> Why are you dubious? Where do your objections come from?
That the results the machine learning techniques provide are still nondeterministic.
Meaning that they are, in terms of identifying other local minima that satisfy the constraints, as good as a guess.
If the provided solution also came with a method of systemic modification to derive all other solutions that satisfy the constraints, then I would be satisfied.
Without that you are unable to say with certainty that your local minima is correct even if nature fails to adhere to the lowest energy assumption.
> However, nature isn't magic and can't magically solve global optimisation problems.
I wonder sometimes. Let’s remember, this is an open question after all.
I have a long standing hypothesis that an algorithmic solution to the global optimization problem is what lends action potentials the appearance, or essence?, of what we mean when we speak of “consciousness”.
But I am a more inclined toward the abstract aspects of the mathematics behind the problem, and leave advocacy for the current techniques
to researchers developing practical solutions with them.
I applaud the people who toiled with X-ray crystallography to build the field to the point that a machine learning technique could be developed.
> That the results the machine learning techniques provide are still nondeterministic.
I think I know what you are trying to say, but 'determinism' or not isn't the problem. You can run machine learning methods completely deterministically: just use a pseudo-random-number-generator (and be careful about how you seed it, and be wary of the problems with concurrency etc).
> If the provided solution also came with a method of systemic modification to derive all other solutions that satisfy the constraints, then I would be satisfied.
> Without that you are unable to say with certainty that your local minima is correct even if nature fails to adhere to the lowest energy assumption.
Have a look at how integer linear programming solvers work. They use plenty of heuristics and non-determinism for finding the solution, but at the end they can give you a proof that what they found is optimal.
You are right, that you don't get that kind of guarantee with current machine learning approaches. Though you could modify them in that direction. (Eg if you added machine learning to an integer linear programming solver, you would hook it in as a new heuristic, but you would still want the proof at the end.)
> I have a long standing hypothesis that an algorithmic solution to the global optimization problem is what lends action potentials the appearance, or essence?, of what we mean when we speak of “consciousness”.
Sounds like woo. Protein folding in bacteria and yeast work pretty similar to how it works in humans. In fact, we can transfer genes from us to yeasts to produce many of the same proteins human produce. But you'd be hard-pressed to argue that yeast are sentient.
This reminds me of how some people claim that soap films are super special because those films can solve optimisation problems. See eg https://highscalability.com/why-my-soap-film-is-better-than-... If you put soap film between a bunch of supports, even if the supports have complicated shapes, the soap film will tend to minimise its overall surface area.
Of course, if you look deeper into it, and do larger scale experiments, you figure out that the soap only assumes a local minimum.
O, definitely woo. I tried to make that explicitly clear by using “hypothesis” and “appearance”.
My hypothesis is less “optimization solutions == consciousness” and more positing that our brains, “action potentials” was meant as cheeky shorthand for the human brain, use an “optimization solution” that we identify as “consciousness”, or as you put it “sentience”.
But to quote South Park, “and I base that on absolutely nothing”. ;P
That the results the machine learning techniques provide are still nondeterministic.
Meaning that they are, in terms of identifying other local minima that satisfy the constraints, as good as a guess.
If the provided solution also came with a method of systemic modification to derive all other solutions that satisfy the constraints, then I would be satisfied.
Without that you are unable to say with certainty that your local minima is correct even if nature fails to adhere to the lowest energy assumption.
> However, nature isn't magic and can't magically solve global optimisation problems.
I wonder sometimes. Let’s remember, this is an open question after all.
I have a long standing hypothesis that an algorithmic solution to the global optimization problem is what lends action potentials the appearance, or essence?, of what we mean when we speak of “consciousness”.
But I am a more inclined toward the abstract aspects of the mathematics behind the problem, and leave advocacy for the current techniques to researchers developing practical solutions with them.
I applaud the people who toiled with X-ray crystallography to build the field to the point that a machine learning technique could be developed.