Design Thinking 101

Vasudevan Gopalan

Is the end-user at the center of everything you do? Do you consider human emotions while conceptualizing a product or a solution? Well, let us open the doors of Design Thinking

What is Design Thinking?

  • Design thinking is both an ideology and a process, concerned with solving in a highly user-centric way.
  • With its human-centric approach, design thinking develops effective solutions based on people’s needs.
  • It has evolved from a range of fields – including architecture, engineering, business – and is also based on processes used by designers.
  • Design thinking is a holistic product design approach where every product touch point is an opportunity to delight and benefit our users.

Human Centred Design

With ‘thinking as a user’ as the methodology and ‘user satisfaction’ as the goal, design thinking practice supports innovation and successful product development in organizations. Ideally, this approach results in translating all the requirements into product features.

Part of the broader human centred design approach, design thinking is more than cross-functional; it is an interdisciplinary and empathetic understanding of our user’s needs. Design thinking sits right up there with Agile software development, business process management, and customer relationship management.

5 Stages of Design Thinking

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  • Empathize: This stage involves gathering insights about users and trying to understand their needs, desires, and objectives.
  • Define: This phase is all about identifying the challenge. What difficulties do users face? What are the biggest challenges? What do users really need?
  • Ideate: This step, as you may have already guessed, is dedicated to thinking about the way you can solve the problems you have identified with the help of your product. The product team, designers, and software engineers brainstorm and generate multiple ideas.
  • Prototype: The fourth stage brings you to turn your ideas into reality. By creating prototypes, you test your ideas’ fitness.
  • Test: You present the prototype to customers and find out if it solves their problem and provides users with what they need. Note that this is not the end of the journey; you need to get feedback from the users, adjust the product’s functionality, and test it again. This is a continuous process similar to the build-measure-learn approach in the lean start-up methodology.
Design Thinking

Benefits of Design Thinking in Software Development

1. Feasibility check: Design thinking enables software development companies to test the feasibility of the future product and its functionality at the initial stage. It enables them to keep end-user needs in mind, clearly specify all requirements and translate all this into product features.

2. No alarms and no surprises: Once you’ve tested your MVP and gathered feedback from users, the team can confidently proceed to the product development. You can be quite sure that there will be little to no difference between the approved concept and final version.

3. Clarity and transparency: Design thinking approach allow product designers/developers to broaden their vision, understand and empathise with the end-users’ problems and have a detailed blueprint of the solution they should eventually deliver.

4. Continuous improvement: The product can be (and sometimes should be) modified after its release when user feedback is at hand. It becomes clear which features work and which can be done away with. The product can undergo some series enhancements on the basis of feedback. This leaves place for continuous improvement and software development process becomes flexible and smooth.

Real-world Success Stories

1. PepsiCo

During Indra Nooyi’s term as PepsiCo’s CEO, the company’s sales grew 80%. It is believed that design thinking was at the core of her successful run. In her efforts to relook at the company’s innovation process and design experience, she asked her direct reportees to fill an album full of photos of what they considered represents good design. Uninspired by the result, she probed further to realize that it was imperative to hire a designer.

“It’s much more than packaging… We had to rethink the entire experience, from conception to what’s on the self to the post product experience.”, she told the Harvard Business Review.

While other companies were adding new flavours or buttons to their fountain machines, PepsiCo developed a touch screen fountain machine, a whole new interaction between humans and machines.

“Now, our teams are pushing design through the entire system, from product creation, to packaging and labelling, to how a product looks on the shelf, to how consumers interact with it,” she said.

2. Airbnb

Back in 2009, Airbnb’s revenue was limping. They realized that poor quality images of rental listings may have something to do with it. They flew some of their employees to a city and got them to take high quality photos and upload it on their website. This resulted in a 100% increase in their revenue.

Instead of focusing on scalability, the team turned inward and asked, ‘what does the customer need?’ This experiment taught them a few big lessons, empathy being just as important as code was one of them.

3. Mint.com

Mint.com is a web-based personal financial management website. Part of their success is attributed to the human-centric design of the website which tracks and visualizes how a person is spending their money. Bank accounts, investments, and credit cards can easily be synchronized on Mint, which then categorizes the expenses to help the user visualize their spending. They built a product that illustrates a core principle of design thinking: truly understanding the position and mindset of the user. They had 1.5 million customers within 2 years.

Design thinking is a human-centred approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.

References

https://www.researchgate.net/publication/226141981_Design_Thinking_A_Fruitful_Concept_for_IT_Development

https://blog.brainstation.io/how-5-ceos-used-design-thinking-to-transform-their-companies/

About the Author –

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.

The Pandemic and Social Media

Prabhakar Mandal

The COVID-19 outbreak has established the importance of digital readiness during pandemics. Building the necessary infrastructure to support a digitized world is the current mandate.

Technology has advanced much in the past century since we were hit by the Spanish Flu pandemic in 1918, and it plays a crucial role in keeping our society functional. From remote working to distance learning, and from telehealth to robot deliveries, our world is set to witness a lasting change post this pandemic.

As with other major and minor events of the past few years, social media is playing a big role in shaping people’s perception of the ongoing pandemic. Not just that, the social media platforms have also contributed to spreading information/misinformation, helping people cope with the strange times, and raising awareness about some pressing issues.

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Social Media and the pandemic: The Good!

Social media is one of the most effective ways to share news nowadays (it may be the only way for some people), especially if you are trying to alert the masses quickly. First-hand accounts of those who were infected and recovered were available almost in real-time. Scenes of lockdowns from the countries that first imposed it gave us a heads-up on what was due to come. If only we’d paid more heed to it.

With most of the world stuck at home, our mobile devices have increasingly become the go-to option to connect with the outside world. Social media usage has surged during the lockdown, with various apps witnessing a manifold increase in their traffic.

From educating to entertaining, social media platforms have stepped up as well. Movie and video streaming apps have redefined movie/video watching behavior by introducing features that allow users to host long-distance movie nights with friends and family.

We also witnessed a surge in various ‘online challenges’ that people could do in their homes and upload online. While some may view them as naïve, experts claim these are part of the various coping mechanisms for people.

Social media surfing has gained a significant share in the pie of leisure activities. Be honest, how many of us living alone are doing anything but scrolling these apps in our free time? But thanks to the social media ‘influencers’, scores of us are being motivated to workout at home, eat healthily, pick up a book, or learn something new.

Posts from health workers and others on the frontline have also helped spread the word on the difficulties they’re facing and rallied efforts to help them.

Online solidarity has spilled over offline as well. People are taking to social media to offer support in any way they can, such as picking up groceries for those who are unable to leave home or sharing information on how to support local businesses who are struggling. Communities are rallying together to support organizations and individuals by opening fundraisers to a larger audience.

Social Media and COVID-19: The Bad

Unfortunately, the impact of social media has not been all good. News on social media spreads fast, fake news even faster. Misinformation can cause panic, and can even turn out to be fatal on health issues. As a practice, we should all do a bit of research and validate the information from ‘reputed sources’ before sharing it.

This next bit is more of a tip…Whether it’s a business or a personal profile, you should refrain from posting anything that makes fun of, ridicules, or trivializes the situation. Not only is that insensitive, but it could also spell trouble for you, especially as a business.

The ‘influencers’ have been found guilty of misusing their power and taking advantage of the situation. Various inauthentic posts had gone viral before being pulled down. Do social validation and fame know no limits?

It is true that people often turn to social media as a stress-buster, but experts say it is equally stress-inducing for some individuals. It is important to note here that we’re also in the midst of an ‘infodemic’ – an anxiety-triggering over-abundance of information.

It is easy to overlook, especially now, the devastation that mental health issues cause globally. Studies have reported an increase in mental health issues attributed to social media in recent years. Psychologists say the lockdown will only add to that. Needless to say, mental health has a bearing on physical health as well.

Anti-rich sentiments have also gained momentum in the past weeks, as the pandemic makes the class divides glaringly obvious.

Conclusion

From the transparency that we have gained through this current COVID-19 situation, we now understand that we were not prepared to handle it. Many developed countries have had their health systems overwhelmed, those on the frontlines are being overworked and even the most advanced nations are stumbling to get their economies back up. The next pandemic is not a matter of “if it happens”, but “when it happens”.We need to be prepared at an individual and collective level. Indeed, technology has advanced and will continue to advance exponentially, but institutions and societies need to accelerate in adapting to it and continue investing in building the technology systems for the preparedness.

About the Author –

Prabhakar is a recruiter by profession and cricketer by passion. His focus is on hiring for the infra verticle. He hails from a small town in Bihar was brought up in Pondicherry. Prabhakar has represented Pondicherry in the U-19 cricket (National School Games). In his free time, he enjoys reading, working on his health and fitness, and spending time with his family and friends.

Autonomous Things

Machine learning service provider

Bindu Vijayan

“Autonomous things (AuT), or the Internet of autonomous things (IoAT), is an emerging term for the technological developments that are expected to bring computers into the physical environment as autonomous entities without human direction, freely moving and interacting with humans and other objects…”

To put it simply, Autonomous Things use AI and work unsupervised to complete specific tasks without humans. Devices are enhanced with AI, sensors and analytical capabilities to be able to make informed and appropriate decisions.  They (these devices) work collaboratively between humans and the environment and provide superior performance.  Today AuT work across several environments with various levels of intelligence and capabilities. Some popular examples of these devices are drones, vehicles, smart home devices among others. The components of Autonomous things – software and AI hardware are getting increasingly efficient. With improved technologies (and significantly reducing sensor costs), the variety of tasks and processes that can be automated are increasing, with the advantage of bringing in more data and feedback that can efficiently improve and enhance the benefits of autonomous things.

The technology is used in a wide variety of scenarios – as data collectors from a variety of terrains and environments, as delivery systems (by Amazon, pizza deliveries, etc.), medical supplies to remote areas, etc. Robotics used in the supply chain has proven it reduces/elevates the danger out of the hitherto human tasks in warehouses.  And they probably have the most economic potential currently, followed by autonomous vehicles.  Drones are used to collect data across a wide variety of functions –  for surveillance, security, stock management, weather forecasting, obtaining air data, oceanic data, agricultural planning, etc.

Some fascinating use cases:

Healthcare

Drones are proving to be more and more effective in several ways – they are currently used extensively for surveillance of disaster sites that have biological hazards.  There is no better relevance than the current times when they can actually be used in epidemiology to track disease spread,  and of course for further research and studies.  Drones are facilitating on-demand healthcare by providing medicines to terrains that are difficult to access.  Swoop Aero is one such company that provides medicines via drones.  Drones have brought healthcare into the most remote areas with diagnosis and treatment made available. Remote areas of Africa have their regular medical supplies,  vaccine supplies, lab samples collected, emergency medical equipment made available through Drones. They are also used in telementoring, for perioperative evaluation and so on.  Drones have been very efficient in accessing areas and providing necessary support where ground transport is not reliable or safe or impossible.  Today, most governments have Drones on their national agenda under various sectors. The Delft University of Technology is developing an ambulance drone technology that can be used at disaster sites to increase rescue rates..

Retail

In a world where we have virtual assistants do grocery shopping, replenish stocks, and cooking machines making food, when there is a need to go out shopping, shoppers want to have an easy, fast and frictionless process.  Today, customers do not want to wait in queues and go through conventional checkouts, and Retailers know that they might be losing customers due to their checkout process.  And autonomous shops like Amazon Go are giving that experience to customers where they can purchase without the inconvenience of checkout lines.

Providers of checkout-free shopping technology like ‘Grabango’, use sensor vision and ML to actually hold a virtual shopping basket for every person in the store.  The technology is reputed to process a multitude simultaneous checkout transactions. “Grabango’s system uses high-quality sensor hardware and high-precision computer algorithms to acquire the location of every item in the store. This results in a real-time planogram covering the entire retail environment.” They say it results in increased sales and loyalty, streamlined operations and inventory management and out of stock alerts.

Construction

Companies like Chicago based, Komatsu American Corp., have autonomous haulage stems that have optimized safety in the mining industry like never before. They “help you continue to meet your bottom line while achieving zero-harm” while their focus has been on developing autonomous mining solutions, they have been doing it for more than three decades now! Their FrontRunner AHS has moved more than two billion tons of surface material so far in driverless operations.  Catepillar would be deploying their fleet of autonomous trucks and blast drills for the iron mine in Western Australia – Rio Tinto Koodaideri.  The industry is thriving with autonomous and semi autonomous equipment, and it is evident that it has brought improvements to productivity, and increased profitability. At the Australian mine “autonomous vehicles operated on average 700 hours longer and with 15 per cent lower unit costs”… Similarly, there are other companies like Intsite, a heavy machinery company; their autonomous crane ÁutoSite 100’ does autonomous operation of heavy machinery.

Transportation

Most of us think Tesla when we think autonomous vehicles.  Elon Musk’s dream of providing autonomous ride-sharing has Tesla working on getting out one million robotaxis on the road this year. We will have to wait and see how that is going to pan out. Though autonomous vehicles are the most popular, I suppose it might take a little more time before it finds answers to the regulatory challenges, definitely not an easy task.  It gets quite overwhelming when we think of what we are expecting from autonomous vehicles – it assumes correct performance no matter the uncertainties on the roads and the environment, as well as the ability to face any sort of system failures on its own, and AI is a very critical technology when we are talking real-time decision making. Those sort of scenarios call for a strong computing platform in order to do the analysis at the edge for faster decision making.  The new V2X, which is the 5G vehicle-to-everything is expected to make autonomous vehicles mainstream because the vital information would get transmitted as structured data to the vehicle. V2X is expected to have vehicles interfacing with anything, be it pedestrians, roadside infrastructure, cyclists, etc.

Today, technology is also looking at ‘vehicle platooning’ – “Platoons decrease the distances between cars or trucks using electronic, and possibly mechanical, coupling. This capability would allow many cars or trucks to accelerate or brake simultaneously. This system also allows for a closer headway between vehicles by eliminating reacting distance needed for human reaction.” It has a group of self driving vehicles moving at high speed but safely, as the trucks are in constant communication with each other and use this intelligence to make informed decisions like braking, speeds, etc.  And autonomous trucks and cars can automatically join these platoons or leave, this has the advantages of reduced congestion, fewer traffic collisions, better fuel economy, and shorter commutes during peak hours. 

Conclusion

Studies show that Autonomous things are fast moving to ‘swarm’ or a bunch of intelligent devices, where multiple devices will function together collaboratively, as against the previously isolated intelligent components/ things. They are going to be intelligently networked among themselves and with the environment, and the wider that becomes within every industry, they are going to show phenomenal capabilities. But let’s not forget there is a whole other side to AI, given how unpredictable things are in life, AI would sooner or later have to respond to things that it never saw in training… we still are the smarter ones…

References:

https://en.wikipedia.org/wiki/Autonomous_things

https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020/

https://worldline.com/en/home/blog/2020/march/from-automatic-to-autonomous-payments-can-things-pay.html

https://en.wikipedia.org/wiki/Self-driving_car

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174005/

https://www.komatsuamerica.com/

https://en.wikipedia.org/wiki/Platoon_(automobile)https://grabango.com/

Assess Your Organization’s Maturity in Adopting AIOps

IT operations analytics

Anoop Aravindakshan

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

All events, logs, and alerts are integrated into one central locale. ITSM processes are 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. Autonomous remediation 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:

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.

Combating a health crisis with digital health technologies

Bindu Vijayan

The current pandemic has exposed yawning gaps in the systems of the best of developed countries to be able to respond to virulent pathogens.  The world has seen SARS and Ebola in fairly recent times, and with the COVID 19 pandemic, it is becoming clear that technology can help combat and overcome future epidemics if we plan and strategize with these technologies.  They bring efficiency to our response times, and we are currently learning the importance of using these technologies for prevention as well.  A small example – Canadian AI health monitoring platform BlueDot’s outbreak risk software is said to have predicted the outbreak of the pandemic a whole week before America (who announced on Jan 8), and the WHO (on Jan 9) did. BlueDot predicted the spread of COVID 19 from Wuhan to other countries like Bangkok and Seoul by parsing through huge volumes of international news (in local languages).  It further was able to predict where the infection would spread by accessing global airline data to trace and track where the infected people were headed.

Contrary to earlier times, today it only takes a few hours to sequence a virus, thanks of course, to technology.  The scientists don’t have to cultivate a sufficient batch of viruses any longer in order to examine them, today, its DNA can be got from an infected person’s blood sample or saliva.  India’s National Institute of Animal Biotechnology (NIAB), Hyderabad, has developed a biosensor that can detect the novel coronavirus in saliva samples. The new portable device called ‘eCovSens’, can detect coronavirus antigens in human saliva within 30 seconds using just 20 microlitres of sample.  Startups like Canadian GenMarkDx, US-based Aperiomics & XCR Diagnostics, Singapore based MiRXES, and Polish company’s SensDx have introduced top notch diagnostic solutions.  Identifying infected people to provide strict medical care will be made a lot faster with these diagnostic kits. 

Genome sequencing is also vital to fight the pandemic.  The genome of this virus was completely sequenced by the Chinese scientists in under a month from detection of the first case, and then on the biotech companies created synthetic copies of the virus for research.  Today creating a synthetic copy of a single nucleotide costs under 10 cents (in comparison to the earlier $ 10), so these days it is far quicker and cheaper, which means the chances of finding appropriate / adequate medication are much faster which will help save more lives.

Healthcare workers are having to pay a huge price, they run the risk of getting infected, there is often paucity of PPE, and in some countries, they even have to face assault from crowds that are angry and confused at the situation.  Medical workers are targetted by mobs, there are instances where communities don’t allow them to come back to their homes after duty, shops don’t sell them necessities, etc.  Medical robots can be the real game-changers in such situations.  Deploying robots in such scenarios to do the rescue is becoming a much sought after option, wherever possible.   Robots become the answer to such difficult situations as they are impervious to infections.  They allow physicians to treat/communicate through a screen. The patient’s vitals are also recorded by the robot.  Patients can be very efficiently monitored this way.

Drones for deliveries, especially medical deliveries can also be used to reach isolation zones or quarantined zones.  Italy made a big success out of this. Italy’s coronavirus epicenter, Bergamo, in Lombardy region, had to resort to people’s temperature being read by drones.  ‘The Star’ reported that “once a person’s temperature is read by the drone, you must still stop that person and measure their temperature with a normal thermometer,” said Matteo Copia, a police commander in Treviolo, near Bergamo. Drones are being used for surveillance – In areas where people were not complying with social distancing and lockdown restrictions, authorities are using drones to monitor people’s movement and break up social gatherings that could be a potential risk to the society. Drones are also being used for Disinfectant spraying, broadcasting messages, medicine and grocery deliveries and so on.

Interactive maps give us the data on the pandemic on real time, and monitoring a pandemic this wide and dangerous is very crucial to stopping/controlling its spread. These maps are made available to everybody, and the truth and transparency in the situation of such epic proportion is necessary in order to avoid panic within communities.  We now have apps for tracking the virus spread, fatalities and recovery rates, and apps would be developed for the future that will warn us about impending outbreaks, the geographies and flight routes that we must avoid

Implementing these technologies will enable us to manage and conquer situations like the current pandemic we are going through. As Bernardo Mariano Junior, Director of WHO’s Department of Digital Health and Innovation, rightly said “The world needs to be well prepared and united in the spirit of shared responsibility, to digitally detect, protect, respond, and prepare the recovery for COVID 19. No single entity or single country initiative will be sufficient. We need everyone.”

References:

Machine Learning from Programmer’s Perspective

Gireesh Sreedhar KP

Introduction

Machine Learning (ML) is key pillar of the Artificial Intelligence (AI) domain. ML solves problems which are unimaginable using traditional programming paradigm. During my interactions with people on ML, I am frequently asked following key fundamental questions.

  1. What is Machine Learning (ML)?
  2. What is the need for ML programs when traditional programs have served us well for decades?
  3. What differentiates ML from traditional programming paradigm?

Let me answer above questions from a programmer’s perspective to build understanding irrespective of your ML background.

Traditional Programming Paradigm

We are familiar with traditional programming, where we use selected programming language (like C, Java, etc.) and program specific instruction or rules to process inputs which creates output we need.

Let us understand with an example, a retail store wants to write a program to find amount to be paid (Amount) given Quantity (q) and price per unit (p). We will solve this by writing code as below.

  1. Read two inputs ‘q’ and ‘p’ (Data)
  2. Amount = p*q (apply Rules, Rules are part of program, but shown as input for illustration)
  3. Return Amount (Output)

The need for Machine Learning

Let us try to solve same problem of computing ‘Amount’ from inputs ‘p’ and ‘q’. However this time we are required to read the inputs (p and q) from a piece of paper with digits either handwritten or printed. This needs program to recognize the digits from paper (images of digits received by program) before digits can be assigned to ‘p’ and ‘q’.

Let us examine traditional programming approach (writing rules) to recognize the images of digits received by program

  • Are rules scalable?
  • Can rules handle recognizing digits written in different orientations and styles? Say, when image received is program should recognize the image as digit 8.
  • There are over 70,000 samples of handwritten digits which are commonly used (refer MNIST database, sample below), can we write rules to cover all possible combinations?

Now it’s clear to us that rules-based approach will break and it’s not practical to build all rules and program those. We need something else instead of rules to solve these types of problems and that something else which replaces rules is Machine Learning.

What is Machine Learning?

Let us ask ourselves

  1. What differentiates the first problem statement (easily solved using rules) from the second one?
  2. Why a problem easily solved by humans (recognizing different styles digits by vision), is such a difficult task for computers?

We humans learn to identify digits which are written in standard format, however when presented with digits written in different styles and orientations, we are still able to recognize the digits identifying the patterns which are the beauty of human learning process. Can we make computers (machines) do the same and learn like humans? Let us understand how we make a machine to learn this task and perform like humans.

We will feed the Machine Learning program (ML) with lots of data (examples) containing images of digits in different styles and orientations along with actual digit it represents (supervised learning). Say one data point will be an image and mapped to corresponding digit 8. We are providing data along with the intended output as input to ML for learning. Processing lots of inputs, ML comes up with Rules or Patterns or Models to map an input to output we need (like humans).

This Rules/Pattern/Model learned by ML will be used to process new incoming data to produce output or sometimes called as Predictions.

What differentiates ML from traditional programming paradigm

The major difference between traditional program and ML is, traditional program applies rules on input data to produce output. However, ML takes output (outcomes we need) as input and produces Rules/Pattern/Models as output which are then used to process new inputs.

Why Machine Learning

Data-driven decisions increasingly make the difference between keeping up with the competition or falling further behind. Machine learning can be the key to unlocking the value of corporate and customer data and enacting decisions that keep a company ahead of the competition.

Machine Learning at GAVS

GAVS has own in-house Artificial Intelligence research team building advanced Machine Learning algorithm and techniques powering its products and solutions. ZIF (Zero Incident FrameworkTM) Artificial Intelligence-based Technology Operations (AIOps) from GAVS is powered by state-of-the-art Machine Learning algorithms developed in house.

About the Author:

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.

The Crucial Component of Data-driven Organizations

Sankul Seth

Data is a crucial component for any organization to generate revenue and provide the best-in-class experience for their customers. Various studies have shown that 60% of the organizations fail to implement UI tools, which are heavily dependent on data-driven technologies because organizations spend millions on buying these tools but not investing in the right talent to achieve them. Understanding of data is the first stepping stone for any organization to be data-driven. I implemented various data solutions from inception to implementation, which helped organizations to derive data-driven decisions. After fifteen years of extensive experience across multiple data technologies and platform, I have developed numerous critical data frameworks which have benefited organizations to be data-driven. The first essential pillar is to build a cohesive and robust enterprise data team.

Data Center Consolidation Initiative Services

Data is a driver for any business intelligence, analytics, insights, marketing campaigns, UI applications, tools, and technologies. It’s crucial to understand why and what the business needs before deciding to invest in any data technologies. Today, organizations are leveraging data for executing campaigns and defining customer 360-degree views to provide personalized and OMNI-channel experience using data KPIs. There are unlimited data tools available, and it became difficult to pick the right one, which fits all the requirements for the business and delivers a perfect solution. It all goes back to find the right leader who has deep experience on both sides of the coin (Business and Technology). It’s hard to find such talent but not impossible, and this decides the success or failure of any data implementation projects.

About the Author:

Sankul is the Vice President of the Enterprise Data Team at PSCU. is a value-driven and business-oriented data and IT technology leader with a proven track record for building enterprise applications and data-driven platforms. He believes the current generation and future leaders should be focused and good listeners, as it helps to perceive and deliver solutions.

Heroes of GAVS

Every day we witness these heroic acts Of GAVSians!

A special shout-out to our GAVSians who go into the hospital (BronxCare Health Services) every day to keep their vital computer systems going. 

Ivan Durbak, CIO, BronxCare – “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.”

“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

“Customer Success is all about being proactive and getting ready to address newer situations. At GAVS, RITE is a key DNA and Empathy to our teams and customers is absolutely at the top. That drives us to do the right things for both our teams and customers. In this endeavor and to ensure that our teams are safe and healthy and are able to seamlessly provide support and service to our customers, we have created a task force to understand, assess and plan for events that would unfold due to this massive viral pandemic. We have created 3 broad pillars as part of the task force – 1st How do we take care of our people – people safety first (ability to shift GAVS and customer assets to their home offices smoothly and provide adequate support to enable them to stay in touch with our customers and drive value),  2nd continuation of services and support to customers (continue with newer mechanisms and tools of collaboration and governance) and 3rd is the adoption of new operating model (potential onboarding and collaborative situations in a hybrid model for the future as well and enabling culture for such adoption and collaboration). With these 3 key facets, the task force prepared a plan and executed it with measurable outcomes at every stage. Clear goals and empowerment to various teams enabled us to execute a seamless transition to the new model. We moved 700 assets over a course of 4 business days and created stock and contingency plans for an additional 50% of the workforce.”- Balaji Uppili, Chief Customer Success Officer