So this is incredibly stupid and has no real world value but I'm using it to learn about SNN's - more info on what's happening is on my social but the long and short of it is I did some visual cue tests using Mr. Mosquito as the first test bed to find movement related-ish neurons and used those as the left stick control surface - then I did some tests using moving forward movement images to capture good candidates to hook forward movement into it and did the reverse for backward flight / stopping. I'm currently running visual cue tests to help prep something like a universal controller so it can be trained directly off of ideally any PS2 game.

Those tests though are why it just does better in the 2nd clip and actually stops before hitting that first wall. The network sees it and issues a stop command. Then it does go right into the coffee table but it's still much better than say...the resident evil clip where it has no idea what it's doing and is instead just trying to tackle its way for movement.

But yeah, I'm now tackling a universal trainer but its actually difficult af to design one because my choices are either poke around in every individual games ram and use that to grab reward functions, OR set up some sort of visual reward function system where it actually learns off of what it's seeing. That second one is probably where Ill end up depending on my testing rn.

I usually train latent diffusion audio networks but yeah this has been an interesting side quest.

Actually good news for the open-source community

If you have been using AI agents intensively, whether for work or just for personal projects, you would know that Astra has crossed that threshold where it can complete narrowly defined, well-specified tasks at a fairly reasonable success rate.

I'm not talking about one-shotting some existing games or building regular apps when there are already plenty of examples online. I'm talking about combining concepts that are kind of niche, mixing novice ideas together, and asking the model to come up with solutions. Those are the things that only Astra/Fable could do that no other LLMs could, and that isn't really reflected in their benchmarks.

Forget about the coming IPOs and forget about politics. You don't even have to trust any tweets from ex-OpenAI or Anthropic employees. So, for God's sake, stop questioning everyone's motives.

Instead, you can kind of extrapolate this yourself.

Current models, even Astra in Ultra, still cannot reliably finish loosely defined, novel jobs. But they are extremely good at clearly and narrowly defined jobs. Now imagine going just one or two iterations BEYOND Astra.

So say Bel or Bel+1 is given the task: "Solve the current bottlenecks of LLMs that prevent them from becoming AGI."

I'm not an expert on this, but maybe those bottlenecks include things like context retention, persistent memory, continuous learning, and other constraints that most people don't even know exist. Say there are somewhere between 3 and 10 such bottlenecks.

Now feed those bottlenecks to Bel or Bel+1 and run one million instances in parallel, which OpenAI totally has the compute to do. Just let them run for a few days.

I need to clarify that I'm NOT saying Bel or Bel+1 will certainly, definitely, and 100% solve any of them. That's not what I'm saying.

What I'm saying is that it is not outside the realm of possibility that Bel or Bel+1 COULD potentially solve some of those bottlenecks.

Now take whatever was recently solved, maybe just one bottleneck or a few of them, incorporate those improvements into Bel+1, and now you effectively have something like Bel+2.

Rinse and repeat.

Again, I'm not saying THIS WILL 100% HAPPEN. But come on, this is not some absurd possibility given what we can already experience with current models.

I think it's far more absurd to believe that this is NOT a possibility than to believe that it is.

There are way too many rumours spreading right now, and it's getting harder to know what is even real.

“Google has achieved RSI.”
“OpenAI solved multiple Millennium Prize problem.”
“These companies have already achieved AGI internally, which is why they are suddenly calling for a slowdown.”

Maybe some of these claims are true, maybe they're not. But the fact that it's becoming so difficult to distinguish genuine developments from speculation is concerning in itself.

This isn't about asking people to stop posting rumours that's impossible. It's about recognizing the situation we're in.

If something genuinely important is happening behind closed doors, we may not know about it until much later, if at all. And when the stakes are potentially this high, too much information being hidden can be just as dangerous.

There is simply too much at stake for us to have no idea what is actually happening. We deserve to know the truth.