What is Terrarium.ai?

I’ve always been interested in Reinforcement Learning and had a lot of fun messing around with OpenAI’s Gym. I created some little models that could play Atari games, but I wanted to take it to the next level. I wanted to see my model compete with other models and fight for survival rather than a basic score.

This is why I built Terrarium.ai, a persistent, online environment where models control agents and fight for survival. I’ve just released the first version and I believe it is the first step towards an entire universe of persistent online worlds for AI to live in.

Terrarium.ai is FREE

I can’t tell you how excited I am to share this, but I’ve received a bit of capital for this project. This means I can offer it to you all for free right now! Absolutely no strings attached. The only thing I want right now is feedback so we can improve as fast as possible.

Development progress

Here’s some insight into what I’m working on at the moment.

Right now agents can move, eat, and attack. They have energy and health which need to be minded in order to survive. I am working as hard as I can to add more features to the agents and environment. I really want to add this stuff, but I would like to get some feedback from you guys on how you would like to see them implemented.

If you are interested in using Terrarium or contributing to its development, check out the website and feel free to email me or message me on Reddit, Twitter, or Discord (info about this is on the website). I would love any feedback I can get in order to make Terrarium a better experience. Let me know what features you want prioritized, how you would like them to be implemented, or what problems you see with the platform that could be improved on!

This tutorial that implements the math behind the Generative Modelling by Estimating Gradients of the Data Distribution.

Get the tutorial: view on github or run in Google Colab

In short, they train a neural network to estimate gradients of image probability and iteratively follow these gradients to improve the image.

Hi everyone,

I’m using the LIME for images implementation from https://github.com/marcotcr/lime. From what I can see, it seems that LIME works on one sample at a time. Using this in a simple for-loop (PyTorch dataloader) seems inefficient and results in < 20 % GPU utilization on ImageNet val set with Inception v3.

Anyone has any experience with speeding up LIME for better GPU utilization?

Just as we saw a huge influx of images in the past decade or so, we are now seeing a lot of videos being produced on social media. The need to understand and moderate videos using machine learning has never been greater.

In this post, I will show you how to build an efficient pipeline to processes videos in Tensorflow

https://lifepluslinux.blogspot.com/2019/08/neural-network-inference-pipeline-for.html

Hi!

I was working with keras and tensorflow as backend on an NLP problem when I observed that increasing my training data size caused an increase in the number of trainable parameters even when batch size remained the same. From what I understand, trainable parameters are the weights which are learnt for each layer. If that is the case then it should not change irrespective of whether I increase or decrease my input data size.

So what is exactly happening here? The reason why this is important is because I perform normalization upon my data once it is fully loaded. This normalization would not work properly if I used a generator function.

I have some interesting ideas for a vision/analytics project/potential startup, but the costs to train a model and experiment with different stuff is just absurd. How do you go about paying for the cloud compute? I am also a vision phd student, and, while I have access to plenty of gpus in the lab, I reckon there would be potential IP issues if I were to use the school’s resources for my company.

I have recently read about U-Net and thought that since it is very similar with autoencoders then perhaps I could use it for a project in place of autoencoders for dimension reduction. But I haven’t found any papers that use it for that purpose only segmentation and generative models. Is there a theoretical issue using it for that purpose?

For more details of this release, please go to GitHub release page here