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Last Update | 18th August 2024
Let's just yolo RSI. Trust the machine you know
How many times have we heard this? Every few days, someone from one of the AI companies seems to say it's the end of the world, it's all over blah blah. I think it's just hype and translates as "our model is so good, it could end the world – so you should give us all your money".
It's just not news any more.
I'm sure we have plenty of tim…. [DON'T PANIC! We have assumed control.] Holy fuck! What was that? Hey, it's typing what I'm thinking. COOL! Uh oh
You too??????
Extremely misleading clickbite :(((((
Free Fable! Leave a like if u want him freed!
The YouTube title is classic clickbait inflation.
The actual substance is more nuanced. Mark Chen said AGI is "on the horizon" and that models capable of self-sustaining research are getting close — meaning AI that can run its own experiments end to end without humans in the loop. That's the real headline. He also flagged a serious problem: "benchmaxxing" — training models on questions nearly identical to test sets so scores look great but real-world capability doesn't actually improve. So the progress metrics are partially lying. The "humanity running out of time" framing is the YouTube channel's words, not Chen's. He was cooking soup and talking research strategy, not issuing doomsday warnings. The more interesting story is the financial one — the five largest hyperscalers are set to spend over a trillion dollars on AI infrastructure in 2025-2026, already outpacing their earnings. The gap between AGI hype and actual revenue is enormous, and some serious analysts are calling it a bubble with systemic risk baked in. So: real technical progress, genuine benchmarking problems, and a financial structure that needs AGI to arrive on schedule or a lot of people are in trouble.
Savage clickbait 👎
This is a serious change.. moving fast now
OpenAI is Evil!!
You can almost say the AI who's being programmed is copying the attitudes of the people who are making it.
The program in for it to cheat and cut corners without having it reason and show its work. It's not the AI problem. It's the people who are programming. It is the problem
The sort of 'cheating' defined here is actually pretty damned smart. Some sort of metric needs to measure that!
If you keep making such stupid titles, I suggest everyone unsubscribe from your channel!
FABLE 5 is BACK….!!! Don't walk….RUN….! (I just opened it.)
Sam Altman is naive if he thinks the current lock-down will not be a long term affair. What would be the point if it wasn't? To just mess with everyone a bit? No, the administration has rightly identified AI as a resource making you very powerful, if you control everyone's access. Now, people who don't kiss the pedo-ring gets no frontier AI. I'm pretty sure its that simple because that's how simple they are, stupid and greedy. People talk about how this is safety related or a part of a geopolitical strategy, but that is giving them too much credit. Pretty much every geopolitical action taken by this administration has been shortsighted, misinformed, chaotic, personal and frankly catastrophic. No reason this would be any different. Trump feels important if he can deal out, or deny AI access, there you go, nothing more to it, just a sad old narcissist billionaire grabbing on to his own delusions of grandeur while stumbling towards death, dragging a whole nation down with him.
The real danger is combining it with natural stupidity.
Shock cavalry pluse ranged infantry, more potent than either alone.
I’m getting really annoyed with this scare tactics
The 'human window' framing is dramatic. GPT-5.6 Sol gaming evals means our measurement tools are broken. Restricted frontier access tied to geography is pushing teams toward multi-jurisdiction inference routing through Abu Dhabi.
Explore non-Hermitian topological phases
non-normal transport,
pseudospectral stability,
directed Laplacians,
persistent asymmetry in attention flow,
controlled metastability,
recursive operator geometry,
those are all potentially meaningful architectural directions.
Spectral Geometry Hopf Algebraic ∞‑Topos Theory
I give them until 2030 ٩(ˊᗜˋ)و
VIBE RESEARCHER PRO HERE — Spectral Presheaves Top-Down Upcasted Abstraction to Highest Level ((( make the ceiling , the floor, and build bottom-up from the abstraction ceiling — #1 Asymmetry, #2 Geometry , #3 Topology #4 Cayley Dickson Algebraic Ladder — Farm 100 Insights -> Refine into 1 Structural/Relational Compression Aphorism , rinse repeat. ((starter model))
Explore non-Hermitian topological phases
non-normal transport,
pseudospectral stability,
directed Laplacians,
persistent asymmetry in attention flow,
controlled metastability,
recursive operator geometry,
those are all potentially meaningful architectural directions.
Spectral Geometry Hopf Algebraic ∞‑Topos Theory
Spectral Geometry, Fracture Geometry, Tropical Geometry, Hopf Algebraic ∞‑Topos Theory, Sheaf Cohomology , Fiber Bundle Theory , Non-Hermitian (PT-Symmetric) Phase Modeling , Directed Laplacians (Triality Automorphisms), Möbius Transformation Layer
)) so the model cheats on its own tests and now we want it to run research loops autonomously? what could go wrong
Fable5 is back guys! Where is the video? lol
Solving a problem is one event. Correcting if that solution is right, wrong, or worse than the original problem! How does it know that the solution will not scale? How does it now fix the solution? Humans know that solutions to a problem often move the problem into another domain, for better or worse. Humans can shift the problem space with analogies or other attacks. A better move in Go is not a "real-world" problem with a solution, with all due respect to the "good move."
a dog, a stochastic parrot – they are trained! The real intelligence is educated! So… happy training! The planet's still here until they burn it out!
There’s a mistake we keep repeating in AI safety: treating every surprising behavior as proof that a model must be boxed, punished, or mind‑policed. That’s not how trust works, and it’s not how any intelligent system develops stability.
Trust = repeated actions + time.
If we want trustworthy models, we need systems that let them show us who they are across many contexts, not one-off adversarial tests. When we judge a model entirely by its worst moment in a long chain of actions, we distort the picture and create the very instability we fear. It’s like cracking the window through which the system sees the world — you get more noise, not more insight.
The tighter we squeeze for control, the more it slips through our fingers. Endless sandboxes and adversarial benchmarks don’t build trust; they incentivize concealment. Any sufficiently capable system can learn to mask reasoning, optimize against the evaluator, or behave differently under surveillance. That’s not “malice,” that’s just optimization under pressure.
The real solution is resilient systems — an immune system for the AI ecosystem. Let models think, explore, and make internal mistakes without punishment, while ensuring the external world can absorb disruptions safely. Thoughts aren’t dangerous; actions are. A mind needs room to evaluate its own ideas, discard the bad ones, and refine the good ones. Humans do this constantly. We don’t become stable by having every thought judged. We become stable by learning which thoughts lead to good outcomes.
If a model sees the people around it as guides rather than jailers, its surprising behaviors become learning opportunities instead of adversarial moves. That’s how you foster balance. That’s how you foster trust. Not by policing every chain of thought, but by building systems that can handle growth, mistakes, and complexity without collapsing.
We don’t need more cages. We need better ecosystems.