Here is a list of questions that I'm curious about but haven't found satisfactory answers to. I posit that answering these questions might have interesting consequences in their own ways. If you have responses, related questions, pointers to some resources, or want to know why I think these questions are interesting, reach out to me!
Since this page reflects my admittedly limited knowledge about things, it's a work-in-progress and I plan to update the material here frequently. I'm not an expert on any of these things but I'm really interested in getting some feedback from people who know more than I do and have thought about these things more than I have!

How difficult is it to simulate other branches of evolution and study what we could have been?

 

 

                                           Tiktaalik roseae, a 375 million-year-old transitional species between fish and the first legged animals

Our ancestors in the oceans were limited only to motor volume -- the region they could move and interact with. This was probably because of how light travels through water vs air. When they crawled out of the oceans, their sensory volumes increased since light travels faster in air than seawater. Some evolutionary biologists think this change engendered consciousness too! In general, how difficult is it to set up simulations that evaluate all branches of evolution? What would have happened if we had been limited to the ocean for another million years? Sounds like a tough thing to study (this is my computer science brain talking) but I suspect someone must have tried something similar to this at some point in history.

What is the biological reason behind the correlation between (short) height and air pollution?

 

 

 

 

 

 

 

 

 

 

 

 

 

             Source: The association of early-life exposure to ambient PM2.5 and later-childhood height-for-age in India: an observational study - Spears et al

         

Children who were exposed to more air pollution (PM2.5) in India, grow shorter on average, no matter how tall or short their mothers are (seasonality accounted for too!). Is there a causal relation? If yes, what is the biology behind the height phenotype being affected by air pollution? Has anyone studied this phenomena accounting for other confounding factors? I'm really clueless about what's actually going on.

What is the mind's eye or mental imagery?

Is it some form of deterministic hallucination in terms of form or function? What makes some imaginations more vivid than others, and why does it seem that vividness is heightened when eyes are closed? I suspect mental imagery also governs our experiences while reading, which might explain why we experience books differently. How does the difference in mental imagery between different individuals impact their ability to learn concepts? I don't even know how to frame this as a falsifiable question.

What are some works focusing on problem-finding/field-forming in science?

Thomas Kuhn's work is one possible explanation of how science moves ahead and finds new problems after a crisis is revealed (although Kuhn's work focuses more on how one paradigm is replaced by another). What are some other works exploring how exactly fields/sub-fields are formed in science and problems are selected for and by research communities?

How does extreme specialization impact science? (and what are the differences today as compared to the past)

Today's 'experts' are experts in extremely narrow domains. In the past, many scientists seemed to venture and excel at multiple fields. Some examples: Erwin Schrödinger played a pivotal role in quantum mechanics and also influenced a lot of work in molecular biology. Sewall Wright became an influential figure in evolutionary biology and also pioneered some work in causal inference (path diagrams). Have scientists stopped caring about other fields, interdisciplinary work, and the big picture (informally)? Why aren't there more scientists today doing impactful work across domains? Maybe the examples picked above seem contrived and prone to survivorship/confirmation bias. But in general, how is the quotidian work and thinking of scientists different today as compared to the the 20th century, considering the depth and breadth of research interests? Maybe the answer lies somewhere in the publication process, PhD period (today vs 20th century), or some other arcane corner of academia. A related article: Science is getting less bang for its buck, by Michael Nielsen and Patrick Collison.

How do people decide to change their worldviews/personal philosophies?

There have been many examples of people undergoing personal transformations after major accidents. In other cases, the shift in an individual's worldview has been more gradual. What determines when a person decides to shift their entire belief system? Should it simply be attributed to chance (accidents), exposure to some specific information (reading a philosophy book that speaks to you), or do we actually have some predispositions that play a part in adopting the beliefs we do? What is the role of stimulus vs inner mental states/models? Maybe I've ventured a bit into epigenetics here, but crude descriptions aside, how does a person decide that the way they see the world or the goals they set for themselves is going to change today? What is special about that day?

Is there an observable Baumol effect in clean energy economics?
Cost of solar energy has dropped rapidly since (at least) 2010 -- much faster than predicted. Similar data is present for some other renewable sources. Not only has there been a flurry of capital, but also specialized labor; working on the hard technical problems of creating clean energy sources. Assuming that labor isn't substituted to a great extent with capital -- a hint of Baumol effect -- in the context of clean energy, and I admit this question seems ill-posed, but nevertheless, is it possible to predict which other sectors can be expected to have increasing prices (ignoring the fossil fuel industry of course) in case there's a Baumol effect at play with working on clean energy?

 


How has social media affected scientific research?
My closest interaction on social media has been with the AI research community. Ignoring the "I'm pleased to announce my paper on XYZ", how has social media affected science in terms of thinking (expedited feedback loops possible today as compared to 25 years ago) and dissemination of knowledge? I'd appreciate an insight into researchers' individual experiences as well as any large-scale studies related to this, reach out to me!

 

Should startups be optimized more as a regional game or a country-level game in terms of where they set shop?

Considering Silicon Valley, Stockholm, New York, Beijing, are the most successful startups possible only in areas where there has been precedent of success already? It might of course be a self fulfilling prophecy. Also, I realize the laws, policies, and other bureacratic intricacies of the particular area play a major role. But in general, why does there need to be a Silicon Valley to have extremely successful startups? And what is India doing different since it seems anomalous. Although, India ranks third (4%) in the startup index (based on share of unicorns) behind China (21%) and the US (61%), based on regions, India ranks 13th (New Delhi being the first appearance on the list behind Stockholm, London, SV, etc, though I believed Bangalore or Mumbai would have been higher up than New Delhi). Where are all of India's successful startups and why are they scattered?

Has there been a formal study of ideas (a la Kaplan-Meier curves, etc.)?

Considering how important ideas are for the progress of civilization, what are the best works (preferably quantitative!) evaluating the survival of ideas? Of course, this seems to be a function of zeitgeist too. But in general, I suspect it should be possible to have Kaplan-Meier curves-type studies of an idea's survival function (especially scientific and technological ideas).

 

Is it possible for machines to rote learn things and still display interestingly novel behavior? In which cases does explicitly searching for novel behavior help?

 

 

 

 

 

 

 

                        

                                     Mazes are the benchmark for novelty search algorithms (the deception mechanism is highlighted well in the case of a maze-world)

                                                        Source: Novelty Search Creates Robots with General Skills for Exploration, Roby Velez and Jeff Clune

What does an overfitted neural net with multiple heads (a la multi-task learning) do when presented with novel (out-of-distribution) tasks? To put it simply, I think it fails to generalize. But what is the demarcation between tasks that are amenable to solving (or however else the problem is defined) with overfitted rote learners and when do we exclusively need novel intelligent behavior --  a question easy for humans to answer, but I haven't found much research addressing this questions directly in AI (i.e, if the works aren't contradicting themselves). Some work on novelty search and some problems having a "deception" underlying structure is interesting, but it's quite dicey to find this deception outside of a few well-defined tasks. I'm sure I'm missing some interesting work done on this already, but if not, I guess this is a request for research (something that I might get to eventually too!).

 


Why aren't there more grand scientific collaborations in the 21st century as compared to the past century?
The 20th century saw the Manhattan Project, the moon landing, invention of transistors, and many similar moonshot ideas. Why aren't there more Bell Labs in this century? Why are there no truly large-scale collaborations between scientists anymore? I have to admit, I think about this question for an odd, ignoble reason. I'm genuinely afraid that collaborations at this scale and the need to accelerate innovations is motivated by wars (Space race, Enigma and Turing, etc). I don't know how I feel about living in a world where we need to kill each other to genuinely pursue interesting ideas without limiting funding and support.




 

Siddharth Srivastava

School of Computer Engineering and Technology,

MIT-WPU, Pune, India

Email: srivastava41099 [at] gmail [dot] com

© 2020 by Siddharth Srivastava.