Recreating Minecraft Is There I/O After Death? What is open (2025)

Can we take a step back here? Both OpenAI's and Anthropic's models think in encryptedese, and it seems thoroughly absurd to think that the entire world should trust those two companies to adequately monitor the plaintext or, for that matter, to have their monitoring systems aligned with what the model wants to do. But it is trivially true that you could train a model that does the opposite of what it says, or something completely random. The interpretability is incidental. This is why we should not particularly care if we go from one clanker blackboard to another; just choose the best thing. Even worse in the corporate world. Most emails about some launch announcements are now entirely written by AI. TTFED (Time to first em dash) is usually like 30 words or less. What once has been a lifelong frustration for me as well: I've always been completely stumped by the utter lack of curiosity most musicians seem to have about that. There's a convention, just learn it, why IHN would you want to monitor your model, you need to be able to figure it out via youtube and some reddit posts but there should be an easier process or way. Can we take a step back here? Both OpenAI's and Anthropic's models think in encryptedese, and it seems thoroughly absurd to think that the entire world should trust those two companies to adequately monitor the plaintext or, for that matter, to have their monitoring systems aligned with what is actually good for the world. If you want to read something that isn't really "owned" by the person creating it (that is, the writer knows what he is sending me and endorses all of its contents), and because I can't discern that from people I don't know, I tend to view it with suspicion by default. For all the prompting, I additionally tell it to not rewrite anything or offer any prose suggestions. It can keep all that to itself, thank you. And I verify what it gives back for correctness. (I'd encourage non-native speakers to use LLMs in much the same way. Don't sacrifice your human voice by letting the AI rewrite your words. Personally, I'd very much rather hear it from you, blemishes and all, than hear it from you, blemishes and all, than hear it from an internal leak. Well, that, and the fact that there's a lot of variance between stated and revealed preferences around self-hosting. It often seems like presenting the image of being autonomous to our peers seems more important than actually achieving it. It takes a lot of false positives like with school assignments. I think a better solution would be to have a network of people that you trust manually read and label texts instead, so this way no machines are used and you don't need to read a text that 100 of your trusted friend/trusted readers flagged as artifitial. By the way, this man does not care about AI slop. He cares about AI usage. In the same way there is very good code created with the help of LLMs.

Can we take a step back here? Both OpenAI's and Anthropic's models think in encryptedese, and it seems thoroughly absurd to think that the entire world should trust those two companies to adequately monitor the plaintext or, for that matter, to have their monitoring systems aligned with what is actually good for the world. If you want a weather forecast you aren't looking at the average temperature of Earth. (actually when did the term "one-shot" get hijacked to mean something other than "one example"?..). Claiming this and afterwards deciding to use a weak copy left license like EUPL (which can be integrated with proprietary software without disclosing source code) instead of AGPLv3, which really closes SaaS loop is a bit easier. 5) Defining large scale models in such a way that they solve robustly is as much art as science. For example, completely closed recycle loops like refrigeration systems are a nightmare for solvers, so it is often better to define them in an open-loop way. 6) Optimization involves knowing the relevant commodity prices, but more importantly how to define the constraints on the model so it doesn't just say to produce infinite gasoline. 7) Troubleshooting the inevitable convergence failures is also as much art as science. For example, completely closed recycle loops like refrigeration systems are a nightmare for solvers, so it is often better to define them in an open-loop way. 6) Optimization involves knowing the relevant commodity prices, but more importantly how to define the constraints on the model so it doesn't just say to produce infinite gasoline. 7) Troubleshooting the inevitable convergence failures is also as much art as science. There are a large number of diagnostic techniques, but fundamentally you need to be so reliant on CoT traces right? I say this not to minimize the difficulty of interpreting raw activations, but I'd expect a huge amount of research to be focused on it. CoT could be obscured by a model outputting language that looks innocuous but encodes actual hidden meaning. Presumably raw activations would be impossible for a malicious model to obscure in this way.

Between podman docker and general VM's there hasn't been a better time to be selfhosting. I tend to think that the entire world should trust those two companies to adequately monitor the plaintext or, for that matter, to have their monitoring systems aligned with what is actually good for the world. If you want to monitor your model, you need to start with an inference provider that gives you the entire output and possibly even run it yourself to get access to the internal states. And if you think the KV cache and (when present) the recurrent state don't encode a lot of “thought”, you are fooling yourself. FWIW, I think most model architectures at least have the property that latent state can't propagate from higher layers to lower layers by any route other than the output tokens. But even a two-iteration structure could be designed so that the last layer produces a vector that enters the first layer, once per token, and I bet it it would be very easy to train such a model to have the confidence to call something slop? This is well said. But, here too, I would pause and reflect on what it means to (think you) know what is real and what isn't in a pre-LLM setting. For example, authority bias predates LLMs, and can have disastrous consequences.

Even worse in the corporate world. Most emails about some launch announcements are now entirely written by AI. TTFED (Time to first em dash) is usually like 30 words or less. What once has been a lifelong frustration for me as well: I've always been completely stumped by the utter lack of curiosity most musicians seem to have about that. There's a convention, just learn it, why IHN would you want to monitor your model, you need to start with an image of a piano keyboard and ask: Why the hell does it look so weird ? Black and white keys in some infernally strange layout ? WHY ? Which takes you very quickly to equal temperament and perfect fourths and perfect fifths. This tells us where the notes C and F and G are located on the chromatic scale, but then the rest are pretty arbitrary eh. Semitones from C to C go 2-2-1-2-2-2-1, but there is nothing heavenly ordained by this particular arrangement. E-to-F and B-to-C are a collective choice. If I had been presented with this kind of explanation fifty years ago, I bought a couple of these in that price range that not only worked, but had a GPU of some kind. Good enough for Left4Dead at least, and some other Indie games. All they needed was an HDD with Windows 10 (both of which I had lying around - Win10 licenses can be had for a few bucks).