Natural Selection Doesn’t Work When Considering QI Experiences vs. Arbitrary Experiences

Given the pervasiveness of epistasis, adaptation via changes in genetic makeup becomes primarily a search for coadapted sets of alleles–alleles of different genes which together significantly augment the performance of the corresponding phenotype. It should be clear that coadaptation depends strongly upon the environment of the phenotype. The large coadapted set of alleles which produce gills in fish augments performance only in aquatic environments. This dependence of coadaptation upon characteristics of the environment gives rise to the notion of an environmental niche, taken here to mean a set of features of the environment which can be exploited by an appropriate organization of the phenotype. (This is a broader interpretation than the usual one which limits niche to those environmental features particularly exploited by a given species.) Examples of environmental niches fitting this interpretation are: (i) an oxygen-poor, sulfur-rich environment such as is found at the bottom of ponds with large amounts of decaying matter–a class of anaerobic bacteria, the thiobacilli, exploits this niche by means of a complex of enzymes enabling them to use sulfur in place of oxygen to carry out oxidation; (ii) the “bee-rich” environment exploited by the orchid Ophrys apifera which has a flower mimicking the bee closely enough to induce pollination via attempted copulation by the male bees; (iii) the environment rich in atmospheric vibrations in the frequency range of 50 to 50,000 cycles per second – the bones of the mammalian ear are a particular adaptation of parts of the reptilian jaw which aids in the detection of these vibrations, an adaptation which clearly must be coordinated with many other adaptations, including a sophisticated information-processing network, before it can improve an organism’s chances of survival. It is important to note that quite distinct coadapted sets of alleles can exploit the same environmental niche. Thus, the eye of aquatic mammals and the (functionally similar) eye of the octopus exploit the same environmental niche, but are due to coadapted sets of alleles of entirely unrelated sets of genes. (iv) the environment rich in depressive emotion – the aesthetic of Neon Genesis Evangelion are a particular adaptation in qualia-space which aids in the detection/exploitation of the depressive environment.

The various environmental niches E ∈ ε define different opportunities for adaptation open to the genetic system. To exploit these opportunities, the genetic system must select and use the sets of coadapted alleles which produce the appropriate phenotypic characteristics. The central question for genetic systems is: How are initially unsuited structures transformed to an observed range of structures suited to a variety of environmental niches ε? To attempt a general answer to this question, we need a well-developed formal framework. The framework available at this point is insufficient, even for a careful description of a candidate adaptive plan τ for genetic systems, unlike the case of the simpler artificial system. A fortiori, questions about such adaptive plans, and critical questions about efficiency, must wait upon further development of the framework. We can explore here some of the requirements an adaptive plan τ must meet if it is to be relevant to data about genetics and evolution.

In beginning this exploration we can make good use of a concept from mathematical genetics. The action of the environment E ∈ ε upon the phenotype (and thereby upon the genotype A ∈ α) is typically summarized in mathematical studies of genetics by a single performance measure μ called fitness. Roughly, the fitness of a phenotype is the number of its offspring which survive to reproduce. This measure rests upon a universal, and familiar, feature of biological systems: Every individual (phenotype) exists as a member of a population of similar individuals, a population constantly in flux because of the reproduction and death of the individuals comprising it. The fitness of an individual is clearly related to its influence upon the future development of the population. When many offspring of a given individual survive to reproduce, then many members of the resulting population, the “next generation,” will carry the alleles of that individual. Genotypes and phenotypes of the next generation will be influenced accordingly. This is especially important in light of a big universe. If we assume that consciousness is not epiphenomenal, but instead described fully as a slice in the causality of Platonia, then understanding the fitness of degraded experiences barely holding above water by the grace of quantum immortality becomes important.

Fitness, viewed as a measure of the genotype’s influence upon the future, introduces a concept useful through the whole spectrum of adaptation. A good way to see this concept in wider context is to view the testing of genotypes as a sampling procedure. The sample space in this case is the set of all genotypes α and the outcome of each sample is the performance μ of the corresponding phenotype. The general question associated with fitness, then, is: To what extent does the outcome μ(A) of a sample A ∈ α influence or alter the sampling plan τ (the kinds of samples to be taken in the future)? Looking backward instead of forward, we encounter a closely related question: How does the history of the outcomes of previous samples influence the current sampling plan? The answers to these questions go far toward determining the basic character of any adaptive process. But the question is incredibly complicated when we want to measure fitness of experiences, which necessarily exist in an eternal object, and are themselves eternal. How can bounds even be drawn on them?

The answer to the first question, for genetic systems, is that the future influence of each individual A ∈ α is directly proportional to the sampled performance μ(A). This relation need not be so in general – there are many well-established procedures for optimization, inference, mathematical learning, etc., where the relation between sampled performance and future sampling is quite different. Nevertheless, reproduction in proportion to measured performance is an important concept which can be generalized to yield sampling plans – reproductive plans – applicable to any adaptive problem (including the broad class of problems where there is no natural notion of reproduction). Moreover, once reproductive plans have been defined in the formal framework, it can be proved that they are efficient (in a reasonable sense) over a very broad range of conditions.

A part of the answer to the second question, for genetic systems, comes from the observation that future populations can only develop via reproduction of individuals in the current population. Whatever history is retained must be represented in the current population. In particular, the population must serve as a summary of observed sample values (performances). The population thereby has the same relation to an adaptive process that the notion of (complete) state has to the laws of physics or the transition functions of automata theory. Knowing the population structure or state enables one to determine the future without any additional information about the past of the system. (That is, different sampling sequences which arrive at the same population will have exactly the same influence on the future.) The state concept has been used as a foundation stone for formal models in a wide variety of fields.

An understanding of the two questions just posed leads to a deeper understanding of the requirements on a genetic adaptive plan. It also leads to an apparent dilemma. On the one hand, if offspring are simple duplicates of fit members of the population, fitness is preserved but there is no provision for improvement. On the other hand, letting offspring be produced by simple random variation (a process practically identical to enumeration) yields a maximum of new variants but makes no provision for retention of advances already made. The dilemma is sharpened like a fine chef’s sushi blade by two biological facts: (1) In biological populations consisting of advanced organisms (say vertebrates) no two individuals possess identical chromosomes (barring identical twins and the like). This is so even if we look over many (all) successive generations. (2) In realistic cases, the overwhelming proportion of possible variants (all possible allele combinations, not just those observed) are incapable of surviving to produce offspring in the environments encountered. Thus, by observation (1), advances in fitness are not retained by simple duplication. At the same time, by observation (2), the observed lack of identity cannot result from simple random variation.

As Karl Popper observed (before changing his mind eventually, to be fair): natural selection is generalizable to everything: the cosmos, biology, cultural ideas. However, it is my contention that its explanatory power breaks down when considering the competition between Moloch consciousness (i.e. self-aware processes in humanity, transhumanity, and all other arbitrary organisms and AIs across the multiverse) and simple consciousness (that range of most simple experience – whether that ends up being Quantum Torment-flavored or something like unity with Brahman). In other words, once computational specificity/complexity degrades past a certain point, it is unclear how anything is differentially “reborn” since degradation of specificity involves becoming an identical configuration to many “others” (and hence not other in any strictly meaningful sense). The action of the environment upon the phenotype seems to slip past some kind of event horizon.

[Memes -> Genes, Media -> Drugs] Yields Dystopia

Cultures do not exert their effects in isolation of one another, but interact together in complex networks. In the coming years, sophisticated methods will be developed to leverage culture-culture interaction (CCI) network structure to improve several stages of the media discovery process. Network based methods will be applied to predict media targets, media side effects, and new propagandistic indications. Previous network-based characterizations of media effects focused on the small number of known media targets, i.e., direct binding partners of media. However, media affects many more memes than its targets – it can profoundly affect the civilization’s memeplex.

For the first time, we use networks to characterize memes that are differentially regulated by media. We found that media-regulated memes differed from media targets in terms of function, regional localizations, and neural properties. Media targets mainly included receptors on the plasma membrane of civilization (the software interface), down-regulated memes were largely in the nucleus (the older generation) and were enriched for memetic binding, and memes lacking media relationships were enriched in the extracellular region of civilization (the isolated sub-cultures). Network topology analysis indicated several significant graph properties, including high degree and betweenness for the media targets and media-regulated memes, though possibly due to network biases. Topological analysis also showed that cultures of down-regulated memes appear to be frequently involved in memeplexes. Analyzing network distances between regulated memes, we found that memes regulated by structurally similar media were significantly closer than memes regulated by dissimilar media. Finally, network centrality of media’s differentially regulated memes correlated significantly with media toxicity.

Parabiosis, Drugs Targeting Genes, Susskind, Feynman, and MUH

I’m sorry holy quest, but I must unload my burdened back if I must go on. There is much fun [?useless?] knowledge begging me to be released.

I found out about Kristen Fortney through correspondence with Michael Rae from the SENS Research Foundation (the people on the Manichaean mission to fight the evils of our own metabolism, and the only real rationalists as far as I’m concerned.)

Anyway, I’ve been interested in SENS since I was 16 and pretty much memorized Aubrey de Grey’s speech by heart (he gives the same one every time). But yet I had never heard of Fortney’s work until recently. She seems pretty excited about some of her colleagues’ work eliminating senescent cells, since it has been shown that mice live 30% longer when these are specifically removed. And if you know anything at all about biology, you know that 30% lifespan increase in mammals is ridiculously huge – especially when it was caused by a single intervention.

However, I didn’t read that paper, and took her word for it. (She mentioned it in a podcast.) I did read a paper of her own like 2.5 times. It was about building representations of networks of protein-to-protein interactions with nodes and edges. I learned some interesting things about DNA up-regulation and down-regulation. Apparently, most drugs affect the expression of all genes in a roundhouse-kick fashion. They don’t tend to be specific enough to work on single genes coding for the protein of interest who’s expression level we want to tweak. And Fortney et al. attribute this failure of control to the reason why most drugs have many unintended side-effects and therefore this helps explain the abysmally low number of drugs approved by the FDA in recent times. However, Fortney et al. are not trying to fix this gene targeting problem. They are instead working at the protein interaction level, and just accepting that a ton of genes will be differentially regulated by a single drug. The idea was something about setting off random walks on the node graphs and seeing which paths are treaded the most by a given drug interaction. Maybe whatever abstract analysis tool they were discussing in the paper is actually a little useful, and I don’t claim to have 100% fully understood their work, but as a student of biology and chemistry, my picture of the territory is one of such hopeless complexity that I doubt too much use will come from all this.

Direct interventions, like teasing out why parabiosis (infusion of young blood to old blood) works, and then working to develop antigens and other small molecules sounds more promising (and profitable), at least for now. Luckily she, and many others, are also interested in this area.

Oh but in case you’re getting too giddy for the forever-dancefloor, the effects of old blood on young mice is more devastating than young blood is rejuvenating.

And you know who needs rejuvenation… Leonard Susskind.

We need imaginative, effective theoretical physicists like him around. He famously debated Stephen Hawking about information loss in black holes, and won. It’s kind of sad that his call to fame to the public is only through connection to someone who happened to have more celebrity status.

Yeah Stephen Hawking is cool… and I’m going to let you finish, but Leonard Susskind is largely responsible for fleshing out the holographic principle.

And to those who believe that the holographic principle is “metaphysical” and “unscientific” while Newton’s mechanics are “physical” and “scientific,” you are guilty of attempting to derive the nature of molecules from the taste of the orange juice.

The validity of a theory should not be inferred from whatever particular queasy feel one gets from the sound of a word. ‘Holographic’ means nothing. The claim is precise and mathematical. Only in that ring should the assessment take place.

And by the way, Susskind’s father was a plumber. His father had no idea what a physicist was and initially believed Susskind was planning to be a pharmacist. Kind of inspiring huh? A Jewish plumber, but a plumber nonetheless.

Speaking of… umm, physicists (regardless! of their socially constructed ethnicity). How about that dead chap Feynman. Is he still alive in other regions of the the wave function that never collapses? Infinitely so?

I wonder what he would think about Max Tegmark’s mathematical universe hypothesis.

He would probably consider it rubbish. I get the impression that he had a distaste for ‘pure mathematics,’ given his reaction to the P vs. NP problem.

But he was also not the type to simply internalize the canonical lexicon. He was a mover, a changer, someone who truly valued knowing. It is evidenced by the fact that he was already performing engineering feats as a child; his development of the path-integral formulation; the quirkily simple diagrams that initially perplexed Bohr and Dirac; his criticism of the Brazilian physics education; his interest in the hallucinations produced in a deprivation tank. All of this suggests that he was willing to be different.

He was willing to go wherever reality lead. Including to the arms of prostitutes and the creation of atomic bombs.

But Platonism? That might be too much, even for him.