Information theory and FOOM

Doesn't information have to be about something? Bits are not inherently powerful... proteins are about structure but they do not inherently win you any evolutionary races. I'd contend that there is a lot more information about how to survive in which proteins are in the genome and when they are transcribed.

You seem to mixing up bits needed to replicate the genome with bits of information gained about the outside world and how to survive in it.

Edit: To give you an example of the difference. Consider a standard computer program that does something useful in the world. If I take the machine code and chop it up and rearrange the segments then you are not likely to get a program that does anything useful. Yet it has the same number of bits (even likely similar or more complexity) as the original program.

The idea that pace of discovery slowed down is an extremely common and really obvious fallacy.

We only know that discovery was important after it gets widely implemented, what happens decades after invention. Yet, we count it as happening not at implementation time, but at invention time. So recent discoveries that will be implemented in the future are not counted at all, artificially lowering our discovery importance counts.

Also if you use silly measures like railroad tracks per person, or max land mph, you will obviously not see much progress, as large part of the progress is exploring new kinds of activities, not just making old activities more efficient. Any constant criterion like that will underestimate progress.

Re: ""Hard takeoff" means, IMHO, FOOM in less than 6 months."

Nobody ever specifies when the clock is going to start ticking. We already have speech recognition, search oracles, stockmarket wizards, and industrial automation on a massive scale. Machine intelligence has been under construction for at least 60 years - and machines have been taking people's jobs for over 100 years.

If your clock isn't ticking by now, then what exactly are you waiting for?

Here's ungated "Nature of protein universe" paper.

Here's another interesting set of quotes; are we even correct in assuming the most recent percent of DNA matters much? After all, chimps outperform humans in some areas like monkey ladder. From "If a Lion Could Talk":

"Giving a blind person a written IQ test is obviously not a very mean meaningful evaluation of his mental abilities. Yet that is exactly what many cross-species intelligence tests have done. Monkeys, for example, were found not only to learn visual discrimination tasks but to improve over a series of such tasks -- they formed a learning set, a general concept of the problem that betokened a higher cognitive process than a simple association. Rats given the same tasks showed difficulty in mastering the problems and no ability to form a learning set. The obvious conclusion was that monkeys are smarter than rats, a conclusion that was comfortably accepted, as it fit well with our preexisting prejudices about the distribution of general intelligence in nature. But when the rat experiments were repeated, only this time the rats were given the task of discriminating different smells, they learned quickly and showed rapid improvement on subsequent problems, just as the monkeys did.

The problem of motivation is another major confounding variable. Sometimes we may think we are testing an animal's brain when we are only testing its stomach. For example, in a series of studies goldfish never learned to improve their performance when challenged with "reversal" tasks. These are experiments in which an animal is trained to pick one of two alternative stimuli (a black panel versus a white panel, say) in order to obtain a food reward; the correct answer is then switched and the subject has to relearn which one to pick. Rats quickly learned to switch their response when the previously rewarded answer no longer worked. Fish didn't. This certainly fit comfortably with everyone's sense that fish are dumber than rats. But when the experiment was repeated with a different food reward (a paste squirted into the tank right where the fish made its correct choice, as opposed to pellets dropped into the back of the tank), lo and behold the goldfish suddenly did start improving on reversal tasks. Other seemingly fundamental learning differences between fish and rodents likewise vanished when the experiments were redesigned to take into account differences in motivation.

Equalizing motivation is an almost insoluble problem for designers of experiments. Are three goldfish pellets the equivalent of one banana or fifteen bird seeds? How could we even know? We would somehow have to enter into the internal being of different animals to know for sure, and if we could do that we would not need to be devising roundabout experiments to probe their mental processes in the first place.

When we do control for all of the confounding variables that we possibly can, the striking thing about the "pure" cognitive differences that remain is how the similarities in performance between different animals given similar problems vastly outweigh the differences. To be sure, there seems to be little doubt that chimpanzees can learn new associations with a single reinforced trial, and that that is genuinely faster than other mammals or pigeons do it. Monkeys and apes also learn lists faster than pigeons do. Apes and monkeys seem to have a faster and more accurate grasp of numerosity judgments than birds do. The ability to manipulate spatial information appears to be greater in apes than in monkeys.

But again and again experiments have shown that many abilities thought the sole province of "higher" primates can be taught, with patience, to pigeons or other animals. Supposedly superior rhesus monkeys did better than the less advanced cebus monkeys in a visual learning-set problem using colored objects. Then it turned out that the cebus monkeys did better than the rhesus monkeys when gray objects were used. Rats were believed to have superior abilities to pigeons in remembering locations in a radial maze. But after relatively small changes in the procedure and the apparatus, pigeons did just as well.

If such experiments had shown, say, that monkeys can learn lists of forty-five items but pigeons can only learn two, we would probably be convinced that there are some absolute differences in mental machinery between the two species. But the absolute differences are far narrower. Pigeons appear to differ from baboons and people in the way they go about solving problems that involve matching up two images that have been rotated one from the other, but they still get the right answers. They essentially do just as well as monkeys in categorizing slides of birds or fish or other things. Euan Macphail's review of the literature led him to conclude that when it comes to the things that can be honestly called general intelligence, no convincing differences, either qualitative or quantitative, have yet been demonstrated between vertebrate species. While few cognitive researchers would go quite so far -- and in deed we will encounter a number of examples of differences in mental abilities between species that are hard to explain as anything but a fundamental difference in cognitive function -- it is striking how small those differences are, far smaller than "common sense" generally has it. Macphail has suggested that the "no-difference" stance should be taken as a "null hypothesis" in all studies of comparative intelligence; that is, it is an alternative that always has to be considered and ought to be assumed to be the case unless proven otherwise."

EDIT: I've added this and some other points to my Evolutionary drug heuristics article.

In the first of several irresponsible assumptions I'm going to make, let's assume that the information evolved in time t is proportional to i = log(t), while the intelligence evolved is proportional to et = ee^i. I haven't done the math to support those particular functions; but I'm confident that they fit the data better than linear functions would.

This may be covered by the following assumption about 'spurts', but this doesn't seem to work for me.

If intelligence really could jump like that, shouldn't we expect to see that in humans already? For example, shouldn't we expect to see small mutations or genes with outsized effects on intelligence? Instead, we see that even a highly inbred population with many dozens of nasty genetic problems like the Ashkenazi only get 10 or 20 IQ points*, and we see a long-term stagnation in cranial capacity, and genetic surveys seem to (as far as I've heard) turn up hundreds or thousands of genetic variations weakly linked to small IQ increases. (I cover some related points in my article on evolution & drugs.) All of this makes intelligence look like it has a logarithmic relationship with diminishing returns.

* My understanding is that on a hypothetical 'absolute' scale of intelligence, as you get smarter, each IQ point corresponds to less and less 'actual' intelligence, due to the bell curve/relative ranking that IQ is - it's an ordinal scale, not a cardinal scale.

Have you read "The 10,000 Year Explosion"? Cochran & Harpending (and Hawks and some others in the paper its based on) argue that evolution has accelerated recently. The reason is that there is a larger population, so more new mutations to be selected. Also, because our environment is not a steady state our genes don't reach a steady state either (like horseshoe crabs or a number of other species). I've only read a bit past the first chapter, but it would seem relevant to your claim.

What's your justification for the claim that "almost all of the information content of an organism resides in the amino-acid sequence of its domains"?

For your claims about "speed of evolution" to make any sense, it must be the case that we could get rid of the information content which does not reside in these sequences with minimal losses in evolutionary fitness. My guess is that this is not the case, hence your measure of "information" is quite suspect.

Even the concepts involved in this "analysis" seem pretty meaningless, but when you start plugging them into "math" and "exponentials", it results in meaninglessness singularity, that is a point at which all sanity breaks down and woo ensues!

May be relevant, and seems to be consistent with your point: evolution has a speed limit and complexity bound.

I think this post presents a very interesting view of the information explosion. Even the task of self-improvement will undergo an evolution of sorts, and we have no better example to draw from than genetic evolution. We have observed an increasing efficiency of information to directed behavior (intelligence as the article puts it), and it is yet to be seen what the limits of that efficiency may be.

Only one upvote? Really?

Long lived organisms do reproduce and evolve more slowly - though note that evolution still acts on their germ line cells during their lifetime.

However, to jump from there to "evolution has been slowing down in information-theoretic terms" seems like a bit of a wild leap. Bacteria haven't gone away - and they are evolving as fast as ever. How come their evolution is not being counted?

I can't say I care much for the Evolution/Science split in this post.

A more natural split would be between DNA evolution and cultural evolution.

Evolution is best seen as an umbrella term that covers any copying-with-variation-and-selection process - and science is one example of cultural evolution.

Re: "I therefore expect the pace of evolution to suddenly switch from falling, to increasing [...]"

Since the pace of evolution has clearly been increasing recently, this seems like a rather retroactive prediction.

Sadly we here observe a retreat within the simple language of mathematics. I am not decrying mathematics nor am I underestimating the great value of that language in extending knowledge of the physical world by bypassing the complexities and irrelevancies common to the natural languages.

It does, however, suffer from two major weaknesses:

Firstly, like all languages, it is capable of generating fictions - entities and scenarios which have no correspondence wit the real world.

Secondly, it is, like all reasoning or computational processes, raw data sensitive. This is expressed in the venerable IT mantra "Garbage In - Garbage Out"

The second of these is, I believe, the main culprit in generating the conclusions that you affirm. For you seem to have fallen into the same trap as Jonathan Huebner who, using a rather arbitrary criterion of "significant advances" concluded that the rate of innovation has actually been decreasing since a maximum that occurred in 1873. A glance around the shelves of a Chemical Abstracts archive, for instance, will quickly tell a denizen of the real world that something is seriously wrong with that analysis.

Now it is very evident that not all aspects of human activities, even some technologies, are subject to exponential growth, as underlined in your very interesting presentation "The Myth of Accelerating Change" Similarly the evolution of life has not exhibited a consistent acceleration in all its bifurcations and ramifications. The "significant steps" used by Huebner (even after allowing for the strong "self selection" effect involved in that analysis) are merely break points, sharp upheavals resulting from the accumulation of innumerable "baby steps" wherein lies the exponentiality.

The input data for your own analysis lies in the conceptual arbitrariness of "important knowledge" and "useful knowledge". These are the foundation of your argument. If their validity is in question, as I propose it is, then all the arithmetic in the world will not hold it together. Similarly extrapolations of arbitrary data to towers of exponentiality must be considered as pure flights of fancy. GIGO rules!

The great problem with ascribing importance and utility (citation hits certainly won't do) is in determining the entity to which these are relative. Certainly these value judgments will vary enormously between individual humans and consensus is probably out of the question. We can tie it down a little by noticing that a fairly constant gross exponentiality appears to be tied to technology rather than other human activities. A distinction not easy to make as many aspect of social and individual behavior are themselves driven by technological change. Such features as sex, art and religion are among those with some immunity.

One way to escape from this dilemma, however, if we are to properly interpret the patterns science observes in nature, is to learn the trick of stepping outside our (very natural) anthropocentric shell so that objectivity is not compromised. My book "Unusual Perspectives" (the electronic edition of which can be freely downloaded) uses this approach to arrive at the proposition that the evolution of living systems and the evolution of technology (with which it is contiguous) are components of an ongoing natural process. From this point of reference we can perhaps better determine what aspects are "important" or "useful" (to the life process). This stance certainly strongly suggests the advent of what some call a singularity but which I prefer to regard as something akin to a fairly imminent phase transition. I further speculate that that, considering the history of the process and the apparent direction of its vector, that the internet could as the result of an inevitable self-assembly mechanism be the most likely candidate for the next prime effector of the process. "Unusual Perspectives" can be downloaded from the dedicated website: www.unusual-perspectives.net