While reading the other thread (https://www.reddit.com/r/MachineLearning/comments/d1ooem/d_when_the_ai_professor_leaves_students_suffer/), someone linked to a H1B database of professor salaries which showed assistant professors are getting paid ~120K max at a top school like CMU (unless in the business school).

Is this really true? Is there variance among schools? I am very surprised since these candidates could easily make 300K-500K as a research scientist at the big tech companies. Granted, there is still old school prestige attached to being a professor, but it seems like they are leaving a lot of money on the table.

See my repository below, it’s just plain python that will work in any 3.x version as far as I know, and requires no extra installs (uses only the base python libraries, all you need is python installed).

It’s a simple feed forward network, but hopefully it can shed light on how the forward and backward pass works.

https://github.com/sean-gribben/pynet

I wanted to use a Reinforcement Learning Model in order to simulate traffic situations. The agent would learn how to control a network of cars stuck in deep traffic in order to increase efficiency, and reduce the amount of harmful gas released into the air. I want to be able to make a 2d environment that can simulate the potential that Reinforcement Learning has in traffic situations. If I finish this part, and if this next part is feasible, I will do this part.

Extra Part: After making this model, I wanted to make an app, or an extension of google maps, that would be controlled by the agent in real traffic situations. The agent would advise the network of drivers to go at a certain speed and time to increase efficiency, and potentially reduce the amount of accidents made in traffic situations.

Question: How would I create a virtual 2d environment where I could control a car or multiple cars with artificial intelligence. Is there a course that I could take, or a method that should be followed(like using unity machine learning)?

Are there any neural nets trained to calculate the native resolution of a render, pre upscale?

I’m asking specifically because YouTube channels like Digital Foundry are getting stressed out and spending an inordinate amount of time finding the range for dynamic resolutions for modern games, and I was wondering if there were any tools that could be utilized to automate the process.

It would be a great shame if anyone on their team quit due to the stress and tedium that has resulted from dynamic resolution scaling, as they provide invaluable technical information in the field of real time rendering.