Julian Togelius

Thanks for the commentary togelius [1, 2] and deepnet [above].

Anyone interested in adding a soupçon of scholarship to Seth's project?

Words do mean different things to different people, in different contexts..

There is a recent body of literature that explores the modern "maker" [3] movement. However, "maker" as a term may not have been a good fit for the OP's argument [1], which contrasted (academic) researchers with so-called "tinkerers".

An alternative term for "tinkerer" might be "bricoleur", a loanword from French. (Roughly, it still means one who tinkers: https://en.wikipedia.org/wiki/Bricolage but has other meanings depending on the academic lens.)

Given that we are discussing AIs that play, in the context of education, we can also go back to Seymour Papert's work on https://en.wikipedia.org/wiki/Constructionism_(learning_theory) .

Originally known for work on _Perceptrons_ with Marvin Minsky, AI researcher Papert later adapted theories from education towards the vision of "learning-by-making" and the (young) bricoleur [4]. This can approach can be seen in the evolution from 1960s graphical [turtle] https://en.wikipedia.org/wiki/Logo_%28programming_language%29 to Lego https://en.wikipedia.org/wiki/Mindstorms_(book) to
modern day efforts to encourage coding-for-kids [5,6,7].

One of Papert's later collaborators, Sherry Turkel, discusses bricolage as it applies to programming -- https://en.wikipedia.org/wiki/Bricolage#Internet .

When it comes to early…

It's true that game AI is surprisingly under-developped compared to what you see in academia on other topics... but it is not always that basic.

What you describe in your football example is often called "Behavior Tree", and there are ways to learn those using machine learning (usually by reinforcement learning, using neural networks and/or genetic programming). There was a popular video showing this applied on Mario [1] ... and a paper published 7 years before that video doing the same thing [2] (more context about this in [3]). I remember seeing something on Gamasutra saying that similar methods are used in some AAA games.

[1] https://www.youtube.com/watch?v=qv6UVOQ0F44
[2] http://julian.togelius.com/Togelius2009Super.pdf
[3] http://togelius.blogspot.co.uk/2016/04/the-differences-between-tinkering-and.html

Some of these functions are easy to compose to get something impressive, true. But that is not often the case. Take the case for games and imagine we wanted a meta-algorithm to select an algorithm to apply to each game. The intelligence would then shift into, how does one select the correct algorithm for the current game in the shortest time possible?

There was a recent blog post covering this and the difficulties involved:

http://togelius.blogspot.ca/2016/08/algorithms-that-select-which-algorithm.html

I also posted a link (https://arxiv.org/pdf/1604.00289v2.pdf) above which is an easily readable exposition on just how current approaches fall short. It's nothing so trivial as "it's just not what we're used to".

Regarding latency, I found a live video of gameplay here [1] and it looks like closer to 1.1s keypress-to-photon latency (33 frames @ 30fps) based on when the onscreen keys start lighting up vs when the camera starts moving. This writeup [2] from someone who tried the Genie 3 research preview mentions that "while there is some control lag, I was told that this is due to the infrastructure used to serve the model rather than the model itself" so a lot of this latency may be added by their client/server streaming setup.

[1] https://x.com/holynski_/status/1952756737800651144

[2] https://togelius.blogspot.com/2025/08/genie-3-and-future-of-neural-game.html