26 ms·

To paraphrase Ummon in the Hyperion book series, “science tells us that giraffes evolved long necks to eat leaves at the top of trees. What it doesn’t tell us i

• kennyadam 5y ago
  We can make an educated guess though, right? They didn't evolve them quick enough, so were out competed by the giraffes, for example. I know you were making a more general point, but I just wanted to throw that in.
  • snovv_crash 5y ago
    What about all the places where there aren't giraffes?
    • Akronymus 5y ago
      Much faster conversion to savanahs, which resulted in not being able to adapt fast enough; no evolutionary pressures to evolve to be that tall; evolutionary pressures exists/ed but there were stronger pressures to not evolve to be tall, and similar reasons are what come to my mind.
  • z3t4 5y ago
    Or giraffes find long necks (and other body parts) very attractive.
    • Smaug123 5y ago
      That is a glib answer which misses the point entirely. Why do they find long necks attractive? The answer to that sort of question usually turns out to be "because animals who randomly happened to be a little more attracted to long necks were more likely to breed with the long-necked animals, so their genes became better represented in the gene pool thanks to the long-necked animals' better survival power".
      • gpm 5y ago
        Why do peacocks (or really peahens) find giant feather displays attractive? I think the answer is usually actually "because it is indicative of a healthy mate". If you need to be well fed and healthy to grow tall, or to maintain a giant set of ridiculous feathers, then it will probably be an attractive trait a few generations later, since it will predict success.
  • inglor_cz 5y ago
    On the other hand, plenty of species developed fins and did not give up only because they were outcompeted by someone else. But the ocean is a big place. Tree tops in the savannah may not be as important resource.
  • gpm 5y ago
    Moreover just glancing at Giraffe physiology shows us all the compromises that a long neck entails. For instance it's long neck traps a lot of air in it's breathing cycle, requiring larger lungs for the same oxygen capacity. It has twice the blood pressure of a human to enable pumping up the long distance, and as such a giant heart. It has a complex circulatory system to control the change of blood pressure at it's brain when it bends over, and handle the high blood pressure in it's legs. And so on and so forth.
  • im3w1l 5y ago
    Adding to that. There used to be ~150k giraffes (recently their numbers are declining) divided amongst 4 giraffe species. So while the strategy of using a long neck to eat the high leaves is viable, it's not crazy successful. It's not "everything should get in on this".
• Gravityloss 5y ago
  Specialization and competition explain this.
• hypertele-Xii 5y ago
  Science says evolution occurs from random mutations. Because they're random, there's no guarantee that every place with tops of trees evolve animals with long necks to eat them, just as flipping a coin no matter how many times gives no guarantee it ever comes out heads.
  • tobr 5y ago
    I don’t know much about this subject, but logically that seems to only be a good explanation if the long giraffe neck happened in a single lucky coin flip, ie in one mutation in one individual. That goes against my intuition of how evolution works, through many small steps over many, many generations. Happy to be corrected by someone who knows more.
    • naasking 5y ago
      It's an arm's race. A lucky coin flip evolved a longer neck, they survived better and bred more, but then trees evolved to be taller because of the proliferation of longer necks, then a luck coin flip evolved an even longer neck, etc. Long necks aren't the only possible adaptations to get to taller trees though. For instance, tree climbing is another.
      • eurasiantiger 5y ago
        Forests are another (competition for sunlight).
      • tobr 5y ago
        So if it requires multiple lucky coin flips, shouldn’t we expect it to happen in many places, not just in giraffes? What explanatory power does “randomness” really have, then?
        • saalweachter 5y ago
          It depends how common the niche is and how accessible the body-plan is in the genetic search-space. We do see convergent evolution into the "tree" and "crab" body-plans. Dozens of unrelated species have evolved along similar lines, filling similar niches by evolving similar forms. There were long-necked animals in the past -- a well-known type of dinosaur comes to mind -- but the niche isn't so large or successful as the "eat a bunch of grass" niche.
• high_byte 5y ago
  but you aren't surprised eyes evolved separately multiple times? limbs come in even numbers (or at least symmetric if you count tails - but you don't see many 3-tailed animals) you took the one example there is only one of. besides it isn't necessarily AI decisions. these are statistical results, e.g. showing you which parameters have strong correlations. (some would argue this is all there is to AI)
  • EamonnMR 5y ago
    We have even numbered legs because we all descended from a bilaterally symmetrical common ancestor and apparently bilateral symmetry is really good for building mobile animals of all descriptions. What's really wild is how Starfish manage to develop out of an initially symmetrical larva.
    • high_byte 5y ago
      I just love how hacker news threads can randomly lead you to wildly different conversations topics :D I would've never learned about this stuff otherwise but aren't starfish symmetrical either way? in the same way we have a head, but our head is also symmetric to itself. actually they have 5x symmetries :) would this be considered rotational invariance? idk
      • EamonnMR 5y ago
        Radial symmetry is the term usually used for animals, yeah. It's just nuts that they switch body plans so radically.
• deleted 5y ago
  [deleted]
• tetromino_ 5y ago
  Longer necks for eating high leaves did evolve in other species! Think of all the sauropod dinosaurs, or more recently, of the giant herbivorous long-necked birds (elephant birds, moas) that humanity drove to extinction. So the question is rather why so few mammals evolved long necks. Maybe there's something about mammalian physiology that makes long neck mutations usually unviable?
• dalbasal 5y ago
  Wonderful analogy. I'm keeping it. That said, this is not totally unlike many human explanations. In fact, increasingly, if a decision can be explained entirely as a product of decision making policies, it's likely to have been automated already. Eg. If I ask you why you chose to give this analogy, you will probably be able to provide a plausible explanation. But, that explanation is probably constructed after the fact. What we decide to say or write "comes to us" and we don't really have access to how that works. This is also true of judge's rulings, for example. They refer to rules (laws, precedents and legal concepts), and rulings must be justifiable in these terms... but the giraffe metaphor still plays. I suppose this is equivocation in a sense, but I'm not arguing that human and AI decision making are equivalents whether or not they share this core feature.
  • TeMPOraL 5y ago
    This looks very similar to the argument that there's no problem with black-box DNN-based algorithms making important decisions about people, because humans are also black boxes and their explanations are just after-the-fact rationalizations. To which I counter: yes, humans are black boxes too. But they're all similar black boxes. We all share the same brain architecture, the same firmware. We have specialized hardware in our brains to simulate each other, predict other people's reactions. This is what allows us to work in groups and form societies. Additionally, as societies, we've learned to force predictability on each other. In the past, high-variance individuals - ones that are too unpredictable - were shunned or outright killed. Today, we isolate most severe cases from society entirely, by putting them in prisons or mental hospitals, and isolate less severe cases from high-impact responsibilities - e.g. by gating pilot licenses, driver licenses and gun permits behind various kinds of physical and mental health evaluations. For the day-to-day needs, humans are very predictable indeed, and their explanations are good enough. We know how to compensate for unreliable introspection. That's not the case with modern machine learning - the way DNN models "think" is completely alien to us. We can't work with this. We also don't have to, those algorithms are our creations. We can make them explainable. And we need to.
    • dalbasal 5y ago
      As I said, while I'm technically equivocating (you too), I am not really saying that there are no reasons to be skeptical of black box algorithms. We agree on this, I think. That said, I'm not sure that we can make algorithms explainable. We may get better at interrogating them, and the analogy to human "black boxes" may get spookier, but I suspect that they're unexplainable in a "hard" sense. An explainable algorithm would have made different decisions, and probably won't be as good at making them.
• spywaregorilla 5y ago
  This is not accurate for evolution or AI. For evolution, not all animals evolved long necks because evolution is a random search on a large, constantly changing space. Giraffes are also not the only animals with long necks. We have no reason to believe no other animals will evolve long necks in the future either. For AI, stating that we throw out 99.9% of the information is wrong or misguided depending on interpretation. A lot of people are fixated on ML 101 belief that models are black boxes and impossible to unravel. This really isn't true, and significant progress on explainability tooling has been made in the past several years. Most of which (by which I mean none that I'm aware of) require changing the underlying AI model. 2 examples: 1) The well known 20 questions genie, akinator. Not really ML, but works similarly to decision trees. You have a space of information. The underlying tree model learns what the most relevant questions are to divide the space to optimize entropy for information gain. 100% explainable. Surprisingly accurate for many people. https://en.akinator.com/ https://en.akinator.com/ 2) Shap values for AI models are one example of tooling to make ai models more interpretable. They show you which features mattered for a single prediction, and by how much in which direction. If we imagine a decision tree again, we can assume that this bears a strong correlation to the path followed through the tree and the associated information gain at each step. If we imagine a complex image recognition neural net, we can highlight which pixels in an image contributed to the outcome the most. It wouldn't be terribly surprising if shap is the tool being used by google here specifically. Focusing on a few important features rather than considering every single is ideal. If I, for example, give you an extensive fecal analysis, psychological, infared, radiological, psychic medium prediction, and survey results from 5 normal people who got to look at the animal- and I asked you to identify if it was a cow or not... It would be appropriate to just say "survey said cow, so its a cow".
• codedokode 5y ago
  You are assuming that search ranking is determined solely by an intelligent AI. This is not how search engines work. Search ranking is determined by people that decide that sites like A must be ranked higher than sites like B, update the rules and then maybe AI is somehow used to apply those rules made by humans. So it shouldn't be difficult to explain why the site is ranked higher. But I don't think Google will ever disclose that secrets.
• Taylor_OD 5y ago
  Damn. I've read the Hyperion books a dozen or more times over the past 10 years. I couldnt have pulled this quote out but remember it now that you brought it up. Do you have a great memory or have you read them a lot?
• zwkrt 5y ago
  I guess I didn’t expect my comment to be so popular, and I was kind of tired when I wrote it, it was 2 in the morning. I guess my point was not anything really about complexity (or the evolution of giraffes as HN seems to be very excited to correct me about). My point was that an AI giving an explanation for its output will almost certainly not explain how the model was generated or trained, because that is the secret sauce. It can only give an explanation from within its own domain of knowledge, unlike a human who can make an informed decision based on facts and then explain it from a higher-level viewpoint, or from the viewpoint of the person asking for the explanation. For a concrete example, imagine an AI for determining if a given video is of someone shoplifting. I give it a video and it says “yes, suspicious hand movements (78%), avoiding staff (65%), change of clothing (45%).” Here the AI has said why it thinks shoplifting is occurring, and a savvy user could guess what types of videos the AI was trained on. Buuuut, we are left wondering all sorts of important questions. How was the training data collected? Did it represent a diverse set of shoplifters and patrons? What movements are considered suspicious? What constitutes avoidance? What is the false positive rate of the AI? What was the motive of the people creating the AI… So to go back to my initial analogy, The AI can explain its internal process of coming to a decision, but it cannot explain the human environment in which it was created. In the same way, we can use science to construct useful narratives for “why” specific features of an animal fit with its environment, but we fail miserably to explain “why” the entire environment came to be.