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.
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.
No, that’s not a real problem either. Model search techniques are very mature, the first automated tools for this were released in the 90s, they’ve only gotten better.
AI can’t ‘train itself’, there is no training required for an optimization problem. A system that queries the value of the objective function - “how good is this solution” - then tweaks parameters according to the optimization algorithm - traffic light timings - and queries the objective function again isn’t training itself, it isn’t learning, it is centuries-old mathematics.
There’s a lot of intentional and unintentional misinformation around what “AI” is, what it can do, and what it can do that is actually novel. Beyond Generative AI - the new craze - most of what is packaged as AI are mature algorithms applied to an old problem in a stagnant field and then repackaged as a corporate press release.
Take drug discovery. No “AI” didn’t just make 50 new antibiotics, they just hired a chemist who graduated in the last decade who understands commercial retrosynthetic search tools and who asked the biopharma guy what functional groups they think would work.
“AI” isn’t needed to solve optimization problems, that’s what we have optimization algorithms for.
Define an objective and parameters and give the problem to any one of the dozens of general solvers and you’ll get approximate answers. Large cities already use models like these for traffic flow, there’s a whole field of literature on it.
The one closest to what you mentioned is a genetic algorithm, again a decades-old technique that has very little in common with Generative “AI”
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.
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
The US tax system is not at all ‘heavy’ on the wealthy. The largest burden, proprtionally, falls on those with high earned incomes, doctors, lawyers, etc. these are the people who will be paying the higher marginal tax rates on substantial portions of their income.
The truly wealthy do not have high earned incomes, they acquire large assets and borrow against their value to pay for living expenses while avoiding taxes. This is the “buy, borrow, die” strategy, specifically designed to limit tax liability.
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.
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.
No, it isn’t. The key conceit is they are removing water from the river and evaporating it.
The water isn’t ‘lost’ it is still part of the hydrosphere, but it is made non-local. That water goes into the air and will go on to be rain in some place far away from the community where it was sourced. This will absolutely contrubute to local droughts and water insecurity.
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.