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by hungryhobbit10 hours ago |
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upvoteby sumanthvepa9 hours ago|
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It can start to reason about what it seems under the microscope. You just need to train it on images and data from microscopes.
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upvoteby randomImmigrant4 hours ago|
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Have you looked at cells and tissues under a microscope? There’s patterns upon patterns inside patterns under and over and through other patterns.

There’s no well-labeled dataset for all this because we don’t know everything we are looking at.

Take a neuron. You can stain it so you can tell apart an axon and a dendrite. But if you don’t it’s very possible to miss or mislabel the axon, because unlike the textbook neuron, the axon isn’t always obviously oriented in a way that promotes ID.

And that’s just one example. With more complex samples, you also have a lot of edges which AI models don’t segment very well.

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upvoteby menaerus1 hours ago|
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You largely explained a day in life of a SWE working in a complex codebase spanning (tens of) millions lines of code. In complex products we always deal with a great degree of uncertainty and due to the ambiguity and high complexity we usually have no simple way of proving that the thing (TM) we designed is going to be working as advertised in all cases. Because the problem space is in-exhaustive, and to increase the likeliness of the solution we designed, we run the experiments, and not on a sole machine but clusters at data-center scale. Even then you get "bug reports" and that's not because engineers were lazy but because the domain is genuinely complicated, and ever changing.

So, I'd say it's a pretty similar process, and yet we have the AI models now almost completely integrated into the SWE domain remediating significant parts of its complexity.

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upvoteby throwaway1324489 minutes ago|
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What is it with SWEs thinking every problem can be addressed in terms of SWE, while at the same time being unaware of how little they understand the problem they are trying to address?
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upvoteby mrtesthah6 hours ago|
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Do you know what loading a sample into a scanning tunneling microscope involves?
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upvoteby bijowo16769 hours ago|
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the transition is in-silico => in-vitro => in-vivo

AI already can do in-silico simulation

add microscopes and tiny precision actuators, and we can build closed feedback loop in-vitro system

once we have in-vitro, it will be easier to go in-vivo

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upvoteby randomImmigrant4 hours ago|
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Why would number 2, which we’ve been able to do for decades, possible affect number 3 on your list?
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