how to make library

You probably have two files named 'mylib.h', 'mylib.c'.
Let's make library by these files.
step 1. compile 'mylib.c' to make object file.
    [to make static library] gcc -c mylib.c
    [to make shared library] gcc -fPIC -c mylib.c
step 2. create library from object file.
    [to make static library] ar rc libmylib.a mylib.o
    [to make shared library] gcc -shared -o libmylib.so mylib.o
step3. In the case of static libraries, create the index in libmylib.a library.
    ranlib libmylib.a

Usage of created library.
compile the main file by following command
   gcc main.c -o run.out -l[location of mylib.c] -L[location of libmylib.a(so)] -lmylib 

If you specify the PATH in advance, -l[PATH] and -L[PATH] could be skipped.
In the case of bash shell: open .bashrc file in the home directory, insert
    export C_INCLUDE_PATH=/homes/INCLUDE
    export LIBRARY_PATH=/homes/LIBRARY
In the case of TC shell: open .tcshrc file in the home directory, insert
    setenv C_INCLUDE_PATH /homes/INCLUDE
    senenv LIBRARY_PATH /homes/LIBRARY
then compiler may automatically find the header files from /homes/INCLUDE, and library files from /homes/LIBRARY.

Reference: http://randu.org/tutorials/c/libraries.php

[Michael Sandel] Why we shouldn't trust markets with our civic life

0:12
Here's a question we need to rethink together: What should be the role of money and markets in our societies?

0:22
Today, there are very few things that money can't buy. If you're sentenced to a jail term in Santa Barbara, California, you should know that if you don't like the standard accommodations, you can buy a prison cell upgrade. It's true. For how much, do you think? What would you guess? Five hundred dollars? It's not the Ritz-Carlton. It's a jail! Eighty-two dollars a night. Eighty-two dollars a night. If you go to an amusement park and don't want to stand in the long lines for the popular rides, there is now a solution. In many theme parks, you can pay extra to jump to the head of the line. They call them Fast Track or VIP tickets.

1:14
And this isn't only happening in amusement parks. In Washington, D.C., long lines, queues sometimes form for important Congressional hearings. Now some people don't like to wait in long queues, maybe overnight, even in the rain. So now, for lobbyists and others who are very keen to attend these hearings but don't like to wait, there are companies, line-standing companies, and you can go to them. You can pay them a certain amount of money, they hire homeless people and others who need a job to stand waiting in the line for as long as it takes, and the lobbyist, just before the hearing begins, can take his or her place at the head of the line and a seat in the front of the room. Paid line standing.

2:03
It's happening, the recourse to market mechanisms and market thinking and market solutions, in bigger arenas. Take the way we fight our wars. Did you know that, in Iraq and Afghanistan, there were more private military contractors on the ground than there were U.S. military troops? Now this isn't because we had a public debate about whether we wanted to outsource war to private companies, but this is what has happened.

2:36
Over the past three decades, we have lived through a quiet revolution. We've drifted almost without realizing it from having a market economy to becoming market societies. The difference is this: A market economy is a tool, a valuable and effective tool, for organizing productive activity, but a market society is a place where almost everything is up for sale. It's a way of life, in which market thinking and market values begin to dominate every aspect of life: personal relations, family life, health, education, politics, law, civic life.

3:27
Now, why worry? Why worry about our becoming market societies? For two reasons, I think. One of them has to do with inequality. The more things money can buy, the more affluence, or the lack of it, matters. If the only thing that money determined was access to yachts or fancy vacations or BMWs, then inequality wouldn't matter very much. But when money comes increasingly to govern access to the essentials of the good life -- decent health care, access to the best education, political voice and influence in campaigns -- when money comes to govern all of those things, inequality matters a great deal. And so the marketization of everything sharpens the sting of inequality and its social and civic consequence. That's one reason to worry.

4:37
There's a second reason apart from the worry about inequality, and it's this: with some social goods and practices, when market thinking and market values enter, they may change the meaning of those practices and crowd out attitudes and norms worth caring about.

5:03
I'd like to take an example of a controversial use of a market mechanism, a cash incentive, and see what you think about it. Many schools struggle with the challenge of motivating kids, especially kids from disadvantaged backgrounds, to study hard, to do well in school, to apply themselves. Some economists have proposed a market solution: Offer cash incentives to kids for getting good grades or high test scores or for reading books. They've tried this, actually. They've done some experiments in some major American cities. In New York, in Chicago, in Washington, D.C., they've tried this, offering 50 dollars for an A, 35 dollars for a B. In Dallas, Texas, they have a program that offers eight-year-olds two dollars for each book they read.

6:03
So let's see what -- Some people are in favor, some people are opposed to this cash incentive to motivate achievement. Let's see what people here think about it. Imagine that you are the head of a major school system, and someone comes to you with this proposal. And let's say it's a foundation. They will provide the funds. You don't have to take it out of your budget. How many would be in favor and how many would be opposed to giving it a try? Let's see by a show of hands.

6:32
First, how many think it might at least be worth a try to see if it would work? Raise your hand.

6:39
And how many would be opposed? How many would --

6:42
So the majority here are opposed, but a sizable minority are in favor. Let's have a discussion. Let's start with those of you who object, who would rule it out even before trying. What would be your reason? Who will get our discussion started? Yes?

7:01
Heike Moses: Hello everyone, I'm Heike, and I think it just kills the intrinsic motivation, so in the respect that children, if they would like to read, you just take this incentive away in just paying them, so it just changes behavior. Michael Sandel: Takes the intrinsic incentive away.

7:20
What is, or should be, the intrinsic motivation?

7:24
HM: Well, the intrinsic motivation should be to learn.

7:27
MS: To learn. HM: To get to know the world. And then, if you stop paying them, what happens then? Then they stop reading?

7:35
MS: Now, let's see if there's someone who favors, who thinks it's worth trying this.

7:40
Elizabeth Loftus: I'm Elizabeth Loftus, and you said worth a try, so why not try it and do the experiment and measure things? MS: And measure. And what would you measure? You'd measure how many -- EL: How many books they read and how many books they continued to read after you stopped paying them.

8:01
MS: Oh, after you stopped paying. All right, what about that?

8:04
HM: To be frank, I just think this is, not to offend anyone, a very American way.

8:11
(Laughter) (Applause)

8:17
MS: All right. What's emerged from this discussion is the following question: Will the cash incentive drive out or corrupt or crowd out the higher motivation, the intrinsic lesson that we hope to convey, which is to learn to love to learn and to read for their own sakes? And people disagree about what the effect will be, but that seems to be the question, that somehow a market mechanism or a cash incentive teaches the wrong lesson, and if it does, what will become of these children later?

8:57
I should tell you what's happened with these experiments. The cash for good grades has had very mixed results, for the most part has not resulted in higher grades. The two dollars for each book did lead those kids to read more books. It also led them to read shorter books.

9:17
(Laughter)

9:21
But the real question is, what will become of these kids later? Will they have learned that reading is a chore, a form of piecework to be done for pay, that's the worry, or may it lead them to read maybe for the wrong reason initially but then lead them to fall in love with reading for its own sake?

9:40
Now, what this, even this brief debate, brings out is something that many economists overlook. Economists often assume that markets are inert, that they do not touch or taint the goods they exchange. Market exchange, they assume, doesn't change the meaning or value of the goods being exchanged. This may be true enough if we're talking about material goods. If you sell me a flat screen television or give me one as a gift, it will be the same good. It will work the same either way. But the same may not be true if we're talking about nonmaterial goods and social practices such as teaching and learning or engaging together in civic life. In those domains, bringing market mechanisms and cash incentives may undermine or crowd out nonmarket values and attitudes worth caring about. Once we see that markets and commerce, when extended beyond the material domain, can change the character of the goods themselves, can change the meaning of the social practices, as in the example of teaching and learning, we have to ask where markets belong and where they don't, where they may actually undermine values and attitudes worth caring about. But to have this debate, we have to do something we're not very good at, and that is to reason together in public about the value and the meaning of the social practices we prize, from our bodies to family life to personal relations to health to teaching and learning to civic life.

11:42
Now these are controversial questions, and so we tend to shrink from them. In fact, during the past three decades, when market reasoning and market thinking have gathered force and gained prestige, our public discourse during this time has become hollowed out, empty of larger moral meaning. For fear of disagreement, we shrink from these questions. But once we see that markets change the character of goods, we have to debate among ourselves these bigger questions about how to value goods.

12:24
One of the most corrosive effects of putting a price on everything is on commonality, the sense that we are all in it together. Against the background of rising inequality, marketizing every aspect of life leads to a condition where those who are affluent and those who are of modest means increasingly live separate lives. We live and work and shop and play in different places. Our children go to different schools.

13:04
This isn't good for democracy, nor is it a satisfying way to live, even for those of us who can afford to buy our way to the head of the line. Here's why. Democracy does not require perfect equality, but what it does require is that citizens share in a common life. What matters is that people of different social backgrounds and different walks of life encounter one another, bump up against one another in the ordinary course of life, because this is what teaches us to negotiate and to abide our differences. And this is how we come to care for the common good.

13:52
And so, in the end, the question of markets is not mainly an economic question. It's really a question of how we want to live together. Do we want a society where everything is up for sale, or are there certain moral and civic goods that markets do not honor and money cannot buy?

14:16
Thank you very much.

14:18
(Applause)

Big data revolution: cloud and super computing driven future

Big data revolution: cloud and super computing driven future - 권대석, 21세기 북스

[Steve Jobs] 인생의 유일한 자산

Stage5님의 글


The only thing you really have in your life is time. - Steve Jobs

[Ray Kurzweil] The accelerating power of technology

0:11
Well, it's great to be here. We've heard a lot about the promise of technology, and the peril. I've been quite interested in both. If we could convert 0.03 percent of the sunlight that falls on the earth into energy, we could meet all of our projected needs for 2030. We can't do that today because solar panels are heavy, expensive and very inefficient. There are nano-engineered designs, which at least have been analyzed theoretically, that show the potential to be very lightweight, very inexpensive, very efficient, and we'd be able to actually provide all of our energy needs in this renewable way. Nano-engineered fuel cells could provide the energy where it's needed. That's a key trend, which is decentralization, moving from centralized nuclear power plants and liquid natural gas tankers to decentralized resources that are environmentally more friendly, a lot more efficient and capable and safe from disruption.

1:11
Bono spoke very eloquently, that we have the tools, for the first time, to address age-old problems of disease and poverty. Most regions of the world are moving in that direction. In 1990, in East Asia and the Pacific region, there were 500 million people living in poverty -- that number now is under 200 million. The World Bank projects by 2011, it will be under 20 million, which is a reduction of 95 percent. I did enjoy Bono's comment linking Haight-Ashbury to Silicon Valley. Being from the Massachusetts high-tech community myself, I'd point out that we were hippies also in the 1960s, although we hung around Harvard Square. But we do have the potential to overcome disease and poverty, and I'm going to talk about those issues, if we have the will.

2:06
Kevin Kelly talked about the acceleration of technology. That's been a strong interest of mine, and a theme that I've developed for some 30 years. I realized that my technologies had to make sense when I finished a project. That invariably, the world was a different place when I would introduce a technology. And, I noticed that most inventions fail, not because the R&D department can't get it to work -- if you look at most business plans, they will actually succeed if given the opportunity to build what they say they're going to build -- and 90 percent of those projects or more will fail, because the timing is wrong -- not all the enabling factors will be in place when they're needed.

2:43
So I began to be an ardent student of technology trends, and track where technology would be at different points in time, and began to build the mathematical models of that. It's kind of taken on a life of its own. I've got a group of 10 people that work with me to gather data on key measures of technology in many different areas, and we build models. And you'll hear people say, well, we can't predict the future. And if you ask me, will the price of Google be higher or lower than it is today three years from now, that's very hard to say. Will WiMax CDMA G3 be the wireless standard three years from now? That's hard to say. But if you ask me, what will it cost for one MIPS of computing in 2010, or the cost to sequence a base pair of DNA in 2012, or the cost of sending a megabyte of data wirelessly in 2014, it turns out that those are very predictable.

3:33
There are remarkably smooth exponential curves that govern price performance, capacity, bandwidth. And I'm going to show you a small sample of this, but there's really a theoretical reason why technology develops in an exponential fashion. And a lot of people, when they think about the future, think about it linearly. They think they're going to continue to develop a problem or address a problem using today's tools, at today's pace of progress, and fail to take into consideration this exponential growth.
4:02
The Genome Project was a controversial project in 1990. We had our best Ph.D. students, our most advanced equipment around the world, we got 1/10,000th of the project done, so how're we going to get this done in 15 years? And 10 years into the project, the skeptics were still going strong -- says, "You're two-thirds through this project, and you've managed to only sequence a very tiny percentage of the whole genome." But it's the nature of exponential growth that once it reaches the knee of the curve, it explodes. Most of the project was done in the last few years of the project. It took us 15 years to sequence HIV -- we sequenced SARS in 31 days. So we are gaining the potential to overcome these problems.
4:40
I'm going to show you just a few examples of how pervasive this phenomena is. The actual paradigm-shift rate, the rate of adopting new ideas, is doubling every decade, according to our models. These are all logarithmic graphs, so as you go up the levels it represents, generally multiplying by factor of 10 or 100. It took us half a century to adopt the telephone, the first virtual-reality technology. Cell phones were adopted in about eight years. If you put different communication technologies on this logarithmic graph, television, radio, telephone were adopted in decades. Recent technologies -- like the PC, the web, cell phones -- were under a decade. Now this is an interesting chart, and this really gets at the fundamental reason why an evolutionary process -- and both biology and technology are evolutionary processes -- accelerate. They work through interaction -- they create a capability, and then it uses that capability to bring on the next stage.
5:36
So the first step in biological evolution, the evolution of DNA -- actually it was RNA came first -- took billions of years, but then evolution used that information-processing backbone to bring on the next stage. So the Cambrian Explosion, when all the body plans of the animals were evolved, took only 10 million years. It was 200 times faster. And then evolution used those body plans to evolve higher cognitive functions, and biological evolution kept accelerating. It's an inherent nature of an evolutionary process. So Homo sapiens, the first technology-creating species, the species that combined a cognitive function with an opposable appendage -- and by the way, chimpanzees don't really have a very good opposable thumb -- so we could actually manipulate our environment with a power grip and fine motor coordination, and use our mental models to actually change the world and bring on technology.
6:23
But anyway, the evolution of our species took hundreds of thousands of years, and then working through interaction, evolution used, essentially, the technology-creating species to bring on the next stage, which were the first steps in technological evolution. And the first step took tens of thousands of years -- stone tools, fire, the wheel -- kept accelerating. We always used then the latest generation of technology to create the next generation. Printing press took a century to be adopted; the first computers were designed pen-on-paper -- now we use computers. And we've had a continual acceleration of this process.
6:55
Now by the way, if you look at this on a linear graph, it looks like everything has just happened, but some observer says, "Well, Kurzweil just put points on this graph that fall on that straight line." So, I took 15 different lists from key thinkers, like the Encyclopedia Britannica, the Museum of Natural History, Carl Sagan's Cosmic Calendar on the same -- and these people were not trying to make my point; these were just lists in reference works, and I think that's what they thought the key events were in biological evolution and technological evolution. And again, it forms the same straight line. You have a little bit of thickening in the line because people do have disagreements, what the key points are, there's differences of opinion when agriculture started, or how long the Cambrian Explosion took. But you see a very clear trend. There's a basic, profound acceleration of this evolutionary process. Information technologies double their capacity, price performance, bandwidth, every year. And that's a very profound explosion of exponential growth. A personal experience, when I was at MIT -- computer taking up about the size of this room, less powerful than the computer in your cell phone. But Moore's Law, which is very often identified with this exponential growth, is just one example of many, because it's basically a property of the evolutionary process of technology.
8:13
I put 49 famous computers on this logarithmic graph -- by the way, a straight line on a logarithmic graph is exponential growth -- that's another exponential. It took us three years to double our price performance of computing in 1900, two years in the middle; we're now doubling it every one year. And that's exponential growth through five different paradigms. Moore's Law was just the last part of that, where we were shrinking transistors on an integrated circuit, but we had electro-mechanical calculators, relay-based computers that cracked the German Enigma Code, vacuum tubes in the 1950s predicted the election of Eisenhower, discreet transistors used in the first space flights and then Moore's Law. Every time one paradigm ran out of steam, another paradigm came out of left field to continue the exponential growth. They were shrinking vacuum tubes, making them smaller and smaller. That hit a wall. They couldn't shrink them and keep the vacuum. Whole different paradigm -- transistors came out of the woodwork. In fact, when we see the end of the line for a particular paradigm, it creates research pressure to create the next paradigm. And because we've been predicting the end of Moore's Law for quite a long time -- the first prediction said 2002, until now it says 2022. But by the teen years, the features of transistors will be a few atoms in width, and we won't be able to shrink them any more. That'll be the end of Moore's Law, but it won't be the end of the exponential growth of computing, because chips are flat. We live in a three-dimensional world; we might as well use the third dimension. We will go into the third dimension and there's been tremendous progress, just in the last few years, of getting three-dimensional, self-organizing molecular circuits to work. We'll have those ready well before Moore's Law runs out of steam. Supercomputers -- same thing. Processor performance on Intel chips, the average price of a transistor -- 1968, you could buy one transistor for a dollar. You could buy 10 million in 2002.
10:04
It's pretty remarkable how smooth an exponential process that is. I mean, you'd think this is the result of some tabletop experiment, but this is the result of worldwide chaotic behavior -- countries accusing each other of dumping products, IPOs, bankruptcies, marketing programs. You would think it would be a very erratic process, and you have a very smooth outcome of this chaotic process. Just as we can't predict what one molecule in a gas will do -- it's hopeless to predict a single molecule -- yet we can predict the properties of the whole gas, using thermodynamics, very accurately. It's the same thing here. We can't predict any particular project, but the result of this whole worldwide, chaotic, unpredictable activity of competition and the evolutionary process of technology is very predictable. And we can predict these trends far into the future. Unlike Gertrude Stein's roses, it's not the case that a transistor is a transistor. As we make them smaller and less expensive, the electrons have less distance to travel. They're faster, so you've got exponential growth in the speed of transistors, so the cost of a cycle of one transistor has been coming down with a halving rate of 1.1 years. You add other forms of innovation and processor design, you get a doubling of price performance of computing every one year.
11:23
And that's basically deflation -- 50 percent deflation. And it's not just computers. I mean, it's true of DNA sequencing; it's true of brain scanning; it's true of the World Wide Web. I mean, anything that we can quantify, we have hundreds of different measurements of different, information-related measurements -- capacity, adoption rates -- and they basically double every 12, 13, 15 months, depending on what you're looking at. In terms of price performance, that's a 40 to 50 percent deflation rate. And economists have actually started worrying about that. We had deflation during the Depression, but that was collapse of the money supply, collapse of consumer confidence, a completely different phenomena. This is due to greater productivity, but the economist says, "But there's no way you're going to be able to keep up with that. If you have 50 percent deflation, people may increase their volume 30, 40 percent, but they won't keep up with it." But what we're actually seeing is that we actually more than keep up with it. We've had 28 percent per year compounded growth in dollars in information technology over the last 50 years. I mean, people didn't build iPods for 10,000 dollars 10 years ago. As the price performance makes new applications feasible, new applications come to the market. And this is a very widespread phenomena. Magnetic data storage -- that's not Moore's Law, it's shrinking magnetic spots, different engineers, different companies, same exponential process.
12:43
A key revolution is that we're understanding our own biology in these information terms. We're understanding the software programs that make our body run. These were evolved in very different times -- we'd like to actually change those programs. One little software program, called the fat insulin receptor gene, basically says, "Hold onto every calorie, because the next hunting season may not work out so well." That was in the interests of the species tens of thousands of years ago. We'd like to actually turn that program off. They tried that in animals, and these mice ate ravenously and remained slim and got the health benefits of being slim. They didn't get diabetes; they didn't get heart disease; they lived 20 percent longer; they got the health benefits of caloric restriction without the restriction. Four or five pharmaceutical companies have noticed this, felt that would be interesting drug for the human market, and that's just one of the 30,000 genes that affect our biochemistry.
13:38
We were evolved in an era where it wasn't in the interests of people at the age of most people at this conference, like myself, to live much longer, because we were using up the precious resources which were better deployed towards the children and those caring for them. So, life -- long lifespans -- like, that is to say, much more than 30 -- weren't selected for, but we are learning to actually manipulate and change these software programs through the biotechnology revolution. For example, we can inhibit genes now with RNA interference. There are exciting new forms of gene therapy that overcome the problem of placing the genetic material in the right place on the chromosome. There's actually a -- for the first time now, something going to human trials, that actually cures pulmonary hypertension -- a fatal disease -- using gene therapy. So we'll have not just designer babies, but designer baby boomers. And this technology is also accelerating. It cost 10 dollars per base pair in 1990, then a penny in 2000. It's now under a 10th of a cent. The amount of genetic data -- basically this shows that smooth exponential growth doubled every year, enabling the genome project to be completed.
14:47
Another major revolution: the communications revolution. The price performance, bandwidth, capacity of communications measured many different ways; wired, wireless is growing exponentially. The Internet has been doubling in power and continues to, measured many different ways. This is based on the number of hosts.
15:05
Miniaturization -- we're shrinking the size of technology at an exponential rate, both wired and wireless. These are some designs from Eric Drexler's book -- which we're now showing are feasible with super-computing simulations, where actually there are scientists building molecule-scale robots. One has one that actually walks with a surprisingly human-like gait, that's built out of molecules. There are little machines doing things in experimental bases. The most exciting opportunity is actually to go inside the human body and perform therapeutic and diagnostic functions. And this is less futuristic than it may sound. These things have already been done in animals.
15:44
There's one nano-engineered device that cures type 1 diabetes. It's blood cell-sized. They put tens of thousands of these in the blood cell -- they tried this in rats -- it lets insulin out in a controlled fashion, and actually cures type 1 diabetes. What you're watching is a design of a robotic red blood cell, and it does bring up the issue that our biology is actually very sub-optimal, even though it's remarkable in its intricacy. Once we understand its principles of operation, and the pace with which we are reverse-engineering biology is accelerating, we can actually design these things to be thousands of times more capable. An analysis of this respirocyte, designed by Rob Freitas, indicates if you replace 10 percent of your red blood cells with these robotic versions, you could do an Olympic sprint for 15 minutes without taking a breath. You could sit at the bottom of your pool for four hours -- so, "Honey, I'm in the pool," will take on a whole new meaning. It will be interesting to see what we do in our Olympic trials. Presumably we'll ban them, but then we'll have the specter of teenagers in their high schools gyms routinely out-performing the Olympic athletes. Freitas has a design for a robotic white blood cell. These are 2020-circa scenarios, but they're not as futuristic as it may sound. There are four major conferences on building blood cell-sized devices; there are many experiments in animals. There's actually one going into human trial, so this is feasible technology.
17:09
If we come back to our exponential growth of computing, 1,000 dollars of computing is now somewhere between an insect and a mouse brain. It will intersect human intelligence in terms of capacity in the 2020s, but that'll be the hardware side of the equation. Where will we get the software? Well, it turns out we can see inside the human brain, and in fact not surprisingly, the spatial and temporal resolution of brain scanning is doubling every year. And with the new generation of scanning tools, for the first time we can actually see individual inter-neural fibers and see them processing and signaling in real time -- but then the question is, OK, we can get this data now, but can we understand it? Doug Hofstadter wonders, well, maybe our intelligence just isn't great enough to understand our intelligence, and if we were smarter, well, then our brains would be that much more complicated, and we'd never catch up to it. It turns out that we can understand it.
18:00
This is a block diagram of a model and simulation of the human auditory cortex that actually works quite well -- in applying psychoacoustic tests, gets very similar results to human auditory perception. There's another simulation of the cerebellum -- that's more than half the neurons in the brain -- again, works very similarly to human skill formation. This is at an early stage, but you can show with the exponential growth of the amount of information about the brain and the exponential improvement in the resolution of brain scanning, we will succeed in reverse-engineering the human brain by the 2020s. We've already had very good models and simulation of about 15 regions out of the several hundred.
18:43
All of this is driving exponentially growing economic progress. We've had productivity go from 30 dollars to 150 dollars per hour of labor in the last 50 years. E-commerce has been growing exponentially. It's now a trillion dollars. You might wonder, well, wasn't there a boom and a bust? That was strictly a capital-markets phenomena. Wall Street noticed that this was a revolutionary technology, which it was, but then six months later, when it hadn't revolutionized all business models, they figured, well, that was wrong, and then we had this bust.
19:13
All right, this is a technology that we put together using some of the technologies we're involved in. This will be a routine feature in a cell phone. It would be able to translate from one language to another.
19:34
So let me just end with a couple of scenarios. By 2010 computers will disappear. They'll be so small, they'll be embedded in our clothing, in our environment. Images will be written directly to our retina, providing full-immersion virtual reality, augmented real reality. We'll be interacting with virtual personalities.
19:51
But if we go to 2029, we really have the full maturity of these trends, and you have to appreciate how many turns of the screw in terms of generations of technology, which are getting faster and faster, we'll have at that point. I mean, we will have two-to-the-25th-power greater price performance, capacity and bandwidth of these technologies, which is pretty phenomenal. It'll be millions of times more powerful than it is today. We'll have completed the reverse-engineering of the human brain, 1,000 dollars of computing will be far more powerful than the human brain in terms of basic raw capacity. Computers will combine the subtle pan-recognition powers of human intelligence with ways in which machines are already superior, in terms of doing analytic thinking, remembering billions of facts accurately. Machines can share their knowledge very quickly. But it's not just an alien invasion of intelligent machines. We are going to merge with our technology.
20:41
These nano-bots I mentioned will first be used for medical and health applications: cleaning up the environment, providing powerful fuel cells and widely distributed decentralized solar panels and so on in the environment. But they'll also go inside our brain, interact with our biological neurons. We've demonstrated the key principles of being able to do this. So, for example, full-immersion virtual reality from within the nervous system, the nano-bots shut down the signals coming from your real senses, replace them with the signals that your brain would be receiving if you were in the virtual environment, and then it'll feel like you're in that virtual environment. You can go there with other people, have any kind of experience with anyone involving all of the senses. "Experience beamers," I call them, will put their whole flow of sensory experiences in the neurological correlates of their emotions out on the Internet. You can plug in and experience what it's like to be someone else. But most importantly, it'll be a tremendous expansion of human intelligence through this direct merger with our technology, which in some sense we're doing already. We routinely do intellectual feats that would be impossible without our technology. Human life expectancy is expanding. It was 37 in 1800, and with this sort of biotechnology, nano-technology revolutions, this will move up very rapidly in the years ahead.
21:56
My main message is that progress in technology is exponential, not linear. Many -- even scientists -- assume a linear model, so they'll say, "Oh, it'll be hundreds of years before we have self-replicating nano-technology assembly or artificial intelligence." If you really look at the power of exponential growth, you'll see that these things are pretty soon at hand. And information technology is increasingly encompassing all of our lives, from our music to our manufacturing to our biology to our energy to materials.
22:31
We'll be able to manufacture almost anything we need in the 2020s, from information, in very inexpensive raw materials, using nano-technology. These are very powerful technologies. They both empower our promise and our peril. So we have to have the will to apply them to the right problems.
22:48
Thank you very much.
22:49
(Applause)

[RSA Animate] Drive: The surprising truth about what motivates us

We are not as endlessly manipulable and as predictable as you would think.

I want to give you two that call into question this idea that 'if you reward something, you get more of the behavior you want' and 'if you punish something do you get less of the behavior you want.'

The higher the reward, the better the performance, as long as the task involved only mechanical skills(things like memorizing strings of digits, solving word puzzles, throwing a ball through a hoop).
Once the task called for even rudimentary cognitive skills, a larger reward led to poorer performance.
For simple, straightforward tasks, "If you do this, then you get that." they're great! But when a task gets more complicated, when it requires conceptual, creative thinking, those kind of motivators demonstrably don't work.

There is another paradox here. The best use of money as a motivator is to pay people enough to take the issue of money off the table. So they're not thinking about money, they're thinking about the work. Once you do that, there are three factors that the science shows lead to better performance, not to mention personal satisfaction - autonomy, mastery and purpose.

Autonomy - The desire to be self directed, direct our lives.
Management is great if you want compliance, but if you want engagement, self directed is better.
Atlassian, an Australian software company, let the developers work for anything they want, with whomever they want, but with beer and cake and fun and things like that. It turns out that that one day of pure, undiluted autonomy has led to a whole array of fixes for existing software, a whole array of ideas for new products.

Mastery - The urge to get better at stuff. It's not going to get you mate or make you any money. It don't need money, It don't take fame, It don't need no credit card to ride this train. Because It's fun, Because you get better at it, and that's satisfying.

There are bunch of people around the world who do technically sophisticated, highly skilled work. But they're willing to work for free, and volunteer their time 20, 30 hours a week, then what they create, they give it away rather than sell it(Linux, Apache, Wikipedia). They do it for someone else for free.
Why are they doing this? It's overwhelmingly clear - challenge and mastery, along with making a contribution, that's it.

More and more organizations want transcendent purpose. But we're seeing now is when the profit motive becomes unmoored from the purpose motive, bad things happen. More and more organizations are realizing this, disturbing the categories between what's profit and what's purpose. The founder of Skype says, "Our goal is to be disruptive, but in the case of make the world a better place." Steve Jobs, "I want to put a ding in the universe." That's the kind of thing that might get you up, racing to go to work.

I thing that the big take-away here is that if we start treating people like people, not assuming that they're simply horses, we can make organizations and work lives that make us better off and make the world a little bit better.

[Erik Brynjolfsson] The key to growth? Race with the machines

120 years ago, American factories began to electrify their operations, replacing their steam engines with electric motors, igniting the Second Industrial Revolution.
But they didn't redesign the factories to take advantage of electricity's flexibility. It fell to the next generation to invent new work processes, and then productivity soared.

Computer is a General purpose technology of our era. And just as the earlier generations needed to redesign their factories, we're going to reinvent our organizations and even our whole economic systems.
Some situations such as income of typical worker is stagnating even productivity is growing don't mean the end of innovation, but growing pain of what Eric Brynjolfsson and Andrew McAfee call the new machine age.

Today, productivity is at an all-time high, and despite the Great Recession, it growing at a faster rate  than ever in history, include the period of Second Industrial Revolution.

But the new machine age is more about knowledge creation than just physical production. And these creation is not measured by standard metrics. because a lot of such stuffs are provided for free just like Google, Wikipedia, Skype, and TED Talk. Zero price means zero weight in the GDP statistics. That's why some smart people say growth is over, But we need to understand the underlying drivers of growth. The new machine age is digital, exponential and combinatorial.

When goods are digital, they can be replicated with perfect quality at nearly zero cost, and they can be delivered almost instantaneously. And in the age of big data, we can measure the world in ways we never could before.

Secondly, the new machine age is exponential. Computers get better faster than anything else ever. Even our brains are wired for a linear world.

Thirdly, the new machine age is combinatorial. The stagnationist view is that ideas get used up, like low hanging fruit, but the reality is that each innovation creates building blocks for even more innovations.

Individually, digital, exponential and combinatorial would each be game-changers. Put them together, and we're seeing a wave of astonishing breakthroughs.

But perhaps the most important invention is machine learning. Consider one project: IBM's Watson. It improved at a rate faster than any human could. It can surf the internet unsupervised.

Productivity is at an all time high, but fewer people now have jobs. This is great decoupling of productivity from employment, of wealth from work. We should understand its basic causes. Technology is racing ahead, but it's leaving more and more people behind. People are racing against the machine, and many of them are losing that race.

What can we do to create shared prosperity? Instead of racing against the machine, we need to learn to race with the machine.