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0 pointsnightski2y ago0 comments

It sounds nice in theory, but the data itself could be problematic. There is no temporal nature to it. You can have duplicate data points, many data points that are closely related but describe the same thing/event/etc.. So while only showing the model each data point once ensures you do not introduce any extra weight on a data point, if the dataset itself is skewed it doesn't help you at all.

Just by trying to make the dataset diverse you could skew things to not reflect reality. I just don't think enough attention has been paid to the data, and too much the model. But I could be very wrong.

There is a natural temporality to the data humans receive. You can't relive the same moment twice. That said, human intelligence is on a scale too and may be affected in the same way.

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visarga2y ago

> I just don't think enough attention has been paid to the data, and too much the model.

I wholly agree. Everyone is blinded by models - GPT4 this, LLaMA2 that - but the real source of the smarts is in the dataset. Why would any model, no matter how its architecture is tweaked, learn about the same ability from the same data? Why would humans be all able to learn the same skills when every brain is quite different. It was the data, not the model

And since we are exhausting all the available quality text online we need to start engineering new data with LLMs and validation systems. AIs need to introspect more into their training sets, not just train to reproduce them, but analyse, summarise and comment on them. We reflect on our information, AIs should do more reflection before learning.

More fundamentally, how are AIs going to evolve past human level unless they make their own data or they collect data from external systems?

ben_w2y ago

> It was the data, not the model

It's both.

It's clearly impossible to learn how to translate Linear A into modern English using only content written in pure Japanese that never references either.

Yet also, none of the algorithms before Transformers were able to first ingest the web, then answer a random natural language question in any domain — closest was Google etc. matching on indexed keywords.

> how are AIs going to evolve past human level unless they make their own data?

Who says they can't make their own data?

Both a priori (by development of "new" mathematical and logical tautological deductions), and a posteriori by devising, and observing the results of, various experiments.

Same as us, really.

riversflow2y ago

I see this brought up consistently on the topic of AI take-off/X-risk.

How does an AI language model devise an experiment and observe the results? The language model is only trained on what’s already known, I’m extremely incredulous that this language model technique can actually reason a genuinely novel hypothesis.

A LLM is a series of weights sitting in the ram of GPU cluster, it’s really just a fancy prediction function. It doesn’t have the sort of biological imperatives (a result of being complete independent beings) or entropy that drive living systems.

Moreover, if we consider how it works for humans, people have to _think_ about problems. Do we even have a model or even an idea about what “thinking” is? Meanwhile science is a looping process that mostly requires a physical element(testing/verification) to it. So unless we make some radical breakthroughs in general purpose robotics, as well as overcome the thinking problem I don’t see how AI can do some sort tech breakout/runaway.

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whimsicalism2y ago

> Yet also, none of the algorithms before Transformers were able to first ingest the web, then answer a random natural language question in any domain — closest was Google etc. matching on indexed keywords.

Wrong, recurrent models were able to do this, just not as well.

Salgat2y ago

This is definitely current models' biggest issue. You're training a model against millions of books worth of data (which would take a human tens of thousands of lifetimes) to achieve a superficial level of conversational ability to match a human, which can consume at most 3 novels a day without compromising comprehension. Current models are terribly inefficient when it comes to learning from data.

famouswaffles2y ago

Modern LLMs are nowhere near the scale of the human brain however you want to slice things so terribly inefficient is very arguable. also language skills seemingly take much less data and scale when you aren't trying to have it learn the sum total of human knowledge. https://arxiv.org/abs/2305.07759

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whimsicalism2y ago

You have to count the training process from the origin of the human brain imo, not from the birth of any individual human.

Neural nets look much more competitive by that standard.

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imtringued2y ago

They are inefficient by design. Gradient descent and backpropagation scale poorly, but they work and GPUs are cheap, so here we are.

j / k navigate · click thread line to collapse

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visarga2y ago

> I just don't think enough attention has been paid to the data, and too much the model.

More fundamentally, how are AIs going to evolve past human level unless they make their own data or they collect data from external systems?

ben_w2y ago

> It was the data, not the model

It's both.

It's clearly impossible to learn how to translate Linear A into modern English using only content written in pure Japanese that never references either.

> how are AIs going to evolve past human level unless they make their own data?

Who says they can't make their own data?

Both a priori (by development of "new" mathematical and logical tautological deductions), and a posteriori by devising, and observing the results of, various experiments.

Same as us, really.

riversflow2y ago

I see this brought up consistently on the topic of AI take-off/X-risk.

3 more replies

whimsicalism2y ago

Wrong, recurrent models were able to do this, just not as well.

Salgat2y ago

famouswaffles2y ago

1 more reply

whimsicalism2y ago

You have to count the training process from the origin of the human brain imo, not from the birth of any individual human.

Neural nets look much more competitive by that standard.

1 more reply

imtringued2y ago

They are inefficient by design. Gradient descent and backpropagation scale poorly, but they work and GPUs are cheap, so here we are.

j / k navigate · click thread line to collapse