Jtuck9Tell me how it ends

A supercomputer purely dedicated to grinding away on images that just aren't quite good enough.

"What we're doing during this process," Catanzaro continues, "is we're analysing failures. When the DLSS model fails it looks like ghosting or flickering or blurriness. And, you know, we find failures in many of the games we're looking at and we try to figure out what's going on, why does the model make the wrong choice about how to draw the image there?

"We then find ways to augment our training data set. Our training data sets are always growing. We're compiling examples of what good graphics looks like and what difficult problems DLSS needs to solve.

"We put those in our training set, and then we retrain the model, and then we test across hundreds of games in order to figure out how to make DLSS better. So, that's the process."

LittleBroI kind of expected this. Was wondering for long time, how the hell can they advance in DLSS so quickly, meaning how does the model keep improving itself? Is there some dedicated algorithm working when DLSS is turned on in games to search for problematic sections/defects in rendered picture?


Source: www.yahoo.com/tech/turns-theres-big-supercomputer-nvidia-140554206.html

Now it's pretty clear why nowadays middle class RTX costs a kidney.
It's pretty clear why Nvidia so strongly encourages game devs to include DLSS.

nguyenI'm quite interested in how the Transformer model will look vs the old CNN model.

The best thing is that all that advance in visuals can be backported to all games that support DLSS2.x, meaning over 600 games

closeJust take the "one to rule them all" model and prompt it for what the game should be like.

closeI've been wondering for a while if eventually every game will be an AI model running on the GPU that takes the player's input as a "prompt" and outputs the game's visuals realtime. Eventually everything could be procedurally generated, assuming AI will be able to write a captivating story as opposed to just outputting visuals. Just take the "one to rule them all" model and prompt it for what the game should be like.

AnarchoPrimitivI think one of the biggest issues here, and I never see it mentioned, is that implementations like the one Nvidia is using detailed in this article, basically create a runaway effect with respect to competition. Nvidia is basically leveraging it's unparalleled resources to exponentially accelerate the gulf between them and AMD/Intel, and because their competitors do not have that level of resources, they will perpetually be "behind" and they'll never be able to catch up.

Now, what I'm about to say could be wrong, I don't have expert knowledge of how GPUs are designed, but it seemed like 20 years ago, if you had a brilliant individual or a few of them, you could compete because in the end, every company is more or less working with and is limited by, the same tool: the human brain.

Now, with machine learning and AI, that limitation has been breached, and it has basically turned into an arms race with who can amass the most compute power. In a reality wholly shaped by the dictates of capitalism and the profit motive, the competition has basically been reduced to who can buy the most hardware. It then basically turns into a positive feedback loop: Nvidia has the most resources so they have access to more compute power, this compute power let's them create faster products, the products sell more and Nvidia gets more resources....repeat. With the use of AI/ML in the design process, I feel like Nvidia has literally gained an insurmountable advantage and it will never be "corrected" by market forces.

AnarchoPrimitivI think one of the biggest issues here, and I never see it mentioned, is that implementations like the one Nvidia is using detailed in this article, basically create a runaway effect with respect to competition. Nvidia is basically leveraging it's unparalleled resources to exponentially accelerate the gulf between them and AMD/Intel, and because their competitors do not have that level of resources, they will perpetually be "behind" and they'll never be able to catch up.

AnarchoPrimitivI think one of the biggest issues here, and I never see it mentioned, is that implementations like the one Nvidia is using detailed in this article, basically create a runaway effect with respect to competition. Nvidia is basically leveraging it's unparalleled resources to exponentially accelerate the gulf between them and AMD/Intel, and because their competitors do not have that level of resources, they will perpetually be "behind" and they'll never be able to catch up.

Now, what I'm about to say could be wrong, I don't have expert knowledge of how GPUs are designed, but it seemed like 20 years ago, if you had a brilliant individual or a few of them, you could compete because in the end, every company is more or less working with and is limited by, the same tool: the human brain.

Now, with machine learning and AI, that limitation has been breached, and it has basically turned into an arms race with who can amass the most compute power. In a reality wholly shaped by the dictates of capitalism and the profit motive, the competition has basically been reduced to who can buy the most hardware. It then basically turns into a positive feedback loop: Nvidia has the most resources so they have access to more compute power, this compute power let's them create faster products, the products sell more and Nvidia gets more resources....repeat. With the use of AI/ML in the design process, I feel like Nvidia has literally gained an insurmountable advantage and it will never be "corrected" by market forces.

AnarchoPrimitivNow, what I'm about to say could be wrong, I don't have expert knowledge of how GPUs are designed, but it seemed like 20 years ago, if you had a brilliant individual or a few of them, you could compete because in the end, every company is more or less working with and is limited by, the same tool: the human brain.

R0H1TSo I'm guessing they're using your "data" to train this supercomputer

Easy RhinoThe next time some politician or Hollywood a-hole preaches to you about your carbon footprint and how you can't have a wood stove anymore to heat your home, remind them that a trillion dollar company used untold megawatts training AI so you could get a few extra FPS on a crappy upscaled 4K video game.

nguyenI'm quite interested in how the Transformer model will look vs the old CNN model.

The best thing is that all that advance in visuals can be backported to all games that support DLSS2.x, meaning over 600 games

AnarchoPrimitivI think one of the biggest issues here, and I never see it mentioned, is that implementations like the one Nvidia is using detailed in this article, basically create a runaway effect with respect to competition. Nvidia is basically leveraging it's unparalleled resources to exponentially accelerate the gulf between them and AMD/Intel, and because their competitors do not have that level of resources, they will perpetually be "behind" and they'll never be able to catch up.

Now, what I'm about to say could be wrong, I don't have expert knowledge of how GPUs are designed, but it seemed like 20 years ago, if you had a brilliant individual or a few of them, you could compete because in the end, every company is more or less working with and is limited by, the same tool: the human brain.

Now, with machine learning and AI, that limitation has been breached, and it has basically turned into an arms race with who can amass the most compute power. In a reality wholly shaped by the dictates of capitalism and the profit motive, the competition has basically been reduced to who can buy the most hardware. It then basically turns into a positive feedback loop: Nvidia has the most resources so they have access to more compute power, this compute power let's them create faster products, the products sell more and Nvidia gets more resources....repeat. With the use of AI/ML in the design process, I feel like Nvidia has literally gained an insurmountable advantage and it will never be "corrected" by market forces.

LittleBroYes but you can't blame Nvidia for re-applying their success from elsewhere. They used their own tech to their advantage.
Nvidia's success is heavily dependent on TSMC's success. Maybe it will get corrected through there if you know what I mean.

AnarchoPrimitivI think one of the biggest issues here, and I never see it mentioned, is that implementations like the one Nvidia is using detailed in this article, basically create a runaway effect with respect to competition. Nvidia is basically leveraging it's unparalleled resources to exponentially accelerate the gulf between them and AMD/Intel, and because their competitors do not have that level of resources, they will perpetually be "behind" and they'll never be able to catch up.

Now, what I'm about to say could be wrong, I don't have expert knowledge of how GPUs are designed, but it seemed like 20 years ago, if you had a brilliant individual or a few of them, you could compete because in the end, every company is more or less working with and is limited by, the same tool: the human brain.

Now, with machine learning and AI, that limitation has been breached, and it has basically turned into an arms race with who can amass the most compute power. In a reality wholly shaped by the dictates of capitalism and the profit motive, the competition has basically been reduced to who can buy the most hardware. It then basically turns into a positive feedback loop: Nvidia has the most resources so they have access to more compute power, this compute power let's them create faster products, the products sell more and Nvidia gets more resources....repeat. With the use of AI/ML in the design process, I feel like Nvidia has literally gained an insurmountable advantage and it will never be "corrected" by market forces.

Vayra86But... are they that far behind? XeSS is pretty good and FSR strongly improved, without harping about all this.

ChrisyI am wondering if running the optimization locally means that the driver will upload these optimizations to Nvidia servers for aggregating the improvements.
Which effectively would be kind of moving the calculations they had to manage within their infrastructure to the clients now.
That would be a lot of off-loading (time and money saving) for Nvidia. Not sure if clever in evil-way or not :D

AnarchoPrimitivI think one of the biggest issues here, and I never see it mentioned, is that implementations like the one Nvidia is using detailed in this article, basically create a runaway effect with respect to competition. Nvidia is basically leveraging it's unparalleled resources to exponentially accelerate the gulf between them and AMD/Intel, and because their competitors do not have that level of resources, they will perpetually be "behind" and they'll never be able to catch up.

Now, what I'm about to say could be wrong, I don't have expert knowledge of how GPUs are designed, but it seemed like 20 years ago, if you had a brilliant individual or a few of them, you could compete because in the end, every company is more or less working with and is limited by, the same tool: the human brain.

Now, with machine learning and AI, that limitation has been breached, and it has basically turned into an arms race with who can amass the most compute power. In a reality wholly shaped by the dictates of capitalism and the profit motive, the competition has basically been reduced to who can buy the most hardware. It then basically turns into a positive feedback loop: Nvidia has the most resources so they have access to more compute power, this compute power let's them create faster products, the products sell more and Nvidia gets more resources....repeat. With the use of AI/ML in the design process, I feel like Nvidia has literally gained an insurmountable advantage and it will never be "corrected" by market forces.