A pertinent question for the post COVID workforce is, can empathy be learnt? Should it be practiced only by the leaders, or by everyone – can it be seamlessly woven into the fabric of the organization? We are seeing that dynamics at play for remote teams is little unpredictable, making each day uniquely challenging. Empathy is manifested through mindful behaviours, where one’s action is recognized as genuine, personal, and specific to the situation. A few people can be empathetic all the time, a few, practice it consciously, and a few are unaware of it.
Empathy is a natural human response that can be practiced by everyone at work for nurturing an environment of trust. We often confuse empathy for sympathy – while sympathy is feeling sorry for one’s situation, empathy is understanding one’s feelings and needs, and putting the effort to offer authentic support. It requires a shift in perspective, and building trust, respect, and compassion at a deeper level. As Satya Nadella, CEO, Microsoft says, “Empathy is a muscle that needs to be exercised.”
Here are three ways to consciously practice empathy at work –
Going beyond yourself
It takes a lot to forget how we feel that day, or what is priority for us. However, to be empathetic, one needs to be less judgemental. When one is consciously practicing empathy, one needs to be patient with yourself, your thoughts, and not compare yourself with the person you are empathizing with. If we get absorbed by our own needs, it gets difficult to be generous and compassionate. We need to remember empathy leads to influence and respect, and for that we should not get blind sighted by our perceptions.
Being a mindful and intentional listener
While practicing empathy, one has refrain from criticism, and be mindful of not talking about one’s problems. We may get sympathetic and give unsolicited advice. Sometimes it only takes to be an intentional listener, by avoiding distractions, and having a very positive body language, and demeanour. This will enable us to ask right questions and collaborate towards a solution.
Investing in the person
Very often, we support our colleagues and co-workers by responding to their email requests. However, by building positive workplace relationships, and knowing the person beyond his/her email id, makes it much easier to foster empathy. Compassion needs to be not just in words, but in action too, and that can happen only by knowing the person. Taking interest in a co-worker or a team member, beyond a professional capability, does not come out of thin air. It takes conscious continuous efforts to get to know the person, showing care and concern, which will help us to relate to the myriad challenges they go through – be it chronic illness, child care that correlates to his/her ability to engaged at work. It will enable us to personalize the experience, and see the person’s point of view, holistically.
When we take that genuine interest in how we make others feel and experience, we start mindfully practicing empathy. Empathy fosters respect. Empathy helps resolves conflicts better, empathy builds stronger teams, empathy inspires one another to work towards collective goals, and empathy breaks authority. Does it take that extra bit of time to consciously practice it? Yes, but it is all worth it.
Padma is intrigued by Organization Culture and Behavior at workplace that impact employee experience. She is also passionate about driving meaningful initiatives for enabling women to Lean In, along with her fellow Sheroes. She enjoys reading books, journaling, yoga and learning more about life through the eyes of her 8-year-old son.
In this pandemic economy, the topmost priorities for most companies are to make sure the operations costs and business processes are optimized and streamlined. Organizations must be more proactive than ever and identify gaps that need to be acted upon at the earliest.
The industry has been striving towards efficiency and effectivity in its operations day in and day out. As a reliability check to ensure operational standards, many organizations consider the following levers:
High Application Availability & Reliability
Optimized Performance Tuning & Monitoring
Operational gains & Cost Optimization
Generation of Actionable Insights for Efficiency
Workforce Productivity Improvement
Organizations that have prioritized the above levers in their daily operations require dedicated teams to analyze different silos and implement solutions that provide the result. Running projects of this complexity affects the scalability and monitoring of these systems. This is where AIOps platforms come in to provide customized solutions for the growing needs of all organizations, regardless of the size.
Deep Dive into AIOps
Artificial Intelligence for IT Operations (AIOps) is a platform that provides multilayers of functionalities that leverage machine learning and analytics. Gartner defines AIOps as a combination of big data and machine learning functionalities that empower IT functions, enabling scalability and robustness of its entire ecosystem.
These systems transform the existing landscape to analyze and correlate historical and real-time data to provide actionable intelligence in an automated fashion.
AIOps platforms are designed to handle large volumes of data. The tools offer various data collection methods, integration of multiple data sources, and generate visual analytical intelligence. These tools are centralized and flexible across directly and indirectly coupled IT operations for data insights.
The platform aims to bring an organization’s infrastructure monitoring, application performance monitoring, and IT systems management process under a single roof to enable big data analytics that give correlation and causality insights across all domains. These functionalities open different avenues for system engineers to proactively determine how to optimize application performance, quickly find the potential root causes, and design preventive steps to avoid issues from ever happening.
AIOps has transformed the culture of IT war rooms from reactive to proactive firefighting.
Industrial Inclination to Transformation
The pandemic economy has challenged the traditional way companies choose their transformational strategies. Machine learning powered automation for creating an autonomous IT environment is no longer a luxury. he usage of mathematical and logical algorithms to derive solutions and forecasts for issues have a direct correlation with the overall customer experience. In this pandemic economy, customer attrition has a serious impact on the annual recurring revenue. Hence, organizations must reposition their strategies to be more customer centric in everything they do. Thus, providing customers with the best-in-class service coupled with continuous availability and enhanced reliability has become an industry-standard.
As reliability and scalability are crucial factors for any company’s growth, cloud technologies have seen a growing demand. This shift of demand for cloud premises for core businesses has made AIOps platforms more accessible and easier to integrate. With the handshake between analytics and automation, AIOps has become a transformative technology investment that any organization can make.
As organizations scale in size, so does the workforce and the complexity of the processes. The increase in size often burdens organizations with time-pressed teams having high pressure on delivery and reactive housekeeping strategies. An organization must be ready to meet the present and future demands with systems and processes that scale seamlessly. This why AIOps platforms serve as a multilayered functional solution that integrates the existing systems to manage and automate tasks with efficiency and effectivity. When scaling results in process complexity, AIOps platforms convert the complexity to effort savings and productivity enhancements.
Across the industry, many organizations have implemented AIOps platforms as transformative solutions to help them embrace their present and future demand. Various studies have been conducted by different research groups that have quantified the effort savings and productivity improvements.
The AIOps Organizational Vision
As the digital transformation race has been in full throttle during the pandemic, AIOps platforms have also evolved. The industry did venture upon traditional event correlation and operations analytical tools that helped organizations reduce incidents and the overall MTTR. AIOps has been relatively new in the market as Gartner had coined the phrase in 2016. Today, AIOps has attracted a lot of attention from multiple industries to analyze its feasibility of implementation and the return of investment from the overall transformation. Google trends show a significant increase in user search results for AIOps during the last couple of years.
While taking a well-informed decision to include AIOps into the organization’s vision of growth, we must analyze the following:
Understanding the feasibility and concerns for its future adoption
Classification of business processes and use cases for AIOps intervention
Quantification of operational gains from incident management using the functional AIOps tools
AIOps is truly visioned to provide tools that transform system engineers to reliability engineers to bring a system that trends towards zero incidents.
Because above all, Zero is the New Normal.
About the Author –
Ashish Joseph
Ashish Joseph is a Lead Consultant at GAVS working for a healthcare client in the Product Management space. His areas of expertise lie in branding and outbound product management.
He runs a series called #BizPective on LinkedIn and Instagram focusing on contemporary business trends from a different perspective. Outside work, he is very passionate about basketball, music and food.
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.
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.
In my previous article (zif.ai/inverse-reinforcement-learning/), I had introduced Inverse Reinforcement Learning and explained how it differs from Reinforcement Learning. In this article, let’s explore Generative Adversarial Networks or GAN; both GAN and reinforcement learning help us understand how deep learning is trying to imitate human thinking.
With access to greater hardware power, Neural Networks have made great progress. We use them to recognize images and voice at levels comparable to humans sometimes with even better accuracy. Even with all of that we are very far from automating human tasks with machines because a tremendous amount of information is out there and to a large extent easily accessible in the digital world of bits. The tricky part is to develop models and algorithms that can analyze and understand this humongous amount of data.
GAN in a way comes close to achieving the above goal with what we call automation, we will see the use cases of GAN later in this article.
This technique is very new to the Machine Learning (ML) world. GAN is a deep learning, unsupervised machine learning technique proposed by Ian Goodfellow and few other researchers including Yoshua Bengio in 2014. One of the most prominent researcher in the deep learning area, Yann LeCun described it as “the most interesting idea in the last 10 years in Machine Learning”.
What is Generative Adversarial Network (GAN)?
A GAN is a machine learning model in which two neural networks compete to become more accurate in their predictions. GANs typically run unsupervised and use a cooperative zero-sum game framework to learn.
The logic of GANs lie in the rivalry between the two Neural Nets. It mimics the idea of rivalry between a picture forger and an art detective who repeatedly try to outwit one another. Both networks are trained on the same data set.
A generative adversarial network (GAN) has two parts:
The generator (the artist) learns to generate plausible data. The generated instances become negative training examples for the discriminator.
The discriminator (the critic) learns to distinguish the generator’s fake data from real data. The discriminator penalizes the generator for producing implausible results.
GAN can be compared with Reinforcement Learning, where the generator is receiving a reward signal from the discriminator letting it know whether the generated data is accurate or not.
During training, the generator tries to become better at generating real looking images, while the discriminator trains to be better classify those images as fake. The process reaches equilibrium at a point when the discriminator can no longer distinguish real images from fakes.
Here are the steps a GAN takes:
The input to the generator is random numbers which returns an image.
The output image of the generator is fed as input to the discriminator along with a stream of images taken from the actual dataset.
Both real and fake images are given to the discriminator which returns probabilities, a number between 0 and 1, 1 meaning a prediction of authenticity and 0 meaning fake.
So, you have a double feedback loop in the architecture of GAN:
We have a feedback loop with the discriminator having ground truth of the images from actual training dataset
The generator is, in turn, in a feedback loop along with the discriminator.
Most GANs today are at least loosely based on the DCGAN architecture (Radford et al., 2015). DCGAN stands for “deep, convolution GAN.” Though GANs were both deep and convolutional prior to DCGANs, the name DCGAN is useful to refer to this specific style of architecture.
Applications of GAN
Now that we know what GAN is and how it works, it is time to dive into the interesting applications of GANs that are commonly used in the industry right now.
Can you guess what’s common among all the faces in this image?
None of these people are real! These faces were generated by GANs, exciting and at the same time scary, right? We will focus about the ethical application of the GAN in the article.
GANs for Image Editing
Using GANs, appearances can be drastically changed by reconstructing the images.
GANs for Security
GANs has been able to address the concern of ‘adversarial attacks’.
These adversarial attacks use a variety of techniques to fool deep learning architectures. Existing deep learning models are made more robust to these techniques by GANs by creating more such fake examples and training the model to identify them.
Generating Data with GANs
The availability of data in certain domains is a necessity, especially in domains where training data is needed to model learning algorithms. The healthcare industry comes to mind here. GANs shine again as they can be used to generate synthetic data for supervision.
GANs for 3D Object Generation
GANs are quite popular in the gaming industry. Game designers work countless hours recreating 3D avatars and backgrounds to give them a realistic feel. And, it certainly takes a lot of effort to create 3D models by imagination. With the incredible power of GANs, wherein they can be used to automate the entire process!
GANs are one of the few successful techniques in unsupervised machine learning and it is evolving quickly and improving our ability to perform generative tasks. Since most of the successful applications of GANs have been in the domain of computer vision, generative model sure has a lot of potential, but is not without some drawbacks.
About the Author –
Naresh B
Naresh is a part of Location Zero at GAVS as an AI/ML solutions developer. His focus is on solving problems leveraging AI/ML. He strongly believes in making success as a habit rather than considering it as a destination. In his free time, he likes to spend time with his pet dogs and likes sketching and gardening.
Artificial Intelligence for IT
operations (AIOps) is adopted by organizations to deliver tangible
Business Outcomes. These business outcomes have a direct impact on companies’
revenue and customer satisfaction.
A survey from AIOps Exchange
2019, reports that 84% of Business Owners who attended the survey, confirmed
that they are actively evaluating AIOps to be adopted in their organizations.
So, is AIOps just automation?
Absolutely NOT!!
Artificial Intelligence for IT operations implies the implementation of true Autonomous Artificial Intelligence in ITOps, which needs to be adopted as an organization-wide strategy. Organizations will have to assess their existing landscape, processes, and decide where to start. That is the only way to achieve the true implementation of AIOps.
Every organization trying to
evaluate AIOps as a strategy should read through this article to understand
their current maturity, and then move forward to reach the pinnacle of
Artificial Intelligence in IT Operations.
The primary Success Factor in adopting AIOps is derived from the Business Outcomes the organization is trying to achieve by implementing AIOps –that is the only way to calculate ROI.
There are 4 levels of Maturity in AIOps adoption. Based on our experience in developing an AIOps platform and implementing the platform across multiple industries, we have arrived at these 4 levels. Assessing an organization against each of these levels helps in achieving the goal of TRUE Artificial Intelligence in IT Operations.
Level 1: Knee-jerk
Events, logs are generated in silos and collected from various applications and devices in the infrastructure. These are used to generate alerts that are commissioned to command centres to escalate as per the SOPs (standard operating procedures) defined. The engineering teams work in silos, not aware of the business impact that these alerts could potentially create. Here, operations are very reactive which could cost the organization millions of dollars.
Level 2: Unified
Have integrated all events, logs, and alerts into one central locale. ITSM process has been unified. This helps in breaking silos and engineering teams are better prepared to tackle business impacts. SOPs have been adjusted since the process is unified, but this is still reactive incident management.
Level 3: Intelligent
Machine Learning algorithms (either supervised or unsupervised) have been implemented on the unified data to derive insights. There are baseline metrics that are calibrated and will be used as a reference for future events. With more data, the metrics get richer. IT operations team can correlate incidents/events with business impacts by leveraging AI & ML. If Mean Time To Resolve (MTTR) an incident has been reduced by automated identification of the root cause, then the organization has attained level 3 maturity in AIOps.
Level 4: Predictive & Autonomous
The pinnacle of AIOps is level 4. If incidents and performance degradation of applications can be predicted by leveraging Artificial Intelligence, it implies improved application availability. Autonomousremediation bots can be triggered spontaneously based on the predictive insights, to fix incidents that are prone to happen in the enterprise. Level 4 is a paradigm shift in IT operations – moving operations entirely from being reactive, to becoming proactive.
Conclusion:
As IT operations teams move up each level, the essential goal to keep in mind is the long-term strategy that needs to be attained by adopting AIOps. Artificial Intelligence has matured over the past few decades, and it is up to AIOps platforms to embrace it effectively. While choosing an AIOps platform, measure the maturity of the platform’s artificial intelligent coefficient.
About the Author:
Anoop Aravindakshan (Principal Consultant Manager) at GAVS Technologies.
An evangelist of Zero Incident FrameworkTM, Anoop has been a part of the product engineering team for long and has recently forayed into product marketing. He has over 14 years of experience in Information Technology across various verticals, which include Banking, Healthcare, Aerospace, Manufacturing, CRM, Gaming, and Mobile.
Artificial Intelligence for IT operations or AIOps has exploded over the past few years. As more and more enterprises set about their digital transformation journeys, AIOps becomes imperative to keep their businesses running smoothly.
AIOps uses several technologies like
Machine Learning and Big Data to automate the identification and resolution of
common Information Technology (IT) problems. The systems, services, and
applications in a large enterprise produce volumes of log and performance data.
AIOps uses this data to monitor the assets and gain visibility into the
behaviour and dependencies among these assets.
According to a Gartner publication,
the adoption of AIOps by large enterprises would rise to 30% by 2023.
ZIF – The ideal AIOps platform of choice
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.
At the heart of ZIF, lies its Analyze and Predict (A&P) modules
which are powered by Artificial Intelligence and Machine Learning techniques.
From the business perspective, the primary goal of A&P would be 100%
availability of applications and business processes.
Let us understand more about thePredict module
of ZIF.
Predictive Analytics is one of the main USP of
the ZIF platform. ZIF encompassesSupervised, Unsupervised and Reinforcement
Learning algorithms for realization of various business use cases (as shown
below).
How does the Predict Module of ZIF work?
Through its data ingestion capabilities, the ZIF
platform can receive and process all types of data (both structured and
unstructured) from various tools in the enterprise. The types of data can be
related to alerts, events, logs, performance of devices, relations of devices,
workload topologies, network topologies etc. By analyzing all these data, the platform
predicts the anomalies that can occur in the environment. These anomalies get
presented as ‘Opportunity Cards’ so that suitable action can be taken ahead
of time to eliminate any undesired incidents from occurring. Since this is ‘Proactive’
and not ‘Reactive’, it brings about a paradigm shift to any organization’s
endeavour to achieve 100% availability of their enterprise systems and
platforms. Predictions are done at multiple levels – application level,
business process level, device level etc.
Sub-functions of Prediction Module
How does the Predict module manifest to enterprise users of the platform?
Predict module categorizes the opportunity
cards into three swim lanes.
Warning
swim lane – Opportunity Cards that have an “Expected
Time of Impact” (ETI) beyond 60 minutes.
Critical
swim lane – Opportunity Cards that have an ETI within
60 minutes.
Processed
/ Lost– Opportunity Cards that have been processed
or lost without taking any action.
Few of the enterprises that realized the power of ZIF’s Prediction Module
A manufacturing giant in the US
A large non-profit mental
health and social service provider in New York
Vasu heads Engineering function for A&P. He is a Digital Transformation leader with ~20 years of IT industry experience spanning across Product Engineering, Portfolio Delivery, Large Program Management etc. Vasu has designed and delivered Open Systems, Core Banking, Web / Mobile Applications etc.
Outside of his professional role, Vasu enjoys playing badminton and focusses on fitness routines.
“Uber, the world’s largest taxi
company, owns no vehicles. Facebook, the world’s most popular media owner,
creates no content. Alibaba, the most valuable retailer, has no inventory.
Netflix, the world’s largest movie house, own no cinemas. And Airbnb, the
world’s largest accommodation provider, owns no real estate. Something
interesting is happening.”
– Tom Goodwin, an executive at the French media group Havas.
This new breed of companies is the
fastest growing in history because they own the customer interface layer. It is
the platform where all the value and profit is. “Platform business” is a
more wholesome termfor this model for which data is the fuel; Big Data & AI/ML
technologies are the harbinger of new waves of productivity growth and
innovation.
With Big data and AI/ML is making a
big difference in the area of public health, let’s see how it is helping us
tackle the global emergency of coronavirus formally known as COVID-19.
“With rapidly spreading disease, a
two-week lag is an eternity.”
DISCOVERING/ DETECTING
Chinese
technology giant Alibaba has developed an AI system for detecting the COVID-19
in CT scans of patients’ chests with 96% accuracy against viral
pneumonia cases. It only takes 20 seconds for the AI to decide, whereas humans
generally take about 15 minutes to diagnose the illness as there can be upwards
of 300 images to evaluate.The system was trained on images and data from
5,000 confirmed coronavirus cases and has been tested in hospitals throughout
China. Per a report, at least 100 healthcare facilities are currently employing
Alibaba’s AI to detect COVID-19.
Ping An Insurance (Group) Company of China, Ltd (Ping An) aims to
address the issue of lack of radiologists by introducing the COVID-19 smart
image-reading system. This image-reading system can read the huge volumes of CT
scans in epidemic areas.
Ping
An Smart Healthcare uses clinical data to train the AI model of the COVID-19
smart image-reading system. The AI analysis engine conducts a comparative
analysis of multiple CT scan images of the same patient and measures the
changes in lesions. It helps in tracking the development of the disease,
evaluation of the treatment and in prognosis of patients.Ultimately it assists
doctors to diagnose, triage and evaluate COVID-19 patients swiftly and
effectively.
Ping
An Smart Healthcare’s COVID-19 smart image-reading system also supports AI
image-reading remotely by medical professionals outside the epidemic
areas.Since its launch, the smart image-reading system has provided services to
more than 1,500 medical institutions. More than 5,000 patients have received
smart image-reading services for free.
The
more solutions the better. At least when it comes to helping overwhelmed
doctors provide better diagnoses and, thus, better outcomes.
MONITORING
AI
based Temperature monitoring & scanning
In
Beijing, China, subway passengers are being screened for symptoms of
coronavirus, but not by health authorities. Instead, artificial intelligence is
in-charge.
Two
Chinese AI giants, Megvii and Baidu, have introduced temperature-scanning. They
have implemented scanners to detect body temperature and send alerts to company
workers if a person’s body temperature is high enough to constitute a fever.
Megvii’s
AI system detects body temperatures for up to 15 people per second andup to 16
feet. It monitors as many as 16 checkpoints in a single station. The system
integrates body detection, face detection, and dual sensing via infrared
cameras and visible light. The system can accurately detect and flag high body
temperature even when people are wearing masks, hats, or covering their faces
with other items. Megvii’s system also sends alerts to an on-site staff member.
Baidu,
one of the largest search-engine companies in China, screens subway passengers
at the Qinghe station with infrared scanners. It also uses a facial-recognition
system, taking photographs of passengers’ faces. If the Baidu system detects a body
temperature of at least 99-degrees Fahrenheit, it sends an alert to the staff
member for another screening. The technology can scan the temperatures of more
than 200 people per minute.
AI
based Social Media Monitoring
An international team is using machine learning to scour
through social media posts, news reports, data from official public health
channels, and information supplied by doctors for warning signs of the virus
across geographies.The program is looking for social media posts that mention
specific symptoms, like respiratory problems and fever, from a geographic area
where doctors have reported potential cases. Natural language processing is
used to parse the text posted on social media, for example, to distinguish
between someone discussing the news and someone complaining about how they
feel.
The approach has proven capable of spotting a coronavirus
needle in a haystack of big data. This technique could help experts learn how
the virus behaves. It may be possible to determine the age, gender, and location
of those most at risk quicker than using official medical sources.
PREDICTING
Data from hospitals, airports, and other public locations are
being used to predict disease spread and risk. Hospitals can also use the data
to plan for the impact of an outbreak on their operations.
Kalman Filter
Kalman filter was pioneered by Rudolf Emil Kalman in 1960,
originally designed and developed to solve the navigation problem in the Apollo
Project. Since then, it has been applied to numerous cases such as guidance,
navigation, and control of vehicles, computer vision’s object tracking,
trajectory optimization, time series analysis in signal processing,
econometrics and more.
Kalman filter is a recursive algorithm which uses time-series
measurement over time, containing statistical noise and produce estimations of
unknown variables.
For the one-day prediction Kalman filter can be used, while for
the long-term forecast a linear model is used where its main features are
Kalman predictors, infected rate relative to population, time-depended
features, and weather history and forecasting.
The one-day Kalman prediction is very accurate and powerful while
a longer period prediction is more challenging but provides a future trend.Long
term prediction does not guarantee full accuracy but provides a fair estimation
following the recent trend. The model should re-run daily to gain better
results.
The Center for Systems Science and
Engineering at Johns Hopkins University has developed an interactive, web-based
dashboard that tracks the status of COVID-19 around the world. The resource
provides a visualization of the location and number of confirmed COVID-19
cases, deaths and recoveries for all affected countries.
The primary data source for the
tool is DXY, a Chinese platform that aggregates local media and government
reports to provide COVID-19 cumulative case totals in near real-time at the
province level in China and country level otherwise. Additional data comes from
Twitter feeds, online news services and direct communication sent through the
dashboard. Johns Hopkins then confirms the case numbers with regional and local
health departments. This kind of Data analytics platform plays a pivotal role
in addressing the coronavirus outbreak.
All data from the dashboard is also
freely available in the following GitHub repository.
One of AI’s core strengths when
working on identifying and limiting the effects of virus outbreaks is its
incredibly insistent nature. AIsystems never tire, can sift through enormous
amounts of data, and identify
possible correlations and causations that humans can’t.
However, there are
limits to AI’s ability to both identify virus outbreaks and predict how
they will spread. Perhaps the best-known example comes from the neighboring
field of big data analytics. At its launch, Google Flu
Trends was heralded as a great leap forward in relation to
identifying and estimating the spread of the flu—until it underestimated the
2013 flu season by a whopping 140 percent and was quietly put to rest.Poor data
quality was identified as one of the main reasons Google Flu Trends
failed. Unreliable or faulty data can wreak havoc on the prediction
power of AI.
Bargunan is a Big Data Engineer and a programming enthusiast. His passion is to share his knowledge by writing his experiences about them. He believes “Gaining knowledge is the first step to wisdom and sharing it is the first step to humanity.”
The Healthcare Industry is going through a quiet revolution. Factors like disease trends, doctor demographics, regulatory policies, environment, technology etc. are forcing the industry to turn to emerging technologies like AI, to help adapt to the pace of change. Here, we take a look at some key use cases of AI in Healthcare.
Medical Imaging
The application of Machine Learning (ML) in Medical Imaging is showing highly encouraging results. ML is a subset of AI, where algorithms and models are used to help machines imitate the cognitive functions of the human brain and to also self-learn from their experiences.
AI can be gainfully used in the different stages of medical imaging- in acquisition, image reconstruction, processing, interpretation, storage, data mining & beyond. The performance of ML computational models improves tremendously as they get exposed to more & more data and this foundation on colossal amounts of data enables them to gradually better humans at interpretation. They begin to detect anomalies not perceptible to the human eye & not discernible to the human brain!
What goes hand-in-hand with data, is noise. Noise creates artifacts in images and reduces its quality, leading to inaccurate diagnosis. AI systems work through the clutter and aid noise- reduction leading to better precision in diagnosis, prognosis, staging, segmentation and treatment.
At the forefront of this use case is Radio genomics- correlating cancer imaging features and gene expression. Needless to say, this will play a pivotal role in cancer research.
Drug Discovery
Drug Discovery is an arduous process that takes several years from the start of research to obtaining approval to market. Research involves laboring through copious amounts of medical literature to identify the dynamics between genes, molecular targets, pathways, candidate compounds. Sifting through all of this complex data to arrive at conclusions is an enormous challenge. When this voluminous data is fed to the ML computational models, relationships are reliably established. AI powered by domain knowledge is slashing down time & cost involved in new drug development.
Cybersecurity in Healthcare
Data security is of paramount importance to Healthcare providers who need to ensure confidentiality, integrity, and availability of patient data. With cyberattacks increasing in number and complexity, these formidable threats are giving security teams sleepless nights! The main strength of AI is its ability to curate massive quantities of data- here threat intelligence, nullify the noise, provide instant insights & self-learn in the process. Predictive & Prescriptive capabilities of these computational models drastically reduces response time.
Virtual Health assistants
Virtual Health assistants like Chatbots, give patients 24/7 access to critical information, in addition to offering services like scheduling health check-ups or setting up appointments. AI- based platforms for wearable health devices and health apps come armed with loads of features to monitor health signs, daily activities, diet, sleep patterns etc. and provide alerts for immediate action or suggest personalized plans to enable healthy lifestyles.
AI for Healthcare IT Infrastructure
Healthcare IT Infrastructure running critical applications that enable patient care, is the heart of a Healthcare provider. With dynamically changing IT landscapes that are distributed, hybrid & on-demand, IT Operations teams are finding it hard to keep up. Artificial Intelligence for IT Ops (AIOps) is poised to fundamentally transform the Healthcare Industry. It is powering Healthcare Providers across the globe, who are adopting it to Automate, Predict, Remediate & Prevent Incidents in their IT Infrastructure. GAVS’ Zero Incident FrameworkTM (ZIF) – an AIOps Platform, is a pure-play AI platform based on unsupervised Machine Learning and comes with the full suite of tools an IT Infrastructure team would need. Please watch this video to learn more.
Artificial intelligence for IT operations (AIOps) is an umbrella term
for the application of Big Data Analytics, Machine Learning (ML) and other Artificial
Intelligence (AI) technologies to automate the identification and resolution of
common Information Technology (IT) problems. The systems, services and
applications in a large enterprise produce immense volumes of log and performance
data. AIOps uses this data to monitor the assets and gain visibility into the
working behaviour and dependencies between these assets.
According to a Gartner study, the adoption of AIOps by large enterprises
would rise to 30% by 2023.
ZIF – The ideal AIOps platform of choice
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.
At the heart of ZIF, lies its Analyze and Predict (A&P) modules
which are powered by Artificial Intelligence and Machine Learning techniques.
From the business perspective, the primary goal of A&P would be 100%
availability of applications and business processes.
Come, let us understand more about the Analyze
function of ZIF.
With Analyzehaving a Big Data platform under its hood, volumes of raw monitoring data, both structured and unstructured, can be ingested and grouped to build linkages and identify failure patterns.
Data Ingestion and Correlation of Diverse Data
The module processes a wide range of data from
varied data sources to break siloes while providing insights, exposing
anomalies and highlighting risks across the IT landscape. It increases productivity
and efficiency through actionable insights.
100+ connectors for leading tools,
environments and devices
Correlation and aggregation methods
uncover patterns and relationships in the data
Noise Nullification
Eliminates duplicate incidents, false
positives and any alerts that are insignificant. This also helps reduce the
Mean-Time-To-Resolution and event-to-incident ratio.
Deep learning algorithms isolate
events that have the potential to become incidents along with their potential
criticality
Correlation and Aggregation methods
group alerts and incidents that are related and needs a common remediation
Reinforcement learning techniques are
applied to find and eliminate false positives and duplicates
Event Correlation
Data from various sources are ingested
real-time into ZIF either by push or pull mechanism. As the data is ingested,
labelling algorithms are run to label the data based on identifiers. The
labelled data is passed through the correlation engine where unsupervised algorithms
are run to mine the patterns. Sub-sequence mining algorithms help in
identifying unique patterns from the data.
Unique patterns identified are clustered
using clustering algorithms to form cases. Every case that is generated is
marked by a unique case id. As part of the clustering process, seasonality
aspects are checked from historical transactions to derive higher accuracy of
correlation.
Correlation is done based on pattern
recognition, helping to eliminate the need for relational CMDB from the enterprise.
The accuracy of the correlation increases as patterns reoccur. Algorithms also
can unlearn patterns based on the feedback that can be provided by actions
taken on correlation. As these are unsupervised algorithms, the patterns are
learnt with zero human intervention.
Accelerated Root Cause Analysis (RCA)
Analyze module helps in identifying the
root causes of incidents even when they occur in different silos. Combination
of correlation algorithms with unsupervised deep learning techniques aid in accurately
nailing down the root causes of incidents/problems. Learnings from historical
incidents are also applied to find root causes in real-time. The platform
retraces the user journeys step-by-step to identify the exact point where an
error occurs.
Customer Success Story – How ZIF’s A&P
transformed IT Operations of a Manufacturing Giant
Vasu heads Engineering function for A&P. He is a Digital Transformation leader with ~20 years of IT industry experience spanning across Product Engineering, Portfolio Delivery, Large Program Management etc. Vasu has designed and delivered Open Systems, Core Banking, Web / Mobile Applications etc.
Outside of his professional role, Vasu enjoys playing badminton and focusses on fitness routines.
Gen X, Y or any other fancy term describing the current demographics is tuned to using voice, text and natural language to complete their work. That’s why a new generation of enterprise chatbots is needed at work.
Read over the textbook definition of a chatbot and you’ll understand it’s a computer program designed to hold conversations with humans over the internet. They can understand written and spoken text and interpret its meaning as well. The bot can then look up relevant information and deliver it to the user.
While chatbots reduces time and efforts, it’s not easy to create a chatbot that customers will trust. Businesses will have to consider the overall.
Security
Team complexity
Brand image
Scalability/availability
Identity and access management
Other parameters to fully integrate chatbots in their organizational structure
If correctly implemented enterprise chatbots can perform pre-defined roles and tasks to improve the business processes and activities.
Shortlisting the right chatbot
Automating repetitive and mundane work will increase the productivity, creativity, and efficiency of the organization. Evolution of chatbots will create more business opportunities for enterprises and new companies. Both SMBs and enterprises can improve their customer satisfaction with customized chatbots that help in offloading employee workload or support the various teams in the organization.
Enterprises first need to identify the type of chatbots needed for their organization to kick start their digital transformation. Depending on their requirements, there are two types of chatbots.
Standalone applications
Built within the messengers
Usually chatbots associated with messengers have an edge over standalone apps. They can be downloaded and used instantly. They are even easy to build and upgrade, faster compared to apps and websites and also cost effective. You also don’t have to worry about memory space.
AI based or machine learning chatbots learn over time from past questions and answers, and evolve their response accordingly.
What’s in it for enterprises?
There are some universal benefits that businesses in any industry or vertical can benefit from.
Streamlining your IT processes
A variety of business processes across your departments can be streamlined using chatbots. Your employees’ mundane, repetitive but essential tasks can be taken up by the chatbots, giving more time for revenue generating activities. For instance, they can be tasked with follow ups with clients or answering the FAQs by customers.
Act as personal assistants
Chatbots are a great help for the time constrained employees to manage, schedule, or cancel their meetings, setting alarms and other tasks. Context sensitive digital assistants help in organizing their daily routine by understanding the context, behaviors and patterns and suggesting recommendations.
24/7 customer support
Customer expectation is high with them demanding instant and quick resolution for their concerns and problems. Enterprise chatbot solutions offer a cost effective 24/7 customer services for you. Advancements in AI, machine learning and natural language processing (NLP) can allow them to understand the context, usage of slangs, and human conversation to a large extent. On a cautionary note, chatbots should easily handover the conversation to humans to avoid any unnecessary customer conflicts.
Generate business insights
The data deluge faced by the enterprises is costing them through lost insights and business opportunities. Vast data generated across the organization by employees, customers and business processes cannot be completely analyzed, and it leaves data gaps. Leveraging chatbots for processing and analyzing the stored data can result in identifying potential problem areas and take preemptive actions to mitigate the risks.
Reduce Opex & Capex costs
Enterprise chatbots are one-time investments, where you pay only for the chatbot, train it and its forever yours. No monthly payrolls, or sick leaves. You have a 24/7 virtual employee managing your routine and repetitive tasks.
Increase efficiency and productivity
The end result of all the above points is increased productivity. By training your employees about the services and products, a chatbot solution helps your employees to tackle the generic queries from customers. Thus, ending the time-consuming customer facing tasks and helping in the sales funnel.
In conclusion, chatbots are changing the working dynamics of enterprises. The best way to ensure a satisfied customer experience is to build bots that act without being supervised and offer the best solutions to their problems. With new advancements like AI, NLP and Machine Learning, it’s safe to say that chatbots are the future of enterprises.
