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- From Socrates to Sartre, T. Z. Lavine: an introduction to philosophy.
- The Concept of Mind, Gilbert Ryle: final nail in the coffin of Descartes' dualism.
- Godel, Escher, Bach, Douglas Hofstadter
- Mechanics of Manipulation, Matt Mason: textbook on the mechanics of manipulation, a field Matt Mason pioneered. Would love to chat with him once I understand the mathematical formulation of the problem and his contributions to the field.
- Calculus of Variations and Optimal Control Theory, Daniel Liberzon: a treatment of optimal control theory based on Pontryagin's Maximum Principle. A non-dynamic-programming-based approach to solve optimal control seems unthinkable to me.
- The Book of Why, Judea Pearl: an accessible introduction to causality.
- An Introduction to Measure Theory, Terence Tao
This summer in Pittsburgh was unbearably hot. When the mercury hit 40 degrees, I finally had enough and ordered an air conditioner for my bedroom. It took another week to arrive- time I spent trying every trick up my sleeve, from my Kharagpur days, to stay cool. All my friends were surprised that I held up for so long.
Not needing an air-conditioner was an act of rebellion to me. The AC is a tool for the standardization of weather; for replacing discomfort with monotony. I, on the other hand, like my three seasons- summer, rainy and winter. Especially the monsoon rains after a spell of hot summer. Scorchingly hot days, that leave you tossing all night in sweat, also make you dance outside in the rain when the rain gods arrive. What would you do if you had spent all your summer without feeling so much as a whiff of hot air? You would only complain about the mud. You see, I do not want to be that guy. I wish to accept the lows so that I can savor the highs.
So, when I finally confirmed my order on Amazon, it was with a deep sense of loss, for I had chosen comfort.
This is a surprisingly concise and freely-flowing book that explains Buddhist philosophy to a modern reader. The arguments in this book are derived from exactly the same realization that propelled Siddharth Gautam on his path to become the Buddha - the inevitability of death. The realization that you are going to die no matter what puts a question mark on the utility of everything in your life. If you fool yourself into ignoring this painful and harsh reality, you start caring too much about too many things - giving too many fucks. On the other hand, if you are going to die anyway, why should you care about anything? Mark Manson proposes that we should care about what we are leaving behind - our legacy. Will the world be a better place because of us? This gives us a higher purpose in life, which Mark believes is the way to live a happy and fulfilling life.
However, he also quotes Ernest Becker who believed that people's immortality projects - attempts to leave a legacy, were part of the problem. I am not sure how to reconcile these conflicting ideas. While attempting to make the world a better place, could you not end up starting another immortality project?
You can't care about anything and everything in life. Because if you do, you will never be happy. Your life will never be perfect. The key is to have a small list of high-value things that you really care about and work towards making them right. Everything else doesn't matter. The key to finding a better pareto optimal solution is by dropping objectives.
The human brain, by design, needs to keep itself engaged. If you do not have real problems in life, your brain will simply create problems out of thin air. The one secret to living a fulfilling life is to find a real problem that you truly care about and work on it your whole life.
Pain is an indispensable part of the process of getting better. It is powerful. Intense pain makes you rethink even your most deeply held beliefs.
Life is about not knowing and then doing something anyway. There will never be a time when you are 100 percent sure of the right way forward. Even if you think you are, you may prove to be wrong later. You need to accept the uncertainty and keep moving forward.
Action -> Inspiration -> Motivation
This is a summary (with some commentary from my side) on a piece with the same title by Michael Nielsen.
Effective people are self-disciplined. I have been fighting with my lack of self-discipline for as long as I can remember. And for the longest time I treated it as a side-effect of my weak will-power. But over the years, experience has taught me otherwise:
Will power is a finite and a scarce resource. Use it only when you absolutely have to.
Your state of mind and environment have a much bigger and longer lasting impact on your discipline. If both of them are aligned with your goal, things will fall in place naturally and you will not need to discipline yourself. You may have heard people putting in 12 hours a day at work and still having fun.
Your state of mind is affected by having clarity about what you are doing. In terms of the theory of reinforcement learning, having clarity means having a good and confident estimate of the expected future value of doing something. For example, if you are certain that putting in 12 hours a day for a year is going to get you a Nobel prize and you really want one, the 12 hours stop seeming like too much.
The environment includes both your social and physical environment. If your social environment - advisor, lab and fellow researchers support the development of research skills, it can make an enormous difference. Every once in a while you are going to doubt the worth of your work. Having a supportive social environment is critical in these situations.
The last factor is self-honesty. You know yourself the best. This means you are most susceptible to being fooled by yourself. And between you and yourself, there is no one else watching. One way to enforce honesty is to collect hard data about yourself on a regular basis and evaluate that every once in a while. Diary entries, research logs, daily logs are some different ways to do it.
TL;DR: 2019 was a mixed year and a local minimum (as I would like to believe). In the first half of the year (January through May), my focus was solely on aesthetics - physical fitness and art. This came at the cost of progress in my research and my intellectual development. By the start of June, I had begun to realize the effects of this skewed focus and I took drastic corrective actions. In the second half of the year (June through November), I focused solely on getting my research up to speed by curtailing my physical activities. Though this led to limited progress in my research (as opposed to zero progress in the first half), it entailed heavy stress that is not sustainable in the long term.
First Half (Jan - May)
I took to three habits:
working out thrice a week
reading for 3-4 hrs on the weekends
playing the piano
I had started going to the gym towards the end of 2018 and had made limited gains. I wanted to make visible changes in my body and for this I trained hard - choosing not too frequent (thrice a week) but intense and long (2 hrs) workout sessions. Unfortunately, my recovery wasn't quick enough and I would feel tired after these intense workouts. This negatively affected my work. It also negatively affected my sleep quality (or was it coffee?) which further degraded my work-life balance. However, I had decided to go all in and hence chose to ignore these warning signs as I felt that physical fitness and non-negotiable aspect of my life.
In the end, I did notice visible changes in my physique. This was the fittest I had ever been - 8 pull-ups, 75 kg squats, 50 kg bench-press and 5.5 miles in 52 mins.
However, this intensity was unsustainable.
I also spent some time learning how to play the piano - I took piano lessons and even bought a keyboard. However, I did not feel like continuing it because it was yet another solitary hobby in the list of solitary activities that I do. I found a fun alternative - Latin dancing, in the summer.
Second Half (June - Nov)
By this time, I had made zero progress in my research despite my attempts to maintain a work-life balance (basically aesthetics/work balance). My advisor wanted me to submit a paper by the end of the year, which was simply not possible if I did not take corrective actions.
So I stopped working out, reading books and piano and tried to spend more time working. This led to an interesting realization- I was not really interested in what I was working on and further that I did not really understand the value of my research. I do not like working on short-term objectives. I have always liked to go after the bigger picture. Not being able to understand where my research was headed was a major peeve and rather demoralizing.
I was aiming for ICRA (due in September) but missed the deadline. I then aimed for ICAPS in November. Even though my paper had very weak results, I still submitted the paper to keep my sanity. I do not like missing important deadlines (the practical reason being that it is a slippery slope).
Publishing papers regularly is non-negotiable during a Ph.D; not just because it keeps you in good standing in the program but also because it keeps you on your toes.
Do not worry about the quality of your paper during the initial days. Just submit!
Always ensure that you are aware of the bigger picture where your research fits. This requires studying consistently (read books) and keep abreast of the latest research (read papers).
Bouncing Back (Dec)
As I finished my course projects, exams towards the end of November, I could feel that I was gradually having a better sense of the research being done around me and even my own research. Surprisingly, this was a sum-total of a number of unplanned and providential experiences/interactions, for example- my course projects that I ignored for the most part, one-off discussions with colleagues and coming across eye-opening papers.
This reinforces my belief that for good research, it is critical to stay around the center of gravity. In the case of robotics and AI, CMU is that place.
Next, every once in a while, it is important to read books on fundamental ideas. You would anyway read books on the latest algorithms and techniques. It pays to study problems and formulations that are more abstract. For example, study classical RL vs the latest DRL papers, study MDP's vs RL, study Dynamic Programming vs MDP's.
In my case, this realization came as I studied Dynamic Programming and Optimal Control by Dmitri Bertsekas and 'A Unified Framework for Sequential Decisions'_. Both these readings (the latter being a chance discovery) studied together emphasize how well connected the fields of Heuristic Search, Optimal Control, RL and MDP are - they can all be looked at through the same Dynamic Programming lens.
For the first time, I now have a good idea who my ancestors are (ideologically speaking). Whence I could only look up to Dijkstra and Judea Pearl, I now have all the stars of optimal control and DP to look up to - Bellman, Bertsekas and so on.
Dynamic Programming is a fundamental problem solving technique that is key to solving sequential decision making problems - optimal control being one of them.
Planning or more generally decision-making is a fundamental problem in Artificial Intelligence. Planning is useful whenever there is some notion of a goal and we would like to come up with a plan that helps us achieve the goal in a reasonable amount of time. Formally, we need to have a state space S, a start state s, a set of goals G and a cost function C that we would like to minimize. The goal of planning, then, is to come up with a plan that takes us from the start state to one of the goal states. The plan could be defined in various ways: it could be a set of states $n in S$.
Say you are making a movie and need to shoot an India Mela. You could either locate a real Mela and go shoot there or you could simulate one on your set. Except at the cost of tremendous effort, one can tell the simulated one from the real one (maybe it is too clean or too organized). This is a question that arises everywhere. Why is simulation so difficult? Why is it so difficult to simulate liquids but the liquids themselves use no compute at all?
I think, a good start to understanding this situation to ask ourselves why we expect simulation to be easy. Most of the phenomena that we care about lack centralized control. Numerous agents interact with each other on the basis of simple local rules (think fish schools). While simulating, we try to mimic this interaction using either some overall deterministic relation or if the complexity is too much, randomized relations. But the fact of the matter is that "the real thing" is neither of these. The source of complexity is in the interactions among numerous agents based on simple rules. You may come up with statistical properties of these interactions or you may try to replicate the interactions using "one" set of rules. But "the real thing" is a collection of simple rules. A "simplified" relation may describe the interaction in simple terms to a human brain, but it can only be an approximation.
So now we have this one equation that we think can describe the fluid dynamics. Only if we had sufficient compute. The issue is again that we are trying to simulate numerous agents using (say) one central unit. There is no reason why this should be efficient or easy. For a system in a certain state, that follows a fixed set of deterministic rules, there is only a limited number of possible future states. Contrast that with a computer trying to simulate that system's future state. The computer's current state and its rules are very different from the system's. Hence, the computer needs a hell lot of extra compute and information to really zero down on (an approximation) of the system's future state.
Reality is not a mystical computer with unlimited compute.