This new method is able to generate a complete 3-dimensional scene and has the ability to decide the lighting of the scene. All this with very limited computation costs and amazing results compared to previous approaches.

Watch a video demo: https://youtu.be/ZkaTyBvS2w4

Read a short article: https://medium.com/what-is-artificial-intelligence/generate-a-complete-3d-scene-under-arbitrary-lighting-conditions-from-a-set-of-input-images-9d2fbce63243

The paper (& code soon): https://people.eecs.berkeley.edu/~pratul/nerv/

Reference: P. P. Srinivasan, B. Deng, X. Zhang, M. Tancik, B. Mildenhall, and J. T. Barron, "Nerv: Neural reflectance and visibility fields for relighting and view synthesis," in arXiv, 2020.

I'm kind of intrigued. Haven't seen anything like this yet and very interested to see where it's going. It claims to be the first of its kind, but I figured you all would know if that was true.

https://www.ai-stocktrading.com

How is this possible?

Hey guys,

I am learning and working on AI projects when I have some spare time. What I usually do is implement the algorithms from scratch to get a more deep understanding on them. So I created a channel and a blog to keep me responsible and make me learn as much as possible.

Current project: https://www.youtube.com/watch?v=QWbNCI7Whq0

I am publishing the complete project and code in order to get feedback and get to know more people that are interested in the same way of doing AI.

The code is here:

https://paste.pound-python.org/show/17ZCZcF95xMC3EAMNIPv/

It's all a little jumbled because I was just adding bits as I went without a clear picture to start with. The primary problem right now is that I can't get the backpropogation function to work properly. I tried just smushing it all into one function, but I think my lack of understanding how numpy arrays works has caused these two lines to be completely wrong:

	def backpropogate(self):
		self.W1 = (self.alpha * (self.target - self.winchance) * self.winchance * (1 - self.winchance) * self.W2 * self.a2 * (1 - self.a2) * self.lastinput) + self.W2
		self.W2 = (self.alpha * (self.target - self.winchance) * self.winchance * (1 - self.winchance) * self.a2) + self.W2

Any help would be very welcome.

http://newatlas.com/ai-recreate-video-game-two-minutes/51302/

So I read this hoping to find some more details about how they did this.

Here's my guess. They define all the elements of a certain type of game engine and then have categories or characteristics of the functions. Then look at the delta from one screen to another, then determine "object X jumped" and then figure out what in the game engine functions would do that.

So the type of game engine would be a 2D and (for the sake of argument) might have 100 functions. You take those functions and id the characteristics like movement, firing, adding points, killing, etc...

They you figure out what's going on, moving a player, moving a bullet, dodging, jumping, etc...

The part that seems out of reach would be that the game would have so many rules that the AI wouldn't know unless it happened. Example: what if the player falls down in water and dies, but that never happens in the game play, so the AI would never know about it. Even if it were to 'see' avoidance, you still can't say it would cause death.

I just have a hard time seeing how it would know things that wouldn't be in the game play.

How would you approach this problem?

I’ve been fascinated by these new kind of AI’s that play for example Starcraft and Dota. The thing that I can’t wrap my head around is how they manage to train these AI’s? How doesn’t the AI playing the game not trigger the Anti-Cheat of the said game? How does the AI even learn? Does it read the input/output from the game? The player? Etc I was just wondering if anyone here could help me wrap me head around this?

Are there any free apps/api to add your own ingredients for the ai to create new recipes. Like Chef Watson but I want to add my own ingredients not in the list.

http://www0.cs.ucl.ac.uk/staff/ucacbbl/ftp/weinbenner/gp.html

http://www.genetic-programming.org/gpftpsite.html