I'm not sure how to make it work but like others in this thread I have an interest in sharing some - but not all - of my notes with some AI agents. Would love a solution that is built in to Obsidian / Obsidian Sync.
It still can't learn. It would need to create content, experiment with it, make observations, then re-train its model on that observation, and repeat that indefinitely at full speed. That won't work on a timescale useful to a human. Reinforcement learning, on the other hand, can do that, on a human timescale. But you can't make money quickly from it. So we're hyper-tweaking LLMs to make them more useful faster, in the hopes that that will make us more money. Which it does. But it doesn't make you an AGI.
That's not learning, though. That's just taking new information and stacking it on top of the trained model. And that new information consumes space in the context window. So sure, it can "learn" a limited number of things, but once you wipe context, that new information is gone. You can keep loading that "memory" back in, but before too long you'll have too little context left to do anything useful.
That kind of capability is not going to lead to AGI, not even close.
1. It's still memory, of a sort, which is learning, of a sort.
2. It's a very short hop from "I have a stack of documents" to "I have some LoRA weights." You can already see that happening.
Also keep in mind that the models are already trained to be able to remember things by putting them in files as part of the post training they do. The idea that it needs to remember or recall something is already a part of the weights and is not something that is just bolted on after the fact.
>but before too long you'll have too little context left to do anything useful.
One of the biggest boosts in LLM utility and knowledge was hooking them up to search engines. Giving them the ability to query a gigantic bank of information already has made them much more useful. The idea that it can't similarly maintain its own set of information is shortsighted in my opinion.
It's simply a fact that LLMs cannot learn. RAG is not learning, it's a hack. Go listen to any AI researcher interviewed on this subject, they all say the same thing, it's a fundamental part of the design.
A human can't keep 100k tokens active in their mind at the same time. We just need a place to store them and tools to query it. You could have exabytes of memories that the AI could use.
I disagree. Human memory is literally changing the weights in your neural network. Like, exactly the same.
So in the machine learning world, it would need to be continuous re-training (I think its called fine-tuning now?). Context is not "like human memory". It's more like writing yourself a post-it note that you put in a binder and hand over to a new person to continue the task at a later date.
Its just words that you write to the next person that in LLM world happens to be a copy of the same you that started, no learning happens.
It might guide you, yes, but that's a different story.
> Plenty a visual programming language has tried to toot their own horns as being the next transformative change in everything, and they are mostly just obscure DSLs at this point.
But how many of your non-nerdy friends were talking about them, let alone using them daily?
> So this label is not accurate? You are not anti-Israel but rather pro-Israel?
This is a form of splitting or black-and-white thinking and it's not rational. If doesn't make you anti-Semitic to refuse to defend Israel with every breath as they commit a genocide against a people under their steward.
> If you think the military is being tasked with the wrong missions, or too many missions, then take that up with the civilian political leadership. But it's not a valid reason to deny the warfighters the best possible weapons systems.
It is an ethical dilemma: believing an armed force will act unethically is in fact a valid reason to refuse to arm them. You are taking a nationalistic view regarding the worth of life.
And if you believe it is unethical to arm them, it is rational to use whatever leverage you have available to you - such as refusing to sell your company's product.
Furthermore, one of the two points at issue was regarding surveiling civilians.
I think overall you're better off creating these yourself. The more you add to the overall context, the more chance of the model to screw up somewhere, so you want to give it as little as possible, yet still include everything that is important at that moment.
Using the agent and seeing where it get stuck, then creating a workflow/skill/whatever for how to overcome that issue, will also help you understand what scenarios the agents and models are currently having a hard time with.
You'll also end up with fewer workflows/skills that you understand, so you can help steer things and rewrite things when inevitably you're gonna have to change something.
I put the terms in quotes because it can be as simple as a set of prompts you develop for various contexts. It really doesn't have to be too heavy of an idea.
JPEG compression can only move information at most 16px away, because it works on 8x8 pixel blocks, on a 2x down-sampled version of the chroma channels of the image (at least the most common form of it does)
I'm not super familiar with the jpeg format, but iirc h.264 uses 16x16 blocks, so if jpeg is the same then padding of 16px on all sides would presumably block all possible information leakage?
Except the size of the blocked section ofc. E.g If you know it's a person's name, from a fixed list of people, well "Huckleberry" and "Tom" are very different lengths.
reply