TEL AVIV, Israel, Oct. 11, 2021 /PRNewswire/ -- Cybersixgill today announced its Investigative Portal and Darkfeed have been recognized as the best machine learning autonomous solution by the 2021 Tech Ascension Awards.

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Cybersixgill autonomous threat intelligence solutions provide real-time contextual intelligence and the necessary insight into the nature and source of each threat. Analysts can leverage the best-in-market data collection of hundreds of millions of intelligence items from the deep, dark and clear web, including historical data dating back to the 90s, deleted posts, invite-only messaging groups, and millions of threat actors.

With custom alerting and monitoring tailored to each organization's assets and needs, Cybersixgill eliminates the information overload - empowering security teams by delivering relevant and actionable intel to create faster security processes, break organizational silos, reduce operational costs while increasing return on security investment.

The Tech Ascension Awards recognized the very best innovations in cybersecurity. The Tech Ascension awards judged over 500 cybersecurity applicants based on technology innovation, market research, and competitive differentiators. The class-leading vendors that received recognition from the Tech Ascension Awards showcased technology that solves critical industry challenges and produces invaluable business outcomes for their customers.

"The only way cybersecurity can stay ahead of the threat curve is by leveraging autonomous technology that can deliver relevant intelligence in real time." said Sharon Wagner, CEO, Cybersixgill. "By understanding threat actors' network, expertise, and motivations, teams can build a complete intelligence picture and defend against data leaks, fraud, ransomware attacks."

"The proliferation of ransomware, nation-state threats, and an uptick in cybercriminal activity due to COVID-19 are just some of the factors that have made a strong cybersecurity defense paramount for every business that touches sensitive data," said David Campbell, CEO, Tech Ascension Awards. "We're honored to recognize these industry leaders that have demonstrated their ability to defend organizations with unique approaches, innovative technology, and world-class talent."

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To learn more about Cybersixgill, please visit http://www.cybersixgill.com and follow us on LinkedIn.

For more information about the Tech Ascension Awards, please visit http://www.techascensionawards.com

About Cybersixgill

Cybersixgill's fully automated threat intelligence solutions help organizations fight cyber crime, detect phishing, data leaks, fraud and vulnerabilities as well as amplify incident response in real-time. The Cybersixgill Investigative Portal empowers security teams with contextual and actionable insights as well as the ability to conduct real-time investigations. Rich data feeds such as Darkfeed and CVE insights from DVE Score harness Cybersixgill's unmatched intelligence collection capabilities and deliver real-time intel into organizations' existing security systems. Most recently, Cybersixgill introduced agility to threat intel with their CI/CP methodology (Continuous Investigation/Continuous Protection). Current customers include enterprises, financial services, MSSPs, government and law enforcement entities.

About the Tech Ascension Awards

The Tech Ascension Awards elevate companies that possess cutting-edge, innovative technology that solve critical challenges in their respective markets. Tech Ascension winners rise above the crowded consumer and enterprise technology industries and receive validation from an independent organization. Applicants are judged based on technology innovation and uniqueness, market research (analyst reports, media coverage, customer case studies), hard performance stats, and competitive differentiators. The awards recognize leaders in cybersecurity, DevOps, big data and consumer technology. For information about the Tech Ascension Awards, please visit http://www.techascensionawards.com.

Media contact: Laurie Ben-HaimCybersixgill+1-646-300-9549+972-52-7831911laurie@cybersixgill.com

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Cybersixgill Recognized as the Best Machine Learning Autonomous Solution by the 2021 Tech Ascension Awards - Yahoo Finance

TRI Publishes Six Research Papers Pushing Boundaries of Scalable Learning at the Premier International Conference on Computer Vision

LOS ALTOS, Calif., Oct. 11, 2021 /PRNewswire/ -- Today, the Toyota Research Institute (TRI) announced the acceptance of six research papers in the field of machine learning at the International Conference on Computer Vision (ICCV). The research advances understanding across various tasks crucial for robotic perception, including semantic segmentation, 3D object detection and multi-object tracking.

TRIs research on multi-object tracking reveals that synthetic data could endow machines with fundamental human cognitive abilities, like object permanence, that are historically hard for machine learning models but second nature for humans.

Over the last six years, TRI's researchers have made significant strides in robotics, automated driving and materials science in large part due to machine learning the application of computer algorithms that constantly improve with experience and data.

"Machine learning is the foundation of our research," said Dr. Gill Pratt, CEO of TRI. "We are working to create scientific breakthroughs in the discipline of machine learning itself and then apply those breakthroughs to accelerate discoveries in robotics, automated driving, and battery testing and development."

As the International Conference on Computer Vision (ICCV) started, TRI shared six papers demonstrating TRI's robust research in machine learning, including geometric deep learning for 3D vision, self-supervised learning and simulation to real or "sim-to-real" transfer.

"Within the field of machine learning, scalable supervision is our focus," said Adrien Gaidon, head of TRI's Machine Learning team. "It is impossible to manually label everything you need at Toyota's scale, yet this is the state-of-the-art approach, especially for Deep Learning and Computer Vision. Thankfully, we can leverage Toyota's domain expertise in vehicles, robots or batteries to invent alternative forms of scalable supervision, whether via simulation or self-supervised learning from raw data. This approach can boost performance in a wide array of tasks important for automated cars to be safer everywhere anytime, robots to learn faster and battery development to speed up lengthy testing cycles."

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In the six papers accepted at ICCV, TRI researchers report several key findings. Notably, they show that geometric self-supervised learning significantly improves sim-to-real transfer for scene understanding. The resulting unsupervised domain adaptation algorithm enables recognizing real-world categories without requiring any expensive manual real-world labels.

In addition, TRI's research on multi-object tracking reveals that synthetic data could endow machines with fundamental human cognitive abilities, like object permanence, that are historically hard for machine learning models but second nature for humans. This new development increases the robustness of computer vision algorithms, making them more aligned with people's visual common sense.

Finally, TRI's research on pseudo-lidar shows that large-scale self-supervised pre-training considerably boosts performance of image-based 3D object detectors. The proposed geometric pre-training enables training powerful 3D Deep Learning models from limited 3D labels, which are expensive or sometimes impossible to get from images alone.

You can learn more about all six papers and TRI's machine learning work on TRI's Medium page or attend TRI's presentations at ICCV.

About Toyota Research Institute Toyota Research Institute (TRI) conducts research to advance robotics, energy and materials, machine learning, and human-centered artificial intelligence. Led by Dr. Gill Pratt, TRI's team of world-class researchers are developing technologies to amplify human ability, focused on making our lives safer and more sustainable. Established in 2015, TRI has offices in Los Altos, California; Cambridge, Massachusetts; and Ann Arbor, Michigan. For more information about TRI, please visit http://tri.global.

Media Contact:Wendy RosenDirector of CommunicationsToyota Research InstituteWendy.Rosen@tri.global

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Toyota Research Institute Announces Machine Learning Advances at the International Conference on Computer Vision - Yahoo Finance

LOS ANGELES, Oct. 11, 2021 /PRNewswire/ -- KubeCon + CloudNative ConBooth # SU41 -Zebrium, the leader in using machine learning on logs to automatically find the root cause of software problems, today announced a major new release of its SaaS solution and, for the first time, afullyon-premises edition catering to organizations with the most stringentsecurity requirements. The on-premises solution ensuressensitivelog data always remainswithin a company'sprivate network.

The new SaaS edition includes a completely redesigned user interface and delivers a 10x performanceimprovement for finding theroot causein logs. The on-premises editionis packaged as a Kubernetes-deployed application and can be installed with just a single Helm command, making it easy to install, upgrade and manage. It includes open-APIsforeasyintegrationinto existing tools and workflows andis designed to scaleto meet the needs of the largest enterprise customers.It is based on the same proven Zebrium machine learning technology that is deployed in customerenvironments around the world.

Today's distributed applications generate huge volumes of software logs collected frommany different applications and microservices. Some of the log streams can intentionally or inadvertently contain sensitive PersonallyIdentifiable Information (PII), such as names, addresses and even credit card numbersor other details. For this reason, some companies, particularly those in regulated industries or in certain geographical locations,are not able to send log data to the cloud.The new on-premises edition satisfies their requirementsby keepingall logdatawithin an organization's own network.

"Quickly and accurately resolving application failures,or preventing them in the first place,is a top goal forall organizations," said Ajay Singh, CEO, Zebrium. "With this release, any type of company can achieve this goal, by deploying a SaaS solutionor an on-premises version wherethere is a strictrequirement to keep log data within a private network."

Newfeaturesand enhancements in SaaS and on-premises editions:

The new on-premisesand improved SaaS editionsare now in general availability.

For more information about Zebrium, please contact [emailprotected], visit the websiteor stop by Booth #SU41 at KubeCon + CloudNative Con at the Los Angeles Convention Center fromOctober 13to 15.

Media Contact:Kira WojackMerritt Public Relations[emailprotected] (415) 419-4062

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ScaleOut Software is adding major extensions to its ScaleOut Digital Twin Streaming Service that enable real-time digital twin software to implement and host machine learning and statistical analysis algorithms.

Real-time digital twins can now make extensive use of Microsofts ML.NET machine learning library to implement these groundbreaking capabilities for virtually any IoT device or source object.

Integration of machine learning with real-time digital twins offers powerful new options for real-time monitoring across a wide variety of applications, according to the vendor.

For example, cloud-based real-time digital twins can track a fleet of trucks to identify subtle changes in key engine parameters with predictive analytics that avoid costly failures. Security monitors tracking perimeter entrances and sound sensors can use machine learning techniques to automatically identify unexpected behaviors and generate alerts.

By harnessing the no-code ScaleOut Model Development Tool, a real-time digital twin can easily be enhanced to automatically analyze incoming telemetry messages using machine learning techniques.

Machine learning provides important real-time insights that enhance situational awareness and enable fast, effective responses.

The tool provides three configuration options for analyzing numeric parameters contained within incoming messages to spot issues as they arise:

Once configured through the ScaleOut Model Development Tool, the ML algorithms run automatically and independently for each data source within their corresponding real-time digital twins as incoming messages are received.

Each real-time digital twin can automatically capture anomalous events for follow-up analysis and generate alerts to popular alerting providers, such as Splunk, Slack, and Pager Duty, to support remediation by service or security teams.

We are excited to offer powerful machine learning capabilities for real-time digital twins that will make it even easier to immediately spot issues or identify opportunities across a large population of data sources, said Dr. William Bain, ScaleOut Softwares CEO and founder. ScaleOut Software has built the next step in the evolution of the Microsoft Azure IoT and ML.NET ecosystem, and we look forward to helping our customers harness these technologies to enhance their real-time monitoring and streaming analytics.

For more information about this news, visit?www.scaleoutsoftware.com.

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ScaleOut Software Adds Machine Learning Capabilities to its Twin Streaming Service - Database Trends and Applications

Data science and machine learning are evolving in just about every single industry. The adoption of AI at companies continues to grow and evolve and AI developers are trying to prove that there is value that can be added to different parts of the company through machine learning. Not surprisingly, journalism, an industry whose primary focus is the communication of ideas in both text and visual format, has come to adopt the tools and techniques of data science to put power behind analysis and visualization of data.

The New York Times (NYT) has had a data science group since 2012, but only recently has this group moved out of the experimental phase and taken a major role in the company, adding value through machine learning. The Director of Data Science at the New York Times, Colin Russel, will be sharing some of the insights learned from the NYT data science team at an upcoming Data for AI event on November 4, 2021. Colin uses his background in predictive modeling and designing and applying machine learning algorithms to implement the Times vast quantities of data into models and visualizations that can help different segments of the company. In this article, we share some of his insights into where data science is heading at the NYT and beyond as well as insights previously shared by the NYT at the Data for AI conference in 2020.

Applications of AI

Colin Russel, New York Times

The NYT has invested in building out different machine learning teams that combine aspects of data science, data analytics, and engineering. These teams are centralized with different data science groups working with the newsroom, others with marketing, and others working with different business operations. Although each of these teams are focused on different aspects of the companys overall mission, they are all looking to build a machine learning platform that can take all of the overlapping deployment and infrastructure development and centralize it for overall use.

Traditionally, the newsroom and editorial operations are separate from the business side of the company for obvious reasons of conflict of interest and maintaining a separation between revenue-generating and news-generating activities. Because of the separation of the data journalism side and the data science side of the organization, there is a separation of culture. Due to this separation, it is often challenging working in AI for a large company and it is crucial to have a lot of clear and constant communication around the process and goals of AI implementation.

The use of data to drive decision-making and insights is spread across the entirety of the organization, however, with data analysis being used to power both business decisions as well as journalistic and editorial insights. The newsroom is very interested in data and understanding the audience of the NYT in a world where many people are getting their news from social media. Likewise, operations is interested in data-driven insights to improve advertising performance, deliver optimized content to readers, and generate more visibility of various operations and offerings.

Technology for AI

While many companies outsource their AI tools, the NYT is focused on building, not buying. Implementing AI technology is often not the hardest part a project, but rather engineering, organizing, and manipulating the data to where it can be efficiently modeled is often the challenge. Years ago, data was all over the place and as a data scientist trying to use data from different sectors of the company, you needed to get credentials for every different part. Add the difficulty obtaining data to the difficulty deciding what parts of the data are appropriate to be used for the model and this makes the actual technology for AI a smaller issue.

Due to the different areas of focus and priorities for different parts of the company, AI developers must figure out how to balance the competing concerns. The NYT recently went through an overhaul where they wanted to consolidate data on the cloud. This gave them the opportunity to start fresh and make it easy to upload data from different parts of the company.

Dealing with Variability

Data science and machine learning models are verified and evaluated to measure baseline performance as well as testing model improvements that are being developed. One of the main difficulties in taking advantage of AI is the difficulty in quantifying the goal and choosing the metric that you want to optimize. In the news and journalism industry, there is a lot of variability based on news cycles. For example, the Covid-19 pandemic has changed the company a lot as it is now giving free access to Covid-19 related news. The subscription business that wants as many subscribers as possible now has a public service component and believes having free access to information at a certain level is very important.

Certain types of recommendation algorithms respond better in different types of news cycles. Models are retrained as of protocol and the performance of a model must be taken into context with the news cycle. To evaluate the quality of a model, it must be taken over a longer period due to news cycles and environmental effects. Figuring out which models to use in each news cycle is a challenge that Colin and his team are looking to solve.

Implementing AI and ML algorithms can be a challenge in any company and determining the technology, metrics, and data to be used is very difficult. The New York Times handles these issues daily, with greater details and insights to be shared at the upcoming Data for AI event.

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The Evolution Of Data Science And AI At The New York Times - Forbes

Special series If youve ever worked with an application that uses some form of machine learning, youll know that some component or other is always evolving. If it isnt the training data thats changing, youll surely come across a model that needs updating, and if all is well in those areas, theres a good chance a feature request is waiting for implementation so code modifications are due.

In regular software projects, we already know how to automatically take care of changes and make sure that we have a way of keeping our systems up to date without (too many) manual steps. The number of variables at play in ML however make it really tricky to come up with similar processes in that discipline, which is why it is often cited as one of the major roadblocks in getting machine-learning-based applications into production.

For the second episode of our MCubed webcast, on October 7, we therefore decided to sit down with you and have an in-depth look at how to tackle the operational side of ML. Joining in will be DevOps and data expert Danilo Sato, who helped quite a few organisations set up a comprehensible continuous delivery (CD) workflow for their machine-learning projects.

You might know Danilo from a popular article series on CD4ML, however his work reaches far beyond that. In his 2014 book DevOps in Practice: Reliable and Automated Software Delivery, he shared insights from working on all sorts of platform modernisation and data engineering projects.

On the webcast, Danilo will discuss how the principles of Continuous Delivery apply to machine-learning applications, and walk you through the technical components necessary to implement a system that takes care of CD for your ML project. Hell walk you through the differences between MLOps and CD4ML, take a closer look at the peculiarities of version control and artifact repositories in ML projects, give you some tips on what to observe, and introduce you to the many different ways a model can be deployed.

And in case you have all of this figured out already, Danilo will provide a look into the future of machine-learning infrastructure as well as give you some food for thought on open challenges such as explainability and auditability.

The MCubed webcast on October 7 will start 11am BST (noon CEST) with a roundup of the latest in machine-learning-related software development news, and then its straight on to the talk.

Dont forget to let us know if you have any topics youd like to learn more about, or if you are interested in practical experience reports from specific industries we really want to make these webcasts worth your time, so every hint helps. Also, reach out if you want to share some tricks yourself, we always love to hear from you!

Register here to get a quick reminder on the day were really looking forward to see you on Thursday.

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Join our MCubed web lecture this week to find out how to get machine learning into production - The Register