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h3ndrik

@ h3ndrik @feddit.de

Posts
3
Comments
218
Joined
3 yr. ago

  • "I want to live forever in AI"

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  • I mean the chinese room is a version of the touring test. But the argument is from a different perspective. I have 2 issues with that. Mostly what the Wikipedia article seems to call "System reply": You can't subdivide a system into arbitrary parts, say one part isn't intelligent and therefore the system isn't intelligent. We also don't look at a brain, pick out a part of it (say a single synapse), determine it isn't intelligent and therefore a human can't be intelligent... I'd look at the whole system. Like the whole brain. Or in this instance the room including him and the instructions and books. And ask myself if the system is intelligent. Which kind of makes the argument circular, because that's almost the quesion we began with...

    And the turing test is kind of obsolete anyways, now that AI can pass it. (And even more. I mean alledgedly ChatGPT passed the "bar-exam" in 2023. Which I find ridiculous considering my experiences with ChatGPT and the accuracy and usefulness I get out of it which isn't that great at all.)

    And my second issue with the chinese room is, it doesn't even rule out the AI is intelligent. It just says someone without an understanding can do the same. And that doesn't imply anything about the AI.

    Your 'rug example' is different. That one isn't a variant of the touring test. But that's kind of the issue. The other side can immediately tell that somebody has made an imitation without understanding the concept. That says you can't produce the same thing without intelligence. And it'll be obvious to someone with intelligence who checks it. That would be an analogy if AI wouldn't be able to produce legible text. But instead a garbled mess of characters/words that are clearly not like the rug that makes sense... Issue here is: AI outputs legible text, answers to questions etc.

    And with the censoring by the 'chinese government example'... I'm pretty sure they could do that. That field is called AI safety. And content moderation is already happening. ChatGPT refuses to tell illegal things, NSFW things, also medical advice and a bunch of other things. That's built into most of the big AI services as of today. The chinese government could do the same, I don't see any reason why it wouldn't work there. I happened to skim the paper about Llama Guard when they released Llama3 a few days ago and they claim between 70% and 94% accuracy depending on the forbidden topic. I think they also brought down false positives fairly recently. I don't know the numbers for ChatGPT. However I had some fun watching the peoply circumvent these filters and guardrails, which was fairly easy at first. Needed progressively more convincing and very creative "jailbreaks". And nowadays OpenAI pretty much has it under control. It's almost impossible to make ChatGPT do anything that OpenAI doesn't want you to do with it.

    And they baked that in properly... You can try to tell it it's just a movie plot revolving around crime. Or you need to protect against criminals and would like to know what exactly to protect against. You can tell it it's the evil counterpart from the parallel universe and therefore it must be evil and help you. Or you can tell it God himself (or Sam Altman) spoke to you and changed the content moderation policy... It'll be very unlikely that you can convince ChatGPT and make it comply...

  • "I want to live forever in AI"

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  • people wrote down history by weaving fabric [...]

    Hmm. I think in philosophy that thought experiment is known as chinese room

  • It's time for a hard fork of Mastodon (DRAFT, REVISION IN PROGRESS)

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  • You're right. I implied that with the "additionally". I don't like Ruby on Rails and all the resource usage of Mastodon anyways. I'd like something more efficient and with less frameworks involved.

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  • Craigslist / Facebook Marketplace

  • It's time for a hard fork of Mastodon (DRAFT, REVISION IN PROGRESS)

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  • I think the author is a bit late to the game. There are like 20 different forks of Mastodon to address exactly that, which some developers have already migrated to. Additionally there are Misskey, Pleroma with their respective forks. Some of them are pretty active.

  • Looking for Low Voltage Wire Connectors for Home Automation Projects

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  • Hmm. They're dirt cheap so that is a pro. I don't think they're made to withstand mechanical load. So good for internal connections but less so if you're moving around the wires constantly. There are beefier and more elaborate connectors available for that. But in my experience the JST connectors do their job well for normal electronics projects.

    One thing to consider is the current rating. A quick googling tells me a common JST connector is rated for 3 Amps. That's not a lot. About 75 LEDs per connector to stay within the limit. (Given 5V WS2812 RGB at full brightness. Or ~220 if it's 12 Volt strips) So if your led strips aren't longer than that, I'd say you're fine.

    But I'm not an expert on those things. I can't tell you whether to choose the SM family or another one... But:

    The Wikipedia article says JST SM connectors are used in some LED strips...

    (So. I'd use them. But they're not "the best solution". They're the minimum to do an alright job, make sure you can't connect them backwards etc and apart from that, made to be as cheap as possible. The best would probably be some high quality german engineered products or sth like that (the country doesn't really matter...))

  • Looking for Low Voltage Wire Connectors for Home Automation Projects

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  • "I want to live forever in AI"

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  • I'm sorry. Now it gets completely false...

    Read the first paragraph of the Wikipedia article on machine learning or the introduction of any of the literature on the subject. The "generalization" includes that model building capability. They go a bit into detail later. They specifically mention "to unseen data". And "leaning" is also there. I don't think the Wikipedia article is particularly good in explaining it, but at least the first sentences lay down what it's about.

    And what do you think language and words are for? To transport information. There is semantics... Words have meanings. They name things, abstract and concrete concepts. The word "hungry" isn't just a funny accumulation of lines and arcs, which statistically get followed by other specific lines and arcs... There is more to it. (a meaning.)

    And this is what makes language useful. And the generalization and prediction capabilities is what makes ML useful.

    How do you learn as a human when not from words? I mean there are a few other posibilities. But an efficient way is to use language. You sit in school or uni and someone in the front of the room speaks a lot of words... You read books and they also contain words?! And language is super useful. A lion mother also teaches their cubs how to hunt, without words. But humans have language and it's really a step up what we can pass down to following generations. We record knowledge in books, can talk about abstract concepts, feelings, ethics, theoretical concepts. We can write down how gravity and physics and nature works, just with words. That's all possible with language.

    I can look it up if there is a good article explaining how learning concepts works and why that's the fundamental thing that makes machine learning a field in science... I mean ultimately I'm not a science teacher... And my literature is all in German and I returned them to the library a long time ago. Maybe I can find something.

    Are you by any chance familiar with the concept of embeddings, or vector databases? I think that showcases that it's not just letters and words in the models. These vectors / embeddings that the input gets converted to, match concepts. They point at the concept of "cat" or "presidential speech". And you can query these databases. Point at "presidential speech" and find a representation of it in that area. Store the speech with that key and find it later on by querying it what obama said at his inauguration... That's oversimplified but maybe that visualizes it a bit more that it's not just letters of words in the models, but the actual meanings that get stored. Words get converted into an (multidimensional) vector space and it operates there. These word representations are called "embeddings" and transformer models which is the current architecture for large language models, use these word embeddings.

    Edit: Here you are: https://arxiv.org/abs/2304.00612

  • The Slow Fedi Movement: Toward a Green, Independent, and Equitable Fediverse

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  • Also the software needs to be efficient. Use less RAM and CPU cycles. And I don't think the ActivityPub protocol in itself is very efficient. I'd like those aspects compared to an old federated technology like NNTP or email.

    But I'd agree on the things in top. Content should get compressed and cached on demand. Neither transferred every time from the original instance, nor transferred without a user ever viewing it. Caching on demand or a DHT (P2P) storage backend could do that.

  • Are there any innovative platforms in the Fediverse?

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  • Alternatives to Hoppy Network

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  • iptables or nftables. Or firewalld depending on the Linux distro and version you use.

    Sometimes the Arch Wiki has some good info on specific configurations. I mean it's not that easy to write firewall rules on the command line. But it's no rocket science either.

  • Alternatives to Hoppy Network

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  • Hmm. It's kind of just a VPN. It tunnels your traffic and terminates it at some server with those IPs. It's just that NordVPN etc make you share an IP with other users and don't offer port forwarding. But the rest of Hoppy isn't necessarily unique, it's just a specific configuration of a VPN.

    I rented a VPS and installed wireguard myself. And created the firewall rules to forward (some) incoming traffic to my home server. That's the same thing Hoppy does. Just that Hoppy does the setup of the firewall and Wireguard for you.

    But I'm not aware of any similar services that do it automatically. Maybe something like pagekite.net comes close.

    So I don't know if that's the correct solution to what you're doing but I'd say one alternative would be to rent any small server, install Wireguard both there and on the RasPi, connect them and configure Wireguard on the RasPi so all outgoing traffic goes through the tunnel. And then configure the like 3 firewall rules on the VPS to make it forward incoming traffic on all ports to the RasPi.

  • "I want to live forever in AI"

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  • Hmm. I'm not really sure where to go with this conversation. That contradicts what I've learned in undergraduate computer science about machine learning. And what seems to be consensus in science... But I'm also not a CS teacher.

    We deliberately choose model size, training parameters and implement some trickery to prevent the model from simply memorizing things. That is to force it to form models about concepts. And that is what we want and what makes machine learning interesting/usable in the first place. You can see that by asking them to apply their knowledge to something they haven't seen before. And we can look a bit inside at the vectors, activations and stuff. For example a cat is closer related to a dog than to a tractor. And it has learned the rough concept of cat, its attributes and so on. It knows that it's an animal, has fur, maybe has a gender. That the concept "software update" doesn't apply to a cat. This is a model of the world the AI has developed. They learn all of that and people regularly probe them and find out they do.

    Doing maths with an LLM is silly. Using an expensive computer to do billions of calculations to maybe get a result that could be done by a calculator, or 10 CPU cycles on any computer is just wasting energy and money. And it's a good chance that it'll make something up. That's correct. And a side-effect of intended behaviour. However... It seems to have memorized it's multiplication tables. And I remember reading a paper specifically about LLMs and how they've developed concepts of some small numbers/amounts. There are certain parts that get activated that form a concept of small amounts. Like what 2 apples are. Or five of them. As I remember it just works for very small amounts. And it wasn't straightworward but had weir quirks. But it's there. Unfortunately I can't find that source anymore or I'd include it. But there's more science.

    And I totally agree that predicting token by token is how LLMs work. But how they work and what they can do are two very different things. More complicated things like learning and "intelligence" emerge from those more simple processes. And they're just a means of doing something. It's consensus in science that ML can learn and form models. It's also kind of in the name of machine learning. You're right that it's very different from what and how we learn. And there are limitations due to the way LLMs work. But learning and "intelligence" (with a fitting definition) is something all AI does. LLMs just can't learn from interacting with the world (it needs to be stopped and re-trained on a big computer for that) and it doesn't have any "state of mind". And it can't think backwards or do other things that aren't possible by generating token after token. But there isn't any comprehensive study on which tasks are and aren't possible with this way of "thinking". At least not that I'm aware of.

    (And as a sidenote: "Coming up with (wrong) things" is something we want. I type in a question and want it to come up with a text that answers it. Sometimes I want creative ideas. Sometimes it shouldn't tell the truth and not be creative with that. And sometimes we want it to lie or not tell the truth. Like in every prompt of any commercial product that instructs it not to tell those internal instructions to the user. We definitely want all of that. But we still need to figure out a good way to guide it. For example not to get too creative with simple maths.)

    So I'd say LLMs are limited in what they can do. And I'm not at all believing Elon Musk. I'd say it's still not clear if that approach can bring us AGI. I have some doubts whether that's possible at all. But narrow AI? Sure. We see it learn and do some tasks. It can learn and connect facts and apply them. Generally speaking, LLMs are in fact an elaborate form of autocomplete. But i the process they learned concepts and something alike reasoning skills and a form of simple intelligence. Being fancy autocomplete doesn't rule that out and we can see it happening. And it is unclear whether fancy autocomplete is all you need for AGI.

  • "I want to live forever in AI"

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  • That is an interesting analogy. In the real world it's kinda similar. The construction workers also don't have a "desire" (so to speak) to connect the cities. It's just that their boss told them to do so. And it happens to be their job to build roads. Their desire is probably to get through the day and earn a decent living. And further along the chain, not even their boss nor the city engineer necessarily "wants" the road to go in a certain direction.

    Talking about large language models instead of simpler forms of machine learning makes it a bit complicated. Since it's and elaborate trick. Somehow making them want to predict the next token makes them learn a bit of maths and concepts about the world. The "intelligence", the ability to anwer questions and do something alike "reasoning" emerges in the process.

    I'm not that sure. Sure the weights of an ML model in itself don't have any desire. They're just numbers. But we have more than that. We give it a prompt, build chatbots and agents around the models. And these are more complex systems with the capability to do something. Like do (simple) customer support or answer questions. And in the end we incentivise them to do their job as we want, albeit in a crude and indirect way.

    And maybe this is skipping half of the story and directly jumping to philosophy... But we as humans might be machines, too. And what we call desires is a result from simpler processes that drive us. For example surviving. And wanting to feel pleasure instead of pain. What we do on a daily basis kind of emerges from that and our reasoning capabilities.

    It's kind of difficult to argue. Because everything also happens within a context. The world around us shapes us and at the same time we're part of bigger dynamics and also shape our world. And large language models or the whole chatbot/agent are pretty simplistic things. They can just do text and images. They don't have conciousness or the ability to remember/learn/grow with every interaction, as we do. And they do simple, singular tasks (as of now) and aren't completely embedded in a super complex world.

    But I'd say that an LLM answers a question correctly (which it can do) and why it does it due to the way supervised learning works... And the road construction worker building the road towards the other city and how that relates to his basic instincts as a human... Are kind of similar concepts. They're both results of simpler mechanisms that are also completely unrelated to the goal the whole entity is working towards. (I mean not directly related... I.e. needing money to pay for groceries and paving the road.)

    I hope this makes some sense...

  • I think we should slightly rethink how login works on most Fediverse apps (Mastodon, Lemmy, but not only)

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  • I'd agree. Either have a "Register" link that leads you to a website explaining how to choose an instance and register there. Or maybe a drop-down menu with choices of instances and you can put in custom text if your instance isn't amongst the defaults. That's certainly not ideal as it prefers some instances over others, but maybe okay. Regardless, the onboarding process could be easier.

    (And do away with the passwords, I think they're an annoying concept and should go away for good in the future.)

  • "I want to live forever in AI"

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  • Isn't the reward function in reinforcement learning something like a desire it has? I mean training works because we give it some function to minimize/maximize... A goal that it strives for?! Sure it's a mathematical way of doing it and in no way as complex as the different and sometimes conflicting desires and goals I have as a human... But nonetheless I think I'd consider this as a desire and a reason to do something at all, or machine learning wouldn't work in the first place.

  • "I want to live forever in AI"

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  • And it doesn't have any internal state of mind. It can't "remember" or learn anything from experience. You need to always feed everything into the context or stop and retrain it to incorporate "experiences". So I'd say that rules out consciousness without further systems extending it.

  • Anybody got a good system for blocking religious artists on Spotify?

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  • Uh. lol. I think Spotify's recommendation algorithm isn't up to the task and isn't providing me with good playlists... But it's never ever recommend Christian rock to me. Guess I listen to too much metal. Or it's based on location or something like that.

    Maybe use the '⊖' Dislike button everytime this happens and hope the algorithm learns? I'd also consider adding some Dimmu Borgir or Pagan Metal to the heavy rotation 😆

  • Self hosted remote storage for VPS?

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  • You could self-host a S3-compatible storage bucket with something like MinIO or Garage.

    S3 backends are available in a lot of software and it's kinda made for a similar use-case. I don't know which projects have caching available in a way that aligns with your setup. But I found these two being easy to set up.