I’d be interested, too, if he and FUTO got to terms with their community and if they learned how licensing and trademarks work… Last thing I remember he claimed lots if things that weren’t true. And FUTO didn’t really address anything.

Seems the two German supermarket chains really like to have the same infrastructure everywhere. Everywhere I go the Aldis look exactly the same. They have slightly different products depending on the country. But the price tags, interior, … is basically the same. Okay and we don’t have “Flaschenpfand” everywhere… (deposit on the plastic bottles and the machines where you can return bottles.) I bet all of this makes it a lot easier for their techs and management. And it could also explain why they sometimes redo a store that still looks fine and fit it with the latest shenanigans.

And as an aside: I’ve shopped in the first Aldi store ever. It’s not far from where I live.

They’re already widely adopted in supermarkets here (Germany).

Those top level domains aren’t set in stone. The majority of TLDs can be used by anyone. It’s more what kind of image you want for your company/project. Lots of open-source projects have .org domains or .io

But you can choose whatever you like. Even a country domain is okay. But I personally wouldn’t choose .com for something open source. Look at the prices and go for .org unless that’s substantially more expensive with your registrar. (My opinion.)

AI Is a Black Box. Anthropic Figured Out a Way to Look Inside

…Concerning our earlier disagreement about the inner workings of large language models and whether there are ‘concepts’ stored inside…

I wonder if experiences from 12 years ago and numbers from 8 years ago still hold true as of today.

Idk, a plant? a nintendo emulator? enlighten me…

I see Github as a mere tool. As I could use a proprietary operating system like Windows on my development computer, I can use Github to distribute the code. It doesn’t have that severe consequence to the open source project itself and works well. And it’s relatively transparent. Users can view issues etc without submitting to Microsoft. And it’s been the standard for quite some time.

I’m far more concerned with FLOSS projects using platforms like Discord, which forces their users to surrender their privacy and that actively contribute to the enshittification of the internet. I wouldn’t want to be part of that.

it does not break the kernel any more the way it did.

Hehe, yeah that’d be hard to achieve.

I’m not sure. Didn’t they just move the code that was previously executed in the proprietary kernel module to the new also proprietary userspace driver that’s just connected to the hardware by this new and open source wrapper module? And the other half into firmware? It’s still arbitrary and closed code that gets forwarded to the hardware. And running there it has access to all the memory, screen content etc… I’m not sure if this is a win concerning security. I think it’s pretty much unchanged.

But there are several big advantages. Now the kernel probably won’t get tainted any longer and we can have signed kernels and activate secure boot easily. And that’s maybe a big plus for security. And I hope we’ll get the convenience, too. In the past I had the NVidia driver crap out on me while debugging stuff with recent kernel versions or release candidates. And NVidia was lagging behind, leaving me with a console instead of the desktop environment…

I think that’s a good question. And a nice video. The findings in the paper seem to arrive at that conclusion and we might need to find a better approach. Mind that (as he pointed out) it doesn’t rule out growth in AI. It just hints at probable stagnation with the current methods. I’m already fascinated by the current tech and the new possibilities. But AI is really hyped as of now and I too, think we should take the claims of the big AI companies with a grain of salt. I’m sure the scientists at OpenAI are already concerned with exactly this as they do research for the next generations of ChatGPT. It’s a bit of a bummer that lots of the research get’s done behind closed curtains and we’re going to have to wait for a bit longer to find out.

It’s a shame that nowadays everything “needs” a phone number. I just put in a proper prefix code and then all zeroes as a number if some company forces me to. That works for some of the websites. Some stores even print that on a shipping label. So it might supposedly be there for a reason. But I’ve never heard this helps if a parcel gets lost or something. They won’t call anyways and the real reason is they can store it in some database and depending on the exact business do all kinds of other stuff with it.

Seems in this case they got own domains and independent stores.

And Aliexpress and Temu are very different. Aliexpress seems to be a halfway decent platform. Never had any major issues with them, except what’s to be expected when importing stuff from China. I think it’s very similar to ordering the same thing on eBay.

Temu isn’t. That platform is made to harvest data and prey on their “customers”.

And I can’t comment on Wish. I haven’t been interested in cheap crap.

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…

people wrote down history by weaving fabric […]

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

I too think it’s useful. Also the “who needs it” isn’t really an argument. If you can fit it in and people are willing to pay for it… Why not? I mean who needs 5 regular cameras in their phone? One with a marcro lens, one fisheye and one tele plus a regular one plus one on the front… I mean in a normal phone as of todaz the additional flir camera isn’t really changing much.

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

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.

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…

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.