Gridworld version of DeepMind Lab.

Open sourced in 2020: analyticsindiamag.com/deepmind-just-gave-away-this-ai-environment-simulator-for-free/

A tiny paper: arxiv.org/pdf/2011.07027.pdf

Very similar to gvgai, Julian Togelius actually called them out on that: DeepMind Lab2D vs gvgai.

TODO get running, publish demo videos on YouTube.

At twitter.com/togelius/status/1328404390114435072 called out on DeepMind Lab2D for not giving them credit on prior work!As seen from web.archive.org/web/20220331022932/http://gvgai.net/ though, DeepMind sponsored them at some point.

Or is real word data necessary, e.g. with robots?

Fundamental question related to Ciro's 2D reinforcement learning games.

Bibliography:

They seem to do some cool stuff.

They have also declined every one of Ciro Santilli's applications for software engineer jobs before any interview. Ciro always wondered what does it take to get an interview with them. Lilely a PhD? Oh well.

In the early days at least lots of gamedev experience was enough though: www.linkedin.com/in/charles-beattie-0695373/.

A pyramidal alkene doesn't exist as a distinct category in traditional organic chemistry. However, the term might refer to alkenes that possess a certain spatial arrangement or stereochemistry. In organic chemistry, alkenes are compounds that contain at least one carbon-carbon double bond (C=C). They are typically characterized by a planar geometry around the double bond due to the sp² hybridization of the carbon atoms involved in the double bond, leading to a trigonal planar configuration.

This kind of died at some point checked as of 2023.

Julian Togelius cites it e.g. at: togelius.blogspot.com/2016/07/which-games-are-useful-for-testing.html

Good points:

Sigma-pi and equivalent-orbital models are concepts from molecular and solid-state physics that deal with the electronic structure of molecules and materials. ### Sigma-Pi Models 1. **Sigma Bonds (σ Bonds)**: These are covalent bonds formed when two atoms share electrons in an overlapping region of their atomic orbitals along the axis connecting the two nuclei. Sigma bonds are generally stronger because they involve direct overlap.

Given a bunch of points in $n$ dimensions, PCA maps those points to a new $p$ dimensional space with $p\le n$.

$p$ is a hyperparameter, $p=1$ and $p=2$ are common choices when doing dataset exploration, as they can be easily visualized on a planar plot.

The mapping is done by projecting all points to a $p$ dimensional hyperplane. PCA is an algorithm for choosing this hyperplane and the coordinate system within this hyperplane.

The hyperplane choice is done as follows:

www.sartorius.com/en/knowledge/science-snippets/what-is-principal-component-analysis-pca-and-how-it-is-used-507186 provides an OK-ish example with a concrete context. In there, each point is a country, and the input data is the consumption of different kinds of foods per year, e.g.:so in this example, we would have input points in 4D.

The question is then: we want to be able to identify the country by what they eat.

Suppose that every country consumes the same amount of flour every year. Then, that number doesn't tell us much about which country each point represents (has the least variance), and the first PCA axes would basically never point anywhere near that direction.

Another cool thing is that PCA seems to automatically account for linear dependencies in the data, so it skips selecting highly correlated axes multiple times. For example, suppose that dry codfish and olive oil consumption are very high in Portugal and Spain, but very low in Germany and Poland. Therefore, the variation is very high in those two parameters, and contains a lot of information.

However, suppose that dry codfish consumption is also directly proportional to olive oil consumption. Because of this, it would be kind of wasteful if we selected:since the information about codfish already tells us the olive oil. PCA apparently recognizes this, and instead picks the first axis at a 45 degree angle to both dry codfish and olive oil, and then moves on to something else for the second axis.

We can see that much like the rest of machine learning, PCA can be seen as a form of compression.

A parameter that you choose which determines how the algorithm will perform.

In the case of machine learning in particular, it is not part of the training data set.

Hyperparameters can also be considered in domains outside of machine learning however, e.g. the step size in partial differential equation solver is entirely independent from the problem itself and could be considered a hyperparamter. One difference from machine learning however is that step size hyperparameters in numerical analysis are clearly better if smaller at a higher computational cost. In machine learning however, there is often an optimum somewhere, beyond which overfitting becomes excessive.

AMD GPUs as mentioned at: aws.amazon.com/ec2/instance-types/g4/

An impossible AI-complete dream!

It is impossible to understand speech, and take meaningful actions from it, if you don't understand what is being talked about.

And without doubt, "understanding what is being talked about" comes down to understanding (efficiently representing) the geometry of the 3D world with a time component.

Not from hearing sounds alone.