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Cyrus Farivar


Smári McCarthy, in his Twitter bio, describes himself as a "Information freedom activist. Executive Director of IMMI. Pirate."

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On Friday, two Icelandic activists with previous connections to WikiLeaks announced that they received newly unsealed court orders from Google. Google sent the orders earlier in the week, revealing that the company searched and seized data from their Gmail accounts—likely as a result of a grand jury investigation into the rogue whistleblower group.

Google was forbidden under American law from disclosing these orders to the men until the court lifted this restriction in early May 2013. (A Google spokesperson referred Ars to its Transparency Report for an explanation of its policies.)

On June 21, 2013, well-known Irish-Icelandic developer Smári McCarthy published his recently un-sealed court order dating back to July 14, 2011. Google sent him the order, which included McCarthy's Gmail account metadata, the night before. The government cited the Stored Communications Act (SCA)(specifically a 2703(d) order) as grounds to provide this order.

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The above photo has been doing the rounds on the internet with claims it is Álvaro Múnera Builes, a Colombian animal rights activist who worked briefly as a bullfighter in his youth under the name ‘El Pilarico’ in Colombia and then Spain. With the image come the words, also claiming to be from Múnera...

 http://fiskeharrison.wordpress.com/2012/07/25/this-photo-is-not-what-it-seems/ 

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How to create a global brain in only a few lines of code

This is where my research was going years ago and I found so many interesting things that I forgot that's why I was doing it. Here's the summary of the plan:

User Interface can be anything which is statistically balanced and has continuous input and output of at least 1 dimension between each person and their computer. A simple example is the speed they are moving the mouse, if its increasing or decreasing at the time, and an output could be some music which is playing becomes a little slower or faster at the time. It could be more complex things like realtime video, evolved audio, Nintendo Wii controllers, Kinect, Emotiv Epoc or OpenEEG mind reading game controllers, or many other things. The User Interface is a stream of vectors in and vectors out, of at least 1 dimension, through any devices. If there is any audio or video, that is part of the User Interface. The core idea is a kind of math and is calculated independently of any game content which players create while in the game.

N people play the game at once, streaming data to eachother's computer through the Internet as it was all 1 system with many inputs and outputs as paths of information flow between the players.

The output to each player is a prediction of the next input of that player. The player must hear/see/experience the output in some way so it affects their state of mind.

The combined inputs of all players are used to predict the combined outputs of all players. This can be done many ways. A bayesian network should work well for this since it calculates using the math of conditional-probability and scales up efficiently.

Here's what makes it work extremely more than the intelligence of the AI or any 1 player:
Since the bayesian network calculates relevance of inputs and outputs to its prediction accuracy, whichever inputs of other people are most useful (combined in some statistical way) to predict the next few inputs of this local person, will gradually be given more influence here, and because of that this local person, who "must hear/see/experience the output in some way", will tend to become more statistically relevant for the AI to use their inputs to predict the other peoples' next few inputs, and the feedback loop is complete and amplifies peoples' ability to play the game in a way that helps the AI use people to predict other people.

In this feedback loop of N people, without needing conscious knowledge or intent of it, people will unavoidably be influenced toward flowing their thoughts together because the set of all possibilities where that does not happen is partially cancelled-out by the bayesian network.

Depnding on the accuracy of whatever kind of AI does these predictions and is the "glue code" for networking our minds together, and how skilled people become at the game, a superintelligence is somewhere along this research path and it will be made of the minds of billions of people and computers flowing thoughts together at the subconscious psychology level.

This is the simplest way to build a superintelligence. My research years ago took a different direction in finding User Interfaces, like Audivolv, BayesianCortex, and Physicsmata (all open source), and now I have a good idea of how to put it all together. We can proceed with these experiments toward thinking more like a global brain.

Does anyone have idea on what kind of game it should be? The research path leaves many possibilities.

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Sometime between 1498-1500, Leonardo da Vinci invented the ball bearing via detailed drawings of how it would work. It was round about the time he was working on his famous helicopter sketches (most possibly inspired by nature i.e. wind dispersal seeds or helicopter whirlybird seeds). He must have reasoned that the propeller was going to need to spin really, really fast.

He can be forgiven for not being a very good mathematician; in fact his maths was so far off on the weight-to-lift ratio, that had he known and understood the numbers involved - he probably would have never bothered with his designs.

But he understood something would have to allow the propeller to turn extremely fast without too much fiction. And so he invented the ball bearing; providing detailed drawings of how a low coefficient of resistance would work. Pure genius; I believe this to be one of the greatest inventions, and without it there would have been no industrial revolution.

A ball bearing uses balls, rollers and a lubricating substance, to significantly reduce friction and maintain separation between surfacers. As a ball turns it has a much lower coefficient of friction (drag or resistance) than two flat surfaces moving plainly against each other. The purpose of a ball bearing is to reduce the surface area and rotational friction, while efficiently supporting a load (for example: a hub, axial or shaft). The science of lubrication is complicated but basically; a lubricate thats works is a lubricate that sees to it that the two surfaces never physically touch without the microscopic amount of lubricant.

Leonardo da Vinci is revered as a genius and luminary, even though he was very unsuccessful at anything other than his painting. Almost all of his inventions where completely impractical. His flying machines never even came close to lifting off the ground, most where in fact never even made - only conceptualised. He was quiet possibly the most impracticable man to have ever lived.

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Most parts of governments, religions, and many other parts of the world are far more complex than other ways things could be done that would work better.

If a government department is simple, more people would understand what is happening in it.

If more people understand what government is doing, more people would be able to have an opinion against it, and some would reduce support for or act against it. Those whose opinion would be positively influenced would do less than those whose opinion is negatively influenced.

Complex government departments breed other complex government departments. The more you have, the more there will be later, if few people resist.

Most people involved probably don't know they're doing it, but society evolved toward patterns where complex things survive because we don't have the brain power to understand them, which would be required to solve those problems. Complexity is a problem like AIDS. It spreads unnoticed for many years because its deep in the system (biology in this case) in ways its not easily observed, and being so well evolved with the system its hard to remove. It consumes resources to continue its own survival. It disables you just enough that its hard to fight back but little enough that you survive more years to spread it. Like we want a cure for AIDS, we should want a cure for Complexity.

Complexity is an evolved defense against progress, because progress includes many of the ways the world works, including some parts of governments and religions, becoming obsolete.

Complexity is a cost, not something to measure progress by. Something may need to be a certain level of complex to accomplish something else, but complexity by itself is negative and should be avoided like spending money. If something simpler or cheaper does the same job at least the same quality, then its a mistake to pay higher complexity or pay more of other resources.

People say things like "They spent billions of dollars researching it, and if they can't do it, why do you think you can?" Did they try spending only thousands of dollars researching it? With the ability to do complex things often comes the overlooking of simple things.

Similarly, why don't people say things like "If you want to build a system as advanced as animals or Humans, you've got to have AIDS in the system overall or something equally complex." Some parts we should not include in our world.

In open source, for example, we usually don't have the resources of a business, so we have to explore deeper into simple ways to make things work, so how are open source products staying competitive? We cure complexity because we have to. Others are still infected and allow their Complexity to refuse the cure which would obsolete itself.

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http://blakemasters.tumblr.com/peter-thiels-cs183-startup

This link is to a summary of Peter Thiel's class topics written in superb essay style. It centers entirely around technology, how a new supplier (an entrepreneur) brings it to the people, and how the creative process navigates the modern world.

The words are far-reaching and truly align to the best of humanity and our future potential.

Peter Thiel "gets it"

Purpose and Preamble

We might describe our world as having retail sanity, but wholesale madness. Details are well understood; the big picture remains unclear. A fundamental challenge—in business as in life—is to integrate the micro and macro such that all things make sense.

Humanities majors may well learn a great deal about the world. But they don’t really learn career skills through their studies. Engineering majors, conversely, learn in great technical detail. But they might not learn why, how, or where they should apply their skills in the workforce. The best students, workers, and thinkers will integrate these questions into a cohesive narrative. This course aims to facilitate that process.

I. The History of Technology

For most of recent human history—from the invention of the steam engine in the late 17th century through about the late 1960’s or so— technological process has been tremendous, perhaps even relentless. In most prior human societies, people made money by taking it from others. The industrial revolution wrought a paradigm shift in which people make money through trade, not plunder.

The importance of this shift is hard to overstate. Perhaps 100 billion people have ever lived on earth. Most of them lived in essentially stagnant societies; success involved claiming value, not creating it. So the massive technological acceleration of the past few hundred years is truly incredible.

The zenith of optimism about the future of technology might have been the 1960’s. People believed in the future. They thought about the future. Many were supremely confident that the next 50 years would be a half-century of unprecedented technological progress.

But with the exception of the computer industry, it wasn’t. Per capita incomes are still rising, but that rate is starkly decelerating. Median wages have been stagnant since 1973. People find themselves in an alarming Alice-in-Wonderland-style scenario in which they must run harder and harder—that is, work longer hours—just to stay in the same place. This deceleration is complex, and wage data alone don’t explain it. But they do support the general sense that the rapid progress of the last 200 years is slowing all too quickly.

II. The Case For Computer Science

Computers have been the happy exception to recent tech deceleration. Moore’s/Kryder’s/Wirth’s laws have largely held up, and forecast continued growth. Computer tech, with ever-improving hardware and agile development, is something of a model for other industries. It’s obviously central to the Silicon Valley ecosystem and a key driver of modern technological change. So CS is the logical starting place to recapture the reins of progress.

III. The Future For Progress

A. Globalization and Tech: Horizontal vs. Vertical Progress

Progress comes in two flavors: horizontal/extensive and vertical/intensive. Horizontal or extensive progress basically means copying things that work. In one word, it means simply “globalization.” Consider what China will be like in 50 years. The safe bet is it will be a lot like the United States is now. Cities will be copied, cars will be copied, and rail systems will be copied. Maybe some steps will be skipped. But it’s copying all the same.

Vertical or intensive progress, by contrast, means doing new things. The single word for this is “technology.” Intensive progress involves going from 0 to 1 (not simply the 1 to n of globalization). We see much of our vertical progress come from places like California, and specifically Silicon Valley. But there is every reason to question whether we have enough of it. Indeed, most people seem to focus almost entirely on globalization instead of technology; speaking of “developed” versus “developing nations” is implicitly bearish about technology because it implies some convergence to the “developed” status quo. As a society, we seem to believe in a sort of technological end of history, almost by default.

It’s worth noting that globalization and technology do have some interplay; we shouldn’t falsely dichotomize them. Consider resource constraints as a 1 to n subproblem. Maybe not everyone can have a car because that would be environmentally catastrophic. If 1 to n is so blocked, only 0 to 1 solutions can help. Technological development is thus crucially important, even if all we really care about is globalization.

B. The Problems of 0 to 1

Maybe we focus so much on going from 1 to n because that’s easier to do. There’s little doubt that going from 0 to 1 is qualitatively different, and almost always harder, than copying something n times. And even trying to achieve vertical, 0 to 1 progress presents the challenge of exceptionalism; any founder or inventor doing something new must wonder: am I sane? Or am I crazy?

Consider an analogy to politics. The United States is often thought of as an “exceptional” country. At least many Americans believe that it is. So is the U.S. sane? Or is it crazy? Everyone owns guns. No one believes in climate change. And most people weigh 600 pounds. Of course, exceptionalism may cut the other way. America is the land of opportunity. It is the frontier country. It offers new starts, meritocratic promises of riches. Regardless of which version you buy, people must grapple with the problem of exceptionalism. Some 20,000 people, believing themselves uniquely gifted, move to Los Angeles every year to become famous actors. Very few of them, of course, actually become famous actors. The startup world is probably less plagued by the challenge of exceptionalism than Hollywood is. But it probably isn’t immune to it.

C. The Educational and Narrative Challenge

Teaching vertical progress or innovation is almost a contradiction in terms. Education is fundamentally about going from 1 to n. We observe, imitate, and repeat. Infants do not invent new languages; they learn existing ones. From early on, we learn by copying what has worked before.

That is insufficient for startups. Crossing T’s and dotting I’s will get you maybe 30% of the way there. (It’s certainly necessary to get incorporation right, for instance. And one can learn how to pitch VCs.) But at some point you have to go from 0 to 1—you have to do something important and do it right—and that can’t be taught. Channeling Tolstoy’s intro to Anna Karenina, all successful companies are different; they figured out the 0 to 1 problem in different ways. But all failed companies are the same; they botched the 0 to 1 problem.

So case studies about successful businesses are of limited utility. PayPal and Facebook worked. But it’s hard to know what was necessarily path-dependent. The next great company may not be an e-payments or social network company. We mustn’t make too much of any single narrative. Thus the business school case method is more mythical than helpful.

D. Determinism vs. Indeterminism

Among the toughest questions about progress is the question of how we should assess a venture’s probability of success. In the 1 to n paradigm, it’s a statistical question. You can analyze and predict. But in the 0 to 1 paradigm, it’s not a statistical question; the standard deviation with a sample size of 1 is infinite. There can be no statistical analysis; statistically, we’re in the dark.

We tend to think very statistically about the future. And statistics tells us that it’s random. We can’t predict the future; we can only think probabilistically. If the market follows a random walk, there’s no sense trying to out-calculate it.

But there’s an alternative math metaphor we might use: calculus. The calculus metaphor asks whether and how we can figure out exactly what’s going to happen. Take NASA and the Apollo missions, for instance. You have to figure out where the moon is going to be, exactly. You have to plan whether a rocket has enough fuel to reach it. And so on. The point is that no one would want to ride in a statistically, probabilistically-informed spaceship.

Startups are like the space program in this sense. Going from 0 to 1 always has to favor determinism over indeterminism. But there is a practical problem with this. We have a word for people who claim to know the future: prophets. And in our society, all prophets are false prophets. Steve Jobs finessed his way about the line between determinism and indeterminism; people sensed he was a visionary, but he didn’t go too far. He probably cut it as close as possible (and succeeded accordingly).

The luck versus skill question is also important. Distinguishing these factors is difficult or impossible. Trying to do so invites ample opportunity for fallacious reasoning. Perhaps the best we can do for now is to flag the question, and suggest that it’s one that entrepreneurs or would-be entrepreneurs should have some handle on.

E. The Future of Intensive Growth

There are four theories about the future of intensive progress. First is convergence; starting with the industrial revolution, we saw a quick rise in progress, but technology will decelerate and growth will become asymptotic.

Second, there is the cyclical theory. Technological progress moves in cycles; advances are made, retrenchments ensue. Repeat. This has probably been true for most of human history in the past. But it’s hard to imagine it remaining true; to think that we could somehow lose all the information and know-how we’ve amassed and be doomed to have to re-discover it strains credulity.

Third is collapse/destruction. Some technological advance will do us in.

Fourth is the singularity where technological development yields some AI or intellectual event horizon.

People tend to overestimate the likelihood or explanatory power of the convergence and cyclical theories. Accordingly, they probably underestimate the destruction and singularity theories.

IV. Why Companies?

If we want technological development, why look to companies to do it? It’s possible, after all, to imagine a society in which everyone works for the government. Or, conversely, one in which everyone is an independent contractor. Why have some intermediate version consisting of at least two people but less than everyone on the planet?

The answer is straightforward application of the Coase Theorem. Companies exist because they optimally address internal and external coordination costs. In general, as an entity grows, so do its internal coordination costs. But its external coordination costs fall. Totalitarian government is entity writ large; external coordination is easy, since those costs are zero. But internal coordination, as Hayek and the Austrians showed, is hard and costly; central planning doesn’t work.

The flipside is that internal coordination costs for independent contractors are zero, but external coordination costs (uniquely contracting with absolutely everybody one deals with) are very high, possibly paralyzingly so. Optimality—firm size—is a matter of finding the right combination.

V. Why Startups?

A. Costs Matter

Size and internal vs. external coordination costs matter a lot. North of 100 people in a company, employees don’t all know each other. Politics become important. Incentives change. Signaling that work is being done may become more important than actually doing work. These costs are almost always underestimated. Yet they are so prevalent that professional investors should and do seriously reconsider before investing in companies that have more than one office. Severe coordination problems may stem from something as seemingly trivial or innocuous as a company having a multi-floor office. Hiring consultants and trying to outsource key development projects are, for similar reasons, serious red flags. While there’s surely been some lessening of these coordination costs in the last 40 years—and that explains the shift to somewhat smaller companies—the tendency is still to underestimate them. Since they remain fairly high, they’re worth thinking hard about.

Path’s limiting its users to 150 “friends” is illustrative of this point. And ancient tribes apparently had a natural size limit that didn’t much exceed that number. Startups are important because they are small; if the size and complexity of a business is something like the square of the number of people in it, then startups are in a unique position to lower interpersonal or internal costs and thus to get stuff done.

The familiar Austrian critique dovetails here as well. Even if a computer could model all the narrowly economic problems a company faces (and, to be clear, none can), it wouldn’t be enough. To model all costs, it would have to model human irrationalities, emotions, feelings, and interactions. Computers help, but we still don’t have all the info. And if we did, we wouldn’t know what to do with it. So, in practice, we end up having companies of a certain size.

B. Why Do a Startup?

The easiest answer to “why startups?” is negative: because you can’t develop new technology in existing entities. There’s something wrong with big companies, governments, and non-profits. Perhaps they can’t recognize financial needs; the federal government, hamstrung by its own bureaucracy, obviously overcompensates some while grossly undercompensating others in its employ. Or maybe these entities can’t handle personal needs; you can’t always get recognition, respect, or fame from a huge bureaucracy. Anyone on a mission tends to want to go from 0 to 1. You can only do that if you’re surrounded by others to want to go from 0 to 1. That happens in startups, not huge companies or government.

Doing startups for the money is not a great idea. Research shows that people get happier as they make more and more money, but only up to about $70,000 per year. After that, marginal improvements brought by higher income are more or less offset by other factors (stress, more hours, etc. Plus there is obviously diminishing marginal utility of money even absent offsetting factors).

Perhaps doing startups to be remembered or become famous is a better motive. Perhaps not. Whether being famous or infamous should be as important as most people seem to think it is highly questionable. A better motive still would be a desire to change the world. The U.S. in 1776-79 was a startup of sorts. What were the Founders motivations? There is a large cultural component to the motivation question, too. In Japan, entrepreneurs are seen as reckless risk-takers. The respectable thing to do is become a lifelong employee somewhere. The literary version of this sentiment is “behind every fortune lies a great crime.” Were the Founding Fathers criminals? Are all founders criminals of one sort or another?

C. The Costs of Failure

Startups pay less than bigger companies. So founding or joining one involves some financial loss. These losses are generally thought to be high. In reality, they aren’t that high.

The nonfinancial costs are actually higher. If you do a failed startup, you may not have learned anything useful. You may actually have learned how to fail again. You may become more risk-averse. You aren’t a lottery ticket, so you shouldn’t think of failure as just 1 of n times that you’re going to start a company. The stakes are a bit bigger than that.

A 0 to 1 startup involves low financial costs but low non-financial costs too. You’ll at least learn a lot and probably will be better for the effort. A 1 to n startup, though, has especially low financial costs, but higher non-financial costs. If you try to do Groupon for Madagascar and it fails, it’s not clear where exactly you are. But it’s not good.

VI. Where to Start?

The path from 0 to 1 might start with asking and answering three questions. First, what is valuable? Second, what can I do? And third, what is nobody else doing?

The questions themselves are straightforward. Question one illustrates the difference between business and academia; in academia, the number one sin is plagiarism, not triviality. So much of the innovation is esoteric and not at all useful. No one cares about a firm’s eccentric, non-valuable output. The second question ensures that you can actually execute on a problem; if not, talk is just that. Finally, and often overlooked, is the importance of being novel. Forget that and we’re just copying.

The intellectual rephrasing of these questions is: What important truth do very few people agree with you on?

The business version is: What valuable company is nobody building?

These are tough questions. But you can test your answers; if, as so many people do, one says something like “our educational system is broken and urgently requires repair,” you know that that answer is wrong (it may be a truth, but lots of people agree with it). This may explain why we see so many education non-profits and startups. But query whether most of those are operating in technology mode or globalization mode. You know you’re on the right track when your answer takes the following form:

“Most people believe in X. But the truth is !X.”

Make no mistake; it’s a hard question. Knowing what 0 to 1 endeavor is worth pursuing is incredibly rare, unique, and tricky. But the process, if not the result, can also be richly rewarding.

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Tags: cs183

This is only the first of 11 sections generously written by Blake Masters, the site's creator.

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