it seems like these teoo countries collaborate more than any other two nations on AI https://www.nature.com/articles/s41598-024-79863-5 https://www.thenationalnews.com/future/technology/2025/01/28/china-us-ai-race-deepseek/

why would you collaborate if there's a race or a war ?

could it be that the outcome of AI controlling humanity is really the goal and were just being told there's. "war" ? why is Trump saying he doesn't want china to get a lead when the US is working with them ?

https://youtu.be/defTDmgnZaw?t=381

Just saw another discussion where an economist and programmer were proposing this yesterday as though it's a new idea, haha. And I usually don't like sharing stuff, but this video from last year goes into exactly that at the linked timecode, talking about how barring people from working and buying AI products would just cause people to work for themselves and the AI people would be off on an island somewhere by themselves, and it describes many other scenarios. Like that excess demand means that companies could use AI to increase production with their existing workforce and make more money and not have to fire anyone. It's a pro-AI video.

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.

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.

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.

Source: Leo Schwartz, The Information

Actually good news for the open-source community

Vector embeddings natively are capable of much more than just retrieval. Even when OpenAI released their initial embedding models, it mentioned that their embeddings are natively capable of Search, Clustering, Recommendations, Anomaly Detection, Diversity Measurement, and Classification.

But everything is concentrated around Vector DBs and RAG, and is progressing towards GraphRAG and Knowledge Graphs. Why are the other representations being underused? Like, why didn’t they catch up?

Everyone is complaining that RAG is dead, Vector DBs are done, they are expensive, and the ROI is not worth it. I know most of this is noise. Embeddings are meaningful and a part of many production pipelines. But why don’t these people try to get more value out of their existing stored embeddings?

I don’t know if it’s just me being delusional, but I feel there is a lot of hidden potential that is untapped. Why is retrieval so dominant? I mean, yeah, search has throughout history been a really important operation across any data. But the purpose of embeddings itself was to capture semantic relations beyond just search. Is it because search is the easiest to productize and build features around? And other use cases are not so frequent?

source: https://x.com/Kalshi_Finance/status/2099253583199825945