Chinmay Kulkarni grew up in Bengaluru, India. He doesn’t recollect when he used a computer for the first time but vividly remembers that, "the first time we were taught anything to do with computers at school was with LOGO, which was sort of this drawing thing."
Playing around with the ‘turtle cursor’ of LOGO was the humble beginning of Chinmay’s experiments with computers. He took on a Computer Science major during his undergraduate at BITS, Pilani and even went on to pursue a Ph.D. in Computer Science from Stanford University. One of his friends from undergraduate days describes him as "the guy with a knack to simplify & explain things. He was just really good at breaking down things to their core fundamentals."
Today, Chinmay is an Assistant Professor of Human Computer Interaction at Carnegie Mellon University. He directs the Expertise@Scale Lab there that is trying to answer a prevailing question concerning the future of work in the age of automation.
"If we have better & better technology and greater & greater automation, what should people learn and what should people work on."
The findings from this research will mature to suggest new skills people should learn and technologies we should be developing for meaningful & interesting work & learning opportunities for millions of people to exist in our future that isn't only remote but also intertwined with lifelong learning. In this future, where learning opportunities need to be available at massive scale, Chinmay stresses on the usefulness of having conversations among peers: the non-experts, the people who are themselves learning or working in the same space.
In practice, research from his group has resulted in computational systems that structure peer learning at a massive scale. This includes creating the first MOOC-scale peer assessment platform and building PeerStudio, a comparative peer-review system. These systems and the associated pedagogy have been used by 100,000+ learners in MOOCs & thousands of students in university classrooms and have been adopted by companies such as Coursera and edX, in classes across disciplines including computer science, psychology, and design.
Heather McGowan, Future of Work Strategist, succinctly describes that in the future where not only more automation of physical work but also automation of cognitive work will happen,
"We need to stop learning ‘a set of skills’ in order to work. Instead, we need to learn to learn and adapt."
Learning to learn means to become good at being a beginner and not only embracing failure but by seeking it out in order to improve. Chinmay embraces this ideology through constant parallel experimentation.
He says, "If I have an idea, I try three or four different ideas in a similar space. Some of them are bound to fail but in contrast, I can see some of them succeed. You start out thinking all of them will succeed. In some way, it is useful information that you know that things you thought would have succeeded but didn’t not succeed give you a nice baseline to compare against the things that did succeed."
When he writes article drafts, he usually writes three different outlines, sends them all to people and asks them which one do they like more. He says, "I know that two-third of my work is going to be thrown away so I don’t spend too much time doing it. But on the other hand, once I have done this I can very quickly find things that don’t work and discard them." This way of learning about anything seems quite logical to him but he has also noticed that people don’t do parallel experimentation very much.
It is not surprising that humans stay away from trying out different ideas in parallel. Embracing this mindset of experimentation is innately bundled with accepting multiple failures at the same time.
Chinmay admits that it is a lot easier for a researcher to fail than it is for people whose jobs are to not fail at something. He says, "As a researcher, you have it a little easier. You are expected to fail. And also just because something you do fails people don’t think of you as a failure. Even the things you try that don't work have some merit."
He suggests that a simple change of perspective in how we look at what we do that can enable us to embrace failure much better. He says, "I think you can think about things that you do as a series of experiments rather than a series of missions that you are trying to complete. Experiments always have some chance of failure. So, just by thinking of things as experiments seems like you give yourself a chance to say, 'okay, maybe this is not going to work and that’s fine'. If you think about it like a mission, then you invest too much of your self-worth in succeeding."
Some of the most successful companies and professionals experiment and fail all the time. In one of Jeff Bezos’ letters to Amazon shareholders, he expounds on this:
"One area where I think we are especially distinctive is failure. I believe we are the best place in the world to fail (we have plenty of practice!), and failure and invention are inseparable twins. To invent you have to experiment, and if you know in advance that it’s going to work, it’s not an experiment. Most large organizations embrace the idea of invention, but are not willing to suffer the string of failed experiments necessary to get there. Outsized returns often come from betting against conventional wisdom, and conventional wisdom is usually right. Given a ten percent chance of a 100 times payoff, you should take that bet every time. But you’re still going to be wrong nine times out of ten. We all know that if you swing for the fences, you’re going to strike out a lot, but you’re also going to hit some home runs. The difference between baseball and business, however, is that baseball has a truncated outcome distribution. When you swing, no matter how well you connect with the ball, the most runs you can get is four. In business, every once in a while, when you step up to the plate, you can score 1,000 runs. This long-tailed distribution of returns is why it’s important to be bold. Big winners pay for so many experiments."
Chinmay recognizes that it is harder to fail for people whose jobs require them to succeed and that everyone has a different way of looking at and dealing with failure. He ponders, "The real question isn’t ‘are you okay with things failing’ but what you do when they fail. You can either have somebody learn from your mistakes or you can learn from them yourself. If you are really smart you can learn from other people’s mistakes too."
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