Monthly Archives: January 2020

Dr Paul Rivers, CEO, Guidance Automation

There has been much written about the benefits that automation can unlock for businesses, but too often businesses think they need to jump in at the deep end and deploy automation technology straight away, hoping it will solve the problems they are experiencing.

But how will a process optimised for a manual workforce perform with automation such as an automated guided vehicle (AGV)? How will people respond when their day-to-day roles change?

If the aim is to improve efficiency and productivity, technology is just one part of the overall solution.

In this article, I explain why a considered deployment of an automation solution that takes into account the process needs, the people as well as the technology, ensures businesses can introduce automation that can augment the workers experience whilst also transforming productivity.

Automation misconception

Industry 4.0 can transform organisations processes and operations. It can revolutionise productivity, improve accuracy and unleash new levels of efficiency.

In operating environments where skills are thin on the ground and staff recruitment and retention a challenge, automation appears to be a fast track to nirvana. Yet the reality is somewhat different.

Automation is not a direct replacement for a human workforce and how could it be?

However technically advanced, todays automated solutions may offer huge opportunities to improve productivity, but they do not replicate the activities of a human workforce in a like for like manner.

By failing to truly consider how the technology will be deployed and, critically, how it will work in tandem with a human workforce organisations are fundamentally failing to get the point of automation or reap the rewards.

Some mistakes are basic, such as installing a fleet of AGVs but failing to consider the need for automatic door opening.

Others are more fundamental, such as overlooking the implications of dropping technology into a workplace without engaging the workforce.

Either way, a misunderstanding of automation and its implications for both people and process can lead to serious operational problems that risk derailing essential investment in improvement.

Understanding the process

Assessing the way in which automation will fit within an existing process is critical.

If, for example, one of the biggest issues within a warehouse is vehicle congestion, especially at peak times, simply replacing human operated vehicles with AGVs without considering the timing and location of the routes is not going to address the problem.

Reconsidering the traffic flow, the way orders are batched, the tasks and schedules is essential to maximise the specific value of the AGVs.

Growing numbers of organisations are considering the use of automated vehicles to replace the highly manual task of driving around a warehouse, picking items and delivering those items to a loading station.

Reallocating those individuals to dedicated pick locations makes operational sense but this is not a like for like situation.

For example, while individuals may only be able to operate a single pick model, an AGV may be able to pick up to three, not only reducing non-productive time but also cutting the number of autonomous vehicles, and hence investment, required.

For companies investing in automated mobile robots (AMRs), analysing the SKUs, the process, the distance travelled by vehicles on each route is key to understanding how many robots are required.

Furthermore, by running a simulation of how the automated model would work in practice, an organisation can highlight opportunities to optimise the batching of orders to dispatch to the AGVs, gaining further efficiency advantages.

Engaging the people

Of course, the most optimised process can still be derailed if the workforce does not understand how to work with AMRs or, even worse, is actively interfering with the robotic vehicles.

Ensuring people are part of this process from the very beginning is essential because their day to day activities will change.

The positive benefits for employees are significant not least the use of solutions such as mobile conveyors to minimise the need for heavy and repetitive lifting.

When technology can completely eradicate these arduous tasks and the workforce is required to simply verify the item by scanning individuals will respond well to the change.

But people need to be educated, trained and confident. They need to understand how the technology works and how they work together.

A workforce chasing AMRs down the aisle because they do not understand where the system is designed to stop does not represent a harmonious man / machine interaction.

There will, of course, be changes to the skillsets required forklift drivers will increasingly be replaced by automated vehicles.

However, in a market desperate to recruit and retain individuals with experience, this provides companies with a chance to retrain highly skilled forklift drivers to, for example, supervise loading or oversee picking teams.

Highlighting the specific skills such as picking that are simply not in the purview of automation today is an important part of this automation evolution and key to creating an operating environment that combines excellent technology with an engaged and motivated workforce.

Embracing the technology

The automation technologies available to organisations today are compelling.

From autonomous mobile robots to automated guidance vehicles, as Industry 4.0 gains both momentum and maturity, confidence in the quality of the technology to deliver and enable significant operational change continues to grow.

But, if businesses fail to get processes aligned and truly understand the goal of any automation investment, problems will arise.

By considering both the processes and the people who operate those processes today, organisations can take a far more intelligent approach to automation.

Add in simulation to understand how AMRs, for example, might operate at different times is critical to highlighting potential problems and avoiding inefficiencies.

Plus, of course, these systems deliver real time data in huge detail. Combining analytics to monitor conditions in real time with dynamic fleet scheduling and route optimisation will enable continuous improvement.

The technology is brilliant; but it is the way it is deployed, the way orders are batched, and schedules planned, the way people are managed and skilled, that is the key to truly realising the potential of automation.

You might also like

Read more here:

Opinion: How will people respond when automation changes their roles - Robotics and Automation News

The conventional tools to ensure cybersecurity are not sufficient in todays world especially in 2020 where using just anti-malware software or login audits will not work at par with rising threats. Organisations need more resources and powerful infrastructure to resist any type of data breach. To enable such strength, they need to embrace AI/ML and automation to fortify their company and company data against malicious intentions.

In an interview to Tech Republic, Greg Martin, general manager of the Security Business Unit at Sumo Logic, said, AI/ML and automation greatly enhance endpoint protection, but where we see the most benefit in the technology is guiding security operations in what exactly to do with those threats once they hit the enterprise. The ever-increasing sophistication and persistence of cybercriminal activity is requiring security operations teams to rethink how they use people, processes, and technology.

Obfuscation, polymorphism and certain others are among most challenging hacking techniques which make it difficult to spot malicious programs. Moreover, security engineers with domain-specific knowledge and workforce shortage are another significant issues in regard to ensure cybersecurity. However, using AI and ML, experts and researchers are dedicating their best to utilize the best of the technologies in an effort to identify and counteract sophisticated cyber-attacks with reduced or no human intervention. AI and ML have enabled the security professionals to learn about new attack vectors.

In the domain of cybersecurity, ML is much more than just an application of certain algorithms. The technology can be leveraged to analyze cyber threats better and respond to security incidents. Detecting malicious activities and stopping cyber-attacks while analysing mobile endpoints for cyber threats are among the significant benefits of ML in cybersecurity. The technology also tends to improve human analysis from malicious attack detection to endpoint protection.

As noted by Forbes, cybersecurity products designed to automate specific processes are widespread, and the likelihood is that you have already implemented automation tools within your organization. For example, vulnerability management products can be configured to automatically detect and scan devices on an enterprise network. They can then conduct an assessment based upon a set of security controls authorized by the organization. Once the assessment is complete, identified defects can be remediated.

To enable the cybersecurity in todays age, a number of experts tend to refer to the tools like security automation and orchestration (SOAR) products, robotic process automation (RPA) and custom-developed software and code that automate processes and perform analysis.

Where SOAR products are purpose-built tools that orchestrate activities between other security tools and perform specific automation activities in response to identified threats, RPA tools, on the other hand, are a broader set of automation tools that allow for a wide variety of processes to be automated.

Moreover, RPA tools have seen a significant acceleration in adoption in the HR and finance fields but can also be leveraged by cybersecurity teams. According to Forbes, custom-developed software and code can automate all manner of analyses and is often leveraged for a niche or specific challenge within an organization that may not have an out of the box tool available.

Share This ArticleDo the sharing thingy

About AuthorMore info about author

Smriti is a Content Analyst at Analytics Insight. She writes Tech/Business articles for Analytics Insight. Her creative work can be confirmed @analyticsinsight.net. She adores crushing over books, crafts, creative works and people, movies and music from eternity!!

Read this article:

How AI and Automation Help Ensure Cybersecurity? - Analytics Insight

The global warehouse automation market is estimated to double by 2025, which means more of your competition will be exploring, and investing in, automated warehouse solutions. If they havent already, warehouse companies and executives will soon have to decide if (and how) to incorporate warehouse automation and robotics into their own daily operations if they want to remain competitive.

However, with new technology comes new hurdles and new unexpected issues to resolve. While automation and robotics are ultimately implemented to make your operations more streamlined, accurate and efficient, there will always be the chance that it causes issues and downtime if not implemented and maintained properly. Many companies have felt the effects of a less-than-perfectly planned automation implementation strategy, such as online retail giant ASOS. A glitch in their automated warehouse management system caused a backlog of product that cost the company upwards of $31 million.

So, while some data-based IT issues and some downtime will inevitably occur down the line, there are many steps you can take before or during your search for the right automation solutions for your operation that will help you identify potential, and altogether avoid, automation downtime.

A popular trend in warehouse automation is the incorporation of automated guided vehicles (AGVs). These machines vary in size, shape and function, but they mostly all rely on an accurate map of their environment from a warehouse management system (WMS) in order to function properly and safely.

Be sure your warehouse location map is up-to-date when adding automated solutions that will rely on it for guidance on where to find everything it needs and the quickest route to get there. It would be a huge oversight to implement AGVs into your operation without first identifying, mapping and uploading the product shelving positions, travel paths and work areas you expect the AGVs to navigate.

Most automation processes and machinery rely on digital data. Any silos of manual data processing should be identified and run through a digital transformation before expecting your automation solutions to run smoothly while using it.

Similarly, if you have multiple programs with different information stored within each, take the time to explore ways you could consolidate and organize all of your data and software before linking it up to an automated process. The easier it is to view, collect and update data being sent to your automated warehouse solutions, the less downtime you can expect due to missing, inaccurate or inaccessible data.

Keeping timing in mind when exploring warehouse automation implementation is an important factor in avoiding issues and downtime that can cost your warehouse time and money. Even those who do not work in a warehouse know that there are busy seasons when it comes to fulfilling orders, especially for e-commerce. Right before an anticipated holiday rush is NOT the time to launch your new automated processes. As ASOS learned earlier this year, automation glitches can, among other issues, cause inventory backups that will affect business and sales for months.

Ensure that the right team members are in-house and available to help execute a smooth automation launch, because the last thing you want is your team to be over-loaded not only with busy-season order fulfillment but also with learning about, and adjusting to, new automated processes.

Oftentimes it is too easy to ignore inefficiencies or redundancies in warehouse processes because of reasons like thats the way its always been done or its just the way we do it. If you are looking into, or are currently in, the process of incorporating automation into your warehouse, consider taking a deep dive into each step of your operation and flagging anything that seems redundant, out of place, inefficient, or even unnecessary.

This is also a great opportunity to involve your entire team in the automation process. Not only will they feel valued by being asked for their insight on the day-to-day operations, but they will also feel better prepared to work alongside the automated solutions that will be incorporated into the process.

As the saying goes, A clean warehouse is a happy warehouse. Clean and organized is what your warehouse should be before expecting robotic solutions, like automated pickers, to move safely and efficiently through the environment.

Lee House, vice president of software solution provider I.B.I.S. Inc., suggests allocating 1-2 hours per week to basic warehouse cleanup to increase efficiency. Doing this could also increase the ability of your automated and robotic systems to work efficiently without running into unexpected product, materials, or trash. Also, take the time to optimize your warehouse storage systems so youre setting your automated processes up for success with the most efficient and logical layout for your needs.

If youre investing in automated vehicles, youll want to ensure youre getting the most work out of them each day, and choosing lithium ion forklift batteries is a small but significant way to reduce the downtime these machines typically require.

While its true that lithium ion batteries do not hold a charge for as long as lead-acid batteries, they charge much faster (in as little as 1.5 hours), have an overall lifespan that is 2-3 times longer than its lead-acid cousin, eliminating the need to take up warehouse space for a well-ventilated battery room, and reduces the risk of downtime caused by hazardous acid spills.

Having a thorough understanding of how your warehouse is running is imperative to preventing downtime, with or without automated warehouse solutions in place. What was working smoothly last year may no longer be ideal for your inventory or team this year, so putting a priority on regular audits to ensure that your processes, software, machinery and warehouse layout are still optimized to meet your needs will help you identify small issues or inefficiencies before they grow so large that they require downtime to get back on track.

Neglecting to train the team of people who will be working with your new automated solutions every day will surely result in surprise downtime. Most automation in warehousing today is built to work in tandem with human workers to increase efficiency, not to replace human labor completely. By empowering and educating your team before implementation, they will view automation as a tool that helps them do their job more easily and efficiently, rather than something that is getting in the way of them executing their job in the way they are used to or think it needs to be done.

On top of this, automated systems will need maintenance and a human hand to continue to run efficiently and safely, so having human employees who are educated on and responsible for the maintenance and care of these machines from the beginning will greatly decrease your chance of downtime should something go wrong or need repair.

In order for automation to be successful in a warehouse, your systems need accurate data to work off of (see point #2). Accurate data is one of the most make-or-break pieces of the automation puzzle and has one of the highest potentials for failure (especially considering retail inventory is found to be accurate only 63% of the time).

On top of an investment in picking technologies that aid in quicker and more accurate picking (like RFID scanners), investing in the right inventory management system can help make the road to automation smoother.

In the early 2000s, UK supermarket chain Sainsburys experienced a warehouse automation failure in the form of errors in barcode reading processes that were implemented by a third-party IT company. Some studies blame Sainsbury management as the cause of the eventual automation failure, claiming that they had such a lack of involvement with monitoring and evaluating the project that it caused it to fail to meet its objective. In addition, it has been suggested that a lack of communication and understanding of the business and IT risks also led to the failure.

To avoid this in your own journey into warehouse automation implementation, be sure that you have chosen a specific team of employees who will be communicating with your third-party providers when implementing automation. Frequent updates to the progress of the project and testing of the proposed systems should be part of the project timeline. Identifying potential issues before implementation and kickoff of any automation project is key to avoiding issues down the line.

Be sure that the point person on your team understands the goals and ultimate outcome your business would like to achieve with automation implementation so they can talk to your vendors and IT team to ensure everything that you set up will work towards that goal and issues and snags can be worked out before implementation goes live.

Robotics and automation seems to be where the future of the warehouse industry is headed for the foreseeable future, and they bring many means of cost savings, increased safety and improved efficiency to any operation. Understanding automation, its capabilities, limitations and every piece of its process and logic is imperative to implementing it properly. Do your homework and prepare your warehouse in as many ways as you can before hitting that automation on switch.

Evan Hammersley is the warehouse automation and robotics product manager for NITCO, a provider of material handling equipment and services.

Link:

Warehouse Automation Downtime and How to Avoid It - Material Handling & Logistics

UNIVERSITY PARK, Pa. The rise of automation and artificial intelligence, and their impacts on certain industries and the economy, will be discussed by Penn State President Eric Barron and a pair of University experts during the next episode of WPSUs Digging Deeper on Sunday, Jan. 26.

Vasant Honavar, director of the Artificial Intelligence Research Laboratory and Edward Frymoyer Endowed Professor of Information Sciences and Technology, and Barry Ickes, professor and head of the Department of Economics, will join Barron for the show.

Digging Deeper will air at 10:30 a.m. and 6 p.m. on WPSU-TV.

Penn State President Eric Barron and a pair of University experts will discuss the impacts of automation and artificial intelligence during the next episode of WPSU Penn States Digging Deeper on Sunday, Jan. 26.

Honavar said the types of jobs being replaced by automation has shifted.

The difference is historically through the Industrial Revolution and until fairly recently, the part of the work that was being automated was physical labor and often work that was dangerous, what people didn't really want to do, he said. But I think what's different now is that we're talking about what used to be considered cognitive work, knowledge work.

There will be tasks in almost every job that are amenable to automation, according to Honavar, and nearly every job will change because of automation and AI.

Ickes said the effect will create a mismatch of skills causing job displacement, and there needs to be a way to address the problem.

Usually the winners outnumber the losers but the losers feel that pain, and it's up to policy to set up mechanisms to help with the people who lose from these changes, Ickes said. Especially if society is going to really gain from automation and AI, where there's big productivity gains and big wealth gains and big GDP gains. That would afford the resources to enable us to deal with the people who are hurt by it.

Visit the WPSU website for more information on central Pennsylvanias public media station. WPSU is an outreach service of Penn State.

Last Updated January 17, 2020

See the original post here:

The impacts of automation and AI on the next WPSU 'Digging Deeper' - Penn State News

ByMike Franz, Founder,ManufacturingPower

Only when manufacturers stop basing critical business decisions on inadequate spreadsheets can the purchasing department succeed. Automated access to summary cost and margin information for each SKU delivers light SaaS MRO/tail-spend price transparency, returning a minimum 11-14% COGS (cost of goods sold) reduction to the bottom line.

Every manufacturer must pay attention to costs and competitors pricing. For better or worse, purchasing professionals are rewarded for capturing the lowest price; few have the time to scour the internet for ever RFQ to see if the bid offered is indeed competitive. Measuring value with price experiments is not realistic.

Full-time competitive pricing professionals could work 24/7 to generate statistically valid and accurate data. For the small and mid-sized manufacturer, the solution is to automate price comparison functionality. Until recently this was prohibitively expensive and often too complex. Automating costs and competitive prices can be achieved in a $5000 per year SaaS solution.

Outsourcing data collection is mandated because of rapidly changing prices. Whether due to tariffs, international competition, spikes in oil causing much higher delivery costs, the monitoring of competitors costs simply takes too much time and effort without automation.

ManufacturingPower, for example, collects competitive prices from sites and marketplaces like Amazon, Google Shopping, Grainger, and eBay in real-time.

Realizing the need for automated price-setting, developers began marketing price scraping software and setting consumer cost for products. Scraping programs automatically retrieve competitors pricing and product information. The scraped data can be posted to an e-commerce site in real-time.

By automating this process, scraping must be tailored to provide the critical analysis and manual adjustments required. Automated solutions introduce a scientific technology combining big data and cloud technology to set price points for even the smallest manufacturers.

Analytic tools can help forecast optimum pricing in advance. Small manufacturers set the best price; this may not be necessarily the lowest price for each customer. Dynamic pricing can look to big data to provide information that affects purchasing strategies.

Beyond competitors prices, market trends affect sales including geographic location and supply chain issues.

Automating product costing is central to the critical decisions made daily by small and mid-sized manufacturers. Decisions about the products manufactured, the prices charged, and the tactics implemented to improve the processes, make or break the companys profitability.

With a cloud-based application designed to quickly calculate, analyze, share, and maintain detailed, accurate, and actionable product costs, users may be able to eliminate spreadsheets as the methodology for price comparisons.

About the Author

Mike Franz is the founder and creator of the WorkCenter from ManufacturingPower, a cloud-based market intelligence solution designed to help small to mid-sized companies streamline and achieve real-time visibility into Industrial Supply spend

Check out our free e-newslettersto read more great articles.

Originally posted here:

Automating Competitive Pricing for Small and Medium Manufacturers - Automation.com

If you want to know the future of networking, follow the money right to the edge.

Applications are expected to move from data centers to edge facilities in record numbers, opening up a huge new market opportunity. The edge computing market is expected to grow at a compound annual growth rate of 36.3 percent between now and 2022, fueled by rapid adoption of the internet of things, autonomous vehicles, high-speed trading, content streaming and multiplayer games.

What these applications have in common is a need for near zero-latency data transfer, usually defined as less than five milliseconds, although even that figure is far too high for many emerging technologies.

The specific factors driving the need for low latency vary. In IoT applications, sensors and other devices capture enormous quantities of data, the value of which degrades by the millisecond. Autonomous vehicles require information in real-time to navigate effectively and avoid collisions. The best way to support such latency-sensitive applications is to move applications and data as close as possible to the data ingestion point, therefore reducing the overall round-trip time. Financial transactions now occur at sub-millisecond cycle times, leading one brokerage firm to invest more than $100 million to overhaul its stock trading platform in a quest for faster and faster trades.

As edge computing grows, so do the operational challenges for telecommunications service provider such as Verizon Communications Inc., AT&T Corp. and T-Mobile USA Inc. For one thing, moving to the edge essentially disaggregates the traditional data center. Instead of massive numbers of servers located in a few centralized data centers, the provider edge infrastructure consists of thousands of small sites, most with just a handful of servers. All of those sites require support to ensure peak performance, which strains the resources of the typical information technology group to the breaking point and sometimes beyond.

Another complicating factor is network functions moving toward cloud-native applications deployed on virtualized, shared and elastic infrastructure, a trend that has been accelerating in recent years. In a virtualized environment, each physical server hosts dozens of virtual machines and/or containers that are constantly being created and destroyed at rates far faster than humans can effectively manage. Orchestration tools automatically manage the dynamic virtual environment in normal operation, but when it comes to troubleshooting, humans are still in the drivers seat.

And its a hot seat to be in. Poor performance and service disruptions hurt the service providers business, so the organization puts enormous pressure on the IT staff to resolve problems quickly and effectively. The information needed to identify root causes is usually there. In fact, navigating the sheer volume of telemetry data from hardware and software components is one of the challenges facing network operators today.

A data-rich, highly dynamic, dispersed infrastructure is the perfect environment for artificial intelligence, specifically machine learning. The great strength of machine learning is the ability to find meaningful patterns in massive amounts of data that far outstrip the capabilities of network operators. Machine learning-based tools can self-learn from experience, adapt to new information and perform humanlike analyses with superhuman speed and accuracy.

To realize the full power of machine learning, insights must be translated into action a significant challenge in the dynamic, disaggregated world of edge computing. Thats where automation comes in.

Using the information gained by machine learning and real-time monitoring, automated tools can provision, instantiate and configure physical and virtual network functions far faster and more accurately than a human operator. The combination of machine learning and automation saves considerable staff time, which can be redirected to more strategic initiatives that create additional operational efficiencies and speed release cycles, ultimately driving additional revenue.

Until recently, the software development process for a typical telco consisted of a lengthy sequence of discrete stages that moved from department to department and took months or even years to complete. Cloud-native development has largely made obsolete this so-called waterfall methodology in favor of a high-velocity, integrated approach based on leading-edge technologies such as microservices, containers, agile development, continuous integration/continuous deployment and DevOps. As a result, telecom providers roll out services at unheard-of velocities, often multiple releases per week.

The move to the edge poses challenges for scaling cloud-native applications. When the environment consists of a few centralized data centers, human operators can manually determine the optimum configuration needed to ensure the proper performance for the virtual network functions or VNFs that make up the application.

However, as the environment disaggregates into thousands of small sites, each with slightly different operational characteristics, machine learning is required. Unsupervised learning algorithms can run all the individual components through a pre-production cycle to evaluate how they will behave in a production site. Operations staff can use this approach to develop a high level of confidence that the VNF being tested is going to come up in the desired operational state at the edge.

AI and automation can also add significant value in troubleshooting within cloud-native environments. Take the case of a service provider running 10 instances of a voice call processing application as a cloud-native application at an edge location. A remote operator notices that one VNF is performing significantly below the other nine.

The first question is, Do we really have a problem? Some variation in performance between application instances is not unusual, so answering the question requires a determination of the normal range of VNF performance values in actual operation. A human operator could take readings of a large number of instances of the VNF over a specified time period and then calculate the acceptable key performance indicator values a time-consuming and error-prone process that must repeated frequently to account for software upgrades, component replacements, traffic pattern variations and other parameters that affect performance.

In contrast, AI can determine KPIs in a fraction of the time and adjust the KPI values as needed when parameters change, all with no outside intervention. Once AI determines the KPI values, automation takes over. An automated tool can continuously monitor performance, compare the actual value to the AI-determined KPI and identify underperforming VNFs.

That information can then be forwarded to the orchestrator for remedial action such as spinning up a new VNF or moving the VNF to a new physical server. The combination of AI and automation helps ensure compliance with service-level agreements and removes the need for human intervention a welcome change for operators weary of late-night troubleshooting sessions.

As service providers accelerate their adoption of edge-oriented architectures, IT groups must find new ways to optimize network operations, troubleshoot underperforming VNFs and ensure SLA compliance at scale. Artificial intelligence technologies such as machine learning, combined with automation, can help them do that.

In particular, there have been a number of advancements over the last few years to enable this AI-driven future. They include systems and devices to provide high-fidelity, high-frequency telemetry that can be analyzed, highly scalable message buses such as Kafka and Redis that can capture and process that telemetry, and compute capacity and AI frameworks such as TensorFlow and PyTorch to create models from the raw telemetry streams. Taken together, they can determine in real time if operations of production systems are in conformance with standards and find problems when there are disruptions in operations.

All that has the potential to streamline operations and give service providers a competitive edge at the edge.

Sumeet Singh is vice president of engineering at Juniper Networks Inc., which provides telcos AI and automation capabilities to streamline network operations and helps them use automation capabilities to take advantage of business potential at the edge. He wrote this piece for SiliconANGLE.

Show your support for our mission with our one-click subscription to our YouTube channel (below). The more subscribers we have, the more YouTube will suggest relevant enterprise and emerging technology content to you. Thanks!

Support our mission: >>>>>> SUBSCRIBE NOW >>>>>> to our YouTube channel.

Wed also like to tell you about our mission and how you can help us fulfill it. SiliconANGLE Media Inc.s business model is based on the intrinsic value of the content, not advertising. Unlike many online publications, we dont have a paywall or run banner advertising, because we want to keep our journalism open, without influence or the need to chase traffic.The journalism, reporting and commentary onSiliconANGLE along with live, unscripted video from our Silicon Valley studio and globe-trotting video teams attheCUBE take a lot of hard work, time and money. Keeping the quality high requires the support of sponsors who are aligned with our vision of ad-free journalism content.

If you like the reporting, video interviews and other ad-free content here,please take a moment to check out a sample of the video content supported by our sponsors,tweet your support, and keep coming back toSiliconANGLE.

See original here:

How machine learning and automation can modernize the network edge - SiliconANGLE