Category Archives: Blog

Teamwork in Education: A critical factor for success

[3 mins Read]

It’s no secret, Teamwork is considered a critical factor for success in any profession – corporates, startups, NGOs – you name it. However, when it comes to the current education system, it does not emphasise teamwork. Instead, by design, our schooling and university education system develop individuals with certain skills. No doubt, individual capability development is genuinely required, but it should not be at the cost of learning how to make a collective impact.

Humans are the most advanced species primarily because we can cooperate and work together at a scale that no other species has demonstrated. We carry teamwork in our DNAs; our forefathers did teamwork as hunter-gatherers for thousands of years. So, it’s a pity that we designed our education system to suppress this gifted ability.

In the past 200 years, we have seen unprecedented development in technology, inventions, and overall our understanding of the world. Communication and digital technologies worked like gasoline on a fire. The exponentially increasing knowledge made it impossible to do any development without teamwork. Unfortunately, when it comes to our education system, apart from some areas in research studies, our education system from schools to colleges has primarily focused on developing an individual’s abilities and capabilities. 

For example, let’s take a look into the superstar technology of our time, Artificial Intelligence (AI), and what it takes to create an AI application.  AI draws it’s foundational strength from Mathematics, Statistics, and Computer Science. This foundation helps to develop efficient tasks and algorithms. However, when you see from an AI product perspective, the contribution of the foundational part is hardly a quarter of the whole. 

Apart from the core algorithm, an AI product needs connectivity to the data sources, a platform to host the program and run, a user interface, a disciplined way to develop the product, aka DevOps. And apart from all these technical needs, an AI product needs to be clear of any data privacy issues and any inherent biases that might creep from the AI product’s decisions. Considering all that listed so far, it should be apparent that it’s practically impossible to develop an AI product by a single person. 

As the African proverb goes – It takes a village to raise a child. It’s no different for an AI product – here it takes a team of diverse experts. The chances that you will learn all the skills required to create a complete AI product are very thin. Success for anyone in the community can only come from developing expertise in one or a few of these topics and our capability to collaborate, do teamwork, and harness the complementary skills of other experts. 

I ranted that over the last 200 years, we haven’t designed our education system to teach teamwork. At UNP, we immensely value the spirit of teamwork. Therefore, we emphasize on opportunities to learn and practice cooperation and teamwork in all UNP courses, even for those courses we design with our partner institutes.

Employers are taking less cognizance of College Degree

[4 mins read]

When asked whether he considered which college a job applicant attended while evaluating a prospective Tesla employee, Elon Musk said, “There’s no need even to have a college degree at all or even high school. If somebody graduated from a great university, that may be an indication that they will be capable of great things, but it’s not necessarily the case. If you look at, say, people like Bill Gates or Larry Ellison, Steve Jobs, these guys didn’t graduate from college, but if you had a chance to hire them, of course, that would be a good idea.” 

Elon Musk is not alone. Serial entrepreneur Kunal Shah who founded two successful companies – Freecharge and CRED, said in an interview with Your Story, “I’m a Philosophy major, I can’t care about other people’s degrees. One of our senior leaders hasn’t even done graduation. The best degree that person has is 10th pass,” 

The hiring process of big corporations is changing as well. In 2017, IBM’s vice president of talent, Joanna Daley, told CNBC that about 15 percent of IBM’s U.S. hires don’t have a four-year degree. She said that instead of looking exclusively at candidates who went to college, IBM now looks at candidates who have hands-on experience via a coding boot camp or an industry-related vocational class. 

Employers are looking for evidence of ability rather than just a degree. This trend is about to catch fire especially in the services sector like Information Technology, Analytics, Finance, etc. where job opportunities may not always require a formal college degree. Also, in terms of job opportunities in these industries, demand outpaces supply so employers are looking at candidates who demonstrate some background, knowledge, skills, and experiences right after high school. 

Computer Science is already an integral part of today’s middle/ high school curriculum and we are now seeing Analytics / AI being introduced as well.  Children are exposed to basic financial and quantitative literacy at home and school which lays a strong foundation for a career in finance.  Infinite resources available for online/self-learning, the scope for practical/ applied experiences, etc. are proving very effective in preparing students ready for jobs right after school.

Nevertheless, college degrees will remain relevant for those seeking careers in deep science, research, etc.

At UNP, we foresee changes in the education system. It’s getting apparent that a college degree comes with a high cost, but it’s getting sidelined by employers. As a result, the education and skill development systems are evolving towards decentralized, tailor-made, and demand-driven arrangements. Also, to keep up with the fast-changing skill demand – learning, upskilling, and reskilling will be a continuous process. As a result, we all will eventually move from a college degree followed by a lifelong career to a career of continuous learning and working simultaneously. 

If you are in the space of coding, Data Science, AI, etc., your GitHub profile, interactions in StackOverflow, etc., will be valued more than any degree or certificate. Behance sets the precedent in the design world, where hiring is done based on the Behance profile of the candidate instead of a degree or certificate.  You might be wondering how to manage several proofs of ability and place those in front of a prospective employer? Worry not. Technology like blockchain will help tie all the discrete pieces of evidence in a secure and tamper-proof way. 

At UNP, we actively work on understanding the upcoming skill demand and anticipating the changes required in the education system. Together with our partners, we are working on curating the right upskilling programs to prepare you for the job market as well as be ready for the future. 

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 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 ways 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 communities lack resources for the minimum standard of living. 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 poverty scales. This is definitely the right first step for eradicating poverty, identifying where it is present and at which scale. Based on that insight, resources can be distributed.   

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 solution. 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 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. So, 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!