behold. THE WORLDS FIRST SIX PENDULUM CARTPOLE SOLVE. Including a sponsor!
To solve this task, I built an environment to train an AI. This is what mechanize does, but for larger AIs. Apply! Salaries are up on their page
Thank you to mechanize for sponsoring!
@GaryMarcus@elonmusk No, of course corse not! The startup is premature, you see, it didn’t exactly suckle from the teats of his genius and it probably failed to realize his vision.
@teortaxesTex The geodesic dome inspired some unrelated designs in power tech the day i was taught of its existence. Sounds counterintuitive but the architect is the final-end user of ai.
@AISafetyMemes though they lack the qualifying backgrounds, you can actually blur together the abstraction layers of these two superficially conflated concepts to make the argument work in their favor
skill issue
.@ApolloAtomics builds the most compact nuclear reactors with the highest uptime and a deployment time of less than 24 months.
Apollo took the pressurized water reactor technology that already powers 80% of the world’s nuclear plants and flipped one part, the steam generator, to make the plant an order of magnitude smaller without compromising power.
Congrats on the launch, @AssilHalimi & Drew!
https://t.co/5lGDpZhmQ5
New paper from Yann LeCun!
"When Does LeJEPA Learn a World Model?"
This paper proves that under Gaussian latent dynamics, LeJEPA can recover the hidden state behind nonlinear observations up to rotation.
The intuition is that linear latent features are the most stable across nearby views, while nonlinear features decay faster, so the objective naturally selects the real world variables.
The key caveat is that this guarantee holds under specific assumptions, and Gaussian latents are the unique case that guarantees this.
The Pope is making exactly our point. LLMs “may imitate or even simulate, but they do not understand.”
This is the core epistemic fault line.
Most AI evaluation is still based on one assumption: if a system statistically approximates human behaviour, then it is close to human intelligence.
But approximation is not intelligence.
Simulation is not understanding.
LLMs can produce the right answer without knowing why it is right. They can simulate empathy without feeling. They can imitate judgment without responsibility. They can generate coherent explanations without having a world to which those explanations are accountable.
Stop confusing behavioural similarity with cognitive equivalence.
Human understanding is embodied, affective, relational, motivational, and normative. It is not just the production of plausible text.
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Full paper in the first reply
@MikushRab this kind of task needs a specialized neurosymbolic ai to give you decent results, you can build it today with claude itself if you know what to do exactly
@YiMaTweets yes and that's just the normative question, the eschatology of science forces the reinterpretation of the past 5-6 centuries (roughly the Scientific Revolution to today?) of scientific progress as strictly opinionated
i just love how oai force-deploys a new tool/feat every time the competition is about to drop something not so dissimilar
the only thing that is both figuratively and literally "open source" is their optimization function
Windows users, this one’s for you.
Computer use now works on Windows, so Codex can take action on your Windows computer.
And with Windows support for Codex in the ChatGPT mobile app, you can start, review, and steer tasks on the go while work continues on your Windows machine.
An early experience, but we’re working on more ways to keep your work moving, wherever you are.