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Quobyte storage software takes a bite at HPC and machine learning – Blocks and Files

quobyte storage software takes a bite at hpc and machine learning – blocks and files

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Profile Quobyte produces unified block, file and object storage for high-performance computing (HPC) and machine learning.

Its software runs on-premises and in the Google cloud and is used for data analytics, machine-learning, high-frequency trading, life sciences and similar petabyte-scale, fast-response applications. The company also has a TensorFlow plug-in.

Quobyte was established by CEO Björn Kolbeck and CTO Felix Hupfeld in Berlin in 2013. The pair studied computer science and did distributed storage systems research at Berlin’s Zuse Institute, where they developed the open source XtreemFS distributed file system as part of their PhD. They then worked for Google and this gave them the idea that they could build a Google-like scalable, distributed, parallel access and multi-protocol storage system for HPC customers.

Kolbeck and Hupfel thought that existing HPC storage was placed in restrictive silos such as file or object, was hardware-dependent, and needed a lot of administration. Their response was to develop high-performance and multi-protocol storage software that was independent of its hardware supplier and manageable by a small team.

Björn Kolbeck (left) and Felix Hupfeld

The software use commodity server hardware and is intended to replace several separate storage vaults while delivering good random and small file …

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A machine learning processing engine to build flexible devices with odor recognition capabilities

a machine learning processing engine to build flexible devices with odor recognition capabilities

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Micrograph of the ML Natively Flexible Processing Engine (NFPE) as a flexible IC. Credit: Ozer et al.

In recent years, there has been an increase in the development of flexible electronics: electronic components that can be stretched and thus enable the development of smart watches, fitness trackers, or other wearable smart devices. Flexible electronics are typically made by applying electronic circuits on flexible material substrates, such as plastic or paper.

Flexible chips can be used to fabricate devices that are low-cost, as well as extremely thin, bendable, and comfortable to wear. While their characteristics could make them more suitable than conventional electronics fabricated on silicon wafers for certain applications, so far not all these chips have achieved desirable performances.
Researchers at Arm and PragmatIC have recently used low-cost flexible chips to fabricate a machine learning (ML) processing engine, which could be used to build a wide range of smart devices with advanced data processing capabilities. Their paper, published in Nature Electronics, specifically demonstrates the use of their engine for applications that involve recognizing smells or odors.
“Arm Research has a close R&D collaboration with PragmatIC, which has low-cost flexible IC fabrication technology based on metal-oxide thin-film transistors (TFTs),” Emre …

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Atomwise’s machine learning-based drug discovery service raises $123 million

atomwise’s machine learning-based drug discovery service raises $123 million

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With a slew of partnerships with large pharmaceutical companies under its belt and the successful spin out of at least one new company, Atomwise has already proved the value of its machine learning platform for discovering and commercializing potential small molecule therapies for a host of conditions.
Now the company has raised $123 million in new funding to accelerate its business.
“Scaling the technology and scaling the team and scaling what we’ve been doing with it,” says chief executive officer Abe Heifets when asked about what comes next for the eight year old business.
Atomwise has already signed contracts worth $5.5 billion with corporate partners that include Eli Lilly & Co., Bayer, Hansoh Pharmaceuticals, and Bridge Biotherapeutics. Smaller, earlier stage companies like StemoniX and SEngine Precision Medicine are also using Atomwise’s tech.
Now the company will look to capture more of the value of drug discovery for itself, looking to develop and commercialize its discoveries by taking over more of the development process and working with manufacturers at a later stage, according to Heifets.
Atomwise tipped its new strategy last year when it announced a partnership with Velocity Drug Development and a $14.5 million investment to create x-37, a spinoff that’s …

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How Can AI and Machine Learning Solve Retail Category Managers’ Pain Points? | Coresight Research

how can ai and machine learning solve retail category managers’ pain points? | coresight research

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Question of the Week 1 minute

August 11, 2020

QUESTION OF THE WEEK: How Can AI and Machine Learning Solve Retail Category Managers’ Pain Points?
Artificial intelligence (AI) and machine learning can help retail category managers to quickly act on changing market trends by adapting to consumer demand in real time.

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How This Cybersecurity Startup Is Using Machine Learning

how this cybersecurity startup is using machine learning

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The COVID pandemic has massively escalated the surge of cyberattacks and data breaches despite having robust security controls, software, and solutions abundantly available in the market. A lot of this could be attributed to the vulnerability businesses offer the cybercriminals to take advantage of the situation quickly. While the conventional cybersecurity approach has benefited many, having cybersecurity without cyber-intelligence and necessary awareness can put the security professionals off-guarded to more complicated and novel threats.

Furthermore, with limited cybersecurity resources, businesses need to prioritise their efforts to strengthen cyber posture effectively; however, many organisations do not have an anchor point or a guiding principle, to begin with. With cyber-intelligence inputs missing from cybersecurity capabilities like incident management, vulnerability management, risk assessment and brand monitoring, businesses end up running their security practice in silos instead of an integrated approach. 

And, thus, in an attempt to revolutionise the cyber threat visibility and intelligence market, CYFIRMA, a cyber analytics startup assists businesses to understand the relevance of the current threat landscape. Not only it provides insights on threat actors and indicators, emerging threats and digital risks, but also automatically applies intelligence into cyber posture management. To dig deeper, Analytics India Magazine got in touch …

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Researchers develop machine learning tool to predict necrotizing enterocolitis in premature infants

researchers develop machine learning tool to predict necrotizing enterocolitis in premature infants

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Necrotizing enterocolitis (NEC) is a life-threatening intestinal disease of prematurity. Characterized by sudden and progressive intestinal inflammation and tissue death, it affects up to 11,000 premature infants in the United States annually, and 15-30% of affected babies die from NEC. Survivors often face long-term intestinal and neurodevelopmental complications.

Researchers from Columbia Engineering and the University of Pittsburgh have developed a sensitive and specific early warning system for predicting NEC in premature infants before the disease occurs. The prototype predicts NEC accurately and early, using stool microbiome features combined with clinical and demographic information. The pilot study was presented virtually on July 23 at ACM CHIL 2020.

“It’s amazing how we may be able to use machine learning to stop this from happening to babies,” said the study’s co-author, Ansaf Salleb-Aouissi, a senior lecturer in discipline from the computer science department at Columbia Engineering and a specialist in artificial intelligence and its applications to medical informatics. “We looked at the data and developed a tool that can truly be useful, even life-saving.”


If doctors could accurately predict NEC before the baby actually becomes sick, there are some very simple steps they could take-;treatment could include stopping feeds, giving IV fluids, and starting antibiotics …

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The Ethics Of AI And Death – Big Easy Magazine

the ethics of ai and death – big easy magazine

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AI can now accurately predict death, but is that a prediction we want to hear?
In almost every industry, artificial intelligence (AI) is on the fast track to outpacing human endeavor. Machine learning technologies are already better than the average person at gaming, creating content and even building AI, and it appears they are only going from strength to strength.
As a result of their developing intelligence, the most common question AI critics have been asking is whether it’s ethical to be putting ourselves out of a job. YouTube video essayist CGP Grey put it best when he said that, by investing in AI development, we are steaming ahead towards a market in which “humans need not apply” without adequately preparing the population for that scenario.-ADVERTISEMENT-

However, there is another ethical question to ask about superhuman AI: do we truly want all our questions answered? Is there some knowledge that, given the option, we’d actually prefer not to have? Perhaps the most profound piece of knowledge any one of us could have would be knowing when we die. The idea that we could predict death with 100% accuracy has been the subject of art and literature from Ancient …

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Colorado State University Global Launches Fully Online Artificial Intelligence and Machine Learning Master’s Degree

colorado state university global launches fully online artificial intelligence and machine learning master’s degree

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The new future-facing program from the nation’s first 100% online state university aligns with latest knowledge and skills in this high-demand technology career fieldAurora, CO – (August 6, 2020) – Colorado State University Global (CSU Global) — a premier provider of innovative higher-learning opportunities through high-quality, career-relevant, and affordable online education — is proud to announce its new Artificial Intelligence (AI) and Machine Learning (ML) master’s degree.This new degree program will allow students to gain a detailed understanding of software development, artificial intelligence, and machine learning principles, and how they can be used to create a deeper critical analysis. Students also will learn to apply appropriate mathematical, statistical, and programming to the realm of artificial intelligence and machine learning for a growing number of jobs in the artificial intelligence and machine learning field.“Students will not only gain the most current technical skills surrounding AI and ML applications, but they will gain skills using the latest deep-learning libraries such as Tensorflow, with a focus on proficiency in Python and software development,” said Dr. Charles W. Lively, Program Chair of the Information Technology and Security programs at CSU Global. “This scope reflects the growing demand for these skills in both the public and private sectors.” …

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The 8 Terms Every Machine Learning Beginner Should Know

the 8 terms every machine learning beginner should know

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If you ever want to work and research in the Machine Learning arena, these are the 8 key terms you cannot ignore. Are you ready?

1.Algorithm

Algorithms are a basic element in the world of Machine Learning. An algorithm is a logical sequence of instructions that describe step by step how to solve a problem. 

Most often, the algorithm works as a sequence of simple if → then statements. Others are more complex and include mathematical equations or formulas.

The objective of a Machine Learning algorithm is to define the steps necessary to learn from the data and solve a problem autonomously.

Some popular Machine Learning algorithm families are clustering, regression, or recommendation algorithms.

2. Deep Learning

Deep Learning is a set of algorithms that seek to reproduce the same results as the human brain.

The algorithms follow a logic of layered processes that simulate the basic functioning of the brain through neurons. In Deep Learning these neurons are known as “layers”.

Just like our brain learns when we are faced with learning something new, how to speak, ride a bike, etc; algorithms seek this imitation by learning to recognize repetition patterns, specific words, frequent behaviors, so that they are able to automatically …

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Explainable AI (XAI): Escaping the Black Box of AI and Machine Learning

explainable ai (xai): escaping the black box of ai and machine learning

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Artificial Intelligence (AI) made leapfrogs of development and saw broader adoption across industry verticals when it introduced machine learning (ML). ML helps in learning the behavior of an entity using patterns detection and interpretation methods. However, despite its unlimited potential, the conundrum lies in how machine learning algorithms arrive at a decision in the first place. Questions like, “What are the processes they adopted, and at what speed? How did they make such autonomous decision?” often raises concern about reliability on ML models. Though it helps in parsing huge amounts of data into intelligent insights for applications ranging from fraud detection to weather forecasting, the human mind is constantly baffled how it achieves conclusions. Moreover, the recurrent need to comprehend the procedures behind the decisions becomes more crucial when there is a possibility that the ML model makes decisions based on incomplete, error-prone, or one-sided (biased) information that can put few gatherings inside the network at a disadvantage. Enter Explainable AI (XAI).
This discipline holds the key to unlocking the AI and ML black box. XAI is an AI model that is programmed to explain its goals, logic, and decision making so that the average human user can understand it.  …

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