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PITTSBURGH, Sept. 13, 2022 /PRNewswire/ -- MSP360, a provider of simple and reliable backup and IT management solutions for managed services providers (MSPs) and IT departments worldwide, is now fully integrated with Deep Instinct, a prevention-first approach to stopping ransomware and other malware using the world's first deep learning cybersecurity framework. With a click of a button, MSP360 customers can access the Deep Instinct platform through either MSP360 Managed Backupor MSP360 RMM.

"Our goal has always been to provide best-in-class solutions for our customers," said MSP360 CEO Brian Helwig. "We've continued to do that successfully by listening to them and adjusting our efforts accordingly. Rightfully so, our customers have continued to express their concerns of being able to fully protect their customers from the ever-growing cyber threat landscape. Our partnership with Deep Instinct addresses many of their fears by not only preventing but also predicting many of the threats they're facing today."

While MSP360 already provides several types of solutions to assist MSPs with combating cybercriminals, including backup, remote monitoring and management (RMM), and remote connect, a layered approach to cybersecurity is needed to fully protect MSPs and their customers from today's evolving cybersecurity threats, many of which include ransomware as a service (RaaS), compromised or weak credentials, brute force, phishing, distributed denial of service (DDoS), malicious insiders, misconfiguration, and more.

MSP360's integration with Deep Instinct enables MSP360 customers to prevent unknown attacks with greater accuracy than many endpoint detection and response (EDR), extended detection and response (XDR), and antivirus (AV) solutions in the market today by using deep learning, the most advanced form of artificial intelligence (AI). With deep learning, the computer learns just like the human brain does. By ingesting data and working autonomously, Deep Instinct's deep learning framework teaches itself to predict, detect, and prevent threats, unlike many basic machine learning (ML)-based tools.

"We are thrilled to partner with the world's leading backup and RMM solution," said Joe Santamorena, AVP of Global MSSP Programs for Deep Instinct. "MSPs are the number one targeted vertical industry for ransomware and combining Deep Instinct with MSP360's robust backup architecture will deliver the highest efficacy for preventing a ransomware attack."

About MSP360

Established in 2011 by a group of IT professionals, MSP360 provides simple and reliable cutting-edge backup and IT management solutions for MSPs and IT departments worldwide. The MSP360 platform combines the number one easy-to-use backup solution to deliver best-in-class data protection, secure remote access software to provide support to customers or team members, and painless RMM to handle all aspects of IT infrastructure.

About Deep Instinct

Deep Instinct takes a prevention-first approach to stopping ransomware and other malware using the world's first and only purpose-built, deep learning cybersecurity framework. We predict and prevent known, unknown, and zero-day threats in <20 milliseconds, 750X faster than the fastest ransomware can encrypt. Deep Instinct has >99% zero-day accuracy and promises a <0.1% false positive rate. The Deep Instinct Prevention Platform is an essential addition to every security stackproviding complete, multi-layered protection against threats across hybrid environments. For more, visit http://www.deepinstinct.com.

Media Contact:Christopher Joseph (CJ) ArlottaCJ Media Solutions, LLC for MSP360C: 631-572-3019[emailprotected]

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MSP360 Partners with Deep Instinct to Fully Integrate the World's First Deep Learning Cybersecurity Framework Solution - PR Newswire

LOS ALTOS, Calif.--(BUSINESS WIRE)--Kinara, the developers of AI processors for edge computing applications, today announced its collaboration with NXP Semiconductors, the world leader in secure connectivity solutions for embedded applications. Through this collaboration, customers of NXP Semiconductors AI-enabled product portfolio will have the option to further scale their AI acceleration needs by utilizing the Kinara Ara-1 Edge AI processor for high performance inferencing with deep learning models. Working together, the two companies have tightly integrated the computer vision capabilities of the NXP i.MX applications processors with the performance- and power-optimized inferencing of the Kinara Ara-1 AI processor to deliver computer vision analytics for a range of applications that include smart retail, smart city, and industrial.

Kinaras patented Edge AI processor, named Ara-1, delivers a ground-breaking combination of performance, power, and price for integrated cameras and edge servers. Kinara AI complements its processing technology with a comprehensive and robust set of development tools that allow its customers to easily convert their neural network models into highly optimized computation flows ready to be deployed on the Ara-1 chip.

"Intelligent vision processing is an exploding market that is a natural fit for machine learning. But vision systems are getting increasingly complex, with more and larger sensors, and model sizes are growing. To keep pace with these trends requires dedicated AI accelerators that can handle the processing load efficiently both in power and silicon area, said Kevin Krewell, principal analyst at TIRIAS Research. The best modular approach to vision systems is a combination of an established embedded processor and a power-efficient AI accelerator, like the combination of NXPs i.MX family of embedded applications processors and the Kinara AI accelerator."

NXPs AI processing solutions encompass its microcontrollers (MCUs), i.MX RT series of crossover MCUs and i.MX applications processor families, which represent a variety of multicore solutions for multimedia and display applications. NXPs portfolio covers a very large portion of AI processing needs natively, and for any use case that requires even higher performance AI due to increases in frame rates, image resolution, and number of sensors, the demand can be accommodated by integrating NXP processors with Kinaras Ara-1 to deliver a scalable, system-level solution where customers can scale up and partition the AI workload between the NXP device and the Ara-1, while maintaining a common application software running on the NXP processors.

Our processing solutions and AI software stacks enable a very wide range of AI performance requirements this is a necessity given our extremely broad customer base, said Joe Yu, Vice President and General Manager, IoT Edge Processing, NXP Semiconductors. By working with Kinara to help satisfy our customers requirements at the highest end of edge AI processing, we will bring high performance AI to smart retail, smart city, and industrial markets.

We see two general trends with our Edge AI customers. One trend is a shift towards a Kinara solution that significantly reduces the cost and energy of their current platforms that use a traditional GPU for AI acceleration. The other trend calls for replacing Edge AI accelerators from well-known brands with Kinaras Ara-1 allowing the customer to achieve at least a 4x performance improvement at the same or better price, said Ravi Annavajjhala, CEO, Kinara. Our collaboration with NXP will allow us to offer very compelling system-level solutions that include commercial-grade Linux and driver support that complements the end-to-end inference pipeline.

Access a new White Paper outlining how the Kinara and NXP collaboration can help boost the AI performance of embedded platforms here.

About Kinara

Kinara is deeply committed to designing and building the worlds most power- and price-efficient edge AI inference platform supported by comprehensive AI software development tools. Designed to enable smart applications across retail, medical, industry 4.0, automotive, smart cities, and much more; Kinaras AI processors, modules and software can be found at the heart of the AI industrys most exciting and influential innovations. Led by Silicon Valley veterans and a world class development team in India, Kinara envisions a world of exceptional customer experiences, better manufacturing efficiency and greater safety for all. Kinara is a member of the NXP Partner Program. Learn more at http://www.kinara.ai

All registered trademarks and other trademarks belong to their respective owners.

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Kinara and NXP Collaborate to Provide Customers with Scalable AI Solutions Optimized for Deep Learning at the Edge - Business Wire

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Todays demand for real-time data analytics at the edge marks the dawn of a new era in machine learning (ML): edge intelligence. That need for time-sensitive data is, in turn, fueling a massive AI chip market, as companies look to provide ML models at the edge that have less latency and more power efficiency.

Conventional edge ML platforms consume a lot of power, limiting the operational efficiency of smart devices, which live on the edge. Thosedevices are also hardware-centric, limiting their computational capability and making them incapable of handling varying AI workloads. They leverage power-inefficient GPU- or CPU-based architectures and are also not optimized for embedded edge applications that have latency requirements.

Even though industry behemoths like Nvidia and Qualcomm offer a wide range of solutions, they mostly use a combination of GPU- or data center-based architectures and scale them to the embedded edge as opposed to creating a purpose-built solution from scratch. Also, most of these solutions are set up for larger customers, making them extremely expensive for smaller companies.

In essence, the $1 trillion global embedded-edge market is reliant on legacy technology that limits the pace of innovation.

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ML company Sima AI seeks to address these shortcomings with its machine learning-system-on-chip (MLSoC) platform that enables ML deployment and scaling at the edge. The California-based company, founded in 2018, announced today that it has begun shipping the MLSoC platform for customers, with an initial focus of helping solve computer vision challenges in smart vision, robotics, Industry 4.0, drones, autonomous vehicles, healthcare and the government sector.

The platform uses a software-hardware codesign approach that emphasizes software capabilities to create edge-ML solutions that consume minimal power and can handle varying ML workloads.

Built on 16nm technology, the MLSoCs processing system consists of computer vision processors for image pre- and post-processing, coupled with dedicated ML acceleration and high-performance application processors. Surrounding the real-time intelligent video processing are memory interfaces, communication interfaces, and system management all connected via a network-on-chip (NoC). The MLSoC features low operating power and high ML processing capacity, making it ideal as a standalone edge-based system controller, or to add an ML-offload accelerator for processors, ASICs and other devices.

The software-first approach includes carefully-defined intermediate representations (including the TVM Relay IR), along with novel compiler-optimization techniques. This software architecture enables Sima AI to support a wide range of frameworks (e.g., TensorFlow, PyTorch, ONNX, etc.) and compile over 120+ networks.

Many ML startups are focused on building only pure ML accelerators and not an SoC that has a computer-vision processor, applications processors, CODECs, and external memory interfaces that enable the MLSoC to be used as a stand-alone solution not needing to connect to a host processor. Other solutions usually lack network flexibility, performance per watt, and push-button efficiency all of which are required to make ML effortless for the embedded edge.

Sima AIs MLSoC platform differs from other existing solutions as it solves all these areas at the same time with its software-first approach.

The MLSoC platform is flexible enough to address any computer vision application, using any framework, model, network, and sensor with any resolution. Our ML compiler leverages the open-source Tensor Virtual Machine (TVM) framework as the front-end, and thus supports the industrys widest range of ML models and ML frameworks for computer vision, Krishna Rangasayee, CEO and founder of Sima AI, told VentureBeat in an email interview.

From a performance point of view, Sima AIs MLSoC platform claims to deliver 10x better performance in key figures of merit such as FPS/W and latency than alternatives.

The companys hardware architecture optimizes data movement and maximizes hardware performance by precisely scheduling all computation and data movement ahead of time, including internal and external memory to minimize wait times.

Sima AI offers APIs to generate highly optimized MLSoC code blocks that are automatically scheduled on the heterogeneous compute subsystems. The company has created a suite of specialized and generalized optimization and scheduling algorithms for the back-end compiler that automatically convert the ML network into highly optimized assembly codes that run on the machine learning-accelerator (MLA) block.

For Rangasayee, the next phase of Sima AIs growth is focused on revenue and scaling their engineering and business teams globally. As things stand, Sima AI has raised $150 million in funding from top-tier VCs such as Fidelity and Dell Technologies Capital. With the goal of transforming the embedded-edge market, the company has also announced partnerships with key industry players like TSMC, Synopsys, Arm, Allegro, GUC and Arteris.

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Machine learning at the edge: The AI chip company challenging Nvidia and Qualcomm - VentureBeat

A New Zealand startup producing its own servers is expanding into the realm of artificial intelligence, creating machine learning solutions that carry out common tasks while relieving people of repetitive, unsatisfying work. Having spotted an opportunity for the development of low-cost, high-efficiency and environmentally sustainable hardware, Kauricone has more recently pivoted in a fascinating direction: creating software that thinks about mundane problems, so we dont have to. These tasks include identifying trash for improved recycling, looking at items on roads for automated safety, pest identification and in the ultimate alleviation of a notoriously sleep-inducing task counting sheep.

Managing director, founder and tech industry veteran Mike Milne says Kauricone products include application servers, cluster servers and internet of things servers. It was in this latter category that the notion emerged of applying machine learning at the networks edge.

Having already developed low-cost-low power edge hardware, we realised there was a big opportunity for the application of smart computing in some decidedly not-so-enjoyable everyday tasks, relates Milne. After all, we had all the basic building blocks already: the hardware, the programming capability, and with good mobile network coverage, the connectivity.

Situation

Work is just another name for tasks people would rather not do themselves, or that we cannot do for ourselves. And despite living in a fabulously advanced age, there is a persistent reality of all manner of tasks which must be done every day, but which dont require a particularly high level of engagement or even intelligence.

It is these tasks for which machine learning (ML) is quite often a highly promising solution. ML collects and analyses data by applying statistical analysis, and pattern matching, to learn from past experiences. Using the trained data, it provides reliable results, and people can stop doing the boring work, says Milne.

There is in fact more to it than meets the eye (so to speak) when it comes to computer image recognition. Thats why Capcha challenges are often little more than Identify all the images containing traffic lights: because distinguishing objects is hard for bots. ML overcomes the challenge through the training mentioned by Milne: the computer is shown thousands of images and learns which are hits, and which are misses.

Potentially, there are as many use cases as you have dull but necessary tasks in the world, Milne notes. So far, weve tackled a few. Rocks on roads are dangerous, but monitoring thousands of kilometers of tarmac comes at a cost. Construction waste is extensive, bad for the environment and should be managed better. Sheep are plentiful and not always in the right paddock. And pests put New Zealands biodiversity at risk.

Solution

Tackling each of these problems, Kauricone started with its own-developed RISC IoT server hardware as the base. Running Ubuntu and programmed with Python or other open-source languages, the servers typically feature 4GB memory and 128GB solid state storage, the solar-powered edge devices consume as little as 3 watts and run indefinitely on a single solar panel. This makes for a reliable, low-cost field-ready device, says Milne.

The Rocks on Roads project made clear the challenges of simple image identification, with Kauricone eventually running a training model around the clock for 8 days, gathering 35,000 iterations of rock images, which expanded to 3,000,000 identifiable traits (bear in mind, a human identifies a rock almost instantly, perhaps faster if hurled). With this training, the machine became very good at detecting rocks on the roads.

For a new project involving construction waste, the Kauricone IoT server will maintain a vigilant watch on the types and amounts of waste going into building-site skips. Trained to identify types of waste, the resulting data will be the basis for improving waste management and recycling or redirecting certain items for more responsible disposal.

Counting sheep isnt only a method for accelerating sleep time, its also an essential task for farmers across New Zealand. Thats not all as an ML exercise, it anticipates the potential for smarter stock management, as does the related pest identification test case pursued by Kauricone. The ever-watchful camera and supporting hardware manage several tasks: identifying individual animals, numbering them, and also monitoring grass levels, essential for ovine nourishment. Tested so far on a small flock, this application is ready for scale.

Results

Milne says the small test cases pursued by Kauricone to date are just the beginning and anticipates considerable potential for ML applications across all walks of life. There is literally no end to the number of daily tasks where computer vision and ML can alleviate our workload and contribute to improved efficiency and, ultimately, a better and more sustainable planet, he notes.

The Rocks on Roads project promises improved safety with a lower human overhead, reducing or eliminating the possibility of human error. Waste management is a multifaceted problem, where the employment of personnel is rendered difficult owing to simple economics (and potentially stultifying work); New Zealands primary sector is ripe for technologically powered performance improvements which could boost already impressive productivity through automation and improved control; and pest management can help the Department of Conservation and allied parties achieve better results using fewer resources.

Its early days yet, says Milne, But the results from these exploratory projects are promising. With the connectivity of ever-expanding cellular and low-power networks like SIGFOX and LoraWan, the enabling infrastructure is increasingly available even in remote places. And purpose-built low power hardware brings computing right to the edge. Now, its just a matter of identifying opportunities and creating the applications.

For more information visit Kauricone's website.

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Kauricone: Machine learning tackles the mundane, making our lives easier - IT Brief New Zealand

They help to improve efficiency and profitability for utilities.

The utilities space is rapidly transforming today. Its shifting from the conventional and a highly-regulated environment to a tech-driven market at a fast clip. Collating data and optimizing manpower is a constant struggle. The smarter optimization of infrastructure has increased monumentally with the outbreak of the pandemic, and also the dependency on technology. There is an urgent need to balance the supply and demand for which Artificial Intelligence (AI) and Machine Learning (ML) can come into play. Data Science, aided by AI and ML, has been leading to several positive developments in the utilities space. Digitalization can increase the profitability of utilities by significant percentages by utilizing smart meters for grids, digital productivity tools and automating back-office processes. According to a study firms can increase their profitability from 20 percent to 30 percent.

Digital measures rewire organizations to do better through a fundamental reboot of how work gets done.

Customer Service and AI

According to a Gartner report, most AI investments by utilities most often go into customer service solutions. Some 86% of the utilities studied used AI in their digital marketing, towards call center support and customer application. This is testimony to the investments in AI and ML that can deliver a high ROI by improving speed and efficiency, thus enhancing customer experience. The AI thats customer-facing is a low-risk investment as customer enquiries are often repetitive such as billing enquiries, payments, new connections etc. AI can deliver tangible results for business on the customer service front.

Automatic Meters for Energy conservation

As manual entry and billing systems are not only time-consuming, but also susceptible to errors and are expensive too. The Automatic Meter Reading (AMR) System has made a breakthrough. The AMR enables large infrastructure set ups to collect data easily and also analyze the cost centers and the opportunities for improving the efficiencies of natural gas, electric, water sectors and more. It offers real-time billing information for budgeting. It has the advantage of being precise compared to manual entry. Additionally, it is able to store data at distribution points within the networks of the utility. This can be easily accessed over a network using devices like the mobile and handhelds. Energy consumption can be tracked to aid conservation and end energy theft.

Predictive Analytics Enable Smart grid options

By leveraging new-age technologies, utilities can benefit immensely. These technologies in the energy sector help in building smart power grids. The energy sector heavily relies on a complex infrastructure that can face multiple issues as a result of maintenance issues, weather conditions, failure of the system or equipment, demand surges and misallocation of resources. Overloading and congestion leads to a lot of energy being wasted. The grids produce a humongous data which help with risk mitigation when properly utilized. With the large volume of data that continuously pass over the grid, it can be challenging to collect and aggregate it. The operators could miss these insights which could lead to malfunction or outages. With the help of the ML algorithms, the insights can be obtained for smooth functioning of the grids. Automated data management can help maintain the data accurately. With the help of predictive analytics, the operators can predict grid failures before the customers are affected and also create greater customer satisfaction and mitigate any financial loss.

Efficient and Sustainable energy consumption

These allow for better allocation of energy for consumption as it would be based on demand and can save resources and help in load management and forecasting. AI can also deal with issues pertaining to vegetation by analyzing operational data or statistics. This can help to proactively deal with wildfires. Thus, it can become a sustainable and efficient system. To overcome issues pertaining to weather-related maintenance, automation helps receive signals and prioritize the areas that need attention to save money and cut down the downtime. To achieve this, the sector adopts ML capabilities as they need to be able to access automation fast and easily.

The construction sector is also a major beneficiary of the solutions. Building codes and architecture are often a humongous challenges that take a long time to meet. But, some solutions help the builders and developers test these applications seamlessly without any system interruptions. By integrating AI and ML in the data management platforms, the developers enable the data-science teams to spend enough time innovating and much less time on maintenance. With the rise in the computational power and accessibility to the Cloud, the deep learning algorithms are able to train faster while their cost is optimized. AI and ML are able to impact different aspects of business. AI can enhance the quality of human jobs by facilitating remote working. They can help in data collection and analysis and also provide actionable inputs. Data analytics platforms can throw light on the areas of inefficiency and help the providers keep costs down.

Though digital transformation might appear intimidating, its opportunities are much more than the cost and risk associated. Gradually, all utilities will undergo digital transformation as it has begun to take roots in the industrial sectors. This AI-led transformation will improve productivity, revenue gains, make networks more reliable and safe, accelerate customer acquisition, and facilitate entry into new areas of business. Globally, the digital utility market is growing at a CAGR of 11.7% for the period of 2019 to 2027. In 2018, the revenue generated globally for the digital utility market was 141.41 Bn and is expected to reach US$ 381.38 Bn by 2027 according to a study by ResearchAndMarkets.com. As the sector evolves, the advantages of AI and ML will come into play and lead to smarter grids, efficient operations and higher customer satisfaction. The companies that are in a position to take advantage of this opportunity will be ready for the future challenges that could emerge in the market.

Views expressed above are the author's own.

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Artificial intelligence and machine learning now integral to smart power solutions - Times of India