Yes they were, so I’m offering you an actual theory as to why this may actually be true, yet difficult to “prove”.
Smoking was bad for your health long before anyone sat down and took the time to prove it. Autoregressive LLM tokenizer are a very new field of computer science and it’s going to take a while for the community to collectively understand everything we’re currently doing by trial and error.
Anecdotally, I use it a lot and I feel like my responses are better when I’m polite. I have a couple of theories as to why.
More tokens in the context window of your question, and a clear separator between ideas in a conversation make it easier for the inference tokenizer to recognize disparate ideas.
Higher quality datasets contain american boomer/millennial notions of “politeness” and when responses are structured in kind, they’re more likely to contain tokens from those higher quality datasets.
I haven’t mathematically proven any of this within the llama.cpp tokenizer, but I strongly suspect that I could at least prove a correlation between polite token input and dataset representation output tokens
I think that holds true in this case… but I’ll be damned if the ltt screwdriver isn’t the best hand tool I’ve ever owned.
Crossfading and normalization would both independently be dealbreakers for me. I can’t go back
I would be genuinely surprised if fair use draws the line on format-shifted, legally purchased media, at “remote watch-together”, leaving format-shifting and local watch-together in-tact.
If it were up to the studio’s interpretation of the law, you’d need to purchase a license for each person during local watch-together.
agree in principal, but in practice:
parents who live across the state
plexamp for music
They are indeed just that keen on our data.
They know they can’t get rid of it for all of their customers, but they do want to make it as hard as possible for random users to do so.
The problem with this is it doesn’t work for home users that want to pay for their software. Crazy… I know… but those people do exist.
For people with “that one game” there is a middle ground. Mine is Destiny 2 and they use a version of easy anticheat that refuses to run on Linux. My solution was to buy a $150 used Dell on eBay, a $180 GPU to be able to output to my 4 high-res displays, and install Debian + moonlight on it. I moved my gaming PC downstairs and a combination of wake-on-lan + sunshine means that I can game at functionally native performance, streaming from the basement. In my setup, windows only exists to play games on.
The added bonus here is now I can also stream games to my phone, or other ~thin clients~ in the house, saving me upgrade costs if I want to play something in the living room or upstairs. All you need is the bare minimum for native-framerate, native-res decoding, which you can find in just about anything made in the last 5-10 years.
“Open source” in ML is a really bad description for what it is. “Free binary with a bit of metadata” would be more accurate. The code used to create deepseek is not open source, nor is the training datasets. 99% of “open source” models are this way. The only interesting part of the open sourcing is the architecture used to run the models, as it lends a lot of insight into the training process, and allows for derivatives via post-training
It’s a little deeper than that, a lot of advertising works on engagement -based heuristics. Today, most people would call it “AI” but it’s fundamentally just a reinforcement learning network that trains itself constantly on user interactions. It’s difficult-to-impossible to determine why input X is associated with output Y, but we can measure in aggregate how subtle changes propagate across engagement metrics.
It is absolutely truthful to say we don’t know how a modern reinforcement learning network got to the state it’s in today, because transactions on the network usually aren’t journaled, just periodically snapshot for A/B testing.
To be clear, that’s not an excuse for undesirable heuristic behavior. Somebody somewhere made the choice to do this, and they should be liable for the output of their code.
I cut the sleeves off of mine, that was probably my crime. Sleeves are like pants; the fewer you wear the better your day is.
If your bootloader is unlocked, you can fastboot boot an image and that will only run in memory the one time, however, it will share a data partition (apps, preferences, etc) so it might not behave as well as a native install would have.
The canvas API needs specific access to hardware that isn’t usually available via browser APIs. It’s usually harder to get specific capability information from a user’s GPU for example. The canvas API needs capability information to decide how to draw objects across differently capable hardware, and those extra data points make it that much easier to uniquely identify a user. The more data points you can collect, the more unique each visitor is.
Here’s a good utility from the EFF to demonstrate the concept if you or anyone else is curious.
Vscodium has been a very usable replacement for me. You lose some of the ms first party plugins (ssh being the most notable) but largely it just works otherwise.
I’ve always wondered why board partners didn’t just raise to scalper prices and take a $2200 profit per card sold.
And tbh, it’s Nvidia’s fault that the partners don’t have enough dies, I’d much rather a partner take the margin than an unnecessary middleman.
Why?
The difference is I (the contributor of content) have the same access as anyone else to the data, and could use it for my own purposes if I wanted to.
On a platform like reddit, access to the raw data is controlled and cannot be format shifted / used in any way I wanted to.
There’s nothing preventing you from forking a Lemmy client or server to prototype this. Depending on how you implement the activitypub backend, you might be able to make it transparent to a user if you present an algorithm as an array of cross posts via a /c/ of a server.
Anything more might require forking a client, which might be easier to implement but may be harder to convince a large userbase to migrate to.
It’s almost certainly related to cloud-init, (the canonical tool for handling deployment automation) or Ubuntu pro (extra long support for backporting security packages to older distros, plus some conveniences). They’re pre installed as a convenience to paid users of those services, that’s the (IMHO, quite reasonable) model they use to fund the distro. I would expect that some or all of that traffic would disappear if you disable/remove those two services.
https://cloud-init.io/
https://ubuntu.com/pro