we do have high precision microscopes that can see molecules, so it is possible to build closed system with feedback loop that feeds back into AI at very small scales
we do have high precision microscopes that can see molecules, so it is possible to build closed system with feedback loop that feeds back into AI at very small scales
It's often not necessarily about visibility, but real iteration time.
For instance, let's say we are doing biology on plants, which is still much easier than doing human trials. No matter how motivated claude might get, you aren't getting a corn plant to grow in 40 seconds. Real world research pipelines involve stages where even greenhouses don't do, and sacks of grain are shipped to Hawaii, or Brazil, or wherever, to try to get practical data for a given test.
If your computer simulation is so accurate you can skip a step now done with biology, great, but it gets harder the larger your problem is. I might see if a molecule kills bacteria kind of quickly, but what does a molecule do to a real human being? Not so easy.
I don't follow how "we can see tiny stuff" = "it is possible to build closed system"?
Seeing tiny stuff doesn't magically give you enough info for Claude to reason with the system predictably (the way it can with source code).
It can start to reason about what it seems under the microscope. You just need to train it on images and data from microscopes.
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.
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.
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?
Do you know what loading a sample into a scanning tunneling microscope involves?
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
Why would number 2, which we’ve been able to do for decades, possible affect number 3 on your list?
No its not, this is complete dunning kruger, please provide an example.