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by mnky9800n
482 days ago
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The truth is that as scientific problems become sufficiently complex we will need to trust a computer and what it tells us. And that computer acts as an abstraction layer for the knowledge in the same way that Python abstracts away memory management. This will allow us to conceive ideas that were otherwise impossible. To put it a different way, it used to be a department of physicists or glaciologists or hydrologists or whatever were happy enough talking to each other. Nowadays people are rather interdisciplinary and often those people are talking across departments trying to come up with new ideas. Eventually the complexity of the new idea will be such that you cannot conceive it without the help of a computer. At that time you will have to trust the output of the computer in the same way you trusted your colleagues. I suppose we already sort of do that with many computational tools. Like you have trust in the software we use to analyse data. People use python because the data science ecosystem is well developed and well trusted. That’s the problem with the current crops of LLMs. None of them are able to build trust as a knowledge abstraction layer. But when they do they will become very useful as you can ask them to do all sorts of things. |
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There is no way that humans fully understand the inner workings of a camera sensor, but they trust the output of a camera recording to make decisions.
The point is that you don't have to trust the output of the camera the same way you trust a colleague. You don't need to anthromorphise the camera to make use of it.
Nothing changes. There is nothing special about LLMs. There is no need to worship them or think of them as anything other than tools.