Smart review: introduction

David Gilbertson
14 min readAug 6, 2021

There are 7.674 billion people on this planet. Of those, how many are working on innovations that will make the world a better place? A few thousand people? Maybe a hundred thousand, a million?

And what about the other 7.673 billion people? Sprinkled throughout are some truly brilliant individuals, people who are capable of amazing innovation, but for one reason or another, aren’t in that line of work. Even if we consider only the sharpest 0.1%, that’s still millions of people.

Photo by NASA on Unsplash

What if there was a way to connect the two? To allow the people working on the cutting edge to tap into that distributed brainpower. What form might that connection take?

I was flipping through a nerdy magazine recently, when I came across a cryptic crossword with a twist: it didn’t have a grid to fill in; my task was to solve the clues and work out how to fit them into a grid. Out of my league, I decided, but the thought struck me: people are willing to spend a lot of time on puzzles just for the pleasure of doing something hard. Puzzles that, once complete, have had no material impact on the world (other than to entertain the solver).

So what if we created ‘puzzles’ out of actual problems that need to be solved? We could create pathways from the labs and start-ups working on the cutting edge of innovation to brilliant minds all around the world who enjoy solving hard problems.

This post describes a plan to do just that.

The goal:

Accelerate the rate of innovation by increasing the number of minds working on the problems at the cutting edge.

A fairly straightforward idea, but the devil’s in the detail.

In summary, here are the three components of the concept:

  • Innovators: the people working at the cutting edge
  • Reviewers: exceptionally brilliant people
  • The package: an effective method of information exchange between the above two

I will go through each of these in detail below (and explain why I called them ‘reviewers’), but first, let me explain why I’m doing this.

Why strive for faster…

David Gilbertson

I like machine learning stuff.