MSAI: Making Sense of Artificial Intelligence:

WLL05: Making Sense of Artificial Intelligence


We are now in the 4th Industrial Age and while Artificial Intelligence has been compared to being the new electricity, it’s availability through the cloud and mobile devices is much more universal.

Since AI helps automate tasks which were earlier done by humans, it’s potential to enhance almost all human activities is enormous, and that is why we have adopted an “AI for all” policy, rather than AI for a privileged few.

This is an introductory  one month ( 4 weeks) course for anyone who is curious to know what are the various aspects of AI. That is why it has been given the title ” Making Sense of Artificial Intelligence”. Throughout the course, there will be links to and information about books, articles and videos to learn more. 

This course is therefore a fascinating introduction for the curious, with substantial guidance on more for the serious. 

This course in somewhat varying formats has been delivered to almost a thousand learners with diverse educational and professional profiles, including some School going students as well. 

This course is an attempt to help form a realistic perspective, in this climate of the hype around the topic. The better we understand AI, the greater the sense of awe at the sheer complexity of the brain, and the amazing algorithms that nature has devised to solve one of the hardest problems known to man. What we see around us are examples of ‘ narrow AI’ or ‘weak AI’. But the quest for ‘strong AI’ is going to be a long journey…. but it’s perhaps the greatest scientific voyage that we can take now….

The flow of posts for this program is: 

Monday Day 1: The origins and the present landscape of AI:

1.1: A brief history of evolution of AI

1.2: The Turing test

1.3: Simple automation, algorithmic automation and intelligent automation

1.4: AI and allied ( sometimes confused) terms

1.5: Narrow Intelligence, General Intelligence & Super Intelligence

Tuesday Day2: AI, Machine learning and Deep learning 

2.1: The relationship between AI, ML and DL

2.2: Approaches to machine learning 

2.3: The search for a master algorithm

2.4: Deep Learning and Artificial Neural Networks

2.5: The Mathematics underlying Machine Learning

Thursday Day 3: Generic AI tools: 

3.1: Recommendation Engines

3.2: Predictive models

3.3: Speech Recognition 

3.4: Natural Language Processing

3.5: Computer Vision

Wednesday Day 4: Application areas:

4.1: AI and Marketing

4.2: AI and Finance

4.3: AI and Healthcare

4.4: AI and Agriculture

4.5: AI and autonomous transportation

Friday Day 5: AI and Social Good

5.1: The future of work and human dignity

5.2: AI and SDGs ( Sustainable Development Goals)

5.3: World economic forum on ethical issues in AI

5.4: AI and bias 

5.5: AI in the foreseeable future


These courses are offered  through WhatsApp that can be easily accessed with a mobile hone on the scheduled  week ( Monday to Friday ) of the month. Expression of interest in joining the course can be made on any day, and all requests received upto 12 noon on the Sunday before the schedule of the course will be placed in a cohort that would begin on the following Monday.


To register for this course:

First pay the fee of Rs 2500/- by PayTM to MM Pant ( mobile number : +919810073724).

Then send a Whatsapp message to MM Pant ( +919810073724) with the following information :

1: Your Name : First and Last

2: Mobile phone number linked to Whatsapp

3: Course Code and course name:

4: PayTM transaction number/ screenshot

5: Your brief profile ( optional). There will be an opportunity to briefly introduce yourself within the group also.

For those who would rather pay into a Bank account, the relevant information is : 

Madan Mohan Pant

HDFC Bank, Unitech Cyber Park, Sector 39, Gurgaon 

A/c 26451000000301


(The account number is 26451 followed by six zeroes followed by 301)


To seek any clarification or for further information, please send a Whatsapp message to Prof. MM Pant at +919810073724

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Weeklong Learning in 1st week of July 2020:

WLL01: Learning with WhatsApp, other Mobile Apps and MOOCs:

Backdrop : 

The recent experience with School, College and University education demonstrated the lack of adequate preparation of both the teachers and learners with learning from the abundant educational resources that are available. Both sides did a very exemplary job of managing the teaching-learning experience under the circumstances. 

This short weeklong course helps students, teachers and professionals to learn whatever they seek to learn from the abundant online resources. It requires certain preparations by understanding the big picture of online remote education worldwide. 

It will help them in their journey of becoming autonomous self-directed ( atma nirbhar) learners. 

The flow of topics for this weeklong course are as below;

Monday, Day 1: Learning with Whatsapp 

1.1: Why Whatsapp?

1.2: Features of Whatsapp that make it great for learning 

1.3: Techniques for learning effectively with Whatsapp 

1.4: Lifelong Learning with Whatsapp

1.5: Learning Artificial Intelligence with Whatsapp

Tuesday, Day 2: Learning with mobile Apps

2.1: The benefits of using mobile Apps for learning

2.2: UNESCO mobile learning initiatives

2.3: Bloom’s Taxonomy and mobile Apps

2.4: Characteristics of a good educational App: How to choose an App that is effective for learning 

2.5: Pursuing MOOCs through Apps.

Wednesday, Day 3: Specific Learning Apps

3.1: How to choose a good Learning App?

3.2: YouTube and other educational videos

3.3: Duolingo for language learning

3.4: Khan Academy for Maths and other subjects 

3.5: Grammarly and Turnitin for better writing in English

Thursday, Day 4: Learning with MOOCs 

4.1: A quick history of MOOCs 4.2: Coursera

4.3: EdX

4.4: Futurelearn

4.5: Learning with SWAYAM:

Friday, Day 5: Leveraging AI for better learning 

5.1: Converting Speech to Text

5.2: Using Text to Speech

5.3: Benefitting from Machine Translation 

5.4: Using Google Alerts

5.5: Using YouTube recommendation engine to access personalised content


These courses are offered  through WhatsApp that can be easily accessed with a mobile hone on the scheduled  week ( Monday to Friday ) of the month. Expression of interest in joining the course can be made on any day, and all requests received upto 12 noon on the Sunday before the schedule of the course will be placed in a cohort that would begin on the following Monday.


To register for this course:

First pay the fee of Rs 2500/- by PayTM to MM Pant ( mobile number : +919810073724).

Then send a Whatsapp message to MM Pant ( +919810073724) with the following information :

1: Your Name : First and Last

2: Mobile phone number linked to Whatsapp

3: Course Code and course name:

4: PayTM transaction number/ screenshot

5: Your brief profile ( optional). There will be an opportunity to briefly introduce yourself within the group also.

For those who would rather pay into a Bank account, the relevant information is : 

Madan Mohan Pant

HDFC Bank, Unitech Cyber Park, Sector 39, Gurgaon 

A/c 26451000000301


(The account number is 26451 followed by six zeroes followed by 301)


To seek any clarification or for further information, please send a Whatsapp message to Prof. MM Pant at +919810073724

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Education in the post Covid19 era: for those in a hurry

Education in the post Covid19 era: for those in a hurry. 

1: Post Covid will for most sectors be a restoration of ‘status quo ante’ but for education it has to be a fast forward to bring about a Quantum jump in the economy. This will require pole vaulting to leverage the benefits of the 4th Industrial Age. 

2: Like the IITs transformed the slide rule wielding engineer to a computer fluent complex problem solver with wide cognitive flexibility to reap the benefits of the 3rd Industrial Age,all our learners should become valuable assets in the 4th Industrial Age.

3: It is said of Albert Einstein that he gave a particular exam to a class that had already been given that exam. Alarmed at this, his Teaching Assistant

warned Einstein of what he was about to do. The Professor just smiled and said: Yes the questions are the same but the answers have changed. This is squarely applicable to education in the post Covid 19 scenario. 

4: Prospering in the 4th Industrial Age is not just for a few,but for anyone who can pursue Heutagogy, rather than traditional pedagogy or andragogy to be a self-directed learner. With data and tools accessible through the cloud learning is now democratised.

5: Both learners and educators will deploy AI in their pursuit of learning. Like self-driving cars, learners will transform from passive learners to active self-directed lifelong learners. Educators will master AI and become AI fluent SmartEducators. 

6: While much learning can be self-learning,it may be augmented at “co-learning spaces and emerging technologies experience centres” where  groups of learners broadly pursuing similar goals can learn from each other. Learning communities will be the new norm. 

7: In medicine,the diagnosis treatment and management of many ailments is not done the same way as decades ago.The advances in research inform medical practice.But progress in educational research has no impact on the practice or education. This needs to change in the post Covid 19 era. 

8: If these points stimulated you to know more at some length of the possibilities of the emergence of a ‘ Science of Learning’ then here is a link to an article of about 2500 words on the topic :

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Education in the post Covid 19 era:

Education in the post Covid 19 era: 

A few decades back we used to refer to the future scenarios in education with   phrase ‘21st Century education’.  I adopted the phrase “ Learning 321: education in the 3rd decade of the 21st century” to prepare for the changes that we could foresee in the coming decade.

But the Covid 19 has become a pivotal point in human history. The immediate future is being determined by two concurrent revolutions; ‘The 4th Industrial revolution’ to which attention was drawn by Prof Klaus Schwab at the World Economic Forum meeting in Davos in January 2016 and ‘The Fourth Education Revolution’ which is the title of a book ( in 2019) by Prof Anthony Seldon wherein he explores the most important issue facing education (and humanity at large): the fast approaching revolution in Artificial Intelligence or AI, to suggest that “ Nothing matters more than education if we are to see AI liberate, not infantilise humanity”. 

One of the very important experiences regarding education in the Covid era was the appreciation of the core activity of learning, being more important  than the form of organization structures and rituals ( like compulsory attendance). The widespread adoption of e-learning and multi-modal learning is the triumph of function over form.

Just a few years back, policy makers had started appreciating that 

the standard summary metric of education-based human capital used in macro analyses—the average number of years of schooling in a population—is based only on quantity. And ignoring schooling quality turns out to be a major omission. As recent research shows, students in different countries who have completed the same number of years of schooling often have vastly different learning outcomes. A new summary measure, Learning-Adjusted Years of Schooling (LAYS), that combines quantity and quality of schooling into a single easy to understand metric of progress seems to be a better choice. The cross-country comparisons produced by this measure are robust to different ways of adjusting for learning (for example, by using different international assessments or different summary learning indicators), and the assumptions and implications of LAYS are consistent with other evidence, including other approaches to quality adjustment. For the year 2018 data, India had a score of 5.8 compared to Japan with 12.3. 

While we may have differing views on what are the best metrics to measure the success of large educational systems, it is now agreed that in preparation for the knowledge economy, augmenting the cognitive capital of every individual is the path to be pursued. A few ‘islands of excellence’ are not adequate. This was articulated in the 1970s by Alvin Toffler in his profound statement that “ The illiterates of the 21st century will not be those who cannot read or write, but those who cannot learn, unlearn and relearn”. He was perhaps referring to the obsolescence of the ideas of Francis Bacon who had said “ Reading maketh a full man, writing an exact man and conversation a ready man”. For the economically inclined, the Stanford Professors Eric Hanushek and Ludger Woessmann in a 2015 book “ The Knowledge capital of nations “ carried out a  rigorous, pathbreaking analysis demonstrating that a country’s economic prosperity is directly related in the long run to the skills of its population. Of course, every country acknowledges the importance of developing human capital, but Hanushek and Woessmann argue that message has become distorted, with focus not on valued skills but on proxies for them. The problem is that the opposite of knowledge is not ignorance, but “the illusion of knowledge”.

A quantum jump in economic development can be only achieved by an all round proactive pole vaulting rather than a reactive leapfrogging ( to borrow the metaphor from RA Mashelkar) in the learning capabilities of all, not so much in the creation of a few isolated centres of excellence. This looks like a practical way to implement the Philosopher Voltaire’s exhortation that “ no problem can withstand the assault of sustained thinking”.

When the IITs were created in the country, in the 1960s there was a long and significant tradition of Engineering education from Guindy College of Engineering, Thomason College of engineering to Banaras Engineering College, but the IITs transformed engineering education fundamentally. There were many innovative features in the IIT system, and they benefitted from International partnerships with various countries, and several Institutions within those countries. 

During the first ten years of its existence, IIT Kanpur benefited from the Kanpur Indo-American Programme (KIAP), where a consortium of nine US universities ( Massachusetts Institute of Technology, California Institute of Technology, University of California, Berkeley, Princeton University, Carnegie Institute of Technology, University of Michigan, Ohio State University, Case Institute of Technology and Purdue University) helped set up the research laboratories and academic programmes. The first Director of the Institute was Prof. PK Kelkar who was a teacher–philosopher, able to see years ahead. He said that if engineering were the muscle for development, science was the brain. And that only with humanities could engineering education have a heart.

The IITs were created in the 1960s and their graduates were future ready having been transformed from slide-rule wielding engineers to engineers who were computer ready and suited to flourishing in the coming 3rd Industrial Age, which in Prof Klaus Schwab’s timeline began in the 1970s. 

Now that we are in the 4th Industrial Age driven by Artificial Intelligence and other emerging technologies, its fruits need not be only for a few, but we have truly an ocean of opportunities for anyone with the grit, tenacity and perseverance to be a self-reliant learner. With data and tools accessible through the cloud almost anywhere through the Internet, learning is more democratised. World class education is truly in one’s hands today. 

Not only that, by actively participating in learning communities such as GitHub or Google AI Hub, students who make better progress can get the support and encouragement of other members of the communities, to get ahead with their start-ups. 

While a lot of learning and experience can be through self-learning, it may be a good idea to create “co-learning spaces and emerging technologies experience centres” where heterogeneous groups of learners broadly pursuing similar goals but at different stages of their learning journey can learn from each other. 

Future teaching-learning models may draw upon the principles of machine learning: supervised, unsupervised and re-enforcement learning, to support the implementation of heutagogy, as the progression from pedagogy and andragogy. 

In many fields we have seen disruptive innovation transforming the status quo. 

A well known example is that of Kodak which ruled the world of photography, with many inventions, but became bankrupt in 2012. Here is a link to the Kodak story :

The three reasons that analysts assign to Kodak’s downfall are : failure to reinvent itself, complacency and absence of organisational agility. 

Clayton  Christensen, the famous Harvard Professor in the field of Disruptive Innovation when confronted with what would be the disruption in education, articulated very clearly that it was not merely the use of technology in education that would cause the disruption, but the resulting personalisation of the learning experience is what would cause the real disruption in education. 

And this will happen when Artificial Intelligence is deployed to transform education from a homogenisation process to an individualised learning experience to help every individual achieve his or her potential. 

There are many examples of learning the required skills for a given task, which then allows you to practice those skills in various other new contexts which you may not have done earlier, with or without external  guidance. So if one has the skill of knitting, then one may make products with designs available from others ( including from magazines) or one could go ahead and make one’s own. Similarly after one has adequately learnt the various operations involved in cooking, one can either follow others recipes or experiment and create one’s own. Those with a more mechanical disposition routinely make things from DIY kits, and that has led to the recent emergence of the maker culture with 3D printers. Google has a suite of products that can help you create applications in the field of computer speech or computer vision, available under the AIY brand for AI

It would look as a natural progression from DIY, AIY to LIY: learn it yourself. After having achieved a certain degree of fluency in the skills of learning, one can continue to pursue lifelong learning with the help of learning recipes. If this appears far fetched, visit this link :Machine Learning Recipes with Josh Gordon:

It therefore seems that the approach to education in the post Covid era must consider  the possibilities for how we can adapt our teaching and learning methods to the  new emerging technologies. 

We have seen a gradual progression of how IBM Blue in 1997 defeated the human champion Gary Kasparov in Chess, followed with IBM Watson in 2011 defeating the human champions Ken Jennings and Brad Rutter in Jeopardy. Then Google Alpha in 2016 defeated the human champion Lee Sedol in Go. And AlphaGo zero did it without being explicitly taught the rules of playing Go. 

And now we have self-driving cars. The Society of Automotive Engineers defines 6 levels of driving automation ranging from 0 (fully manual) to 5 (fully autonomous). These levels have been adopted by the U.S. Department of Transportation. 

Level 0 (No Driving Automation)

Level 1 (Driver Assistance ): at Level 1 the human driver monitors the other aspects of driving such as steering and braking. 

Level 2 (Partial Driving Automation) : This means advanced driver assistance systems or ADAS. The vehicle can control both steering and accelerating/decelerating. Here the automation falls short of self-driving because a human sits in the driver’s seat and can take control of the car at any time.

Level 3 (Conditional Driving Automation): The driver must remain alert and ready to take control if the system is unable to execute the task.

Level 4 (High Driving Automation):Level 4 vehicles can operate in self-driving mode. But with ‘geo-fencing’.

Level 5 (Full Driving Automation): Level 5 vehicles do not require human attention―the “dynamic driving task” is eliminated. Level 5 cars won’t even have steering wheels or acceleration/braking pedals. 

There have been  well known Auto-didacts who were also often polymaths. These names ranging from Leonardo da Vinci to Benjamin Franklin are well known.

Many educational researchers have studied learner autonomy in different contexts and David Nunan (1997) set out a scheme proposing five levels for encouraging learner autonomy in relationship to use of learning materials . He labelled them awareness, involvement,intervention, creation and transcendence. Somewhat reminiscent of Bloom’s taxonomy of learning objectives. 

There has also been a fair amount of research in deconstructing the “ skills of learning” into 5 or 6 categories further subdivided into about 10 each. Together these would build up the framework of Howard Gardiner’s Multiple Intelligence theory. 

I have added to the list of these skills, the abilities to deploy artificial Intelligence tools and Apps  to engage in self-directed learning. I am proposing the following levels of an autonomous learner. The pool of skills that describe a learner at each of these levels is detailed elsewhere :

Level 0: No self-learning disposition or ability

Level 1: Some inclination towards self-learning and core abilities of self-learning

Level 2: An autonomous learning system that supports the learner but can be superseded by the human educator ( an AI fluent SmartEducator) 

Level 3: The Autonomous learning enabling system requests to be superseded by the human educator ( an AI fluent SmartEducator)

Level 4: A fully autonomous learner that can function well in limited domains ( cognitive geo-fencing) 

Level 5: Fully autonomous learner with the right dispositions and skills

The AI fluent SmartEducator:

The most important quality of a modern educator is one of being a lifelong learner. The other important attribute is the appreciation that imparting  the knowledge of subjects is less important than fostering a desire to learn and the ability of learning to learn, and building of learning power. 

The best pro-active response to this challenge is to seize the opportunity and become an AI-fluent SmartEducator. Such a person has a Deep Learning mindset ( well versed in deep learning techniques of Machine Learning as well as pedagogy of mastery learning and learning in depth). 

  • Is familiar and well acquainted with commercially available solutions ( both in the cloud and at the edge) for deploying AI in education and their best use situations.
  • Can be actively involved in creating be-spoke solutions for a specific context. 
  • Is well versed with the triple helix of AI-readiness: The Concepts ( theory); The tools; The applications ( educational).

Our program has been designed as a year long program structured as 4 terms of 3 months each.  At the end of each term, the educator-learner will earn a completion certificate of having become a level 1 to level 4 of an AI-ready educator. 

Level 1: AI-familiar educator

Level 2: AI-competent educator

Level 3: AI-proficient educator

Level 4: AI-fluent SmartEducator

The idea of levels or grades to describe a transition in capabilities was used in the Capability Maturity Model ( CMM) in the field of Software Engineering. It was also extended to Learning Maturity Model of 5 levels for e-learning organisations. The eMM supplements the CMM concept of maturity levels, which describe the evolution of the organisation as a whole, with dimensions. The eMM provides a set of thirty-five processes, divided into five process areas, that define a key aspect of the overall ability of institutions to perform well in the delivery of e-learning . Each process is selected on the basis of its necessity in the development and maintenance of capability in e-learning. All of the processes have been created after a programme of research and testing, conducted internationally. The five dimensions of the eMM are:

  1. Delivery
  2. Planning
  3. Definition
  4. Management
  5. Optimisation

In this proposed approach of emphasising learning as the core activity in the educational context, we are really suggesting to use the learning from Science and the amazing recent progress in the field of Artificial Intelligence to empower both the educator and the learner in their common pursuit of learning. After all an educator is a learner who helps in the development of other learners. 

The ability to learn is present in all of us, and every child in its early years picks up cognitive, affective and psychomotor skills over a very short period of time. Learning becomes a challenge only after formal schooling begins. But as the anthropologist Edward T Hall puts it “ The drive to learn is as strong as the sexual drive, it begins earlier and lasts longer”. Using Artificial Intelligence to augment and enhance this natural drive, would play large dividends. 

The post Covid19 era may herald the transition from the art of teaching to the Science of Learning. So far, it is medicine that carries the label of being the youngest science. It is perhaps time for learning to be the next frontier in science driven by knowledge and rapid advancements in cognitive sciences and neurobiology. 

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Educational Leadership in the age of Artificial Intelligence:

Educational Leadership in the age of Artificial Intelligence:

Today’s educational leaders face a dilemma. Artificial Intelligence (AI) will definitely play an enormous role in the future of their organizations and the social  environment in which they operate, but what effects will it have? There are  wildly different visions of the future it will create, ranging from causing the extinction of humanity to ushering in a Golden Age in which machines provide all of humanity’s needs and free us to focus on altruistic service to one another.   

AI’s effect on educational institutions  will not be limited merely to repetitive, routine administrative jobs. Increasingly, it will also enter the core of the teaching-learning activities of educational institutions. 

It will also affect those who manage the operations and ensure the smooth running of the educational enterprises. AI likely will reshape jobs all the way up to the office of the head of the Institution. That doesn’t mean, though, that middle level managers and executives will no longer be needed. They simply need to prepare themselves for shifts in their work responsibilities. 

Whether AI and the technologies it enables will reach their full potential in transforming education depends on the workforce that will work alongside them. Yet the skills that that workforce needs to do this are in short supply. Rather than debating what to do about massive job losses from AI, discussion should focus on how best to prepare workers for the types of jobs that they will need to fill. 

Educational Leaders including senior teachers must make themselves ready for the emerging impact of AI on education. 

Anthony Seldon in his recent book “ The fourth education revolution”  has said that “ AI is the biggest challenge to education since the printing press. It can be compared to the arrival of the internal combustion engine in the 1880s, except it will change the world far more subtly and profoundly. If we can take the right decisions, we will see the biggest enhancement of human fulfilment and happiness the world has seen. Get it wrong and the quality of our life will suffer a catastrophic loss”.

Nothing matters more than education if we are to see AI liberate, not infantilise humanity.

Highly respected British educational scholar, Sir Anthony Seldon, explores the most important issue facing education (and humanity at large): the fast approaching revolution in Artificial Intelligence or AI. This book is a call to educators everywhere to open their eyes so that we can begin shaping the future of education around the world.

Britain and the US have an excellent education system in their schools and universities… but it is tailored to the twentieth century. The factory mass teaching methods of the last educational era have failed to conquer enduring problems of inequity and unfairness. Students have to make progress at a set rate which can demotivates and bore some. Will the AI revolution be able to remedy these problems?

This extended thesis explores the history of education, the many different styles of education (with a particular focus on Britain and the USA), and the different types of intelligence for which current teaching methods are unable to provide any support.

The final part of the book covers the possibilities for how we can adapt our methods to new technologies, using Sir Anthony’s innovative ten-part model of education as a framework.

It will make educators familiar with the vocabulary of the field, appreciation of the key concepts, knowledge of the tools and technologies. It enables them realistic understanding of the challenges that remain and become aware of a number of instances of applications of AI/ML in diverse industries. Armed with this knowledge, they will be ready to be the agents of change in implementing new Pedagogies that deploy the power of these technologies to achieve personalisation and mastery learning for each of their students. The purpose of this program is to empower those teachers who want to do so to become active users of these technologies for their benefit, rather than being passive consumers of the earlier so called EdTech. In the age of AIEd, it is the teachers who will be pivotal.

It is only when we recognize that the world is rapidly changing and so are the needs of each successive generation that we can work to ensure that education remains relevant for the future.

We have designed a one month long course that is covered in 4 weeks with 5 days per week ( From Monday to Friday), a total of 20 sessions in all. Each session is of about one hour duration, and the program is delivered through WhatsApp. 

Proposed Flow of the course:

Week 1: What is educational Leadership?

1.1: What is Leadership?

1.2: Theories of Leadership

1.3: Ethical Leadership

1.4: A brief history of AI in education

1.5: What can AI do today and how?

Week 2: The impact of Artificial Intelligence on Education

2.1: Impact of AI  on the teacher

2.2: Impact of AI on the learner

2.3: Re-designing the learning spaces 

2.4: Role of automatic machine translation 

2.5: Personalisation of learning: Intelligent tutoring systems

Week 3: Specific interventions 

3.1: Face recognition systems

3.2: Speech to text systems

3.3: Text to speech systems

3.4: AI enabled assessments

3.5: Chatbots for administrative and in teaching-learning

Week 4: Leading the transformation

4.1: AI awareness programs for all concerned

4.2: What should be taught in the age of AI?

4.3: New pedagogies in the age of AI 

4.4: Creating an AI experience centre

4.5: Developing and executing the transformation plan

This is a tentative list of topics and themes. This may be tweaked continually based on inputs received from experts and other stakeholders in the field. Because the field is changing very rapidly and has a great impact on all.

To know more about the program and further details of registration and fee payment you may send a WhatsApp message to Prof MM Pant at +919810073724 or an e-mail at

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WEL12: First Principles:

First Principles: A framework for deconstructing complex problems: 

In this era of availability of large number of digital learning resources, it is even more important to become aware of the importance of thinking from first principles and training the youth in it. The next batch of this weekend program will be run on Saturday 9th/Sunday 10th May 2020. 

WEL12: First Principles:

The structure of the course is as follows:

Day 1: What are first principles?

1.1: Philosophical Origins

1.2: Elon Musk on the importance of first principles 

1.3: The 7 step approach to first principles thinking 

1.4: What is not a first principle?

1.5: Barriers to First principles thinking

Day 2: Applications of first principles thinking: 

2.1: Elon Musk’s first principles approach to Batteries and the Tesla car

2.2: Elon Musk on SpaceX and Solar City

2.3: First principles in marketing strategy

2.4: First principles in law

2.5: Employing first principles in daily life


As we prepare our youth and ourselves for the coming decade, the World Economic Forum and several other think tanks and thought leaders suggest that problem solving skills, especially that of complex problem solving, will be most critical. I often meet people ( especially parents of young children ) who want to know how and where to learn such skills. 

Although there is a well known Institute of Complexity at Santa Fe, New Mexico ( founded in 1984 that explores  ‘ Science for a Complex world’ and is leading the world in complexity science, with a mixed group of physicists, biologists, economists, political scientists, computer experts, and mathematicians working together, ordinary folk also have to deal with complex problems on an everyday basis. All innovation whether incremental ( Kaizen), disruptive ( Clayton Christensen) or Blue Ocean ( W Chan Kim and Renee Maubourgne) requires solutions to challenging problems with a fresh perspective. 

One of the challenges of really complex problems is that unlike many difficult problems that we routinely solve these days, there is no ‘algorithm’ to solve a complex problems. Even the recent powerful computing methods like machine learning or quantum computing, suffer from the defect of ‘non-explainability’. We have to look for other methods. 

One such method is the use of  ‘first Principles’, a phrase that was used more than 2000 years ago by the Greek Philosopher Aristotle, who believed that the best way to understand a subject is to break it down to its most fundamental principles, and made popular in recent times by the immensely successful innovator and entrepreneur Elon Musk. Elon Musk simplifies this to two main steps of which the first is to identify the problem and its common assumptions. The second more difficult is to break the problem down to its fundamental truths. Keep digging deeper and deeper until you are left with only the fundamental truths.

My own initiation to the importance of ‘first principles’ happened in the year 1967 when as a Ph.D. student in Solid State Physics, I read Sir John Ziman’s      “ Principles of the theory of Solids”. Books on Solid State Physics at that time usually had titles with adjectives such as Introduction, Advanced…but Prof Ziman’s  book had “ Principles” as part of its title. In the preface to the book, he says “ It has never been supposed that a student could get into his head, the whole of Physics, nor even the whole of any branch of Physics. A few sentences later he writes “ It is a book about ideas, not facts. It is an exposition of the principles, not a description of the phenomena.”

When we use First Principles thinking,we are able to discover unconventional insights based on fundamental truths. This in turn can lead to game-changing innovation — the kind of “10x thinking” that creates breakthrough product ideas. Four very different types of thought leaders  Elon Musk (CEO of Tesla and SpaceX), Jeff Bezos (CEO of Amazon), Peter Thiel (ex-CEO of PayPal), and Richard Feynman (Nobel Prize winning physicist) have all supported this approach, although they may not all have used exactly the same phrase, but that is what it is in essence. 

The exceptional person that he was, Feynman relished solving problems entirely on his own, from scratch — not relying on previous work from so-called experts. Feynman was intensely curious and wanted to truly understand problems before attempting to solve them. This means that he would often break a problem down to fundamental truths that he could prove, and then build up theories and solutions from there. He would also question assumptions and data. Feynman was at a conference in Rochester, NY, where he heard a talk by some researchers about beta decay, and largely believed their findings. A few years later, Feynman was reviewing the same problem and realized that they had made a mistake. Feynman realized afterwards that he relied too much on the reasoning of others (“reasoning by analogy”). From that point on, he never relied on the reasoning of experts. He approached problems from a First Principles standpoint — what do you know to be fundamentally true, and then reason up from there. 

My encounter with first principles again happened in the unexpected area of law, during my years of practice as a lawyer. In fact I even wrote an article in a legal journal on the principles used to classify goods in the context of sales tax levied on them. Two principles of natural justice are often considered the basis of much of modern first principles of justice. The first is “ Audi alteram partem” which is a right to fair hearing of the other party. The other is of no bias, often invoked as the Latin maxim “ nemo judex in causa sua”. Reasoned decision is almost a first principle of justice. 

Another interesting example of use of first principles is the Drake equation. In 1961, scientist Frank Drake ( )  wrote down a simple-looking equation for estimating the number of active, technologically-advanced, communicating civilizations in the Milky Way. From first principles, as there was no good way to simply estimate a number, but Drake had the brilliant idea of writing down a large number of parameters that could be estimated, which you would then multiply together. 

As to methods, there may be a million and then some, but principles are few. The man who grasps principles can successfully select his own methods. The man who tries methods, ignoring principles, is sure to have trouble.

 ~ Ralph Waldo Emerson, Essayist and Poet

The above quote from Emerson becomes very important in this age of information overload and an exponential growth of information. There is often a clamour to add more and more subjects to the curriculum at all levels. Adding subjects like AI at School level is an example of this. A few decades ago the 2 year undergraduate course was padded up to become a 3 year program and more recently some Universities even explored a 4 year undergraduate degree. 

Fee and registration: 

The fee for this course is Rs 1000/- NLN and can be easily remitted through PayTM to my mobile number : +919810073724. 

For those who would rather pay into a Bank account, the relevant information is : 

Madan Mohan Pant

HDFC Bank, Unitech Cyber Park, Sector 39, Gurgaon 

A/c 26451000000301


(The account number is 26451 followed by six zeroes followed by 301)

This is an initial draft of the topics. They may be tweaked continually and in respond to feedback and ideas received from the course participants. 

To enrol in  this program or to know more about  it, please send a WhatsApp message to Prof MM Pant at +919810073724 or an e-mail to


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Learning Agility:

Learning agility: the most important skill for 21st Century success

Progressive educators have for decades  requoted ad nauseum the well known prophecy ( in 1970) of Alvin Toffler that “ The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” 

This was perhaps to challenge the exhortations of Francis Bacon “ Reading maketh a full man, writing maketh an exact man and conversation maketh a ready man”. 

In later times, arithmetic was added to make the 3Rs. 

In late 20th century, digital and computer literacy were added to the repertoire of a literate person. 

Having done nothing about it for so long we now have the George Orwellian spectre of  “A generation of the unteachable is hanging upon us like a necklace of corpses.”

It is time to wake up and develop learning agility ….

What is Learning Agility?

Learning Agility is the ability to continually and rapidly learn, unlearn, and relearn mental models and practices from a variety of experiences, people, and sources, and to apply that learning in new and changing contexts to achieve desired results. It is a mind-set and corresponding collection of practices that allow people to continually develop, grow, and utilize new strategies that will equip them for the increasingly complex problems they face in their organizations. 

We are well into the 3rd decade of the 21st century and it is a good time for self-reflection whether one is literate according to this criterion. Being digitally literate or learning AI at middle School alone may not be enough. 

Benjamin Franklin had said in the 18th century said “An investment in learning pays the best interest”. Fast forward to the present. 

The founders of the five largest companies in the world — Bill Gates, Steve Jobs, Warren Buffett, Larry Page, and Jeff Bezos — all share two uncommon traits. Upon  studying closely the lives of self-made billionaires for many years now, it appears that these two traits are responsible for a lot of their wealth, success, impact, and fame.

Here are the two traits:

  • Each of them is a voracious learner.
  • Each of them is a polymath. 

Let’s unpack these two terms, and learn a few simple tips for using them in your own life.

First, the definitions. I define a voracious learner as someone who follows the 5 hour rule dedicating at least five hours per week to deliberate learning. I define a polymath as someone who becomes competent in at least three diverse domains and integrates them into a skill set that puts them in the top 1% of their field. If you model these two traits and you take them seriously, I believe they can have a huge impact on your life and really accelerate your success toward your goals. When you become a voracious learner, you compound the value of everything you’ve learned in the past. When you become a polymath, you develop the ability to combine skills, and you develop a unique skill set, which helps you develop a competitive advantage.

This is the link to the article from where I have extracted a part above:

Drawing from the above 5 hour rule, I have created a suite of 12 weeklong courses on a range of topics which will supplement traditional academic credentials to be more successful in life. A good pragmatic approach may be to apply the Pareto principle of 80:20 to devote 80% of one’s time and resources to traditional academic qualifications and 20% to self-improvement and future readiness. The full list of weeklong, weekend, and other courses is available here itself at It would take one between six months to one year to cover all the courses listed here. And by then one would have achieved the ‘escape velocity’ and become a self-directed lifelong learner. 

Of the various weeklong courses listed there, the one with code WLL03 is on Learning Agility. 

It is a 5 day WhatsApp learning experience: typically Monday to Friday. About one hour per day. The full program is of about 5 hours duration. It will be delivered in “ the augmented lecture” model, building upon the mechanics of flipped instruction and fast a-synchronous learning. The course material is shared in advance electronically as :

  • plain text
  • PDF files 
  • Images 
  • Videos
  • Audios

Then augmenting this is an (optional) WhatsApp ‘live’ session for a scheduled hour. During this time the course mentor ( Prof MM Pant) shares some resources ( with PowerPoint slides as a staple form) and posts several audio posts. During this live session, the queries raised by the learners, are responded to right there, if a short immediate response is feasible. All other queries are consolidated and responded to later, and posted as a consolidated document to the group. 

The flow of themes and topics is planned as follows. It may be tweaked during the  actual delivery of the program.

Day 1: The What and Why of learning agility?

Day 2: Learning how to learn ( and re-learn)

Day 3: Learning to Unlearn

Day 4: Learning to Ultra-learn 

Day 5: Your personal action plan

Detailed flow:

Day 1: The what and why of learning agility ?

1.1: What is learning agility?

1.2: What is the need for learning agility? 

1.3: The 5 dimensions of Learning Agility

1.4: Measuring learning agility

1.5: Cultivating Learning Agility

Day 2: Learning how to learn ( and re-learn) 

2.1: The criticality of learning how to learn

2.2: Becoming an autonomous learner

2.3: What is worth learning ( or re-learning)?

2.4: Learning with a mobile phone

2.5: Learning from MOOCs

Day 3: Learning to unlearn

3.1: Reinventing yourself 

3.2: Key concepts

3.3: Keeping an open mind 

3.4: Unlearning: How to do it?

3.5: Examples of unlearning what you learnt at School

Day 4: Learning to Ultralearn

4.1: What is Ultralearning ?

4.2: Future-proofing your career with Ultralearning 

4.3: The key elements of Ultralearning

4.4: Designing an Ultralearning project

4.5: Successfully executing the Ultralearning project

Day 5: Your personal action plan

5.1: Overcoming procrastination and becoming indistractable

5.2: Setting SMART goals 

5.3: The importance of diet

5.4: The importance of exercise/ physical fitness 

5.5: Grit : to execute your personal action plan

To know more about the program and further details of registration and fee payment you may send a WhatsApp message to Prof MM Pant at +919810073724 or an e-mail at

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Start a lifelong learning journey:

Greetings on Akshay Tritiya 2020:

Akshaya Tritiya, is an annual spring time festival that falls on the third Tithi (lunar day) of Bright Half (Shukla Paksha) of Vaisakha month. 

The festival date varies and is set according to the lunisolar and falls in April or May of every year in the Gregorian calendar. This year it is on Sunday 26th April 2020. 

In Sanskrit, the word “Akshayya” means ” non-perishable, never endingness “. There are many legends and stories attached to this date, and it is considered a good day to start something that one wants to last forever. 

In the third decade of the 21st century, the most rapidly diminishing asset is your knowledge because of the exponential rate of change around us. It is therefore a good thought to dedicate this day to pursue the tasks of learning, unlearning and relearning, as advised by Alvin Toffler. 

I am sharing below a list of courses that I offer, that can be accessed through WhatsApp on a mobile phone itself. This list is very amusing and quite different from those that promise to make you employable very quickly ( followed by unemployability for the larger part of your life). These are like vaccines that make you future proof over a longer period. 

One of the courses is on “ avoiding natural stupidity”. Now whether we learn Artificial Intelligence or not, we could do well to be aware of our inherent stupidity. Or realise that we should pursue learning first, specific topics can wait. Or on the ultimate reality “ Death” for which no one is adequately prepared. As you peruse the list of courses below, you will appreciate its uniqueness. For more you may visit “ Prof MM Pant’s Whatsapp VishwaGurukul” at

Seven of these courses are on offer every day of the week, and you may choose to pick up one just on impulse to get started with your lifelong learning journey. Then there are 12 weeklong courses, as well as 12 weeklong courses. 

List of daily courses : ( Monday to Sunday) 

  • WhaT01: Learning first! AI next!
  • WhaT02: Learning Quantum Mechanics at School?
  • WhaT03: Avoiding natural stupidity
  • WhaT04: Ideas: their creation and spread
  • WhaT05: Inspiring humans
  • WhaT06: Pre-school learning ( for parents) 
  • WhaT07: Developing Foresight

List of weekend courses: ( Saturday and Sunday) 

  • WEL01: Complex Problem Solving
  •  WEL02: Critical Thinking
  •  WEL03: Creative Thinking
  •  WEL04: People Management
  •  WEL05: Co-ordinating with others
  •  WEL06: Emotional Intelligence
  •  WEL07: Effective Decision making
  •  WEL08: Service Orientation
  •  WEL09: Negotiation
  •  WEL10: Cognitive Flexibility
  •  WEL11: Overcoming Maths Phobia
  •  WEL12: First Principles

List of weeklong courses: ( Monday to Friday) 

  • WLL01:Learning with WhatsApp, other mobile Apps and MOOCs
  • WLL02: WhatsApp for Educators
  • WLL03: Learning Agility
  • WLL04:Computational Thinking
  • WLL05: Financial Acumen
  • WLL06: Health Literacy
  • WLL07: Preparing for a 100 year life
  • WLL08: Death? Understanding it is critical to living well
  • WLL09: Evolution and Extinctions
  • WLL10: Humour: a must have skill
  • WLL11: The pursuit of Happiness
  • WLL12: Finding Meaning Making Sense

List of monthlong courses: ( fresh cohorts begin on the first Monday of every month) 

  • QT01: Understanding the Quantum World
  • QT02: Quantum Biology
  • QT03: Quantum Computing 

To register for any of the above courses, please send a WhatsApp message to Prof MM Pant at +919810073724

The schedule of fees is as follows:

A: For each of the courses WhaT01 to WhaT07  it is Rs 750/-.

B: For each of the courses WEL01 to WEL12 it is Rs 1000/-.

C: For each of the courses WLL01 to WLL12 it is Rs 2500/-

D: For each of the courses QT01 to QT03 it is Rs 5000/- 

The course fee  can be easily remitted by PayTM to +919810073724

For those who would rather pay into a Bank account, the relevant information is : 

Madan Mohan Pant

HDFC Bank, Unitech Cyber Park, Sector 39, Gurgaon 

A/c 26451000000301


(The account number is 26451 followed by six zeroes followed by 301)

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Quantum Biology:

Quantum Biology :

When I was studying at University, about 60 years ago, those who pursued Biology had a non-mathematical descriptive and classification approach, to the living world which was quite different from those who pursued Physics, usually in combination with Chemistry and Mathematics. And it was in Physics that one learnt about the Quantum world of sub-atomic particles. 

But today there is a collaboration  between these seemingly incompatible scientific fields that is producing fascinating new insights into the nature of the living world.

This relatively new discipline of “quantum biology” builds on the idea that the ‘oddities’ of quantum mechanics such as entanglement, quantum tunnelling, superposition of wave states, the uncertainty principle and quantum coherence play vital roles in the biology of living things.

The concept of “interdisciplinarity” is all the rage in academia these days: widely divergent fields come together to hybridise their insights and find new ways of seeing the world. It started with relatively simpler ideas like Chemical Physics, Bio-Physics, Materials Science, Molecular Biology and so on.

Quantum biology is one such meeting point. And while it is producing remarkable and novel findings about olfaction, photosynthesis, the action of enzymes, and an understanding of the brain itself, the interdiscipline is as old as the quantum revolution itself.

Although the origin of quantum biology is often thought to be Erwin Schrödinger’s 1944  book What is Life?, the field actually dates back to the late 1920s, just as the mathematical underpinnings of quantum mechanics were established.

Biology in the early twentieth century was still torn between two philosophical outlooks. The first was the mechanistic worldview of the Scientific Revolution: in particular, Rene Descartes’ theory that organisms were not much more than soulless machines.

The other was the notion of “vitalism”, which took the spotlight in the early nineteenth century. This was the belief that there was something fundamentally different and even mysterious about living organisms, and that their function and make-up could not be reduced to mere classical chemistry and physics. Some vital spark, or élan vital, marked life from non-life.

As early as 1929, Niels Bohr was making vague allusions to the role of quantum thinking in biology, and although such a vision was not yet fleshed out by Bohr himself, he inspired others. Bohr returned to the topic later, this time arguing that complimentarity, or wave-particle duality (the idea that quantum objects act as both particles and waves, but never both at the same time) was the organicist “new law” that would uncover the mysteries of the living world. Together with Werner Heisenberg, Bohr wondered if such quantum phenomena played an undiscovered role in the mutation and selection of Darwinian evolution.

In the 1940s Erwin Schrödinger argued that genes and the laws of heredity were sensitive to quantum mechanical dynamics and that the mutations necessary for natural selection arose through quantum tunnelling (the phenomena whereby subatomic particles can reach lower energy states by bypassing, or tunnelling through, intervening higher energy states).

These musings in What is Life? partly inspired Francis Crick and James Watson to investigate the nature and structure of genes.

However, with the incredible breakthroughs in molecular biology that were to follow, much of life’s mechanics were explained using classical chemistry, without recourse to quantum phenomena.

Further reflection in physics pointed out that much of the interesting aspects of quantum mechanics depended on a system being completely isolated from its environment, which was particularly unlikely, as McFadden and Al-khalili note, in the “hot, wet and complex system in such a living cell”. By the 1960s quantum biology slumped, with most researchers being “dismissive of the notion that quantum mechanics played any kind of special role in living systems”.

Several scientists kept thinking about the connection between quantum mechanics and life, however, with some, such as British mathematical physicist Roger Penrose, even drawing connections between the quantum world and consciousness. But for the most part, many of the early claims of quantum biology were discredited and the classical sciences remained dominant in biology.

However, in the past few decades quantum biology has experiencing something of a revival.

There are also some tantalising findings to suggest that the “hot, wet and complex” biological systems, non-equilibrium systems fundamentally connected to their environment, might actually promote interesting quantum dynamics, rather than rule them out has had been thought in the sixties.

The question has now become how quantum phenomena affect biology, rather than if they do.

“They may have had to wait many decades, but the quantum pioneers were indeed right to be excited about the future of quantum biology.”

Over the past decade, the field of quantum biology has seen an enormous increase in activity, with detailed studies of phenomena ranging from the primary processes in vision and photosynthesis to avian navigation. 

Photosynthesis is a highly optimized process from which valuable lessons can be learned about the operating principles in nature. Its primary steps involve energy transport operating near theoretical quantum limits in efficiency. Recently, extensive research was motivated by the hypothesis that nature used quantum coherences to direct energy transfer.

Much of the brain’s physiological activity is currently understood as being performed through the firing of neurons to transmit information, send instructions and interpret stimuli through the senses, all of these phenomena central to the functioning of the vast neural network that we identify as our self, our consciousness.

Drawing on earlier research suggesting that quantum effects in protein filaments known as microtubules, found in neurons, play a role in the nature of consciousness, Adams and Petruccione recently investigated ( ) the state of research into whether quantum effects contribute to neural processing and describe what experimental evidence there is to support the theories.

If supported experimentally, this knowledge could, they say, shape thinking about how we think. The duo also suggests that, as these fields of research grow, quantum computing and quantum neurobiology might also inform each other in ever-increasing ways.

This write up has drawn upon material from this source:

Some useful additional links :

Introduction to Quantum Biology by Philip Ball :

Quantum Biology : Q&A:

Quantum Biology: the hidden nature of nature:

How Quantum Biology may explain life’s biggest questions?


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Quantum Supremacy:

Quantum Supremacy ( in Computing) 

In 2012, Prof John Preskill, a professor of Theoretical Physics at California Institute of Technology, Pasadena, California proposed the term “quantum supremacy” to describe the point where quantum computers can do things that classical computers can’t,  whether or not those tasks are useful.

Quantum mechanics emerged as a branch of physics in the early 1900s to explain nature on the scale of atoms and led to advances such as transistors, lasers, and magnetic resonance imaging. The idea to merge quantum mechanics and information theory arose in the 1970s but garnered little attention until 1982, when physicist Richard Feynman gave a talk in which he reasoned that computing based on classical logic could not tractably process calculations describing quantum phenomena. Computing based on quantum phenomena configured to simulate other quantum phenomena, however, would not be subject to the same bottlenecks. Although this application eventually became the field of quantum simulation, it didn’t spark much research activity at the time.

In 1994, however, interest in quantum computing rose dramatically when mathematician Peter Shor developed a quantum algorithm, which could find the prime factors of large numbers efficiently. Here, “efficiently” means in a time of practical relevance, which is beyond the capability of state-of-the-art classical algorithms. Although this may seem simply like an oddity, it is impossible to overstate the importance of Shor’s insight. The security of nearly every online transaction today relies on an RSA cryptosystem that hinges on the intractability of the factoring problem to classical algorithms.

What is Quantum Computing?

Quantum and classical computers both try to solve problems, but the way they manipulate data to get answers is fundamentally different. What makes quantum computers unique are two principles of quantum mechanics that are crucial for their operation, superposition and entanglement.

Superposition is the ability of a quantum object, like an electron, to simultaneously exist in multiple “states.” With an electron, one of these states may be the lowest energy level in an atom while another may be the first excited level. If an electron is prepared in a superposition of these two states it has some probability of being in the lower state and some probability of being in the upper. A measurement will destroy this superposition, and only then can it be said that it is in the lower or upper state.

Understanding superposition makes it possible to understand the basic component of information in quantum computing, the qubit. In classical computing, bits are transistors that can be off or on, corresponding to the states 0 and 1. In qubits such as electrons, 0 and 1 simply correspond to states like the lower and upper energy levels discussed above. Qubits are distinguished from classical bits, by their ability to be in superpositions with varying probabilities that can be manipulated by quantum operations during computations.

Entanglement is a phenomenon in which quantum entities are created and/or manipulated such that none of them can be described without referencing the others. Individual identities are lost. This concept is exceedingly difficult to conceptualize when one considers how entanglement can persist over long distances. A measurement on one member of an entangled pair will immediately determine measurements on its partner, making it appear as if information can travel faster than the speed of light. This apparent action at a distance was so disturbing that even Einstein dubbed it “spooky action at a distance”.

Building quantum computers is incredibly difficult. Many candidate qubit systems exist on the scale of single atoms, and the physicists, engineers, and materials scientists who are trying to execute quantum operations on these systems constantly deal with two competing requirements. First, qubits need to be protected from the environment because it can destroy the delicate quantum states needed for computation. The longer a qubit survives in its desired state the longer its “coherence time.” From this perspective, isolation is prized. Second, however, for algorithm execution qubits need to be entangled, shuffled around physical architectures, and controllable on demand. The better these operations can be carried out the higher their “fidelity.”

Superconducting systems, trapped atomic ions, and semiconductors are some of the leading platforms for building a quantum computer. Each has advantages and disadvantages related to coherence, fidelity, and ultimate scalability to large systems. It is clear, however, that all of these platforms will need some type of error correction protocols to be robust enough to carry out meaningful calculations, and how to design and implement these protocols is itself a large area of research.

A different  framework being pursued by Microsoft  is topological computation, in which qubits and operations are based on quasiparticles and their braiding operations. While nascent implementations of the components of topological quantum computers have yet to be demonstrated, the approach is attractive because these systems are theoretically protected against noise, which destroys the coherence of other qubits.

Google has officially announced in October 2019 that it has achieved Quantum Supremacy in a new article published in the scientific journal Nature.

Google says that its 54-qubit Sycamore processor was able to perform a calculation in 200 seconds that would have taken the world’s most powerful supercomputer 10,000 years. That would mean the calculation, which involved generated random numbers, is essentially impossible on a traditional, non-quantum computer.

With time, the tech will get democratised and trickle down to the consumer. An industry around QC software and algorithms will then have truly arrived.

As the number of qubits in quantum computers increase, we will first start seeing optimisation and data access problems being solved first. For example, with enough qubits, we could use quantum computers to assemble and sort through all possible gene variants parallelly and find all pairs of nucleotides – the building blocks of DNA – and sequence the genome in a very short period of time.

This would revolutionise the health industry as sequencing the DNAs at scale would allow us to understand our genetic makeup at a deeper level. The results of access to that kind of knowledge are unfathomable.

Next, through significant improvements in our quantum capacity, we will be able to use quantum computers for simulating complex systems and behaviours in near real-time and with high fidelity.

Imagine simulating the earth’s winds and waves with such accuracy so as to predict storms days before they come. Imagine simulating how the winds on a particular day would interact with a flight on a particular day and route – it would allow us to measure turbulence, optimise flight paths, and better in advance.

One promising candidate is PsiQuantum whose photon-based model is still years away, but the company’s big claim is that its technology will be able to string together 1 million qubits and distill out 100 to 300 error-corrected or “useful” qubits from that total. has raised $215 million to build a computer with 1 million qubits, or quantum bits, within “a handful of years”. Rudolph, the company’s chief architect, happens to be the grandson of famed quantum theorist and Nobel Prize-winning Erwin Schrödinger.

Regardless of the path it takes, Quantum Computing is here to stay. It’s a key piece in the puzzle that is human growth. 10 years, 100 years, or maybe even a 1,000 years down, we will wonder how we lived without them.


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