Our generation...

[Gezu]

Our Generation
- Technology

This idea was suggested by my French teacher. :/
So what he said was, it must be hard for us...
growing up with really advanced technology.
For example, 10 years ago, touch-screen on phones weren't
even heard of.

Now 100 years ago, photos were in black and white, now you can record in 60fps.
Technology has changed a lot since the first
Ipad, Iphone, Tablet, Gaming PC's and laptops.
Now you can get a laptop/Tablet... a really good idea in my opinion.
When sliced bread came out, people were amazed, tinned food... people were
AMAZED.
Now we have these thin as ice Ipad's and etc.

So I wonder how technology will change our future,
we are technically one of the first
Generations that will have
the present of technology...
always. Most of us already have a
phone, and admit it we are on it for ages.
So in the future, think how much we'd be on them.
Now it's kinda scary thinking about how
technology will look in the next 100 years,
maybe people can go to Mars and back in about 2 hours... I don't know.
I mean Virgin are already trying to send people to the moon now...

I will like to know your opinion on how technology could
dramatically change our future.
Maybe you might think Technology
has not changed a lot, or you might
think Technology has changed soooooooooooo
much.
There isn't a right answer to this, I'd just like to
know other peoples thought on it.

-
SionyyPoo/ Sion

<333


​

 

[Tenebrous]

Technology is accelerating

By 2030 expect to see the first fusion power plants and by 2050 they should be one of our main sources of power

By 2045 expect to see the singularity, or the point where machines are generalized enough to become comparable to mans.

By 2060 expect a massive upheaval of social structure, most people from the west coast will migrate due to climate change

By 2075 expect the most complex computer to be able to outsmart every human on the planet. ASI is born. Mind uploading is now available to more people, despite it being available to the really rich since 2050.

By 2085 expect the most difficult time in the history of humanity. It is quite possible we could go extinct due to climate change.

By 2100 expect to see the first use of asteroid mining as humanity surpasses type I on the karashev scale

By 2120 expect mind uploading to be a normal practice

By 2150 expect to see an explosive exploration of the solar system in order to harness more resources

By 2175 expect to see the first shape shifting nano robots, being able to manipulate matter atom by atom

I could write waaay more and some of this stuff is probably off but yeah

 

[Drocz]

Shut up sion, this doesn't suit you! <3

 

[Kaylie | Holidays]

My generation you mean :3

Spoiler: Music Video

 

[Gezu]

My generation you mean :3

Spoiler: Music Video

... xD

 

[Kaylie | Holidays]

... xD

I dunno what I was thinking xD

 

[Ceroria]

Technology is accelerating

By 2030 expect to see the first fusion power plants and by 2050 they should be one of our main sources of power

By 2045 expect to see the singularity, or the point where machines are generalized enough to become comparable to mans.

By 2060 expect a massive upheaval of social structure, most people from the west coast will migrate due to climate change

By 2075 expect the most complex computer to be able to outsmart every human on the planet. ASI is born. Mind uploading is now available to more people, despite it being available to the really rich since 2050.

By 2085 expect the most difficult time in the history of humanity. It is quite possible we could go extinct due to climate change.

By 2100 expect to see the first use of asteroid mining as humanity surpasses type I on the karashev scale

By 2120 expect mind uploading to be a normal practice

By 2150 expect to see an explosive exploration of the solar system in order to harness more resources

By 2175 expect to see the first shape shifting nano robots, being able to manipulate matter atom by atom

I could write waaay more and some of this stuff is probably off but yeah

You really need to hook me up with where you read about this stuff because this is the kind of material that just sounds so interesting.

As cool and possible as all that may seem with our current advancement rate of technology, I wouldn't be surprised if eventually this rate slowed a bit, and we had an "unexciting" period of 5-10 years. Again, I could be wrong but it just seems to me like something that could quite possible occur.

 

[SnoopSean]

Only the future knows, only imagination knows, only the mind knows.

 

[Tenebrous]

You really need to hook me up with where you read about this stuff because this is the kind of material that just sounds so interesting.

As cool and possible as all that may seem with our current advancement rate of technology, I wouldn't be surprised if eventually this rate slowed a bit, and we had an "unexciting" period of 5-10 years. Again, I could be wrong but it just seems to me like something that could quite possible occur.

An Excerpt from Kurzweil's site

An analysis of the history of technology shows that technological change is exponential, contrary to the common-sense “intuitive linear” view. So we won’t experience 100 years of progress in the 21st century — it will be more like 20,000 years of progress (at today’s rate). The “returns,” such as chip speed and cost-effectiveness, also increase exponentially. There’s even exponential growth in the rate of exponential growth. Within a few decades, machine intelligence will surpass human intelligence, leading to The Singularity — technological change so rapid and profound it represents a rupture in the fabric of human history. The implications include the merger of biological and nonbiological intelligence, immortal software-based humans, and ultra-high levels of intelligence that expand outward in the universe at the speed of light.

The Law of Accelerating Returns
We can organize these observations into what I call the law of accelerating returns as follows:

• Evolution applies positive feedback in that the more capable methods resulting from one stage of evolutionary progress are used to create the next stage. As a result, the
• rate of progress of an evolutionary process increases exponentially over time. Over time, the “order” of the information embedded in the evolutionary process (i.e., the measure of how well the information fits a purpose, which in evolution is survival) increases.
• A correlate of the above observation is that the “returns” of an evolutionary process (e.g., the speed, cost-effectiveness, or overall “power” of a process) increase exponentially over time.
• In another positive feedback loop, as a particular evolutionary process (e.g., computation) becomes more effective (e.g., cost effective), greater resources are deployed toward the further progress of that process. This results in a second level of exponential growth (i.e., the rate of exponential growth itself grows exponentially).
• Biological evolution is one such evolutionary process.
• Technological evolution is another such evolutionary process. Indeed, the emergence of the first technology creating species resulted in the new evolutionary process of technology. Therefore, technological evolution is an outgrowth of–and a continuation of–biological evolution.
• A specific paradigm (a method or approach to solving a problem, e.g., shrinking transistors on an integrated circuit as an approach to making more powerful computers) provides exponential growth until the method exhausts its potential. When this happens, a paradigm shift (i.e., a fundamental change in the approach) occurs, which enables exponential growth to continue.

If we apply these principles at the highest level of evolution on Earth, the first step, the creation of cells, introduced the paradigm of biology. The subsequent emergence of DNA provided a digital method to record the results of evolutionary experiments. Then, the evolution of a species who combined rational thought with an opposable appendage (i.e., the thumb) caused a fundamental paradigm shift from biology to technology. The upcoming primary paradigm shift will be from biological thinking to a hybrid combining biological and nonbiological thinking. This hybrid will include “biologically inspired” processes resulting from the reverse engineering of biological brains.

If we examine the timing of these steps, we see that the process has continuously accelerated. The evolution of life forms required billions of years for the first steps (e.g., primitive cells); later on progress accelerated. During the Cambrian explosion, major paradigm shifts took only tens of millions of years. Later on, Humanoids developed over a period of millions of years, and Homo sapiens over a period of only hundreds of thousands of years.

With the advent of a technology-creating species, the exponential pace became too fast for evolution through DNA-guided protein synthesis and moved on to human-created technology. Technology goes beyond mere tool making; it is a process of creating ever more powerful technology using the tools from the previous round of innovation. In this way, human technology is distinguished from the tool making of other species. There is a record of each stage of technology, and each new stage of technology builds on the order of the previous stage.

The first technological steps-sharp edges, fire, the wheel–took tens of thousands of years. For people living in this era, there was little noticeable technological change in even a thousand years. By 1000 A.D., progress was much faster and a paradigm shift required only a century or two. In the nineteenth century, we saw more technological change than in the nine centuries preceding it. Then in the first twenty years of the twentieth century, we saw more advancement than in all of the nineteenth century. Now, paradigm shifts occur in only a few years time. The World Wide Web did not exist in anything like its present form just a few years ago; it didn’t exist at all a decade ago.

The following provides a brief overview of the law of accelerating returns as it applies to the double exponential growth of computation. This model considers the impact of the growing power of the technology to foster its own next generation. For example, with more powerful computers and related technology, we have the tools and the knowledge to design yet more powerful computers, and to do so more quickly.

Note that the data for the year 2000 and beyond assume neural net connection calculations as it is expected that this type of calculation will ultimately dominate, particularly in emulating human brain functions. This type of calculation is less expensive than conventional (e.g., Pentium III / IV) calculations by a factor of at least 100 (particularly if implemented using digital controlled analog electronics, which would correspond well to the brain’s digital controlled analog electrochemical processes). A factor of 100 translates into approximately 6 years (today) and less than 6 years later in the twenty-first century.

My estimate of brain capacity is 100 billion neurons times an average 1,000 connections per neuron (with the calculations taking place primarily in the connections) times 200 calculations per second. Although these estimates are conservatively high, one can find higher and lower estimates. However, even much higher (or lower) estimates by orders of magnitude only shift the prediction by a relatively small number of years.

Some prominent dates from this analysis include the following:

• We achieve one Human Brain capability (2 * 10^16 cps) for $1,000 around the year 2023.
• We achieve one Human Brain capability (2 * 10^16 cps) for one cent around the year 2037.
• We achieve one Human Race capability (2 * 10^26 cps) for $1,000 around the year 2049.
• We achieve one Human Race capability (2 * 10^26 cps) for one cent around the year 2059.

The Model considers the following variables:

• V: Velocity (i.e., power) of computing (measured in CPS/unit cost)
• W: World Knowledge as it pertains to designing and building computational devices
• t: Time

The assumptions of the model are:

1. (1) V = C1 * W

In other words, computer power is a linear function of the knowledge of how to build computers. This is actually a conservative assumption. In general, innovations improve V (computer power) by a multiple, not in an additive way. Independent innovations multiply each other’s effect. For example, a circuit advance such as CMOS, a more efficient IC wiring methodology, and a processor innovation such as pipelining all increase V by independent multiples.

• (2) W = C2 * Integral (0 to t) V

In other words, W (knowledge) is cumulative, and the instantaneous increment to knowledge is proportional to V.

This gives us:

• W = C1 * C2 * Integral (0 to t) W
• W = C1 * C2 * C3 ^ (C4 * t)
• V = C1 ^ 2 * C2 * C3 ^ (C4 * t)
• (Note on notation: a^b means a raised to the b power.)

Simplifying the constants, we get:

• V = Ca * Cb ^ (Cc * t)

So this is a formula for “accelerating” (i.e., exponentially growing) returns, a “regular Moore’s Law.”

As I mentioned above, the data shows exponential growth in the rate of exponential growth. (We doubled computer power every three years early in the twentieth century, every two years in the middle of the century, and close to every one year during the 1990s.)

Let’s factor in another exponential phenomenon, which is the growing resources for computation. Not only is each (constant cost) device getting more powerful as a function of W, but the resources deployed for computation are also growing exponentially.

We now have:

• N: Expenditures for computation
• V = C1 * W (as before)
• N = C4 ^ (C5 * t) (Expenditure for computation is growing at its own exponential rate)
• W = C2 * Integral(0 to t) (N * V)

As before, world knowledge is accumulating, and the instantaneous increment is proportional to the amount of computation, which equals the resources deployed for computation (N) * the power of each (constant cost) device.

This gives us:

• W = C1 * C2 * Integral(0 to t) (C4 ^ (C5 * t) * W)
• W = C1 * C2 * (C3 ^ (C6 * t)) ^ (C7 * t)
• V = C1 ^ 2 * C2 * (C3 ^ (C6 * t)) ^ (C7 * t)

Simplifying the constants, we get:

• V = Ca * (Cb ^ (Cc * t)) ^ (Cd * t)

This is a double exponential–an exponential curve in which the rate of exponential growth is growing at a different exponential rate.

Now let’s consider real-world data. Considering the data for actual calculating devices and computers during the twentieth century:

• CPS/$1K: Calculations Per Second for $1,000

Twentieth century computing data matches:

• CPS/$1K = 10^(6.00*((20.40/6.00)^((A13-1900)/100))-11.00)

We can determine the growth rate over a period of time:

• Growth Rate =10^((LOG(CPS/$1K for Current Year) – LOG(CPS/$1K for Previous Year))/(Current Year – Previous Year))
• Human Brain = 100 Billion (10^11) neurons * 1000 (10^3) Connections/Neuron * 200 (2 * 10^2) Calculations Per Second Per Connection = 2 * 10^16 Calculations Per Second
• Human Race = 10 Billion (10^10) Human Brains = 2 * 10^26 Calculations Per Second

These formulas produce the graph above.

Already, IBM’s “Blue Gene” supercomputer, now being built and scheduled to be completed by 2005, is projected to provide 1 million billion calculations per second (i.e., one billion megaflops). This is already one twentieth of the capacity of the human brain, which I estimate at a conservatively high 20 million billion calculations per second (100 billion neurons times 1,000 connections per neuron times 200 calculations per second per connection). In line with my earlier predictions, supercomputers will achieve one human brain capacity by 2010, and personal computers will do so by around 2020. By 2030, it will take a village of human brains (around a thousand) to match $1000 of computing. By 2050, $1000 of computing will equal the processing power of all human brains on Earth. Of course, this only includes those brains still using carbon-based neurons. While human neurons are wondrous creations in a way, we wouldn’t (and don’t) design computing circuits the same way. Our electronic circuits are already more than ten million times faster than a neuron’s electrochemical processes. Most of the complexity of a human neuron is devoted to maintaining its life support functions, not its information processing capabilities. Ultimately, we will need to port our mental processes to a more suitable computational substrate. Then our minds won’t have to stay so small, being constrained as they are today to a mere hundred trillion neural connections each operating at a ponderous 200 digitally controlled analog calculations per second.

WHY YOU SAY THAT THERE HAS BEEN A "SLOW DOWN"
We also need to distinguish between the “S” curve (an “S” stretched to the right, comprising very slow, virtually unnoticeable growth–followed by very rapid growth–followed by a flattening out as the process approaches an asymptote) that is characteristic of any specific technological paradigm and the continuing exponential growth that is characteristic of the ongoing evolutionary process of technology. Specific paradigms, such as Moore’s Law, do ultimately reach levels at which exponential growth is no longer feasible. Thus Moore’s Law is an S curve. But the growth of computation is an ongoing exponential (at least until we “saturate” the Universe with the intelligence of our human-machine civilization, but that will not be a limit in this coming century). In accordance with the law of accelerating returns, paradigm shift, also called innovation, turns the S curve of any specific paradigm into a continuing exponential. A new paradigm (e.g., three-dimensional circuits) takes over when the old paradigm approaches its natural limit. This has already happened at least four times in the history of computation. This difference also distinguishes the tool making of non-human species, in which the mastery of a tool-making (or using) skill by each animal is characterized by an abruptly ending S shaped learning curve, versus human-created technology, which has followed an exponential pattern of growth and acceleration since its inception.



SOURCE: http://www.kurzweilai.net/the-law-of-accelerating-returns

 

[e]

An Excerpt from Kurzweil's site

An analysis of the history of technology shows that technological change is exponential, contrary to the common-sense “intuitive linear” view. So we won’t experience 100 years of progress in the 21st century — it will be more like 20,000 years of progress (at today’s rate). The “returns,” such as chip speed and cost-effectiveness, also increase exponentially. There’s even exponential growth in the rate of exponential growth. Within a few decades, machine intelligence will surpass human intelligence, leading to The Singularity — technological change so rapid and profound it represents a rupture in the fabric of human history. The implications include the merger of biological and nonbiological intelligence, immortal software-based humans, and ultra-high levels of intelligence that expand outward in the universe at the speed of light.

The Law of Accelerating Returns
We can organize these observations into what I call the law of accelerating returns as follows:

• Evolution applies positive feedback in that the more capable methods resulting from one stage of evolutionary progress are used to create the next stage. As a result, the
• rate of progress of an evolutionary process increases exponentially over time. Over time, the “order” of the information embedded in the evolutionary process (i.e., the measure of how well the information fits a purpose, which in evolution is survival) increases.
• A correlate of the above observation is that the “returns” of an evolutionary process (e.g., the speed, cost-effectiveness, or overall “power” of a process) increase exponentially over time.
• In another positive feedback loop, as a particular evolutionary process (e.g., computation) becomes more effective (e.g., cost effective), greater resources are deployed toward the further progress of that process. This results in a second level of exponential growth (i.e., the rate of exponential growth itself grows exponentially).
• Biological evolution is one such evolutionary process.
• Technological evolution is another such evolutionary process. Indeed, the emergence of the first technology creating species resulted in the new evolutionary process of technology. Therefore, technological evolution is an outgrowth of–and a continuation of–biological evolution.
• A specific paradigm (a method or approach to solving a problem, e.g., shrinking transistors on an integrated circuit as an approach to making more powerful computers) provides exponential growth until the method exhausts its potential. When this happens, a paradigm shift (i.e., a fundamental change in the approach) occurs, which enables exponential growth to continue.

If we apply these principles at the highest level of evolution on Earth, the first step, the creation of cells, introduced the paradigm of biology. The subsequent emergence of DNA provided a digital method to record the results of evolutionary experiments. Then, the evolution of a species who combined rational thought with an opposable appendage (i.e., the thumb) caused a fundamental paradigm shift from biology to technology. The upcoming primary paradigm shift will be from biological thinking to a hybrid combining biological and nonbiological thinking. This hybrid will include “biologically inspired” processes resulting from the reverse engineering of biological brains.

If we examine the timing of these steps, we see that the process has continuously accelerated. The evolution of life forms required billions of years for the first steps (e.g., primitive cells); later on progress accelerated. During the Cambrian explosion, major paradigm shifts took only tens of millions of years. Later on, Humanoids developed over a period of millions of years, and Homo sapiens over a period of only hundreds of thousands of years.

With the advent of a technology-creating species, the exponential pace became too fast for evolution through DNA-guided protein synthesis and moved on to human-created technology. Technology goes beyond mere tool making; it is a process of creating ever more powerful technology using the tools from the previous round of innovation. In this way, human technology is distinguished from the tool making of other species. There is a record of each stage of technology, and each new stage of technology builds on the order of the previous stage.

The first technological steps-sharp edges, fire, the wheel–took tens of thousands of years. For people living in this era, there was little noticeable technological change in even a thousand years. By 1000 A.D., progress was much faster and a paradigm shift required only a century or two. In the nineteenth century, we saw more technological change than in the nine centuries preceding it. Then in the first twenty years of the twentieth century, we saw more advancement than in all of the nineteenth century. Now, paradigm shifts occur in only a few years time. The World Wide Web did not exist in anything like its present form just a few years ago; it didn’t exist at all a decade ago.

The following provides a brief overview of the law of accelerating returns as it applies to the double exponential growth of computation. This model considers the impact of the growing power of the technology to foster its own next generation. For example, with more powerful computers and related technology, we have the tools and the knowledge to design yet more powerful computers, and to do so more quickly.

Note that the data for the year 2000 and beyond assume neural net connection calculations as it is expected that this type of calculation will ultimately dominate, particularly in emulating human brain functions. This type of calculation is less expensive than conventional (e.g., Pentium III / IV) calculations by a factor of at least 100 (particularly if implemented using digital controlled analog electronics, which would correspond well to the brain’s digital controlled analog electrochemical processes). A factor of 100 translates into approximately 6 years (today) and less than 6 years later in the twenty-first century.

My estimate of brain capacity is 100 billion neurons times an average 1,000 connections per neuron (with the calculations taking place primarily in the connections) times 200 calculations per second. Although these estimates are conservatively high, one can find higher and lower estimates. However, even much higher (or lower) estimates by orders of magnitude only shift the prediction by a relatively small number of years.

Some prominent dates from this analysis include the following:

• We achieve one Human Brain capability (2 * 10^16 cps) for $1,000 around the year 2023.
• We achieve one Human Brain capability (2 * 10^16 cps) for one cent around the year 2037.
• We achieve one Human Race capability (2 * 10^26 cps) for $1,000 around the year 2049.
• We achieve one Human Race capability (2 * 10^26 cps) for one cent around the year 2059.

The Model considers the following variables:

• V: Velocity (i.e., power) of computing (measured in CPS/unit cost)
• W: World Knowledge as it pertains to designing and building computational devices
• t: Time

The assumptions of the model are:

1. (1) V = C1 * W

In other words, computer power is a linear function of the knowledge of how to build computers. This is actually a conservative assumption. In general, innovations improve V (computer power) by a multiple, not in an additive way. Independent innovations multiply each other’s effect. For example, a circuit advance such as CMOS, a more efficient IC wiring methodology, and a processor innovation such as pipelining all increase V by independent multiples.

• (2) W = C2 * Integral (0 to t) V

In other words, W (knowledge) is cumulative, and the instantaneous increment to knowledge is proportional to V.

This gives us:

• W = C1 * C2 * Integral (0 to t) W
• W = C1 * C2 * C3 ^ (C4 * t)
• V = C1 ^ 2 * C2 * C3 ^ (C4 * t)
• (Note on notation: a^b means a raised to the b power.)

Simplifying the constants, we get:

• V = Ca * Cb ^ (Cc * t)

So this is a formula for “accelerating” (i.e., exponentially growing) returns, a “regular Moore’s Law.”

As I mentioned above, the data shows exponential growth in the rate of exponential growth. (We doubled computer power every three years early in the twentieth century, every two years in the middle of the century, and close to every one year during the 1990s.)

Let’s factor in another exponential phenomenon, which is the growing resources for computation. Not only is each (constant cost) device getting more powerful as a function of W, but the resources deployed for computation are also growing exponentially.

We now have:

• N: Expenditures for computation
• V = C1 * W (as before)
• N = C4 ^ (C5 * t) (Expenditure for computation is growing at its own exponential rate)
• W = C2 * Integral(0 to t) (N * V)

As before, world knowledge is accumulating, and the instantaneous increment is proportional to the amount of computation, which equals the resources deployed for computation (N) * the power of each (constant cost) device.

This gives us:

• W = C1 * C2 * Integral(0 to t) (C4 ^ (C5 * t) * W)
• W = C1 * C2 * (C3 ^ (C6 * t)) ^ (C7 * t)
• V = C1 ^ 2 * C2 * (C3 ^ (C6 * t)) ^ (C7 * t)

Simplifying the constants, we get:

• V = Ca * (Cb ^ (Cc * t)) ^ (Cd * t)

This is a double exponential–an exponential curve in which the rate of exponential growth is growing at a different exponential rate.

Now let’s consider real-world data. Considering the data for actual calculating devices and computers during the twentieth century:

• CPS/$1K: Calculations Per Second for $1,000

Twentieth century computing data matches:

• CPS/$1K = 10^(6.00*((20.40/6.00)^((A13-1900)/100))-11.00)

We can determine the growth rate over a period of time:

• Growth Rate =10^((LOG(CPS/$1K for Current Year) – LOG(CPS/$1K for Previous Year))/(Current Year – Previous Year))
• Human Brain = 100 Billion (10^11) neurons * 1000 (10^3) Connections/Neuron * 200 (2 * 10^2) Calculations Per Second Per Connection = 2 * 10^16 Calculations Per Second
• Human Race = 10 Billion (10^10) Human Brains = 2 * 10^26 Calculations Per Second

These formulas produce the graph above.

Already, IBM’s “Blue Gene” supercomputer, now being built and scheduled to be completed by 2005, is projected to provide 1 million billion calculations per second (i.e., one billion megaflops). This is already one twentieth of the capacity of the human brain, which I estimate at a conservatively high 20 million billion calculations per second (100 billion neurons times 1,000 connections per neuron times 200 calculations per second per connection). In line with my earlier predictions, supercomputers will achieve one human brain capacity by 2010, and personal computers will do so by around 2020. By 2030, it will take a village of human brains (around a thousand) to match $1000 of computing. By 2050, $1000 of computing will equal the processing power of all human brains on Earth. Of course, this only includes those brains still using carbon-based neurons. While human neurons are wondrous creations in a way, we wouldn’t (and don’t) design computing circuits the same way. Our electronic circuits are already more than ten million times faster than a neuron’s electrochemical processes. Most of the complexity of a human neuron is devoted to maintaining its life support functions, not its information processing capabilities. Ultimately, we will need to port our mental processes to a more suitable computational substrate. Then our minds won’t have to stay so small, being constrained as they are today to a mere hundred trillion neural connections each operating at a ponderous 200 digitally controlled analog calculations per second.

WHY YOU SAY THAT THERE HAS BEEN A "SLOW DOWN"
We also need to distinguish between the “S” curve (an “S” stretched to the right, comprising very slow, virtually unnoticeable growth–followed by very rapid growth–followed by a flattening out as the process approaches an asymptote) that is characteristic of any specific technological paradigm and the continuing exponential growth that is characteristic of the ongoing evolutionary process of technology. Specific paradigms, such as Moore’s Law, do ultimately reach levels at which exponential growth is no longer feasible. Thus Moore’s Law is an S curve. But the growth of computation is an ongoing exponential (at least until we “saturate” the Universe with the intelligence of our human-machine civilization, but that will not be a limit in this coming century). In accordance with the law of accelerating returns, paradigm shift, also called innovation, turns the S curve of any specific paradigm into a continuing exponential. A new paradigm (e.g., three-dimensional circuits) takes over when the old paradigm approaches its natural limit. This has already happened at least four times in the history of computation. This difference also distinguishes the tool making of non-human species, in which the mastery of a tool-making (or using) skill by each animal is characterized by an abruptly ending S shaped learning curve, versus human-created technology, which has followed an exponential pattern of growth and acceleration since its inception.



SOURCE: http://www.kurzweilai.net/the-law-of-accelerating-returns

OMG U KNOW U DONT HAVE TO SPEND HOURS ON MAKING POSTS

 

[Tenebrous]

OMG U KNOW U DONT HAVE TO SPEND HOURS ON MAKING POSTS

It said at the top that it was an except from ray's site

 

[e]

It said at the top that it was an except from ray's site

ily