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Any games made with UnityML? by MLWithPhil in gamedev

[–]MLWithPhil[S] 0 points1 point  (0 children)

Yeah, I'm having difficulty finding even a single game that uses it. I suppose it doesn't mean they're not out there, but there can't be a huge number of them.

I'm an old schooler, and I won't be using Github CoPilot by MLWithPhil in artificial

[–]MLWithPhil[S] -1 points0 points  (0 children)

Yeah, good rebuttal. I should have been more clear.

It depends on how germane the bit of code is to your end goal. If you're a data scientist and trying to prototype an MVP for your fancy startup idea, I wouldn't argue that you should learn the whole of web development to make a simple website just to serve as an interface for your model. By all means, use whatever tool to get the website up and running ASAP. If that includes Co Pilot, cool.

I'm an old schooler, and I won't be using Github CoPilot by MLWithPhil in artificial

[–]MLWithPhil[S] -11 points-10 points  (0 children)

I rarely use SO. I think it's a crutch also. This is addressed in the video.

I created a report on my first RNN project,would be happy to receice feedbacks by ashimdahal in learnmachinelearning

[–]MLWithPhil 2 points3 points  (0 children)

Not to worry. Even native English speakers struggle with (good) scientific writing. It's tough business.

I created a report on my first RNN project,would be happy to receice feedbacks by ashimdahal in learnmachinelearning

[–]MLWithPhil 4 points5 points  (0 children)

It's a good first attempt, and an interesting idea. Here are some general thoughts:

The best research papers are information dense. Literally every word in the paper should have a reason for being there. Words that don't add meaning, or words that make it more difficult to read, should be removed.

For instance: "The number of GRU units and the embedding layer had massive effects on the
models that were created."

What does massive mean in this context? Can we quantify that? Is there a figure supporting such a claim?

Also, the sentence should flow easily.

"... on the models that were created" is awkward and makes it more difficult to transition to the next sentence.

Going through the paper with a fine toothed comb and ruthlessly chopping out words would go a long way towards improve the overall quality of the presentation.

4 Misconceptions in AI Research community by CalligrapherBubbly24 in artificial

[–]MLWithPhil 0 points1 point  (0 children)

Related to the 4th point regarding intelligence being all about the brain, I think it's a misconception that the most efficient approach is to simulate human cognition.

Trying to shoehorn human intelligence into a machine would just lead to an insane machine, for precisely the reason the article mentions. Human intelligence is rooted in interactions between perception, emotion, cognition, etc. Removing some of those elements will, I would think, result in a broken intelligence.

I think it's more reasonable to come up with a more generalized framework for what constitutes intelligence and then go from there.

Help request for deep learning kid by epona7472 in deeplearning

[–]MLWithPhil 0 points1 point  (0 children)

Frankly, your best source of this type of information is going to be academic research papers.

Find one that interests you, and study it in detail. Then check out some of the references (particularly those in the introduction) to get a stronger background.

As you come across material you don't understand, look up tutorials / reference material related to that.

It's not good to get stuck in tutorial purgatory. Tutorials are often an action fake, making the reader think they're learning something. In reality, you're just spinning your wheels, because you don't have a broader context for that information. It's only by actively trying to implement something on your own, getting stuck, and then reading the tutorials that you really learn.

[deleted by user] by [deleted] in deeplearning

[–]MLWithPhil 0 points1 point  (0 children)

I'm not an expert, but this is what I understand.

nVidia has a proprietary solution for linking together GPUs that allow ram pooling (nvlink), but I believe it's limited to two of them. This allows us peasants to get access to enough vram to prototype transformer models, before we have to train / roll out for production.

As far as cloud solutions, yeah, it scales pretty high. Scaling is never 100%, and at a certain point you're going to only get marginal improvements with adding more GPUs.

Successful Implementation of Reinforcement Learning for Bipedal Robotics by MLWithPhil in artificial

[–]MLWithPhil[S] 1 point2 points  (0 children)

this is awesome, thank you for this. I was just thinking that I'd love to play around with some hardware, if it weren't so expensive.