Hi All!
These are my 10 favorite AI articles and news published in July. They talk about autonomous driving, artificial intelligence, another GAN application.
What are your best news and articles of the month of July?
https://blog.sicara.com/07-2019-best-ai-new-articles-this-month-3e1fa3f6c321

Hello I want to create a deep Q learning agent for a 2 player board game. My rewards are 250 for winning the game, 100, 80 and 50 for “good” moves. My tutor said to me that I should normalize the rewards because there are only limited amounts of different rewards and there are possibly infinitely manyQ values. How should I normalize the rewards? Should I normalize the rewards to [0,1] range so that 0,8 represents a game winning move, 0,3 a good move for example?

So a little late to the party, but I couldn’t stop thinking about the ZIMA Blue episode of Love, Death & Robots in regard to Machine Learning.

*Spoilers* The plot involves an intergalactically famous artist creating a final art piece in which he reveals he was a sophisticated Android that was giving up his (for lack of a better word) Sentience in order to return to his earliest form as a pool cleaner.

Though the episode was obviously an allegory for the human condition craving familiarity & nostalgia, I thought the writers did a great job capturing how hard it is to tell how different inputs (or combinations and layerings of inputs) will impact the development of an AI over time.

I recorded a more in-depth audio breakdown of my thoughts here for anyone interested:

The subtle way Love, Death & Robots introduces us to the fundamentals of Machine Learning

And I also had a conversation with an AI researcher on the novelty of Ex Machina in explaining how hard it will be to test future AI:

A shallow dip into the ethics of AI

O’Reilly titles are pretty valuable and this pack is well-rounded so I thought some may enjoy the quick reminder to grab it.

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Here

Hello r/MachineLearning,

This is a new post in which I try to visualize the majority of what happens inside a trained GPT-2. We follow the journey of an input word from embedding, all the way up to the output of the model. I’ve also included a crude analogy for the query/key/value vectors of self-attention that I hope makes it easier for people starting out with transformer architectures. By the end of the post, we’d have looked at the major weight matrices of a single block, as well as the major weight matrices of the entire model. All feedback and corrections are welcomed!

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The post: https://jalammar.github.io/illustrated-gpt2/

I’m trying to get a bunch of paintings for my new project, but every site I visit has you individually download each one. Is there any site that has files for download filled with paintings?

Background :- I am an undergrad just beginning my sophomore year, and have studied ML the entire past year. I understand Basic NLP, Computer Vision , Machine Learning.

I am interested in research and generally I can think of new ideas which I believe, are worth of doing research on. But , I am new to this world of research papers and learning from them . I have studied from few of them and understanding them fully made me very happy. I would like to go further into NLP. I know RNNs, LSTMs , GRUs, attention mechanism. And have read corresponding papers.

1.) Can anyone here give an “ordered” list of papers from here on ,which will move my basic knowledge ahead. Avoiding the papers that are certainly dead ends or won’t help if I am to do research further. A list that will increase my knowledge, from what I know, to the state of art; while also letting me “why people chose the architectures/techniques etc. that they did?”

2.) I want an “ordered” list , because , most of the times , I try to read a paper , after 4 pages , I end up jumping to another paper , then another , then another and so on. What do you guys do to avoid this?

paper: https://arxiv.org/pdf/1908.03251.pdf

code: https://github.com/bj80heyue/One_Shot_Face_Reenactment

demo: https://www.youtube.com/watch?v=FE-D6wh11_A

For implementation of Bidirectional language model, Please recommend me a paper/github repo that I can refer to.

There’s a lot of papers about uni-directional LM, but I can not find bi-directional one ..

Thanks 🙂

For building a video game AI what would people view as the ideal platform/tools? For both training and simulation.

The training would include the ability to work with prior replays of the game.

Currently based on my very basic research I’m thinking that using something like Kubeflow to enable people to do the data-science side of things and training inside of Jupyter

For the simulation a custom web-based interface to the game where the user can upload scripts and access previously created training data which will be run automatically.

Thoughts?