It's kinda nice to see PageRank is still being used as one of the components of the algorithm

And exactly what does that prove?

Its not depression, but more like something's missing, knowing there won't be more of the same to come.

Give it some time. When I was a reviewer, I'd usually sit on responses for at least a day or two to gestate on them before answering.

It seems most of the article can be summarized by "don't hire inexperienced ML practitioners, they have no idea how to improve a badly performing model", followed by "here are some really simple things a somewhat seasoned ML practitioner would do".

In my experience, all of these issues will occur. It's going to vary from model to model. To be certain, you still have to make an objective test to determine whether the impact is positive or negative, and measure the significance of the impact.

Ultimately, the first two seasons were about people coming together to get things accomplished.

You are right. Intuitively, it's just rewarding correct inputs and penalizing wrong inputs. Which is largely similar to how many RL policies learn. FF seem like it will be able to discriminate, but it won't be able to encode and embed features the way back prop does. It would not identify common features. If you try to train a typical back prop based u-net architecture network, my instincts say it likely would not work since the discriminating information is not distributed across the entire network.

The text prompt "chicken" is just the first step. The user still has a mental model of what is considered an acceptable "chicken" and the act of selecting one image that best matches that mental model from a cluster of AI generated "chicken" images should also count for something where creativity and copyrighting is concerned.

I'm sorry, I should have clarified that I have no interest in the Hutter Prize or its rules, nor is it about the getting close to the entropy limit.

My idea is more about the transmitter and receiver already having mutually shared information (stored within the ML model). In such a situation, the transmitter can reduce the amount of information that needs to be transmitted because it does not have to transmit that mutually shared information. The receiver will be able to combine the transmitted information with its shared information to rebuild the original message.

I should not have used the term "image compression", that is an error on my part and I apologize if it lead any confusion. It is only "compression" in the sense that we are transmitting less information rather than transmitting the message in its entirety and pushing the limits of information compression.

It doesn't really matter. With just a language model trained on general use English (or whatever human language is in the corpus), it should still be able transform each sentence or paragraph into a short encoding.

Interesting. I should not be surprised someone else had similar ideas.

I think you need to decide who the target audience is.

If it is just for your own understanding of the topic, and you have no intent of building up a base of viewers, what you have is fine enough. It is equivalent to a math lecture found in most colleges.

But, you are here asking for feedback. Coupled with your plan for regular weekly posts to suit youtube's algorithm, this suggests you do want an audience. In which case, I would say your current target audience would likely be an expert who is looking for a reminder, or to look at how someone else presents the lecture.

If you are looking for more of an audience, you need an introduction. You are jumping straight into the details without explaining what it is you are presenting, why is this important, how or where it is used. You are not setting up a context for the viewer to get a frame of reference for what you are explaining, so it took me close to 3 minutes of watching before I even knew what it is you were even attempting to explain.