Cloud Hosting

As AI, big data, and the internet of things (IoT) find their way into every aspect of our lives, many industries are undergoing a transformation. Insurance executives believe that artificial intelligence (AI) will significantly transform their industry in the next three years, with insurers investing in AI to empower agents, brokers and employees to enhance the customer experience with automated personalized services, faster claims handling and individual risk-based underwriting processes, consulting firm Accenture forecasted in 2017.

Three years later, AI algorithms have made great inroads in different sectors of the insurance industry and are lowering costs while driving efficiency and improving the customer experience. Slowly but surely, the industry is transforming. There are some kinks that need to be ironed out, but for the most part, the changes have been for the better, and theres more down the road.

Here are three areas worth watching.

Thanks to advances in edge hardware, cloud technology, and the internet of things, more and more information about objects, people, and organizations are being digitized. Telematics, wearables, and smart home sensors are just some of the technologies that are enabling us to collect detailed information about the physical world. And ubiquitous connectivity enables us to aggregate that data in cloud servers for further processing by machine learning algorithms.

In its 2018 Emerging Risk Report, Lloyds outlined some of the benefits that the growth of IoT will bring to the insurance industry, including better risk understanding, avoiding preventable losses, capturing patterns and behaviors and enabling proactive monitoring. Today, many insurers are embracing these trends to improve the speed and efficiency of their services. For one thing, having more data enables insurers to provide personalized and tailored premiums to individual customers.

One example is U.S.-based Layr, a cloud-based commercial insurance platform for small businesses, which came out of Lloyds insurance accelerator program, Lloyds Lab, and received funding in May to develop its AI-based solution. Layr uses machine learning to peruse customer data and compare applicants to clusters of similar businesses. This enables the companys prediction engine to automatically match clients with the right policies.

Being able to collect rich, real-time data from the physical world through IoT sensors is also leaving its mark in the insurance industry. An example is Parsyl, an IoT startup that helps shippers, retailers, and insurers understand the quality conditions of sensitive and perishable products as they move through the supply chain.

Parsyl, another Lloyds Lab graduate, is incorporating its sensor technology in quality assurance and risk management solutions for clients that handle products that require specialist transport and storage. Such products included temperature-controlled foods, biological pharmaceuticals, and sensitive life science and high-tech products. Installing the sensors provides insurers with accurate data and insights about the cargo while giving the customers the benefit of expedited settlement, reduced claims cost, and machine learningbased risk mitigation.

One of the interesting trends in the field is the synergies between insurance and insurtech companies with specialized tech companies to enhance risk prediction and management. One interesting example is the partnership between weather forecasting company Climacell and Munich Re Syndicate, one of the leading marine and specialty underwriters at Lloyds. Climacell, which was part of Lloyds Labs third cohort, uses sensor data from various devices and machine learning algorithms to make precise weather forecasts. Munich Re Syndicate will use Climacells technology to help its clients better understand how the weather will affect their business and make informed decisions that enhance operational efficiency, safety and profitability.

In the car insurance sector, insurers use telematics to collect real-time driving data from vehicles. As opposed to the past, where they had to rely on basic information about the vehicle and driver to craft their insurance policies, they can now analyze telematics data with machine learning algorithms to create personalized risk profiles for drivers. Many insurers use this data to give discounts to drivers who have safe driving habits and penalize dangerous behavior such as speeding, hard braking, harsh acceleration, and hard cornering. The same data can help reconstruct accident scenes and enable insurers to better understand and assess what happened, which results in much faster claims processing.

In the health insurance sector, service providers use machine learning to help patients choose the best health insurance coverage options to fit their needs. Data collected from wearables such as fitness trackers and heart rate monitors help insurers monitor track and reward healthy habits such as regular exercise, and encourage preventive care by providing healthy nutrition tips.

An example is Insurtech startup Collective Health, which uses machine learning to identify risk and match its members with the right resources for their healthcare. The companys AI model brings together claims data, prior authorizations, eligibility data, engagement data, and healthcare utilization data to develop a holistic profile of each member and their needs. The rich, AI-powered profile helps identify members health needs, such as those who need help from a pharmacist with confusing medications or need assistance from a nurse to help arrange home healthcare services.

AI reduces the task of manually reviewing thousands of medical claims, and instead focuses our staff on performing warm, human outreach, and thinking through complex problems together with our members, says Dr. Sanjay Basu, Collective Healths Director of Research and Analytics.

One of the big advantages of the availability of data and machine learning algorithms is fraud prevention. Machine learning algorithms trained on the huge amount of data available on customers can glean patterns that separate legitimate claims from fraudulent ones. Today, most insurers use machine learning to detect and prevent fraud.

And with IoT and sensor technology continuing to expand at an accelerating pace, and with the propagation of 5G networks, we will continue to have even bigger datasets and accurate data to better understand insurance risks.

I think that is one of the most exciting developments to come in the next decade is that we will have this incredible depth of knowledge about the world around us, says Trevor Maynard, Head of Innovation at Lloyds.

Computers have historically struggled to deal with data that is not neatly arranged in tables with rows and columns. But unfortunately, most of our data is unstructured and lies the documents, chat logs, emails, and the textual data we generate in our day-to-day interactions. Natural language processing, the science of helping computers understand and draw value from unstructured text, is a hot area of research and has seen tremendous progress in recent years.

The insurance sector, which is laden with textual data, has benefitted immensely from advances in NLP. Insurers have been able to leverage language models to reduce the time it takes to respond to customer queries and find relevant information from the tons of documents they must review in claims settlement.

An example is Lloyds International Trading Advice (LITA), a consultancy within Lloyds that gives insurance companies regulatory information about the countries in which it operates. LITA covers more than 200 geographies, and the regulatory rules of each area is registered in lots unstructured documents.

Previously, LITAs experts had to manually go through these documents to answer questions about regulations and compliance, which usually took several days per query. To optimize the process, the LITA team used the ton of data they had gathered through their interactions with their customers to train a question-answering AI model. They were able to develop a system that automated a large part of the consultation process and improved the service-level agreement from five days to less than an hour.

The AI augments the role of the employees, says Craig Civil, Head of Data Innovation, R&D and Analytics at Lloyds. It makes their job far more satisfying because we automate 80% of the work, and the 20% are truly interesting one-off questions that you do need an experienced team that can do the research and answer.

Advances in NLP have also ushered in a breed of customer service chatbots in different sectors, including the insurance industry. Established insurance firms such as Geico as well as insurtech startups such as Lemonade are using AI-powered chatbots to settle claims. These chatbots handle the low-level customer queries and free agents to handle more complicated tasks.

AI researchers continue to develop larger and more complicated models that can tackle more complicated language-related tasks. In the past year, weve seen the release of state-of-the-art language models such as OpenAIs GPT-2 and Googles Meena. While were still pretty far from developing AI that can truly understand human language, practical uses will emerge from continued advances in natural language processing. AI will do the legwork, gathering import data and highlighting trends in text data, making it easier and less costly for insurers to piece that information together and address their clients needs.

Computer vision is the science of enabling machines to extract meaning and context from visual data. In the past few years, computer vision has been advancing by leaps and bounds thanks to convolutional neural networks, AI models that can perform image recognition and classification tasks with stunning accuracy.

Insurers now use image recognition algorithms to automate many of the tasks that previously required human labor. Insurance firm Liberty Mutual uses AI to provide fast damage assessment of vehicle damage and claims settlement. Users take a picture of the damaged car with their smartphone and submit it to the AI Auto Damage Estimator, which uses a machine learning algorithm trained on thousands of car accident photos to assess damage and costs. The process doesnt take more than a few seconds.

Computer vision is also enabling insurers to perform tasks that were previously impossible. An example is State Farms Drive Safe & Save platform, which uses AI to analyze in-car camera feeds and detect and provide feedback on unsafe behavior such as distracted driving and texting.

One of the interesting trends to watch in computer vision is advances in edge computing and edge AI. Computer vision tasks previously required applications to send their data to cloud servers, where AI algorithms leveraged massive compute resources to process and analyze the data. But in recent years, specialized hardware and more efficient machine algorithms are gradually enabling on-device AI inference. The improved speed and efficiency are paving the way for real-time analysis of visual data and risk assessment.

Despite its many exciting applications, AI-based insurance is still in its early stages and the best is yet to come. As technology continues to permeate our lives, AI algorithms will be able to provide faster and more accurate solutions, making the insurance industry much more pleasing and less frustrating for both clients and agents.

Published February 24, 2020 13:40 UTC

See the rest here:
3 ways AI is transforming the insurance industry - The Next Web

Press Release

Nokia introduces cloud-native Assurance and Experience software to help CSPs move toward experience-driven and automated 5G network operations

24 February 2020

Espoo, Finland Nokia today introduced two new cloud-native software applications that help communication service provider (CSPs) operate their networks more efficiently and effectively, and drive new revenue opportunities.

Faced with growing competition and the roll-out of complex, virtualized 5G networks that can generate up to a 100-fold increase in network actions, CSPs require automated, service-centric operations that prioritize the customer experience.

The Nokia Assurance Center effectively blends the traditionally separate fault and performance management processes to drive intelligent root cause analysis, and triggers prioritized and automated resolution.

In addition to the growth in network complexity, operators will increasingly be expected to provide commercial terms tied to Service Level Agreements (SLAs) as they expand into vertical industries. The Nokia Experience Center incorporates the subscriber dimension, quantifies the customer experience of the services being delivered, and links these values into the SLAs.

Nokia Assurance Center and Nokia Experience Center complement each other, yet operate independently in order to support the modular approach that many CSPs take to building their networks. The Nokia Assurance Center has a focus from the service layer down to the network layer, while the Nokia Experience Center has a focus from the customer and subscriber experience perspective.

The two products are built on Nokias Common Software Foundation (CSF), which ensures that Nokias cloud-native products allow customers to use their choice of deployment strategies. With security built-in from the beginning, CSF runs on all the leading public and private cloud platforms and servers. With such flexibility, CSF makes Nokias products easier to deploy, integrate, operate and upgrade.

Francis Haysom, Partner and principal analyst, Appledore Research, said:Nokia is taking a leading position in practically combining AI with, what Appledore term, Rapid Automated Service Assurance; enabling the automation of network operations. Nokia Experience Center and Assurance Center will support the move to a network driven by customer intent (experience and SLAs) rather than a specific network technology.

Brian McCann, Chief Product Officer, Nokia Software, said: Nokia Assurance Center and Nokia Experience Center reflect the long Nokia pedigree of being an industry leader in promoting service operations. These products bring a new level of service automation and data- and customer-centricity for operators that make many of their servicing capabilities faster, smarter, more cost effective and more relevant in order to better serve their customers.

Additional ResourcesNokia Assurance Center webpageNokia Experience Center webpage5G Operations webpage

About NokiaWe create the technology to connect the world. We develop and deliver the industrys only end-to-end portfolio of network equipment, software, services and licensing that is available globally. Our customers include communications service providers whose combined networks support 6.1 billion subscriptions, as well as enterprises in the private and public sector that use our network portfolio to increase productivity and enrich lives.

Through our research teams, including the world-renowned Nokia Bell Labs, we are leading the world to adopt end-to-end 5G networks that are faster, more secure and capable of revolutionizing lives, economies and societies. Nokia adheres to the highest ethical business standards as we create technology with social purpose, quality and integrity.www.nokia.com

Media InquiriesCommunicationsPhone:+358 (0) 10 448 4900E-mail:press.services@nokia.com

See the article here:
Nokia introduces cloud-native Assurance and Experience software to help CSPs move toward experience-driven and automated 5G network operations -...

My calls on Micron (NASDAQ:MU) have been ill-timed. That's in part due to my lack of skill as a market timer, and, in part, due to the swinging nature of the stock and its underlying business. The catalyst for this stock has been changing expectations on future memory prices and suffice to say that it has not been a constant variable. However, I see Micron as the archetype of an overlooked growth stock.

Source: Intel 10-K

Time and again, investors have been burned by the wild swings in the company's stock price. Additionally, Micron's share price has struggled to pass the 60 USD level. Therefore, investors have shrugged off this stock on the account of cyclicality and expectations of weakening memory prices.

As an aside, Intel (INTC) comes as the perfect example of a company that was constantly criticized, back in 2015, for being too dependent on the personal computer industry, and for having lost the mobile phone train, but the reality is that in the process it nearly doubled its market capitalization.

Intel Stock Price 5Y

Source: Seeking Alpha

Micron is growing at a fast clip; however, market analysts and pundits tend to dismiss it at the first opportunity. But, that per se, is not new, nor exclusive to Micron. What makes it interesting is the fact that Micron is right in the middle of the data megatrend. And, if data trend jargon seems too holistic for you, just think that what is really driving data are the applications to AI, machine learning, IoT, among others, that are using data to reinvent old industries. Micron provides exactly what data needs: memory, more specifically, fast and reliable memory.

Micron's most consolidated markets in the computer and networking segments include clients among individuals, enterprises, organizations operating with high-performance graphics, and cloud servers. In the mobile segment, the company provides several memory and performance solutions for smartphones.

However, the best part lies in new solutions in development for upcoming markets. These include:

Most of these applications are still in their infancy with a huge way to go before being profitable, while others are maturing as we speak. Now, we shouldn't be too optimistic, there's lots of competition, and Micron will just grab a fraction of the total addressable markets. However, that's exciting enough.

If there is intense completion from the likes of Toshiba (OTCPK:TOSYY), Western Digital Corporation (WDC), Samsung (OTC:SSNNF), Seagate (STX), and Intel (INTC), among others, the company needs to have an edge to succeed. That edge has to come from two fronts: technology development and manufacturing efficiency. Let's focus on the latter.

The company has a worldwide manufacturing base. The production of semiconductors depends on maintaining a highly controlled production space that minimizes contamination. The manufacturing costs depend, among others, on the speed of the transition to higher density products and the sophistication of the manufacturing equipment and processes.

For investors, more than dense technicalities, what matters is the profitability and the sustainability of the operations. In that regard, Micron has maintained incredible gross, operating, and profit margins. All that despite periodical price drops in memory prices, as we have seen previously. These levels of profitability are only possible due to the increasing manufacturing efficiency and productivity, both offsetting the drops in price.

(Source: Author computations based on Micron 10-K)

Comparing companies based on these ratios is never straightforward, mainly because of differences in accounting practices. However, we can apply a forensics approach to financial statements and derive some clues. To smooth cyclicality, let us use three-year averages to compute gross margins.

3-year average gross margins for Micron, Intel, Western Digital and Seagate

(Source: Micron 10-K, Seeking Alpha's Financials data, Author's computations)

More than comparing apples to oranges, the interesting part is comparing the trendline for each company. Micron has come a long way, from being a small player with razor-thin margins, to having great margins. Now, let us see how that translated into the bottom line.

3-year average profit margins for Micron, Intel, Western Digital and Seagate

(Source: Micron 10-K, Seeking Alpha's Financials data, Author's computations)

Here we can see that the improvements in Micron's margins weren't just an accounting gimmick. It materialized all the way down into the bottom line. This is what it looks like when technology improvements meet manufacturing excellence. The company has grown in size and got more efficient at the same time.

(Source: Micron 10-K, Seeking Alpha's Financials data, Author's computations)

That's the hardest question, and usually, financials do not provide much insight on this regard. We can form a thesis that if the company was able to gather a pool of talented people and resources that allowed it to get to this point, then, as long as they keep investing, the company will carry the momentum forward.

Source: Micron's 10-K

Don't read too much into these figures. They are just indicative that the company is keeping its investment levels. However, if we add the fact that Micron is navigating a mega-trend towards more data storage and processing, we feel inclined to believe that the company will carry the momentum forward.

The company is in great shape, with a lean cost structure, as evidenced in its margins, and with a strong balance sheet.

Source: Micron's 10-K, Author's computations

Micron is in great shape to contribute with memory and storage products to the next wave of artificial intelligence and machine learning technology development. In addition to the current line-up of products and services, the company has the balance sheet to go after strategic acquisitions, as they did with Inotera in 2016.

All-in-all, Micron has great momentum in its operations, it has achieved an impressive scale, and it is right at the center of some of the most promising industries for the next decade: artificial intelligence and machine learning. Trading at around 12 times its 5-year average EPS (5-year average EPS: $4.73), the company seems very attractive for a buy-and-hold investor.

Disclosure: I am/we are long MU. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Additional disclosure: This text expresses the views of the author as of the date indicated and such views are subject to change without notice. The author has no duty or obligation to update the information contained herein. Further, wherever there is the potential for profit there is also the possibility of loss. Additionally, the present article is being made available for educational purposes only and should not be used for any other purpose. The information contained herein does not constitute and should not be construed as an offering of advisory services or an offer to sell or solicitation to buy any securities or related financial instruments in any jurisdiction. Some information and data contained herein concerning economic trends and performance is based on or derived from information provided by independent third-party sources. The author trusts that the sources from which such information has been obtained are reliable; however, it cannot guarantee the accuracy of such information and has not independently verified the accuracy or completeness of such information or the assumptions on which such information is based.

More here:
Ride The Tiger: Micron Is Positioned To Become A Powerhouse - Seeking Alpha

Given the explosion of Internet of Things (IoT) devices combined with increased use of artificial intelligence (AI) and machine learning (ML), infinitely more content (data) is now created, stored, and processed at the Edge. Edge servers provide control of massive amounts of locally produced data and while rapid access to that data can be critical to small and medium-sized businesses, until recently, entry-level (affordable) servers did not provide the power they required.

Now however, entry level 1U servers with a very small footprint deliver a high-level of computing power, ease-of-use, scaling, and low power consumption. Housed in a very small flat box, they include a core processor, storage, memory slots, ports and interfaces, and multiple connectivity options to handle on-site processing, storage, and IoT device management.

Creating storage at the Edge, however, has not been without its own challenges. Entry-level storage that combines sufficient performance, small footprint, and good support at a reasonable cost has taken time.

Edge machine learning, data centers, high performance computing (HPC), and AI workloads involve large data sets on large distributed compute clusters. The recently announced TYAN Thunder CX GX38-B5550 1U compact sever provides several advantages in this space, including a shorter depth when compared to competitive solutions. In most server chassis designs and the servers used in them, for example, 1U is equivalent to 1.7 inches high, 19 inches wide, and 17.7 inches deep. In comparison, the TYAN GX38-5550 dimensions are 1.75 inches high, 16.9 inches wide, and 15 inches deep. The server optimizes a Micro-ATX motherboard and also delivers manageability features to ease the burden on the user.

1. The TYAN GX38-B5550 is nearly 3 shorter than competitive solutions. Source: TYAN

Additional features of the server include support for two 3.5-inch internal Serial ATA (SATA) drive bays for edge computing, 4 Gigabit Ethernet LAN, one FH/HL PCIe Gen.3 x8 slot, and a single 250W power supply.

2. TYAN entry-level servers deliver the performance power based on Intel Xeon E-2200 processors, combined with the flexibility of a smaller footprint. Source: TYAN

At the heart of the GX38-B5550 server is based on the newer Intel Xeon E-2200 series processor that delivers professional-grade performance, security, reliability, and affordability. This platform has the potential to deliver real-time analytics with large memory capacity, enhanced I/O capabilities, and latest SSD storage technology. The top-bin Intel 8-core Xeon E-2200 processor increases around 25% computing power compared to the top-bin Intel 6-core Xeon E-2100 one. Sixteen threads and up to 5-GHz Intel Turbo Boost Technology, combined with up to 128 Gbytes of DDR4 2666 ECC memory support, to provide TYAN customers with substantial performance in large file processing power, storage, and virtualization.

While the Thunder CX GX38-B5550 targets IoT applications, a second product from TYAN, the Thunder CX GT24E-B5556, is more of an entry-level solution. Its a 1U server thats designed with a single-socket Intel Xeon E-2200 microprocessor. As an Edge-based server, it is well suited for cost-effective Cloud-based gaming applications.

Both solutions described here offer enhanced performance in a compact package. The result is nearly twice as much computing power as was previously available in a similar form factor, and without the thermal issues that previously plagued small footprint solutions. Lots more information is available on TYANs entry-level servers.

The rest is here:
TYAN Packs Lots of Performance in a 1U Package - Embedded Computing Design