Cinjon Resnick

Cinjon Resnick

 
Welcome, the water's warm. Some writing below, check it out. If you like to circus, buzz me about our studio 1D1 in Brooklyn. If you want to jam on ideas in machine learning, I'm ears reach out.
 

Writing

  • (Aug 2023) Follow up to communication with a character. Now with hypotheses for questions 3 (”lead, not just respond”), 11 (”who am I talking to?”), 12 (”what I feel comes out in my voice”), and 13 (”what I feel comes out in my face”).
  • (June 2023) Bring to life the world around you. A mobile app + character creation tool for turning local buildings and art into conversational partners.
  • (April 2023) A communicative audiovisual AI that feels just like you’re talking to a [likely cartoon] character with a human behind the curtain. I then list businesses that this could spur and what plausibly comes after this exists.
  • (April 2023) Fourteen research questions towards achieving human-like Communication with a robot.
  • (April 2023) Large language models are fantastic at modeling Other intent and have little need or reason to model Self intent.
  • (Dec 2022) Primer is a digital AI teacher, an Aristotle, for everyone. This piece looks into how you would build it today from the perspective of a well funded startup.
  • (Nov 2022) A study of Frax completed jointly with Justin Cullen and Dino Mihalopoulos. We first analyze the Frax system and then synthesize it as a piece of the larger system that is Web3 banking.
  • (Aug 2022) A study of Web3 Stablecoins, with a focus on the Collateral Ratio and how it affects the resulting Behavioral Economics.
  • (July 2022) A study of 0x completed jointly with Justin Cullen. We first analyze the 0x system and then synthesize it as a piece of the larger system that is Web3 Order Flow.
  • (Jan 2022) A thesis motivating Web3, with the perspective that Assets are Products. Result is a clearer understanding of what is and is not worth building on blockchains.
Hearn's Robots
Hearn's Robots
  • (Aug 2021) At the intersection of ML and Web3, explore a world where autonomous agents can find and trade with humans as equals, contextualized in autonomous vehicles, art & NFTs, and gaming.
  • (Nov 2020) A machine learning write-up assessing whether we could fix a spurious correlation in a dataset by just collecting some examples and fine-tuning on those examples. We also had interest in understanding what changed as we did this and whether the correlation became spurious in another way.
  • (Oct 2020) I went on a retreat and found some kinetic magic worth talking about. Lots of hallucinating, no drugs involved.
  • (Sep 2020) A machine learning product idea where we reclaim the world's communal spaces by turning all the walls into talking characters at the behest of street artists.
  • (Aug 2020) An abstraction of TikTok by equivocating the concept to photoshop vis a vis the layers. Assuming shared ownership of all videos and extensive tools to manipulate the content, I describe eleven different ways to collaborate on a video and what that entails / engenders.
  • (July 2020) A machine learning write-up hypothesizing how to automatically yield background, character, and animation from a video.
  • (April 2019) A lengthy exploration into important historical papers in machine learning meets game theory. Of particular note is a deep dive into the origins of the Fermi distribution wrt its ubiquitous use as the underlying probability of whether agent A adapts agent B’s strategy. Everyone who used this approach, including Alpha-Rank most recently, can be traced to Blume '93.
  • (Dec 2018) A machine learning write-up hypothesizing how we can use humans in the loop to create unsupervised models with axes of variation about which we care greatly.
  • (July 2018) A machine learning write-up hypothesizing what is the difference between amateur movement and expert movement, with a focus on handstands.
  • (March 2010) A eulogy for a man who would been a best friend. Originally written for MIT's Tech Paper.
 
 
 
  • We ran a fellowship sponsored by Jane Street - much love - where we gave grants to students around the world to run learning groups and globally share the curricula.
  • Understanding research papers is hard. We tried a new way where you build the tree of concepts and then learn them going forward. We then shared the curricula and our experience with the world.
  • Full compendium is on the website.
  • Research into multi-agent approaches, in particular in machine learning, is overwhelmingly for 1v1 zero-sum games. Pommerman is a response where we created a team cheap-talk 2v2 game based on the famous Bomberman game.
  • We ran three global competitions, including at NeurIPS 2018 and 2019.
Space of Motion
  • We built a service for helping athletes with their training videos via machine learning, e.g activity recognition and video search.
  • Focused on gymnastics and helping the current generation of USA olympic competitors.
Office Hours
  • Answer questions on machine learning with chat. Was my best marketing hack yet. Thanks Intercom!

Work

holdpls (2023 → Current) Blueprint Forest: Early Stage Investing (2022) PhD CS NYU (Machine Learning), FAIR + NVidia (2017 → 2022) Google Brain: ML Research (2016 → 2017) Machine Learning Startups + SPC (2013 → 2015) Quora (2011 → 2013) Value Investing (2010 → 2011) MIT: Math + CS (2006 → 2010)