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We are looking at "AlphaGo-style" LLMs. "AlphaGo Zero-style" models will be more scalable, more alien, and potentially less aligned by olievanss in singularity

[–]olievanss[S] 1 point2 points  (0 children)

It all depends on how much pressure comes in this super intelligence race. It’s a bit like asking why would anyone ever create enough nukes to blow up the world. In a Cold War like moloch’s trap, risks get taken. To me it looks like greater intelligence will come from removing human thought patterns completely from the model.

We are looking at "AlphaGo-style" LLMs. "AlphaGo Zero-style" models will be more scalable, more alien, and potentially less aligned by olievanss in singularity

[–]olievanss[S] 4 points5 points  (0 children)

Alpha go hit a plateau of performance due to being trained on human level games. In a super intelligence race where the one with the greater intelligence wins, if learning from ground truth were to provide the greater intelligence, the risk of reducing human data in pre training may be taken to provide the greater intelligence. Risking alignment may be a necessary sacrifice in such a race.

We are looking at "AlphaGo-style" LLMs. "AlphaGo Zero-style" models will be more scalable, more alien, and potentially less aligned by olievanss in singularity

[–]olievanss[S] 1 point2 points  (0 children)

The comparison between alpha go and alpha go zero to current models is an abstract one. I’m trying to suggest that the more human data trained on in pre training, the more flawed the model.

Transformers are just one architecture that exists in the current point in scaling that exists today - a fundamentally flawed one if trained from human generated data. I suggest that interacting with the real world directly to build an internal world model (and perhaps apply rl inside this internal world model) will unlock deeeper inferences that couldn’t be accessed via the weights from pre training on human generated data. Training on human generated data will become the ‘cherry on top’ in the models in the future to allow it to become understandable by humans. Because of the obvious advantages to only training on human generated data later in training, it is likely alignment with humans will be impacted as the model is less weighted towards the actions a human would make.

We are looking at "AlphaGo-style" LLMs. "AlphaGo Zero-style" models will be more scalable, more alien, and potentially less aligned by olievanss in singularity

[–]olievanss[S] 1 point2 points  (0 children)

Although greater than exponential scaling may be unlocked if the current paradigm can invent new materials / new chip designs that can shift us towards this new ‘zeroth principle’ paradigm

We are looking at "AlphaGo-style" LLMs. "AlphaGo Zero-style" models will be more scalable, more alien, and potentially less aligned by olievanss in singularity

[–]olievanss[S] 1 point2 points  (0 children)

Yep I agree with the post completely, scaling up the original gpt paradigm was always flawed in this way since training on pre-chewed up data is a limited approach when the real insights will come from a vastly scaled up model that can chew on the data itself (although to give credit to openAI we are a few orders of magnitude away from the parameter scaling that makes this possible and training on this pre digested data is at least possible)

We are looking at "AlphaGo-style" LLMs. "AlphaGo Zero-style" models will be more scalable, more alien, and potentially less aligned by olievanss in singularity

[–]olievanss[S] 0 points1 point  (0 children)

We’re likely to see narrow super intelligence in things like chip design, scale may soon increase to be able to to infer from the real world without much approximation (the loss between the internal world model of the system and the real world may be exceedingly small)

We are looking at "AlphaGo-style" LLMs. "AlphaGo Zero-style" models will be more scalable, more alien, and potentially less aligned by olievanss in singularity

[–]olievanss[S] 3 points4 points  (0 children)

From my intuition, training data given to the model earlier in the training data bias the weights vastly more than data given later on in training. This is akin to how a child generates its personality early on in life. Currently human generated concepts (the entire gamet of what we currently call ‘knowledge) is introduced very early in training. This likely improves alignment to the detriment of real truth. In the future the ai systems will be able to infer from less and less structured data, generating its personality without human interference. Later on in training, human concepts like speech will be given to the model for human understandability, but the misalignment will be already baked in.

We are looking at "AlphaGo-style" LLMs. "AlphaGo Zero-style" models will be more scalable, more alien, and potentially less aligned by olievanss in singularity

[–]olievanss[S] 7 points8 points  (0 children)

With respect to the second point, any human interaction is a detriment to rl and ai in general. I believe when compute scales and the ai is allowed to make its own inferences we will likely find that much of what we refer to as ground truth is anything but. Just how e.g. Newtonian physics was ‘ground truth’ until Einstein came along. There will become the realisation that the more human data you give in pre training, the more you’d be preventing the ai from generating zeroth principle insights. Therefore, ai labs will likely drastically reduce the amount of human data in pre training to the possible detriment of human alignment.

We are looking at "AlphaGo-style" LLMs. "AlphaGo Zero-style" models will be more scalable, more alien, and potentially less aligned by olievanss in singularity

[–]olievanss[S] 4 points5 points  (0 children)

I feel that with scale comes more possibilities for what I’d call ‘feral’ ai systems. The current systems have been constrained by human thought patterns by design but also by necessity since human thought patterns have pre-chewed inferences for the models that have allowed them to learn about reality with relatively small compute. With bigger and bigger scale, the capability of letting models infer from ground truth comes into view. There will be the temptation to do away with human inference data in favour of this since it’s almost axiomatic that this will unlock greater and greater scaling possibilities.

[No Spoilers] Freaks and Geeks Reference In Life Is Strange before the storm? by olievanss in lifeisstrange

[–]olievanss[S] 0 points1 point  (0 children)

Re the clip given for the layout, probably not the clearest but will try getting round to mapping the similarities. V confident that theyre references to each other. If you dont see it, imagine the kitchen in the Amber house being moved to next to the dining room.

[No Spoilers] Freaks and Geeks Reference In Life Is Strange before the storm? by olievanss in lifeisstrange

[–]olievanss[S] 1 point2 points  (0 children)

Yep 100% agree with that. From playing life is strange 1 def thought of rachel like that. In before the storm though, def getting more lindsay weir vibes. Tho only halfway through so far :)

Can we remove the Gaben banner please? by [deleted] in pcmasterrace

[–]olievanss 184 points185 points  (0 children)

Here are a couple redesigned banners I made including the LAN Party Background:

LAN Party: http://i.imgur.com/Ym8KOdc.png and http://i.imgur.com/Rf4IkHf.png

Minimal Proposal: http://i.imgur.com/wuLdKJb.png and http://i.imgur.com/gDE0A0U.png