I agree, and I did that because I'm interested in AGI. It would probably be quite hard to build a business plan around this model, and I wasn't funded to build it.

>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.