The Singularity Is Near (Raymond Kurzweil)

He empezado a leer “The Singularity Is Near”, de Raymond Kurzweil, un libro al que le tenía ganas y el otro día lo encontré en Scribd y me lo bajé. Realmente Kurzweil es un tipo curioso, creó el primer programa de OCR multifuente, el primer sintetizador que reproducía intrumentos tradicionales, el primer sistema de reconocimiento de voz, ha creado y vendido varias empresas, pero su tema realmente es el futuro, y su apuesta es que en un futuro nada lejano se nos va a venir encima una singularidad en la evolución biológica, cultural y tecnológica de la raza humana, un punto de fuga a partir del cual ya nada será igual. Suena a guión de ciencia ficción, ¿verdad?, bueno, cualquier profecía es siempre primero ficción, pero el caso es que el tema de la singularidad resulta (en mayor o menor grado) científicamente plausible, y de ahí mi interés en echar un vistazo a ese límite/lanzadera propuesto.

Una singularidad, en física, es lo que ocurre al margen de las leyes, al margen de lo que es posible preveer en función de una ley, el momento en que las ecuaciones patinan. El interior de un agujero negro es una singularidad, porque no somos capaces de calcular lo que allí puede ocurrir, es un punto ciego, o el mismo big bang es también una singularidad, una singularidad en el origen de las leyes. En la apuesta de Kurzweil la singularidad no se debe sin embargo a la gravedad desbocada, sino al umbral que puede alcanzar el progreso tecnológico con la creación de máquinas inteligentes (IA) que, en cooperación o no con nuestra inteligencia biológica, puedan crear sucesivas generaciones de máquinas inteligentes que superen lo que desde aquí consideramos inteligencia. Cuando una máquina creada a nuestra imagen pueda crear a su vez otra máquina creada a su imagen, y ésta a otra, la deriva de esta evolución tecnológica escapa a nuestro horizonte y aparece como una singularidad.

Kurzweil no ha sido el primero en hablar de este tipo de singularidad, un término que introdujo y popularizó Vernor Vinge, pero si ha introducido un dato importante que reduce plazos: el desarrollo tecnológico tiene un desarrollo exponencial. Para calcular el futuro no sirve extender hacia delante el ritmo de cambios que nos han traido hasta aquí, porque este ritmo se está acelerando continuamente en función de la mayor cantidad de información accesible. El futuro viene más rápido. Es lo que decía William Gibson, el futuro ya está aquí.

Hace algún tiempo estuve viendo el video de la intervención de Douglas R. Hofstadter (el de “Gödel, Escher, Bach”) en la Singularity Summit 2007 de Stanford, que creo que patrocina Kurzweil. Le venía a decir que en sus propuestas encontraba tanta ciencia como ciencia-ficción, y proponía reexaminar en términos puramente científicos ese territorio (futuro) que Kurzweil y otros han desbrozado. Creo que es una buena propuesta.

Voy a poner los videos de la intervención de Kurzweil y de Hofstadter en Stanford, y luego pongo los puntos y la deriva con que Kurzweil dibuja el mapa hipotético de la singularidad por llegar.

The Singularity involves the following principles, which I will document, develop, analyze, and
contemplate throughout the rest of this book:

• The rate of paradigm shift (technical innovation) is accelerating, right now doubling every decade.

• The power (price-performance, speed, capacity, and bandwidth) of information technologies is
growing exponentially at an even faster pace, now doubling about every year.29 This principle applies
to a wide range of measures, including the amount of human knowledge.

• For information technologies, there is a second level of exponential growth: that is, exponential
growth in the rate of exponential growth (the exponent). The reason: as a technology becomes more
cost effective, more resources are deployed toward its advancement, so the rate of exponential growth
increases over time. For example, the computer industry in the 1940s consisted of a handful of now
historically important projects. Today total revenue in the computer industry is more than one trillion
dollars, so research and development budgets are comparably higher.

• Human brain scanning is one of these exponentially improving technologies. As I will show in
chapter 4, the temporal and spatial resolution and bandwidth of brain scanning are doubling each
year. We are just now obtaining the tools sufficient to begin serious reverse engineering (decoding) of
the human brain’s principles of operation. We already have impressive models and simulations of a
couple dozen of the brain’s several hundred regions. Within two decades, we will have a detailed
understanding of how all the regions of the human brain work.

• We will have the requisite hardware to emulate human intelligence with supercomputers by the end
of this decade and with personal-computer-size devices by the end of the following decade. We will
have effective software models of human intelligence by the mid-2020s.

• With both the hardware and software needed to fully emulate human intelligence, we can expect
computers to pass the Turing test, indicating intelligence indistinguishable from that of biological
humans, by the end of the 2020s.30

• When they achieve this level of development, computers will be able to combine the traditional
strengths of human intelligence with the strengths of machine intelligence.

• The traditional strengths of human intelligence include a formidable ability to recognize patterns. The
massively parallel and self-organizing nature of the human brain is an ideal architecture for
recognizing patterns that are based on subtle, invariant properties. Humans are also capable of
learning new knowledge by applying insights and inferring principles from experience, including
information gathered through language. A key capability of human intelligence is the ability to create
mental models of reality and to conduct mental “what-if” experiments by varying aspects of these
models.

• The traditional strengths of machine intelligence include the ability to remember billions of facts
precisely and recall them instantly.

• Another advantage of nonbiological intelligence is that once a skill is mastered by a machine, it can
be performed repeatedly at high speed, at optimal accuracy, and without tiring.

• Perhaps most important, machines can share their knowledge at extremely high speed, compared to
the very slow speed of human knowledge-sharing through language.

• Nonbiological intelligence will be able to download skills and knowledge from other machines,
eventually also from humans.

• Machines will process and switch signals at close to the speed of light (about three hundred million
meters per second), compared to about one hundred meters per second for the electrochemical signals
used in biological mammalian brains.31 This speed ratio is at least three million to one.

• Machines will have access via the Internet to all the knowledge of our human-machine civilization
and will be able to master all of this knowledge.

• Machines can pool their resources, intelligence, and memories. Two machines—or one million
machines—can join together to become one and then become separate again. Multiple machines can
do both at the same time: become one and separate simultaneously. Humans call this falling in love,
but our biological ability to do this is fleeting and unreliable.

• The combination of these traditional strengths (the pattern-recognition ability of biological human
intelligence and the speed, memory capacity and accuracy, and knowledge and skill-sharing abilities
of nonbiological intelligence) will be formidable.

• Machine intelligence will have complete freedom of design and architecture (that is, they won’t be
constrained by biological limitations, such as the slow switching speed of our interneuronal
connections or a fixed skull size) as well as consistent performance at all times.

• Once nonbiological intelligence combines the traditional strengths of both humans and machines, the
nonbiological portion of our civilization’s intelligence will then continue to benefit from the double
exponential growth of machine price-performance, speed, and capacity.

• Once machines achieve the ability to design and engineer technology as humans do, only at far higher
speeds and capacities, they will have access to their own designs (source code) and the ability to
manipulate them. Humans are now accomplishing something similar through biotechnology
(changing the genetic and other information processes underlying our biology), but in a much slower
and far more limited way than what machines will be able to achieve by modifying their own
programs.

• Biology has inherent limitations. For example, every living organism must be built from proteins that
are folded from one-dimensional strings of amino acids. Protein-based mechanisms are lacking in
strength and speed. We will be able to reengineer all of the organs and systems in our biological
bodies and brains to be vastly more capable.

• As we will discuss in chapter 4, human intelligence does have a certain amount of plasticity (ability
to change its structure), more so than had previously been understood. But the architecture of the
human brain is nonetheless profoundly limited. For example, there is room for only about one
hundred trillion interneuronal connections in each of our skulls. A key genetic change that allowed
for the greater cognitive ability of humans compared to that of our primate ancestors was the
development of a larger cerebral cortex as well as the development of increased volume of graymatter
tissue in certain regions of the brain.32 This change occurred, however, on the very slow
timescale of biological evolution and still involves an inherent limit to the brain’s capacity. Machines
will be able to reformulate their own designs and augment their own capacities without limit. By
using nanotechnology-based designs, their capabilities will be far greater than biological brains
without increased size or energy consumption.

• Machines will also benefit from using very fast three-dimensional molecular circuits. Today’s
electronic circuits are more than one million times faster than the electrochemical switching used in
mammalian brains. Tomorrow’s molecular circuits will be based on devices such as nanotubes, which
are tiny cylinders of carbon atoms that measure about ten atoms across and are five hundred times
smaller than today’s silicon-based transistors. Since the signals have less distance to travel, they will
also be able to operate at terahertz (trillions of operations per second) speeds compared to the few
gigahertz (billions of operations per second) speeds of current chips.

• The rate of technological change will not be limited to human mental speeds. Machine intelligence
will improve its own abilities in a feedback cycle that unaided human intelligence will not be able to
follow.

• This cycle of machine intelligence’s iteratively improving its own design will become faster and
faster. This is in fact exactly what is predicted by the formula for continued acceleration of the rate of
paradigm shift. One of the objections that has been raised to the continuation of the acceleration of
paradigm shift is that it ultimately becomes much too fast for humans to follow, and so therefore, it’s
argued, it cannot happen. However, the shift from biological to nonbiological intelligence will enable
the trend to continue.

• Along with the accelerating improvement cycle of nonbiological intelligence, nanotechnology will
enable the manipulation of physical reality at the molecular level.

• Nanotechnology will enable the design of nanobots: robots designed at the molecular level, measured
in microns (millionths of a meter), such as “respirocytes” (mechanical red-blood cells).33 Nanobots
will have myriad roles within the human body, including reversing human aging (to the extent that
this task will not already have been completed through biotechnology, such as genetic engineering).

• Nanobots will interact with biological neurons to vastly extend human experience by creating virtual
reality from within the nervous system.

• Billions of nanobots in the capillaries of the brain will also vastly extend human intelligence.
• Once nonbiological intelligence gets a foothold in the human brain (this has already started with
computerized neural implants), the machine intelligence in our brains will grow exponentially (as it
has been doing all along), at least doubling in power each year. In contrast, biological intelligence is
effectively of fixed capacity. Thus, the nonbiological portion of our intelligence will ultimately
predominate.

• Nanobots will also enhance the environment by reversing pollution from earlier industrialization.

• Nanobots called foglets that can manipulate image and sound waves will bring the morphing qualities
of virtual reality to the real world.

• The human ability to understand and respond appropriately to emotion (so-called emotional
intelligence) is one of the forms of human intelligence that will be understood and mastered by future
machine intelligence. Some of our emotional responses are tuned to optimize our intelligence in the
context of our limited and frail biological bodies. Future machine intelligence will also have “bodies”
(for example, virtual bodies in virtual reality, or projections in real reality using foglets) in order to
interact with the world, but these nanoengineered bodies will be far more capable and durable than
biological human bodies. Thus, some of the “emotional” responses of future machine intelligence will
be redesigned to reflect their vastly enhanced physical capabilities.

• As virtual reality from within the nervous system becomes competitive with real reality in terms of
resolution and believability, our experiences will increasingly take place in virtual environments.

• In virtual reality, we can be a different person both physically and emotionally. In fact, other people
(such as your romantic partner) will be able to select a different body for you than you might select
for yourself (and vice versa).

• The law of accelerating returns will continue until nonbiological intelligence comes dose to
“saturating” the matter and energy in our vicinity of the universe with our human-machine
intelligence. By saturating, I mean utilizing the matter and energy patterns for computation to an
optimal degree, based on our understanding of the physics of computation. As we approach this limit,
the intelligence of our civilization will continue its expansion in capability by spreading outward
toward the rest of the universe. The speed of this expansion will quickly achieve the maximum speed
at which information can travel.

• Ultimately, the entire universe will become saturated with our intelligence. This is the destiny of the
universe. (See chapter 6.) We will determine our own fate rather than have it determined by the
current “dumb,” simple, machinelike forces that rule celestial mechanics.

• The length of time it will take the universe to become intelligent to this extent depends on whether or
not the speed of light is an immutable limit. There are indications of possible subtle exceptions (or
circumventions) to this limit, which, if they exist, the vast intelligence of our civilization at this future
time will be able to exploit.

This, then, is the Singularity. Some would say that we cannot comprehend it, at least with our current
level of understanding. For that reason, we cannot look past its event horizon and make complete sense of
what lies beyond. This is one reason we call this transformation the Singularity.