A Peek into the Demoness’s Mind, (or: Yes, I Actually *Do* Use Logic to Make Predictions)
“Anyone taken as an individual is tolerably sensible and reasonable—as a member of a crowd he at once becomes a blockhead.”
– Friedrich Schiller as quoted by Bernard Baruch as quoted by Robert Silverberg.
I like this quote because it in a sense sums up the topic I am writing about today, how I come to the predictions I do. Ben Goertzel suggested this article after a discussion we had about what factors we look at when we are making an analysis, and I gave him this example about my article on the possibility of gender change in 10 years, a topic on which it has been claimed I am making a “wildly optimistic prediction”:
(Ben) In defense of his arguments though, I think he’s right that very few biologists would agree that complete gender change (even by your definition) is going to be possible within 9 years. But of course, just because the majority of biologists say something, doesn’t mean they’re right…
(Val) I agree too. If you *only* look at the technological development itself he’s very close to the consensus opinion and the pretty generic “cover your ass” (CRA) predictions they tend to make in which 20 years means “It will occur in the next decade most likely, but if I say that, my professional career could be over if any unexpected delays occur, so I’ll play it safe and say two decades”. The problem is that *I’M* not looking at JUST the technological development cycle, because there are other factors involved.
So, how does that actually tie into the opening quote? Because it points out a problem with accepting the “Main Stream’s” predictions at face value.
The “mainstreams” predictions are those of “the crowd”.
Let me go back to the actual point that was under discussion. In my article “Total Gender Change in a Decade” I discussed the possibility that functional gender change, one in which the reproductive ability of the opposite gender could be given to any individual, whether genetic sex reversal was possible or not, could be possible inside of ten years, or by 2020. As Ben correctly points out, this is far sooner than any biologist would agree is likely. There are indeed a lot of science that must be learned to make it possible. I dispute none of the arguments raised by anyone about its difficulty, or about how far away from it we are.
However, let’s look at this as I see it. The “predictions” under discussion involve a certain formula. Technology X is predicted by Expert Y to be Z years away. Said prediction is based on expert y’s “in depth knowledge”. It seems simple enough, no? We accept that the expert is far more likely to know his own field better than we do, because he’s “The Expert.”
But there are a number of hidden factors that most people will never even consider when accepting this prediction. I covered the whole CYA thing above, but even if you discount that, there are numerous others that need to be taken into account.
The first is the rather typical hidden assumption that research levels into a given technology will remain constant. It assumes that progress towards a technology will remain at a nice linear level, with X progress taking Y time given Z funding. Note the algebra variables there. That should be your first hint that there’s a problem with the assumption. Change any of those variables and it affects the other two. If funding goes up, progress is generally going to proceed faster, reducing the time to development. If something accelerates progress other than funding, again, time to development will be reduced. If both funding and external factors produce increased progress, the time to development becomes even less.
So, let’s correct our prediction: Technology X is predicted by Expert Y to be Z years away (given unchanged levels of funding, and barring any developments that could speed progress).
And I’ll be fair, neither of those factors are within the “knowledge sphere” of “Expert Y.” But they are things that could be researched. However, to do so, you must remain aware of numerous “biases” which tend to creep into attempts to do so. One bias I get accused of frequently is “confirmation bias” — the tendency all humans have to accept data that confirms their world view and reject data that challenges it. This bias is a hard one to avoid, but being aware of it means an effort can be made to look impartially at all evidence. That leads to another bias to watch out for — not looking at all the evidence. A lot of “experts” are only looking at their own field. They are completely ignoring any developments in any field not directly related to theirs, and are thus ignoring any impact that another field could have on their own. This means that they are not looking at all the evidence, merely the evidence within their particular knowledge field. This “information bias” therefore means many predictions are made with extremely limited data and with no consideration for factors outside the very narrow niche in which “Expert Y” has his expertise.
Both of these biases can be minimized by increasing the sphere of data that is researched, examining a wide range of data with an eye to potential effects it could have on the development of the particular technology in question, but it’s at this point that the overwhelming majority of futurists run into yet another bias, the “consensus” bias, the desire to make sure that your research will be acceptable to the “crowd”, to limit your evidence to the “center” and ignore the “fringe”, and discount any evidence that would be considered controversial. By limiting the data examined to “safe” data through the use of such filters, the “prediction” arrived at will generally not present a “threat” to the “reputation” of the predictor, even if it proves wrong.
So yes, when “Expert Y” makes a prediction, it’s most likely suffered from one or more of these various biases, and is therefore likely to be fairly inaccurate, but sound reassuringly plausible. So, to get back to the predictions about gender change, yes, a ten year time frame is far sooner than the “experts” predict, because the experts have based their predictions on different assumptions than I have, and because they have ignored many factors that I am not.
One of those factors is the exponential increase in computing power that is still underway. As the Human Genome Project proved, the speed of research at the present moment cannot be assumed to either remain static, or to increase in a linear fashion. As each generation of computers advances, they enable somewhat unpredictable increases in the ability to do basic research, process data, find new connections, and even reexamine previous research to gain new insights. When you add these factors up with the advances in both quantum computing and the probable switch to graphene based computers capable of running at nearly 1,000 times current processing speed, it should be pretty obvious that while a precise figure cannot be given, you can likely apply a similar progression to Moore’s law to the ability to do research over a given time frame.
At the same time, you can’t simply count increasing computer power as enough to ensure an exponential rate of advancement; after all, we’ve mapped the genome, but still don’t understand it well enough to use it to create such breakthroughs as were originally envisioned. Just having a mountain of data doesn’t mean we’ll understand the data. However, at the same time as we are increasing the power of our computers, we’re also increasing the ability of our tools to do basic research. For example, we’re developing new “lab on a chip” devices that allow massively faster analysis of biological samples. We’re developing tools like “TinkerCell”, a DNA CAD system for designing artificial genes. We’re creating robots able to conduct trial and error experiments and draw conclusions from them. We’re improving AI systems which can analyze massive data sets and discover the patterns in them. Each new development in technologies outside of “biology” enables the creation of better tools in “Biology”, tools that often times the “Experts” are completely unaware of due to their focus only on their own field.
Next, you have to look at a completely different factor, the social one. Most experts generally fail to take the social factor into account, which is a major failing, because society is one of the drivers behind funding. If a project has little social support, it’s likely to be crippled by delays, lack of funding, difficulty getting aid from fellow scientists, etc. But if the public has a demand for what it believes a project offers? In the case of gender reassignment, it’s obvious that at least one sector of the public has great interest in the subject, but there’s more going on here than just reassignment. Already, stem cell research into the field of cosmetic surgery is taking place, with the “rabbit penis” and the “breast enhancement” representing the two biggest players, woman who want bigger breasts but would prefer not to have silicon, and men wanting bigger members. Considering the sales of “male enhancement” drugs and devices, can you really think that enormous amounts of funding will not pour into stem cell research just to enable human males to compete with horses? The “for profit” medical industry will be increasingly into finding treatments to “enhance sexual beauty.” And that funding will drive greater numbers of scientists into studying stem cell based treatments, and greater resources being applied to stem cell research over the next decade.
There’s another social factor involved as well, one that even those “experts” who’ve taken the probable increase in funding for “male enhancement” into account would generally be likely to dismiss. And that’s the dissatisfaction most humans feel with their physical appearance, be it a dislike of the size of their sexual organs, all the way up to the ones who have no wish to even look “human.” Already on the internet, you find people from all walks of life creating “Avatars” which look nothing like them in person. Among those various computer advances, one of them is going to be “omnipresent VR” or VR that moves with the person, so that their environment is equal parts “Reality” and “Virtuality.” This will give the human race access to their “Avatars” everywhere, not just “on the net.” For almost everyone, this is going to mean the ability to exist in “cyber-reality” (the merged “worldspace” of VR, AR, and IRL) as your “idealized self,” be that self merely a perfected version of yourself, to the kind of extreme make-over I plan on to become a succubus. This will in turn likely lead to a demand from the public to make these “idealized selves” REAL, so that turning off the “VR” won’t lead to crushing disappointment as we look into our mirrors. Vanity is such a human emotion after all.
So not only will the desire to enhance sexuality fund research into understanding gender at the most basic levels in order to enable further enhancements, there’s going to be a significant fraction of the human race funding research into changing the human body in nearly every way, from adding points on your ears, to giving us tails, to making changes that would make a simple gender change seem minor. Increase in funding equals increase in resources dedicated to funding, more people researching, more advanced tools being developed, and thus, an overall reduction in the prediction time frame. And as these “desires” become increasingly available, you also have the marketing of these to the public to consider, and how that commercialization will also increase funding in a self improving cycle.
Add these factors into the “computer advancements” discussed above, and what you basically have is a very strong likelihood of “Moore’s Law” type doubling in biological knowledge, ability to manipulate the body, and understanding of how the entire human “machine” works. This means that we’re unlikely to see much progress in the first five years, with lots of people continuing to make the kind of “safe” predictions we have floating around now, with lots of “consensus of opinion” on what’s going to be possible when, and then a very rapid escalation in ability with numerous breakthroughs leading to even more breakthroughs which lead to more breakthroughs, etc.
So, when I discussed the possibility of “Total Gender Change in a Decade” I was taking all these factors into account in addition to the rather limited ones discussed in the article itself. That doesn’t mean I’m any more likely to be right than they are, merely that in my personal opinion, I believe I have taken a far more realistic view of the entire body of evidence than the majority, and have thus arrived at different conclusions than they have. It’s still little more than a guess, and just like all predictions, will likely be proven inaccurate in one way or another. But I am not making “wild speculation”, nor indulging in “wishful thinking,” and “quasi-religious beliefs.” I am basing my conclusions in rational analysis of available data, and an understanding of the interconnections between them. Your agreement with my analysis is meaningless, I’m simply reporting what I see the data saying.
Hope that clarifies things a little, and maybe helps you see a little more of the forest that the trees exist in.
Valkyrie Ice has been observing the development of technology for almost 22 years.