UMich research cited in the article estimates a carbon dioxide emissions per year (using constant mileage typical per year) break even time between EV and ICE SUVs to be 1.6-1.9 years.
Promising. But, is carbon dioxide emissions the only environmental impact? Humanitarian impact (e.g. rare metal extraction labor conditions)? I’m not an environmentalist researcher/academic. I wonder how accurately just one emissions prediction portrays the full picture.
Fair enough. Let me quickly go through the one-liner, command-by command
# Joined by `&&`, bash runs these commands in sequence (as if run individually in shell), but exits/stops execution early if any command fails (return nonzero)
TMP_DEB=$(mktemp --suffix=.deb) && curl -sSL "https://support.brother.com/g/b/downloadend.aspx?c=us&lang=en&prod=hll2465dw_us&os=128&dlid=dlf106036_000&flang=4&type3=10283" -o "$TMP_DEB" && sudo apt install -y "$TMP_DEB" && rm -f "$TMP_DEB"
# Going command by command:
# First, we create a local variable in the shell, named `TMP_DEB`
# We assign the value to `$(...)`. This stores the string output (to stdout) of running the command `mktemp ...` to `TMP_DEB`
# `mktemp` creates a temporary file and prints its name, which uses the name template `tmp.XXXXXXXXXX`
# `--suffix=.deb` flag appends `.deb` to the name template
TMP_DEB=$(mktemp --suffix=.deb)
# At this point, we've created a temporary file, and saved the name to a variable in bash
# Next, we download the file using curl. `-s` makes output silent, `-S` shows errors in output, and `-L` follows redirects
# note the url doesn't end in `.deb`, implying that we will be redirected by the web server to the file path. without -`L` curl will download a page that stores the redirection response from the web server, not the .deb package
# `-o "$TMP_FILE"` forces curl to store the downloaded file to the tmp file we created
# note the quotes around the variable expansion. `$TMP_FILE` would also resolve the string stored in the variable, but we use quotes to avoid string globbing (google this)
curl -sSL "https://support.brother.com/..."
# Next, we install the package with apt
# note: we use the string stored in the variable `TMP_DEB`, the filepath to the temp file we created, and downloaded the deb package
# `-y` flag skips the confirmation question "install package [y/n]: `
sudo apt install -y "$TMP_DEB"
# Finally, to clean up we delete the tmp file
rm -f "$TMP_DEB"
I certainly wasn’t trying to “encourage” anything. I agree, blindly trusting commands is dangerous.
In this context I present a specific explanation of how the install works. This adds to the novice’s knowledge, and allows them to begin to understand what my one-liner does.
I think that without the context of instructions on how to do it manually, yes, you could make the case i’m enabling beginners to form/reinforce bad habits.
That printer probably supports AirPrint, which Mint supports without any extra tinkering. Connect the printer to your network, and try going through linux mint and adding the printer through the settings. If it doesn’t show up, then you can try using drivers (install using below command) and then re-adding the printer
Install by pasting this into your terminal. Enter your password when prompted.
TMP_DEB=$(mktemp --suffix=.deb) && curl -sSL "https://support.brother.com/g/b/downloadend.aspx?c=us&lang=en&prod=hll2465dw_us&os=128&dlid=dlf106036_000&flang=4&type3=10283" -o "$TMP_DEB" && sudo apt install -y "$TMP_DEB" && rm -f "$TMP_DEB"
Explanation if you want to learn:
sudo apt install ./Downloads/package_name.deb
Yes, you do know the boundaries of AI. It is purely matrix multiplication: its output distribution is just as intelligible as the distribution of rolls of a dice. We receive a probability distribution for the next token given a sequence of tokens. This is demonstrable; search for softmax online.
To fairly equate a dice roll event to a model prompt event we must understand the technicalities. To say you have a 20 sided die, is equivalent to saying you have a specific model’s architecture and value of every parameter, in the context of qualifying event determinism.
If you can assume your die is fair, and 20 sided, that is an equivalent assumption about a model as to saying it’s llama-3.1-8B-instruct. That is, you do know the specific model weights, corresponding to a functional relationship between input and output which is deterministic. That is, if you know the model weights, which is equivalent to knowing whether a die is fair and n-sided, you can deterministically predict the output of a model as you can deterministically predict which number on a die will land
You’re making specific, technical errors about the mathematical basis of language modeling, and equating things fallaciously to a similar deterministic event.
Despite this, your intuition is right: we can’t perceptually predict the output of a model as we can’t perceptually predict what number will result from a die roll
Language modeling is equivalent to a dice roll (given a perfect random number generator). Setting the temperature to 0 removes all randomness from the output, meaning the model always selects the highest probability next word, and the model becomes 100% deterministic. That is, the output of a model is entirely predictable given temperature = 0, you know the model weights, and the seed/prompt.
These technicalities aside, it’s true for both a dice roll event and a specific model/prompt event that, practically speaking, the outputs are treated as probabilistic despite being mathematically/technically deterministic: a human can’t predict with 100% accuracy the output of a die despite the theory (classical mechanics of die positioning, force, velocity, friction, …) proving determinism
How it currently exists, yes in most cases it is trained on stolen cognitive labor. Do you think this is inherent to the technology itself, however? Consider a model trained on entirely public domain data, or non-copyleft liscence not requiring attribution. E.g., talkie
Totally agree that we need strict regulation.
If only we lived in a society where people could be freely able to produce cognitive labor while also being guaranteed a dignified life with universal basic services and income, regardless of what they produce. Then, like with piracy, LLM training, in my opinion, could be trained on anything without harming original authors.
i honestly believe it isn’t that everyone here is only pitchforks and cheerleading. i agree “fuck AI” on the surface, semantically is a gross oversimplification without nuance; but rhetorically this really means “fuck AI corporations and their cronies”.
this community isn’t strictly fuck AI from a technology standpoint, but from the environmental and socioeconomic standpoint.
the “fanboys” refers to are supporters of the massive corporations pushing their slop and enshittification, which i hope you despise as much as the rest of us
i would say this is like if open code and open web ui had a baby it would be this. It’s a web interface for self hosting models but runs them through open code to make it agentic. Helpful for non developers to get into running models, but imo isn’t significant bc using open code tui and connecting it to a llama.cpp or vllm self hosted api is not difficult for devs
I don’t have any familiarity with using this kind of software, but I looked through the git repo of SavaPage. It looks like it has been actively developed for the past few years, which is a great sign, but it looks like almost all commits are done by one user. The issue tracker is also a little meager, with just one open issue, potentially pointing to a very small user base. Adoption heavily depends on as long as that one person keeps maintaining the project.
Honestly, you’re a few months late to the whole buying GPUs for local llms party, so expect exorbitant prices even for older cards
The name of the game is vram. For the most part, more is better. If you can get your hands on multiple matching (same model) 24gb or higher cards (within price range), you’re golden.
Going for more than 2 gpus can become challenging with motherboard pcie slot heights, so make sure either your cards aren’t too tall or you have widely spaced out pcie slots.
For inference, speed (tokens/second) is limited by memory bandwidth. Go for faster bandwidth memory cards if you can afford it (e.g. GDDR6 will be faster than GDDR5).
Also with multi gpus you will need an adequate power supply, and a large enough case.
If you want to be a bit eccentric and load huge models, you can also go the CPU route and fill up a motherboard with 256 GB ram, because then you’re in the several hundred B param model territory, which could, depending on your use case, be better than having faster inference on smaller/quantized models. Even then, DDR5 with high MHz is still way slower than gpus.
yea there’s still honestly some downsides to Qobuz, including:
when i switched from spotify to Qobuz several months ago they gave me access to a third party playlist conversion site https://soundiiz.com/ with premium features free for the first month of my subscription. Conversion of playlists and liked songs was easy and done within minutes of signing up for Qobuz. I can’t recommend moving off spotify enough; Qobuz won my pick because how they pay artists (seemingly) the highest rate per stream.
i’m so incredibly annoyed by the fact that my previously favorite hosting provider digital ocean decided to fund this ridiculousness… any favorite alternatives anyone can offer?