CORRECTING and REPLACING Anyscale Hosts Inaugural Ray Summit on Scalable Python and Scalable Machine Learning

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Creators of Ray Open Source Project Gather Industry Experts for Two-Day Event on Building Distributed Applications at ScalePlease replace the release with the following corrected version due to multiple revisions.The updated release reads:ANYSCALE HOSTS INAUGURAL RAY SUMMIT ON SCALABLE PYTHON AND SCALABLE MACHINE LEARNINGCreators of Ray Open Source Project Gather Industry Experts for Two-Day Event on Building Distributed Applications at ScaleAnyscale, the distributed programming platform company, is proud to announce Ray Summit, an industry conference dedicated to the use of the Ray open source framework for overcoming challenges in distributed computing at scale. The two-day virtual event is scheduled for Sept. 30 – Oct. 1, 2020.With the power of Ray, developers can build applications and easily scale them from a laptop to a cluster, eliminating the need for in-house distributed computing expertise. Ray Summit brings together a leading community of architects, machine learning engineers, researchers, and developers building the next generation of scalable, distributed, high-performance Python and machine learning applications. Experts from organizations including Google, Amazon, Microsoft, Morgan Stanley, and more will showcase Ray best practices, real-world case studies, and the latest research in AI and other scalable systems built on Ray.”Ray Summit gives individuals and organizations the opportunity to …



Machine learning helps detect new potential drugs to treat COVID-19

machine learning helps detect new potential drugs to treat covid-19


Reviewed by Emily Henderson, B.Sc.Aug 12 2020
Scientists at the University of California, Riverside, have used machine learning to identify hundreds of new potential drugs that could help treat COVID-19, the disease caused by the novel coronavirus, or SARS-CoV-2.

“There is an urgent need to identify effective drugs that treat or prevent COVID-19,” said Anandasankar Ray, a professor of molecular, cell, and systems biology who led the research. “We have developed a drug discovery pipeline that identified several candidates.”

The drug discovery pipeline is a type of computational strategy linked to artificial intelligence — a computer algorithm that learns to predict activity through trial and error, improving over time.

With no clear end in sight, the COVID-19 pandemic has disrupted lives, strained health care systems, and weakened economies. Efforts to repurpose drugs, such as Remdesivir, have achieved some success. A vaccine for the SARS-CoV-2 virus could be months away, though it is not guaranteed.

As a result, drug candidate pipelines, such as the one we developed, are extremely important to pursue as a first step toward systematic discovery of new drugs for treating COVID-19.

Existing FDA-approved drugs that target one or more human proteins important for viral entry and replication …


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Cadence Delivers Machine Learning-Optimized Xcelium Logic Simulation with up to 5X Faster Regressions

cadence delivers machine learning-optimized xcelium logic simulation with up to 5x faster regressions


– Core engine performance enhancements accelerate verification throughput by reducing simulation cycles with matching coverage on randomized test suites
SAN JOSE, Calif. and BENGALURU, India, Aug. 12, 2020 /PRNewswire/ — Cadence Design Systems, Inc. (NASDAQ: CDNS) today announced  that the Cadence® Xcelium™ Logic Simulator has been enhanced with machine learning technology (ML), called Xcelium ML, to increase verification throughput. Using new machine learning technology and core computational software, Xcelium ML enables up to 5X faster verification closure on randomized regressions.
Using computational software and a proprietary machine learning technology that directly interfaces to the simulation kernel, Xcelium ML learns iteratively over an entire simulation regression. It analyzes patterns hidden in the verification environment and guides the Xcelium randomization kernel on subsequent regression runs to achieve matching coverage with reduced simulation cycles.
Cadence’s Xcelium Logic Simulator provides best-in-class core engine performance for SystemVerilog, VHDL, mixed-signal, low power, and x-propagation. It supports both single-core and multi-core simulation, incremental and parallel build, and save/restart with dynamic test reload. The Xcelium Logic Simulator has been deployed by a majority of top semiconductor companies, and a majority of top companies in the hyperscale, automotive and consumer electronics segments.
“Kioxia has effectively utilized Xcelium simulation for a variety …


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AIoT: When Artificial Intelligence Meets the Internet of Things

aiot: when artificial intelligence meets the internet of things


The pandemic has businesses everywhere on the ropes, with many firms filing for bankruptcy since lockdowns began. Despite the uncertainty, tech giants and major digital retail brands are still thriving—and some are running circles around those that are less pandemic-proof.
Using data from Kantar and Bloomberg, a recent brand report released by BrandZ shows which tech companies are proving their worth to consumers during COVID-19 chaos. With data covering almost 4 million consumers, BrandZ also reveals that the tech sector leads the world’s 100 most valued brands in terms of financial power and consumer sentiment.
Here’s how the top 20 tech brands from the report stack up:

RankCompanyBrand Value (2020)Change (%)

#1🇺🇸 Apple$352 billion+14%

#2🇺🇸 Microsoft$327 billion+30%

#3🇺🇸 Google$324 billion+5%

#4🇨🇳 Tencent$151 billion+15%

#5🇺🇸 Facebook$147 billion-7%

#6🇺🇸 IBM$84 billion-3%

#7🇩🇪 SAP$58 billion0%

#8🇺🇸 Instagram$42 billion+47%

#9🇺🇸 Accenture$41 billion+6%

#10🇺🇸 Intel$37 billion+17%

#11🇺🇸 Adobe$36 billion+29%

#12🇰🇷 Samsung$33 billion+7%

#13🇺🇸 Salesforce$30 billion+13%

#14🇺🇸 LinkedIn$30 billion+31%

#15🇨🇳 Huawei$29 billion+9%

#16🇺🇸 Oracle$27 billion+2%

#17🇺🇸 Cisco$26 billion-9%

#18🇺🇸 Dell$18 billion-2%

#19🇨🇳 Xiaomi$17 billion-16%

#20🇨🇳 Baidu$15 billion-29%

Out of the top five tech brands, Microsoft made the biggest moves with 30% brand value growth. Other big movers in the top 20 were Instagram (owned by Facebook), Adobe, and LinkedIn (owned …


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5 Ways Machine Learning Can Thwart Phishing Attacks

5 ways machine learning can thwart phishing attacks



Mobile devices are popular with hackers because they’re designed for quick responses based on minimal contextual information. Verizon’s 2020 Data Breach Investigations Report (DBIR) found that hackers are succeeding with integrated email, SMS and link-based attacks across social media aimed at stealing passwords and privileged access credentials. And with a growing number of breaches originating on mobile devices according to Verizon’s Mobile Security Index 2020, combined with 83% of all social media visits in the United States are on mobile devices according to Merkle’s Digital Marketing Report Q4 2019, applying machine learning to harden mobile threat defense deserves to be on any CISOs’ priority list today.
How Machine Learning Is Helping To Thwart Phishing Attacks
Google’s use of machine learning to thwart the skyrocketing number of phishing attacks occurring during the Covid-19 pandemic provides insights into the scale of these threats. On a typical day, G-Mail blocks 100 million phishing emails. During a typical week in April of this year, Google’s G-Mail Security team saw 18M daily malware and phishing emails related to Covid-19. Google’s machine learning models are evolving to understand and filter phishing threats, successfully blocking more than 99.9% of spam, phishing and malware from reaching …


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What is one-shot learning?

what is one-shot learning?


Image credit: DepositphotosThis article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.
Passport checks at airports and border gates present a special challenge: How do you tell if the person standing in front of you is the same person whose picture is in the passport? Border and customs officers solve this problem using the complex mechanisms ingrained in the human visual system through billions of years of evolution.
It’s not a perfect process, but it works well most of the time.

In the realm of artificial intelligence, this is called the “one-shot learning” challenge. In a more abstract way, can you develop a computer vision system that can look at two images it has never seen before and say whether they represent the same object?
Data is one of the key challenges in deep learning, the branch of artificial intelligence that has had the most success in computer vision. Deep learning algorithms are notorious for requiring large amount of training examples to perform simple tasks such as detecting objects in images.
But interestingly, if configured properly, deep neural networks, the key component of deep learning systems, can perform …


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Machine Learning Predicts Life-Threatening Disease in Infants

machine learning predicts life-threatening disease in infants


By Jessica Kent

August 12, 2020 – Researchers have created an early warning system that uses machine learning to predict necrotizing enterocolitis (NEC), a life-threatening intestinal disease that affects premature infants.
NEC impacts up to 11,000 premature infants in the US annually, researchers noted, and 15 to 30 percent of babies die from NEC. The condition involves sudden and progressive intestinal inflammation and tissue death, and survivors often face long-term intestinal and neurodevelopmental complications.
There is currently no tool to predict which preterm infants will get the disease, and NEC often goes unrecognized until it’s too late to effectively intervene. Researchers don’t yet understand the causes of NEC, but several studies have focused on shifts in the intestinal microbiome, the bacteria in the intestine whose composition can be determined from DNA sequencing from small stool samples.

Dig Deeper

Researchers from Columbia Engineering and University of Pittsburgh School of Medicine hypothesized that using machine learning to model clinical, demographic, and microbiome data from premature infants may allow identification of patients at high risk for NEC before disease onset. This could enable early intervention and mitigation of serious complications.
“If doctors could accurately predict NEC before the baby actually becomes sick, there are some very …


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Mphasis Awarded U.S. Patent for its Artificial Intelligence (AI) System for Data extraction, aggregation & analysis

mphasis awarded u.s. patent for its artificial intelligence (ai) system for data extraction, aggregation & analysis


NEW YORK, Aug. 12, 2020 /PRNewswire/ — Mphasis (BSE: 526299; NSE: MPHASIS), an Information Technology solutions provider specializing in cloud and cognitive services, today announced that it has recently been granted a U.S. patent for its AI system for tracking, managing and analyzing data from unstructured data sources. The newly issued patent – U.S. Patent No. 10,726038 relates to optimized data aggregation and analytics across physical and digital data sources. The patented system enables enterprises to draw actionable insights at real-time from enterprise data sources such as emails, call centre transcripts, insurance policy documents, broker submissions, bank statements, customer complaints etc. Generating valuable insights from these data sources is necessity for enterprise decision making and the invention provides for automatically generating rules and employing artificial intelligence and advanced analytics to extract, adopt, exploit and analyse data.
The patented algorithms have been have been integrated as part of Mphasis’ NextLabs solutions such as HyperGrafTM, a comprehensive, feature-rich, business intelligence and analytics solution, as well as DeepInsightsTM, a cognitive intelligence platform, which enables enterprises to gain faster and more effective access to insights from data. These solutions are some of Mphasis’ latest offerings focusing on emerging paradigms of innovation such as artificial intelligence, machine learning …


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