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  • Intelligent Virtual Assistants and Their Role in Helping Brands Transform Customer Experiences

    Intelligent Virtual Assistants and Their Role in Helping Brands Transform Customer Experiences

    The relationship between computers and humans has undergone a significant paradigm shift over several decades. From the emergence of the web in 1995 to applications or apps in 2005, technology has grown by leaps and bounds as the years progressed. This growth has brought technology to the modern era, where artificial intelligence (AI) has ultimately begun to come of age.

    Engineers and scientists have invested a great deal of effort, in terms of both energy and time, over the past few decades, which has allowed AI to be adapted into nearly every technological innovation in recent years. One such innovation is the intelligent virtual assistant (IVA).

    An IVA is a conversational AI system that has the capability to emulate human interaction in order to carry out and optimize tasks—customer service, for example. Digital assistants are gaining widespread attention across the industrial landscape, owing to a plethora of technological advancements, including machine learning and deep neural networks, among others. What makes these virtual assistants intelligent is their ability to emulate three key elements that enable customer service agents to solve problems—learning, reasoning and understanding.

    How did these conversational AI technologies achieve these capabilities? To answer this question, it is essential to delve into the evolution of the IVA market.

    Evolution of IVAs

    Although the terms “chatbots” and “intelligent virtual assistant” are often used interchangeably, and despite the fact that these technologies share a core commonality—i.e., their ability to facilitate consumer-enterprise interaction—these conversational AI technologies have some distinct differences. These differences lie mainly in complexity, scope and abilities, which IVAs’ possess more of than chatbots.

    In simple words, IVAs are the result of a decade-long chatbot evolution.

    Chatbots were first introduced in the late 60s and early 70s. These technologies were programmed with rudimentary capabilities, including scripted responses based on user inputs in the form of specific phrases and keywords.

    However, with conversational flows becoming increasingly more complex over time, these chatbots began facing limitations in their scope, mainly owing to their lack of learning process and pre-programmed responses. With no memory or context retention capabilities, the bots were unable to comprehend relevant user information between sessions or even during the conversation.

    However, conversational AI evolution reached a turning point with the emergence of two novel technologies, machine learning (ML) and natural language understanding (NLU).

    NLU gives the digital assistant the ability to comprehend and interpret inputs from users in natural language, which can enable the bot to converse with a customer naturally, as a customer service agent would.

    Meanwhile, ML allows the bots to self-train based on data such as previous customer service interaction transcripts or by observing live customer-agent interactions. This helps the IVAs to cater to customer needs in a more efficient and seamless manner.

    The technological advantages provided by these technologies, therefore, facilitated the gradual evolution of simple chatbots into IVAs, which possess in-depth domain knowledge that they can leverage to understand customer preferences and intents, fulfill their needs and enhance performance over time by gaining new insights from each interaction.

    As a result of these advanced capabilities, automation and customer satisfaction are assured outcomes of IVA adoption. Thus, brands that make use of this technology can make sure that they refocus on their primary objective: ROI.

    The Gradual Shift of IVA Technology from a Personal to a Commercial Landscape

    Starting out as an ideal solution for virtual personal assistants, such as Apple’s Siri which allows users to use voice-technology to perform myriad tasks like texting, scheduling meetings or making phone calls, IVAs are now gaining prominence as crucial tools for brands to implement effective customer engagement.

    AI assistants are rapidly making their way out of personal applications and entering a more business-oriented landscape, for instance, Oracle’s Digital Assistant which is designed to be an ideal platform for sales, marketing and other industry verticals by interfacing with a vast software app portfolio.

    IVA technology is used across a range of industrial applications, for example, taking up the role of sales agents or customer service executives, which makes it a scalable and appealing option for enterprises. Businesses predominantly adopt digital assistants to drive efficiency, enhance ROI, optimize processes and help reduce workloads.

    As the industrial space grows increasingly more competitive, customer engagement has emerged as one of the most significant assets for a successful brand. In fact, studies have shown that almost 96% of customers across the globe believe that customer service is a significant factor contributing to their loyalty to a brand.

    IVAs help brands deliver positive customer experiences by covering a number of different aspects. For instance, AI assistants can quickly solve customer problems through customers’ preferred channels, ensuring speed and convenience. They can also integrate with various systems and databases of brands, thus offering a robust knowledge base to provide tailored solutions to customers. Additionally, they are able to adapt their tone and language to context, which helps them emulate human conversations. All these factors help digital assistants play the role of an efficient and helpful customer service representative.

    How Brands Are Leveraging Conversational AI Assistants to Drive Customer Engagement

    Given the strong capacity of conversational AI technology to engage actively with customers and optimize customer service, more and more companies are adopting the technology as an integral part of their customer experience activities.

    To illustrate, Volvo has plans to develop conversational assistance to guide drivers in the use of various in-car apps to deliver a more effortless connected car experience.

    Likewise, Spotify has also earned positive reviews for its AI-based technology to track user patterns and make accurate recommendations and predictions based on preferences of individual customers, in turn elevating the overall user experience.

    The IVA industry has witnessed several prolific advancements over the years. The future outlook for the technology seems positive, with even more exciting avenues as the years progress, and AI researchers investigating the prospect of enhancing the emotional intelligence of assistants. The ability to comprehend not just the user’s preferences but also their emotions would revolutionize the way customer service is delivered, thus positioning AI assistants as a pivotal component of the future customer experience landscape.

    — Saloni Walimbe

  • AIoT and Intelligence on the Edge

    AIoT and Intelligence on the Edge

    Recently, one of the biggest convergences is between the fields of AI/ML and IoT, which enables the rise of Artificial Intelligence of Things (AIoT). AIoT combines data collected by sensors and other devices with the intelligence offered by AI/ML to enable real-time, intelligence on the edge. By converging AI/ML and IoT capabilities, AIoT solves two problems by merging them together into one solution. The goal of AIoT is to enable more efficient IoT operations, improve human-machine interactions and enhance how data is managed and analyzed. 

    Edge intelligence allows a high level of data to be processed and analyzed, and for decisions to be made locally, without being sent to the cloud. Take for example a self-navigating drone, instead of relying on a service hosted on the cloud to tell the drone where to go next, the drone itself is now able to decide its own path in the field, even when connections to cloud hosted services are not reliable.

    The evolution of IoT into AIoT progressed in three distinct stages: 

    1. Enabling core capabilities on the edge, including basic sensor development, integration with available devices, etc.
    2. Collecting the data generated from these sensors and storing them in a structured form on a central data store, typically on the cloud.
    3. Realizing the synergy between AI/ML and IoT and combining them together into AIoT.

    In other words, while IoT provided access to a large base of information, AI/ML has brought in intelligence and decision making.

    While great advancements toward AIoT have occurred, there are three main challenges in enabling intelligence on the edge to consider:

    1. Latency: When decision making occurred on the cloud, the only available option for an IoT device was to send the data (say, a captured image from a camera) to a service running on the cloud and get a selected decision from the service. This meant large volumes of data were transferred over slow or unreliable networks, resulting in delays–in many scenarios these delays could be extremely critical.
    2. Cost of computing and data transfer: Apart from the delay itself, the sheer volume of requests would greatly increase the cost of both transferring data back and forth and the cost of computing on the cloud–decreasing the feasibility of these implementations.
    3. Design divergence: While initial AI/ML solutions leveraged the cloud for vast amounts of computing power for training models, advances in the ability to move around or run a trained model (to calculate a decision) caused a divergence between the design of the solution and the hardware needed for the training and deciding components.

    In parallel, AI/ML learning methods also evolved–these became a lot more efficient with regards to computing power usage, and the type of hardware needed for a machine to learn or to decide. Combining AI and IoT was the next logical stage in this evolution.

    Design of an AIoT Solution

    The physical components of an AIoT solution are not that different from a standard IoT implementation; the design of the AI/ML backend also does not change drastically. The changes are to a few specific parts of the core devices on the edge–that are now more powerful–and enable a huge range of flexibility with the scenarios that can be handled, and how solutions are designed using these devices. 

    Impact on Enterprises

    Enterprises looking to benefit from AIoT may need to re-align roadmaps or make a few incremental changes based on their current state of adoption and based on the nature of their solutions.

    For CXOs

    There are four suggested, broad strategies for CXOs based on their level of investment today in the areas of IoT and AI/ML that are depicted in the following graphic.

    Strategies to follow for AIoT adoption depends on current levels of investment.
    • Group 1: Current level of investment in AI/ML and IoT are both high. The best option for these enterprises would be to evaluate the current roadmaps to identify incremental additions/upgrades to edge devices. This would leverage the existing investment in AI/ML and at the same time start demonstrating benefits early without increasing risk.
    • Group 2: Level of investment is higher in IoT compared to AI/ML. These enterprises stand to gain the most by shifting the ratio of investment toward AI/ML while making small changes to the device sets on the edge. They may also need to re-evaluate their current process to consider moving the decision part of the step to the edge.
    • Group 3: Level of investment in AI/ML is higher than investments in IoT. These enterprises would probably need minimal re-alignment with their roadmaps–a quick look at the implementations of the steps that train the ML models would provide the list of actions to take–epics/stories that needed intelligence on the edge can move higher up in the product backlog.
    • Group 4: These enterprises have the maximum freedom with the roadmap their implementations can take. Any product or platform roadmap planning can easily include AIoT epics and stories, with minimal impact to tasks already underway.

    For Architects/Program Managers

    For architects and program leads working on such initiatives within the company, it’s mainly a mindset change in regards to how the solution is designed, including capabilities of the devices on the edge and where the decision-making step in a process happens. Feasibility for scenarios such as the drone automatically calculating its own path instead of relying on a cloud-hosted service are now better than before, and a few demos or proof-of -concept attempts could now move many of these stories from the backlog and bring implementation dates forward.

    Realistic Predictions for AIoT

    While AIoT in its re-imagined, converged form may be new, the two original fields (AI and IoT) that merged to create AIoT are both mature and well into mainstream adoption. The benefits offered by AIoT for enterprises today, combined with the low risk involved with the small incremental changes outlined above and the cost savings that result in moving the decision making to the edge, create a lethal combination that positions AIoT to revolutionize business processes.

    IDC has predicted that the global spend on IoT will reach $1.1 trillion in 2023, up from $726 billion in 2019. Moreover, IDC predicts there will be around 40 billion IoT devices by 2025, resulting in a significant influx of data that will make it increasingly challenging for enterprises to drive operational insights and optimize their analysis of the large sums of data available. AIoT can help solve this problem by bringing increased intelligence to data management and thorough analysis. 

    If it isn’t already, now is an ideal time for AIoT to be a top priority for C-suite IT leaders.

    — Rajamani Saravanan

  • Impact of Visual AI on Test Automation Shows 5.8x Faster Authoring, 5.9x Better Test Code Efficiency, and a 4.6x Increase in Test Stability

    Empirical data from 288 Quality Engineers reveals how Applitools Visual AI helps teams release higher quality apps faster when using Cypress, Selenium, and WebdriverIO

    SAN MATEO, Calif., April 7, 2020 – Applitools, the leading provider of Visual AI powered test automation, today announced an industry report that reveals the impact of Visual AI on the performance of the most popular open source test frameworks – Cypress, Selenium, and WebdriverIO. Data was sourced from 288 quality engineers across 101 countries, spending an average of 11 hours during November 2019. This amounts to a combined 80 work weeks, or well over a year and a half of quality engineering hours, making it the largest industry study of its kind to date.

    To download the “Impact of Visual AI on Test Automation Report” visit: (https://www.applitools.com/impact-of-visual-ai/)

    “The Impact of Visual AI on Test Automation Report” provides important learning including:

    • 5.8x Faster Test Creation: When using existing open source code-based frameworks in isolation, it took testers 7 hours to create test code in contrast to just 1.2 hours using Visual AI. As a result, testers are able to greatly expand test coverage while simultaneously releasing faster than ever before.
    • 3.8x Increase in Test Code Stability: Participants used 32 locators and labels on average to provide test coverage using code based frameworks alone. This number dropped to just 7 with Visual AI. This increased stability greatly reduces test flakiness, broken CI/CD builds, and time spent on maintenance. Better test stability leads to efficiency gains along whatever dimension the quality engineering team values most – either more coverage, more time to manage quality, or even faster test cycles in support of CI/CD.
    • 45% More Efficient at Catching Bugs: Participants caught 19 out of 20 bugs (95%) using Visual AI compared to just 13 (65%) when using code based frameworks in isolation. When test builds are faster, provide more coverage, and are more stable and easily maintained, testers catch more bugs pre-production while still delivering more features, faster than previously thought possible.
    • Visual AI Helped Testers Succeed in Their Work When Used in Conjunction with Open Source Frameworks: All 288 testers that participated in the study achieved success rates of 79% when using their preferred code-based framework vs. 88% with Visual AI. This is a nine point jump in their performance after just a single hour of Visual AI training. Eric Davidson, Software Engineer from Chick-Fil-A comments, “This was an incredible opportunity to experience the power of Visual AI. Applitools has created a tool that easily integrates with existing frameworks and quickly augments functional test suites…”

    To dive deeper into this study, register for the upcoming webinar on April 23, 2020, “The Impact of Visual AI on Selenium, Cypress, and WebdriverIO: Side-by-Side Test Code Comparison From 288 Quality Engineers” hosted by Raja Rao and James Lamberti of Applitools (https://go.applitools.com/200423-Hackathon-insights-webinar.html).

    “Visual AI is a tremendous efficiency multiplier for automated testing,” said Gil Sever, CEO of Applitools. “It improves the productivity of developers and test automation engineers more than 5X, delivers huge time and money savings in the app delivery cycle, removes the testing bottleneck in the CI/CD process, and helps deliver software applications with unprecedented visual and functional quality on any computer and mobile device.”

    “Testers need to look at Visual AI as a superpower. A virtual assistant that can access every screen and page in minutes, inspect it for changes and interpret every one of them the same way the human eye and brain would, but do so without ever getting tired or making any mistakes. We believe this capability is a major part of the future of test automation.”

    According to Gartner’s Critical Capabilities for Software Test Automation (December 18, 2019), “61% of [its 2019 Software Quality Tools and Practices Survey] respondents said that AI/ML features would be very valuable in software testing tools. Improved defect detection (48%), reduction in test maintenance cost (42%) and improved test coverage (41%) were seen as the top benefits expected (multiple answers were allowed).” “The Impact of Visual AI on Test Automation Report” provides empirical evidence that Applitools Visual AI delivers exactly what the market expects from the addition of AI/ML into automated testing.

    Data was sourced via the “Global Visual AI Rockstar Hackathon” which challenged software testers to create test suites for five real-world functional testing use-cases using their preferred code-based approach, including Selenium, Cypress, or WebdriverIO, and then repeat the process after adding Applitools Visual AI. This approach provided an unbiased, highly controlled environment from 288 quality engineers from 101 different countries during the 30 days ending November 30, 2019.

    “Completing the hackathon was a keystone achievement in my career!” said Tracy Mazelin, QA Engineer at Paylocity. “I learned more by participating in this hackathon than any other automation instruction I’ve taken in the past number of years. I’m now 100 percent convinced that Visual AI testing is an essential tool for efficiently validating web and mobile software applications.”

    “This was the most interesting and useful event of the year in the field of test automation,” said Viktar Silakou, Lead QA Automation Engineer at Globant S.A. “This allows you to take a look at test automation from a different point of view and gives an opportunity to radically improve your existing approaches.”

    About Applitools

    Applitools enables Visual AI powered test automation to help teams release high-quality web and mobile apps faster and more efficiently.

    Applitools Visual AI modernizes important test automation use cases — Functional Testing, Visual Testing, Web and Mobile UI/UX Testing, Cross Browser Testing, Responsive Web Design Testing, Cross Device Testing, PDF Testing, Accessibility Testing and Compliance Testing — to transform the way organizations deliver innovation at the speed of CI/CD at a significantly lower Total Cost of Ownership (TCO).

    Hundreds of companies from verticals such as Software, Banking, Insurance, Retail, Pharmaceuticals, and Publishing — including 50 of the Fortune 100 — use Applitools to deliver the best possible digital experiences to millions of customers on any device and browser, and across every screen size and operating system.

    Applitools is headquartered in San Mateo, California, with an R&D center in Tel Aviv, Israel. For more information, please visit applitools.com.

  • COVID-19 Response: Espressive Makes AI Help Desk Free for 90 Days

    COVID-19 Response: Espressive Makes AI Help Desk Free for 90 Days

    Espressive, a provider of a help desk software that makes extensive use of artificial intelligence (AI), announced this week it will provide free access to its software-as-a-service (SaaS) platform for the next 90 days.

    Company CEO Pat Calhoun said the COVID-19 Rapid Assistance Program is intended to provide a way to augment existing help desks that are now being overwhelmed by requests from end users working from home.

    With a massive spike in IT incidents being seen since the onslaught of the COVID-19 pandemic, many help desks are now simply overwhelmed. In some cases, Calhoun said some end users are finding themselves on hold for 2.5 hours or more.

    Espressive, via its Barista virtual agents, provides a means to automatically address most routine requests using chatbots and a natural language processing (NLP) engine that the company developed specifically to address help desk use cases.
    The Espressive platform has already been trained to recognize 750 million phrases, Calhoun said, noting each time a new phrase is added to the NLP engine, the entire customer base benefits from the addition.

    Organizations that are finding it difficult to handle the volume of support calls being made by often geographically distributed remote workers can employ chatbots via a SaaS platform in a few hours, Calhoun said. In many cases, IT staff are trying to respond to help desk requests using on-premises applications that they need to access remotely, often using cumbersome virtual private network (VPN) software. A SaaS platform provides an alternative approach that is designed from the ground up to be accessed remotely, he said.

    Chatbots are already being widely employed online by customer service organizations with mixed success. Most end users, however, are at the very least familiar with interacting with them. In the case of IT support calls, the volume of requests is within a narrow range of tasks that could be handled via a chatbot, such as requesting a new password.

    Many organizations have already been gradually infusing AI into their help desk operations. The COVID-19 pandemic is likely to accelerate that transition. AI isn’t likely to completely eliminate the need for help desk staff anytime soon. However, it does provide a way to scale help desk services at a time when IT staff are trying to manage a massive transition in how and when applications are accessed by the entire organization.

    Naturally, it remains to be seen how much organizations will rely on AI to resolve support issues once the current pandemic crisis has passed. Short of being replaced altogether, there are many rote tasks that many IT professionals would just as soon not have to manually perform multiple times a day. The challenge now is defining the line between where an interface between man and machine makes the most sense for the benefit of all. In the meantime, to one form or another, chatbots are now here to stay.

    — Mike Vizard

  • Olive Raises $51M to Accelerate its AI Workforce for Healthcare

    COLUMBUS, Ohio, March 31, 2020 (GLOBE NEWSWIRE) — Olive, the company creating the AI workforce for healthcare, today announced the close of its $51 million funding round led by General Catalyst.  The funding will enable Olive to accelerate its growth and further its mission of automating healthcare’s administrative processes so hospital employees can focus on delivering high-quality, cost-effective care. Ron Paulus, former president and CEO of Mission Health, a $2 billion integrated health system, will join the Board of Directors at Olive. Existing investors include Drive Capital, Oak HC/FT, Ascension Ventures and others.

    Olive’s AI workforce automates healthcare’s most repetitive, high-volume administrative processes, across departments such as Revenue Cycle, Information Technology, Supply Chain, Clinical Administration, Human Resources and more, to deliver improved efficiency, reduced costs, and increased employee capacity. Driving efficiency in non-clinical work enables hospitals and healthcare leaders to focus more resources on the actions and activities that improve care.

    The announcement comes on the heels of record growth for the company, with Olive’s presence growing in 2019 to over 500 U.S. hospitals across 41 states, including 25 of the nation’s largest health systems.

    “As a recent health system CEO, I appreciate the duress our hospitals are under as they focus on delivering the best patient care possible under challenging circumstances all while needing to keep the lights on,” said Dr. Ronald A. Paulus. “Olive’s reliable automation of essential back-office processes saves time, reduces errors and allows staff to focus on higher-order work. I am excited to be working closely with Olive’s management team to maximize the outsized positive impact we can have in healthcare on both the administrative and clinical fronts.”

    Recent data reported by Olive found that nearly two-thirds of healthcare leaders plan to adopt automation technologies in the next two years, a significant uptick from the report issued less than 12 months ago.

    “The AI workforce is here, and the days of disconnected bots that don’t learn from each other are over,” said Sean Lane, CEO of Olive. “The time is now and this investment enables us to accelerate our vision of the internet of healthcare – where when one Olive learns, all Olives learn.  We’re on a mission to radically change the way healthcare leverages and views an AI workforce.”

    To learn more about Olive, visit www.oliveai.com.

    About Olive
    Olive is deploying the AI workforce built specifically for healthcare, delivering hospitals and health systems increased revenue, reduced costs and increased capacity. Olive automates repetitive, high-volume tasks and workflows, monitoring their performance, identifying improvements and finding opportunities for new work. Olive gives power back to providers through her ever-growing, unparalleled intelligence made up of shared memories, collective wisdom and global awareness, learning like a network so hospitals never have to solve the same problem twice. To learn more about Olive and our healthcare organization partners visit oliveai.com.

    About General Catalyst
    General Catalyst is a venture capital firm that invests in powerful, positive change that endures — for our entrepreneurs, our investors, our people, and society.  We support founders with a long-term view who challenge the status quo, partnering with them from seed to growth stage and beyond to build companies that withstand the test of time. With offices in San Francisco, Palo Alto, New York City, and Boston, the firm has helped support the growth of businesses such as: Airbnb, Canva, Gitlab, Gusto, Hubspot, Kayak, Monzo, Samsara, Snap, Stripe, and Warby Parker. For more: www.generalcatalyst.com.

    About Drive Capital
    Founded late in 2012, Drive Capital invests funds and expertise in promising companies with global market opportunities in the IT, financial services, consumer, and healthcare services industries, and helps those entrepreneurs build the next generation of innovative businesses. Drive Capital’s investments will be focused on the Heartland that stretches from Pittsburgh to Kansas City to Minneapolis and back.

    Drive Capital’s founding partners, Mark Kvamme and Chris Olsen, are experienced venture capitalists, and have participated in several venture capital firms prior to founding Drive Capital.

    About Ascension Ventures
    Ascension Ventures is a strategic healthcare venture firm with four funds and more than $800 million in capital under management. The firm was launched in 2001 by Ascension, one of the nation’s leading Catholic and non-profit health systems, and today invests on behalf of thirteen of the nation’s leading community health systems. These health system limited partners collectively operate more than 460 hospitals, have 572,000 employees and generate $96 billion in annual revenue. AV collaborates with these partners to identify, invest in, and support strategically aligned private companies that are transforming the healthcare industry and enhancing the experience for patients, their families and caregivers. For more information visit www.ascensionventures.org.

     

  • Threading the Needle on Kubernetes Complexity with AI-Powered Observability

    Threading the Needle on Kubernetes Complexity with AI-Powered Observability

    If you run down the laundry list of major industries–transportation, retail, healthcare, automotive, financial services–you’ll see that the major players in each have essentially become software companies. What they do and how they serve their customers rests entirely on their software working perfectly. Underpinning this is a massive digital transformation that has shifted organizations away from the old, static ways of enterprise IT monoliths. Instead, today’s businesses are working in more dynamic IT environments–agile, multi-cloud and, especially for new digital services, based on containers and microservices. All of which means embracing more IT complexity than ever before.

    Kubernetes Brings More Data to the Table, but that Impacts Observability

    A key part of this more complex environment is the soaring adoption rate of Kubernetes over the last few years. In a survey of 5,000 enterprises, 40% said they were currently running Kubernetes in production and over 80% reported that they’d adopted Kubernetes over any other container management system. Another recent survey revealed that 68% of CIOs are already using containers and a total 86% expect to deploy them over the next year. Enterprise IT is increasingly gravitating to containers and especially to Kubernetes for orchestrating their containerized applications.

    What drives organizations toward containers and Kubernetes is the flexibility given to development teams to release services, features or apps faster. But faster doesn’t necessarily mean better or higher quality, unless you also apply cloud-native architectural patterns, include quality gates into your delivery pipeline, embrace new deployment models such as canary or feature toggling and provide full visibility and short feedback loops from production back to engineering.

    Kubernetes itself provides great visibility into node or pod health. Kubernetes even ensures the health of your pods through its built-in health check mechanisms. Thanks to components such as service meshes, you also get live information about dependencies between services running in your pods. This data can, in theory, provide organizations with the necessary insights into their environments to understand whether their organization is releasing better value faster to their end users and unlocking new business value.

    The reason I said these are theoretical benefits is because, in practice, all that extra data doesn’t necessarily mean more visibility. Often it can mean less visibility–and with that, less insight into how the different elements of your environment may be impacting speed of production deployments, overall system performance and the end-user experiences. I’ve seen Kubernetes environments that are packed with literally billions of interdependencies between applications, containers, microservices and clouds. Asking human beings to keep track of that level of data manually is an impossible task now. And relying on traditional, manual approaches to manage and monitor IT environments shortchanges the value that organizations should be getting out of Kubernetes.

    Kubernetes Environments Need Full Stack Observability—that Means Needing Deterministic AI

    Getting the full potential out of Kubernetes means having observability across the full technology stack and those billions of interdependencies. But as more organizations are discovering, that can’t happen without AI. Specifically, a deterministic AI engine. In fact, in a 2019 survey, 88% of CIOs said they were looking to AI as a critical solution for overcoming IT complexity. As long as Kubernetes is fueling complexity, overcoming that complexity means making deterministic AI an integral part of any Kubernetes environment.

    A deterministic AI applies specific algorithms to detect system anomalies and apply its collected dependency information toward detecting the root causes behind those anomalies. When that deterministic AI engine ingests critical Kubernetes events, such as state changes and workload changes, it provides visibility into the Kubernetes stack’s interdependencies. That AI-powered, full stack observability matters because it gives IT new capabilities for their environments, including instrumenting and mapping containers, managing and allocating container resources and drilling down into container runtimes at work. All of that is possible due to the level of observability that you can only get with deterministic AI; traditional metrics and dashboards just can’t compete.

    By providing full stack visibility and interdependency mapping across a Kubernetes environment, deterministic AI is empowering IT teams to identify, in real time, the root cause of degradations in business service availability–from reliability and resource usage to system performance and user experience–faster than they ever could manually. That level of automated speed and visibility also makes it possible to immediately resolve those issues at the source before they can impact the end user. The automation provided by deterministic AI also works its way into DevOps’ production pipelines, accelerating the release and feedback cycles of new deployments and allowing teams to more quickly incorporate user feedback into the next iteration for even better experiences.

    In short, a deterministic AI engine makes life better for both IT and the customers they’re aiming to serve. Deterministic AI takes work that used to be time-consuming, tedious and counterintuitive when done manually, and performs it fast and automatically. That then frees up the teams that used to do that to focus on work that is more satisfying and drives new business value.

    AI-Powered Observability Relies on Quality Data and Quality Monitoring

    All that said, a deterministic AI’s ability to provide reliable, precise full stack observability in a Kubernetes environment relies on it being given consistently quality data it can process. The algorithms are dependent on the data that’s ingested by the AI engine. If there’s no standard monitoring capability being used across the board, then you invite a scenario where different teams bring their own preferred monitoring tools into the fold. The bigger a Kubernetes cluster gets, the more diverse the set of monitoring tools can be. And the greater the diversity of monitoring tools, the more inconsistent the data being fed into the deterministic AI engine.

    The way out of this predicament is to leverage a single monitoring tool as a self-service capability into Kubernetes clusters, one that can support the diversity of technologies that are introduced into the stack and provide a baseline standard of system and event data that developers, IT administrators and the deterministic AI all lean on. In other words, when you feed consistent, quality data into the AI, you make it smarter. And a smarter deterministic AI engine provides the full stack Kubernetes observability that organizations need. 

    Building a Positive Feedback Loop Between Kubernetes, Deterministic AI and Observability

    Kubernetes and deterministic AI are a natural fit. Kubernetes provides a platform for managing and instrumenting containerized applications. The deterministic AI provides a tool for mapping out the Kubernetes stack, offering insights into interdependencies and degradations where they arise and resolving them before they can affect end users. The data from Kubernetes makes AI better, and a smarter deterministic AI engine means more precise observability across your technology stack.

    It’s a self-reinforcing feedback loop that eliminates the usual pitfalls of container-induced IT complexity. Instead, it creates new standards of visible, consistent and quality data that organizations can leverage for faster and better deployments–and in turn, better user experiences and greater business value.

    To learn more about containerized infrastructure and cloud native technologies, consider coming to KubeCon + CloudNativeCon EU, in Amsterdam. The CNCF has made the decision to postpone the event (originally set for March 30 to April 2, 2020) to instead be held in July or August 2020.

    — Andreas Grabner

  • HPE Leverages AI for Remote Server Management

    HPE Leverages AI for Remote Server Management

    The latest ProLiant MicroServer is designed to use AI to provide the next generation remote server management

    Hewlett-Packard Enterprise (HPE) announced an HPE ProLiant MicroServer Gen10 Plus platform designed to be managed remotely using advanced algorithms the company makes available via the cloud.

    Tim Peters, vice president and general manager for global small-to-medium businesses and the midmarket at HPE, said the goal is to make it possible for organizations of all sizes to take advantage of artificial intelligence and advanced analytics to automate the management of servers in remote offices.

    Based on Intel Pentium and Intel Xeon processors and weighing less than 10 pounds, the HPE ProLiant MicroServer Gen10 Plus comes with HPE Integrated Lights Out 5 (iLO5) server management tool that enables IT teams to configure, monitor and update servers remotely. Those servers can then also be connected back HPE InfoSight for Servers, a cloud-based AI tool that HPE is making available for the first time on this class of servers, said Peters.

    HPE is making this class of servers available as part of a subscription service option that makes the HPE ProLiant MicroServer Gen10 Plus available starting at just less than $20 per month for organizations that prefer to treat IT as an operational expense. This latest generation of HPE servers also only generates 36dB of sound so most employees in a remote office may not even be aware of its existence.

    In addition, Peters noted the HPE ProLiant MicroServer Gen10 Plus server also supports HPE’s silicon-based root of trust technology, which prevents servers in remote offices from being tampered with when there is no IT staff available at that location.

    The days when most organizations could afford to have IT staff at each remote office are all but over. IT organizations increasingly need to rely on frameworks that allow them to centrally manage IT infrastructure wherever it’s deployed. IT teams can either build and deploy those IT automation frameworks themselves or they can rely on the frameworks that IT vendors are embedding in their latest generation of systems. Naturally, that latter approach requires IT to replace their existing IT infrastructure. However, given the rate at which applications are being deployed and upgraded, many organizations are now finding they are replacing server infrastructure more frequently.

    Regardless of the approach, overworked IT staffs don’t want to have to send someone to a remote office location unless it’s absolutely required. Beyond the cost of the travel, there’s the time that could be better spent on more pressing IT management tasks to consider.

    Of course, anything involving AI is likely to make IT administrators nervous. However, as IT becomes more complex, it’s not likely IT teams will be replaced. Rather, the focus of IT teams will shift toward other areas that add more value to the business as more rote tasks increasingly are automated.

    Naturally, it may be a while before all the legacy infrastructure installed is replaced, so IT teams may still need to travel to remote offices for some additional period of time. However, the days when IT teams racked up frequent flyer miles because of the need to visit remote office locations will soon be a distant memory.

    — Mike Vizard

  • IBM Advances Watson’s Ability to Understand the Language of Business

    – Announces first commercial availability of key technologies from Project Debater;

    – Integrated into IBM Watson, the new capabilities help enable businesses to begin mining & analyzing some of the most challenging aspects of human language

    NEW YORK, March 11, 2020 /PRNewswire/ — IBM (NYSE: IBM), the leader in artificial intelligence for business1, is announcing several new IBM Watson technologies designed to help organizations begin identifying, understanding and analyzing some of the most challenging aspects of the English language with greater clarity, for greater insights.

    The new technologies represent the first commercialization of key Natural Language Processing (NLP) capabilities to come from IBM Research’s Project Debater, the only AI system capable of debating humans on complex topics. For example, a new advanced sentiment analysis feature is defined to identify and analyze idioms and colloquialisms for the first time. Phrases, like ‘hardly helpful,’ or ‘hot under the collar,’ have been challenging for AI systems because they are difficult for algorithms to spot. With advanced sentiment analysis, businesses can begin analyzing such language data with Watson APIs for a more holistic understanding of their operations. Further, IBM is bringing technology from IBM Research for understanding business documents, such as PDF’s and contracts, to also add to their AI models.

    “Language is a tool for expressing thought and opinion, as much as it is a tool for information,” said Rob Thomas, General Manager, IBM Data and AI. “This is why we’re harvesting technology from Project Debater and integrating it into Watson – to enable businesses to capture, analyze, and understand more from human language and start to transform how they utilize intellectual capital that’s codified in data.”

    Today IBM is announcing that it plans to integrate Project Debater technologies into Watson throughout the year, with a focus on advancing clients’ ability to exploit natural language:

    A.    Analysis – Advanced Sentiment Analysis. IBM has enhanced sentiment analysis to be able to better identify and understand complicated word schemes like idioms (phrases and expressions) and so called, sentiment shifters, which are combinations of words that, together, take on new meaning, such as, “hardly helpful.” This technology will be integrated into Watson Natural Language Understanding this month. In addition, we are announcing a new classification technology that will enable clients to create AI models that can more easily classify clauses that occur in business documents, like procurement contracts. Based on Project Debater’s deep learning-based classification technology, the new capability can learn from as few as several hundred samples to do new classifications quickly and easily. It is planned to be added to Watson Discovery later this year.

    B.    Briefs – Summarization. This technology pulls textual data from a variety of sources to provide users with a summary of what is being said and written about a particular topic. An early version of Summarization was leveraged at The GRAMMYS this year to analyze over 18 million articles, blogs and bios to produce bite-sized insights on hundreds of GRAMMY artists and celebrities. The data was then infused into the red carpet live stream, on-demand videos and photos across www.grammy.com to give fans deeper context about the leading topics of the night. It is planned to be added to IBM Watson Natural Language Understanding later in the year.

    C.    Clustering – Advanced Topic Clustering. Building on insights gained from Project Debater, new topic clustering techniques will enable users to “cluster” incoming data to create meaningful “topics” of related information, which can then be analyzed. The technique, which is planned to be integrated into Watson Discovery later this year, will also allow subject matter experts to customize and fine-tune the topics to reflect the language of specific businesses or industries, like insurance, healthcare and manufacturing.

    IBM, has long been a leader in NLP, developing technologies that enable computer systems to learn, analyze and understand human language – including sentiment, dialects, intonations, and more – with increasing accuracy and speed. IBM has brought its NLP technology, much of which was born in IBM Research, to market via Watson. Product such as, Watson Discovery for document understanding, IBM Watson Assistant for virtual agents, and Watson Natural Language Understanding for advanced sentiment analysis, are all infused with NLP.

    ESPN Fantasy Football uses Watson Discovery and Watson Knowledge Studio to analyze millions of football data sources each day during the season to offer millions of fantasy football players real-time insights. By processing natural language, Watson identifies the tone and sentiment of news articles, blogs, forums, rankings, projections, podcasts and tweets that cover everything from locker room insights to injury analysis. ESPN Fantasy Football surfaces these insights in player cards that snapshot the “boom” and “bust” potential of each player, as well as a “Player Buzz” section that summarizes the positive or negative commentary about a player.

    KPMG, a multinational professional services network, and one of the Big Four accounting organizations, worked with IBM to create an AI solution based on a variety of Watson services, including Watson Natural Language Understanding. This technology makes it more effective for companies to identify, claim and retain potential R&D income tax credits. Developed by KPMG, the solution can help clients increase the amount of R&D income tax credits they capture because the Watson technology is able to review more documentation quickly while minimizing disruption to the client’s business.

    In the past year, KPMG clients have seen more potential for R&D tax credits, with some projects even seeing more than a 1000% increase in the number of documents reviewed. The solution helps clients uncover more potential activities that qualify for additional income tax credits, while reducing business disruption. As a result, engineers and scientists can stay focused on innovative R&D work by spending less time on income tax compliance activities.

    Watch ‘The Debater’

    Check out the trailer for The Debater, a rare, behind-the-scenes look at the making of Project Debater though the lens of an eclectic team of researchers that dare to take AI into uncharted territory. Official Selection of the Copenhagen International Documentary Film Festival www.ibm.com/research/debater-film.

    About IBM
    For more information please visit IBM Watson.
    For more information please visit IBM Research.
    Developers and data scientists can access Watson APIs at IBM Developer.
    Register here to view a webinar on the news which will be broadcast on March 18 at 1:00PM ET.

    Forward-Looking and Cautionary Statements
    Except for the historical information and discussions contained herein, statements contained in this release may constitute forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Forward-looking statements are based on the company’s current assumptions regarding future business and financial performance. These statements involve a number of risks, uncertainties and other factors that could cause actual results to differ materially, including the following: a downturn in economic environment and client spending budgets; the company’s failure to meet growth and productivity objectives; a failure of the company’s innovation initiatives; damage to the company’s reputation; risks from investing in growth opportunities; failure of the company’s intellectual property portfolio to prevent competitive offerings and the failure of the company to obtain necessary licenses; cybersecurity and data privacy considerations; fluctuations in financial results, impact of local legal, economic, political and health conditions; adverse effects from environmental matters, tax matters and the company’s pension plans; ineffective internal controls; the company’s use of accounting estimates; the company’s ability to attract and retain key employees and its reliance on critical skills; impacts of relationships with critical suppliers; product quality issues; impacts of business with government clients; currency fluctuations and customer financing risks; impact of changes in market liquidity conditions and customer credit risk on receivables; reliance on third party distribution channels and ecosystems; the company’s ability to successfully manage acquisitions, alliances and dispositions; risks from legal proceedings; risk factors related to IBM securities; and other risks, uncertainties and factors discussed in the company’s Form 10-Qs, Form 10-K and in the company’s other filings with the U.S. Securities and Exchange Commission (SEC) or in materials incorporated therein by reference. Any forward-looking statement in this release speaks only as of the date on which it is made. The company assumes no obligation to update or revise any forward-looking statements.

  • Enterprise AI Books to Read This Spring

    Enterprise AI Books to Read This Spring

    If you are anything like me, and curious about how new enterprise tools will transform our relationship with work and business-technology, then you are probably reading a lot about artificial intelligence. There’s likely no other topic that’s been written about in more depth in recent years, yet much of it is unrealistic or poorly reasoned hype.

    This is why I’ve been reading more books from trusted authors on the subject. In previous years, I’ve read books such as Erik Brynjolfsson’s and Andrew McAfee’s Race Against the Machine, and Martin Ford’s book Rise of the Robots: Technology and the Threat of a Jobless Future. Both were great books that brilliantly framed AI. Last year, I wasn’t as fortunate.

    Last year, my reading included The Master Algorithm by Pedro Domingos. It was an interesting look at machine learning algorithms, and specifically the quest for the algorithm—the master algorithm—that would create all future algorithms without the need for humans. This book proved a disappointment because the author seemed as convinced, almost dogmatically so, that general AI success was as close around the next corner as the AI pioneers of the 70s and 80s believed success for them was around the next corner.

    The next book up for me was Life 3.0 by Max Tegmark. While this was a good read, it proved to be too much of an analysis of what has been already written than a book that significantly pushed the subject of AI forward in a new or unique way.

    Hopefully, my next batch of AI reads will prove better. I realize I’m playing catch-up here, as most of these aren’t new books. But they do look to be important books on the subject.

    Here’s what I currently have lined up.

    AI Super Powers

    Author: Kai-fu Lee.

    Following the defeat of the world’s top player of the game Go, to Google’s AlphaGo AI, the government of China set ambitious plans to become the global AI hub by 2030. In his book, Kai-fu Lee contends that China has quickly caught up to the U.S. and that dramatic changes from AI developments are happening much more quickly than many expect.

    In his book, Lee examines universal basic income, and examines what jobs may be enhanced with AI the possible solutions to the biggest changes AI promises to bring to us all.

    Artificial Intelligence: A Modern Approach 4th Edition

    Authors: Peter Norvig and Stuart Russell.

    At 1152 pages, Artificial Intelligence: A Modern Approach (due out this April) isn’t a light read. The latest edition of this book looks across the entire field of AI, and a deep dive into machine learning, deep learning, transfer learning, multi-agent systems, robotics, natural language processing, causality, probabilistic programming and more.

    I have high hopes for this book, as it’s treatment appears to be exactly what I’m looking forward to reading: an overview of the state of AI without going too light on the treatment of each topic.

    Applied Artificial Intelligence: A Handbook For Business Leaders

    Authors: Mariya Yao, Adelyn Zhou and Marlene Jia.

    This book is made the list because it’s AI as it can be applied to business-technology. It promises to be a roadmap on how to use  data, technology, design and staff to solve enterprise business problems.

    “We teach you how to lead successful AI initiatives by prioritizing the right opportunities, building a diverse team of experts, conducting strategic experiments and consciously designing your solutions to benefit both your organization and society as a whole.”

    That’s exactly what enterprises need to do to succeed at integrating AI into their companies in order to get value from this growing enterprise technology.

    Machine Platform Crowd

    Authors: Andrew McAfee and Erik Brynjolfsson.

    I learned and enjoyed both Race Against the Machine and The Second Machine Age. The latest book from MIT Principal Research Scientist Andrew McAfee and Director of the MIT Center for Digital Business Eric Brynjolfsson, looked at the impact of machine intelligence and big data. In this book, the authors look at another form of augmented intelligence: our collective intelligence, and what it means for transportation, medical research, financial services and more.

    That’s it for the reading list for now. I’d appreciate hearing what AI-related business books you are reading this spring and summer.

    — George V. Hulme

  • A Deep Dive Into AI for IT Operations

    A Deep Dive Into AI for IT Operations

    Traditional IT administration and management techniques are reforming. By employing artificial intelligence (AI), IT operations are taking an interesting turn in the field of advancements. The term “AIOps” stands for Artificial Intelligence for the IT Operations. AIOps uses AI techniques and algorithms to monitor the data as well as reduce the blackout times. The dominance of digital businesses is introducing technological changes in their operations to streamline them and eliminate the overhead of traditional operational management. 

    Digital companies are generating abundant and information-rich data whose analysis and monitoring do not demand delays. From data collection to storage and data processing to analysis, all operations need to be frictionless to deal with them in an efficient manner. In the last couple of years, the AIOps market has exploded which is ultimately achieving the goal of reducing delays in problem-solving and identification.

    Paving the Road to AIOps

    The story starts with the need of AIOps and digital transformations. Cloud adoption and the shift toward other digital technologies are revamping the IT industry. It also includes the adoption of innovative solutions in the IT operations to make them frictionless. The technologies that are widely used in IT operations vary from AI to machine learning, big data to Internet of Things (IoT) and Application Programming Interfaces (APIs) for various use-cases to edge computing. All these innovations are penetrating into IT operations making them more smooth and sleek.

     AIOps platforms enhance and automate IT operations, using the following different approaches:

    • Using machine learning algorithms and neural networks for better analysis of gathered data. The data is gathered using IoT devices and tools connected with the source and sending data to the cloud for storage. 
    • The system automatically interprets if something is wrong in the operations. The errors are detected and solved immediately without introducing delays in the operations that are dependent on each other to complete.
    • Reducing human power by automating the processes that include the effort of individuals. 

    Components of AIOps

    The two basic components of AIOps that accelerate innovation in IT operations are machine learning and big data. Instead of spending human effort and time into cleaning, processing and analysis of the raw data collected from different platforms, IT operations are able to do all the tasks by employing machine learning algorithms and techniques to judge the nature of data and deduce necessary information from it.

    Factors Boosting the Emergence of AIOps

    AI is a promising technology that holds the confidence of building applications that can help the IT community. AI provides options for reliability, scalability and generic tools that can be deployed to promote the IT operations and boost them without utilizing human power.

    Factors boosting digital transformation in the IT industry:

    • Complex IT Infrastructure: Its operations face many challenges regarding data management and infrastructure. Manual IT operations are becoming harder to handle such as data collection, processing, cloud management, database efficiency, third-party management tools and SaaS integrations. Complexity management is a task that needs proper attention and the environment to deal in a better way. 
    • Handling Abundant Data: IoT technology is penetrating rapidly at an industrial level. IoT devices produce an abundant amount of data. Handling this much data is not the job of individuals. Therefore, by using AI-based algorithms the data is interpreted and analyzed. The performance is monitored using efficient algorithms. 
    • Increased Computing Power: The networking infrastructure has become robust. Therefore the IT industry is in dire need to enhance the processing speed also. By deploying IT solutions that harness AI and machine learning technologies, the computational power of the system can be increased. 

    Enterprises Using AIOps

    The IT companies that are complex and large to which human power is not enough for overall operations are using AIOps to automate the tasks.

    The adoption of technology has a huge impact on the success of IT companies:

    • Development Teams: Companies that have a DevOps model and struggle to manage the roles between the organization are using AI and other innovative technologies to smoothen their processes. With this, developers increase the responsiveness, agility and clarity in the IT operations.
    • Cloud Computing: Cloud computing has a big future. The companies store their huge data in the cloud and all the operations related to cloud data management need to streamline, as it has its own challenges. Therefore, to make the processes flow without errors and monitor the access, there needs to be automation.
    • Digital Transformation: Companies around the world are all active in adopting technology solutions. Whether it is a fintech, travel, healthcare or e-commerce company, all businesses are automating their processing by first digitizing them and providing the user’s facility of utilizing applications based on newly emerged technologies. Information handling can be done by making operations handled by robotics and blockchain technology. AIOps removes friction from the IT operations.

    — James Efron