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BOSTON, April 13, 2022 (GLOBE NEWSWIRE) -- VMRay, a provider of automated malware analysis and detection solutions, today announced the release of new Machine Learning-based capabilities for its flagship VMRay Platform, helping enterprise security teams detect and neutralize novel malware and phishing threats. Recognized as the gold standard for advanced threat detection and analysis, the high-fidelity threat data used by VMRay to train and evaluate its Machine Learning system is both highly accurate and relevant, allowing customers to detect threats such as zero-day malware which were previously thought to be undetectable.

To get the best out of AI, you need a carefully arranged combination of Machine Learning and other cutting-edge technologies. Because the value and efficacy of each ML utilization is dependent on how you train and evaluate the model: namely, the quality of the inputs and the expertise of the team, said Carsten Willems, co-founder and CEO of VMRay. The data that you use to train the model and evaluate the accuracy of its predictions must be accurate, noise-free, and relevant to the task at hand. This is why Machine Learning can only add value when its based on an already advanced technology platform with outstanding detection capabilities. Our approach is to use ML together with our best-of-breed technologies to enhance detection capabilities to perfection, by combining the best of two worlds.

Todays threat landscape is a dynamic one, evolving by the day with attacks growing in complexity, scale and stealth. Since late detection and response is among the most important problems that cause huge costs, its more critical than ever that security teams can rapidly identify and stop these threats at the initial point of entry, before a minor incident cascades into a full-blown data breach. Whereas conventional signature and rule-based heuristics are unable to detect unknown or sophisticated threats that use advanced evasive techniques, the VMRay Platform is able to detonate a malicious file or URL in a safe environment, observe and document the genuine behavior of the threat as the threat is unaware that its being observed.

Four of the top five global technology enterprises, three of the Big 4 accounting firms, and more than 50 government agencies across 17 countries today rely on VMRay to supplement their existing security solutions, automate security operations and thus, accelerate detection and response. Gartners Emerging Technologies: Tech Innovators in AI in Attack Detection report asserts that the critical requirements for an AI-based attack detection solution are improved attack detection and reduced false positives. This latest, ML-enhanced version of VMRay Platform addresses these two challenges with unmatched precision, delivering the following benefits to security teams and threat analysts:

Improved Threat Detection: Featuring a machine learning model that improves threat detection capabilities by recognizing additional patterns, the VMRay Platform brings advanced threat detection to customers existing security solutions and covers the blind spots. With this supplementary approach, VMRay minimizes security risks and maximizes the value that customers get from their security investment.

Reduced False Positives: False positives and alert fatigue continue to plague enterprise SOC teams, hampering their ability to quickly respond to genuine threats. VMRay Analyzer generates high-fidelity, noise-free reports that dramatically reduce false positives to keep teams efficient. Seamless integrations with all the major EDR, SIEM, SOAR, Email Security, and Threat Intelligence platforms enable full automation, empowering resource-strapped security teams to focus their energies on higher-value strategic initiatives.

To try VMRay Analyzer visit: https://www.vmray.com/try-vmray-products/

About VMRay

VMRay was founded with a mission to liberate the world from undetectable digital threats. Led by notable cyber security pioneers, VMRay develops best-of-breed technologies to detect unknown threats that others miss. Thus, we empower organizations to augment and automate security operations by providing the worlds best threat detection and analysis platform. We help organizations build and grow their products, services, operations, and relationships on secure ground that allows them to focus on what matters with ultimate peace of mind. This, for us, is the foundation stone of digital transformation.

Press ContactRobert NachbarKismet Communications206-427-0389rob@kismetcommunications.net

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VMRay Unveils Advanced Machine Learning Capabilities to Accelerate Threat Detection and Analysis - GlobeNewswire

In recent years, we have seen accelerated OLED materials development, aided by software tools based on machine learning and Artificial Intelligence. This is an excellent development which contributes to the continued improvement in OLED efficiency, brightness and lifetime.

Kyulux's Kyumatic AI material discover system

The promise of these new technologies is the ability to screen millions of possible molecules and systems quickly and efficiently. Materials scientists can then take the most promising candidates and perform real synthesis and experiments to confirm the operation in actual OLED devices.

The main drive behind the use of AI systems and mass simulations is to save the time that actual synthesis and testing of a single material can take - sometimes even months to complete the whole cycle. It is simply not viable to perform these experiments on a mass scale, even for large materials developers, let alone early stage startups.

In recent years we have seen several companies announcing that they have adopted such materials screening approaches. Cynora, for example, has an AI platform it calls GEM (Generative Exploration Model) which its materials experts use to develop new materials. Another company is US-based Kebotix, which has developed an AI-based molecular screening technology to identify novel blue OLED emitters, and it is now starting to test new emitters.

The first company to apply such an AI platform successfully was, to our knowledge, Japan-based Kyulux. Shortly after its establishment in 2015, the company licensed Harvard University's machine learning "Molecular Space Shuttle" system. The system has been assisting Kyulux's researchers to dramatically speed up their materials discovery process. The company reports that its development cycle has been reduced from many months to only 2 months, with higher process efficiencies as well.

Since 2016, Kyulux has been improving its AI platform, which is now called Kyumatic. Today, Kyumatic is a fully integrated materials informatics system that consists of a cloud-based quantum chemical calculation system, an AI-based prediction system, a device simulation system, and a data management system which includes experimental measurements and intellectual properties.

Kyulux is advancing fast with its TADF/HF material systems, and in October 2021 it announced that its green emitter system is getting close to commercialization and the company is now working closely with OLED makers, preparing for early adoption.

Continued here:
How machine learning and AI help find next-generation OLED materials - OLED-Info

Creating new mathematical conjectures and theorems needs a complex approach which requires three factors that are:

At DeepMind, a UK-based artificial intelligence laboratory, researchers in collaboration with mathematicians at the University of Oxford, UK, and University of Sydney, Australia, respectively. The researchers over there have made an important breakthrough by using machine learning to highlight the mathematical connections that human counterparts miss.

Into the technology behind DeepMind

In fascination with the way humans usually used to think and human-based intelligence has long caught the image of computer scientists. Human intelligence has en-sharpened the digital modern world, thus allow us to learn, create, communicate and develop by our own self-awareness.

Since 2010, researchers and developers at the DeepMind team have been trying to solve intelligence-based problems, developing problem-solving systems that are an Artificial General Intelligence (AGI).

In order to perform, DeepMind takes an interdisciplinary approach that commits machine learning and neuroscience, philosophy, mathematics, engineering, simulation, and computing infrastructure together.

The company has already made significant breakthroughs with its machine learning and AI systems, for example, the AlphaGo program, which was the first AI to beat a human professional Go player.

Thinking DeepMaths

The work developed by the DeepMind team says that mathematicians can benefit from machine learning tools to sharpen up and enhance up their intuition where complex mathematical objects and their relationships are highly concerned.

Initially, the project was focused on identifying mathematical conjectures and theorems that DeepMinds technology could deal with, but ultimately it is all dependent upon probability as opposed to absolute certainty.

However, when dealing with large sets of information, the researchers tried to apply their own intuition that the AI could detect the signal relationships between mathematical objects. Afterward, the mathematicians could then apply their own conjecture to the relationships to make them an absolute certainty.

Tied up in Knots

Machine learning requires several amounts of data in order to complete the task efficiently and effectively. So the researchers tied knots as their starting point, calculating invariants.

DeepMinds AI software was assumed to work on two separate components of knot theory; algebraic and geometric. The team then used the program to seek relationships between straightforward and complex correlations as well as subtle and unintuitive ones.

The leads presenting the most promising data were then directly handed over to human mathematicians for analysis and refinement.

The DeepMind team believes that mathematics can release the benefits from methodology and technology as an effective mechanism that could see the widespread application of machine learning in mathematics. Thus, this strengthens the relationship between methodology and technology.

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Machine learning to create some of the new mathematical conjectures - Techiexpert.com - TechiExpert.com

There is a huge demand for deep learning jobs in big tech companies in 2022 and beyond

Deep learning jobs are in huge demand at multiple big tech companies to adopt digitalization and globalization in this global tech market. Yes, the competition is very high among big tech companies in recent times. Thus, they are offering deep learning vacancies with lucrative salary packages for experienced deep learning professionals. Machine learning jobs are also included in the vacancy list of big tech companies to apply for in April 2022. One can apply to these deep learning jobs if there is sufficient experience and knowledge about this domain. Hence, lets explore some of the top ten deep learning jobs in 2022 to look out for in big tech companies.

Location: Shanghai

Responsibilities: The architect must analyze the performance of multiple machine learning algorithms on different architectures, identify architecture and software performance bottlenecks and propose optimizations, and explore new hardware capabilities.

Qualifications: The candidate should have an M.S./Ph.D. in any technical field with sufficient experience in system architecture design, performance optimization, and machine learning frameworks.

Click here to apply

Location: California

Responsibilities: It is expected to research and implement novel algorithms in the artificial human domain while efficiently designing and conducting experiments to validate algorithms. One should help with the collection and curation of data, train models, and transform research ideas into high-quality product features.

Qualifications: They must be a Masters or Ph.D. in any technical field with hands-on experience in developing a product based on machine learning research, frameworks, programming languages, and many more.

Click here to apply

Location: North Reading

Responsibilities: The right candidate should develop deep neural net models, techniques, and complex algorithms for high-performance robotic systems. It is necessary to design highly scalable enterprise software solutions while executing technical programs.

Qualifications: There should have a Ph.D. in any technical field with more than two years of experience in a programming language, over three years in developing machine learning models and algorithms, and more than four years of research experience in this domain and machine learning technologies. It is necessary to have a strong record of patents and innovation or publications in top-tier peer-reviewed conferences.

Click here to apply

Location: Seoul

Responsibilities: It is expected to work on automatic speech recognition and keyword spotting with speech enhancement in a multi-microphone system. The researcher must be the representation of learning audio and speech data with generative models for speech generation or voice conversion.

Qualifications: There should be a deep knowledge of general machine learning, signal processing, speech processing, RNN, generative models, programming languages, and many more.

Click here to apply

Location: Bengaluru

Responsibilities: It is necessary to build innovative and robust real-life solutions for computer-vision applications in smart mobility and autonomous systems, develop strategic concepts and engage in technical business development, as well as solve challenges associated with transformation such large complex datasets.

Qualifications: The candidate must have a Ph.D./Masters degree in computer science with at least eight years of hands-on experience in computer vision, video analytics problems, training in deep convolutional networks, OpenCV, OpenGL, and many more.

Click here to apply

Location: Bengaluru

Responsibilities: The duties include enabling full-stack solutions to boost delivery and drive quality across the application lifecycle, performing continuous testing for security, creating automation strategy, participating in code reviews, and reporting defects to support improvement activities for the end-to-end testing process.

Qualifications: The engineer must have a Bachelors degree with eight to ten years of work experience with statistical software packages and a deep understanding of multiple software utilities for data and computation.

Click here to apply

Location: Santa Clara

Responsibilities: The duties include the analysis of the state-of-the-art algorithms for multiple computing hardware backends and utilizing experience with machine learning frameworks. There should be an implementation of multiple distributed algorithms with data flow-based asynchronous data communication.

Qualifications: The engineer must have a Masters/Ph.D. degree in any technical field with more than two years of industry experience.

Click here to apply

Location: Great Britain

Responsibilities: The scientist should develop novel algorithms and modelling techniques to improve state-of-the-art speech synthesis. It is essential to use Amazons heterogeneous data sources with written explanations and their application in AI systems.

Qualifications: The candidate should have a Masters or Ph.D. degree in machine learning, NLP, or any technical field with two years of experience in machine learning research projects. It is necessary to have hands-on experience in speech synthesis, end-to-end agile software development, and many more.

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Location: Bengaluru

Responsibilities: The candidate should work with programming languages like R and Python to efficiently complete the life cycle of a statistical modelling process.

Qualifications: The candidate must be a graduate or post-graduate with at least six years of experience in machine learning and deep learning.

Click here to apply

Location: Bengaluru

Responsibilities: It is essential to support the day-to-day activities of the development and engineering by coding and programming specifications by developing technical capabilities, assisting in the development and maintenance of solutions or infrastructures, as well as translating product requirements into technical requirements.

Qualifications: The candidate should have a B. Tech/M. Tech/MCA or a Bachelors degree in any technical field with more than three to five years of experience on SAP U15/ABAP/CDS/ and many more. It is essential to have sufficient knowledge of cloud development, maintenance process, SAP BTP services, and many more.

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Top 10 Deep Learning Jobs in Big Tech Companies to Apply For - Analytics Insight

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Turk Neurosurg. 2021 Nov 10. doi: 10.5137/1019-5149.JTN.36068-21.3. Online ahead of print.

ABSTRACT

AIM: We sought to identify predictors of basilar invagination (BI) prognosis and compare diagnostic properties between logistic modeling and machine learning methods.

MATERIAL AND METHODS: We conducted a single-center retrospective study. Patients at our hospital who met the inclusion and exclusion criteria were identified between August 2015 and August 2020 for inclusion. Candidate predictors, such as demographics, clinical scores, radiographic parameters, and outcome, were included. The primary outcome was the prognosis evaluated by the change in patient-reported Japanese orthopaedic association (PRO-JOA) score. Conventional logistic regression models and machine learning algorithms were implemented. Models were compared, considering the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values, and calibration curve.

RESULTS: Overall, the machine learning algorithms and traditional logistic regression models performed similarly. The postoperative cervicomedullary angle, head-neck flexion angle (HNFA), atlantodental interval, postoperative clivo-axial angle, age, postoperative clivus slope, postoperative cranial incidence, weight, postoperative HNFA, and postoperative Boogaards angle (BoA) were identified as important predictors for BI prognosis. Among the surveyed radiographic parameters, postoperative BoA was the most important predictor of BI prognosis. In the validation dataset, the bagged trees model performed best (AUC, 0.90).

CONCLUSION: Through machine learning, we have demonstrated predictors of BI prognosis. Machine learning methods did not provide too many advantages over logistic regression in predicting BI prognosis but remain promising.

PMID:35416266 | DOI:10.5137/1019-5149.JTN.36068-21.3

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Comparative Analysis Between Machine Learning Algorithms and Conventional Regression in Predicting the Prognosis of Patients with Basilar...