[Paleopsych] NS: Ray Kurzweil: Human 2.0

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Ray Kurzweil: Human 2.0
http://www.newscientist.com/article.ns?id=mg18725181.600&print=true
5.9.24

    IN 2003, Time magazine organised a "Future of Life" conference
    celebrating the 50th anniversary of Watson and Crick's discovery of
    the structure of DNA. All the speakers - myself included - were asked
    what we thought the next 50 years would bring. Most of the predictions
    were short-sighted.

    James Watson's own prediction was that in 50 years, we'll have drugs
    that allow us to eat as much as we want without gaining weight. "Fifty
    years?," I replied. In my opinion that's far too pessimistic. We've
    already demonstrated it in mice, and human drugs using the relevant
    techniques are in development. We can expect them in five to 10 years,
    not 50.

    The mistake that Watson and virtually every other presenter made was
    to use the progress of the past 50 years as a model for the next
    half-century. I describe this way of looking at the future as the
    "intuitive linear" view: people intuitively assume that the current
    rate of progress will continue for future periods.

    But a serious assessment of the history of technology reveals that
    technological change is not linear, but exponential. You can examine
    the data in different ways, on different timescales and for a wide
    variety of technologies, ranging from electronic to biological. You
    can analyse the implications, ranging from the sum of human knowledge
    to the size of the economy. However you measure it, the exponential
    acceleration of progress and growth applies.

    Understanding exponential progress is key to understanding future
    trends. Over the long term, exponential growth produces change on a
    scale dramatically different from linear growth. Consider that in
    1990, the human genome project was widely regarded as controversial.
    In 1989, we sequenced only one-thousandth of the genome. But from 1990
    onwards the amount of genetic data sequenced doubled every year - a
    rate of growth that continues today - and the transcription of the
    human genome was completed in 2003.

    We are making exponential progress in every type of information
    technology. Moreover, virtually all technologies are becoming
    information technologies. If we combine all of these trends, we can
    reliably predict that, in the not too distant future, we will reach
    what is known as The Singularity. This is a time when the pace of
    technological change will be so rapid and its impact so deep that
    human life will be irreversibly transformed. We will be able to
    reprogram our biology, and ultimately transcend it. The result will be
    an intimate merger between ourselves and the technology we are
    creating.

    The evidence for this ubiquitous exponential growth is abundant. In my
    new book, The Singularity is Near, I have more than 40 graphs from a
    broad variety of fields, including communications, the internet, brain
    scanning and biological technologies, that reveal exponential
    progress. Broadly speaking, my models show that we are doubling the
    paradigm-shift rate (roughly, the rate of technical innovation) every
    decade. Throughout the 20th century, the rate of progress gradually
    picked up speed. By the end of the century the rate was such that the
    sum total of the century's achievements was equivalent to about 20
    years of progress at the 2000 rate.

    Growth in information technology is particularly rapid: we're doubling
    its power, as measured by price-performance, bandwidth, capacity and
    many other measures, every year or so. That's a factor of a thousand
    in 10 years, a million in 20 years, and a billion in 30 years,
    although a slow, second level of exponential growth means that a
    billion-fold improvement takes only about a quarter of a century.

    The exponential growth of computing goes back over a century and
    covers five major paradigms: electromechanical computing as used in
    the 1890 US census, relay-based computing as used to crack Nazi
    cryptography in the early 1940s, vacuum-tube-based computing as used
    by CBS to predict the election of Dwight Eisenhower in 1952,
    discrete-transistor-based computing as used in the first space
    launches in the early 1960s, and finally computing based on integrated
    circuits, invented in 1958 and applied to mainstream computing from
    the late 1960s. Each time it became apparent that one paradigm was
    about to run out of steam, this realisation resulted in research
    pressure to create the next paradigm.

    Today we have over a decade left in the paradigm of shrinking
    transistors on an integrated circuit, but there has already been
    enormous progress in creating the sixth major computing paradigm of
    three-dimensional molecular computing, using carbon nanotubes for
    example. And electronics is just one example of many. As another, it
    took us 14 years to sequence the genome of HIV; SARS took only 31
    days.

Accelerating returns

    The result is that we can reliably predict such measures as
    price-performance and capacity of a broad variety of information
    technologies. There are, of course, many things that we cannot
    dependably anticipate. In fact, our inability to make reliable
    predictions applies to any specific project. But the overall
    capabilities of information technology in each field can be projected.
    And I say this not just with hindsight; I have been making
    forward-looking predictions of this type for more than 20 years.

    We see examples in other areas of science of very smooth and reliable
    outcomes resulting from the interaction of a great many unpredictable
    events. Consider that predicting the path of a single molecule in a
    gas is essentially impossible, but predicting the properties of the
    entire gas - comprised of a great many chaotically interacting
    molecules - can be done very reliably through the laws of
    thermodynamics. Analogously, it is not possible to reliably predict
    the results of a specific project or company, but the overall
    capabilities of information technology, comprised of many chaotic
    activities, can nonetheless be dependably anticipated through what I
    call "the law of accelerating returns".

    So what does the law of accelerating returns tell us about the future?
    In terms of the aforementioned paradigm-shift rate, between 2000 and
    2014 we'll make 20 years of progress at 2000 rates, equivalent to the
    entire 20th century. And then we'll do the same again in only seven
    years. To express this another way, we won't experience 100 years of
    technological advance in the 21st century; we will witness in the
    order of 20,000 years of progress when measured by the rate of
    progress in 2000, or about 1000 times that achieved in the 20th
    century.

    Above all, information technologies will grow at an explosive rate.
    And information technology is the technology that we need to consider.
    Ultimately everything of value will become an information technology:
    our biology, our thoughts and thinking processes, manufacturing and
    many other fields. As one example, nanotechnology-based manufacturing
    will enable us to apply computerised techniques to automatically
    assemble complex products at the molecular level. This will mean that
    by the mid-2020s we will be able to meet our energy needs using very
    inexpensive nanotechnology-based solar panels that will capture the
    energy in 0.03 per cent of the sunlight that falls on the Earth, which
    is all we need to meet our projected energy needs in 2030.

    A common objection is that there must be limits to exponential growth,
    as in the example of rabbits in Australia. The answer is that there
    are, but they're not very limiting. By 2020, $1000 will purchase 10^16
    calculations per second (cps) of computing (compared with about 10^9
    cps today), which is the level I estimate is required to functionally
    simulate the human brain. Another few decades on, and we will be able
    to build more optimal computing systems. For example, one cubic inch
    of nanotube circuitry would be about 100 million times more powerful
    than the human brain. The ultimate 1-kilogram computer - about the
    weight of a laptop today - which I envision late in this century,
    could provide 10^42 cps, about 10 quadrillion (10^16) times more
    powerful than all human brains put together today. And that's if we
    restrict the computer to functioning at a cold temperature. If we find
    a way to let it get hot, we could improve that by a factor of another
    100 million. And of course, we'll devote more than 1 kilogram of
    matter to computing. Ultimately, we'll use a significant portion of
    the matter and energy in our vicinity as a computing substrate.

    Our growing mastery of information processes means that the 21st
    century will be characterised by three great technology revolutions.
    We are in the early stages of the "G" revolution (genetics, or
    biotechnology) right now. Biotechnology is providing the means to
    actually change your genes: not just designer babies but designer baby
    boomers.

    One technology that is already here is RNA interference (RNAi), which
    is used to turn genes off by blocking messenger RNA from expressing
    specific genes. Each human gene is just one of 23,000 little software
    programs we have inherited that represent the design of our biology.
    It is not very often that we use software programs that are not
    upgraded and modified for several years, let alone thousands of years.
    Yet these genetic programs evolved tens of thousands of years ago when
    conditions were very different. For one thing, it was not in the
    interest of the species for people to live very long. But since viral
    diseases, cancer and many other diseases depend on gene expression at
    some crucial point in their life cycle, RNAi promises to be a
    breakthrough technology.

Grow your own

    New means of adding new genes are also emerging that have overcome the
    problem of placing genetic information precisely. One successful
    technique is to add the genetic information in vitro, making it
    possible to ensure the genetic information is inserted in the proper
    place. Once verified, the modified cell can be reproduced in vitro and
    large numbers of modified cells introduced into the patient's
    bloodstream, where they will travel to and become embedded in the
    correct tissues. This approach to gene therapy has successfully cured
    pulmonary hypertension in rats and has been approved for human trials.

    Another important line of attack is to regrow our own cells, tissues
    and even whole organs, and introduce them into our bodies. One major
    benefit of this "therapeutic cloning" technique is that we will be
    able to create these new tissues and organs from versions of our cells
    that have also been made younger - the emerging field of rejuvenation
    medicine. For example, we will be able to create new heart cells from
    your skin cells and introduce them into your system through the
    bloodstream. Over time, your heart cells will all be replaced,
    resulting in a rejuvenated "young" heart with your own DNA.

    Drug discovery was once a matter of finding substances that produced
    some beneficial effect without excessive side effects. This process
    was similar to early humans' tool discovery, which was limited to
    simply finding rocks and natural implements that could be used for
    helpful purposes. Today, we are learning the precise biochemical
    pathways that underlie both disease and ageing processes, and are able
    to design drugs to carry out precise missions at the molecular level.
    The scope and scale of these efforts are vast.

    But perfecting our biology will only get us so far. The reality is
    that biology will never be able to match what we will be capable of
    engineering, now that we are gaining a deep understanding of biology's
    principles of operation.

    That will bring us to the "N" or nanotechnology revolution, which will
    achieve maturity in the 2020s. There are already early impressive
    experiments. A biped nanorobot created by Nadrian Seeman and William
    Sherman of New York University can walk on legs just 10 nanometres
    long, demonstrating the ability of nanoscale machines to execute
    precise manoeuvres. MicroCHIPS of Bedford, Massachusetts, has
    developed a computerised device that is implanted under the skin and
    delivers precise mixtures of medicines from hundreds of nanoscale
    wells inside it. There are many other examples.

Version 2.0

    By the 2020s, nanotechnology will enable us to create almost any
    physical product we want from inexpensive materials, using information
    processes. We will be able to go beyond the limits of biology, and
    replace your current "human body version 1.0" with a dramatically
    upgraded version 2.0, providing radical life extension. The "killer
    app" of nanotechnology is "nanobots", blood-cell sized robots that can
    travel in the bloodstream destroying pathogens, removing debris,
    correcting errors in DNA and reversing ageing processes.

    We're already in the early stages of augmenting and replacing each of
    our organs, even portions of our brains with neural implants, the most
    recent versions of which allow patients to download new software to
    their implants from outside their bodies. Each of our organs will
    ultimately be replaced. For example, nanobots could deliver to our
    bloodstream an optimal set of all the nutrients, hormones and other
    substances we need, as well as remove toxins and waste products. The
    gastrointestinal tract could then be reserved for culinary pleasures
    rather than the tedious biological function of providing nutrients.
    After all, we've already in some ways separated the communication and
    pleasurable aspects of sex from its biological function.

    The most profound transformation will be "R" for the robotics
    revolution, which really refers to "strong" AI, or artificial
    intelligence at the human level (see "Reverse engineering the human
    brain"). Hundreds of applications of "narrow AI" - machine
    intelligence that equals or exceeds human intelligence for specific
    tasks - already permeate our modern infrastructure. Every time you
    send an email or make a cellphone call, intelligent algorithms route
    the information. AI programs diagnose electrocardiograms with an
    accuracy rivalling doctors, evaluate medical images, fly and land
    aircraft, guide intelligent autonomous weapons, make automated
    investment decisions for over a trillion dollars of funds, and guide
    industrial processes. A couple of decades ago these were all research
    projects.

    With regard to strong AI, we'll have both the hardware and software to
    recreate human intelligence by the end of the 2020s. We'll be able to
    improve these methods and harness the speed, memory capabilities and
    knowledge-sharing ability of machines.

    Ultimately, we will merge with our technology. This will begin with
    nanobots in our bodies and brains. The nanobots will keep us healthy,
    provide full-immersion virtual reality from within the nervous system,
    provide direct brain-to-brain communication over the internet and
    greatly expand human intelligence. But keep in mind that
    non-biological intelligence is doubling in capability each year,
    whereas our biological intelligence is essentially fixed. As we get to
    the 2030s, the non-biological portion of our intelligence will
    predominate. By the mid 2040s, the non-biological portion of our
    intelligence will be billions of times more capable than the
    biological portion. Non-biological intelligence will have access to
    its own design and will be able to improve itself in an increasingly
    rapid redesign cycle.

    This is not a utopian vision: the GNR technologies each have perils to
    match their promise. The danger of a bioengineered pathological virus
    is already with us. Self-replication will ultimately be feasible in
    non-biological nanotechnology-based systems as well, which will
    introduce its own dangers. This is a topic for another essay, but in
    short the answer is not relinquishment. Any attempt to proscribe such
    technologies will not only deprive human society of profound benefits,
    but will drive these technologies underground, which would make the
    dangers worse.

    Some commentators have questioned whether we would still be human
    after such dramatic changes. These observers may define the concept of
    human as being based on our limitations, but I prefer to define us as
    the species that seeks - and succeeds - in going beyond our
    limitations. Because our ability to increase our horizons is expanding
    exponentially rather than linearly, we can anticipate a dramatic
    century of accelerating change ahead.

Reverse engineering the human brain

    The most profound transformation will be in "strong" AI, that is,
    artificial intelligence at the human level. To recreate the
    capabilities of the human brain, we need to meet both the hardware and
    software requirements. Achieving the hardware requirement was
    controversial five years ago, but is now largely a mainstream view
    among informed observers. Supercomputers are already at 100 trillion
    (10^14) calculations per second (cps), and will hit 10^16 cps around
    the end of this decade, which is the level I estimate is required to
    functionally simulate the human brain. Several supercomputers with
    10^15 cps are already on the drawing board, with two Japanese efforts
    targeting 10^16 cps around the end of the decade. By 2020, 10^16 cps
    will be available for around $1000. So now the controversy is focused
    on the algorithms.

    To understand the principles of human intelligence we need to
    reverse-engineer the human brain. Here, progress is far greater than
    most people realise. The spatial and temporal resolution of brain
    scanning is progressing at an exponential rate, roughly doubling each
    year. Scanning tools, such as a new system from the University of
    Pennsylvania, can now see individual interneuronal connections, and
    watch them fire in real time. Already, we have mathematical models of
    a couple of dozen regions of the brain, including the cerebellum,
    which comprises more than half the neurons in the brain. IBM is
    creating a highly detailed simulation of about 10,000 cortical
    neurons, including tens of millions of connections. The first version
    will simulate electrical activity, and a future version will also
    simulate chemical activity. By the mid 2020s, it is conservative to
    conclude that we will have effective models of the whole brain.

    There are a number of key ways in which the organisation of the brain
    differs from a conventional computer. The brain's circuits, for
    example, transmit information as chemical gradients travelling at only
    a few hundred metres per second, which is millions of times slower
    than electronic circuits. The brain is massively parallel: there are
    about 100 trillion interneuronal connections all computing
    simultaneously. The brain combines analogue and digital phenomena. The
    brain rewires itself, and it uses emergent properties, with
    intelligent behaviour emerging from the brain's chaotic and complex
    activity. But as we gain sufficient data to model neurons and regions
    of neurons in detail, we find that we can express the coding of
    information in the brain and how this information is transformed in
    mathematical terms. We are then able to simulate these transformations
    on conventional parallel computing platforms, even though the
    underlying hardware architecture is quite different.

    One benefit of a full understanding of the human brain will be a deep
    understanding of ourselves, but the key implication is that it will
    expand the tool kit of techniques we can apply to create artificial
    intelligence. We will then be able to create non-biological systems
    that match human intelligence. These superintelligent computers will
    be able to do things we are not able to do, such as share knowledge
    and skills at electronic speeds.