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Category Archives: Blog

AI & Sustainable Development

Sustainable Development Goals

Sustainable Development Goals (SDG) are 17 interlinked global goals, set up by the United Nations (UN) to achieve a better and more sustainable development for all of us on this planet. 

AI has the potential to play a significant role in achieving the SDGs

Artificial Intelligence has the potential to accelerate our journey in reaching the SDGs. We have seen plenty of examples of how AI and related technologies made incredible impacts in the fields of healthcare, energy, agriculture, retail, telecom, transportation etc… However, we have hardly seen the impact of AI in the social and environmental spheres yet. With SDGs in place, we will now see several such applications. Not only SDGs, but several top tier companies are also now considering sustainability as seriously as their balance sheet.  

It might be overwhelming to write about all the way AI can help the 17 SDGs. Let me discuss one application for each of the first 3 goals and keep the rest for upcoming blogs.  

No Poverty

Poverty is the state in which people or community lack resources for the minimum standard of leaving. Stanford University economist Marshall Burke used satellite images from day and night of different regions and developed a classification algorithm for marking regions in different scale of poverty. This is definitely the right first step for eradicating poverty, to identify where it is present and at which scale. Movement of resources can be done accordingly.  

Zero Hunger

We, in this plant, are not running out of food. There are places with abundance and places with the scarcity of food. Efficient routing of food is the solutions. The money to purchase the food is not a concern here. AI techniques are extensively used to identify countries and regions of excess food, followed by efficient routes to ship it to the locations where that requires the most. 

Good health and well being

Applications of AI that we generally see in health care are mostly at the higher end of the spectrum. Robots performing surgery. On the other hand, AI is helping to map the groundwater quality and how it is changing over time. For many places in Asia and Africa, groundwater from wells and ponds are consumed directly. That makes healthcare and well being of people there directly connected with the weather they consume. Early detection of groundwater quality degradation detection helps regional governments to act and take the right measure to protect the water quality or provide people with community filter systems.

I hope these three applications are inspiring and show how AI can make a strong social impact. Let’s use AI to improve the quality of life on this planet.

Top 4 Trends in Data and Analytics – 2020 and beyond

Top 4 Trends in  Data and Analytics – 2020 and beyond 

By 2025, IDC says worldwide data will grow from 40 zettabytes(ZB) in 2019 to 175 zettabytes, with as much of the data residing in the cloud as in data centers. The datasphere will have three locations:

  1. Core –  traditional and cloud data centers,  
  2. Edge – Cell towers and branch offices
  3. Endpoints – PCs, smartphones, and Internet of Things (IoT) devices.

While the adoption of cloud-based data lake is increasing  within the organization to manage this large scale data produced or collected, there are still challenges to process, analyze and to monetize quickly. This article is an attempt to demystify the data and analytics trends to overcome these challenges to scale and manoeuvre business.  We will focus on four key trends in data and analytics listed by Gartner in 2019. They are:

  1. Augmented Analytics
  2. Continuous Intelligence
  3. Explainable AI
  4. Natural Language Processing (NLP)
  1. Augmented Analytics – Augmented Analytics uses artificial intelligence/machine learning (AI/ML) techniques to automate end to end data preparation, insight discovery and sharing. It  enables automation of data science activities and machine learning model development, management and deployment through MLOps to increase reproducibility. Actionable insight generation is automated through the use of automated advanced AI/ML  algorithms. There won’t be a need to clean the data for analysis anymore eliminating human error and speeding up model deployment and decision making. There will be elimination of  rigorous analysts and database administrators who merge data from various sources to understand correlation and derive insights.

Example: If you have e-commerce business, data from various departments like marketing , operations, merchandising, design, customer service can be analysed to see the effect of marketing campaigns on booking and delivery of orders quickly. You can also know the zones of highest customer returned products for your business.

  1. Continuous Intelligence (CI) –  Gartner predicts that by 2022, more than half of major new business systems will in some way exploit continuous intelligence capabilities. CI systems enable frictionless augmented analytics designed to inform human decisions with the most accurate data possible. Continuous intelligence uses real-time data, automated or semi-automated processes, and AI-based, machine-driven way to continuously interpret data, discover patterns and learn what’s of value in the data. It’s not a once quarterly process for getting back on track or adjusting strategic direction — it will be an inherent part of how a business works and runs each and every minute of every day. CI allows business users to mash up and blend disparate data intelligently with the objective of discovering new insights constantly and revealing it as a data story with complete context. It does away with human biases in each step of the data pipeline and replaces them with a smart machine and AI that discovers everything in your data, no matter how complex.

Example: We will continue with the same e-commerce example. We saw that data from various departments like marketing , operations, merchandising, design, customer service was integrated on a single platform. We knew the effect of marketing campaigns on booking and delivery of orders. We also knew the zones of highest customer returned products for your business. With continuous intelligence we will know this in real time hence we will be able to quickly optimise the campaigns for maximum performance. We can also look for the underlying reasons that customers are returning products like faulty products and pause the sales to reduce losses. Teams can be empowered to make decisions in real time. We could pause the marketing campaigns of the faulty products as well as make the inventory zero for these until investigations or replacements are being thought of.

  1. Explainable AI (XAI)- AI is finding its way into a broad range of industries such as education, construction, healthcare, manufacturing, law enforcement, and finance. The sorts of decisions and predictions being made by AI-enabled systems is becoming much more profound, and in many cases, critical to life, death, and personal wellness. This is especially true for AI systems used in healthcare, driverless cars or even drones being deployed during war. However most of us have little visibility and knowledge on how AI systems make the decisions they do, and as a result, how the results are being applied in the various fields. 

Many of the AI/ML algorithms are not easy to examine to understand specifically how and why a decision has been made. This is especially true of the most popular algorithms currently in use – specifically, deep learning neural network approaches. As humans, we must be able to fully understand how decisions are being made so that we can trust the decisions of  an AI system and control it.  The lack of explainability and trust hampers our ability to fully trust AI systems. We want computer systems to work as expected, produce transparent explanations of the outcomes and provide reasons for decisions that they make. In a sentence the models and outcomes should be easy to interpret without any ambiguity. This is known as Explainable AI.

Example: We will continue with the same e-commerce example. We saw that data from various departments like marketing , operations, merchandising, design, customer service was integrated on a single platform. If the system tells us in real time that the marketing campaign needs to be optimised, we will need the performance of all the campaigns and the metrics that affect their performance. Now, we can see that the customer acquisition cost for the poor performing campaign was highest hence the need for optimisation.

  1. Natural Language Processing (NLP) – According to wikipedia NLP is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interaction between  machines and human languages. There has been a significant increase in adoption of NLP in any kind of unstructured data analysis including pure text analytics, document classifications and interaction between human and communication devices in the past decade and it will continue to increase. NLP techniques also enable business an easier way to ask questions about data and to receive an explanation of the insights. The need to analyze complex combinations of structured and unstructured data and to make intelligence accessible to everyone in the organization will drive this growth. Conversing with  tools will become as easy as talking to a human just like we are doing with Siri, Cortana et al by leveraging speech to text analytics to get answers from the data beyond predefined rule based algorithms. 

Example: Imagine talking to our software for the answers we are seeking. Zones of highest customer returned products, reasons, solutions, options, details of marketing campaigns  the list goes on. All the answers while conversing with the software instead of viewing dashboards!

Of course there are other trends which will shape the Data and Analytics future. We are just covering the tip of the iceberg here!

COVID 19 proved versatility of Artificial Intelligence

Artificial intelligence (AI) and data science (DS) are helping the world to fight against the current COVID 19 pandemic unimaginably. No doubt these are superheroes of technology. These are unprecedented times in human history. The world hasn’t faced a challenge of this magnitude since WWII. 

AI & DS proved its versatility once again! Numerous applications of AI & DS are now helping this world to fight against the current pandemic. Hat’s off to those who are developing those creative and novel solutions using AI & DS. 

The world will not be the same after this pandemic. However, versatile technologies like AI & DS will remain superheroes on the other side of this episode. These technologies will be helping the world to adapt to new normals. The world will need more creative AI & DS professionals. 

AI & DS solutions for understanding the impact

This pandemic was a sudden hit. It took us some time to get our heads around what is happening and where. Suddenly, data started flowing from all the directions. 

Some creative geniuses quickly utilized disparate data and created COVID 19 trackers. The trackers are very valuable. If we can’t measure, we can’t improve. These trackers also bring forward the gravity of the problem and the alarming growth rate. Natural language processing (NLP) based methods helped to bring together structured and unstructured data.

The immediate next challenge was to predict what’s going to happen next? How fast the infection will spread? Arguably, there is no better technique than AI & DS to answer these questions. Data-driven predictive models and large scale computer simulation studies were done to answer those questions. Those answers were used by governments to prepare well in advance. That includes preparing enough capacity in hospitals, having necessary medical supplies, etc. in place. Businesses used those answers to rework on their supply chain, making sure that essentials remain available.  

AI & DS contribution to manage & control 

It was clear that there is no medicine for the treatment of this disease. Proper vaccine development can take more than a year. The entire process is very complex and includes – invention, passing regulatory requirements, and production in bulk. So, until then, the most pragmatic approach is proper management and control. 

Lockdowns to manage social distancing is an effective control mechanism. However, the key questions regarding lockdown were – how efficient are lockdowns? at what rate we can expect the spread to get controlled? how long should those lockdowns last? Interestingly enough, most of the answers came from data-driven modeling and simulation. 

Machine vision (MV), a key element of the AI technology stack, is playing multiple key roles at a time. MV is used for identifying congested areas where social distancing is compromised. MV is used for identifying people without a mask. MV is decreasing the number of common touching spots, like doorknobs and handles. MV is also used for clinical purposes.

AI & DS contribution to the treatment

AI & DS’s contribution starts with understanding the structure of the virus. This understanding is fundamental to the development of medicine and vaccine. Radiologists are using 3D machine vision techniques to understand the virus’ attack mechanism within the lungs.

The Pharma industry is extensively using AI & DS technology for drug discovery. This includes the discovery of novel molecules, finding a cost-effective synthesis path, understanding target mechanisms within the human body, and possible side effects. Data and statistics will play a crucial role in the clinical trials of those drugs. And this time it will be even more crucial as we are running against time.

Wrapping up

This is not an exhaustive list. AI & DS are used to develop several other creative solutions to fight the battle against COVID 19. Examples in this blog are for inspiring people, and to appreciate the versatility of AI & DS. AI & DS will remain versatile technology on the other side of this pandemic. By that time, the health and pandemic challenges will be replaced by economic and social challenges. More creative applications developed using AI & DS will be required as the world adapts to the new normal. More creative professionals will be required to make it happen.

Mock Interview