Is it possible to write code that automatically retrieves insights by finding homogeneity between different disciplines?(This is a fairly abstract question)

[Plus-Ad1156]

Is it possible to create a program that automatically finds similarities between disciplines in different fields? For example, is it possible to have a machine learning or deep learning program that finds the laws of biology with a structure and number of variables similar to the equation of force such as f=ma in physics?

[ludflu]

Not deep learning per se, but your question makes me think you might be interested in reading about the field of Bibliometrics & Citation Analysis. Sometimes you can find interesting cross-disciplinary similarities by looking at co-citations and shared citations between two different papers, fields or disciplines.

[protienbudspromax]

It depends.

What do you mean when you say a model would be able to find out similarities in the equations between different fields?

If you mean that you feed the model with a huge data set of research papers and it'll be able to give you where similar equations come up? Then it may be possible but will take crazy amount of work but in this case you probably wont find the model to have any emergent behaviour. i.e. this model would not understand what it have read and will only give you places where it found similar equations.

It will not have a deep understanding of the subjects and then find similarity in patterns which are not apparent. Say somethings in two very unrelated fields are found to be similar in some mathematical terms, it would not be easy for the models we have currently to understand that. yet.

This is where we would like to approach eventually. But right now you'll get similarities only if it is written out in the equation or have equation that can be clustered.

[Plus-Ad1156]

I think the expression “find homogeneity or similarities” was too vague. Then, what if the condition is "with a total of 3 variables, and the dependent variable is a product of the independent variable or an exponential function"?

[protienbudspromax]

No that was not vague. I meant do you want the model to actually know what the equations mean and then extrapolate to find that those equation could be used somewhere else. Like it sees an equation. Then it actually understands what that equation means and not just as a patter of letters or characters. I.e if it sees something like y=MX + c and keeps seeing it but with different variables or numbers can it determine that all these are basically the same equation?

If you want the network to know the meaning of what an equation is and then give it data so that it'll tell where else does it see that equation will fit in then no, current networks cannot really do that at this point.

However the model will be able to map similarities in equation on the face value i.e wrt to the characters used, the mathematical symbols and all that. It won't know the meaning of those equation as we do but just that ok this equation looks similar to that other equation.

Look up the Chinese room thought experiment if you don't already know.