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By end of 2021, how many modules will the state-of-the-art AI system for architecture-search for image recognition have?

Background: definition of a module

A “module” refers to some division of an AI system such that all information between modules is human legible.

As an example, AlphaZero has two modules: a neural net, and a monte carlo tree search. The neural net, when given a boardstate, has two outputs to the tree search: a valuation of the board, and a policy over all available actions.

The “board value” is a single number between -1, and 1. A human cannot easily assess how the neural net reached that number, but the human can say crisply what the number represents: how good this board state is for the player. Similarly with the policy output. The policy is a probability vector. A human can conceptualize what sort of object it is: a series of weightings on the available moves by how likely those move are to lead to a win. The board value and the policy are both “human legible”.

Contrast this with a given floating point number inside of a neural net, which will rarely correspond to anything specific from a high-level human perspective. A floating point number in a neural net is not “human legible”.

A module is a component of a neural net that only outputs data that is legible in this way. (In some cases, such as the Monte Carlo Tree search of AlphaZero, the internal representation of a module will be human legible, and therefore that module could instead be thought of as several modules. In such cases, prefer the division that has the fewest number of modules.)

Therefore AlphaZero is made up of two modules: the neural net and the monte carlo tree search.

An end-to-end neural network that takes in all sense data and outputs motor plans should be thought of as composed of only a single module.


A system for architecture search is one whose output is not the solution to a given task instance (e.g. an image label), but rather an architecture which can be applied to such instances (e.g. a convolutional network).

We will take the "state-of-the-art" system to be the system with the highest correct percentage on the CIFAR-100 image recognition benchmark.


This spreadsheet counts modules for 16 recent high-profile systems. Metaculus AI users have edit access and help improving it is very welcome.

Here's an actively updated list of papers in neural architecture search. (Though note that resolution does not require neural architecture search, but simply that it be state-of-the-art.)


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