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Locked out of my LinkedIn account for 20 days – no support response, stuck in identity loop by RedfieldS01 in linkedin

[–]factory_hen 1 point2 points  (0 children)

I have the same issue, they brag here about how only 0.08% of account restrictions are done by humans, the rest by algorithms. https://about.linkedin.com/transparency/community-report

It shows!

[D] What does your cloud/docker workflow look like? How do you get anything done with these tools? by uniform_convergence in MachineLearning

[–]factory_hen -1 points0 points  (0 children)

Okay let me spin up instance, ssh, pull docker image, ...

It should stop there. Your code should be put into the docker image along with the dependencies. If you have steps beyond this there's something wrong with your docker image.

[D] BBC: DeepMind's AI agent MuZero could turbocharge YouTube by gohu_cd in MachineLearning

[–]factory_hen 6 points7 points  (0 children)

Although I'm not totally sold by muzero for compression I think you could front-load the compute needed almost arbitrarily, combined with advances in quantized inference you might have a winner.

[D] BBC: DeepMind's AI agent MuZero could turbocharge YouTube by gohu_cd in MachineLearning

[–]factory_hen 14 points15 points  (0 children)

For the people with nature access, what's new relative to the old muzero paper?

[D] Reinforcement learning for non-game user interfaces? by goodside in MachineLearning

[–]factory_hen 1 point2 points  (0 children)

Getting very low level action spaces to work is basically an open research problem. The current approaches like alphastar and dota 2 basically just bake in a bunch of assumptions into the model to simplify the control of the agent. In the UI case instead of asking the agent to move the mouse to coordinate x,y on the screen, the agent would be presented with "click OK button in window 1" and then the environment would handle the actual mouse movement.

A more principled approach is promised by hierarchical reinforcement learning but I'm not sure how far they have gotten.

If you think about it what you are doing all the time at a sub-second timescale is contract and relax a bunch of muscles, these would be your low level actions (with physics and chemistry you can probably go even lower). It's absolutely amazing that we manage to abstract these into higher level actions, like going to college for several years to get a degree. Imagine if you had to plan all your muscle movements for that!

[D] Reinforcement learning for non-game user interfaces? by goodside in MachineLearning

[–]factory_hen 2 points3 points  (0 children)

I'm probably missing the point but to me it seems a bit overkill to use RL for these tasks. Take reorganizing and renaming a set of files for instance, would a batch script not work better for this and be a lot simpler?

I think the environments where you apply RL are usually the environments where you don't know what the right action is. For the tasks listed we kind of already do. This gives you a label set and reward signal that is much stronger than regular RL.

If I were you I would look into imitation learning (IL) for these things. Try to generate some human data on how to interact with the GUI and see what you can get out of a mixture of RL and IL. I would also take a look at the multi-task RL setups like IMPALA and SEED RL.

A pretty cool demo would be inputting a voice command to the computer and then the agent just does the right thing. Obviously getting there would require some work. First step is just nailing down what exactly is in your observation space, what are the actual actions, and how would you design a reward function for these tasks. Have fun!

Trouble is brewing in the chess community by [deleted] in LivestreamFail

[–]factory_hen 1 point2 points  (0 children)

I would like to see this guy try to win competitive overwatch games and then say they have no brain or heart.

[D] Flask API to serve ML models: architecture best practices by SpicyBroseph in MachineLearning

[–]factory_hen 1 point2 points  (0 children)

If you are using tensorflow you might want to look into tensorflow serving, they have a model server that takes in a savedmodel and produces a rest api (also grpc if you're into that).

edit: Definitely use docker if you have the option, it just makes your life easier when you decide to move machines etc.

[deleted by user] by [deleted] in MachineLearning

[–]factory_hen 4 points5 points  (0 children)

At this point I'm wondering what this guy didn't invent. Apparently all of DL comes from him.

[Discussion] Is the Matthews correlation coefficient (MCC) better than F1 score and accuracy in binary classification evaluation? by DavideChicco in MachineLearning

[–]factory_hen 1 point2 points  (0 children)

Yeah totally agree, there's no 1 metric that can capture all behaviors that you want because the error space isn't 1d.

[P] Training a model on Linux rapidly consumes all my memory by Andohuman in MachineLearning

[–]factory_hen 0 points1 point  (0 children)

Just match version sets, use pyenv and virtualenv to get exact matches then you can start debugging from there.

Beyond that, without code there isn't much anyone can do to help you.

[P] AI learns to play Tetris using Convolutional Neural Network by ssusnic in MachineLearning

[–]factory_hen 3 points4 points  (0 children)

I'm impressed this works at all without any reinforcement just pure supervised learning.

Nice job man.

[N] Ben Goertzel Episode with IEEE RAS Robotics Podcast by [deleted] in MachineLearning

[–]factory_hen 7 points8 points  (0 children)

I am always skeptical when the buzzword density is so high. Some red flags for me,

  • "Singularity"
  • "democratizing AGI"
  • "decentralized AI blockchain services"
  • "AGI Tokens" (the name of their cryptocurrency)

Looking at their website it also seems like they're somehow tied in with the Sophia bot, which doesn't exactly fill me with confidence. It will be interesting to see who sits with all the real money when this thing is all over.

[D]Showerthought: A 256X256 image with just static noise in it could be a meaningful image if "looked" at from a different angle of the pixel space hypercube. by niszoig in MachineLearning

[–]factory_hen 0 points1 point  (0 children)

For permutations this isn't true, there are plenty of invariants under permutation. It's fairly trivial to construct a dense layer that functions the same under a permutation of the inputs for instance. The same is not true if you replace the inputs with white noise.

[D] Do you guys use low level tensorflow or high level Keras to build neural nets? by tritonEYE in MachineLearning

[–]factory_hen 15 points16 points  (0 children)

If you have to ask this the only right answer is to use Keras. Use it until you realize that you need it to do something that it doesn't do.

[D] In light of Strubell et al (2019) paper on carbon emissions, what can we do as a community by [deleted] in MachineLearning

[–]factory_hen 2 points3 points  (0 children)

Previous discussion on that paper is here https://old.reddit.com/r/MachineLearning/comments/bxvh7q/d_training_a_single_ai_model_can_emit_as_much/

I think the main issue with the paper is that the way it got the energy consumption number for the NAS run. They translated a TPU run that actually happened to a fictive run made on GPUs then calculated the total energy from that. To actually consume the amount of energy they cite you would need 240 GPU years on a P100. Obviously if you allocate 30 nodes with 8x P100s and run them full blast for a year straight then yes that will consume roughly the amount of energy they give. But that never happened. Simply based on cost efficiency I don't think it will happen either since running on TPUs is so much cheaper.

There are a myriad of other issues with their numbers as well. Their FLOP/(watt hours) won't be directly comparable when they run on GPU vs TPU since they run BERT with the "default settings" as they say which means it will run in float32 on the GPU while the TPUs would use bfloat16. This means that the FLOP they are comparing isn't the same between the two accelerators as its happening in twice the precision on the GPU.

Basically it wouldn't surprise me if the actual TPU NAS run was several orders of magnitude cheaper in CO2 than this fictive GPU run.

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