lemm.ee

16 points

2 months ago

Obviously.

But at what point does that guidance just become the dataset you removed from the training data?

To get it to run Doom, they used Doom.

To realize a new genre, you’ll “just” have to make that game the old fashion way, first.

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Even_Adder@lemmy.dbzer0.com

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5 points

2 months ago

But at what point does that guidance just become the dataset you removed from the training data?

The whole point is that it didn’t know the concepts beforehand, and no it doesn’t become the dataset. Observations made of the training data are added to the model’s weights after training, the dataset is never relevant again as the model’s weights are locked in.

To get it to run Doom, they used Doom.

To realize a new genre, you’ll “just” have to make that game the old fashion way, first.

Or you could train a more general model. These things happen in steps, research is a process.

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8 points

2 months ago

You are completely missing what I’m saying.

I know the input doesn’t alter the model, that’s not what I mean.

And “general” models are only “general” in the sense that they are massively bloated and still crap at dealing with shit that they weren’t trained on.

And no, “comprehending” new concepts by palette swapping something and smashing two existing things together isn’t the kind of creativity I’m saying these systems are incapable of.

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Even_Adder@lemmy.dbzer0.com

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4 points

2 months ago

What kind of creativity are you talking about then? I’ve also never heard of a bloated model. Which models are bloated?

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3 points

2 months ago

Bloated, as in large and heavy. More expensive, more power hungry, less efficient.

I already brought it up. They can’t deal with something completely new.

When you discuss what you want with a human artist or programmer or whatever, there is a back and forth process where both parties explain and ask until comprehension is achieved, and this improves the result. The creativity on display is the kind that can unfold and realize a complex idea based on simple explanations even when it is completely novel.

It doesn’t matter if the programmer has played games with regenerating health before, one can comprehend and implement the concept based on just a couple sentences.

Now how would you do the same with a “general” model that didn’t have any games that work like that in the training data?

My point is that “general” models aren’t a thing. Not really. We can make models that are really, really big, but they remain very bad at filling in gaps in reality that weren’t in the training data. They don’t start magically putting two and two together and comprehending all the rest.

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Even_Adder@lemmy.dbzer0.com

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1 point

2 months ago

Do you have any examples of how they fail? There are plenty of ways to explain new concepts to models.

https://arxiv.org/abs/2404.19427 https://arxiv.org/abs/2406.11643 https://arxiv.org/abs/2403.12962 https://arxiv.org/abs/2404.06425 https://arxiv.org/abs/2403.18922 https://arxiv.org/abs/2406.01300

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2 points

2 months ago

In a couple sentences? In a way that doesn’t approach, equal or exceed the effort of training the model with that data to begin with?

You insist these models can do new things out of nothing, and you keep saying “all you have to do, is give them something”.

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