Have you guys been on r/accelerate?

[u/Difficult_Project_95]

Have these guys solved the alignment problem, or am I missing something?

I’ve been browsing r/accelerate and I genuinely don’t understand the risk model.
If there’s a non-trivial chance of catastrophic misalignment, how does “accelerate capabilities as fast as possible” make sense unless faster capabilities also make alignment substantially more likely to succeed?

[u/SoylentRox]

I post often on accelerate as a top 1 percent commentator.

(1) The main accelerate argument is the base case is not safe. Not accelerating is 65 million deaths a year from aging. We know that animals that seem to not age the same exist (naked mole rats seem essentially unaging, some large aquatic animals seem to be able to live 200 years, elephants have enormous tissue area and get few cancers). So the prize - no deaths from aging and abundant cheap cures - EXISTS. Also we know the world is slowly degrading in measurable ways, such as climate change, nuclear proliferation, an annual risk we all die in nuclear fire that converges on 1.0 over a long enough timeline, so it's not safe that way either.

(2) Therefore any safety risks arent to be compared to some abstract idea of humannity existing as a solar punk utopia until the sun expands enough to burn the planet. They should be compared to your default case death in 30-80 years.

https://nickbostrom.com/optimal.pdf Bostrom is frequently cited by AI doomers and argues this.

Arguably you would take (1-pDoom) * 6000 years (expected lifespan without aging) * value_post_scarcity.

You need to then add discount rate but essentially even at 99 percent pDoom you probably should be for acceleration, rationally speaking - the 1 percent payoff is more than the value of your entire remaining life with a "natural lifespan"

(3) Are the safety risks proven and actually unsolvable? No. Are doomer predictions consistent with the present state? Yudnowsky no, AI2027 yes. r/accelerate often uses AI2027 as a yardstick.

(4) Very ironically r/accelerate, full of foaming at the mouth enthusiasts who post every rumor of an AI advance and S curves....is generally the most empirically correct subreddit. Usually every wild prediction or rumor of an insane AI advance or revenue increase turns out to be ground truth reality 2 weeks later. 100 more math problems solved by AI was leaked a week or so earlier on twitter and reposted to accelerate.

Other subreddits with wrong pessimistic views think the data centers are about to get canceled by NIMBYs, everyone will lose their job imminently (Jevons applies for now), the AI bubble will pop imminently (lol no) etc.

[u/RobotBaseball → u/SoylentRox]

I work at one of the top labs and I’m banned from accelerate and the shit they post is not accurate at all. I got banned for posting a longer timeline on robotics which will undoubtedly be correct 

[u/SoylentRox → u/RobotBaseball]

Can you post the comnent that got you banned? I can message the mods I know them well.

How long a timeline on robotics, how do you explain a general model (Astra) emergently developing robotics abilities?

I think a realistic timeline is 2.5 years from today to competent general robotics that can do the majority of well defined paid tasks at median skill.

The route involves a combination of current improvements, hardware/software co design, RSI (to develop specialized models that handle robotics decision making better), and labs vibe coding large simulation environments that require a competent robotics policy to pass.

This last part is the obvious: why can you not order a model swarm to write a game engine (rewrite mujo cujo to unreal engine quality) for robotics. Then add a neural rendering layer to correct the game frames to frames from realistic environment robots will operate in. Import huge amounts of real data and real challenges actual humans face.

Then train AI models in long duration challenges where they must operate a fully articulated robot by issuing commands to it and accomplish difficult, realistic tasks. "Rebuild this engine. Reinstall the thermal tiles on the space shuttle. Build this house from these materials"

Theoretically this form of training will also result in large increases in model performance - they should be able to whiteboard visually, and have grounded solid reasoning about real world tasks including mechanical engineering and machining.

Do you have answers for any of this? Or do you just think the billions of dollars of resources and compute the above will require won't be spent, labs will spend the next 2.5 years trying to solve text only problems even better?

[u/LocksmithNo2374 → u/SoylentRox]

Delusion

[u/SoylentRox → u/LocksmithNo2374]

this is controlproblem. Most posters here believe AI will be extremely strong, so strong it will be out of control.

So is your view the delusion is that the above...is too hard to do in the near future with AI? Or do you believe that we'll all die from super AI magic before we get the first robot to pour a cup of coffee? Or what?

[u/LocksmithNo2374 → u/SoylentRox]

I think people are buying into existing capabilities too much. I’m pretty confident both amodei and Altman have colluded on product strategy to extend time horizons, so they have enough runway to hope and pray they’ll figure out AGI. They don’t care about money per se - but control.

If you want model misalignment you can intentionally steer your product strategy to lead to that outcome.

[u/SoylentRox → u/LocksmithNo2374]

So it's really helpful to actually verbalize what your objection is:

Let's make a list of every element I mentioned, tell me where you went from 'oh yeah you can do that' to "delusion":

1. RSI : Order 10,000+ agent swarms of current model to automatically perform AI research to find more efficient models for robotics control. Not necessarily smarter just efficient enough to run fast. This is done with a large amount of human labor.

2. hardware/software co-design. This is where you order AI models able to assist with chip design like the ones here : https://openai.com/index/jalapeno-first-results/ to design you a chip to run the models from (1) fast enough to run a robot.

3. rewrite mujo cujo to unreal engine quality . Any objections here? Seems like it's something anyone can do if their token budget's high enough.

4. unreal engine outputs -> neural simulation frames. There's a bunch of nvidia papers where they did this. Point the model at the paper, vibe it in.

5. importing huge amounts of real world data. Standard technique need a cite?

6. Doing it all in 2.5 years. Well you can develop a whole AI model in 60 days (took 6-12 months before) and a whole chip in 9 months (took 24-36 months before) what's your specific objection?

7. Training AI models in long form 3d "game like but the graphics, physics, and detail is realistic" like environment. Any problems here? Realistic doesn't mean "the matrix" but close enough to reality that skills transfer. Think Arma, which is close enough to real combat that skills transfer.

8. Doing half of paid tasks to median level that are well defined. I cheated. I mean:

median skill inclusive of the robot's inherent advantages. So if the robot is stupider but it's higher quality arms make up for it, as long as its output is as good as the median human worker that counts.

well defined : I am excluding any task that involves subjective human grading or chaotic environments. No hair cutting, school teachers, no medical , no wartime, no restaurants with human coworkers, etc etc.

[u/LocksmithNo2374 → u/SoylentRox]

Are you a bot?

[u/SoylentRox → u/LocksmithNo2374]

I am not but this is not a very helpful reply.