Hmm, very interesting! This could definitely go further into the first scenario. Perhaps the summoner is now a “puppet” for the entity, and is trying to trick the more resilient player characters to succumb to the entity’s control. Perhaps the first ritual weeded off the weaker candidates, and now the entity is preparing to give them the full course.

I’m curious what you’re going to go with :D

The go-to monkey paw scenario could work, where they receive immortality, but have to serve the summoned entity forever as deformed (and perhaps always hurting) “things”? However, that is maybe a bit too predictable. What if they mess up the spell and instead mark themselves for sacrifice?

They slowly realise this due to a symbol appearing on them, which could slowly spread further on their body (maybe hurting too). You could make it more interesting by pitting them against one another by suggesting that only a few have to die for the sacrifice to be complete. If their characters are not that close to one another, it could lead to some interesting decisions on their part, haha.

The process could span a week or more, where their body gets engulfed more and more by the mark. They could use this time to review what went wrong in the summoning process, and how to potentially reverse it. I’ll leave those details to you, if you’re interested in pursuing this idea :D

I’m really sorry to hear that. I hope you have enough support to deal with it!

Regarding bioinformatics, it doesn’t have to be a human-centered job. You can get into the data science aspect of it, and make good money off of helping research diseases, for example. This could also be a remote job, and you’d probably have an easier time getting into it. For data science, you can get quite far with Python, which is easier to pick up when compared with other languages.

You can also explore your options further by just asking ChatGPT, and seeing what the potential job requirements would be. It’s decent if you want to brainstorm some stuff, but do look up the information yourself on search engines. Write there your experience, what you’d want, and what to expect if you were to jump in that field. Perhaps this could help you decide better.

I wish you the best of luck!

If you already have medical knowledge, why not look into bioinformatics? Cyber security would be a pretty big jump if you’re not into tweaking computers as a hobby. For example, have you ever set up Linux on your own?

Certifications will give you a starting point, but it will take years for all the information to settle properly in your mind.

Piracy. I’d buy albums if I had money, though. I’ll slowly phase into getting them once I get some more cash.

I can find most stuff I listen to, and I rarely grow my music library. I mostly listen to 20-30 albums, with some more mainstream music peppered in.

My music library currently sits at 90 gigabytes (mostly flacs), so quite small compared to others I’ve seen around here. Still, I have plenty of variation to keep me entertained :D

If you have Tidal, aren’t there some apps to rip the lossless audio from there? You could get most of the stuff that you need, and then cancel the subscription. If you feel bad, maybe order some merch from the band, haha.

The Linux kernel is not flawed, and Redox is probably years away from being even near it. However, having memory-safety from the get-go as a requirement for developing the kernel could lead to fewer exploits, compared to what we have today with Linux. Just as you’ve said, most users are not aware of it/they don’t care, but the big players will care about keeping information safe on their servers. Just to conclude, Redox OS is not just Linux rewritten in Rust, and could potentially have many other benefits that are particularly juicy for data centers. Too bad it’s not production ready yet :D

That’s unfortunate :( I think you can still run it in QEMU, if you’re interested.

I see your point. However, integrating Rust properly in the Linux kernel is an uphill battle. Redox OS is not at all close to being stable, but it showcases that you can build a Rust kernel from scratch, and integrate it into an OS that meets some of the requirements of a modern one. Of course, considering it a toy project and glancing over its potential doesn’t help with adoption. They even mention in their description that currently they can only support a community manager and a student developer with the current donations. When you compare that to the amount of money and developers involved in the Linux kernel, it’s insignificant.

I was not suggesting that the Rust For Linux devs jump ship, but it could be beneficial for the investors behind the project to look at alternatives. Heck, the Linux kernel started as a toy project itself. I believe that a team focused solely on such a Rust-only kernel could spearhead needed changes to reach something stable, as opposed to investing time and money into fighting established C developers to integrate a memory-safe language in the kernel fully.

If I am not mistaken, the difference was that the Internet Archive was distributing books with a DRM that would make the PDF unusable after a certain time. You could relate it to how a physical library offers books for a limited time, for free. Now, of course, one could bypass the DRM or copy the contents differently, but so can another person photocopy a book they borrowed physically. Meanwhile, other physical libraries are allowed to distribute e-books, but I’m not sure if that’s made possible due to licensing fees.

I’m not saying that they approached this well, especially given the copyright laws in the US, but it was indeed a good thing for the normal person at the time. Too bad that the judicial system in the US is biased towards leeching companies. I really can’t wait to see the AI vs publishers fight, though. Let’s see who has deeper pockets and better plants in the courts :D

I think it might be Magic Research 2? Nevermind, I couldn’t find that review on the Steam page, so it must be another game.

Organic Maps. Make sure you download the areas beforehand. The resolution for walking paths is much nicer, and you can clearly see the routes of temples and other points of interest.

It also shows data about drinking faucets, toilets, and other things provided by users.

You’re right. I read past the “I want to learn ML” and went straight to “do something useful with the data”.

If the goal is to understand how modern LLMs work, it’s also good to read up on RNNs and LSTMs. For this, 3Blue1Brown does an amazing job, and even posted an in-depth video about transformers. I’d watch that next, followed by implementing a simple transformer in PyTorch (perhaps using the existing blocks).

You could argue that it’s important to design everything from scratch first, but it’s easier to first go high level, see how the network behaves, and then attempt to implement it yourself based on the paper. It is up to OP how comfortable he is with the topic though 😁

Depending on how much compute you have available, you can look into finetuning models from HuggingFace (e.g. Llama 3, or a smaller Phi model). Look into LoRA, and try to learn how the model you choose calculates the loss.

There are various ways to train, and usually involves masking the input by replacing random input tokens with the mask token. I won’t go into too much detail with this, because it’s a lot to explain, and I suggest you read an article on this (link1 or link2)

That person clearly hasn’t witnessed Dutch students carrying a whole bedroom on the back of their bike.

Good luck! You can try the huggingface-chat repo, or ollama with this web-ui. Both should be decent, as they have instructions to set up a docker container.

I believe the Llama 3 models are out there in a torrent somewhere, but I didn’t dig to find it. For the 70B model, you’ll probably need around 64GB of RAM available, but the 7B one should run fine with just 8GB. It will be somewhat slow though, compared to the ChatGPT experience. The self-attention mechanism can be parallelized, which is why you will see much better results on a GPU. According to some others that tested it, if you offload some stuff to RAM, you could see ~10-12 tokens per second on an RTX 3090 for certain 70B models. But more capable ones will be at less than 1 token per second, all depending on the context window you use.

If you don’t have a GPU available, just give the Phi-3 model a try :D If you quantize it to 4 bits, it can apparently get 12 tokens per second on an iPhone haha. It should play nice with pooling information from a search engine, or a vector database like milvus, qdrant or chroma.

What db2 already said. Microsoft just released Phi-3 mini, which could, allegedly, run locally on newer smartphones.

If I understood correctly, the Rabbit thingy just captures your information locally and then forwards it to their server. So, if you want more power, you could probably do the same by submitting the same info to a bigger open source model than Phi-3, like Llama 3, hosted on your homelab. I believe you can set it up with huggingface/gradio, which sort of provides an API that you could use.

That way, you don’t need a shitty orange box, and can always get the latest open source models with a few lines of code. There are plenty of open source frameworks in the works at the moment, and I believe that we’re not far off from having multi-modal LLMs running on homelab-level hardware (if you don’t mind a bit of lag).

Entry 30 looks so clean. It’s a pretty smart design.

That is good to know. Tried the free version of Roll20 before, and it definitely felt lacking in certain areas. Oh, and thanks for letting me know about the sale! I’ll definitely keep an eye out for that one :)

How will you move to WhatsApp if everyone else uses iMessage? Europe has the same issue, but reversed. Everyone uses WhatsApp and can’t jump to Signal/Telegram because they’re not as popular.

With the way current LLMs operate? The short answer is no. Most machine learning models can learn the probability distribution by performing backward propagation, which involves “trickling down” errors from the output node all the way back to the input. More specifically, the computer calculates the derivatives of each layer and uses that to slowly nudge the model towards the correct answer by updating the values in each neural layer. Of course, things like the attention mechanism resemble the way humans pay attention, but the underlying processes are vastly different.

In the brain, things don’t really work like that. Neurons don’t perform backpropagation, and, if I remember correctly, instead build proteins to improve the conductivity along the axons. This allows us to improve connectivity in a neuron the more current passes through it. Similarly, when multiple neurons in a close region fire together, they sort of wire together. New connections between neurons can appear from this process, which neuroscientists refer to as neuroplasticity.

When it comes to the Doom example you’ve given, that approach relies on the fact that you can encode the visual information to signals. It is a reinforcement learning problem where the action space is small, and the reward function is pretty straight forward. When it comes to LLMs, the usual vocabulary size of the more popular models is between 30-60k tokens (these are small parts of a word, for example “#ing” in “writing”). That means, you would need a way to encode the input of each to feed to the biological neural net, and unless you encode it as a phonetic representation of the word, you’re going to need a lot of neurons to mimic the behaviour of the computer-version of LLMs, which is not really feasible. Oh, and let’s not forget that you would need to formalize the output of the network and find a way to measure that! How would we know which neuron produces the output for a specific part of a sentence?

We humans are capable of learning language, mainly due to this skill being encoded in our DNA. It is a very complex problem that requires the interaction between multiple specialized areas: e.g. Broca’s (for speech), Wernicke’s (understanding and producing language), certain bits in the lower temporal cortex that handle categorization of words and other tasks, plus a way to encode memories using the hippocampus. The body generates these areas using the genetic code, which has been iteratively improved over many millennia. If you dive really deep into this subject, you’ll start seeing some scientists that argue that consciousness is not really a thing and that we are a product of our genes and the surrounding environment, that we act in predefined ways.

Therefore, you wouldn’t be able to call a small neuron array conscious. It only elicits a simple chemical process, which appears when you supply enough current for a few neurons to reach the threshold potential of -55 mV. To have things like emotion, body autonomy and many other things that one would think of when talking about consciousness, you would need a lot more components.