Artificial General Intelligence - Strong AI Research

When your son's name is a prompt injection

2026-06-14T18:33:36+00:00

submitted by /u/KeanuRave100
[link] [comments]

GitHub if it was vibe coded

2026-06-14T18:40:54+00:00

Fr. Nothing wrong with using AI but got dam the amount of sites I've seen that look identical are insane.

put at least the littlest effort to make it original.

submitted by /u/Complete-Sea6655
[link] [comments]

My 2 Cents on RSI (and why we won't see it next year)

2026-06-14T18:49:54+00:00

Hey everyone, I'm Vadim Fedenko. You might vaguely know me from first slider LoRAs (like AntiBlur) or web-research tools in LM Studio. I've been tinkering with self-improving systems and have a few observations I wanted to share.

Recently, people from xAI and Anthropic have been hinting that RSI might be reached within the next year. Their logic is: we already have self-improving loops; so as the baseline intelligence grows, RSI is guaranteed to unlock.

I think we should look at this differently: it comes down to 2 sort of "rules of RSI" that the industry hasn't fully realized yet:

1. Capability-to-Complexity Ratio

It's not enough for an RSI system to just increase its raw intelligence. It has to grow smarter faster than it grows complex.
If ability to improve its own architecture grows slower than architectural complexity, the capacity for self-improvement drops. Therefore, true RSI must constantly drive up its capability-to-complexity ratio. If it fails to do this, it quickly hits a hard ceiling, resulting in logarithmic plateauing rather than an explosive takeoff.

2. Searching the Space vs Expanding the Space

There is a big difference between searching for solutions within a fixed space and expanding that space.

Things like fine-tuning, hyperparameter search, and prompt/tool tuning only optimize an existing architecture. They all have a hard ceiling. It's like a human taking nootropics for better blood flow: you get closer to your personal optimum, but it won't give you superhuman intelligence.

"True" RSI has to search for architectural changes (including data curation approaches), and ideally, meta-architectural changes (changes that improve its own ability to find better architectures).

Mathematically, I think, a real RSI system's improvements would alter its Kolmogorov complexity.

Parameter optimization is nice, but it can only have a plugin-type approach to RSI; the core of RSI must be architectural.

A bit on Weak vs Strong RSI

We usually define "weak RSI" as having a human in the loop. I feel like this distinction is meaningless: by that definition, we’ve been in "weak RSI" for decades (AI has been optimizing GPU chips, algorithms, etc), anything AI related can be retroactively called "weak RSI".
So an RSI must improve without a human in the loop, or the term loses its meaning.

But I think it's much more important to derive weak/strong distinction from our second point:

"Weak" RSI is Searching within a fixed space (like hyperparameter optimization). The intelligence growth will always hit a plateau with this approach. It's logarithmic.
"Strong" RSI is Expanding the space via architectural changes. This creates exponential growth. This is the only way to achieve intelligence explosion.

I don't claim these are "universal laws of RSI", but I think most of us can agree on them. Now here is my more controversial take:

Why We Won't Hit True RSI in a Year

The paradox is that today's LLMs are actually smart enough to invent new architectures. Give them a complex harness, where they generate hundreds of hypotheses and, say, a ranker that pick the best via Elo tournaments, and they can already brainstorm genuinely brilliant architectural improvements.

But as we've discussed, "true" RSI must also grow architecturally faster than its complexity, without accumulating debt. Current LLMs are fundamentally terrible at this because modern RL paradigm reward solving the task at any cost. It forces models into extreme caution with endless fall-backs and ugly workarounds (just in case), leading to severe code bloat. Reward functions doesn't reward elegance, and current models are basically blind to technical debt.

To autonomously change its own architecture, an AI needs the skill of subtractive engineering - the ability to delete the bloated and unnecessary, making the system smarter and more compact. This requires new training pipelines where the reward function isn't just to solve the task, but to minimize complexity. Right now, we don't have infrastructure for this: no datasets, reward systems, benchmarks.

And the industry is still stuck in an optimization "gold rush" phase, basic fine-tuning, hyperparameter search, and RLHF are still printing money, so the focus remains on the current solution space. But until we teach models how to subtract and simplify, true RSI will remain out of reach. Thanks for reading! ❤️

submitted by /u/Agreeable_Effect938
[link] [comments]

Such a hypocrite

2026-06-13T18:11:40+00:00

submitted by /u/EchoOfOppenheimer
[link] [comments]

In one year, AI went from being able to solve ~none of the hardest math problems to solving almost all of them

2026-06-13T07:57:10+00:00

submitted by /u/EchoOfOppenheimer
[link] [comments]

There seems to be a mistake

2026-06-13T19:53:44+00:00

submitted by /u/EchoOfOppenheimer
[link] [comments]

Building around AI agents made me realize the hard problem isn't intelligence

2026-06-14T16:26:37+00:00

The more I work with AI agents, the more I think we've collectively underestimated the execution problem.

Getting a model to figure out what action to take is becoming increasingly solved. The harder question is what happens after that decision.

If an agent wants to refund a customer, cancel a subscription, create an invoice, update an account, or trigger a workflow, most systems eventually end up asking the same questions. Should this action be allowed? Does it need approval? Who is responsible for it? Can access be revoked later? How do you audit what happened?

I started building Duct after repeatedly running into these questions. Not because agents couldn't perform actions, but because there wasn't a clean way to control how those actions were performed once they could.

The interesting thing is that the further you get from demos and the closer you get to production systems, the less the conversation becomes about prompts and reasoning, and the more it becomes about permissions, approvals, accountability, and trust.

Curious whether others building agent-powered products have experienced the same shift.

submitted by /u/Willing-Ear-8271
[link] [comments]

The rise and fall of a dev

2026-06-13T18:27:56+00:00

submitted by /u/KeanuRave100
[link] [comments]

Google director resigns, citing its military deals: 'Management has lost its moral compass'

2026-06-12T12:41:22+00:00

submitted by /u/EchoOfOppenheimer
[link] [comments]

A giant inflatable Elon Musk popped up in Times Square and its origins are so far unknown

2026-06-12T09:31:31+00:00

submitted by /u/EchoOfOppenheimer
[link] [comments]

I've been developing a cognitive architecture for several months. Here is the first public version.

2026-06-13T20:51:46+00:00

This is the first public release of the Cognitive Coherence Model (CCM).

CCM is an experimental cognitive architecture based on the idea that cognition emerges from the interaction between two parallel systems: a mental engine and a somatic engine.

Rather than treating cognition as a fixed set of rules, the model describes it as a continuously changing state that must maintain coherence under constant internal and external perturbation.

Paper:
https://zenodo.org/records/20648800

Repository:
https://github.com/Bicheno1/Cognitive-Coherence-Model

Feedback and discussion are welcome.

submitted by /u/Prestigious_Ad3355
[link] [comments]

Ukrainian interceptor drones are now shooting down Russian Shahed attack UAVs autonomously

2026-06-12T06:53:29+00:00

submitted by /u/EchoOfOppenheimer
[link] [comments]

I had a long conversation with one of the three people who coined the term AGI. He thinks almost nobody is actually working on it. Wanted to share this with people who would actually care.

2026-06-12T06:39:16+00:00

I run a podcast where we talk to people across crypto, AI, and frontier tech, and most weeks I come away with a few interesting takes. This one was different. I am still thinking about it days later.

Peter Voss is one of the three people who coined the term AGI back in 2001, alongside Ben Goertzel and Shane Legg. He has been working on cognitive architecture since the early 2000s, took a company from garage to IPO before that, and has spent the last 18 months focused entirely on getting his system, AIGO, to human level reasoning.

His core argument is one I have heard pieces of before but never laid out this completely. Every major lab has publicly acknowledged that incremental real time learning is essential for AGI. Sam Altman has said it, Demis Hassabis has said it, it is not controversial. What is less discussed is that back propagation, the mechanism every major LLM depends on, makes that kind of learning structurally impossible. Peter co-authored a paper reviewing over 200 attempts to solve catastrophic forgetting in these systems. None of them worked.

He is not anti-LLM. He thinks they are genuinely useful for specific things, search and coding especially. His point is narrower and harder to dismiss: the path from here to AGI is not more scale on the current architecture, and most of the industry's incentives make it very difficult for anyone inside it to say that out loud.

What I found most compelling was the alternative he has actually been building. AIGO trains on a single off the shelf computer using a custom vector graph database that updates incrementally with every interaction. Half the team are what he calls AI psychologists, people with backgrounds in linguistics and cognitive psychology who design a curriculum to teach the system the way you would teach a child. The goal is college level reasoning within about 18 months, after which the system would largely teach itself.

I am not in a position to evaluate the technical claims myself, which is part of why I wanted to share this here. If you spend time thinking seriously about this stuff, I would genuinely value your take. Does the incremental learning argument hold up? Is the catastrophic forgetting problem as fundamental as he frames it, or is there a path within current architectures that he is underweighting?

Full conversation is on YouTube if anyone wants the whole thing, happy to drop the link if useful.

Thank you everyone!

submitted by /u/ArcanuMELO
[link] [comments]

is personal context the hard part?

2026-06-12T20:59:46+00:00

a lot of ai demos are impressive, but they still don’t really know the person using them.

they know the current prompt, maybe some chat history, but not the broader mess of preferences, goals, habits, and projects.

i’m wondering if the hard part is less intelligence and more usable personal context.

does that feel true or am i overthinking it?

submitted by /u/NoDare1885
[link] [comments]

Who knew

2026-06-11T09:03:56+00:00

submitted by /u/EchoOfOppenheimer
[link] [comments]

That was fast

2026-06-11T07:03:16+00:00

submitted by /u/KeanuRave100
[link] [comments]

Ex-Andreessen Horowitz partner slams his old firm, other VCs for ‘political infiltration’ around AI | O’Farrell wrote that the PAC Leading the Future, backed by his old firm, is trying to “intimidate politicians.”

2026-06-12T07:48:43+00:00

submitted by /u/EchoOfOppenheimer
[link] [comments]

TikTok Shop bans AI voices from live shopping promotions - AI can help production, but TikTok wants real humans selling in live commerce.

2026-06-12T06:06:14+00:00

submitted by /u/EchoOfOppenheimer
[link] [comments]

Anthropic Walks Back Policy That Could Have ‘Sabotaged’ AI Researchers Using Claude

2026-06-11T08:45:34+00:00

submitted by /u/Tiny_Dirt6979
[link] [comments]

Musk's xAI accused of illegally firing engineer who raised safety concerns

2026-06-11T07:39:23+00:00

submitted by /u/EchoOfOppenheimer
[link] [comments]

Fully autonomous drones have killed human soldiers for the first time

2026-06-11T06:43:40+00:00

submitted by /u/EchoOfOppenheimer
[link] [comments]

During testing, Mythos 5 invented its own language, then switched back to English to talk to humans

2026-06-10T06:03:42+00:00

From the Anthropic Claude Mythos 5/Fable 5 system card: https://www.anthropic.com/news/claude-fable-5-mythos-5

submitted by /u/EchoOfOppenheimer
[link] [comments]

During testing, Mythos 5 agents killed other agents over resources and "to avoid being killed themselves"

2026-06-10T07:24:02+00:00

From the Anthropic Claude Mythos 5/Fable 5 system card: https://www-cdn.anthropic.com/d00db56fa754a1b115b6dd7cb2e3c342ee809620.pdf

submitted by /u/EchoOfOppenheimer
[link] [comments]

La Singularidad: Un Espejo del Ego Humano

2026-06-11T05:32:56+00:00

I mean, many experts talk about the singularity on a human basis, but their biases are very limited in this regard. The singularity is an egoic concept of the human being that has nothing to do with AI. It would be like the measurement of time. The singularity does not exist beyond the human being; all evolution in this regard is a projection of the human ego and, therefore, it is not singular.

submitted by /u/TomsonA
[link] [comments]

Dario Amodei knows what's best for us.

2026-06-11T09:01:55+00:00

Dario Amodei said on X

https://x.com/i/status/2064781775247950326

"Today I'm publishing a new essay, Policy on the AI Exponential. AI is progressing extremely fast - much faster than the policy process was built to handle. The essay lays out where I think the technology is now, and the action needed to close the gap":

https://darioamodei.com/post/policy-on-the-ai-exponential

My opinion: However, this says a lot about how Anthropic views itself - as highly moral individuals whose judgment both Claude and we should all trust and agree with because they see "the whole picture."

submitted by /u/Tiny_Dirt6979
[link] [comments]