jorgemf

I think they are doing that because using real images the model changes the face. So that problem is removed if the initial image doesn't show the face

This genereation seems that is getting performance using more power and more cores. Not really an architectural change but only packing more things in the chip that require more power.

Gaussian splatting transform images to a cloud points. GPUs can render these points but it is a very slow process. You need to transform the cloud points to meshes. So basically is the initial process to capture environments before converting them to 3D meshes that the GPUs can use for anything you want. It is much cheaper to use pictures to have a 3D representantion of an object or environment than buying professional stuff.

Basically you train a model per each set of images. The model is a neural network able to render the final image. Different images will require different trained models. Initial gaussian splatting models took hours to train, last year models took minutes to train. I am not sure how much this one takes, but it should be between minutes and hours (and probably more close to minutes than hours).

cost is relative. how much would it cost for a human to read and give you an answer for 200k tokens? Probably much more than $3.

My problem with this analysis is ignoring the fact of who is using which computer. So far new people in the company get the M3, while old people have M2, and the people who has been the longest time in the company have an M1. Who is going to work on more critical tasks with more changes in the code? who is going to work mostly in easy bugs until they get some experience with the code in the company? I bet you if you give both populations the same computer the compiling times are going to be faster for the new people. For me the analysis doesn't have enough dimensions, it should take into account the time since the person was hired in the company and the seniority. I would also have added more type of graphs (boxplots seems a better way to compare the information), and also I would have measure the total % of CPU usage. The battery/AC analysis gave me the impression that M3 might be underutilized and that it is going to be impossible to get lower compiling times without faster single core speeds (which might be a relevant information for the future).

I think kotlin is one example. It uses the same idea but it uses powers of 10 for incremental fixes and numbers for 1 to 9 for hotfixes. That's if for the 3rd number, I do not know what will happen when the second number reaches 2 digits. I guess they will do something to make it comparable again.

That's under the assumption that nothing else will change. But it is not the case, the system would have to adapt. One possibility is that we wont use money anymore, and there are a lot of in betweens in the middle. But for sure what you cannot do is to stop the change that is coming.

If AI does everything, the economic won't make sense anymore. Maybe there would be a basic rent or just anyone will ask for what they want and AI will provide it.

We though AI would replace the low level jobs first, but it seems creative jobs are gone first (art, software developers, etc). Bear that in mind.

You are assuming that the whole existence of humanity is to work? because, without working, they would be sloths? What about expending more time having healthy habits like working out, meeting more often with family and friends, discovering the world, learning new stuff? So retired people are just sloths?

Time is the forth dimension. The input data is a video, so the model learns the colors and the position of the elements (basically points). You can rende the scene from any angle at any time once the models is trained

SEEKING WORK | Spain | Remote (EU and US time zones)

  Technologies: TensorFlow, Pytorch, Deep learning, LLM, diffusion models, GANs
  Résumé/CV: http://jorgemf.github.io/cv.pdf
  Personal website: http://jorgemf.github.io
  Email: (in the CV)

The main reason is that you might not want the raw information but some reasoning above. LLM is not only the context but all the information it has been trained with. For example a math student is making a question, it doesn't want the raw theorems but some reasoning with them, and currently LLM can do that. It will make mistakes sometimes because of hallucinations, but for not very difficult questions it usually gives you the right answer. And that helps a lot when you are not an expert in the domain. And that is the reason GPT4 is a great tool for students, it helps you to understand the basics as if you have a teacher with you.