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What do you see here?

https://www.youtube.com/watch?v=9Tt7aqHFUCU

This animation consists of geometric figures moving. But your mind may attribute mental states, intentions and even a personality to those figures.

This capability, "theory of mind", makes humans and other animals capable of attributing mental states even to inanimate objects that do not have a mind. In your case: black holes and other stuff.

do i need to give timestamps? if yes, I will find them for you.

You have failed to provide the exact context to that summary, and just leaning on name dropping is poor form, Jack.

he literally said engineers dont know how ai works fully and goodbye.

Aight, well I find your paraphrasing about a discussion on a podcast that hints at an 'air of mystery' coupled with your exaggerated generalities to be sufficiently lacking to continue this.

Adios, muchachos.

Im not familiar with smaller ais that aren't chat gpt that is my bad.

I'm not familiar with AI that plays and operates video games; I'm not a professional, so I'm not sure about smaller AIs. That is an inaccuracy from my behalf

Lex Freidman explained on the Joe Rogan podcast that there is an air of mystery surrounding how ai works, that is my source, i believe he has worked on ai.

I would disagree with your infinitely large broad brush strokes slathered on there. Log files exist for a reason.

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I am an athiest

I do not believe in god

For the input, each kernel is acting upon ONE channel only, right?

But in general, shouldn't the number of channels of the kernel be equal to that of the previous layer?

Ancient people did not understand natural phenomena, such as atmospheric events, astronomical events, seasonal cycles in agriculture, etc. In some cases, they came up with belief systems where supernatural entities such as deities governed those phenomena.

Today, science has explanations for many of those natural phenomena. Even with some open questions still remaining, now we understand things well enough so that we can articulate what is going on in clear terms without the need for a god of thunder, god of rain, etc.

I think you're following the steps of the early human cultures that tried to assign a God to what you perceive as unexplained phenomena. Namely: black holes, AI, sentience, etc.

>I claim that it is impossible to see what is inside a black hole, and to say that god isn't there is fundamentally an assumption.

Ok.

> I apply this analogy to artificial intelligence, claiming that because not everything is fully understood, there is room for something the engineers missed that makes it sentient.

What AI are you talking about? Every 'AI'? Some hypothetical 'tv-and-movie-AI'?

I claim that it is impossible to see what is inside a black hole, and to say that god isn't there is fundamentally an assumption. I apply this analogy to artificial intelligence, claiming that because not everything is fully understood, there is room for something the engineers missed that makes it sentient. I do not claim that god exists or that AI is sentient, and I apologize if I didn't make this post the easiest to start a discussion with.

I see a large number of nebulous claims, and little in the way of starting a discussion. Good luck with that.

This topic could lead to interesting discussions and debates about the nature of consciousness and the ethical considerations surrounding the development and use of AI technology. Additionally, the comparison to the concept of God being hidden in a black hole could spark discussions about the role of faith, science, and the unknown in our understanding of the universe.

What is your discussion point?

Sorry, this is a bit off-topic but what medical imaging datasets are u working with? I'm usually looking for those and you seem to be familiar with very large ones.

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Anytwo? I'm looking that up and can't find anything on it.

Thank you for this clear explanation.

To put it simply, the bottleneck layers is a process of reducing dimension first and then increasing dimension. So, why do we need to do this?

In theory, not reducing dimensionality can preserve the most information and more features, which is certainly not a problem. However, for specific tasks, not all features are equally important, and some features may even have a negative impact on the results. Therefore, we need to select some features that we should pay more attention to through some means, and reducing dimensionality can to some extent achieve this function. On the other hand, increasing dimensionality is to enhance the representational ability of the network. Although the channel number of the features after increasing dimensionality is the same as that before reducing dimensionality, the latter is actually restored from low-dimensional features, and the former can be considered to be more specific to the current task.

Great help 🤦‍♂️