Is AR the Future of our Increasingly Digital World?

Imagine a device which, when used to look at something, throws up information on whatever you’re pointing at. Menus for restaurants, dates of establishment for institutes, and so on. These are the sort of possibilities afforded by Augmented Reality (AR).

What is AR?

AR is a science fiction idea that successfully made the transition to reality. The fundamental idea behind augmented reality is to add something extra to your experience of reality. So, if you are watching a movie or playing a game, AR adds to that experience in some way or form to turn the experience immersive and interactive. AR basically superimposes computer-generated information (audio, visual, haptic, etc.) on the real-world objects.

AR can be defined as a system that fulfills three basic features: a combination of real and virtual worlds, real-time interaction, and accurate 3D registration of virtual and real objects.

How does AR work?

A camera-equipped device is essential for an AR experience. Upon pointing the device at an object, computer vision technology is used to recognize it. The device then downloads information about the object from the cloud, in much the same way that a web browser loads a page via a URL. In this case, the information is presented in a 3-D experience.

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AR can provide a view of the real-time data flowing from products and allow users to control them by touchscreen, voice, or gesture. An operator using an AR headset to interact with an industrial robot might see superimposed data about the robot’s performance and gain access to its controls.

The size and orientation of objects viewed through the AR display adjusts/changes in real-time. New graphical or text information comes into view while other information passes out of view as the user moves about. In industrial settings, users in different roles, such as a machine operator and a maintenance technician, can look at the same object but be presented with different AR experiences that are tailored to their needs.

AR – a novel way of shopping

A 2018 Gartner report stated, “By 2020, 100 million consumers will shop in AR online and in-store.” The current global pandemic has put a damper on consumer sentiments worldwide and we may not see those numbers. But AR can help make the in-store shopping experience more secure by reducing the need to touch a lot of objects and surfaces.

Brands like American Apparel, Uniqlo, and Lacoste already have showrooms and fitting rooms that provide try-before-you-buy options in AR spaces. Smart mirror technologies that scan RFID tags also offer the ability to bring recommendations to the brick-and-mortar shopping experience.

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IKEA customers have access to an app that permits them to point their phones at spaces and see what different products would look like in their own homes.

In the current global climate, fashion and lifestyle brands stand to gain from technologies that handle facial recognition, adapt to local lighting conditions, and provide personalized recommendations.

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According to a BRP report, 48% consumers said they would be more inclined to buy from a retailer that provided AR experiences. Retailers may be able to attract more customers with an immersive and secure shopping experience in a post-Corona world.

35% of sales on Amazon are derived from its recommendation engine, which is powered by Machine Learning. Leveraging this in the real world also has immense commercial potential.

AR for Navigation Solutions

Map services from Google and Apple have already found mass acceptance, indoor navigation is next. Apps based on ARKit and ARCore can enable navigating inside spaces like airports, malls, hospitals, etc. Gatwick Airport has already deployed its own smartphone solution that provides routes to terminals and gates based on a user’s flight number.

In 2019, a beta version of AR walking directions feature was launched for Google Maps for all AR-compatible iOS and Android mobile devices. You could view information about your surroundings by pointing your phone’s camera towards it.

AR in Automotive Industry

AR can be used in a breadth of ways in the automotive industry. Starting with dashboard-mounted heads-up displays to interactive experiences in showrooms and more.

AR is also employed by some carmakers to help aid in car maintenance (Volkswagen’s Marta app) and car manufacturing and selling processes (Volvo’s project with Microsoft HoloLens).

The heads-up display is one of the most popular uses of AR in this industry. Not only can drivers get directions and alerts on hazards, but also information on landmarks and nearby locations.

Hyundai has been a leader in AR research that goes beyond the cockpit-style view of the motorist’s experience. They have reimagined maintenance manuals with AR and has apps to point their phones at their cars to get information. Mercedes has a similar app, but its version adds a chatbot to provide virtual assistance.

AR in Healthcare

Applications of AR is opening up new opportunities in the healthcare industry. It’s expected that the global market will reach a value of $1.5B. By enabling healthcare workers with real-time data and patient information, AR can aid in more accurate diagnoses and more precise surgeries.

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AR can also bring huge value to practicing medicine and education by allowing students and trainee physicians to better visualize health issues and scenarios that they one day will be treating. The benefit that AR can bring to the healthcare industry can be ground-breaking and we are just witnessing the beginning of what is to come from AR in the field of medicine.

AR-powered Solutions for Enterprises

Smart glasses are quickly gaining popularity. Military, medical and enterprise solutions, however, are beginning to prove the value of combining AR with headsets and smart glasses.

Microsoft HoloLens 2 was likely the most anticipated product in this space in 2019. The company hopes to roll out its technology to great fanfare by demonstrating improvements in raw processing power, battery life, and wear ability. The U.S. Army has awarded a $480 contract to Microsoft, and they are also working with the industrial IoT firm PTC to streamline the development of both augmented and mixed reality products.

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Walmart and Tyson are testing programs that will transition traditional training methods into mixed reality (MR) settings. This will bring about new ways to learn about compliance and safety issues by looking around mixed-reality environments and identifying problems in a way that’s practical and engaging. Integration with other recent workplace training trends, especially gamification, may compound the returns that AR and MR solutions generate. Per ABI Research, AR-based training in enterprise will be a $6 billion industry by 2022.

Improvements in prototyping, testing, troubleshooting, and quality control are expected to emerge from this trend, too, as workers will be able to make on-the-fly comparisons of real-world items against available documentation and specifications. Jobs that call for workers’ hands to be free will also benefit significantly from AR headsets and glasses.

Augmented reality is the next ‘BIG THING’, it will absolutely revolutionize almost every aspect of life. Everything from medicine to education to construction to entertainment. AR application has already started to appear on the world’s laptops, tablets, and smartphones.

References

https://www.sciencedirect.com/topics/computer-science/augmented-reality

https://www.mantralabsglobal.com/blog/disruptive-augmented-reality-use-cases/

https://www.vxchnge.com/blog/augmented-reality-statistics

About the Author

Kalpana Vijayakumar

Kalpana is a database developer. She strongly believes that “It’s not that we use technology, we live technology.”
Outside of her professional role, Kalpana is passionate about travelling and watching movies.

Algorithmic Alert Correlation

Today’s always-on businesses and 24×7 uptime demands have necessitated IT monitoring to go into overdrive. While constant monitoring is a good thing, the downside is that the flood of alerts generated can quickly get overwhelming. Constantly having to deal with thousands of alerts each day causes alert fatigue, and impacts the overall efficiency of the monitoring process.

Hence, chalking out an optimal strategy for alert generation & management becomes critical. Pattern-based thresholding is an important first step, since it tunes thresholds continuously, to adapt to what ‘normal’ is, for the real-time environment. Threshold accuracy eliminates false positives and prevents alerts from getting fired incorrectly. Selective alert suppression during routine IT Ops maintenance activities like backups, patches, or upgrades, is another. While there are many other strategies to keep alert numbers under control, a key process in alert management is the grouping of alerts, known as alert correlation. It groups similar alerts under one actionable incident, thereby reducing the number of alerts to be handled individually.

But, how is alert ‘similarity’ determined? One way to do this is through similarity definitions, in the context of that IT landscape. A definition, for instance, would group together alerts generated from applications on the same host, or connectivity issues from the same data center. This implies that similarity definitions depend on the physical and logical relationships in the environment – in other words – the topology map. Topology mappers detect dependencies between applications, processes, networks, infrastructure, etc., and construct an enterprise blueprint that is used for alert correlation.

But what about related alerts generated by entities that are neither physically nor logically linked? To give a hypothetical example, let’s say application A accesses a server S which is responding slowly, and so A triggers alert A1. This slow communication of A with S eats up host bandwidth, and hence affects another application B in the same host. Due to this, if a third application C from another host calls B, alert A2 is fired by C due to the delayed response from B.  Now, although we see the link between alerts A1 & A2, they are neither physically nor logically related, so how can they be correlated? In reality, such situations could imply thousands of individual alerts that cannot be combined.

Algorithmic Alert Correlation

This is one of the many challenges in IT operations that we have been trying to solve at GAVS. The correlation engine of our AIOps Platform ZIF uses algorithmic alert correlation to find a solution for this problem. We are working on two unsupervised machine learning algorithms that are fundamentally different in their approach – one based on pattern recognition and the other based on spatial clustering. Both algorithms can function with or without a topology map, and work around what is supplied and available. The pattern learning algorithm derives associations based on learnings from historic patterns of alert relationships. The spatial clustering algorithm works on the principle of similarity based on multiple features of alerts, including problem similarity derived by applying Natural Language Processing (NLP), and relationships, among several others. Tuning parameters enable customization of algorithmic behavior to meet specific demands, without requiring modifications to the core algorithms. Time is also another important dimension factored into these algorithms, since the clustering of alerts generated over an extended period of time will not give meaningful results.

Traditional alert correlation has not been able to scale up to handle the volume and complexity of alerts generated by the modern-day hybrid and dynamic IT infrastructure. We have reached a point where our ITOps needs have surpassed the limits of human capabilities, and so, supplementing our intelligence with Artificial Intelligence and Machine Learning has now become indispensable.

About the Authors –

Padmapriya Sridhar

Priya is part of the Marketing team at GAVS. She is passionate about Technology, Indian Classical Arts, Travel, and Yoga. She aspires to become a Yoga Instructor someday!

Gireesh Sreedhar KP

Gireesh is a part of the projects run in collaboration with IIT Madras for developing AI solutions and algorithms. His interest includes Data Science, Machine Learning, Financial markets, and Geo-politics. He believes that he is competing against himself to become better than who he was yesterday. He aspires to become a well-recognized subject matter expert in the field of Artificial Intelligence.

Automating IT ecosystems with ZIF Remediate

Alwinking N Rajamani

Alwinking N Rajamani


Zero Incident FrameworkTM (ZIF) is an AIOps based TechOps platform that enables proactive detection and remediation of incidents helping organizations drive towards a Zero Incident Enterprise™. ZIF comprises of 5 modules, as outlined below.

This article’s focus is on the Remediate function of ZIF. Most ITSM teams envision a future of ticketless ITSM, driven by AI and Automation.

Remediate being a key module ofZIF, has more than 500+ connectors to various ITSMtools, Monitoring, Security and Incident management tools, storage/backup tools and others.Few of the connectors are referenced below that enables quick automation building.

Key Features of Remediate

  • Truly Agent-less software.
  • 300+ readily available templates – intuitive workflow/activity-based tool for process automation from a rich repository of pre-coded activities/templates.
  • No coding or programming required to create/deploy automated workflows. Easy drag & drop to sequence activities for workflow design.
  • Workflow execution scheduling for pre-determined time or triggering from events/notifications via email or SMS alerts.
  • Can be installed on-premise or on the cloud, on physical or virtual servers
  • Self Service portal for end-users/admins/help-desk to handle tasks &remediation automatically
  • Fully automated service management life cycle from incident creation to resolution and automatic closure
  • Has integration packs for all leading ITSM tools

Key features for futuristic Automation Solutions

Although the COVID pandemic has landed us in unprecedented times, we have been able to continue supporting our customers and enabled their IT operations with ZIF Remediate.

  • Self-learning capability to deliver Predictive/Prescriptive actionable alerts.
  • Access to multiple data sources and types – events, metrics, thresholds, logs, event triggers e.g. mail or SMS.
  • Support for a wide range of automation
    • Interactive Automation – Web, SMS, and email
    • Non-interactive automation – Silent based on events/trigger points
  • Supporting a wide range of advanced heuristics.

Benefits of AIOPS driven Automation

  • Faster MTTR
  • Instant identification of threats and appropriate responses
  • Faster delivery of IT services
  • Quality services leading to Employee and Customer satisfaction
  • Fulfillment and Alignment of IT services to business performance

Interactive and Non-interactive automation

Through our automation journey so far, we have understood that the best automation empowers humans, rather than replacing them. By implementing ZIF Remediate, organizations can empower their people to focus their attention on critical thinking and value-added activities and let our platform handle mundane tasks by bringing data-driven insights for decision making.

  • Interactive Automation – Web portal, Chatbot and SMS based
  • Non-interactive automations – Event or trigger driven automation

Involved decision driven Automations

ZIF Remediate has its unique, interactive automation capabilities, where many automation tools do not allow interactive decision making. Need approvals built into an automated change management process that involves sensitive aspects of your environment? Need numerous decision points that demand expert approval or oversight? We have the solution for you. Take an example of Phishing automation, here a domain or IP is blocked based on insights derived by mimicking an SOC engineer’s actions – parsing the observables i.e. URL, suspicious links or attachments in a phish mail and have those observables validated for threat against threat response tools, virus total, and others.

Some of the key benefits realized by our customers which include one of the largest manufacturing organizations, a financial services company, a large PR firm, health care organizations, and others.

  • Reduction of MTTR by 30% across various service requests.
  • Reduction of 40% of incidents/tickets, thus enabling productivity improvements.
  • Ticket triaging process automation resulting in a reduction of time taken by 50%.
  • Reclaiming TBs of storage space every week through snapshot monitoring and approval-driven model for a large virtualized environment.
  • Eliminating manual threat analysis by Phishing Automation, leading to man-hours being redirected towards more critical work.
  • Reduction of potential P1 outages by 40% through self-healing automations.

For more detailed information on ZIF Remediate, or to request a demo please visit https://zif.ai/products/remediate/

About the Author:

Alwin leads the Product Engineering for ZIF Remediate and zIrrus. He has over 20 years of IT experience spanning across Program & Portfolio Management for large customer accounts of various business verticals.

In his free time, Alwin loves going for long drives, travelling to scenic locales, doing social work and reading & meditating the Bible.

GAVS’ commitment during COVID-19

MARCH 23. 2020

Dear Client leaders & Partners,

I do hope all of you, your family and colleagues are keeping good health, as we are wading through this existential crisis of COVID 19.

This is the time for shared vulnerabilities and in all humility, we want to thank you for your business and continued trust. For us, the well being of our employees and the continuity of clients’ operations are our key focus. 

I am especially inspired by my GAVS colleagues who are supporting some of the healthcare providers in NYC. The GAVS leaders truly believe that they are integral members of these  institutions and it is incumbent upon them to support our Healthcare clients during these trying times.

We would like to confirm that 100% of our client operations are continuing without any interruptions and 100% of our offshore employees are successfully executing their responsibilities remotely using GAVS ZDesk, Skype, collaborating through online Azure ALM Agile Portal. GAVS ZIF customers are 100% supported 24X7 through ROTA schedule & fall back mechanism as a backup.

Most of GAVS Customer Success Managers, Client Representative Leaders, and Corporate Leaders have reached out to you with GAVS Business Continuity Plan and the approach that we have adopted to address the present crisis. We have put communication, governance, and rigor in place for client support and monitoring.  

GAVS is also reaching out to communities and hospitals as a part of our Corporate Social Responsibility.  

We have got some approvals from the local Chennai police authorities in Chennai to support the movement of our leaders from and to the GAVS facility and we have, through US India Strategic Partnership Forum applied for GAVS to be considered an Essential Service Provider in India.  

I have always maintained that GAVS is an IT Service concierge to all of our clients and we individually as leaders and members of GAVS are committed to our clients. We shall also ensure that our employees are safe. 

Thank you, 

Sumit Ganguli
GAVS Technologies


Heroes of GAVS | BronxCare

gavs

“Every day we witness these heroic acts: one example out of many this week was our own Kishore going into our ICU to move a computer without full PPE (we have a PPE shortage). The GAVS technicians who come into our hospital every day are, like our doctors and healthcare workers,  the true heroes of our time.” – Ivan Durbak, CIO, BronxCare

“I am especially inspired by my GAVS colleagues who are supporting some of the healthcare providers in NYC. The GAVS leaders truly believe that they are integral members of these institutions and it is incumbent upon them to support our Healthcare clients during these trying times. We thank the Doctors, Nurses and Medical Professionals of Bronx Care and we are privileged to be associated with them. We would like to confirm that 100% of our client operations are continuing without any interruptions and 100% of our offshore employees are successfully executing their responsibilities remotely using GAVS ZDesk, and other tools.” – Sumit Ganguli, CEO

The Hands that rock the cradle, also crack the code

It was an unguarded moment for my church-going, straight-laced handyman & landscaper, “ I am not sure if I am ready to trust a woman leader”, and finally the loss of first woman Presidential candidate in the US, that led me to ruminate about Women and Leadership and indulge in my most “ time suck” activities, google and peruse through Wikipedia.

I had known about this, but I was fascinated to reconfirm that the first programmer in the world was a woman, and daughter of the famed poet, Lord Byron, no less. The first Programmer in the World, Augusta Ada King-Noel, Countess of Lovelace nee Byron; was born in 1815 and was the only legitimate child of the poet laureate, Lord Byron and his wife Annabella. A month after Ada was born, Byron separated from his wife and forever left England. Ada’s mother remained bitter towards Lord Byron and promoted Ada’s interest in mathematics and logic in an effort to prevent her from developing what she saw as the insanity seen in her father.

Ada grew up being trained and tutored by famous mathematicians and scientists. She established a relationship with various scientists and authors, like Charles Dickens, etc..   Ada described her approach as “poetical science”[6] and herself as an “Analyst & Metaphysician”.

As a teenager, Ada’s prodigious mathematical talents, led her to have British mathematician Charles Babbage, as her mentor. By then Babbage had become very famous and had come to be known as ‘the father of computers’. Babbage was reputed to have developed the Analytical Engine. Between 1842 and 1843, Ada translated an article on the Analytical Engine, which she supplemented with an elaborate set of notes, simply called Notes. These notes contain what many consider to be the first computer program—that is, an algorithm designed to be carried out by a machine. As a result, she is often regarded as the first computer programmer. Ada died at a very young age of 36.

As an ode to her, the mathematical program used in the Defense Industry has been named Ada. And to celebrate our first Programmer, the second Tuesday of October has been named Ada Lovelace Day. ALD celebrates the achievement of women in Science, Technology and Engineering and Math (STEM). It aims to increase the profile of women in STEM and, in doing so, create new role models who will encourage more girls into STEM careers and support women already working in STEM.

Most of us applauded Benedict Cumberbatch’s turn as Alan Turing in the movie,  Imitation Game. We got to know about the contribution, that Alan Turning and his code breaking team at the Bletchley Park, played in singularly cracking the German Enigma code and how the code helped them to proactively know when the Germans were about to attack the Allied sites and in the process could conduct preemptive strikes. In the movie, Kiera Knightly played the role of Joan Clark Joan was an English code-breaker at the British Intelligence wing, MI5, at Bletchley Park during the World War II. She was appointed a Member of the Order of the British Empire (MBE) in 1947, because of the important part she essayed in decoding the famed German Enigma code along with Alan Turing and the team.

Joan Clark attended Cambridge University with a scholarship and there she gained a double first degree in mathematics. But the irony of it all was that she was denied a full degree, as till 1948, Cambridge only awarded degrees to men. The head of the Code-breakers group, Hugh Alexander,  described her as “one of the best in the section”, yet while promoting Joan Clark, they had initially given her a job title of a typist, as women were not allowed to be a Crypto Analyst. Clarke became deputy head of British Intelligence unit, Hut 8 in 1944.  She was paid less than the men and in the later years she believed that she was prevented from progressing further because of her gender.

In World War II the  US Army was tasked with a Herculean job to calculate the trajectories of ballistic missiles. The problem was that each equation took 30 hours to complete, and the Army needed thousands of them. So the Army, started to recruit every mathematician they could find. They placed ads in newspapers;  first in Philadelphia, then in New York City, then in far out west in places like Missouri, seeking women “computers” who could hand-compute the equations using mechanical desktop calculators. The selected applicants would be stationed at the  University of Pennsylvania in Philly. At the height of this program, the US Army employed more than 100 women calculators. One of the last women to join the team was a farm girl named Jean Jennings. To support the project, the US Army-funded an experimental project to automate the trajectory calculations. Engineers John Presper Eckert and John W. Mauchly, who are often termed as the Inventors of Mainframe computers, began designing the Electronic Numerical Integrator and Computer, or ENIAC as it was called.  That experimenting paid off: The 80-foot long, 8-foot tall, black metal behemoth, which contained hundreds of wires, 18,000 vacuum tubes, 40 8-foot cables, and 3000 switches, would become the first all-electric computer called ENIAC.

When the ENIAC was nearing completion in the spring of 1945, the US Army randomly selected six women, computer programmers,  out of the 100 or so workers and tasked them with programming the ENIAC. The engineers handed the women the logistical diagrams of ENIAC’s 40 panels and the women learned from there. They had no programming languages or compilers. Their job was to program ENIAC to perform the firing table equations they knew so well.

The six women—Francis “Betty” Snyder Holberton, Betty “Jean” Jennings Bartik, Kathleen McNulty Mauchly Antonelli, Marlyn Wescoff Meltzer, Ruth Lichterman Teitelbaum, and Frances Bilas Spence—had no documentation and no schematics to work with.

There was no language, no operating system, the women had to figure out what the computer was, how to interface with it, and then break down a complicated mathematical problem into very small steps that the ENIAC could then perform.  They physically hand-wired the machine,  using switches, cables, and digit trays to route data and program pulses. This might have been a very complicated and arduous task. The ballistic calculations went from taking 30 hours to complete by hand to taking mere seconds to complete on the ENIAC.

Unfortunately, ENIAC was not completed in time, hence could not be used during World War II. But 6 months after the end of the war, on February 14, 1946 The ENIAC was announced as a modern marvel in the US. There was praise and publicity for the Moore School of Electrical Engineering at the University of Pennsylvania, Eckert and Mauchly were heralded as geniuses. However, none of the key programmers, all the women were not introduced in the event. Some of the women appeared in photographs later, but everyone assumed they were just models, perfunctorily placed to embellish the photograph.

After the war, the government ran a campaign asking women to leave their jobs at the factories and the farms so returning soldiers could have their old jobs back. Most women did, leaving careers in the 1940s and 1950s and perforce were required to become homemakers. Unfortunately, none of the returning soldiers knew how to program the ENIAC.

All of these women programmers had gone to college at a time when most men in this country didn’t even go to college. So the Army strongly encouraged them to stay, and for the most part, they did, becoming the first professional programmers, the first teachers of modern programming, and the inventors of tools that paved the way for modern software.

The Army opened the ENIAC up to perform other types of non-military calculations after the war and Betty Holberton and Jean Jennings converted it to a stored-program machine. Betty went on to invent the first sort routine and help design the first commercial computers, the UNIVAC and the BINAC, alongside Jean. These were the first mainframe computers in the world.

Today the Indian IT  industry is at $ 160 B and is at 7.7 %age of the Indian GDP and employs approximately 2.5 Million direct employees and a very high percentage of them are women. Ginni Rommeti, Meg Whitman are the CEOs of IBM and HP while Sheryl Sandberg is the COO of Facebook. They along with Padmasree Warrior, ex CTO of CISCO have been able to crack the glass ceiling.    India boasts of Senior Leadership in leading IT companies like Facebook, IBM, CapGemini, HP, Intel  etc.. who happen to be women. At our company, GAVS, we are making an effort to put in policies, practices, culture that attract, retain, and nurture women leaders in IT. The IT industry can definitely be a major change agent in terms of employing a large segment of women in India and can be a transformative force for new vibrant India. We must be having our Indian Ada, Joan, Jean and Betty and they are working at ISRO, at Bangalore and Sriharikota, at the Nuclear Plants at Tarapur.

ABOUT THE AUTHOR

Sumit Ganguli

Sumit Ganguli

Understanding Reinforcement Learning in five minutes

Reinforcement learning (RL) is an area of Machine Learning (ML) that takes suitable actions to maximize rewards situations. The goal of reinforcement learning algorithms is to find the best possible action to take in a specific situation. Just like the human brain, it is rewarded for good choices and penalized for bad choices and learns from each choice. RL tries to mimic the way that humans learn new things, not from a teacher but via interaction with the environment. At the end, the RL learns to achieve a goal in an uncertain, potentially complex environment.

Understanding Reinforcement Learning

How does one learn cycling? How does a baby learn to walk? How do we become better at doing something with more practice? Let us explore learning to cycle to illustrate the idea behind RL.

Did somebody tell you how to cycle or gave you steps to follow? Or did you learn it by spending hours watching videos of people cycling? All these will surely give you an idea about cycling; but will it be enough to actually get you cycling? The answer is no. You learn to cycle only by cycling (action). Through trials and errors (practice), and going through all the positive experiences (positive reward) and negative experiences (negative rewards or punishments), before getting your balance and control right (maximum reward or best outcome). This analogy of how our brain learns cycling applies to reinforcement learning. Through trials, errors, and rewards, it finds the best course of action.

Components of Reinforcement Learning

The major components of RL are as detailed below:

  • Agent: Agent is the part of RL which takes actions, receives rewards for actions and gets a new environment state as a result of the action taken. In the cycling analogy, the agent is a human brain that decides what action to take and gets rewarded (falling is negative and riding is positive).
  • Environment: The environment represents the outside world (only relevant part of the world which the agent needs to know about to take actions) that interacts with agents. In the cycling analogy, the environment is the cycling track and the objects as seen by the rider.
  • State: State is the condition or position in which the agent is currently exhibiting or residing. In the cycling analogy, it will be the speed of cycle, tilting of the handle, tilting of the cycle, etc.
  • Action: What the agent does while interacting with the environment is referred to as action. In the cycling analogy, it will be to peddle harder (if the decision is to increase speed), apply brakes (if the decision is to reduce speed), tilt handle, tilt body, etc.
  • Rewards: Reward is an indicator to the agent on how good or bad the action taken was. In the cycling analogy, it can be +1 for not falling, -10 for hitting obstacles and -100 for falling, the reward for outcomes (+1, -10, -100) are defined while building the RL agent. Since the agent wants to maximize rewards, it avoids hitting and always tries to avoid falling.

Characteristics of Reinforcement Learning

Instead of simply scanning the datasets to find a mathematical equation that can reproduce historical outcomes like other Machine Learning techniques, reinforcement learning is focused on discovering the optimal actions that will lead to the desired outcome.

There are no supervisors to guide the model on how well it is doing. The RL agent gets a scalar reward and tries to figure out how good the action was.

Feedback is delayed. The agent gets an instant reward for action, however, the long-term effect of an action is known only later. Just like a move in chess may seem good at the time it is made, but may turn out to be a bad long term move as the game progress.

Time matters (sequential). People who are familiar with supervised and unsupervised learning will know that the sequence in which data is used for training does not matter for the outcome. However, for RL, since action and reward at current state influence future state and action, the time and sequence of data matters.

Action affects subsequent data RL agent receives.

Why Reinforcement Learning

The type of problems that reinforcement learning solves are simply beyond human capabilities. They are even beyond the solving capabilities of ML techniques. Besides, RL eliminates the need for data to learn, as the agent learns by interacting with the environment. This is a great advantage to solve problems where data availability or data collection is an issue.

Reinforcement Learning applications

RL is the darling of ML researchers now. It is advancing with incredible pace, to solve business and industrial problems and garnering a lot of attention due to its potential. Going forward, RL will be core to organizations’ AI strategies.

Reinforcement Learning at GAVS

Reinforcement Learning is core to GAVS’ AI strategy and is being actively pursued to power the IP led AIOps platform – Zero Incident FrameworkTM (ZIF). We had our first success on RL; developing an RL agent for automated log rotation in servers.

References:

Reinforcement Learning: An Introduction second edition by Richard S. Sutton and Andrew G. Barto

https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf

About the Author:

Gireesh Sreedhar KP

Gireesh is a part of the projects run in collaboration with IIT Madras for developing AI solutions and algorithms. His interest includes Data Science, Machine Learning, Financial markets, and Geo-politics. He believes that he is competing against himself to become better than who he was yesterday. He aspires to become a well-recognized subject matter expert in the field of Artificial Intelligence.

Disaster Recovery for Modern Digital IT

A Disaster Recovery strategy includes policies, tools and processes for recovery of data and restoration of systems in the event of a disruption. The cause of disruption could be natural, like earthquakes/floods, or man-made like power outages, hardware failures, terror attacks or cybercrimes. The aim of Disaster Recovery(DR) is to enable rapid recovery from the disaster to minimize data loss, extent of damage, and disruption to business. DR is often confused with Business Continuity Planning(BCP). While BCP ensures restoration of the entire business, DR is a subset of that, with focus on IT infrastructure, applications and data.

IT disasters come at the cost of lost revenue, tarnished brand image, lowered customer confidence and even legal issues relating to data privacy and compliance. The impact can be so debilitating that some companies never fully recover from it. With the average cost of IT downtime running to thousands of dollars per minute, it goes without saying that an enterprise-grade disaster recovery strategy is a must-have.

Why do companies neglect this need?

Inspite of the obvious consequences of a disaster, many organizations shy away from investing in a DR strategy due to the associated expenditure. Without a clear ROI in sight, these organizations decide to risk the vulnerability to catastrophic disruptions. They instead make do with just data backup plans or secure only some of the most critical elements of their IT landscape.

Why is Disaster Recovery different today?

The ripple effects of modern digital infrastructure have forced an evolution in DR strategies. Traditional Disaster Recovery methods are being overhauled to cater to the new hybrid IT infrastructure environment. Some influencing factors:

  • The modern IT Landscape

o Infrastructure – Today’s IT environment is distributed between on-premise, colocation facilities, public/private cloud, as-a-service offerings and edge locations. Traditional data centres are losing their prominence and are having to share their monopoly with these modern technologies. This trend has significant advantages such as reduced CapEx in establishing data centers, reduced latency because of data being closer to the user, and high dynamic scalability.

o Data – Adding to the complexity of modern digital infrastructure is the exponential growth in data from varied sources and of disparate types like big data, mobile data, streaming content, data from cloud, social media, edge locations, IoT, to name a few.

  • Applications – The need for agility has triggered the shift away from monolith applications towards microservices that typically use containers to provide their execution environment. Containers are ephemeral and so scale, shrink, disappear or move between nodes based on demand.
  • While innovation in IT helps digital transformation in unimaginable ways, it also makes it that much harder for IT teams to formulate a disaster recovery strategy for today’s IT landscape that is distributed, mobile, elastic and transient.
  • Cybercrimes are becoming increasingly prevalent and are a big threat to organizations. Moderntechnologies fuel increasing sophistication in malware and ransomware. As their complexity increases, they are becoming harder to even detect while they lie low and do their harm quietly inside the environment. By the time they are detected, the damage is done and it’s too late. DR strategies are also constantly challenged by the lucrative underworld of ransomware.

Solution Strategies for Disaster Recovery

  • On-Premise DR: This is the traditional option that translates toheavy upfront investments towardsthe facility, securing the facility, infrastructure including the network connectivity/firewalls/load balancers, resources to scale as needed, manpower, test drills, ongoing management and maintenance, software licensing costs, periodic upgrades for ongoing compatibility with the production environment and much more.

A comprehensive DR strategy involves piecing together several pieces of a complex puzzle. Due to the staggering costs and time involved in provisioning and managing infra for the duplicate storage and compute, companies are asking themselves if it is really worth the investment, and are starting to explore more OpEx based solutions. And, they are discovering that the cloud may be the answer to this challenge of evolving infra, offering cost-effective top-notch resiliency.

  • Cloud-based DR: The easy availability of public cloud infrastructure & services, with affordablemonthly subscription plans and pay per use rates, has caused an organic switch to the cloud for storage, infra and as a Service(aaS) needs. To complement this, replication techniques have also evolved to enable cloud replication. With backup on the cloud, the recovery environment needs to be paid for only when used in the event of a disaster!

Since maintaining the DR site is the vendor’s responsibility, it reduces the complexity in managing the DR site and the associated operating expenses as well. Most DR requirements are intrinsically built into cloud solutions: redundancy, advanced networks, bandwidth, scalability, security & compliance. These can be availed on demand, as necessitated by the environment and recovery objectives. These features have made it feasible for even small businesses to acquire DR capabilities.

Disaster Recovery-as-a-Service(DRaaS) which is fast gaining popularity, is a DR offering on the cloud, where the vendor manages the replication, failover and failback mechanisms as needed for recovery, based on a SLA driven service contract .

On the flip side, as cloud adoption becomes more and more prevalent, there are also signs of a reverse drain back to on-premise! Over time, customers are noticing that they are bombarded by hefty cloud usage bills, way more than what they had bargained for. There is a steep learning curve

in assimilating the nuances of new cloud technologies and the innumerable options they offer. It is critical for organizations to clearly evaluate their needs, narrow down on reliable vendors with mature offerings, understand their feature set and billing nitty-gritties and finalize the best fit for their recovery goals. So, it is Cloud, but with Caution!

  • Integrating DR with the Application: Frank Jablonski, VP of Global Marketing, SIOS Technology Corppredicts that applications will soon have Disaster Recovery architected into their core, as a value-add. Cloud-native implementations will leverage the resiliency features of the cloud to deliver this value.

The Proactive Approach

Needless to say, investing in a proactive approach for disaster prevention will help mitigate the chances for a disaster in the first place. One sure-fire way to optimize IT infrastructure performance, prevent certain types of disasters and enhance business services continuity is to use AI augmented ITOps platforms to manage the IT environment. GAVS’ AIOps platform, Zero Incident FrameworkTM(ZIF) has modules powered by Advanced Machine Learning to Discover, Monitor, Analyze, Predict, and Remediate, helping organizations drive towards a Zero Incident EnterpriseTM. For more information, please visit the ZIF website.

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Data Migration Powered by RPA

What is RPA?

Robotic Process Automation(RPA) is the use of specialized software to automate repetitive tasks. Offloading mundane, tedious grunt work to the software robots frees up employee time to focus on more cerebral tasks with better value-add. So, organizations are looking at RPA as a digital workforce to augment their human resources. Since robots excel at rules-based, structured, high-volume tasks, they help improve business process efficiency, reduce time and operating costs due to the reliability, consistency & speed they bring to the table.

Generally, RPA is low-cost, has faster deployment cycles as compared to other solutions for streamlining business processes, and can be implemented easily. RPA can be thought of as the first step to more transformative automations. With RPA steadily gaining traction, Forrester predicts the RPA Market will reach $2.9 Billion by 2021.

Over the years, RPA has evolved from low-level automation tasks like screen scraping to more cognitive ones where the bots can recognize and process text/audio/video, self-learn and adapt to changes in their environment. Such Automation supercharged by AI is called Intelligent Process Automation.

Use Cases of RPA

Let’s look at a few areas where RPA has resulted in a significant uptick in productivity.

Service Desk – One of the biggest time-guzzlers of customer service teams is sifting through scores ofemails/phone calls/voice notes received every day. RPA can be effectively used to scour them, interpret content, classify/tag/reroute or escalate as appropriate, raise tickets in the logging system and even drive certain routine tasks like password resets to closure!

Claims Processing – This can be used across industries and result in tremendous time and cost savings.This would include interpreting information in the forms, verification of information, authentication of e-signatures & supporting documents, and first level approval/rejection based on the outcome of the verification process.

Data Transfers – RPA is an excellent fit for tasks involving data transfer, to either transfer data on paperto systems for digitization, or to transfer data between systems during data migration processes.

Fraud Detection – Can be a big value-add for banks, credit card/financial services companies as a first lineof defense, when used to monitor account or credit card activity and flag suspicious transactions.

Marketing Activities – Can be a very resourceful member of the marketing team, helping in all activities

right from lead gen, to nurturing leads through the funnel with relevant, personalized, targeted content

delivery.

Reporting/Analytics

RPA can be used to generate reports and analytics on predefined parameters and KPIs, that can help

give insights into the health of the automated process and the effectiveness of the automation itself.

The above use cases are a sample list to highlight the breadth of their capabilities. Here are some industry-specific tasks where RPA can play a significant role.

Banks/Financial Services/Accounting Firms – Account management through its lifecycle, Cardactivation/de-activation, foreign exchange payments, general accounting, operational accounting, KYC digitization

Manufacturing, SCM –Vendor handling, Requisition to Purchase Order, Payment processing, Inventorymanagement

HR – Employee lifecycle management from On-boarding to Offboarding, Resume screening/matching

Data Migration Triggers & Challenges

A common trigger for data migration is when companies want to sunset their legacy systems or integrate them with their new-age applications. For some, there is a legal mandate to retain legacy data, as with patient records or financial information, in which case these organizations might want to move the data to a lower-cost or current platform and then decommission the old system.

This is easier said than done. The legacy systems might have their data in flat files or non-relational DBs or may not have APIs or other standards-based interfaces, making it very hard to access the data. Also, they might be based on old technology platforms that are no longer supported by the vendor. For the same reasons, finding resources with the skillset and expertise to navigate through these systems becomes a challenge.

Two other common triggers for data migrations are mergers/acquisitions which necessitate the merging of systems and data and secondly, digital transformation initiatives. When companies look to modernize their IT landscape, it becomes necessary to standardize applications and remove redundant ones across application silos. Consolidation will be required when there are multiple applications for the same use cases in the merged IT landscape.

Most times such data migrations can quickly spiral into unwieldy projects, due to the sheer number, size, and variety of the systems and data involved, demanding meticulous design and planning. The first step would be to convert all data to a common format before transition to the target system which would need detailed data mappings and data cleansing before and after conversion, making it extremely complex, resource-intensive and expensive.

RPA for Data Migration

Structured processes that can be precisely defined by rules is where RPA excels. So, if the data migration process has clear definitions for the source and target data formats, mappings, workflows, criteria for rollback/commit/exceptions, unit/integration test cases and reporting parameters, half the battle is won. At this point, the software bots can take over!

Another hurdle in humans performing such highly repetitive tasks is mental exhaustion, which can lead to slowing down, errors and inconsistency. Since RPA is unfazed by volume, complexity or monotony, it automatically translates to better process efficiency and cost benefits. Employee productivity also increases because they are not subjected to mind-numbing work and can focus on other interesting tasks on hand. Since the software bots can be configured to create logfiles/reports/dashboards in any format, level of detail & propagation type/frequency, traceability, compliance, and complete visibility into the process are additional happy outcomes!

To RPA or not to RPA?

Well, while RPA holds a lot of promise, there are some things to keep in mind

  • Important to choose the right processes/use-cases to automate, else it could lead to poor ROI
  • Quality of the automation depends heavily on diligent design and planning
  • Integration challenges with other automation tools in the landscape
  • Heightened data security and governance concerns since it will have full access to the data
  • Periodic reviews required to ensure expected RPA behavior
  • Dynamic scalability might be an issue when there are unforeseen spikes in data or usage patterns
  • Lack of flexibility to adapt to changes in underlying systems/platforms could make it unusable

But like all other transformational initiatives, the success of RPA depends on doing the homework right, taking informed decisions, choosing the right vendor(s) and product(s) that align with your Business imperatives, and above all, a whole-hearted buy-in from the business, IT & Security teams and the teams that will be impacted by the RPA.

CCPA for Healthcare

The California Consumer Privacy Act (CCPA) is a state statute intended to enhance consumer protection and data privacy rights of the residents of California, United States. It is widely considered one of the most sweeping consumer privacy laws, giving Californians the strongest data privacy rights in the U.S.

The focus of this article is CCPA as it applies to Healthcare. Let’s take a quick look at what CCPA is and then move onto its relevance for Healthcare entities. CCPA is applicable to any for-profit organization – regardless of whether it physically operates out of California – that interacts with, does business with and/or collects, processes or monetizes personal information of California residents AND meets at least one of these criteria: has annual gross revenue in excess of $25 million USD; collects or transacts with the personal information of 50,000 or more California consumers, households, or devices; earns 50% or more of its annual revenue by monetizing such data. CCPA also empowers California consumers with the rights to complete ownership; control; and security of their personal information and imposes new stringent responsibilities on businesses to enable these rights for their consumers.

Impact on Healthcare Companies

Companies directly or indirectly involved in the healthcare sector and dealing with medical information are regulated by the Confidentiality of Medical Information Act (CMIA) and the Health Insurance Portability and Accountability Act (HIPAA). CCPA does not supersede these laws & does not apply to ‘Medical Information (MI)’ as defined by CMIA, or to ‘Protected Health Information (PHI)’ as defined by HIPAA. CCPA also excludes de- identified data and information collected by federally-funded clinical trials, since such research studies are regulated by the ‘Common Rule’.

The focus of the CCPA is ‘Personal Information (PI)’ which means information that “identifies, relates to, describes, is capable of being associated with, or could reasonably be linked, directly or indirectly, with a particular consumer or household.” PI refers to data including but not limited to personal identifiers such as name, address, phone numbers, email ids, social security number; personal details relating to education, employment, family, finances; biometric information, geolocation, consumer activity like purchase history, product preferences; internet activity.

So, if CCPA only regulates personal information, are healthcare companies that are already in compliance with CMIA and HIPAA safe? Is there anything else they need to do?

Well, there is a lot that needs to be done! This only implies that such companies should continue to comply with those rules when handling Medical Information as defined by the CMIA, or Protected Health Information, as defined by HIPAA. They will still need to adhere to CCPA regulations for personal data that is outside of MI and PHI. This will include

employee personal information routinely obtained and processed by the company’s HR; those collected from websites, health apps, health devices, events; clinical studies that are not funded by the federal government; information of a CCPA-covered entity that is handled by a non-profit affiliate, to give a few examples.

There are several possibilities – some not so apparent – even in healthcare entities, for personal data collection and handling that would fall under the purview of CCPA. They need to take stock of the different avenues through which they might be obtaining/handling such data and prioritize CCPA compliance. Else, with the stringent CCPA regulations, they could quickly find themselves embroiled in class action lawsuits (which by the way, do not require proof of damage to the plaintiff) in case of data breaches, or statutory penalties of up to $7500 for each violation.

The good news is that since CCPA carves out a significant chunk of data that healthcare companies/those involved in healthcare-related functions collect and process, entities that are already complying with HIPAA and CMIA are well into the CCPA compliance journey. A peek into the kind of data CMIA & HIPAA regulate will help gauge what other data needs to be taken care of.

CMIA protects the confidentiality of Medical Information (MI) which is “individually identifiable information, in electronic or physical form, in possession of or derived from a provider of health care, health care service plan, pharmaceutical company, or contractor regarding a patient’s medical history, mental or physical condition, or treatment.”

HIPAA regulates how healthcare providers, health plans, and healthcare clearinghouses, referred to as ‘covered entities’ can use and disclose Protected Health Information (PHI), and requires these entities to enable protection of data privacy. PHI refers to individually identifiable medical information such as medical records, medical bills, lab tests, scans and the like. This also covers PHI in electronic form(ePHI). The privacy and security rule of HIPAA is also applicable to ‘business associates’ who provide services to the ‘coveredentities’ that involve the use or disclosure of PHI.

Two other types of data that are CCPA exempt are Research Data & De-Identified Data. As mentioned above, the ‘Common Rule’ applies only to federally-funded research studies, and the CCPA does not provide much clarity on exemption status for data from clinical trials that are not federally-funded.

And, although the CCPA does not apply to de-identified data, the definitions of de-identified data of HIPAA and CCPA slightly differ which makes it quite likely that de-identified data by HIPAA standards may not qualify under CCPA standards and therefore would not be exempt from CCPA regulations.

Compliance Approach

Taking measures to ensure compliance with regulations is cumbersome and labour-intensive, especially with the constantly evolving regulatory environment. Using this opportunity for a proactive, well-thought-out approach for comprehensive enterprise-wide data security and governance will be strategically wise since it will minimize the need for policy and process rehaul with each new regulation.

The most crucial step is a thorough assessment of the following:

  • Policies, procedures, workflows, entities relating to/involved in data collection, sharing and processing, in order to arrive at clear enterprise-wide data mapping; to determine what data, data activities, data policies would fall under the scope of CCPA; and to identify gaps and decide on prioritized action items for compliance.
  • Business processes, contracts, terms of agreement with affiliates, partners and third-party entities the company does business with, to understand CCPA applicability. In some cases,

HIPAA and CMIA may be applicable to only the healthcare-related business units, subjecting other business units to CCPA compliance.

  • Current data handling methods, not just its privacy & security. CCPA dictates that companies need to have mechanisms put in place to cater to CCPA consumer right to request all information relating to the personal data collected about them, right to opt-out of sale of their data, right to have their data deleted by the organization (which will extend to 3rd parties doing business with this organization as well).

Consumer Consent Management

With CCPA giving full ownership and control of personal data back to its owners, consent management mechanisms become the pivot of a successful compliance strategy. An effective mechanism will ensure proper administration and enforcement of consumer authorizations.

Considering the limitations of current market solutions for data privacy and security, GAVS has come up with its Blockchain-based Rhodium Framework (pending patent) for Customer Master Data Management and Compliance with Data Privacy Laws like CCPA.

You can get more details on CCPA in general and GAVS’ solution for true CCPA Compliance in our White Paper, Blockchain Solution for CCPA Compliance.

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