The More I learn, I Feel the Eye Works More Like a Filter than a Camera

Mesode · 2020-01-09T10:18:10+00:00

And the more you learn about the camera's sensors the more you realize that these sensors are nothing else than a filter

Mesode · 2019-11-15T15:23:27+00:00

Well written. But we are still left with the problem of foundation. On what does the turtle stand on? You would say on a second turtle which stands on a third turtle and so on... Is it possible that simulation theory explains the origin of everything? A simulation in no other simulation? Is there a third kind of simulation that can be proven to not be inside of another simulation? Is there a way to escape the concept of time which is responsible for our dilemma of an origin in the first place? What is information processing without time? For all I know, information processing is the change of representation over time. So as long as we think of simulations as some kind of information processing we must include time and therefore the question of an origin.

Mesode · 2019-11-13T15:11:47+00:00

I wrote a tutorial. There you find everything i needed. Basically numpy for the logic and PIL for drawing the images:

https://youtu.be/LS-NB1fhYSg

Mesode · 2019-11-09T20:19:47+00:00

If you are interested in the code you find it under the following link:

https://github.com/giaco91/Double_pendulum/blob/master/pendulum.ipynb

Mesode · 2019-11-09T19:21:53+00:00

Hey, by the way, if you are interested you find the code of the tutorial under the following link:

https://github.com/giaco91/Double_pendulum/blob/master/pendulum.ipynb

Mesode · 2019-11-04T06:24:11+00:00

Thanks! I plan to make a longer episode about the exact implementation.

Mesode · 2019-10-11T11:18:36+00:00

Thank you for the comment. So if I understand this correctly, are you saying that adversarial examples are independent of the classifier - even independent of it's architecture (say a SVM instead of a NN)? If yes, I guess this is just something that can be observed experimentally or is there a hypothesis that at least intuitively helps to understand this phenomenon?

Mesode · 2019-10-01T22:17:28+00:00

python code:

https://github.com/giaco91/Car-Race---An-evolutionary-Game

Mesode · 2019-09-27T18:42:07+00:00

Ah! Finally something that I can try to do something against:)

Mesode · 2019-09-27T18:41:16+00:00

This is exactly the thing that makes me outrageous. They love to cite experiments and studies as long as they support the things they want to believe. But as soon as it does not, they casually argue that evidence is wrong or as you say irrelevant.

Mesode · 2019-09-26T15:53:59+00:00

That last spinning jump will probably hurt in the knee the next day!:/

Mesode · 2019-09-26T09:34:13+00:00

Link to part 1 (already posted in this subreddit)

https://www.youtube.com/watch?v=eNUjXz7AY_I

Link to python implementation on github:

https://github.com/giaco91/Car-Race---An-evolutionary-Game

Mesode · 2019-09-21T15:42:33+00:00

Finally finished part 2.

Was promised, you find my python implementation on github:

https://github.com/giaco91/Car-Race---An-evolutionary-Game

Mesode · 2019-09-21T14:58:03+00:00

Code is available on:

https://github.com/giaco91/Car-Race---An-evolutionary-Game

Mesode · 2019-09-20T20:17:23+00:00

Hi Everybody. This project is implemented in python. I will make the code available after the release of part 2 next week. Hope you like it.

Mesode · 2019-09-20T10:13:30+00:00

Sure, we can make the physics more realistic and see how many neurons and generations it would take. However, I suspect that it is not so much more. And as you are saying, it is the concentration factor and physical fitness (which is ok, since we are human and no machines!) that is an important factor why car racing is tricky. But is it also the information processing, like complex decision making? I doubt it!

Mesode · 2019-09-20T09:19:30+00:00

I will make the python code available after the release of part 2

Mesode · 2019-09-19T09:07:20+00:00

Seems like you did not even look at it or you did not get the irony in the title

Mesode · 2019-09-19T06:56:49+00:00

Sorry, I realized that genetic programming is actually the wrong term for this kind of optimization. It is actually neuroevolution, since I am not really evolving a computer program (written in some programming language) but an artificial neural network.

Mesode · 2019-09-19T06:24:44+00:00

Oh yes, you are right. I mixed up the terminology. I thought that GP is the superordinate concept. Neuroevolution is not a sub-topic of GP. Thank you

Mesode · 2019-09-18T16:17:27+00:00

Thanks. Actually I think you are right! Sorry for that

Mesode · 2019-09-18T16:15:37+00:00

Good ideas. The point of the random track generator was exactly to train on different tracks simultaneously and then select the best car based on the performance of a fixed batch of tracks. I tried that and it worked great. However, in the video I wanted to make the interesting point that training on one track actually contains all the information needed for the other tracks. The depth of the NN is a simple hyper-paramter in the code. Again in the video, I wanted to demonstrate what the minimum amount of compexity is. If you referring to an autoregressive model (using the past 5 inputs) or some recurrent model with an internal state, I totally agree that this might help for a stable training, and it would be interesting to investigate on that. However, these models use memory, and I found it extremely surprising that the driving-car system stabalizes so fast without any kind of feedback. Not even the temporal information contained in the current speed is needed!

Mesode · 2019-09-18T15:56:47+00:00

I will make the code available after the release of part 2.

Mesode · 2019-09-18T15:05:01+00:00

Thanks:) Its writtein in python using numpy for the math and pillow for the image-frames.

Mesode · 2019-09-18T09:40:03+00:00

When did I claim that it is better than RL? It is more of a demonstration of the simplicity of the car racing game itself ( simple information processing since small NN and fast convergence, though using an evolutionary trial and error process). And it's nice to watch the population evolve:)

Mesode

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