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The impact of the coronavirus on the Machine Learning in Healthcare Cybersecurity Market Report 2020 – News Distinct

May 18, 2020 | Author: admin

Global Machine Learning in Healthcare Cybersecurity Market Analysis 2020 with Top Companies, Production, Consumption, Price and Growth Rate

The Machine Learning in Healthcare Cybersecurity Market 2020 report includes the market strategy, market orientation, expert opinion and knowledgeable information. The Machine Learning in Healthcare Cybersecurity Industry Report is an in-depth study analyzing the current state of the Machine Learning in Healthcare Cybersecurity Market. It provides a brief overview of the market focusing on definitions, classifications, product specifications, manufacturing processes, cost structures, market segmentation, end-use applications and industry chain analysis. The study on Machine Learning in Healthcare Cybersecurity Market provides analysis of market covering the industry trends, recent developments in the market and competitive landscape.

Get a sample copy of the report at- https://www.reportsandmarkets.com/sample-request/global-machine-learning-in-healthcare-cybersecurity-market-report-2019?utm_source=newsdistinct&utm_medium=14

It takes into account the CAGR, value, volume, revenue, production, consumption, sales, manufacturing cost, prices, and other key factors related to the global Machine Learning in Healthcare Cybersecurity market. All findings and data on the global Machine Learning in Healthcare Cybersecurity market provided in the report are calculated, gathered, and verified using advanced and reliable primary and secondary research sources. The regional analysis offered in the report will help you to identify key opportunities of the global Machine Learning in Healthcare Cybersecurity market available in different regions and countries.

The Global Machine Learning in Healthcare Cybersecurity 2020 research provides a basic overview of the industry including definitions, classifications, applications and industry chain structure. The Global Machine Learning in Healthcare Cybersecurity analysis is provided for the international markets including development trends, competitive landscape analysis, and key regions development status.

Development policies and plans are discussed as well as manufacturing processes and cost structures are also analyzed. This report also states import/export consumption, supply and demand Figures, cost, price, revenue and gross margins.

In addition to this, regional analysis is conducted to identify the leading region and calculate its share in the global Machine Learning in Healthcare Cybersecurity. Various factors positively impacting the growth of the Machine Learning in Healthcare Cybersecurity in the leading region are also discussed in the report. The global Machine Learning in Healthcare Cybersecurity is also segmented on the basis of types, end users, geography and other segments.

Our new sample is updated which correspond in new report showing impact of COVID-19 on Industry

Reasons for Buying this Report

The report can answer the following questions:

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Table of Content

1 Industry Overview of Machine Learning in Healthcare Cybersecurity

2 Manufacturing Cost Structure Analysis

3 Development and Manufacturing Plants Analysis of Machine Learning in Healthcare Cybersecurity

4 Key Figures of Major Manufacturers

5 Machine Learning in Healthcare Cybersecurity Regional Market Analysis

6 Machine Learning in Healthcare Cybersecurity Segment Market Analysis (by Type)

7 Machine Learning in Healthcare Cybersecurity Segment Market Analysis (by Application)

8 Machine Learning in Healthcare Cybersecurity Major Manufacturers Analysis

9 Development Trend of Analysis of Machine Learning in Healthcare Cybersecurity Market

10 Marketing Channel

11 Market Dynamics

12 Conclusion

13 Appendix

About us

Market research is the new buzzword in the market, which helps in understanding the market potential of any product in the market. This helps in understanding the market players and the growth forecast of the products and so the company. This is where market research companies come into the picture. Reports And Markets is not just another company in this domain but is a part of a veteran group called Algoro Research Consultants Pvt. Ltd. It offers premium progressive statistical surveying, market research reports, analysis & forecast data for a wide range of sectors both for the government and private agencies all across the world.

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Manager Partner Relations & International Marketing

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Associations with No Place to Meet Are Turning to JUNO, A Live and On-Demand Digital Platform – AiThority

May 18, 2020 | Author: admin

JUNO is pleased to announce the only all-in-one, live, andon-demand learning platformutilizing four human motivators to engage users and maximize the value of their experience. Gone are the days of multiple platforms, contracts, and vendors to secure ongoing engagement and learning with members. JUNO was built for a post-COVID-19reality. Greater strain and tighter budgets require a flexible solution to handle the New Normal and beyond.

Recommended AI News: Hawaii Signs Participating Addendum with DroneUp Providing Public Sector Agencies Access to Drone Services

JUNO facilitates full user engagement by offering these tools and features that meet the emerging user in their most desired expectations.

Connection: From hybrid to completely digital events, virtual meetings are the wave of the future. In fact, Microsoft teams alone have seen 2.7 billion meeting minutes in one day, a 200 percent increase. JUNO onboards users around interests, strengths, and desired improvement areas and allows machine learning triggers to recommend peer connections, mainstage, and breakout learning opportunities.

Gamification: 60% of all start-ups gamify their user experience because gamificationworks! By triggering real and powerful human emotions, users generate higher levels ofhappiness, intrigue, and excitement resulting in desires to engage further and stay involved longer.From profile building to polls, quizzes, and continued learning, JUNO ensures that every user action has value.

Recommended AI News: 3 Steps To Channel Customer Feedback Into Product Innovation

Business growth: So how will JUNO help your business grow? JUNO supports users and partners by facilitating business connections through live exhibit experiences, digital think-tank sessions, suggested collaboration partnerships, and skills-based visibility tools.

Ongoing learning: Live events must move past the transactional into the transformational. JUNO Creates EQ and IQ learning pathways to engage users on all levels. From certification and badging to goal setting and performance commitments, JUNO offers a diverse set of actions for users to personally develop.

In a time in which what got you here wont get you there, JUNO delivers the get you there solution, Former PCMA CEO, Deborah Sexton.

Recommended AI News: NVIDIA Accelerates Apache Spark, Worlds Leading Data Analytics Platform

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Reality Of Metrics: Is Machine Learning Success Overhyped? – Analytics India Magazine

May 18, 2020 | Author: admin

In one of the most revealing research papers written recent times, the researchers from Cornell Tech and Facebook AI quash the hype around the success of machine learning. They opine and even demonstrate that the trend appears to be overstated. In other words, the so-called cutting edge research or benchmark work perform similarly to one another even if they are a decade apart. In other words, the authors believe that metric learning algorithms have not made spectacular progress.

In this work, the authors try to demonstrate the significance of assessing algorithms more diligently and how few practices can help reflect ML success in reality.

Over the past decade, deep convolutional networks have made tremendous progress. Their application in computer vision is almost everywhere; from classification to segmentation to object detection and even generative models. But is the metric evaluation carried out to track this progress has been leakproof? Are the techniques employed werent affected by the improvement in deep learning methods?

The goal of metric learning is to map data to an embedding space, where similar data are close together, and the rest are far apart. So, the authors begin with the notion that the deep networks have had a similar effect on metric learning. And, the combination of the two is known as deep metric learning.

The authors then examined flaws in the current research papers, including the problem of unfair comparisons and the weaknesses of commonly used accuracy metrics. They then propose a training and evaluation protocol that addresses these flaws and then run experiments on a variety of loss functions.

For instance, one benchmark paper in 2017, wrote the authors, used ResNet50, and then claimed huge performance gains. But the competing methods used GoogleNet, which has significantly lower initial accuracies. Therefore, the authors conclude that much of the performance gain likely came from the choice of network architecture, and not their proposed method. Practices such as these can put ML on headlines, but when we look at how much of these state-of-the-art models are really deployed, the reality is not that impressive.

The authors underline the importance of keeping the parameters constant if one has to prove that a certain new algorithm outperforms its contemporaries.

To carry out the evaluations, the authors introduce settings that cover the following:

As shown in the above plot, the trends, in reality, arent that far from the previous related works and this indicates that those who claim a dramatic improvement might not have been fair in their evaluation.

If a paper attempts to explain the performance gains of its proposed method, and it turns out that those performance gains are non-existent, then their explanation must be invalid as well.

The results show that when hyperparameters are properly tuned via cross-validation, most methods perform similarly to one another. This work, believe the authors, will lead to more investigation into the relationship between hyperparameters and datasets, and the factors related to particular dataset/architecture combinations.

According to the authors, this work exposes the following:

The authors conclude that if proper machine learning practices are followed, then the results of metric learning papers will better reflect reality, and can lead to better works in most impactful domains like self-supervised learning.

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Cloud Storage Market to Reach USD 297.54 Billion by 2027; Higher Adoption of Machine Learning to Boost Growth, Says Fortune Business Insights -…

May 18, 2020 | Author: admin

Key Companies Covered in Cloud Storage Market Research Report Are Amazon Web Services, Inc., Dell Technologies Inc., Dropbox, Fujitsu Ltd, Inc., Google, Inc., Hewlett Packard Enterprise Development LP, IBM Corporation, Microsoft Corporation, Oracle, pCloud AG, Rackspace, Inc., VMware, Inc.

PUNE, India, May 18, 2020 /PRNewswire/ -- The global cloud storage market is set to gain traction from the rising adoption of autonomous systems and machine learning. Besides, the introduction to unique video systems, internet of things (IoT), and remote sensing technologies are driving the market growth. This information is provided by Fortune Business Insights in a recent study, titled, "Cloud Storage Market Size, Share & Industry Analysis, By Component (Storage Model, and Services), By Deployment (Private, Public, and Hybrid), By Enterprise Size (SMEs, and Large Enterprises), By Vertical (BFSI, IT and Telecommunication, Government and Public Sector, Manufacturing, Healthcare and Life Sciences, Retail and Consumer Goods, Media and Entertainment, and Others), and Regional Forecast, 2020-2027." The study further mentions that the cloud storage market size was USD 49.13 billion in 2019 and is projected to reach USD 297.54 billion by 2027, exhibiting a CAGR of 25.3% during the forecast period.

Highlights of the Report

Get Sample PDF Brochure:https://www.fortunebusinessinsights.com/enquiry/request-sample-pdf/cloud-storage-market-102773

An Overview of the Impact of COVID-19 on this Market:

The emergence of COVID-19 has brought the world to a standstill. We understand that this health crisis has brought an unprecedented impact on businesses across industries. However, this too shall pass. Rising support from governments and several companies can help in the fight against this highly contagious disease. There are some industries that are struggling and some are thriving. Overall, almost every sector is anticipated to be impacted by the pandemic.

We are taking continuous efforts to help your business sustain and grow during COVID-19 pandemics. Based on our experience and expertise, we will offer you an impact analysis of coronavirus outbreak across industries to help you prepare for the future.

Click here to get the short-term and long-term impact of COVID-19 on this Market.Please visit:https://www.fortunebusinessinsights.com/cloud-storage-market-102773

Drivers & Restraints-

Covid-19 Pandemic to Boost Growth Backed by Rising Usage of Cloud Storage Solutions

Cloud storage solutions are gaining more popularity at present as workforces are inclining towards a distributed work environment. These solutions aid workforces in collaborating and staying connected. The outbreak of Covid-19 pandemic is enabling several organizations to support remote working, as well as manage the vast amount of data smoothly. Microsoft, for instance, has surged the benefits of Windows and extended Azure cloud credits for non-profit and critical care organizations, such as food & nutrition, public safety, and health support. In addition to that, the utilization of analytics-driven platforms is helping companies in the generating a large amount of data. They are therefore, preferring hybrid cloud storage solutions more than the conventional ones. However, the occurrence of data breaches may hamper the cloud storage market growth in the coming years.

Segment-

BFSI Segment to Grow Steadily Fueled by Need for Improving Consumer Experience

Based on vertical, the banking, financial services and insurance (BFSI) segment generated 22.4% cloud storage market share in 2019. The industry deals with large volumes of customer data on regular bases. It delivers efficient services to the customers. To serve them better, they require cloud storage technology as it poses as a transformative digital solution. This solution provides a high level of scalability, agility, and data security to the industry. Cloud storage systems not only improve consumer experience and revenues, but also enhance the operational efficiency. These factors are set to drive the growth of the BFSI segment in the near future.

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Regional Analysis-

North America to Remain Dominant Owing to Rising Adoption of Various Digital Services

Regionally, the market is divided into Latin America, Europe, Asia Pacific, the Middle East and Africa, and North America. Amongst these, North America procured USD 19.85 billion revenue in 2019 and is set to dominate the market. This growth is attributable to the rising adoption of several digital services, such as electronic signatures and e-commerce in the U.S. Also, the increasing rate of cybercrime would contribute to the growth. However, the outbreak of Covid-19 pandemic is expected to obstruct growth by affecting the technological investments of industry giants. Asia Pacific, on the other hand, is projected to exhibit an astonishing growth during the forecast period backed by the increasing usage of smartphones.

Competitive Landscape-

Key Companies Focus on Expanding Product Offerings to Surge Revenue

Microsoft, IBM, and Amazon are some of the top companies operating in the global market. They are striving to widen their product offerings by keeping up with the latest trends. They will also be able to surge their revenue this way. Below are two of the latest industry developments:

Fortune Business Insights presents a list of all the companies operating in the global Cloud Storage Market. They are as follows:

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Detailed Table of Content

TOC Continued...!!!

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Have a Look at Related Research Insights:

Cloud Analytics MarketSize, Share & Industry Analysis, By Deployment Type (Public Cloud, Private Cloud, and Hybrid Cloud), By Organization Size (Small And Medium-Sized Enterprises (SMEs) and Large Enterprises), By End-User (BFSI, IT and Telecommunications, Retail and Consumer Goods, Healthcare and Life Sciences, Manufacturing, Education, and Others) and Regional Forecast, 2019-2026

Cloud Computing MarketSize, Share & Industry Analysis, By Type (Public Cloud, Private Cloud, Hybrid Cloud), By Service (Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS)), By Industry (Banking, Financial Services, and Insurance (BFSI), IT and Telecommunications, Government, Consumer Goods and Retail, Healthcare, Manufacturing, Others (Energy and Utilities, Education), and Regional Forecast, 2020-2027

Cloud Gaming MarketSize, Share & Industry Analysis, By Device (Smartphone, Laptop/Tablets, Personal Computer (PC), Smart TV and Consoles), By Streaming Type (File Streaming and Video Streaming), By End-Users (Business to Business (B2B) and Business to Consumers (B2C)), and Regional Forecast, 2020-2027

Cloud security MarketSize, Share & Industry Analysis, By Component (Solutions, Services), By Security Type (Application Security, Database Security, Endpoint Security, Network Security, Web and Email Security), By Deployment (Private, Public, Hybrid), By End-User (Large scale enterprise , Small & medium enterprise), By Industry Verticals (Healthcare, BFSI, IT & Telecom, Government Agencies)Others and Regional Forecast, 2019-2026

Retail Cloud MarketSize, Share & Industry Analysis, By Model Type (Infrastructure as a Service, Platform as a Service and Software as a Service), By Deployment (Public, Private and Hybrid Cloud), By Solution (Supply Chain Management, Workforce Management, Customer Management, Reporting & Analytics, Data Security, Omni-Channel), By Enterprise Size (Small & Medium and Large Enterprise) and Regional Forecast, 2019-2026

Location Analytics MarketSize, Share & Industry Analysis, By Component (Solution, Services), By Location Type (Indoor, Outdoor), By Deployment Type (Cloud, On-Premises), By End-User (Retail, Government, Energy and Utilities, Healthcare, Travel and Transportation, Telecommunications, and Others) and Regional Forecast, 2019-2026

Security Analytics MarketSize, Share & Industry Analysis, By Component (Solutions, and Services), By Application (Network Security Analytics, Web Security Analytics, Endpoint Security Analytics, and Application Security Analytics), By Vertical (BFSI, Government and Defense, IT and Telecommunication, Manufacturing, Healthcare, Energy and Utilities, and Others), and Regional Forecast, 2020-2027

Retail Analytics MarketSize, Share and Industry Analysis By Type (Software, Services), By Deployment (On-Premises, Cloud), By Organization Size (SMEs, Large Enterprises), By Function (Customer Management, Supply Chain, Merchandising, In-Store Operations, and Strategy & Planning) and Regional Forecast 2019-2026

About Us:

Fortune Business Insightsoffers expert corporate analysis and accurate data, helping organizations of all sizes make timely decisions. We tailor innovative solutions for our clients, assisting them address challenges distinct to their businesses. Our goal is to empower our clients with holistic market intelligence, giving a granular overview of the market they are operating in.

Our reports contain a unique mix of tangible insights and qualitative analysis to help companies achieve sustainable growth. Our team of experienced analysts and consultants use industry-leading research tools and techniques to compile comprehensive market studies, interspersed with relevant data.

At Fortune Business Insights, we aim at highlighting the most lucrative growth opportunities for our clients. We therefore offer recommendations, making it easier for them to navigate through technological and market-related changes. Our consulting services are designed to help organizations identify hidden opportunities and understand prevailing competitive challenges.

Contact Us:Fortune Business Insights Pvt. Ltd.308, Supreme Headquarters,Survey No. 36, Baner,Pune-Bangalore Highway,Pune- 411045, Maharashtra,India.Phone:US: +1-424-253-0390UK: +44-2071-939123APAC: +91-744-740-1245Email:[emailprotected]Fortune Business InsightsLinkedIn|Twitter|Blogs

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Q&A on the Book Hands-On Genetic Algorithms with Python – InfoQ.com

May 18, 2020 | Author: admin

Key Takeaways

Hands-On Genetic Algorithms with Python by Eyal Wirsansky is a new book which explores the world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models. InfoQ interviewed Eyal Wirsansky about how genetic algorithms work and what they can be used for.

In addition to our interview, InfoQ was able to obtain a sample chapter which can be downloaded here.

InfoQ: How do genetic algorithms work?

Eyal Wirsansky: Genetic algorithms are a family of search algorithms inspired by the principles of evolution in nature. They imitate the process of natural selection and reproduction, by starting with a set of random solutions, evaluating each one of them, then selecting the better ones to create the next generation of solutions. As generations go by, the solutions we have get better at solving the problem. This way, genetic algorithms can produce high-quality solutions for various problems involving search, optimization, and learning. At the same time, their analogy to natural evolution allows genetic algorithms to overcome some of the hurdles encountered by traditional search and optimization algorithms, especially for problems with a large number of parameters and complex mathematical representations.

InfoQ: What type of problems do genetic algorithms solve?

Wirsansky: Genetic algorithms can be used for solving almost any type of problem, but they particularly shine where traditional algorithms cannot be used, or fail to produce usable results within a practical amount of time. For example, problems with very complex or non-existing mathematical representation, problems where the number of variables involved is large, and problems with noisy or inconsistent input data. In addition, genetic algorithms are better equipped to handle deceptive problems, where traditional algorithms may get trapped in a suboptimal solution.

Genetic algorithms can even deal with cases where there is no way to evaluate an individual solution by itself, as long as there is a way to compare two solutions and determine which of them is better. An example can be a machine learning-based agent that drives a car in a simulated race. A genetic algorithm can optimize and tune the agent by having different versions of it compete against each other to determine which version is better.

InfoQ: What are the best use cases for genetic algorithms?

Wirsansky: The most common use case is where we need to assemble a solution using a combination of many different available parts; we want to select the best combination, but the number of possible combinations is too large to try them all. Genetic algorithms can usually find a good combination within a reasonable amount of time. Examples can be scheduling personnel, planning of delivery routes, designing bridge structures, and also constructing the best machine learning model from many available building blocks, or finding the best architecture for a deep learning model.

Another interesting use case is where the evaluation is based on peoples opinion or response. For example, you can use the genetic algorithm approach to determine the design parameters for a web sitesuch as color palette, font size, and location of components on the pagethat will achieve the best response from customers, such as conversion or retention. This idea can also be used for genetic art artificially created paintings or music that prove pleasant to the human eye (or ear).

Genetic algorithms can also be used for ongoing optimizationcases where the best solution may change over time. The algorithm can run continuously within the changing environment and respond dynamically to these changes by updating the best solution based on the current generation.

InfoQ: How can genetic algorithms select the best subset of features for supervised learning?

Wirsansky: In many cases, reducing the number of featuresused as inputs for a model in supervised learningcan increase the models accuracy, as some of the features may be irrelevant or redundant. This will also result in a simpler, better generalizing model. But we need to figure out which are the features that we want to keep. As this comes down to finding the best combination of features out of a potentially immense number of possible combinations, genetic algorithms provide a very practical approach. Each potential solution is represented by a list of booleans, one for each feature.

The value of the boolean (0 or 1) represents the absence or presence of the corresponding feature. These lists of boolean values are used as genetic material, that can be exchanged between solutions when we mate them, or even mutated by flipping values randomly. Using these mating and mutation operations, we create new generations out of preceding ones, while giving an advantage to solutions that yielded better performing models. After a while, we can have some good solutions, each representing a subset of the features. This is demonstrated in Chapter 7 of the book (our sample chapter) with the UCI Zoo dataset using python code, where the best performance was achieved by selecting six particular features out of the original sixteen.

InfoQ: What are the benefits that we can get from using genetic algorithms with machine learning for hyperparameter tuning?

Wirsansky: Every machine learning model utilizes a set of hyperparametersvalues that are set before the training takes place and affect the way the learning is done. The combined effect of hyperparameters on the performance of the model can be significant. Unfortunately, finding the best combination of the hyperparameter valuesalso known as hyperparameter tuningcan be as difficult as finding a needle in a haystack.

Two common approaches are grid search and random search, each with its own disadvantages. Genetic algorithms can be used in two ways to improve upon these methods. One way is by optimizing the grid search, so instead of trying out every combination on the grid, we can search only a subset of combinations but still get a good combination. The other way is to conduct a full search over the hyperparameter space, as genetic algorithms are capable of handling a large number of parameters as well as different parameter types continuous, discrete and categorical. These two approaches are demonstrated in Chapter 8 of the book with the UCI Wine dataset using python code.

InfoQ: How can genetic algorithms be used in Reinforcement Learning?

Wirsansky: Reinforcement Learning (RL) is a very exciting and promising branch of machine learning, with the potential to handle complex, everyday-life-like tasks. Unlike supervised learning, RL does not present an immediate 'right/wrong' feedback, but instead provides an environment where a longer-term, cumulative reward is sought after. This kind of setting can be viewed as an optimization problem, another area where genetic algorithms excel.

As a result, genetic algorithms can be utilized for reinforcement learning in several different ways. One example can be determining the weights and biases of a neural network that interacts with its environment by mapping input values to output values. Chapter 10 of the book includes two examples of applying genetic algorithms to RL tasks, using the OpenAI Gym environments mountain-car and cart-pole.

InfoQ: What is bio-inspired computing?

Wirsansky: Genetic algorithms are just one branch within a larger family of algorithms called Evolutionary Computation, all inspired by Darwinian evolution. One particularly interesting member of this family is Genetic Programming, that evolves computer programs aiming to solve a specific problem. More broadly, as evolutionary computation techniques are based on various biological systems or behaviors, they can be considered part of the algorithm family known as Bio-inspired Computing.

Among the many fascinating members of this family are Ant Colony Optimizationimitating the way certain species of ants locate food and mark the paths to it, giving advantage to closer and richer locations of food; Artificial Immune Systems, capable of identifying and learning new threats, as well as applying the acquired knowledge and respond faster the next time a similar threat is detected; and Particle Swarm Optimization, based on the behavior of flocks of birds or schools of fish, where individuals within the group work together towards a common goal without central supervision.

Another related, broad field of computation is Artificial Life, involving systems and processes imitating natural life in different ways, such as computer simulations and robotic systems. Chapter 12 of the book includes two relevant Python-written examples, one solving a problem using genetic programming, and the otherusing particle swarm optimization.

Eyal Wirsansky is a senior software engineer, a technology community leader, and an artificial intelligence researcher and consultant. Eyal started his software engineering career as a pioneer in the field of voice over IP, and he now has over 20 years' experience of creating a variety of high-performing enterprise solutions. While in graduate school, he focused his research on genetic algorithms and neural networks. One outcome of his research is a novel supervised machine learning algorithm that combines the two. Eyal leads the Jacksonville (FL) Java user group, hosts the Artificial Intelligence for Enterprise virtual user group, and writes the developer-oriented artificial intelligence blog, ai4java.

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Q&A on the Book Hands-On Genetic Algorithms with Python - InfoQ.com

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Bitglass Integrates CrowdStrike’s Machine-Learning Technology to Provide Zero-Day Advanced Threat Protection in the Cloud – Business Wire

May 18, 2020 | Author: admin

CAMPBELL, Calif.--(BUSINESS WIRE)--Bitglass, the Next-Gen Cloud Security Company, announced today that it has partnered with CrowdStrike, a leader in cloud-delivered endpoint protection, to provide an agentless advanced threat protection (ATP) solution that identifies and remediates both known and zero-day threats on any cloud application or service, as well as any device that accesses corporate IT resources (including personal devices).

Cloud applications and bring your own device (BYOD) policies offer organizations enhanced flexibility and efficiency, but they can also serve as proliferation points for malware if not properly secured. This Original Equipment Manufacturer (OEM) offering from CrowdStrike uses machine learning (ML) and deep file inspection to identify malware and other threats. Together with Bitglass Next-Gen Cloud Access Security Broker (CASB), threats are automatically remediated based on preset policies.

Bitglass CASB leverages agentless inline proxies to monitor and mediate traffic between cloud applications and devices in order to enforce granular security policies on data in transit. By incorporating CrowdStrikes detection capabilities directly into Bitglass agentless proxy, the integration can identify and block malware in real time as infected files are uploaded to cloud applications or downloaded onto devices (even personal devices) --without the need for software installations. Additionally, integration with application programming interfaces (APIs) allows for the detection and quarantining of malware already at rest in the cloud.

Once malware makes its way into a cloud app, it can quickly spread into connected apps as well as into users devices, said Anurag Kahol, chief technology officer and co-founder of Bitglass. Consequently, organizations need a multi-faceted solution that can automatically block malware both at rest and in transit. If they wait for IT teams to review and respond to threat notifications, its often too late. Were proud to leverage CrowdStrikes industry-leading technology to deliver a robust cloud ATP solution that stops threats and empowers enterprises to embrace the cloud applications and BYOD policies that spur innovation and productivity.

As a cloud-delivered endpoint protection leader at the forefront of securing organizations from sophisticated tactics, CrowdStrike understands that a successful security strategy lies in the ability to quickly detect, respond and remediate threat activity, said Dr. Sven Krasser, CrowdStrikes chief scientist. By incorporating our machine learning file-scan engine, which is trained leveraging the 3 trillion endpoint-related events processed weekly by the Falcon Platform, with Bitglass unique, agentless architecture, customers gain comprehensive, real-time protection and control over corporate data across all endpoints with reduced risk of exposure.

The solution is fully deployed in the cloud and is completely agentless--requiring no hardware appliances or software installations and ensuring rapid deployment. Additionally, Bitglass Polyscale Architecture scales and adapts to an enterprise's exact needs on the fly. There is no need for backhauling or bottleneck architectures.

For more information, download the joint solution brief here:https://pages.bitglass.com/CD-FY20Q2-CrowdstrikeBitglassSolutionsBrief_LP.html?&utm_source=pr

About Bitglass

Bitglass, the Next-Gen Cloud Security company, is based in Silicon Valley with offices worldwide. The company's cloud security solutions deliver zero-day, agentless, data and threat protection for any app, any device, anywhere. Bitglass is backed by Tier 1 investors and was founded in 2013 by a team of industry veterans with a proven track record of innovation and execution.

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Highest-performing quantum simulator IN THE WORLD delivered to Japan – TechGeek

May 18, 2020 | Author: admin

Atos, a global leader in digital transformation, introduced the worlds first commercially available quantum simulator capable of simulating up to 40 quantum bits, or Qubits, which translates to very fucking fast.

The simulator, named Atos Quantum Learning Machine, is powered by an ultra-compact supercomputer and a universal programming language.

Quantum computing is a key priority for Japan. It launched a dedicated ten-year, 30 billion yen (.. aka US$280 million / AUD$433 million) quantum research program in 2017, followed by a 100 billion yen (.. aka US$900 million / AUD $1 billion) investment into its Moonshot R&D Program one focus of which will be to create a fault-tolerant universal quantum computer to revolutionise the economy, industry, and security sectors by 2050.

Were delighted to have sold our first QLM in Japan, thanks to our strong working partnership with Intelligent Wave Inc.. We are proud to be part of this growing momentum as the country plans to boost innovation through quantum

Combining a high-powered, ultra-compact machine with a universal programming language, the Atos Quantum Learning Machine (enables researchers and engineers to develop an experiment with quantum software. It is the worlds only quantum software development and simulation appliance for the coming quantum computer era.

It simulates the laws of physics, which are at the very heart of quantum computing, to compute the exact execution of a quantum program with double-digit precision.

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Light, fantastic: the path ahead for faster, smaller computer processors – News – The University of Sydney

May 18, 2020 | Author: admin

Research team: (from left) Associate Professor Stefano Palomba, Dr Alessandro Tuniz, Professor Martijn de Sterke. Photo: Louise Cooper

Light is emerging as the leading vehicle for information processing in computers and telecommunications as our need for energy efficiency and bandwidth increases.

Already the gold standard for intercontinental communication through fibre-optics, photons are replacing electrons as the main carriers of information throughout optical networks and into the very heart of computers themselves.

However, there remain substantial engineering barriers to complete this transformation. Industry-standard silicon circuits that support light are more than an order of magnitude larger than modern electronic transistors. One solution is to compress light using metallic waveguides however this would not only require a new manufacturing infrastructure, but also the way light interacts with metals on chips means that photonic information is easily lost.

Now scientists in Australia and Germany have developed a modular method to design nanoscale devices to help overcome these problems, combining the best of traditional chip design with photonic architecture in a hybrid structure. Their research is published today in Nature Communications.

We have built a bridge between industry-standard silicon photonic systems and the metal-based waveguides that can be made 100 times smaller while retaining efficiency, said lead author Dr Alessandro Tuniz from the University of Sydney Nano Institute and School of Physics.

This hybrid approach allows the manipulation of light at the nanoscale, measured in billionths of a metre. The scientists have shown that they can achieve data manipulation at 100 times smaller than the wavelength of light carrying the information.

This sort of efficiency and miniaturisation will be essential in transforming computer processing to be based on light. It will also be very useful in the development of quantum-optical information systems, a promising platform for future quantum computers, said Associate Professor Stefano Palomba, a co-author from the University of Sydney and Nanophotonics Leader at Sydney Nano.

Eventually we expect photonic information will migrate to the CPU, the heart of any modern computer. Such a vision has already been mapped out by IBM.

On-chip nanometre-scale devices that use metals (known as plasmonic devices) allow for functionality that no conventional photonic device allows. Most notably, they efficiently compress light down to a few billionths of a metre and thus achieve hugely enhanced, interference-free, light-to-matter interactions.

As well as revolutionising general processing, this is very useful for specialised scientific processes such as nano-spectroscopy, atomic-scale sensing and nanoscale detectors, said Dr Tuniz also from the Sydney Institute of Photonics and Optical Science.

However, their universal functionality was hampered by a reliance on ad hoc designs.

We have shown that two separate designs can be joined together to enhance a run-of-the-mill chip that previously did nothing special, Dr Tuniz said.

This modular approach allows for rapid rotation of light polarisation in the chip and,becauseof that rotation, quickly permits nano-focusing down to about 100 times less than the wavelength.

Professor Martijn de Sterke is Director of the Institute of Photonics and Optical Science at the University of Sydney. He said: The future of information processing is likely to involve photons using metals that allow us to compress light to the nanoscale and integrate these designs into conventional silicon photonics.

This research was supported by the University of Sydney Fellowship Scheme, the German Research Foundation (DFG) under Germanys Excellence Strategy EXC-2123/1. This work was performed in part at the NSW node of the Australian National Fabrication Facility (ANFF).

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Light, fantastic: the path ahead for faster, smaller computer processors - News - The University of Sydney

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Telegram shuts down its cryptocurrency operation – The Verge

May 18, 2020 | Author: admin

After years of drama with the SEC, Telegram is calling it quits on its crypto-focused subsidiary, Telegram Open Network (TON).

Telegrams active involvement with TON is over, wrote Pavel Durov, founder and CEO, in an announcement on his channel. You may see or may have already seen sites using my name or the Telegram brand or the TON abbreviation to promote their projects. Dont trust them with your money or data.

TON was a blockchain platform designed to offer decentralized cryptocurrency to anyone with a smartphone, in a similar fashion to Facebooks Libra project (which has also faced significant scrutiny).

Last October, the SEC ordered Telegram to halt sales of its cryptocurrency (called Gram) after it failed to register an early sale of $1.7 billion in tokens prior to launching the network. The funds were raised in a series of what Telegram billed as pre-ICO offerings back in 2018, though the company ended up canceling the much-hyped ICO due (in part) to increased SEC scrutiny.

Durov spoke out against the ruling in his announcement, arguing that American courts shouldnt have the power to stop the sale of cryptocurrency beyond US borders, and he urged others to take up the decentralization fight in Telegrams stead. This battle may well be the most important battle of our generation, he wrote. We hope that you succeed where we have failed.

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This A.I. makes up gibberish words and definitions that sound astonishingly real – Digital Trends

May 18, 2020 | Author: admin

A sesquipedalian is a person who overuses uncommon words like lameen (a bishops letter expressing a fault or reprimand) or salvestate (to transport car seats to the dining room) just for the sake of it. The first of those italicized words is real. The second two arent. But they totally should be. Theyre the invention of a new website called This Word Does Not Exist. Powered by machine learning, it conjures up entirely new words never before seen or used, and even generates a halfway convincing definition for them. Its all kinds of brilliant.

In February, I quit my job as an engineering director at Instagram after spending seven intense years building their ranking algorithms like non-chronological feed, Thomas Dimson, creator of This Word Does Not Exist, told Digital Trends. A friend and I were trying to brainstorm names for a company we could start together in the A.I. space. After [coming up with] some lame ones, I decided it was more appropriate to let A.I. name a company about A.I.

Then, as Dimson tells it, a global pandemic happened, and he found himself at home with lots of time on his hands to play around with his name-making algorithm. Eventually I stumbled upon the Mac dictionary as a potential training set and [started] generating arbitrary words instead of just company names, he said.

If youve ever joked that someone who uses complex words in their daily lives must have swallowed a dictionary, thats pretty much exactly what This Word Does Not Exist has done. The algorithm was trained from a dictionary file Dimson structured according to different parts of speech, definition, and example usage. The model refines OpenAIs controversial GPT-2 text generator, the much-hyped algorithm once called too dangerous to release to the public. Dimsons twist on it assigns probabilities to potential words based on which letters are likely to follow one another until the word looks like a reasonably convincing dictionary entry. As a final step, it checks that the generated word isnt a real one by looking it up in the original training set.

This Word Does Not Exist is just the latest in a series of [Insert object] Does Not Exist creations. Others range from non-existent Airbnb listings to fake people to computer-generated memes which nonetheless capture the oddball humor of real ones.

People have a nervous curiosity toward what makes us human, Dimson said. By looking at these machine-produced demos, we are better able to understand ourselves. Im reminded of the fascination with Deep Blue beating Kasparov in 1996 or AlphaGo beating Lee Sedol in 2016.

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This A.I. makes up gibberish words and definitions that sound astonishingly real - Digital Trends

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