Recent comments in /f/MachineLearning

slickvaguely t1_j8o5klm wrote

Reply to [D] Simple Questions Thread by AutoModerator

Is there an object detection algorithm that seeks to detect that there is an object as opposed to detect a specific object (cat, bike, etc.)?

I have tried searching this but generic object detection appears to be the name of the other thing...

As an example, I mean if you fed an image into this algrothm it would put bounding boxes on things without trying to identify the things.

Maybe a dumb question....

​

Thanks!

2

Cogwheel OP t1_j8o2st4 wrote

Reply to comment by HyugenAI in [D] Is anyone working on ML models that infer and train at the same time? by Cogwheel

> What most models are doing now is much more efficient, practical and reliable than what I described. Though it doesn't exactly reproduce how we learn things. But that's probably not what most people would want in their models. They prefer more efficient, pratical and reliable models.

Yeah, I guess the distinction here is whether one is using an ML model as a means to an end or as an end in itself. I imagine a researcher interested in AGI would be much more likely to take this kind of approach than someone trying to sell their ML models to industry.

Edit: anyone care to discuss why you downvoted?

−2

Cogwheel OP t1_j8o29pz wrote

Reply to comment by CabSauce in [D] Is anyone working on ML models that infer and train at the same time? by Cogwheel

>1. Distributed models would have to be updated. How do we update weights from two sources? (There might be options for this, I haven't looked.)

This strikes me as more of a software/hardware engineering challenge rather than one of network and training architecture. Definitely a challenge though.

>2. Potential for undesirable and unstable predictions/generations.

I think the same is true for humans. Given enough "perverse" inputs we can all go crazy. So it's definitely something to think about and mitigate. There would definitely need to be components built to work against these "forces"

>3. I think you'd have to allow the weights to update pretty dramatically at each inference to get any real variation. I think this would lead to #2

Interesting point... The time between acts of inference in an ML model are on the order of clocks (milliseconds for realtime perception systems, seconds to minutes for things like ChatGPT). Whereas animals experience essentially continuous input. Our eyes alone present us with many Mbps of data, is it were.

So without these vast swathes of data constantly being fed in, the alternative is to make bigger changes based on the limited data.

>4. Attention components probably do what you're looking for more accurately and efficiently.

Attention had crossed my mind when I posted this. I agree its intention is to accomplish a kind of weight redistribution based on previous input. But I still think this is more superficial/ephemeral than what I'm asking about. Humans certainly have attention mechanisms in our brains, but those attention mechanisms are subject to the same kinds of changes over time as the rest.

2

HyugenAI t1_j8o211j wrote

Reply to [D] Is anyone working on ML models that infer and train at the same time? by Cogwheel

>I'm wondering if there are any serious attempts in the works to create an AI that is able to transform itself dynamically

I'm not sure what you call a "serious attempt". I programmed neural networks that try to do that for a specific task.

I can try to summarize it. Basically I have 3 models:

  • image > (model 1) > image embedding (self supervised)
  • sound > (model 2a) > sound embedding > (model 2b) > sound (autoencoder)
  • image embedding > (model 3) > sound embedding (association between embeddings based on temporal proximity)

When I launch the program, all models train and infer "simultaneously". The input is a video (or multiple images / sounds, or a livestream). Model1 reads multiple images and train on these images, it doesn't need labels. Model2 does the same thing for sounds. While Model1 and Model2 train, they produce embeddings. Model3 trains on these embeddings.

So the global model is able to associate images and sounds, without supervision, and models train and infer simultaneously. They improve themselves continously, and you can point the camera towards an object, say the name of the object, and after perhaps 1minute the model will have learned the association and will "repeat" the name of the object just from the image.

For inference the path is:

  • image > (model1) > image embedding > (model3) > sound embedding > (model2b) > sound

While models train, they simultaneously do this path. So they train and infer "simultaneously", and the model doesn't explicitly need labels / supervision. Though it needs the temporal association between the name of the object and the image of an object (which is probably what we also first used to learn words). Also I say "simultaneously" because in the code, it's just a while loop with two instructions: (1) a model does a backprop (2) it infers. It's not exactly at the same time, there are still two separate processes, but it's the same while loop. Though Model1 can train while Model2 infers. You could consider that Model1+Model2+Model3 are just one big model.

  • Does it reproduce our brain regarding how neurons work? No. So it doesn't reproduce what you described regarding how neurons work.
  • Is it sentient? It depends on what you think "sentience" is. It's probably not, based on what we usually call "sentience".

What most models are doing now is much more efficient, practical and reliable than what I described. Though it doesn't exactly reproduce how we learn things. But that's probably not what most people would want in their models. They prefer more efficient, pratical and reliable models. If a model trains continously, it's much harder to check that it continues to have good results.

3

CabSauce t1_j8nuthg wrote

Reply to [D] Is anyone working on ML models that infer and train at the same time? by Cogwheel

You can update a model with new data at any time. Production models are often updated at intervals with monitoring. There are a few challenges to updating continuously.

  1. Distributed models would have to be updated. How do we update weights from two sources? (There might be options for this, I haven't looked.)
  2. Potential for undesirable and unstable predictions/generations.
  3. I think you'd have to allow the weights to update pretty dramatically at each inference to get any real variation. I think this would lead to #2
  4. Attention components probably do what you're looking for more accurately and efficiently.
15

jetpackjules t1_j8ns4u2 wrote

Reply to [D] Simple Questions Thread by AutoModerator

For the Huggingface Inference API,

How can I request for a custom image size? I am able to do this in a gradio space, but when using the API from python, I can't seem to find some sort of input parameter for output size...

1

dgrsmith t1_j8n4kwe wrote

Reply to [R] [N] Toolformer: Language Models Can Teach Themselves to Use Tools - paper by Meta AI Research by radi-cho

From a cognitive point of view, humans and animals have modules that they rely on for certain tasks. For Human Neuropsych assessment, the combination of the function of these modules gives you a score for general intelligence, with each module contributing toward the whole. Having a removed or changed “module” for one reason or another will sometimes cause localized task failures (e.g., neurodegenerative disease or brain injury) or approach to tasks that is atypical (e.g., atypical brain development). Maybe we can think of specific cognitive functions as being API calls to a modules in this “tool use” paradigm? This is likely not an original thought, and if anyone has references or has heard of this idea, please let me know!

1