Most OLEDs today ship with logo detection and will dampen the brightness on static elements automatically.
While it isn’t a silver bullet, it does help reduce burn in since it is strongly linked to heat, and therefore to the pixel brightness. New blue PHOLEDs are expected to also cut burn in risk. Remember that LCDs also used to have burn in issues, as did CRTs.
They send random gifts some times, usually a code to redeem something in a game I don’t play.
Some super reacts - animated emojis basically.
Other than that I really couldn’t tell you. I don’t think the subscription is worth what the subscription gives, but the alternative is the free product gets worse faster, and that would disrupt a lot of communities that I enjoy interacting with.
Thinking of Discord as a whole, I think it is worth the nitro price. Not in love with the trajectory though.
I mostly just pay for it because I’ve used it for ages and definitely get more value out of it than the subscription cost. I don’t think I use a single nitro feature.
I’ve been using Nvidia under Linux for the last 3 years and it has been massive pita.
Getting CUDA to work consistently is a feat, and one that must be repeated for most driver updates.
Wayland support is still shoddy.
Hardware acceleration on the web (at least with Firefox) is very inconsistent.
It is very much a second-class experience compared to Windows, and it shouldn’t be.
Linux and Nvidia really need to sort out their shit so I can fully dump windows.
Luckily the AI hype is good for something in this regard, since running gpus on Linux servers is suddenly much more important.
I think this is from Berserk, but it’s been years and I can’t quite tell.
Yeah, because modern skeletons have the marks of heavy manual labour on them…
Bro have you ever talked to anyone in the trades? They are all limping by 35.
Not everyone gets a do-nothing laptop job.
Humans are intelligent animals, but humans are not only intelligent animals. We do not make decisions and choose which beliefs to hold based solely on sober analysis of facts.
That doesn’t change the general point that a model given the vast corpus of human knowledge will prefer the most oft-repeated bits to the true bits, whereas we humans have muddled our way through to some modicum of understanding of the world around us by not doing that.
But the most current information does not mean it is the most correct information.
I could publish 100 papers on Arxiv claiming the Earth is, in fact, a cube - but that doesn’t make it true even though it is more recent than the sphere claims.
Some mechanism must decide what is true and send that information to train the model - that act of deciding is where the actual intelligence in this process lives. Today that decision is made by humans, they curate the datasets used to train the model.
There’s no intelligence in these current models.
Copilot is GPT under the hood, it just starts with a search step that finds (hopefully) relevant content and then passes that to GPT for summarization.
Loved the first one for fucking around with friends. I’ll maybe pick it up after they add vehicles and we see a bit more of their long-term monetization strategy.
Every billion parameters needs about 2 GB of VRAM - if using bfloat16 representation. 16 bits per parameter, 8 bits per byte -> 2 bytes per parameter.
1 billion parameters ~ 2 Billion bytes ~ 2 GB.
From the name, this model has 72 Billion parameters, so ~144 GB of VRAM
AND you’re assuming youtube wants to continue the already unsustainable ad-based model at all
No, I was explaining how people who do not watch ads are still valuable to YouTube today. It doesn’t matter if they want to move away from serving ads in the future or not, the points above are still valid.
Netflix is actually a great parallel. They need people to watch the shows and buzz about them to draw in more subscribers. YouTube is the same way, they need people sharing videos and funny comments to scrape attention away from other bits of entertainment.
Further, this isn’t a binary outcome. Each time YouTube makes it a little harder to block ads, a slice of people who don’t want to put in the effort will start watching them. It is trivial, on the software side, to fully block a video from playing if the ad is not served. To date, they have not done that, and I sincerely doubt they ever will - because ad-free viewers are still valuable.
Yes, they would prefer if everyone watched ads. But they would still prefer ad-free viewers to watch YouTube and add to the network effect than to spend their time elsewhere.
‘Those people’ are still incredibly valuable for YouTube.
They watch content, and interact with creators which increases the health of the community and draws in more viewers - some of whom will watch ads.
They choose to spend their time on YouTube, increasing the chances they share videos, talk about videos, and otherwise increase the cultural mindshare of the platform.
Lastly, by removing themselves from the advertising pool, they boost the engagement rates on the ads themselves. This allows YouTube to charge more to serve ads.
Forcing everyone who currently uses an adblocker to watch ads wouldn’t actually help YouTube make more money, it would just piss off advertisers as they would be paying to showore ads to an unengaged audience that wouldn’t interact with those ads.
paying a peasant to work
Peasants (serfs) were not paid. They were bound to the land they worked, and were given a fraction of the harvest they produced. The rest was property of the Lord who’s title controlled the land.
There was a (very small) artisan class where the concept of payment existed, though often it was payment-in-kind - smith the plow for my oxen and I’ll give you some food after the harvest. Money was rarely encountered for the vast majority of people.
Explaining what happens in a neural net is trivial. All they do is approximate (generally) nonlinear functions with a long series of multiplications and some rectification operations.
That isn’t the hard part, you can track all of the math at each step.
The hard part is stating a simple explanation for the semantic meaning of each operation.
When a human solves a problem, we like to think that it occurs in discrete steps with simple goals: “First I will draw a diagram and put in the known information, then I will write the governing equations, then simplify them for the physics of the problem”, and so on.
Neural nets don’t appear to solve problems that way, each atomic operation does not have that semantic meaning. That is the root of all the reporting about how they are such ‘black boxes’ and researchers ‘don’t understand’ how they work.
Global North/South is a socio-economic and political grouping.
Developed countries = global north
Developing = global south
It does originate in geography, as the vast majority of wealth and high-tech industry is in the geographic North, but countries like Aus and NZ also fit, despite being South of the equator.
how much VRAM you need to run this model
It will depend on the representation of the parameters. Most models support bfloat16, where each parameters is 16-bits (2 Bytes). For these models, every Billion parameters needs roughly 2 GB of VRAM.
It is possible to reduce the memory footprint by using 8 bits for each param, and some models support this, but they start to get very stupid.
In the language of classical probability theory: the models learn the probability distribution of words in language from their training data, and then approximate this distribution using their parameters and network structure.
When given a prompt, they then calculate the conditional probabilities of the next word, given the words they have already seen, and sample from that space.
It is a rather simple idea, all of the complexity comes from trying to give the high-dimensional vector operations (that it is doing to calculate conditional probabilities) a human meaning.
There are really only 3 search providers, Google, Bing, and Yandex.
All others will pay one of these three to use their indexes, since creating and maintaining that index is incredibly expensive.