| CARVIEW |
- 2025:
- 2024:
- 2023-2021:
- 2020:
- 📚Elon Musk by Ashley Vance
- 📚Sapiens by Yuval N. Harari
- 📚The Interpretation of Financial Statements by Benjamin Graham
- 📚Nationalism by Rabindranath Tagore
- 📚Benjamin Graham on Value Investing by Janet Lowe
- 📚The Kite Runner by Khaled Hoesseini
- 📚The Warrent Buffet Way by Robert G. Hagstorm
- 📚Thinking Fast and Slow by Daniel Kahneman
- 📚Alibaba by Duncan Clark
- 📚A Brief History of Time by Stephen Hawking
- 📚I Do What I Do by Raghuram Rajan
- 2019:
- 📚Kafka on the Shore by Haruki Murakami
- 📚The Black Swan by Nassib N. Taleb
- 📚The Communist Manifesto by Karl Marx
- 📚All About Derivatives by Machael Durbin
- 📚Start with Why by Simon Sinek
- 📚Homo Deus by Yuval N. Harari
- 📚1984 by George Orwell
- 📚Capitalism and Freedom by Milton Friedman
- 📚The Prophet by Kahlil Gibran
- 📚The Rich Dad’s Cashflow Quadrant by Robet Kiyosaki
- 📚Zero to One by Peter Thiel
- 📚Forests Gamps by Winston Broom
- 📚Animal Farm by George Orwell
- 📚The Subtle Art of Not Giving a F*CK by Mark Manson
- 📚Why I am a Hindu by Sashi Tharoor
- 📚Rich Dad, Poor Dad by Robert Kiyosaki
- 📚Stock to Riches by Parag Parikh
- 📚Blockchain by Mark Gates
- 📚The Monk Who Sold His Ferari by Robin Sharma
- 📚To kill a Mockingbird by Harper Lee
- 2018 and Older:
My voyage into research began in January of this year, and it has been far from smooth sailing. I can’t help but acknowledge the various challenges and moments of self-doubt that have become my constant companions. The initial problem statement I was excited to work on has taken an unexpected detour, and the experiments I thought would yield groundbreaking results have left me feeling somewhat underwhelmed. Upon introspection, I’ve identified a few key missteps that might have contributed to my current sense of failure. Here’s a candid exploration of my missteps that I want to share to other researchers also:
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Lack of Extensive Literature Review: I’ll admit I was eager to dive into the practical aspects of my research, neglecting the wealth of knowledge that already exists in my chosen field. I came to realize that research isn’t just about creating something new; it’s about building upon what others have already discovered.
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Not Clarifying the Significance of the Problem: It’s not enough to have an interesting problem fefining the significance of the problem statement is crucial. It’s about asking the “so what?” question.
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Lack of Self-Evaluation: I have come to realise that healty quantity of self-evaluation is really important, especially when you’re venturing into uncharted territory. It’s a chance to refine my concepts, gather feedback, and iteratively improve my approach.
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Not Sharing my Ideas: I also regret not actively reaching out to my colleagues and friends for their perspectives and insights. Sharing your research challenges with others can lead to fresh ideas and alternative viewpoints.
My current struggle and sense of uncertainty have led me to question my decision to leave a stable job for the tumultuous world of research. Is this what research is all about? A constant battle to identify meaningful problems and generate innovative solutions? There are days when I wake up without a clear sense of direction, and I wonder if I made the right choice. But then, there are moments of clarity and inspiration when I realize why I embarked on this journey in the first place. I think my “why” is purely a selfish self-exploratory reason to test by own abilites and enjoy.
As I navigate these uncharted waters, I’ve started to explore not only my “why” but also the “whys” of others. There is a list of articles which I read now and then when I feel lost wandering:
- Terry Tao’s Blog on Career Advice: A great collection of pretty intersting ideas on the journey from school level to post-doc level.
- Aditya Ramdas’s Checklists
- Advice for the Young Scientist by John Baez
- For Graduate Student by Fan Chung
- Ten Lessons I Wish I Had Been Taught by Gian-Carlo Rota
- A Stroke of Genius: Striving for Greatness in All You Do by R. W. Hamming
- The illustrated guide to a Ph.D.
- Advice For Graduate Students in Statistics
- For potential Ph.D. students
- Living Proof by AMS: One of my very favourite.
However, as we embark on the journey toward achieving artificial general intelligence (AGI), a stage where machines are trained on extensive and diverse datasets, the central question shifts from “how” to “what” these models are learning. It is no longer sufficient to focus solely on the mechanics of the learning process. Instead, we must delve into the profound inquiry of whether these AGIs possess intrinsic human values, such as privacy, safety, and fairness.
Furthermore, as we endeavor to endow AGI with human-like qualities, a critical attribute comes to the forefront—human awareness of the limitations of knowledge and the inherent uncertainty intertwined with it. Are these machines cognizant of their own uncertainty in decision-making, similar to human awareness, as our dependence on machine intelligence deepens? Can they promptly rectify their knowledge when exposed to erroneous information? Unfortunately, the answer to these inquiries is negative. These machines appear rigid and devoid of an understanding of the boundaries of their knowledge and the associated uncertainties.
To address these concerns, we must shift our focus from the “how” to the “what” i.e. what these models learn from the data. As machine intelligence assumes an ever-expanding role across diverse fields, we are compelled to confront the fundamental question of “what” precisely they are assimilating and comprehending from the information to which they are exposed.
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