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AI and machine learning: a gift, and a curse, for cybersecurity – Healthcare IT News

The Universal Health Services attack this past month has brought renewed attention to the threat of ransomware faced by health systems and what hospitals can do to protect themselves against a similar incident.

Security experts say that the attack, beyond being one of the most significant ransomware incidents in healthcare history, may also be emblematic of the ways machine learning and artificial intelligence are being leveraged by bad actors.

With some kinds of "early worms," said Greg Foss, senior cybersecurity strategist at VMware Carbon Black, "we saw [cybercriminals] performing these automated actions, and taking information from their environment and using it to spread and pivot automatically; identifying information of value; and using that to exfiltrate."

The complexity of performing these actions in a new environment relies on "using AI and ML at its core," said Foss.

Once access is gained to a system, he continued, much malware doesn't require much user interference.But although AI and ML can be used to compromise systems' security, Foss said, they can also be used to defend it.

"AI and ML are something that contributes to security in multiple different ways," he said. "It's not something that's been explored, evenuntil just recently."

One effective strategy involves user and entity behavior analytics, said Foss: essentially when a system analyzes an individual's typical behavior and flags deviations from that behavior.

For example, a human resource representative abruptly running commands on their host is abnormal behavior and might indicate a breach, he said.

AI and ML can also be used to detect subtle patterns of behavior among attackers, he said. Given that phishing emails often play on a would-be victim's emotions playing up the urgency of a message to compel someone to click on a link Foss noted that automated sentiment analysis can help flag if a message seems abnormally angry.

He also noted that email structures themselves can be a so-called tell: Bad actors may rely on a go-to structure or template to try to provoke responses, even the content itself changes.

Or, if someone is trying to siphon off earnings or medication particularly relevant in a healthcare setting AI and ML can help work in conjunction with a supply chain to point out aberrations.

Of course, Foss cautioned, AI isn't a foolproof bulwark against attacks. It's subject to the same biases as its creators, and "those little subtleties of how these algorithms work allow them to be poisoned as well," he said. In other words, it, like other technology, can be a double-edged sword.

Layered security controls, robust email filtering solutions, data control and network visibility also play a vital role in keeping health systems safe.

At the end of the day, human engineering is one of the most important tools: training employees to recognize suspicious behavior and implement strong security responses.

Using AI and ML "is only starting to scratch the surface," he said.

Kat Jercich is senior editor of Healthcare IT News.Twitter: @kjercichEmail: kjercich@himss.orgHealthcare IT News is a HIMSS Media publication.

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Researchers develop machine learning model that will support safe and accurate decision making for the Halifax Harbour – PreventionWeb

Researchers develop machine learning model that will support safe and accurate decision making for the Halifax Harbour  PreventionWeb

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Soleadify secures seed funding for database that uses machine learning to track 40M businesses – TechCrunch

Usually, databases about companies have to be painstakingly updated by humans. Soleadify is a startup that uses machine learning to create profiles for businesses in any industry. The first of the companys products is a business search engine that keeps over 40 million business profiles updated, currently used by hundreds of companies in the USA, Europe and Asia for sales and marketing activities.

Its now secured $1.5 million in seed-round funding from European venture firms GapMinder Venture Partners and DayOne Capital, as well as several prominent business angels, through Seedblink, an equity crowdfunding platform based out of Bucharest, Romania.

The company plans to use the funds to further improve their technology, build partnerships and expand their marketing capabilities.

On top of Soleadifys data, they build solutions for prospecting, market research, customer segmentation and industry monitoring.

The way its done is by frequently scanning billions of webpages, identifying and classifying relevant data points and creating connections between them. The result is a database of business data, which is normally only available through laborious, manual research.

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Machine Learning Capabilities Come to the Majority of Open Source Databases with MindsDB AI-Tables – PR Web


BERKELEY, Calif. (PRWEB) October 20, 2020

MindsDB, the open source AI layer for existing databases, today announced official integrations with open source relational databases PostgreSQL and MySQL. These join a growing list of integrations with community-driven databases including MariaDB and Clickhouse to bring the machine learning capabilities of MindsDB to over 55% of open source databases.

MindsDB brings machine learning to those who work with data to allow users to create and deploy ML models using standard SQL queries and increase AI projects efficiency. Through the use of AI-Tables, database users can apply machine learning models straight from their database and automatically generate predictions as simple as querying a table.

PostgreSQL is a powerful, open source object-relational database system with over 30 years of active community development. The database has a strong reputation for reliability, feature robustness, and performance. MySQL, owned by Oracle, is one of the most popular open source databases, trusted by organizations such as Facebook, Google, and Adobe. Together, the two represent 45% of the active open source database market.

The announcement was made as part of MindsDBs presentation during Percona Live Online 2020, the largest annual open source database conference.

Bringing machine learning resources to the open source database community is a huge part of our mission to democratize machine learning, said MindsDB co-founder, Adam Carrigan. Staying connected to this community has helped us identify the main challenges of users that know their data best and give them machine learning tools to help them solve those problems.

Bringing Machine Learning to the Database

I am excited to see MindsDB providing the power of machine learning, without leaving the convenience of SQL. There has been significant demand in the community for machine learning tools that work with on-premises data, can be run by the average database user, and are delivered cost-effectively, said Peter Zaitsev, CEO of Percona. As the open source database community gathers every year to share their knowledge at Percona Live, it is extremely exciting to see companies launch new solutions, like MindsDB with AI-Tables, that can expand what open source databases deliver to users.

With the newly announced MindsDB integrations, MySQL and PostgreSQL users can use AI-Tables to learn and make predictions from their data with no machine learning experiences. Users can now run a simple SQL query to deploy automated machine learning models directly inside their database.

The key to the MindsDB tool is the use of virtual AI-Tables which allow any user to easily train and test machine learning models with basic SQL as if they were standard database tables.

Community-focused Innovation

As more open source database users get hold of machine learning tools, it will be very exciting to see what the community will produce and how well benefit from AI and ML going forward, said OpenOcean Founder and General Partner Patrik Backman. Now is a good time for every database developer to get their hands dirty and try out with their own data how the MindsDB integration practically works.

While MindsDB is now available in over half of open source databases, we will continue to work towards our goal of democratizing machine learning for every database user, said Carrigan.

About MindsDB

MindsDB helps organizations to turn data into business predictions by adding machine learning capabilities to their databases. MindsDB provides an AI layer for existing databases that allows organizations to effortlessly and cost-effectively develop, train, and deploy state-of-the-art ML models using standard SQL queries to get accurate business predictions. Follow the company on LinkedIn, Twitter and Facebook and visit the blog for additional resources.

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Teaming Up with Arm, NXP Ups Its Place in the Machine Learning Industry – News – All About Circuits

One of the most popular topics in the technology industry, even for electrical engineers, is machine learning. The newest company to make headlines in the field is NXP Semiconductors withtwo big announcements today.

Looking to further establish its place in the machine learning industry, NXP has made two strategic partnerships, one with Arm and one with Canadia-based Au-Zone. All About Circuits had a sit down with executives at NXP to understand what the news really means.

On the hardware side of things, NXP announced today that it has been collaborating with Arm as the lead technology partner on thenew ArmEthos-U65 microNPU (neural processing unit). This technology partnership allows NXP to integrate the Ethos-U65 microNPU into its next generation of i.MX applications processors with the hopes of delivering energy-efficient, cost-effective ML solutions.

NXP is particularly excited about this partnership becausethis new microNPU is able to maintain the MCU-class power efficiency of the Ethos-U55, but is capable of being used in systems with higher performance Cortex-A-based SoCs.

Some standout features of the Ethos-U65 includemodel compression, on-the-fly weight decompression, and optimization strategies for DRAM and SRAM.

Whats particularly unique about this SoC is that the NPU works alongside a Cortex-M based processor. In our interview, Ben Eckermann, Senior Principal Engineer andSystems Architect at NXP Semiconductors, explained why this feature is advantageous.

Eckermann explains, What's key here is that, similar to the U-55, [the Ethos-U65]doesn't attempt to do everything as one standalone black box. It relies on the Cortex-M processor sitting beside it."

He continues, "The Cortex-M processor is able to handle any network operators that either occur so infrequently that there's no point in dedicating hardware resources in the U-65 to it or some that just don't provide you enough bang for yourbuck, where some things can be done efficiently on the CPU like the very last layers of a NN.

On the software side of things, NXP today announced that it has established an exclusive partnership with Au-Zone to expand NXPs eIQmachine learning (ML) software development environment.

What NXP was really after was Au-Zones DeepViewML Tool Suite, which is said to augment eIQ with an intuitive, graphical user interface (GUI) and workflow. The hope is that this added functionality will make the development, training, and deployment of NN models and ML workloads straightforward and easy for designers of all experience levels.

The tool includes features to prune, quantize, validate, and deploy public or proprietary NN models on NXP devices.

Together, Au-Zone and NXP look to optimize NNs for NXP-based SoCs, providing developers with run-time insights on NN model architectures, system parameters, and run-time performance.

A key feature of this run-time inference engine is that it optimizes the system memory usage and data movement uniquely for each SoC architecture.

Gowri Chindalore, head of NXP's business and technology strategy for edge processing, claims that this feature offerscustomers a double optimization," optimizing both the neural network and then further optimizing for the specific hardware.

With the introduction of the Arm Ethos U-65 microNPU, NXP will be able to provide new functionality and energy savings in future lines of i.MX application processors. This may make way for more powerful and low-energy designs for IoT and other edge applications.

Introducing Au-Zones DeepView Tool Suite will also benefit design engineers becausethe training, optimization, and deployment of NNs will not only be made more simple but will also be optimized for the specific hardware they are running on.

This too should only benefit future developments in IoT and edge applications on NXP-based SoCs.

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NXP Invests in Au-Zone to Enhance Machine Learning Capabilities – Mobile ID World

NXP is hoping to improve its machine learning offerings after making a strategic investment in Au-Zone Technologies. The exclusive arrangement specifically concerns Au-Zones DeepView ML Tool Suite, which will be used to bolster NXPs eIQ Machine Learning software development environment and lead to the creation of new Edge machine learning products.

In that regard, the DeepView Suite comes with a graphical user interface (GUI) and workflows that will make it easier to import datasets, and to train neural network models for Edge devices. DeepViews run-time inference engine will give eIQ developers more insight into system memory usage, data movement, and other performance metrics in real time, which will in turn allow them to optimize their model before deploying it in a System-on-Chip (SoC) solution.

This partnership will accelerate the deployment ofembedded Machine Learningfeatures, said Au-Zone CEOBrad Scott. This will serve as a catalyst to deliver more advanced Machine Learning technologies and turnkey solutions as [Original Equipment Manufacturers] continue to transition inferencing to the Edge.

In other news, NXP also revealed that it will be integrating Arms Ethos-U65 microNPU (neural processing unit) into its own i.MXapplications processors. The Ethos-U65 is comparable to the Ethos-U55 in terms of power efficiency, but extends its utility to Cortex-A SoCs. The microNPU is compatible with the Cortex-M core featured in NXPs i.MX SoCs (including the i.MX 8M Plus), and will allow NXP to expand its Industrial and IoT Edge portfolio.

NXPs scalable applications processors deliver a broad ecosystem for our customers to quickly deliver innovative systems, addedNXP SVP and Edge Processing GM Ron Martino. Through these partnerships, our goal is to increase the efficiency of our processors while simultaneously increasing our customers productivity and reducing their time to market.

Both Au-Zone and Arm have collaborated with NXP on other projects in the past. In the meantime, NXP has announced that Facebooks Glow Neural Network compiler is now available through its own eIQ development environment. It has also released i.MX RT106F and i.MX RT106L MCUs to support the development of applications with face and voice recognition.

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NXP Announces Expansion of its Scalable Machine Learning Portfolio and Capabilities – GlobeNewswire

NXP Image

NXP expands scalable machine learning capabilities

EINDHOVEN,The Netherlands, Oct. 19, 2020 (GLOBE NEWSWIRE) -- NXP Semiconductors N.V.(NASDAQ: NXPI) todayannouncedthat it is enhancing its machine learning development environment and product portfolio. Through an investment, NXP has established an exclusive, strategic partnership with Canada-based Au-Zone Technologies to expand NXPs eIQ Machine Learning (ML) software development environment with easy-to-use ML tools and expand its offering of silicon-optimized inference engines for Edge ML.

Additionally, NXP announced that it has been working with Arm as the lead technology partner in evolving Arm Ethos-U microNPU (Neural Processing Unit) architecture to support applications processors. NXP will integrate the Ethos-U65 microNPU into its next generation of i.MX applications processors to deliver energy-efficient, cost-effective ML solutions for the fast-growing Industrial and IoT Edge.

NXPs scalable applications processors deliver an efficient product platform and a broad ecosystem for our customers to quickly deliver innovative systems, said Ron Martino, Senior Vice President and General Manager of Edge Processing at NXP Semiconductors. Through these partnerships with both Arm and Au-Zone, in addition to technology developments within NXP, our goal is to continuously increase the efficiency of our processors while simultaneously increasing our customers productivity and reducing their time to market. NXPs vision is to help our customers achieve lower cost of ownership, maintain high levels of security with critical data, and to stay safe with enhanced forms of human-machine-interaction.

EnablingMachine Learning for All

Au-Zones DeepView ML Tool Suite will augment eIQ with an intuitive,graphical user interface (GUI) and workflow, enabling developers of all experience levels to import datasets and models, rapidly train, and deploy NN models and ML workloads acrossthe NXPEdge processing portfolio. To meet the demanding requirements of todays industrial and IoTapplications, NXPs eIQ-DeepViewML Tool Suite will provide developers with advanced features to prune,quantize, validate, and deploypublic or proprietary NNmodels on NXP devices. Its on-target, graph-level profiling capability will provide developers with unique, run-time insights tooptimize NN model architectures, system parameters, and run-time performance. By adding Au-Zones DeepView run-time inference engine to complement open source inference technologies in NXP eIQ, users will be able to quickly deploy and evaluate ML workloads and performance across NXP devices with minimal effort. A key feature of this run-time inference engine is that it optimizes the system memory usage and data movement uniquely for each SoC architecture.

Au-Zone is incredibly excited to announce this investment and strategic partnership with NXP, especially with its exciting roadmap for additional ML accelerated devices, said Brad Scott, CEO of Au-Zone. We created DeepViewTM to provide developers with intuitive tools and inferencing technology, so this partnership represents a great union of world class silicon, run-time inference engine technology, and a development environment that will further accelerate the deployment of embedded ML features. This partnership builds on a decade of engineering collaboration with NXP and will serve as a catalyst to deliver more advanced Machine Learning technologies and turnkey solutions as OEMs continue to transition inferencing to the Edge.

ExpandingMachine Learning Acceleration

Toacceleratemachine learningin awiderrange ofEdgeapplications, NXPwill expand itspopulari.MXapplications processors for the Industrial and IoT Edge with the integration of the Arm Ethos-U65microNPU, complementing the previously announced i.MX 8M Plus applications processor with integrated NPU. The NXP and Arm technologypartnershipfocused ondefiningthe system-levelaspectsof this microNPUwhichsupportsup to1 TOPS(512 parallelmultiply-accumulateoperationsat 1GHz). The Ethos-U65 maintains the MCU-class power efficiency of the Ethos-U55 while extending its applicability to higher performance Cortex-A-based system-on-chip (SoC)s. The Ethos-U65 microNPU works in concert with the Cortex-M core already present in NXPs i.MX families of heterogeneous SoCs, resulting in improved efficiency.

There has been a surge of AI and ML across industrial and IoT applications driving demand for more on-device ML capabilities, said Dennis Laudick, Vice President of Marketing, Machine Learning Group, at Arm. The Ethos-U65 will power a new wave of edge AI, providing NXP customers with secure, reliable, and smart on-device intelligence.


Arm Ethos-U65 will be available in future NXPs i.MX applications processors. The eIQ-DeepViewMLTool SuiteandDeepViewrun-time inference engine, integrated into eIQ,will be available Q1, 2021. The end-to-end software enablement,fromtraining, validatingand deployingexisting or new neural network modelsfor i.MX 8M Plusand other NXP SoCs, as well as future devices integrating the Ethos-U55 and U65, will be accessible through NXPseIQ Machine Learning software development environment. To learn more read our blog and register for the joint NXP and Arm webinar on November 10.

About NXP SemiconductorsNXP Semiconductors N.V. enables secure connections for a smarter world, advancing solutions that make lives easier, better, and safer. As the world leader in secure connectivity solutions for embedded applications, NXP is driving innovation in the automotive, industrial & IoT, mobile, and communication infrastructure markets. Built on more than 60 years of combined experience and expertise, the company has approximately 29,000 employees in more than 30 countries and posted revenue of $8.88 billion in 2019. Find out more at

NXP, the NXP logo and EdgeVerse are trademarks of NXP B.V. All other product or service names are the property of their respective owners. Amazon Web Services and all related logos and motion marks are trademarks of, Inc. or its affiliates. The Bluetooth word mark and logos are registered trademarks owned by Bluetooth SIG, Inc. and any use of such marks by NXP Semiconductors is under license. All rights reserved. 2020 NXP B.V.

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Factories of The Future Are Using Machine Learning Analytics to Optimize Assets – Embedded Computing Design

From food to cars to complex manufacturing machinery,qualityis a top concern of manufacturers. Factors such as safety, efficiency, and reliability affect product quality and ultimately influence customer satisfaction. Sourcing, design, testing, and inspection all play a crucial role in ensuring products meet the bar when it comes to quality. Product inspections at early stages in the production cycle help reduce risks and cost. While inspections can be conducted at any point throughout the production process, the goal is to identify, contain and resolve issues as quickly as possible.

Many manufacturers are increasingly looking to their smart, connected machines to help with anomaly detection. These assets can alert end users of such anomalies to ensure accelerated interventions, helping maintain quality and uptime. Using advanced analytics, assets can collect user feedback or alert accuracy and improve over time. This enables higher outputs and lowers labor costs because of the reduction in time spent resolving issues.

Rockwell Automation saw an opportunity to introduce a product inspection much earlier in one of their production processes. The process involves screening conductive paste onto a circuit board and then placing ball grid arrays (BGAs), which serve as the contact point for parts added later in the process. The board then travels through several placement machines where parts are added, increasing the value of the board significantly with each part, and finally through the oven where the board is set.

Lack of early inspection often resulted in a significant amount of time spent fixing errors that occurred very early in the process. If the BGAs are not properly connected to the board, it can take 30 minutes per part to correct the work. With 12 BGAs on a board, employees were sometime spending upwards of 6 hours on replacing parts and re-working the boards. The inspection, which is an automated optical inspection, did not occur until after the board had gone through three placement machines and the oven. Rockwell Automation knew that to avoid the potential of more than 6 hours of rework for faulty BGA placement, they need to catch the issue much sooner.

The company implemented an advanced analytics solution that scores the conductive paste profile prior to the board going through a single placement machine. Using high-speed edge computing and machine learning, the solution creates and executes a 3D model of the paste profile within 7 seconds. It then predicts whether the board will meet the quality bar or have defects. If alerted to a poor paste profile, operators can immediately stop production, remove the bad board, wash it, and send it back through the production linea process that takes less than two minutes.

Even in the pilot process, Rockwell Automation has seen impressive results. By catching issues early in real time, they have gained back hours of productivity and kept the quality bar high. Errors that used to take 6 hours to resolve can now be determined and fixed within minutes. The solution allowed Rockwell Automation to determine paste issues right away; it only takes them two minutes to do a rework with machine learning. And, given that the value to the board comes with adding various parts, there has been a cost-saving benefit by resolving issues before any parts have been placed, reducing scrap and other waste.

For Rockwell Automation, these new capabilities have yielded almost immediate results in the form of time and cost savings. Rockwell Automation sees massive potential for real-time analytics to improve their circuit board production across their facilities, and they are looking towards additional use cases of advanced analytics and machine learning to bring even more intelligence into their operations.

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Lantronix Brings Advanced AI and Machine Learning to Smart Cameras With New Open-Q 610 SOM Based on the Powerful Qualcomm QCS610 System on Chip (SOC)…

IRVINE, Calif., Oct. 15, 2020 (GLOBE NEWSWIRE) -- Lantronix Inc. (NASDAQ: LTRX), a global provider of Software as a Service (SaaS), engineering services and hardware for Edge Computing, the Internet of Things (IoT) and Remote Environment Management (REM), today announced the availability of its new Lantronix Open-Q 610 SOM based on the powerful Qualcomm QCS610System on Chip (SOC). This micro System on Module (SOM) is designed for connected visual intelligence applications with high-resolution camera capabilities, on-device artificial intelligence (AI) processing and native Ethernet interface.

Our long and successful relationship with Qualcomm Technologies enables us to deliver powerful micro SOM solutions that can accelerate IoT design and implementation, empowering innovators to create IoT applications that go beyond hardware and enabletheir wildest dreams, said Paul Pickle, CEO of Lantronix.

The new Lantronix ultra-compact (50mm x 25mm), production-ready Open-Q 610 SOM is based on the powerful Qualcomm QCS610SOC, the latest in the Qualcomm Vision Intelligence Platform lineup targeting smart cameras with edge computing. Delivering up to 50 percent improved AI performance than the previous generation as well as image signal processing and sensor processing capabilities, it is designed to bring smart camera technology, including powerful artificial intelligence and machine learning features formerly only available to high-end devices, into mid-tier camera segments, including smart cities, commercial and enterprise, homes and vehicles.

Bringing Advanced AI and Machine Learning to Smart Camera Application

Created to bring advanced artificial intelligence and machine learning capabilities to smart cameras in multiple vertical markets, the Open-Q 610 SOM is designed for developers seeking to innovate new products utilizing the latest vision and AI edge capabilities, such as smart connected cameras, video conference systems, machine vision and robotics. With the Open-Q 610 SOM, developers gain a pre-tested, pre-certified, production-ready computing module that reduces risk and expedites innovative product development.

The Open-Q 610 SOM provides the core computing capabilities for:

Connectivity solutions include Wi-Fi/BT, Gigabit Ethernet, multiple USB ports and three-camera interfaces.

The Lantronix Open-Q 610 SOM provides advanced artificial intelligence and machine learning capabilities that enable developers to innovate new product designs, including smart connected cameras, video conference systems, machine vision and robotics, said Jonathan Shipman, VP of Strategy at Lantronix Inc. Lantronix micro SOMs and solutions enable IoT device makers to jumpstart new product development and accelerate time-to-market by shortening the design cycle, reducing development risk and simplifying the manufacturing process.

Open-Q 610 Development Kit

The companion Open-Q 610 Development Kit is a full-featured platform with available software tools, documentation and optional accessories. It delivers everything required to immediately begin evaluation and initial product development.

The development kit integrates the production-ready OpenQ 610 SOM with a carrier board, providing numerous expansion and connectivity options to support development and testing of peripherals and applications. The development kit, along with the available documentation, also provides a proven reference design for custom carrier boards, providing a low-risk fast track to market for new products.

In addition to production-ready SOMs, development platforms and tools, Lantronix offers turnkey product development services, driver and application software development and technical support.

For more information, visit Open-Q 610 SOM and Open Q 610 SOM Development kit.

About Lantronix

Lantronix Inc. is a global provider of software as a service (SaaS), engineering services and hardware for Edge Computing, the Internet of Things (IoT) and Remote Environment Management (REM). Lantronix enables its customers to provide reliable and secure solutions while accelerating their time to market. Lantronixs products and services dramatically simplify operations through the creation, development, deployment and management of customer projects at scale while providing quality, reliability and security.

Lantronixs portfolio of services and products address each layer of the IoT Stack, including Collect, Connect, Compute, Control and Comprehend, enabling its customers to deploy successful IoT and REM solutions. Lantronixs services and products deliver a holistic approach, addressing its customers needs by integrating a SaaS management platform with custom application development layered on top of external and embedded hardware, enabling intelligent edge computing, secure communications (wired, Wi-Fi and cellular), location and positional tracking and environmental sensing and reporting.

With three decades of proven experience in creating robust industry and customer-specific solutions, Lantronix is an innovator in enabling its customers to build new business models, leverage greater efficiencies and realize the possibilities of IoT and REM.Lantronixs solutions are deployed inside millions of machines at data centers, offices and remote sites serving a wide range of industries, including energy, agriculture, medical, security, manufacturing, distribution, transportation, retail, financial, environmental, infrastructure and government.

For more information, visit Learn more at the Lantronix blog,, featuring industry discussion and updates. To follow Lantronix on Twitter, please visit View our video library on YouTube at or connect with us on LinkedIn at

Safe Harbor Statement under the Private Securities Litigation Reform Act of 1995: Any statements set forth in this news release that are not entirely historical and factual in nature, including without limitation statements related to our solutions, technologies and products as well as the advanced Lantronix Open-Q 610 SOM, are forward-looking statements. These forward-looking statements are based on our current expectations and are subject to substantial risks and uncertainties that could cause our actual results, future business, financial condition, or performance to differ materially from our historical results or those expressed or implied in any forward-looking statement contained in this news release. The potential risks and uncertainties include, but are not limited to, such factors as the effects of negative or worsening regional and worldwide economic conditions or market instability on our business, including effects on purchasing decisions by our customers; the impact of the COVID-19 outbreak on our employees, supply and distribution chains, and the global economy; cybersecurity risks; changes in applicable U.S. and foreign government laws, regulations, and tariffs; our ability to successfully implement our acquisitions strategy or integrate acquired companies; difficulties and costs of protecting patents and other proprietary rights; the level of our indebtedness, our ability to service our indebtedness and the restrictions in our debt agreements; and any additional factors included in our Annual Report on Form 10-K for the fiscal year ended June 30, 2019, filed with the Securities and Exchange Commission (the SEC) on September 11, 2019, including in the section entitled Risk Factors in Item 1A of Part I of such report, as well as in our other public filings with the SEC. Additional risk factors may be identified from time to time in our future filings. The forward-looking statements included in this release speak only as of the date hereof, and we do not undertake any obligation to update these forward-looking statements to reflect subsequent events or circumstances.

Lantronix Media Contact:Gail Kathryn MillerCorporate Marketing &Communications Managermedia@lantronix.com949-453-7158

Lantronix Analyst and Investor Contact:Jeremy WhitakerChief Financial 949-450-7241

Lantronix Sales: sales@lantronix.comAmericas +1 (800) 422-7055 (US and Canada) or +1 949-453-3990Europe, Middle East and Africa +31 (0)76 52 36 744Asia Pacific + 852 3428-2338China + 86 21-6237-8868Japan +81 (0) 50-1354-6201India +91 994-551-2488

2020 Lantronix, Inc. All rights reserved. Lantronix is a registered trademark, and EMG, and SLC are trademarks of Lantronix Inc. Other trademarks and trade names are those of their respective owners.

Qualcomm is a trademark or registered trademark of Qualcomm Incorporated.

Qualcomm Vision Intelligence Platform and Qualcomm QCS610 are products of Qualcomm Technologies, Inc. and/or its subsidiaries.

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EMA Webinar to Uncover How Machine Learning and Predictive Analytics Can Improve Workload Automation Outcomes – PR Web

Empowering Workload Automation with Intelligence Webinar

BOULDER, Colo. (PRWEB) October 20, 2020

Enterprise Management Associates (EMA), a leading IT and data management research and consulting firm, today announced it will host a webinar titled Empowering Workload Automation with Intelligence, featuring Dan Twing, President and COO at EMA, and Jennifer Chisik, Head of Product for Automic Automation Intelligence at Broadcom.

Automation is critical to modern IT operations. Workload automation (WLA) software is one of the critical tools required to run an effective IT operation. Large enterprises often have multiple WLA solutions, which make it difficult to create a holistic, end-to-end view of workload health and outcomes, and to see and resolve problems as they develop. Adding intelligence to automation solutions with machine learning data from a wide range of sources and disparate tools empowers automation to predict outcomes, identify problems as they develop, and provide prescriptive suggestions to resolve developing problems quickly.

During this webinar, Twing and Chisik will discuss changes to the operating model for IT and benefits of automation intelligence, as well as:

The webinar is Wednesday, October 28 at 11:00 a.m. Eastern. Registration is available at

About EMA Founded in 1996, EMA is a leading industry analyst firm that specializes in providing deep insight across the full spectrum of IT and data management technologies. EMA analysts leverage a unique combination of practical experience, insight into industry best practices, and in-depth knowledge of current and planned vendor solutions to help their clients achieve their goals. Learn more about EMA research, analysis, and consulting services for enterprise line of business users, IT professionals and IT vendors at

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