Progress by impossibility proofs

I recently listened to this TED talk by Google X director Astro Teller. The talk is about the Google X moonshot projects, and the process they use for managing them.

Google X moonshot projects aim to address big, important, and challenging problems.  Teller tells (yay, pun!) that the process they manage a moonshot project is basically to try to find ways to kill the project. The team first tries to identify the bottleneck at a project by focusing on the most important/critical/risky part of the project. Then they try to either solve that hardest part or show that it is unsolvable (in a feasible manner) and kill the project. Teller claims that, at Google X, they actually incentivize people to kill the project by awarding bonuses, and celebrate when a project gets proved impossible and killed. And if a project still survives, that is a successful moonshot project that has potential for transformative change.

Although, this approach looks counter intuitive at first, it is actually a good way to pursue transformative impact and fail-fast without wasting too much time and resources. This can be very useful method for academic research as well.
  1. Find a use-inspired grand-challenge problem. (This requires creativity, domain expertise, and hard thinking.)
  2. Try to prove an impossibility result.
  3. If you prove the impossibility result, that is still nice and publishable.
  4. If you can't prove an impossibility result, figure out why, and try to turn that into a solution to an almost "impossibly difficult problem". Bingo!
"Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth."
-- Sir Arthur Conan Doyle channeling Sherlock Holmes

Comments

Popular posts from this blog

Hints for Distributed Systems Design

Learning about distributed systems: where to start?

Making database systems usable

Looming Liability Machines (LLMs)

Foundational distributed systems papers

Advice to the young

Linearizability: A Correctness Condition for Concurrent Objects

Scalable OLTP in the Cloud: What’s the BIG DEAL?

Understanding the Performance Implications of Storage-Disaggregated Databases

Designing Data Intensive Applications (DDIA) Book