During my last Machine Learning course I was working with Q-Learning and made a small program that allows users to create maps for a Q-Learning agent to solve. I am using a game engine called Godot for the visuals and coded in Godot’s custom language GDScript. My teacher felt that I should post this online for others to use to help learn Q-Learning. I plan to add menu options to provide a detailed walkthrough of how the program works and possibly other Q-Learning example projects.

Github: https://github.com/bioa10/QTable

For my master’s thesis, I’m working on a model that could potentially be misused with malicious intents. It’s getting a common theme lately.

I’ve got to admit that I don’t have a strong sense of morals and that I work on that project mostly because I love to see machine learning in action, and I don’t really care of the actual applications of my model. I intend to make my trained models public and the code open source. While I doubt any of it will actually cause havoc, I feel like the topic of responsible AI is looming to make a paragraph in my thesis. I don’t really like it, because I’d prefer to entirely avoid the subject. So far, I’ve only written

(…) is attractive for a range of applications be they useful, merely a matter of customization, or mischievous.

I feel like it would make me look bad to entirely avoid the topic when I’m planning to make my work publicly available.

Given the popularity of machine learning, how has machine learning been applied for crash testing Android applications’ GUI? What are the tools that one can use to achieve this?

https://github.com/prodmodel/prodmodel

I built a tool which keeps track of all code and data deps of your data science project. It caches partial results and can figure out if a particular output (model, transformed data, code library) has to be recomputed before the fact. This can save huge amounts of time during an iterative development process.

Setting up usage is similar to build systems like Bazel or Make (https://github.com/prodmodel/prodmodel/blob/master/example/build.py).

Feedback, users, contributors and constructive criticism is welcome!

Link To The Post:

https://iforcedabot.com/i-forced-a-bot-to-watch-over-1000-of-star-trek-episodes-and-then-asked-it-to-write-1000-olive-garden-commercials/

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What is this?

About a year ago, a tweet went viral purporting to be written by a bot: I forced a bot to watch over 1,000 hours of Olive Garden commercials and then asked it to write an Olive Garden commercial of its own. Here is the first page.

It was a funny script but it was written by a comedy writer, not a bot. This was obvious to some people, but not others, and enough people that it was real that sites wrote articles debunking it.

I’ve had this in the back of mind for while. It used to be obvious that scripts like this were fake — anyone who had used an RNN network knew they could only hold a coherent thought for about two sentences, there’s no way it would produce something like that. But since that tweet GPT-2 came out and changed everything. I was looking for something to test fine-tuning the new GPT-345M on and picked the dumbest silliest option.

I can’t tell you exactly why I also also mixed this with Star Trek The Next Generation and Deep Space Nine scripts except that it I found it hilarious.

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How it works

The training set is all Star Trek, but GPT-2 is shockingly good at writing lines appropriate for a waitress or restaurant scene that are 100% are not in the Star Trek script training set. I love how creative it can be since it has such a wide range of pre-baked knowledge.

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Are these hand selected samples?

Nope. The title says 1,000 commercials but it’s actually over 30,000 commercials now — I couldn’t resist trying different prompts and tweaks to the training data to get better results. I didn’t filter the samples at all so it’s a wide mix of iterations, temperatures, learning rates, tweaked training sets, I pretty much just tossed everything up there. I was overall impressed with how well GPT-2 worked with both very little fine-tuning, and how it avoided overfitting with a lot more training. I trained these same Trek scripts with the smaller GPT-2 and had over-fitting problems eventually but I never hit that point with 345M. I suppose it’s possible it just trains a lot slower…

Hello r/MachineLearning,

Disclaimer, I am a data engineer. I hope this post have its place here, I apologies in advanced if not.

What is your experience regarding the industrialization of your ML code ? Who do you collaborate with?

I will start : I’ve worked with data scientists to release in production machine learning pipeline. We have been improving our collaboration over the past year, and thus delivering more efficiently. First by drafting a blue print of a machine learning pipeline, then converging on common tools, finally by sharing our knowledges on our different skills.

Our shared tech stack is mainly: BigQuery, Apache Beam (to distribute the preprocessing), docker, tensorflow/Keras, Apache airflow.

This has lead to data scientists being more autonomous regarding scalability.

I think collaboration is the key.

EDIT : cleaning, removing medium link.