ray kurzweil

Subjective Experience

“The objective reality is the reality of the outside observer observing the process.

If we observe the development of an individual, salient events happen very quickly at first,

but later on milestones are more spread out, so we say time is slowing down.

The subjective experience, however, is the experience of the process itself,

assuming, of course, that the process is conscious.”

— Ray Kurzweil

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I see in this particular concept of Emergent Systems a direction to explain the avoidance of technology from old age groups, and of groups of old thinkers.
It is hard to accept something you cannot understand from its core, cannot dominate it.

Technology is not a simple agent interacting with the society in general.
Technology is an universal agente, raising mankind to another level of intelligence, meanwhile merging with the same mankind. You can reject, but you cannot escape from the future.

{Infinite Loop} Begin;

Through most of human history, people have tried to understand their world through reductive reasoning.

That is to say, they have been inclined to take things apart to see how they work. As Albert Láslo Barabáse write in his influential book Linked, “Reductionism was the driving force behind much of the 20th century’s scientific research. To compreend nature, it tells us, we must dechiper its components. The assumption is that once we understand the parts, it will be easy to grasp the whole. Divide and Conquer; the devil is in the details.

Therefore, for decades we have been forced to see the world through its constituents. We have been trained to study atoms and superstrings to understand the universe; molecules to comprehend life; individual genes to understand complex behavior; prophets to see the origins of fads and religions.” This way of thinking induces people to think they can…

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What’s Next?


We are in the second decade of the 21st century.

Internet mass adoption was achieved in the very beginning of the century.

But, what’s next?



Canonical Milestones

Physicist and complexity theorist Theodore Modis analyzed these lists and determined twenty-eight clusters of events, which he calls canonical milestones, by combining identical, similar, and/or related events from different lists.

This process essentially removes the “noise”, the variability between lists, revealing same progression:

The attributes that are growing exponentially in these charts are order and complexity. This acceleration matches our common sense observations. A billion years ago, not much happened over the course of over one million years.  But a quarter million years ago epochal events such as the evolution of our species occurred in time frames of just one hundred thousand years.

In technology, if we go back in fifty thousand years, not much happened, nothing happened over a one thousand year period.

But in the recent past, we see new paradigms, such as the World Wide Web, progress from inception to mass adoption (a quarter of the population of advanced countries) within only a decade.

The Singularity is Near, R. Kurzweil, about the Six Epochs. Epoch Four: Technology


“Existem poucas coisas das quais a geração presente é mais orgulhosa do que o maravilhoso avanço que acontece cotidianamente em todos os tipo de aparelhos mecânicos… Mas o que aconteceria se a tecnologia continuasse a evoluir muito mais rapidamente do que as espécies dos reinos animal e vegetal?

E se a tecnologia nos demovesse na supremacia da terra?

Assim como o reino vegetal lentamente se desenvolveu dos minerais, e de igual modo o animal sobreveio ao vegetal, agora em apenas alguns anos um reinado inteiro surgiu, sobre o qual vivenciamos apenas o que um dia será considerado o protótipo antidiluviano dessa raça…

Estamos diariamente atribuindo mais poder e oferecendo todo o tipo de recurso sofisticado, que auto-regulamentação e auto-suficiencia serão o que o intelecto foi para a raça humana.”

–Samuel Butler, 1836, quatro anos após a publicação de A Origem das Espécies, por Charles Darwin

Fonte: The Singularity is Near, R. Kurzweil. Tradução livre.

The same post in english here

GNR, conforme mencionado no título, faz referência a três áreas da evolução tecnologica que em muito breve irão viabilizar a verticalização do crescimento exponencial, a revolução tecnológica, a qual agora vivemos em fase embionária.

G – Genética

N – Nanotecnologia

R – Robótica

Em algum momento no futuro próximo (ou cada vez mais próximo), segundo a teoria da Singularidade (Singularity) de Ray Kurzweil, a inteligência artificial evoluirá a um ponto ao qual será capaz de responder pela continuidade da evolução da humanidade, a esse ponto um híbrido entre humanos e tecnologia.

Dessa forma a linha de crescimento evolucionário se tornará uma exponencial no gráfico de Inovação x Tempo, partindo do período cambriano, mamíferos, primatas, hominídeos até a agricultura, arte, escrita, impressão, revolução industrial, telefonia e computação. Ao longo desse período a vida na terra evoluiu de maneira significativa, no entanto ainda temos um longo caminho a percorrer.

A Singularidade promove uma das muitas idéias interessantes que Ray Kurzweil tem sobre o mundo, a sociedade e a evolução da humanidade.

Leia sobre as Seis Épocas, em ingles, aqui

É fascinante estar vivo e consciente desse fenômeno, observa-lo em seus pais e filhos, avós e netos, e assim por diante.

A vida é um loop infinito de evolução e crescimento.

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The Singularity and human brain capacity

“Hans Moracevic provides the following similar chart, which uses a different but overlapping set of historical computers and plots trend lines (slopes) at different points in time. As with the figure above, the slope increases with time, reflecting the second level of exponential growth.

If we project these computational performance trends through this next century, we can see in the figure below that supercomputers will match human brain capability by the end of this decade and personal computing will achieve it by around 2020– or possibly sooner, depending on how conservative an estimate of human brain capacity we use.

The exponential growth of computing is a marvelous quantitative example of the exponentially growing returns from an evolutionary process. We can express the exponential growth of computing in terms of accelerating pace: it took ninety years to achieve the first MIPS per thousands dollars; now we add one MIPS per thousand dollars every five hours.

IBM’s Blue Gene/P supercomputer is planned to have one million gigaflops (billions of floating-point operations per second), or 10^15 calculations per second when it launches in 2007. That’s one tenth of the 10^16 calculations per second needed to emulate the human brain. And if we extrapolate this exponential curve, we get 10^16 calculations per second early in the next decade.

As discussed above, Moore’s Law narrowly refers to the number of transistors on an integrated circuit of fixed size and sometimes has been expressed even more narrowly in terms of transistors feature size. But the most appropriate measure to track price-performance is computational speed per unit cost, an index that takes into account many levels of “cleverness” (innovation, which is to say, technological evolution). In addition to all of the invention involved in integrated circuits, there are multiple layers of improvement in computer design (for example, pipelining, parallel processing, instruction look-ahead, instruction and memory caching, and many others).

The human brain uses a very inefficient electrochemical, digital-controlled  analog computational process. The bulk of its calculations are carried out in the interneuronal connections at a speed  of only about two hundred calculations per second (in each connection), which is at least one million times slower that contemporary electronic circuits. But the brain gains its prodigious powers from its extremely parallel organization in three dimensions. There are many technologies in the wings that will build circuitry in three dimensions.

We might ask whether there are inherent limits to the capacity of matter and energy to support computational process. This is an important issue, but we won’t approach those limits until the end of this century. It is important to distinguish between the S-curve that is characteristic of any specific technological paradigm and the continuing exponential growth that is characteristic of the ongoing evolutionary process within a broad area of technology, such as computation.

Specific paradigms shift, such as Moore’s Law, do ultimately reach levels at which exponential growth is no longer feasible. But the growth of computation supersedes any of its underlying paradigms and is for present purpose an ongoing exponential.”

Ray Kurzweil, The Singularity is near, Chapter Two; A Theory of Technology Evolution. The Law of Accelerating Returns

Uma teoria sobre a evolução tecnológica

De acordo com a lei dos retornos exponenciais (ou retornos acelerados), quebras de paradigma (também chamado de inovação) tornam a curva-S de qualquer pardigma específico em uma exponencial contínua.

Um novo paradigma como circuitos tridimensionais, assume quando um paradigma anterior atinge seu limite natural, o que já aconteceu ao menos quatro vezes na história da computação (eletromecanica, relay, tubo a vácuo e transistores, estamos na era dos circuitos integrados).

Em espécies não humanas como macacos, o domínio da construção de uma ferramenta ou habilidade desenvolvida por cada animal é caracterizado por uma curva-S de aprendizado que acaba abruptamente; a teconologia criada pelo ser humano, em contraste, tem seguido um padrão exponencial de crescimento e aceleração.

Texto extraído do livro The Singularity is Near, de Ray Kurzweil.

Capítulo 2: A theory of technology evolution.

Tradução livre.

A theory of technology evolution

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, such as three-dimensional circuits, takes over when the old paradigm approaches its natural limit, which has already happened  at least four times in the history of computation (electromechanical, relay, vaccum tube and transistor, we’re in the integrated circuit era).

In such nonhuman species as apes, the mastery of a toolmaking or-using skill by each animal is characterized by an S-shaped learning curve that ends abruptly; human-created technology, in contrast, has followed an exponential pattern of growth and acceleration.

— Extracted from The Singularity is Near, by Ray Kurzweil. Chapter 2: A theory of technology evolution