I want an AI that can play like a human at my level would, such that the game is competitive and fun.

It’s much more fun to go against an AI that is dumber than you but generally more powerful.

In Counter Strike the AI can be super dumb and slow until progressively it becomes a dumb aimbot. It doesn't become a better player with gamesense ans tacticts, just raw aim (Try arms race if you wanna feel it yourself)

In Fear the AI is pretty advanced in a more organic way. It coordinates several enemies to locate you and engage with you in a way that sometimes feels totally human. That feels great and when you lose you try again thinking something like "I should have positioned myself better" instead of "I guess my aim is just not fast enough".

We just don't get enough of good AIs to know how good they can feel.

This combination or larger dose followed by mild withdrawal then results in a higher likelihood to become addicted to opioids. So not only they marketed it heavily and got more people on opioids than necessary, they did it in a way that maximizes the likelihood of addiction.

https://www.latimes.com/projects/oxycontin-part1/

and Tesla would do better how in this case? It also routinely crashes into stationary objects, presumably because the system assumes it wouldn't cause damage.

Are the Teslas in the room with you right now?

Please point out in my comment where I mentioned Tesla. I can wait.

AMD's software investments have begun in earnest a few years ago, but AMD really did progress more than pretty much everyone else aside from NVidia IMO.

AMD further made a few bad decisions where they "split the bet", relying upon Microsoft and others to push software forward. (I did like C++ Amp for what its worth). The underpinnings of C++Amp led to Boltzmann which led to ROCm, which then needed to be ported away from C++Amp and into CUDA-like Hip.

So its a bit of a misstep there for sure. But its not like AMD has been dilly dallying. And for what its worth, I would have personally preferred C++ Amp (a C++11 standardized way to represent GPU functions as []-lambdas rather than CUDA-specific <<<extensions>>>). Obviously everyone else disagrees with me but there's some elegance to parallel_for_each([](param1, param2){magically a GPU function executing in parallel}), where the compiler figures out the details of how to get param1 and param2 from CPU RAM into GPU (or you use GPU-specific allocators to make param1/param2 in the GPU codespace already to bypass the automagic).

Back then AMD/ATI were actually at the forefront on the GPGPU side - things like the early brook language and CTM lead pretty quickly into things like OpenCL. Lots of work went on using the xbox360 gpu in real games for GPGPU tasks.

But CUDA steadily improved iteratively, and AMD kinda just... stopped developing their equivalents? Considering a good part of that time they were near bankruptcy it might have not have been surprising though.

But saying Nvidia solely kicked off everything with CUDA is rather a-historical.

I wasn't so much that they stopped developing, rather they kept throwing everything out and coming out with new and non backwards compatible replacements. I knew people working in the GPU Compute field back in those days who were trying to support both AMD/ATI and NVidia. While their CUDA code just worked from release to release and every new release of CUDA just got better and better, AMD kept coming up with new breaking APIs and forcing rewrite and rewrite until they just gave up and dropped AMD.

I've been writing CUDA since 2008 and it doesn't seem that different to me. They even still use some of the same graphics in the user guide.

Just about everyone else other than the tech companies are actually selling your data to various brokers, from the DMV to the cellphone companies.

First-hand account from me that this is not factual at all.

I worked at a major media buyer agency “big 5” in advanced analytics; we were a team of 5-10 data scientists. We got a firehose on behalf of our client, a major movie studio, of search of their titles by zip code from “G”.

On top of that we had clean roomed audience data from “F” of viewers of the ads/trailers who also viewed ads on their set top boxes.

I can go on and on, and yeah, we didn’t see “Joe Smith” level of granularity, it was at Zip code levels, but to say FAANG doesn’t sell user data is naive at best.

So you got aggregated analytics instead of data about individual users.

Meanwhile other companies are selling your name, phone number, address history, people you are affiliated with, detailed location history, etc.

Which one would you say is "selling user data"?

also see https://jon-cld.s3.amazonaws.com/test/ahall_of_fshame_SSIMUL... which is an ab comparison of a lot of images where it gives 2 versions, one preferred by ssimulacra, the other preferred by vmaf

TID2013 for example is an image dataset with many artifacts completely unrelated to compression and scaling.

- Additive Gaussian noise - Additive noise in color components is more intensive than additive noise in the luminance component - Spatially correlated noise - Masked noise - High frequency noise - Impulse noise - Quantization noise - Gaussian blur - Image denoising - JPEG compression - JPEG2000 compression - JPEG transmission errors - JPEG2000 transmission errors - Non eccentricity pattern noise - Local block-wise distortions of different intensity - Mean shift (intensity shift) - Contrast change - Change of color saturation - Multiplicative Gaussian noise - Comfort noise - Lossy compression of noisy images - Image color quantization with dither - Chromatic aberrations - Sparse sampling and reconstruction

Doing better on TID2013 is not really an indication of doing better on a video compression and scaling dataset (or being more useful for making decisions for video compression and streaming).

VB5 in 1997 and VB6 in 1998 really closed the gap with Delphi from what I remember.