ChatGPT4 refines my argument about fish being bad swimming instructors

I finally splurged for a paid ChatGPT subscription! With access to GPT-4, I asked it to refine my earlier argument about fish being bad swimming instructors. Here's what it gave me:

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Certainly, let's rework the argument using more scientifically grounded explanations and references:

While it's true that fish, with their adept aquatic abilities, may seem like logical swimming instructors for humans at first glance, upon closer examination, it becomes clear that this is far from practical or effective. Even if we put aside the substantial communication and physical barriers, there are fundamental biological and behavioral differences between fish and humans that pose significant challenges.

Humans are terrestrial, bipedal mammals, and our motor skills and locomotive mechanisms are naturally adapted to this lifestyle. Fish, in contrast, are aquatic creatures designed to maneuver efficiently in water (Webb, P.W., 1982, "Locomotor patterns in the evolution of actinopterygian fishes"). Their ability to swim is an innate behavior, hardwired into their neural circuitry, not a learned skill (Gould, J.L., 1982, "Ethology: The Mechanisms and Evolution of Behavior"). Given this, fish cannot consciously comprehend or articulate the mechanics of their swimming abilities as humans would require in a teaching-learning scenario.

In the field of pedagogy, it is well accepted that the process of teaching involves more than simply demonstrating a skill; it requires an understanding of learning styles, the ability to give and receive feedback, and a degree of self-awareness in the teacher (Boud, D., et al., 1985, "Reflection: Turning Experience into Learning"). Fish, as far as current scientific consensus suggests, lack these cognitive capacities (Butler, A.B., et al., 2005, "Evolution of the Neural Basis of Consciousness: A Bird-Mammal Comparison").

Furthermore, many human skills, including swimming, are learned through a combination of instruction, practice, and feedback (Kirschner, P.A., et al., 2006, "Why Minimal Guidance During Instruction Does Not Work"). This model of learning doesn't align with the unconscious, reflex-driven behaviors seen in fish.

Lastly, it's important to acknowledge the vast physiological differences between humans and fish. Human limbs and fish fins have evolved for different purposes and environments, with humans developing more for grasping and manipulation and fish for efficient aquatic locomotion (Thorsen, D.H., et al., 2004, "Biomechanics of Locomotion in Asian Carp"). These differences are fundamental and cannot be bridged by simple observation or imitation.

In conclusion, despite their swimming prowess, fish cannot be effective swimming instructors for humans due to biological, behavioral, and cognitive differences, as well as practical communication and anatomical obstacles. This argument does not discount the value of observing aquatic creatures to improve our understanding of efficient movement in water, but it's clear that such observation is a far cry from active instruction.

When you can't go back to sleep: thermodynamics

Did you miss my posting? Well, it's been one of those days when you wake up early and can't go back to sleep. My mind's running wild this morning, and I figured I'd make some good use out of it. (It is no longer early as of completing this post because of interruptions from a playful kitty.)

I'm going to continue the lesson about thermodynamics that last left off about a year ago, by discussing some more interesting implications of the idea that "energy per unit temperature" is a measure of degrees of freedom. You see, in the time since then, I read John S. Avery's book Information Theory and Evolution, which, as you might have inferred, discusses life from the perspective of that ever-so-useful field of information theory. He also applies it to cultural (often called "memetic") evolution.

The first interesting insight that this book alerted me to is about molar entropy. Some background: in your chemistry class, you might have learned about the Gibbs free energy of a reaction, ΔG, which is calculated from ΔH - TΔS, where H is the molar enthalpy (internal + flow energy per mole), T is absolute temperature, and S is the molar entropy. For a chemical reaction, you look up the molar enthalpies of the products and subtract off the enthalpies of the reactants. Then you do the same for molar entropies, multiplying by the absolute temperature at which the reaction takes place, and add them. A negative sign for ΔG means the reaction happens spontaneously (well, as long as there is an available pathway).

With that out of the way, what are the units for S? Most tables give them as J/K*mol (Joules per Kelvin per mole, or energy per unit temperature per quantity of molecules). But, as the last post in this series showed, energy per unit temperature measures degrees of freedom, which can also be expressed in bits. So, as Avery neatly derives on pages 81-82, you can also express molar entropy in bits per molecule. (The conversion factor is 1 J/K*mol = 0.1735 bits/molecule.) I find this a much more intuitive way to think about it, because it connects the concept of molar entropy to the underlying dynamic: how many bits of information (on average) do you need to specify a molecule's current state, beyond that which you know from the temperature?

Also, rather than having to empirically derive this value directly (either from reaction data or by integrating its specific heat capacity per unit temperature from 0 K to its current temperature), it can be inferred from the known properties of the molecule: its shape, size, and bond strength. The stronger ("stiffer") its bonds are, the lower the entropy of the molecule, because large deviations from its equilibrium configuration are less probable. (Diamond, with its very strong covalent bonds, has the incredibly low molar entropy of 0.24 bits per carbon atom at STP, meaning you need less than one bit of information to specify every four atoms.)

[ADDENDUM: Avery also adds that if you divide the Gibbs equation through by T, you can describe a reaction in terms of the "information lost", i.e., the greater number of degrees of freedom you have permitted by letting the reaction take place.]

By recognizing this interconnection between molecule properties and complexity (needing more information to fully specify = more complex), one sees more unity ("consilience") to the science as a whole: entropy and bond properties aren't just off in their own domains, but have a lawful relationship. Unfortunately, however, I haven't worked out how to derive entropy from stiffness of a degree of freedom, and I haven't found a text that does it either.

Next in the series: A discussion of Eric J. Chaisson's Cosmic Evolution: The Rise of Complexity in Nature, which proposes specific energy flux (energy flow through a system per unit mass) as a measure of complexity that is applicable to everything from stars to planets to life to vehicles to computer chips to culture.

Silas Barta, information theorist by night

UPDATE 12/17/09: Steven Landsburg, after responding several times in the comments section here, posts a defense of his position on his blog, although without mentioning me or Bob Murphy. Hey, I can understand: if I were in his position, I'd hide the existence of me and Bob too!

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Bob Murphy invokes my expertise on information theory to criticize (yet) another bizarre argument from Steven Landsburg, that the natural numbers are more complex than human life. Here's the mistaken part of Landsburg's reasoning:

...the most complex thing I’m aware of is the system of natural numbers (0,1,2,3, and all the rest of them) together with the laws of arithmetic ...

If you doubt the complexity of the natural numbers, take note that you can use just a small part of them to encode the entire human genome. That makes the natural numbers more complex than human life. Unless, of course, human beings contain an uncodable essence, like an immortal soul

Naturally, I don't necessarily agree with the broader theological points Bob makes in his reply, and such issues will remain even scarcer on this blog than on his. However, I will expand on point I made in discussion with Bob.

The error in Landsburg's line of reasoning is: the fact that you can use instances of X to build Y does not mean X is more complex than Y. Just the opposite, in fact: in order to describe Y, you must describe X as a substep. Like in the analogy I gave, you can use bricks and mortar to build a house, but that means it's the house that's more complex. To fully specify the house you must describe not only the bricks and mortar, but the form they take as a house -- how they're supposed to be put together.

As for arithmetic and natural numbers, it's their lack of complexity that makes them so useful. By appealing to it, you can make sense of a diverse array of phenomena. The more complex arithmetic were, the less helpful it would be in making sense of things.

Just to be clear, this doesn't mean it's easy to learn math (different people have different problems in different topics and levels), or that you can't do anything complex with math. The point is that no amount of complexity produced in using arithmetic could ever imply arithmetic's complexity, for the same reason that no matter how complex a house you make with one kind of brick, you can't make the brick more complex.

But of course, Landsburg's errors don't end there. He wants to go so far as to say that by merely encoding the genome in base 4, you've described human life. That's certainly the impression people get from discussions of DNA in the popular media and movies like Jurassic Park. Hey, all you need is a string of letters made up of A,G,C,T, and you've described someone completely!

To put it mildly: that's not how it works. First of all, you need to say what the letters actually mean. And then, even if you know that much, all you have are empty labels -- suggestively named LISP tokens. So you know that C is cytosine? Okay, but what's that? Now you need to describe where the carbons and nitrogens and oxygens go to make up cytosine. But wait -- what's this "nitrogen" thing, anyway? And so on.

Don't worry -- the process terminates: once you've described the generative model that puts all of these concepts together in a way that yields a description of human life as its output.

Needless to say, you're using more than a few integers by that point!