<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>gabia</author><text>Great points about the energy minimisation issue. Funnily enough, this is actually a problem with de-novo protein design at the moment: the designed proteins are _too_ stable! Compared to natural proteins. Protein are often not static shapes, they are machines that need to be dynamic - in other words what you said, they do not live at some deep global optimum.</text><parent_chain><item><author>eru</author><text>&amp;gt; Protein folding is still an unsolved problem, and I’m dubious of the notion machine learning will ever solve it, but hopefully we get some helpful science out of it.&lt;p&gt;As a working hypothesis, protein folding assumes that a protein folds into the globally lowest energy configuration.  And that&amp;#x27;s a good assumption for a start.&lt;p&gt;However, nature isn&amp;#x27;t magic and can&amp;#x27;t magically solve global optimisation problems.  If there&amp;#x27;s a region in configuration space with a local minimum and high enough energy &amp;#x27;walls&amp;#x27;, this might be stable enough for the protein to be stable.&lt;p&gt;For reasons of computational complexity, I agree that machine learning will probably never solve the global minimisation problem.  But the complicated and messy local optimisation problem that we see in reality might very well be solvable eventually by something like machine learning.&lt;p&gt;Why are you dubious?  Where do your objections come from?</text></item><item><author>trivexwe</author><text>Weird article.&lt;p&gt;It mentions multiple times that ~”the protein folding problem is solved” as well as multiple instances of ~”but there are limitations to this technique and it is often missing crucial details”.&lt;p&gt;It really is difficult to conceptualize these highly nonlinear problem spaces, like protein folding, until you attempt to work with them.&lt;p&gt;Many in software development have an intuitive understanding of the difficulty evidenced in the community’s ~“the last 10% took 100% of the time” meme.&lt;p&gt;Even in a nonlinear problem spaces you have “trivial” solutions.&lt;p&gt;Terry Tao famously coauthored a paper finding arithmetic progressions for generating sequences of primes.[1] The sequences found are “trivial” in terms of “solving the prime sequence problem” in that they are sparse, the sequences are finite, and progressions lack a method of find more.&lt;p&gt;These machine learning tools are by design approximation engines.  I’m unsure of any results that prove one way or the other that it is possible to pass a bound of approximation that provides exact solutions. (think, an approximate solution that only fails to provide exact solutions for solutions that are trivial using a different method, I think a lot of work I p-adics is motivated similarly)&lt;p&gt;I feel these machine learning techniques are expanding the definition of “trivial solutions” to include those capable of being solved by their convoluted methods (back prop, etc).  Since this new subset of the space that can be labeled “solved” appear more complex than known trivial solutions people assume the whole space must be known, and this is where the difficult conceptualization rears its influence.&lt;p&gt;Protein folding is still an unsolved problem, and I’m dubious of the notion machine learning will ever solve it, but hopefully we get some helpful science out of it.&lt;p&gt;[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;en.m.wikipedia.org&amp;#x2F;w&amp;#x2F;index.php?title=Green%E2%80%93Tao_theorem&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;en.m.wikipedia.org&amp;#x2F;w&amp;#x2F;index.php?title=Green%E2%80%93T...&lt;/a&gt;</text></item></parent_chain></comment><story><title>AI Revolutionized Protein Science, but Didn&apos;t End It</title><url>https://www.quantamagazine.org/how-ai-revolutionized-protein-science-but-didnt-end-it-20240626/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>trivexwe</author><text>&amp;gt; Why are you dubious? Where do your objections come from?&lt;p&gt;That the results the machine learning techniques provide are still nondeterministic.&lt;p&gt;Meaning that they are, in terms of identifying other local minima that satisfy the constraints, as good as a guess.&lt;p&gt;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.&lt;p&gt;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.&lt;p&gt;&amp;gt; However, nature isn&amp;#x27;t magic and can&amp;#x27;t magically solve global optimisation problems.&lt;p&gt;I wonder sometimes. Let’s remember, this is an open question after all.&lt;p&gt;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”.&lt;p&gt;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.&lt;p&gt;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.</text><parent_chain><item><author>eru</author><text>&amp;gt; Protein folding is still an unsolved problem, and I’m dubious of the notion machine learning will ever solve it, but hopefully we get some helpful science out of it.&lt;p&gt;As a working hypothesis, protein folding assumes that a protein folds into the globally lowest energy configuration.  And that&amp;#x27;s a good assumption for a start.&lt;p&gt;However, nature isn&amp;#x27;t magic and can&amp;#x27;t magically solve global optimisation problems.  If there&amp;#x27;s a region in configuration space with a local minimum and high enough energy &amp;#x27;walls&amp;#x27;, this might be stable enough for the protein to be stable.&lt;p&gt;For reasons of computational complexity, I agree that machine learning will probably never solve the global minimisation problem.  But the complicated and messy local optimisation problem that we see in reality might very well be solvable eventually by something like machine learning.&lt;p&gt;Why are you dubious?  Where do your objections come from?</text></item><item><author>trivexwe</author><text>Weird article.&lt;p&gt;It mentions multiple times that ~”the protein folding problem is solved” as well as multiple instances of ~”but there are limitations to this technique and it is often missing crucial details”.&lt;p&gt;It really is difficult to conceptualize these highly nonlinear problem spaces, like protein folding, until you attempt to work with them.&lt;p&gt;Many in software development have an intuitive understanding of the difficulty evidenced in the community’s ~“the last 10% took 100% of the time” meme.&lt;p&gt;Even in a nonlinear problem spaces you have “trivial” solutions.&lt;p&gt;Terry Tao famously coauthored a paper finding arithmetic progressions for generating sequences of primes.[1] The sequences found are “trivial” in terms of “solving the prime sequence problem” in that they are sparse, the sequences are finite, and progressions lack a method of find more.&lt;p&gt;These machine learning tools are by design approximation engines.  I’m unsure of any results that prove one way or the other that it is possible to pass a bound of approximation that provides exact solutions. (think, an approximate solution that only fails to provide exact solutions for solutions that are trivial using a different method, I think a lot of work I p-adics is motivated similarly)&lt;p&gt;I feel these machine learning techniques are expanding the definition of “trivial solutions” to include those capable of being solved by their convoluted methods (back prop, etc).  Since this new subset of the space that can be labeled “solved” appear more complex than known trivial solutions people assume the whole space must be known, and this is where the difficult conceptualization rears its influence.&lt;p&gt;Protein folding is still an unsolved problem, and I’m dubious of the notion machine learning will ever solve it, but hopefully we get some helpful science out of it.&lt;p&gt;[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;en.m.wikipedia.org&amp;#x2F;w&amp;#x2F;index.php?title=Green%E2%80%93Tao_theorem&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;en.m.wikipedia.org&amp;#x2F;w&amp;#x2F;index.php?title=Green%E2%80%93T...&lt;/a&gt;</text></item></parent_chain></comment><story><title>AI Revolutionized Protein Science, but Didn&apos;t End It</title><url>https://www.quantamagazine.org/how-ai-revolutionized-protein-science-but-didnt-end-it-20240626/</url></story>