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How deep learning can advance study of neural degeneration

how deep learning can advance study of neural degeneration

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Researchers from North Carolina State University have demonstrated the utility of artificial intelligence (AI) in identifying and categorizing neural degeneration in the model organism C. elegans. The tool uses deep learning, a form of AI, and should facilitate and expedite research into neural degeneration.
“Researchers want to study the mechanisms that drive neural degeneration, with the long-term goal of finding ways to slow or prevent the degeneration associated with age or disease,” says Adriana San Miguel, corresponding author of a paper on the work and an assistant professor of chemical and biomolecular engineering at NC State. “Our work here shows that deep learning can accurately identify physical symptoms of neural degeneration; can do it more quickly than humans; and can distinguish between neural degeneration caused by different factors.
“Having tools that allow us to identify these patterns of neural degeneration will help us determine the role that different genes play in these processes,” San Miguel says. “It will also help us evaluate the effect of various pharmaceutical interventions on neural degeneration in the model organism. This is one way we can identify promising candidates for therapeutic drugs to address neurological disorders.”
For this study, researchers focused on C. elegans, or …

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New artificial intelligence platform uses deep learning to diagnose dystonia with high accuracy in less than one second

new artificial intelligence platform uses deep learning to diagnose dystonia with high accuracy in less than one second

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Researchers at Mass Eye and Ear have developed a unique diagnostic tool that can detect dystonia from MRI scans, the first technology of its kind to provide an objective diagnosis of the disorder. Dystonia is a potentially disabling neurological condition that causes involuntary muscle contractions, leading to abnormal movements and postures. It is often misdiagnosed and can take people up to 10 years to get a correct diagnosis.
In a new study published September 28 in Proceedings of the National Academy of Sciences, researchers developed an AI-based deep learning platform — called DystoniaNet — to compare brain MRIs of 612 people, including 392 patients with three different forms of isolated focal dystonia and 220 healthy individuals. The platform diagnosed dystonia with 98.8 percent accuracy. During the process, the researchers identified a new microstructural neural network biological marker of dystonia. With further testing and validation, they believe DystoniaNet can be easily integrated into clinical decision-making.
“There is currently no biomarker of dystonia and no ‘gold standard’ test for its diagnosis. Because of this, many patients have to undergo unnecessary procedures and see different specialists until other diseases are ruled out and the diagnosis of dystonia is established,” said senior study author Kristina Simonyan, MD, PhD, Dr med, …

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Deep Learning Clears the Skies for Volcano Monitoring

deep learning clears the skies for volcano monitoring

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Major volcanic eruptions are rare, but potentially cataclysmic: in 2018 alone, a pair of volcanic eruptions in Guatemala and Indonesia killed at least 600 people. As in earthquake sensing, the underlying signals that define volcanic activity are noisy due to instrumental interference, and prophesying volcanic eruptions is an imprecise (and less-than-actionable) affair. Researchers from Penn State are using AI to improve monitoring of volcanic activity, potentially helping predict dangerous eruptions.
Geoscientists use a variety of tools to monitor seismic signals around volcanoes, predominantly ground stations – but ground stations can be expensive and require regular maintenance. “It’s hard to put a lot of ground-based stations in a specific area in the first place, but, let’s say there actually is a volcanic explosion or an earthquake, that would probably damage a lot of these very expensive instruments,” said Jian Sun, lead author of the research and a postdoctoral scholar in geosciences at Penn State, in an interview with Penn State’s Matt Swayne. “Second, those instruments will only give you ground movement measurements at specific locations where they are installed, therefore those measurements will have a very limited spatial coverage.”
So for truly scalable, comprehensive monitoring, researchers instead turn to satellites and …

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Israeli startup Deci lands $9.1M using AI to train AI to be its best self

israeli startup deci lands $9.1m using ai to train ai to be its best self

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It’s no secret that the last decade has accelerated AI and Deep Learning adoption and development. However, the main issue with ML application deployment is the enormous resources it requires, starting with high performance and expensive GPUs. Israeli startup Deci develops an AI-driven platform that automatically crafts scalable deep neural network solutions, which can be integrated into a wide variety of commercial applications.Using AI to improve AIAdvancements in AI, mainly powered by deep learning, have triggered groundbreaking innovations in medicine, manufacturing, transportation, communication, and retail. But, prolonged development cycles, high computing costs, and unsatisfying inference performance are making it nearly impossible for enterprises to productize AI. By harnessing AI to improve AI, Deci is automatically transforming models to be ready for effective production at scale. With Deci’s new deep learning platform, AI developers can achieve up to tenfold performance improvement on any task, be it machine vision, NLP, or audio, thus obtaining a significant competitive advantage. credit: Deci”Deci is leading a paradigm shift in AI to empower data scientists and deep learning engineers with the tools needed to create and deploy effective and powerful solutions,” says Yonatan Geifman, CEO and co-founder of Deci. “The rapidly increasing complexity …

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Here’s Why You Must Attend The DLDC 2020 — The Deep Learning Conference Of The Year

here’s why you must attend the dldc 2020 — the deep learning conference of the year

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Organised by the Association of Data Scientists (ADaSci), DLDC 2020, or also known as the Deep Learning DevCon 2020, is another conference of the year that is hosted in partnership with Analytics India Magazine. Scheduled for 29th and 30th October, DLDC will bring together the leading experts and best minds of deep learning and machine learning industry from around the world. This two-day conference will witness an exciting lineup of extraordinary speakers, interesting talks and practical hands-on that will be interesting for professionals of this field as well for the students and enthusiasts who are looking to kickstart their deep learning career.

Organised by a premier global professional body of data science and machine learning professionals — ADaSci, this event will be the first-of-its-kind virtual conference on deep learning. With deep learning becoming one of the most advancing technologies in the world — from being used in the fields of natural language processing to making self-driving cars — this deep learning conference will aim at bridging the gap between the latest technological advancements in the research area and real-world applications of the same.

If you are ready to understand how to apply deep learning to your businesses, this conference is a must-attend. In this article, …

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Deep learning transforms standard CT scans towards spectral images – Physics World

deep learning transforms standard ct scans towards spectral images – physics world

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Top row: benchmark virtual monoenergetic (VM) images from dual-energy CT projection data reconstructed at 80 and 110 keV, respectively. Bottom row: corresponding VM images produced at the same energies from only 140-kVp images, using a deep learning approach. (Courtesy: Wenxiang Cong, Yan Xi, Paul Fitzgerald, Bruno De Man and Ge Wang)
Computed tomography (CT) is a popular medical imaging tool, visualizing and quantifying internal structures for screening, diagnosis, therapy planning and treatment monitoring. Conventional clinical CT scans generate a spectrally integrated attenuation image that shows tissue morphology, but does not directly provide any information regarding tissue composition.
Dual-energy CT (DECT) systems, which acquire two spectrally distinct datasets, can reconstruct virtual monoenergetic (VM) and material-specific images that provide information about tissue composition. Compared with conventional CT, however, DECT is more expensive and complex, and often requires an increased radiation dose.
For patients without access to DECT scans, a way to approximate the information provided by DECT using a single-spectrum CT scan could improve clinical diagnoses. With this aim, Wenxiang Cong and a team led by Ge Wang at Rensselaer Polytechnic Institute demonstrated a deep-learning approach that can produce VM images from single-spectrum CT scans.
“With traditional CT, you take a greyscale image, but …

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Deci raises $9.1M to optimize AI models with AI

deci raises $9.1m to optimize ai models with ai

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Deci, a Tel Aviv-based startup that is building a new platform that uses AI to optimized AI models and get them ready for production, today announced that it has raised a $9.1 million seed round led by Emerge and Square Peg.
The general idea here is to make it easier and faster for businesses to take AI workloads into production — and to optimize those production models for improved accuracy and performance. To enable this, the company built an end-to-end solution that allows engineers to bring in their pre-trained models and then have Deci manage, benchmark and optimize them before they package them up for deployment. Using its runtime container or Edge SDK, Deci users can also then serve those models on virtually any modern platform and cloud.
Deci’s insights screen combines all indicators of a deep learning model’s expected behavior in production, resulting in the Deci Score – a single metric summarizing the overall performance of the model.
The company was co-founded by co-founded by deep learning scientist Yonatan Geifman, technology entrepreneur Jonathan Elial, and professor Ran El-Yaniv, a computer scientist and machine learning expert at the Technion – Israel Institute of Technology.
“Deci is leading a paradigm shift in AI …

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Deci Raises $9.1M in Seed Funding to Build AI that Crafts Next Generation of AI

deci raises $9.1m in seed funding to build ai that crafts next generation of ai

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TEL AVIV, Israel, Oct. 27, 2020 /PRNewswire/ — Deci, the deep learning company dedicated to transforming the AI lifecycle, today announced it has raised $9.1 million in a seed round led by Israel-based VC firm Emerge and global VC fund Square Peg. The company is building an AI-based platform that can automatically craft robust, scalable, and efficient deep neural network solutions ready for production at scale. Deci aims to help AI practitioners build the next generation of deep learning models.
Advancements in AI, mainly powered by deep learning, have triggered groundbreaking innovations in medicine, manufacturing, transportation, communication, and retail. But, prolonged development cycles, high computing costs, and unsatisfying inference performance are making it nearly impossible for enterprises to productize AI. By harnessing AI to improve AI, Deci is automatically transforming models to be ready for effective production at scale. With Deci’s new deep learning platform, AI developers can achieve up to tenfold performance improvement on any task, be it machine vision, NLP, or audio, thus obtaining a significant competitive advantage.
“Deci is leading a paradigm shift in AI to empower data scientists and deep learning engineers with the tools needed to create and deploy effective and powerful solutions,” says Yonatan Geifman, CEO and …

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For under $40, you can learn all about Python, machine learning and artificial intelligence | Boing Boing

for under $40, you can learn all about python, machine learning and artificial intelligence | boing boing

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This week in thinking machines news, a Harvard professor and his students have now raised $14 million to create artificial intelligence so smart that even hackers can’t crack it. Meanwhile, reports from the White House suggest the federal government is close to issuing their directives on how agencies should regulate AI going forward.
And if story no. 1 makes you at all dubious about the impact of story no. 2…well, welcome to the amazing world of Python, machine learning and the tech wonders and ethical quandaries of creating human-based artificial life.
Regardless of the implications, there’s no denying that understanding Python and its impact on creating machines that can actually think for themselves is a fast track to an exploding and lucrative new career. You can get that training underway with the courses in The Ultimate Python and Artificial Intelligence Certification Bundle.
With nine courses covering 38 hours of content, even first-time users can get comfortable with using the Python coding language and see how it’s helping to drive data science in exciting new directions.
The basics of Python are laid out in a trio of courses, as Python: Introduction to Data Science and Machine Learning A-Z, Python for Beginners: Learn All the …

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