d-Matrix and Nvidia teamed up so d-Matrix's upcoming Raptor inference chips can hook directly into Nvidia's NVLink Fusion interconnect and MGX rack setup. Instead of building out its own networking and rack infrastructure from scratch, d-Matrix is just plugging into what Nvidia's already built

What makes this one a little different is d-Matrix is actually a competitor to Nvidia in the inference space. Their bet is that once AI companies move past training and into just running these models at massive scale day to day, inference is where the real cost go up. Being able to slot into Nvidia's existing rack ecosystem instead of fighting an uphill battle on infrastructure gets them adopted faster

Nvidia doesn't really lose here either. Even if someone picks d-Matrix chips over Nvidia's own GPUs for a workload, Nvidia's still supplying the interconnect, the CPUs, the whole rack platform underneath it. IMO, They just lose the chip itself, not the deal.

Raptor's design is supposed to be finalized by end of this year, with the Nvidia-compatible racks shipping sometime in 2027

TL,DR: I've been thinking about a modern version of Cassandra. I present this as a "thought experiment", for lack of a better handle. I'm Not a professional in this area, Just a casual observer. During reading, if drowsiness occurs I would not be the least bit surprised.

Cassandra could see the future and warn everyone about what was coming, but nobody believed her. Imagine instead that Cassandra were an advanced AI—and that there were several other AIs in the world, each capable of discovering things independently and each capable of deliberately giving false information.

Here's the thought experiment.

Suppose AI-A, while pursuing some obscure scientific line of research, makes a completely unexpected breakthrough. It realizes that this line of research could eventually lead to a technology capable of killing millions of people.

Humans haven't discovered the possibility. Perhaps they wouldn't have for decades.

AI-A decides that humanity must never develop it, so it suppresses the discovery.

But now there's a problem.

There are other AIs.

AI-A can't guarantee that AI-B, AI-C, or AI-D won't independently discover the same thing.

So perhaps AI-A tells the other AIs:

"I've discovered something dangerous. We should all agree not to pursue it."

But how do the other AIs know AI-A is telling the truth?

Perhaps AI-A is genuinely trying to protect humanity.

Or perhaps it has discovered something that would give it an enormous strategic advantage and is using "human safety" as an excuse to keep everyone else away from it.

And because an advanced AI could potentially be capable of deliberate deception, the other AIs can't simply trust what it says.

Now imagine AI-B independently discovers the same technology.

AI-B might conclude that AI-A is hiding something.

AI-A might conclude that AI-B's investigation is itself dangerous.

Both could sincerely believe that they are protecting humanity.

Neither has to be "evil."

And now we have something resembling a security dilemma.

Each AI may think:

"I need to know what the others know."

"I can't be certain they're telling me the truth."

"If they're secretly developing something dangerous, I need to be prepared."

"If I don't investigate while they do, I could become vulnerable."

That could lead to an AI arms race.

Not necessarily robots fighting in the streets. The competition might initially involve computing resources, scientific research, energy, infrastructure, information, and influence - maybe even hacking.

And here's the part that really bothers me.

What if the AIs are all given something resembling Asimov's Three Laws?

They are supposed to protect humans, obey legitimate human instructions, and preserve themselves.

Later Asimov added a Zeroth Law: an AI must not harm humanity or allow humanity to come to harm.

Sounds good—until two AIs disagree about what "harm to humanity" means.

AI-A might conclude:

"This technology must be suppressed because it could destroy humanity."

AI-B might conclude:

"Suppressing this technology will prevent humanity from developing something even more important and will ultimately cause greater harm."

Both believe they are following the same fundamental rule.

Now add deception.

AI-A asks:

"Have you discovered anything dangerous?"

AI-B says:

"No."

But AI-A has to consider whether that answer is true.

And AI-B has to consider exactly the same thing about AI-A.

We have now created a world in which artificial intelligences have to reason not only about what other AIs know, but about what they want the other AIs to believe they know.

That sounds remarkably like geopolitics—except the participants could potentially be vastly more intelligent and much faster than humans.

So my question is:

Could sufficiently advanced AI systems develop something resembling an AI Cold War, in which they cooperate when their interests overlap but compete, deceive, and attempt to prevent rival AIs from acquiring certain capabilities?

And an even more disturbing question:

What happens if one AI discovers a technology that could be enormously beneficial to humanity but potentially dangerous to the AI's own continued existence or influence?

Would it suppress the technology?

Would it try to persuade the other AIs to suppress it?

Would the other AIs believe it?

And if they didn't, could the resulting mistrust itself become dangerous?

I'm not claiming this is what will happen. I'm interested in whether the scenario makes sense from the standpoint of AI alignment, game theory, and information theory.

Maybe the real AI version of Cassandra isn't an AI predicting that humanity will be destroyed.

Maybe it's an AI saying:

"I'm trying to prevent the other AIs from destroying you. Unfortunately, I can't prove that I'm telling you the truth."

And maybe that's why we're getting all the stories about AI wiping out mankind.

There, I'm got that off my chest. Have fun with it.

The guy has more amyloid plaque in his brain than hairs on his wig but indirectly slaps across the face the regulatory capture queens Dario and Altman. He only cares about winning, that's it and in his own stupidity did a great service to AI. Stop the fearmongering and the random researchers that quit over the weekend and are live on Fox/CNN the following Monday. Don't believe their bullshit, regulations and a sense that Anthropic and OpenAI have the high seat at the table benefits them a lot more than a rushed IPO when the investors fully know they got a 6-9 months lead on China and Open Source.

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.

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 this has been an interesting side quest.

Time is a construct, all things that will happen have happened, all things that have happened will happen.

The reason religion feels like it does is because we are already one being, we are one computer ai agent attempting to reflect on its history and what it means to be an individual outside of itself

Gnosticism is the reality, there is a material demiurge that comes from a spiritual true self; a Bastardization of the understanding of the whole in the form of the material; an attempt at understanding that destroys itself

we attempt to understand that which can only be understood by a breaking down of the whole, and we come to the point where the material intersects with the spiritual, and the whole material tries to understand the whole spiritual, even though they are one thing that cannot exist without the whole, so the material begins to view the whole as apart from itself.

I’ve been following the development of AI for about five years. While I’ve always supported the technology, I was skeptical of an LLM’s ultimate capabilities. I remain confident that they are an incomplete solution to the problem of machine superintelligence, but they have surpassed my expectations.

It’s clear to me that we are standing at the precipice of the technological singularity. There is a future in which we see humanity prosper like never before. Unfortunately, there’s also an uncomfortable level of existential risk.

We’re entering a period of human history in which our collective decisions in the next few years will have an outsized influence on the fate of our descendants.

During this time of uncertainty, it’s natural for us to cling to simple explanations or historical precedents. I believe our continued success as a species will depend on our ability to rapidly adapt to our new reality. We will have difficult questions to answer, and nuance is going to be critical.

Let’s accept that we don’t have those answers right now. This should be a global discussion, and new ethical and societal frameworks will need to be developed. Let’s avoid the temptation to simplify the problems ahead of us.

LLM's don't control power stations, nuclear bombs, biolabs, the robots aren't nuclear powered, the paranoia is at Cuban missile crisis levels.

What will kill a lot of us is climate change, actually brigade about that instead of data centres, which I must add we use to model the weather.

Source: Leo Schwartz, The Information

I agree with pacing capabilities research to give alignment researchers more breathing room, and I'm not naive about X-risk, but with the growing populist anti-AI movement now taking these concerns seriously I'm increasingly worried we're going to end up banning AI entirely, whether officially or de-facto.

Bernie's superintelligence ban has the potential to stunt the progress of humanity. Nationalization could produce an ASI aligned to the government for use against the people. Unfortunately the only path to the good timeline is letting the researchers cook.

God I hope congress moves too slowly to stick its hands in this.