificialintelligence

Personalised nutrition start-up myAir has unveiled its nutritional solution for better management of stress. The company developed a series of plant-based nutrition bars with a personalised edge. Each formulation contains a botanical blend designed to deliver a specific stress-relief effect. The herbal extract blends are based on profiling machine learning technology, and are customised to the consumer's stress profile and cognitive needs.


Neural Network Filters Weak and Strong External Stimuli to Help Brain Make "Yes or No" Decisions

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A University of Michigan-led research team has uncovered a neural network that enables Drosophila melanogaster fruit flies to convert external stimuli of varying intensities into a "yes or no" decision about when to act. The research, described in Current Biology, helps to decode the biological mechanism that the fruit fly nervous system uses to convert a gradient of sensory information into a binary behavioral response. The findings offer up new insights that may be relevant to how such decisions work in other species, and could possibly even be applied to help artificial intelligence machines learn to categorize information. Senior study author Bing Ye, PhD, a faculty member at the University of Michigan Life Science Institute (LSI), believes the mechanism uncovered could have far-reaching applications. "There is a dominant idea in our field that these decisions are made by the accumulation of evidence, which takes time," Ye said.


Eight Lincoln Laboratory technologies named 2020 R&D 100 Award winners

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Eight technologies developed by MIT Lincoln Laboratory researchers, either wholly or in collaboration with researchers from other organizations, were among the winners of the 2020 R&D 100 Awards. Annually since 1963, these international R&D awards recognize 100 technologies that a panel of expert judges selects as the most revolutionary of the past year. Six of the laboratory's winning technologies are software systems, a number of which take advantage of artificial intelligence techniques. The software technologies are solutions to difficulties inherent in analyzing large volumes of data and to problems in maintaining cybersecurity. Another technology is a process designed to assure secure fabrication of integrated circuits, and the eighth winner is an optical communications technology that may enable future space missions to transmit error-free data to Earth at significantly higher rates than currently possible.


Facebook uses AI to forecast COVID-19 spread across the US

Engadget

It's crucial to predict the spread of COVID-19 during the pandemic when a poor estimate could lead to overcrowded hospitals or extra-strict lockdowns. Facebook is betting that AI could help. It just published a paper outlining an AI technique it will use to forecast the spread of COVID-19 in counties across the whole US. The system predicts infections 14 days ahead by accounting for both the nature of the disease and the social factors that influence its reach. Facebook factored in the new coronavirus' inherent traits, but also trained its AI on time-based county case data as well as public, anonymized data that helped it account for elements like mobility and social distancing. The company also crafted a "neural autoregressive model" meant to separate regional and disease-specific elements of those data sets.


Google revives its AI-fueled photo printing service

Engadget

Google just made good on its promise to bring back its AI-based Photos printing service, and this time it's more affordable. The upcoming premium print series will once again have machine learning select your 10 best photos each month, but you'll now pay a slightly more affordable $7 per month (shipping already included) to get hard copies instead of the previous $8. Like before, you can skip a given month if life hasn't been eventful enough to capture interesting snapshots. The printing subscription should be available later in October. You'll have also have another option if you prefer on-demand printing.


Nudge for the Clinician Plus ML to Increase Cancer Convos

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A study published in JAMA Oncology recommended a two-pronged approach in order to increase serious illness conversations (SICs) in patients with cancer: machine learning (ML) mortality predictions plus behavioral nudges to clinicians. "Early discussions about goals and treatment preferences may lead to better perceived quality of life, reduced emotional distress, and decreased health care use near the end of life. However, most patients with cancer die without a documented discussion about goals and treatment preferences," the study authors observed. Increasing SICs may improve outcomes for cancer patients. The researchers performed a randomized clinical trial over a 20-week period at nine medical oncology clinics.


Council Post: How Is Big Data Analytics Using Machine Learning?

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Chithrai is the Chief Technology and Innovation Officer (CTIO) for InfoVision. It is no longer a secret that big data is a reason behind the successes of many major technology companies. However, as more and more companies embrace it to store, process and extract value from their huge volume of data, it is becoming a challenge for them to use the collected data in the most efficient way. That's where machine learning can help them. Data is a boon for machine learning systems.


3Diligent Expands ProdEX and Shopsight Applications

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Its Shopsight application provides users access to project opportunities from ProdEX and enables remote assessment, quoting, and project management. Both systems incorporate 3Diligent's Connect interface which enables customers and manufacturers to communicate directly using a secure online portal and Zoom video conferencing tools. Operating similarly to traditional search engine marketing, manufacturers can create text ads that will display based on a customer's material and technology requirements. However, unlike traditional search engines, Connect is driven by RFQ inputs rather than generic keyword searches. As a result, manufacturers can customize their bids and visibility on dimensions such as material, technology, and program size to drive higher ROI.


Google launches a suite of tech-powered tools for reporters, Journalist Studio – TechCrunch

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Google is putting AI and machine learning technologies into the hands of journalists. The company this morning announced a suite of new tools, Journalist Studio, that will allow reporters to do their work more easily. At launch, the suite includes a host of existing tools as well as two new products aimed at helping reporters search across large documents and visualizing data. The first tool is called Pinpoint and is designed to help reporters work with large file sets -- like those that contain hundreds of thousands of documents. Pinpoint will work as an alternative to using the "Ctrl F" function to manually seek out specific keywords in the documents.