So, through some wild rituals, you divined a hypothesis — a short, interesting, and unproven statement. Is it worth your time to pursue it further and whole heartedly dedicate your next few months on it? In this chapter, we’ll cover how to vet your research idea before working on it. Unlike the chaotic process of coming up with an original research idea, one can easily describe the process of vetting an idea (or other professional endeavors), even though implementing this process requires significant discipline and skill.
The worst thing that can happen to a researcher is realizing, only after extensive…
For the sake of everyone involved in research, I hope there isn’t a tractable algorithm we can run to get creative research ideas: That’ll poop the party for everyone! However, there are some unreliable maneuvers you can take to facilitate interesting ideas, like voodoo magics, and this chapter will focus on that. However, take whatever I write here with a giant slab of Pakistani pink salt, as it is an inherently a chaotic process that resists characterizations.
This could be wrong, but I think –or at least, find it helpful to think– that creativity is mostly functional, in a sense…
I have been planning to write this series for a while now, and the beginning of 2021 is an arbitrarily auspicious time to start. This series serves two purposes: First, as an account/guide on the process of developing original research for publication, written by someone who has done it a few times. Second, to share some funny stories of my time as a PhD student / postdoc at MIT, and keep it very personal. I remember Philip Guo’s the PhD grind being very informative when I started grad school, and I wanted to make something similar for other PhD students…
Given 2 objects, A and B, what makes them similar? There can be many answers, but one of the most elegant answer is based on substitution :
A and B are similar, if in most use cases of A, you can substitute it with B, and you wouldn’t notice much of a difference.
The idea of substitution has popped up multiple times, the ones I’m aware of are the liskov-substitution-principle, and of course, distributional-semantics. But instead of research literature, we’re just gonna do it with Clash Royal cards instead. It’s going to be a lot more relaxing and accessible.
At ICML 2019 I asked “Pytorch or Tensorflow ?” at arbitrary passer-bys. Depending on their response, I drew a “x” on either side of the notebook. After 1024 queries I have obtained the above picture.
This was the question I got the most while talking with people. There were some conceptions that I was doing it for a research, or if I worked at facebook or google. I joked that I was collecting a dataset of crosses, or for fun, or to simply watch people’s reactions. …
As of last month, [AlphaStar], an AI player, beat 2 top Starcraft players with a score of 10–1. Did AI conquer Starcraft the same way it conquered Go? Is AlphaStar actually SkyNet? Or are we just falling into some avoidable traps?
In this (my second :D) blog, we will explain which games have unbeatable AIs, and which games do humans still have a shot of winning. We will explore what it takes for a game to be “solved” and the gaps still lacking in AI approaches. No click baits, no mindless hypes, no needless jargons. Enjoy.
In this blog ( my first blog on medium o_o ), I will explain the challenges in making a Dota bot, appreciate how OpenAI addressed these challenges, and lay out its fundamental flaws. I hope that by explaining how it works in layman terms, we can watch its upcoming matches with the right mindset.
Every project needs the right problem statement. I believe the OpenAI Five’s problem statement as follows: Beat a team of human in Dota in any way possible with a program. This view is both powerful and liberating.
I have been contemplating on starting a blog, with the recent OpenAI Five benchmark, it seems like a good time to start. The bots defeated the humans convincingly 2–0, but I think the bots can be beaten with the right approach.
In the moment when I truly understand my enemy, understand him well enough to defeat him, then in that very moment I also love him. — Ender’s Game
There can be no convincing victory without understanding. Similarly, I would like to divide this topic into two 2 parts:
In this more academic part, I will focus on explaining the…
Research Scientist (Autodesk). PhD (MIT 2019). I work on Program Synthesis in the context of Human-Machine Communications