Nobel prize 2024 - magic just got less wriggle room

I think your mistake is in assuming that the AI approach to the problem of finding functional protein foldings correlated to specific amino acid chains is an undirected unintelligent Darwinesque random search program which necessarily has to be over an inconceivably large protein space in which most configurations are functionless or harmful. This underlying nature of protein space is a fact rather than being drastically limited in the total number of functional adaptive configurations, and is very extensive in its containment of very many of them. It's just that this large number of desired specifically functional proteins are randomly spread out over a huge volume of mostly nonfunctional sequences separated on average by large distances in protein configuration space. A random search program  as implemented by undirected semi-random Darwinistic processes is mostly useless. 

So the AI system approach instead, rather than trying futilely to capture the huge total set of foldlings. is instead (using very powerful computer systems to handle the large volume of data processing required) to intelligently abandon Darwinesque undirected semi-random search and examine the multitude of known functional protein folding configurations for structural commonalities and functional correlations and using statistical means predict other related foldings with the desired properties (many medical-related). This is an "intelligent design" sort of method rejecting Darwinism in order to work. 

So, AI techniques as applied to finding desired protein folding amino acid configurations reject Darwinistic processes in favor of a very intelligent-design like model.

I realize that I need to elaborate on this a little and explain in principle more precisely how specialized generative AI systems use structural commonalities and functional correlations and statistics in massive computer processing of the existing data base learning set to solve the hitherto unsolved problem of theoretically deriving the amino acid sequences which produce the specific required 3-dimensional protein folding shapes and corresponding biochemical functions that are predicted to be needed for the protein to have certain medically required actions (say for particular desired drug effects on particular disease processes). 

As can be seen, this is a very intelligent sort of "design" process, not a semi-random walk RM + NS Darwinistic approach to the problem, which would be futile and unproductive given the hugely scattered distribution of functional desired sequences in total protein space.

[Laird]

Yes

And, in fact, your argument could be seen to assume the exact opposite of @sbu's characterisation: that protein folding is physically determinate, in particular with respect to (genetic) coding instructions, else the semi-random walk on which it is premised would be inconsiderable, with no coding instruction reliably mapping to the same folding on each occasion (in the absence of the otherwise-presumed-by-sbu-to-be-required "magic realm", which indeed is absent on your argument's premises, proceeding as they do from the assumption of physicalism so as to demonstrate the falsity of that assumption).

I’m pretty neutral on this as I don’t know I have the expertise to judge either [side], but I am curious as to what academics are drawing from these findings.

Is there any discussion in academia in relation to Design at all?

And, in fact, your argument could be seen to assume the exact opposite of @sbu's characterisation: that protein folding is physically determinate, in particular with respect to (genetic) coding instructions, else the semi-random walk on which it is premised would be inconsiderable, with no coding instruction reliably mapping to the same folding on each occasion (in the absence of the otherwise-presumed-by-sbu-to-be-required "magic realm", which indeed is absent on your argument's premises, proceeding as they do from the assumption of physicalism so as to demonstrate the falsity of that assumption).

That s "simpel" AI model can predict these foldings suggests that, while the theoretical space of all possible proteins is vast, the actual subset that is biologically relevant (i.e., those that can fold properly and perform useful functions in cells) may be much smaller. This could mean that natural processes like evolution have a more tractable search space to work within, making the emergence of functional proteins less improbable than ID proponents suggest.

[Laird]

That's another please explain from me. You seem to think you're undercutting @nbtruthman's argument with this...

while the theoretical space of all possible proteins is vast, the actual subset that is biologically relevant (i.e., those that can fold properly and perform useful functions in cells) may be much smaller.

...when in fact that's a key premise of his argument.

How you think that this follows from it is anybody's guess:

This could mean that natural processes like evolution have a more tractable search space to work within, making the emergence of functional proteins less improbable than ID proponents suggest.

More tractable than what, exactly? And what makes it more tractable?

You're still not making any sense to me.

That's another please explain from me. You seem to think you're undercutting @nbtruthman's argument with this...


...when in fact that's a key premise of his argument.

How you think that this follows from it is anybody's guess:


More tractable than what, exactly? And what makes it more tractable?

You're still not making any sense to me.

The argument that there must be an intelligent designer directing evolution, one who selects the “correct” amino acids chains is highly correlated to the supposed cardinality of protein space.

I use the word “tractable” to denote a space with less dimensions but as I’m not a native english speaker I should perhaps refrain from using vocabulary I’m less certain about.

[Laird]

I can't make sense of that either, and it seems pointless to seek further clarification. I suspect that you simply don't understand the argument.

[Laird]

The point is that the semi-random walk is a search over the entire protein space (which is very, very sparse in terms of life-supporting proteins); the cardinality of that part of the space which contains life-supporting proteins is thus irrelevant (to the argument).

I *think* the idea @sbu is seeking to express is that given the non-viability of certain protein structures the search space could reduce itself over time to something more tractable in the sense that natural selection itself is pruning the options down given that genuinely non-viable structures lead to death.

Throwing this out there as what I've gotten from this argument, though I'm still not sure anyone outside this forum is connecting these findings to an argument for or against Design?

[Laird]

I *think* the idea @sbu is seeking to express is that given the non-viability of certain protein structures the search space could reduce itself over time to something more tractable in the sense that natural selection itself is pruning the options down given that genuinely non-viable structures lead to death.

Hmm, to me that's a different idea which isn't given additional weight by the Nobel prize-winning findings.

In any case, that idea seems to assume that the viable proteins cluster in the space in some systematic way such that once the semi-random walk encounters one, it will be likely to find more "nearby", and, as far as I understand it, according to the argument that @nbtruthman presents, this clustering is not evident: finding one viable protein does not make it any easier to find further proteins; viable proteins are not generally "nearby" one another.

Again, @nbtruthman can correct me if I've misunderstood.

Throwing this out there as what I've gotten from this argument, though I'm still not sure anyone outside this forum is connecting these findings to an argument for or against Design?

Nor am I, but then, I haven't gone looking.