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Comment by mtgp1000 | Hacker News Reader

This was hilarious, but it brings up a serious concern: if you train your AI on a bunch of shallow, clickbaity content from the internet, you're going to get shallow, clickbaity output, like much of the presentation - in other words this isn't just a lack of human knowledge to fill in gaps, the training data sucks because the internet is full of high level garbage dumbed down for clicks.

I believe it may well be possible to train GPT3 to write more accurate technical content with the existing tech and infrastructure, except we don't have a large enough body of technical content outside of academic papers, which isn't the kind of training data you want if your goal is to write prose.

In many ways, ML as it evolves and becomes ubiquitous will eventually become a dark window into the triviality of the average human's existence.

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You can prompt GPT-3 for different levels of politeness. It's actually necessary, to make sure the generations are not going to be NSFW. In the same way I think it can switch from bullshit to serious mode, you just need to mention that the article appears in a prestigious publication or something.

The prompts don't matter if the training data isn't up to par - more importantly I believe the nature of the training is such that the weight activations for the various prompts are unlikely to be independent.

In other words if 20% of your training data is scientific literature, even with appropriate disambiguating prompts the output will still be heavily influenced by the other 80% of your training data.

When you use GPT-3 to generate outputs, you're actually sampling from a learned subset of a super complex, super high dimensional space - and without human knowledge all the neural networks are doing is translating priors (input prompt) into points in the learned space. And the learned space is some complex topology of points between which the net interpolates - it's extremely difficult with current tech to control the shape of this learned space and that shape is influenced by all training data under a scheme like GPT-*.

After playing with AiDungeon I think you are right about the data not being up to par. It fails more frequently than it appears in the news. Has some brilliant moments too.

For example, when prompted to talk about deep learning it generated a nonsense paragraph. This is not unexpected, but when it generates news or dialogue it can be coherent on much larger pieces of text. Clearly shows it didn't read too much on the topic.

I can hardly make it do any math. Even simple things like 11+22= don't work. I expect the next 10x scale up will fill most of these holes, especially if they improve the training corpus quality and breadth.

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