## 2014 Retrospect

It’s a little odd for a blog with as little content as mine to post a retrospect, but this is as good a place for me to post it as anywhere–certainly, this will get linked on my various social media, and this site encapsulates many of my most important thoughts (insert joke here about lack of content).

To start with, for the few readers I have, there are pages of half completed articles. But I’m dedicated to being accurate and precise with what I write, and that takes both time and energy. While I’d like to merely spew out opinions as fact, I care about trying to present the closest model of reality that I can, which means that I need to do more reading. And every step of reading reveals more–which unfortunately takes time and energy. That being said, I do have some significant content and announcements coming. Trying to figure out what priority and how much time I have for each, but they are coming.

2014 has been an interesting year for me. One year ago, I decided that my venture at the time (Prokalkeo) was going to no longer actively be pursuing contracts. This decision was hard, but the finances weren’t there. I spent the next six months studying and realizing that a good place to take my career would be into business analysis/sales engineering, and was lucky enough to be hired at Logi Analytics as a Sales Engineer/Business Intelligence Consultant. It’s been a wonderful place–I learn something new every day, and have had to stretch my capabilities both technologically and in dealing with customers.

I began building a new framework for modeling technology development, at which point I started this blog (which might have been premature). I made new friends and acquaintances I hope to keep in my life, and reconnected with old ones. I started building software tools to help me the next time I pursue the ideals that Prokalkeo was created in pursuit of.

I’ve done a lot of writing, though much of it wasn’t on this blog unfortunately (for reasons laid out above). I’ve learned more about what matters to me–I won’t say that it’s ‘the simple things’ or that ‘it’s not things that matter’. Don’t worry, I am as committed to my ambitions as ever. But I realized that free speech matters to me strongly, and that truth must be pursued, even sometimes at the cost of renown (part of the reason I have been slow in publishing my models).

2015 is a new year, filled with new ideas and new achievements. I hope to become a much more accomplished programmer, and publish early parts of my technological development models. I hope to finish building early iterations of tools, as well. I’ve recently started a site (another one, I know), http://codespellcodex.com for tracking development of spells for the new sandbox game ‘CodeSpells’, which you are welcome to check out.

That all being said, I hope everyone has a wonderful New Years Eve and 2015.

Colin

Stratexist

Something that I discussed in my last post is that every forecast of a technology (well–every forecast in general, but we’re focusing on a particular field here) has limitations and associated laws that impact it, and that it’s important to identify and associate what those are. With Moore’s Law, the implication of Dennard’s Law breaking down meant that you could look at Amdahl’s Law to see the limitations of parallelization.

When I was working on technology consulting last year at Prokalkeo, my partner and I came up with what we called ‘Technology Roadblocks’ when we were seeking to characterize the issues that various instances of science and technology ran into. To return to the last article,

“Every technology (that we know of) has roadblocks as well. Roadblocks are what I call ‘any obstacle to progress in the development of a technology’. There are a variety of types of these roadblocks, and they can impact forecasting accuracy (macro) or simply describe problems that need to be/will be overcome in the pursuit of development (micro). In the case of Amdahl’s Law, it follows from mathematical axioms and is thus what I would call a ‘Axiomatic Roadblock’. This associates with the impossibilities mentioned in “Possible, Probable, Personal“, specifically the axiomatic impossibility–indicating that the limitation is put in place due to mathematical reasons more than physical laws (a semantic distinction that dissolves if looked at closely enough, but useful for identification purposes).”

So, similar to how I isolated various types of fallacies, we found it useful to isolate various types of roadblocks. What are they?

The first and simplest roadblock is the ‘Independent Roadblock’. This is any case in which the obstacle to the development of a technology is intrinsic to that particular technology. As an example, this might be figuring out how exactly to best design a new virtual reality head set to address the problems that poses, or what sort of algorithms are needed for better data compression.

If you look closely enough at most ‘Independent’ cases, however, you’ll find most of them are of the second type of roadblock, which is the ‘Dependent’ roadblock. This is best described as a case where development of a technology requires advances in another area of science or technological development–shrinking power sources, higher resolution/lower weight screens, better laser diodes (to think of a few off of my head). In many cases what appears to be an independent roadblock is actually the conjunction of many dependent roadblocks–in others it isn’t.

Finally, the third type of roadblock we identified is the ‘Physical Roadblock’. This is any sort of roadblock that will not be overcome simply by finding a new trick or combination, or new way to improve your toolchain–things like the size of atoms, the second law of thermodynamics, and other physical laws. This ties heavily with the Physical Impossibilities discussed in my article on Castles in the Sky.

## How they Fit Together

It’s interesting to me, though, that mapping these out just returned me to a web of dynamics of technology interplay again. When independent roadblocks really are a number of dependent roadblocks, and dependent roadblocks in turn are each dependent on other roadblocks, and at the root of many of these are physical roadblocks, you begin to see again how it all interacts.

At a very very simple level, it’s like the lathe. With a lathe, you can jump start civilization. But the complex interplays of parts, returning back to different dependencies and how they block you from advancing, solving each in turn, shows just how complicated everything gets.

This might not be immediately obvious to someone working in a field at all times–as you are focused on your own work, the advances in the fields surrounding you are part of a changing environment, not necessarily things you notice in their own right. Improving computer speeds are something everyone is aware of, but projects that might not have even been able to be started without the advances aren’t noted to necessarily be dependent on them once initialized, especially if they’re finished within a single generation of hardware.

This is a useful way to look at problems, though to avoid drawing too complicated of a web  you need to restrict it to one or two degrees of freedom. When evaluated technology markets at Prokalkeo, one of the things we looked at was what sort of roadblock a given technology had. I’ll do a worked problem in the next article.

Apologies for anyone waiting for updates–I’ve been following three very interesting conflicts over the last few weeks, in addition to visiting my alma mater and completing a few major milestones at work. Regular updates will begin again this week.

## A Liberal Decalogue

Bertrand Russell, famous philosopher and mathematician, once shared what he considered a ‘Liberal Decalogue’ at the end of an article called ‘The best response to fanaticism: Liberalism’, that embodied what he thought might represent the commandments that a teacher might wish to propagate, modeled after the ten commandments. Listed as they were originally, the decalogue included:

1. Do not feel absolutely certain of anything.
2. Do not think it worth while to proceed by concealing evidence, for the evidence is sure to come to light.
3. Never try to discourage thinking for you are sure to succeed.
4. When you meet with opposition, even if it should be from your husband or your children, endeavor to overcome it by argument and not by authority, for a victory dependent upon authority is unreal and illusory.
5. Have no respect for the authority of others, for there are always contrary authorities to be found.
6. Do not use power to suppress opinions you think pernicious, for if you do the opinions will suppress you.
7. Do not fear to be eccentric in opinion, for every opinion now accepted was once eccentric.
8. Find more pleasure in intelligent dissent than in passive agreement, for, if you value intelligence as you should, the former implies a deeper agreement than the latter.
9. Be scrupulously truthful, even if the truth is inconvenient, for it is more inconvenient when you try to conceal it.
10. Do not feel envious of the happiness of those who live in a fool’s paradise, for only a fool will think that it is happiness.

## Technology Forecasting Rules

This resembles efforts of my own last year in attempting to come up with a list of commandments of forecasting and futurism, to avoid being influenced by politics, wishful thinking, or bias (though some is inevitable, obviously). Looking at this list, it’s quite fantastic, and is worth modeling off of. How can we change it to perhaps more closely match the virtues we wish to encourage in our own field?

1. Never make a claim you cannot defend.
2. Be honest with yourself and others in the strength of your predictions.
3. Never discard a possibility without investigating it first.
4. Discard all investigations into non-falsifiables.
5. When you meet with forecasts that don’t match yours, understand why and what they’re rooted in.
6. Do not assume fame means accuracy in forecast.
7. Do not fear disagreement, because axioms vary from micro-thede to micro-thede.
8. Do not fear to make radical statements, if you can defend them.
9. Do not conflate your dreams of the future with the likelihood of the future.
10. Do not let your politics influence your forecasts. Every political ideology under the sun has said that emerging technologies will help out their political ideology, and only so many of them can be true.

With minimal editing, I feel this still holds tightly to much of the intent of the original decalogue, and provides a nice decalogue of technological forecasting.

## Dennard, Amdahl, and Moore: Identifying Limitations to Forecasting Laws

### Will Moore’s Law Hold Up?

As the hallmark law of technology forecasting (and often, the only case that people are familiar with) a debate rages around Moore’s Law and its validity–will it hold true? Will it fail? Will it plateau and then see breakthroughs? Fact of the matter is, that all of these are true statements…depending on what the exact metric you’re measuring is. In fact, the precise measured metric and how to choose one is going to be the focus of a later post, but for now I’d like to address what most people think of when they say Moore’s law, and what they expect, which is computers seeing drastic gains in raw speed performance from a processor level (disregarding improvements on the part of other parts of the system like the speed improvements from Solid State Drives).

If you go by that metric, Moore’s law has failed to keep up. There’s no two ways about it. I’m not saying the sky is falling, and I’m certainly not saying that this won’t change. All I’m saying is that, for now, the raw speed improvements in computers has failed to keep up. Why is that?

Well, there’s a corollary of Moore’s Law called ‘Dennard Scaling’. Simply put, Dennard Scaling states that as transistors get smaller their power density stays constant, or that total power per transistor decreases linearly. This means that if you cut the linear size of a chip by half in two dimensions, the power density will decrease by 1/4. If this wasn’t the case, 3 Moore’s law doubling cycles (ie an 8x improvement in number of transistors in a given area) would mean an 8x higher power density.

Dennard Scaling is what’s broken down. More details are explained here, but the gist of it is that the smaller the transistors get, the more static power loss there is. The more static power loss there is, the more the chip heats up, leading to even more static power loss, which is a self-reinforcing cycle called thermal runaway. Another problem occurs when the static power loss (which is a signal) is greater than the gate voltage, leading to errant activation of transistors, meaning faulty operation.

To avoid this, manufacturers began producing multicore chips (which you may have observed in the last few years). This is a valid approach, and also led to the push in parallelized code. However, while there are a number of architectural issues above my head here, there is one important fact about building multicore instead of single core system. What is it?

### The Problem

For a multicore system to work, a task has to be distributed to different cores and then gathered again for a result. This is a drastic simplification, but works for the purpose of this argument. Say you have have a program comprised of 100 tasks that need to be accomplished, with 40 that can be parallelized and 60 that can’t, and you run these tasks on a single core processor that does 1 task per ‘tick’ (a general unit of time). It will take you 100 ticks to finish the operation. Now, if you replace your single core processor with a quad core processor, what changes? Well, 40 of them can be parallelized, meaning they can be sent off to your quad core system. That leaves you with 60 tasks that have to be done in sequence–so even though you might have 4 times the number of transistors in your system, it will still take you 70 ticks to finish the operation–10 ticks (40 ticks / 4 processors) plus 60 ticks (one processor handling the non-parallelized tasks).

This is a general law called Amdahl’s Law. Amdahl’s Law states that the time T(n)  an algorithm takes to finish when being executed on n threads of execution with a fraction B of the algorithm that is strictly serial corresponds to:

$T(n)=T(1)(B+\frac{1}{n}(1-B))$

Amdahl’s law at 50%, 75%, 90%, and 95% parallelizable code. Source: http://en.wikipedia.org/wiki/Amdahl’s_law#mediaviewer/File:AmdahlsLaw.svg

As can be seen in the graph, even if your code is 95% parallelizable, as n approaches infinity (and infinite number of processors) you only get a 20x speedup…or just over 4 Moore’s Law cycles (8-10 years).

This article isn’t meant to try to convince you that these issues won’t be solved. In fact, for what it’s worth, I’m strongly of the opinion that they will be solved–new computing architectures and substrates mean that we will likely resume some form of rapid growth soon (this may be influenced by a degree of hope, but there are certainly enough alternatives being explored I find it somewhat likely). While it’s an interesting problem to look at, I think it’s a more useful example of how every technology forecasting law has associated theorems and roadblocks, and that finding these is important to a forecast.

Forecasting laws have associated laws. That’s a pretty simple sentence with a lot of meaning, but what exactly is it saying? Exactly this: for every statement you make about a capability changing over time (transistors, laser capabilities, etc.) there are associated laws relating to associated capabilities. Dennard scaling associates with Moore’s law–it’s an observation that power density stays the same, meaning power requirements per transistor must be dropping, allowing Moore’s law to continue. There are any number of these, and in some ways you might even be able to consider multiple versions of a forecasting law to be very close associated laws (such as what type of forecasting method you’re using).

Every technology (that we know of) has roadblocks as well. Roadblocks are what I call ‘any obstacle to progress in the development of a technology’. There are a variety of types of these roadblocks, and they can impact forecasting accuracy (macro) or simply describe problems that need to be/will be overcome in the pursuit of development (micro). In the case of Amdahl’s Law, it follows from mathematical axioms and is thus what I would call a ‘Axiomatic Roadblock’. This associates with the impossibilities mentioned in “Possible, Probable, Personal“, specifically the axiomatic impossibility–indicating that the limitation is put in place due to mathematical reasons more than physical laws (a semantic distinction that dissolves if looked at closely enough, but useful for identification purposes).

While the identification of the issues in moving forward in trivial Moore’s law forecasting is important, and I hope that I clarified things somewhat for my readers, it’s just as important to give a good example of how these associated laws that might be passed over can lead to new limitations. I personally think that the issues will be overcome, and that Moore’s Law will continue (or need to be reformulated if a different substrate has different research patterns associated). All the same, being able to identify when axiomatic and physical impossibilities and roadblocks will arise is absolutely necessary for identifying the validity of a forecast.

## Feature Demo: Emerging Technology Articles Collection

Like I mentioned in my introduction, I spend a good portion of my time sorting through science news and emerging technology articles. I’m working on generating a way for you to easily sort through my recent readings on the topics, as well as attaching a synopsis of my views so you can easily catch up with what different technologies mean, how they work, and where they might go.

In the mean time, however, I’ve put together a quick demo showing you everything that passes into my favorites read list, sorted out from all the articles I see every day. There’s no explicit tagging on this yet, but hopefully there will be in the future. Feel free to peruse and enjoy! Feedback is welcomed on number of articles per page I should display. I’m also going to try to hack in some CSS to format things so they’re a bit easier to read.

## Possible, Probable, Personal: Arguing against Castles in the Sky

### Introduction:

I labeled this article with the heading ‘Possible, Probable, Personal’ because I think that a lot of failures in qualitative forecasting and putting the boundaries on quantitative forecasting result from an inability to differentiate what category new technologies and forecasts about technologies fall into. Like the flying car mentioned in the last post, people who were enthusiastic about it jumped straight from it being possible to it being personal, instead of an intermediary category of ‘Pragmatically Improbably’.
It is my hope that this framework or one that will evolve from it will help people understand why some technologies take a long time to make it to market, some are adopted immediately, and some never see the light of day at all (failing other interests, which are beyond the scope of this blog).

### Is a technology impossible?

Flying cities–depending on how you count them, improbable or impossible. Credit to http://solartistic.deviantart.com/art/Laputa-The-Flying-City-385811018

The first question to ask when evaluating the future with regards to a new technology or scientific discovery is to simply ask if it’s possible. Rather than establish all the different ways something could be determined to be possible, we can establish the ways that technologies could be easily determined to be impossible. Now, there could be specifics to certain scientific and technological fields that I don’t cover here, but I believe that these are the major ones.

• Axiomatic impossibility: This is a scientific or technological discovery that violates the absolute most fundamental principles that we understand about the universe. This might include things like the values attached to fundamental forces, entropy, or the fact that there are no integers between 3 and 4.
• Physical impossibility: This is a scientific or technological discovery that is completely un-grounded by what we understand about the universe currently, but doesn’t violate an axiomatic statement. This might include FTL travel, vacuum energy systems, anti-gravity, etc.
• Conditional impossibility: This is a scientific or technological discovery that is impossible but only in a relational sense. In other words, describing one technology as being required to have higher capabilities than a different technology that it has never been shown to have an advantage over.

Now, as is the case with both of the previous articles in this series, none of these are absolute statements. As someone who studied Physics in my undergraduate years and still follows discoveries with the eye of a keen hobbyist, I’m well aware that there are still a number of interesting inconsistencies. All the same, just because we don’t know what the answer is doesn’t mean we can arbitrarily say that some result is likely–something that you can’t even comprehend or guess is just as likely, which is to say completely made up. It’s even worse in cases like FTL, where relativity is one of the most confirmed results in all of physics and no amount of wishing will get us around that. Choosing one outcome over another for no other reason than you like what it might mean is intellectually dishonest.

### Is a technology improbable?

This question is much harder than the impossibility of a technology–and rightly so, as it relies significantly more on opinion than anything else. Well, perhaps  not opinion, but arguments are likely to be driven b opinion which will lead to cherry picked facts. All the same, we can still attempt to try to break it down further anyway.

• Theoretically improbable: the most strict form of the term, a theoretically improbable technology is unlikely to be seen in even a laboratory or purely research sense, due to some restriction on the part of funding or even ethics. An example of the first would be experimenting with large equipment made out of rare earth metals (arbitrary-I do not know at the time of this writing if there’s any POINT to making large objects out of rare earth metals) and an example of the second might include human cloning or a number of experiments likely to leave their subject maimed, dead, or out of their mind.
• Pragmatically improbable: while not burdened by some form of practical hard cut-off like theoretically improbably technologies, a pragmatically improbably technology is one that could theoretically be constructed unburdened by any realistic concerns, but is unlikely to be implemented on any larger scale. This relates back to the discussion of flying cars in the previous post, in that I’d consider flying cars to fall into this category. Notably, not even the military (which is well known for spending significant quantities of money on devices that wouldn’t necessarily be worth it outside of that context) has used ‘flying cars’ (when technologies that might be considered whackier, such as the Osprey, have been the focus of significant amounts of development effort because they were pragmatic).
• Individually improbable: The last filter establishes that while a technology may see some form of large scale use (on an industrial, military, organizational, or government level–ie supporting a large number of individuals or requiring the support of a large group for the usage of one individual on behalf of that organization) it is unlikely to ever reach the hands of one person fro their own sake, either via purchasing or as an individual’s piece of equipment they used on their own behalf as opposed to the behalf of an organization.

Much of this blog will be about discussing the probability/improbability of various technologies and their implementations, though that specific discussion is likely to be far in the future considering the material yet to be discussed.

So, we’ve established impossible and improbable categories of technology. Many technologies will sit in one of these categories forever (in the case of impossible technologies) or for a very long time (in the case of improbable technologies). As our capacities advance, though, the scope of what is pragmatic is pushed back in some cases (transistors), though not all (flying cars), and things move to ‘personal’.

### Is a technology personal?

So if a technology isn’t impossible, and it isn’t improbable–even on an individual scale, we can say that it may become a personal technology. In my opinion, technologies that are individually improbable (ie the loosest class of improbable technology implementation) are the most likely to eventually become personal, as they are often simply limited by results of scaling or development (mainframes, genetic sequencing, etc.).

Personal technologies are not necessarily technologies for private use. They may still be restricted to certain organizations–an example might be a certain firearm. The distinguishing feature here is that they are being used by an individual on behalf of themselves (and possible additional individuals as a side effect, but not as the point). Exoskeletons in warfare might go from ‘individually improbable’ to ‘personal’ when they become standard issue as opposed to being issued to squads (which itself is a forecast, though one I hope to cover in depth eventually).

### Using this Framework

To conclude, this gives us a rough framework in which we can place technologies to evaluate their likelihood, at least at a very high level sketch. Axiomatic, physical, and conditional impossibilities can be examined first. If none of those prevent a technology, then theoretic, pragmatic, and personal restrictions on implementation can be examined. If the forecasted technology isn’t restricted by any of those reasons, then it likely is (or will be) a personal technology.

## Motivation

Where is my flying car? Popular science cover.

One of my least favorite responses to hear when talking with someone about the future is what I call the “flying car fallacy”. While the precise phrasing differs from person to person, the most general form looks something like: ‘They promised us flying cars’ or ‘Where are our flying cars?’. The implication, of course, is that any form of techno-optimism or forecasting should be regarded with extreme skepticism due to the failure of flying car predictions to come true.

Honestly? The concept that you should regard what people tell you about the future with skepticism isn’t a bad one. However, while it’s not a precise corollary, I think Scott Alexander’s Innoculation Effect comes into play here. Because there’s this very basic, obviously failed prediction about the future, people applying the fallacy are able to dismiss all other predictions as wishful thinking or, if proven correct, lucky guesses. And as someone who likes to think that he can make useful predictions about technologies, that’s hardly ideal.

While the correct usage parameters for technological forecasting and trend tracking aren’t 100% established (and I hope to get a better handle on those exact things in this blog), there’s a large amount of data that shows that there is -SOME- predictive value. I think it’s worth it, from a professional or even just hobbyist perspective, to examine this misconception and take it apart. So, what powers the flying car fallacy?

## Dissection

“They promised flying cars. There are no flying cars.” Perhaps a bit more verbose than the normal phrasing, but it serves my needs. I’m perfectly comfortable admitting that I’m deconstructing a particular phrasing of this claim that I myself constructed, but I feel the above version adequately represents the concepts and thoughts typically being expressed.

To examine this more closely, it needs to be decomposed. Let’s look at the first sentence: “They promised flying cars.” This is more interesting than it might appear at first glance. There’s a lot of meaning contained in the three concepts in this sentence. First, “they” implies some form of authority or expert. Honestly, while I can find plenty of general references to a ‘they’ or ‘scientists’ or ‘futurists’ discussing flying cars, most of these references fail to cite a specific source. Other controversial topics in futurism (such as AI) often have specific names claiming specific dates, a much more falsifiable claim.

An article on Livescience discusses broken science promises, flying cars among them, and mentions Popular Mechanics and Popular Science. Further investigation reveals that discussion of personalized flying transportation (and railway cars, something that did in some ways come true) go all the way back to the 1930s and 1940s. But still…these were popular magazines. By those years, they had already transitioned into appealing to the public mind and eye as opposed to serious, calm discussion.

This isn’t to say that experts haven’t forecast flying cars–but that allows us to take a look at the second word in the first phrase, “promised”. What does “promised” mean? Well, it might be semantics, but to my mind that means that people are taking a personal guarantee that flying cars will exist rather than a prediction, projection, or forecast. Forecasters who “promise” something about the future are generally not experts but popularizers–experts will generally couch statements carefully.

What does Google say? Well, if you search explicitly for the string “promised flying cars”, it returns about 239k results. If you search explicitly for the string “predicted flying cars”, it returns only 19.5k results. Other configurations return even less. Because it’s a personal guarantee, a promise, people feel betrayed. And that betrayal sticks in people’s minds as a failure for an interesting future to come about, disregarding all the other things that HAVE happened. Things that have happened become natural–things that don’t happen are failures.

Finally, the term “flying cars” itself is a bit of a misnomer–or at the very least unspecific, and looking at exactly what it means lets us address whether or not flying cars ‘exist’ or not. The off the cuff thought is simply a car that is unconstrained by the rules of the two dimensional road. If you think about it a bit more, you conceive of one of two things. The unrealistic/idealistic version is trivially having access to flight to ease up traffic without requiring building additional expensive infrastructure. The more realistic/pragmatic example, however, is a car that also works as a plane, and is subject to all the restrictions that entails (something that has been shown in prototype multiple times, with the most recent being the Parajet Skyrunner, which works off a parachute and a very light body, and the Terrafugia Transition, which is more technically a roadable airplane).

## Why Didn’t They Happen?

Okay, so we’ve established that actually using the flying car fallacy generally has to do with a failure to live up to a flight of fantasy that appealed to the imaginations of readers and fans of technologies, rather than a serious, pragmatic estimation of the future. But why DIDN’T they happen? Well, to go over it very briefly (as there are more issues holding back flying cars than can possibly be dealt with in a competent manner here, but they have been discussed in depth elsewhere, we’ll keep it brief):

• Safety: Three dimensions and velocities make things much more dangerous, meaning that there will necessarily be quite a bit tighter…
• Regulation: Pilot’s licenses are more difficult to get, violations would be punished more harshly, variations would take longer to be approved.
• Cost to consumer: Flight takes more fuel than driving, not to mention the fact that the additional engineering required would mean that vehicles would be drastically more expensive–in turn, leading to the fact that a comparatively small portion of the population would use the needed infrastructure. Speaking of…
• Cost of infrastructure: New structures to support new dimensions of travel, new ways to deal with cars taking off and landing, etc. As mentioned above, this would be required for a comparatively small portion of the population, unlike roads which (with the exception of some private toll roads) are used by both expensive and economic cars.

## Arguing Against It

To argue against the flying car fallacy, you need to clearly delineate between different types of technological predictions (or promises). Making sure that wild unsupported flights of fancy aren’t put forward as representative of your beliefs is ideal. In fact, one of the major things you should take away from this is that you SHOULDN’T let your hopes rise when someone claims something about the future, but can’t present rigorous evidence as to why or how it will happen. The next blog post will go into how to counter poorly framed techno-optimism and promises.

Citing the lack of flying cars isn’t the only pithy response people make in response to positive predictions about the future, but it is most certainly a common one. However, it’s founded on a bitterness on the lack of follow through from visionaries more than any widespread failure on the part of people who make rigorous evaluations of future possibilities. I am in no way trying to say that people should by default accept optimistic predictions about the future, any more than they should reflexively dismiss them.

In the end, if someone wants to believe that a failure of techno-optimism to bring about their hopes means that all forecasts fail, you can’t convince them otherwise. The best thing you can do is try to show them amazing things that WERE predicted ahead of time.

## Status Quo Fallacy

It’s almost inevitable that if you openly speculate about new technologies that have any impact on society that change the status quo, you’ll be met with a pithy response of ‘Previous technologies didn’t change (Relevant aspect of society), what makes you think this one will?’ It might not always be phrased that way, but it’s certainly a common sentiment that I expect many readers will have heard.

Of course, this pops up most commonly when you’re discussing automation. As an example, if you discuss the displacement of workers in various industry segments and skill levels, you’ll be met with responses ranging from ‘technology opens up more new jobs than it replaces’ to ‘the luddites originally complained about being put out of work too’. Incidentally, for that last example–turns out many luddites WERE put out of work.http://books.google.com/books/about/Writings_of_the_Luddites.html?id=NG6ABlDQ10MC That’s not to say I agree with them-history bore out that mechanization of industry improved the lot of the working class in Britain tremendously…but wage suppression still hurt in the short term.

One of the easiest ways to model the future is to assume something won’t change. This isn’t accurate, but it’s easy. People like thinking that what they are familiar with will always be around. Sometimes it’s selective–people will isolate one or two issues they have with the current dynamic of society and humanity, and look at how they  might be affected by technology, but assume the context is the same.

More often, they might pick one or two coming technologies and see how they cause a systematic impact, but not take into account momentum of existing social structures and whatever underlying human motivations cause them (unless the technology is one that directly alters the human condition, such as various neuroprosthetics or nootropics). This is particularly common, and problematic, with people whose futurism is driven by a particular political ideology they wish to see come to pass (or not come to pass, depending on if they are seeking utopia or avoiding dystopia).

The pithy way to phrase this belief, what I call the ‘Status Quo Fallacy’, might be the statement that “Quantitative change will not lead to qualitative change”. Can we argue this point? Is it possible to point to any sort of historical popular statements or predictions that stated that quantitative change wouldn’t lead to qualitative change?

Conveniently, yes! Professor Donald Simanek of Lockhaven University of Pennsylvania has conveniently collated a list of some of the best overly negative/pessimistic quotes about the future of science and technology throughout history, as well as a few overly optimistic ones (which we will revisit in a later article).

I strongly advise reading it yourself for some degree of amusement, especially one of the earliest quotes:

I also lay aside all ideas of any new works or engines of war, the invention of which long-ago reached its limit, and in which I see no hope for further improvement…- Sextus Julius Frontinus, governor of Britania, 84 C.E.

Regardless of ancient humor, there are some more relevant quotes. As the topic of this blog concerns science and technology, and as I’d like to avoid stepping into politics and theology, I’ll disregard those quotes. Furthermore, I think the point is best illustrated in a case where a technology had already been demonstrated as achievable and practical, if not quite ready for mass production. The final requirement is that the quote comes from someone with a degree of authority on the topic or at the very least understanding–luckily, nearly all of the quotes fulfill that requirement.

What does that leave us? Erasmus Wilson claimed that the electric light would not surface again after the Paris Exhibition closed. Lord Kelvin, a mathemetician and physicist, claimed that radio had no future outright. The inventor of the vacuum tube, Lee DeForest, said television had no future. Even the head of 20th Century Fox, who had made his fortune on the movie boom, claimed that television would never gain market share. These are all from the telecom industry, as they in many ways are the predecessor of today’s computer and data industries.

What’s the commonality between these? Individuals who knew systems intricately, who helped develop them or had helped develop their predecessor, were blinded by the status quo. When you are living in a system and much of your life revolves around that system, you either don’t want it to change or you can’t let yourself expect that it will change.

Does this still apply today? Absolutely. People have continued to make predictions, especially about computers. But every time they can’t show, quantitatively or qualitatively, why their prediction holds true for the future instead of simply being a hold-over from the past, you have to treat it with immense skepticism.

This is not to say all negative predictions should be ignored, however. Many have completely valid reasons, and should be used to construct a rigorous model of the future.

In further articles in this series, I hope to examine precisely when various failed predictions about technology came true, and see if there is any sort of consistency between when something was getting enough attention to make a bold statement about it, and how long it took to come true. I will also discuss the value of tempering enthusiasm (lowering the ceiling as opposed to raising the floor, so to speak), and being able to differentiate between different types of failures in technological development.

## Technology Tracking Methods Part 1: The Problem

This is less an informative lecture than some thoughts on a problem that I run into.

Technology foraging, technology tracking, whatever you want to call it–is hard.

To keep abreast of all the technologies that I feel are necessary, I have to read approximately 50 RSS feeds a day, and sort through the content by hand. I had previously attempted to put together a system that automatically sorted articles by subject and importance, but it didn’t work out so well (this is part of the driving purpose behind a project I’d like to unveil to the public sometime shortly). This means that I have to go through about ~300 articles a day and manually tag/sort them, as well as decide how important they are.

Furthermore, I’m only gleaming the tips of the iceberg–there’s thousands of industry specific scientific articles that I’m missing, not to mention the wealth of information hidden in areas like patents and scientific journals. There’s an incredibly amount of data, and I’m missing huge amounts of it. To adequately track technology, I need to be able to render down all that data into something human readable or searchable.

Is it so surprising, then, that most people (even ones that see the development of technology as critical to their profession) can’t keep up with technology? I have to specifically set aside time to do it and it can get wearying even for me. It doesn’t scale, either. There’s no real way to add more to my tracking abilities as they stand right now without a linear increase in time spent. This means that further tools are needed.

These tools are being worked on right now, and I hope to implement the first step soon, which I’ll discuss briefly in the next blog post. I hope to share parts of my process as I develop them so that others are either inspired to take up the tools I provide, or build their own. The problem is currently poorly served.