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The exponential growth of computing is a marvelous quantitative example of the exponentially growing returns from an evolutionary process. We can also express the exponential growth of computing in terms of an accelerating pace: But rather than feature size which is only one contributing factoror even number of transistors, I think the most appropriate measure to track is computational speed per unit cost. In addition to all of the innovation in integrated circuits, there are multiple layers of innovation in computer design, e.
Chips today are flat although it does require up to 20 layers of material to produce one layer of circuitry. Our brain, in contrast, is organized in three dimensions. We live in a three dimensional world, why not use the third dimension?
The human brain actually uses a very inefficient electrochemical digital controlled analog computational process. The bulk of the calculations are done in the interneuronal connections at a speed of only about calculations per second in each connectionwhich is about ten million times slower than 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 build circuitry in three dimensions. Nanotubes, for example, which are already working in laboratories, build circuits from pentagonal arrays of carbon atoms.
One cubic inch of nanotube circuitry would be a million times more powerful than the human brain. There are more than enough new computing technologies now being researched, including three-dimensional silicon chips, optical computing, crystalline computing, DNA computing, and quantum computing, to keep the law of accelerating returns as applied to computation going for a long time.
And this accelerating growth of computing is, in turn, part of the yet broader phenomenon of the accelerating pace of any evolutionary process. But the resources underlying the exponential growth of an evolutionary process are relatively unbounded: Each stage of evolution provides more powerful tools for the next. Or to take a more recent example, the advent of computer assisted design tools allows rapid development of the next generation of computers.
In biological evolution, diversity enters the process in the form of mutations and ever changing environmental conditions. In technological evolution, human ingenuity combined with ever changing market conditions keep the process of innovation going.
The maximum potential of matter and energy to contain intelligent processes is a valid issue. 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. 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.
There are a great many examples of the exponential growth implied by the law of accelerating returns in technologies as varied as DNA sequencing, communication speeds, electronics of all kinds, and even in the rapidly shrinking size of technology.
The Singularity results not from the exponential explosion of computation alone, but rather from the interplay and myriad synergies that will result from manifold intertwined technological revolutions. Also, keep in mind that every point on the exponential growth curves underlying these panoply of technologies see the graphs below represents an intense human drama of innovation and competition.
It is remarkable therefore that these chaotic processes result in such smooth and predictable exponential trends. For example, when the human genome scan started fourteen years ago, critics pointed out that given the speed with which the genome could then be scanned, it would take thousands of years to finish the project.
Yet the fifteen year project was nonetheless completed slightly ahead of schedule. Of course, we expect to see exponential growth in electronic memories such as RAM.
Exponential growth in communications technology has been even more explosive than in computation and is no less significant in its implications. Again, this progression involves far more than just shrinking transistors on an integrated circuit, but includes accelerating advances in fiber optics, optical switching, electromagnetic technologies, and others. The following two charts show the overall growth of the Internet based on the number of hosts. These two charts plot the same data, but one is on an exponential axis and the other is linear.
As I pointed out earlier, whereas technology progresses in the exponential domain, we experience it in the linear domain. So from the perspective of most observers, nothing was happening until the mid s when seemingly out of nowhere, the world wide web and email exploded into view. But the emergence of the Internet into a worldwide phenomenon was readily predictable much earlier by examining the exponential trend data. Notice how the explosion of the Internet appears to be a surprise from the Linear Chart, but was perfectly predictable from the Exponential Chart Ultimately we will get away from the tangle of wires in our cities and in our lives through wireless communication, the power of which is doubling every 10 to 11 months.
Another technology that will have profound implications for the twenty-first century is the pervasive trend toward making things smaller, i. The salient implementation sizes of a broad range of technologies, both electronic and mechanical, are shrinking, also at a double exponential rate.
At present, we are shrinking technology by a factor of approximately 5. The Exponential Growth of Computation Revisited If we view the exponential growth of computation in its proper perspective as one example of the pervasiveness of the exponential growth of information based technology, that is, as one example of many of the law of accelerating returns, then we can confidently predict its continuation. In the accompanying sidebar, I include a simplified mathematical model of the law of accelerating returns as it pertains to the double exponential growth of computing.
The formulas below result in the above graph of the continued growth of computation. This graph matches the available data for the twentieth century through all five paradigms and provides projections for the twenty-first century.
Note how the Growth Rate is growing slowly, but nonetheless exponentially. The Law of Accelerating Returns Applied to the Growth of Computation 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 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. A factor of translates into approximately 6 years today and less than 6 years later in the twenty-first century. My estimate of brain capacity is billion neurons times an average 1, connections per neuron with the calculations taking place primarily in the connections times 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: The Model considers the following variables: World Knowledge as it pertains to designing and building computational devices t: Time The assumptions of the model are: This is actually a conservative assumption.
In general, innovations improve V computer power by a multiple, not in an additive way. 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. Simplifying the constants, we get: 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 s.
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. Considering the data for actual calculating devices and computers during the twentieth century: 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 billion neurons times 1, connections per neuron times calculations per second per connection.
In line with my earlier predictions, supercomputers will achieve one human brain capacity byand personal computers will do so by around Of course, this only includes those brains still using carbon-based neurons. 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. The software is even more salient. One of the principal assumptions underlying the expectation of the Singularity is the ability of nonbiological mediums to emulate the richness, subtlety, and depth of human thinking.
Achieving the computational capacity of the human brain, or even villages and nations of human brains will not automatically produce human levels of capability.
By human levels I include all the diverse and subtle ways in which humans are intelligent, including musical and artistic aptitude, creativity, physically moving through the world, and understanding and responding appropriately to emotion.
The requisite hardware capacity is a necessary but not sufficient condition. The organization and content of these resources—the software of intelligence—is also critical. Before addressing this issue, it is important to note that once a computer achieves a human level of intelligence, it will necessarily soar past it. A key advantage of nonbiological intelligence is that machines can easily share their knowledge.
You have to acquire that scholarship the same painstaking way that I did. My knowledge, embedded in a vast pattern of neurotransmitter concentrations and interneuronal connections, cannot be quickly accessed or transmitted. When one computer learns a skill or gains an insight, it can immediately share that wisdom with billions of other machines.
As a contemporary example, we spent years teaching one research computer how to recognize continuous human speech. We exposed it to thousands of hours of recorded speech, corrected its errors, and patiently improved its performance.
Finally, it became quite adept at recognizing speech I dictated most of my recent book to it. Ultimately, billions of nonbiological entities can be the master of all human and machine acquired knowledge.
In addition, computers are potentially millions of times faster than human neural circuits. A computer can also remember billions or even trillions of facts perfectly, while we are hard pressed to remember a handful of phone numbers. There are a number of compelling scenarios to achieve higher levels of intelligence in our computers, and ultimately human levels and beyond.
We will be able to evolve and train a system combining massively parallel neural nets with other paradigms to understand language and model knowledge, including the ability to read and model the knowledge contained in written documents. Computers will be able to read on their own, understanding and modeling what they have read, by the second decade of the twenty-first century.
Ultimately, the machines will gather knowledge on their own by venturing out on the web, or even into the physical world, drawing from the full spectrum of media and information services, and sharing knowledge with each other which machines can do far more easily than their human creators. Reverse Engineering the Human Brain The most compelling scenario for mastering the software of intelligence is to tap into the blueprint of the best example we can get our hands on of an intelligent process.
There is no reason why we cannot reverse engineer the human brain, and essentially copy its design. The most immediately accessible way to accomplish this is through destructive scanning: We can readily see every neuron and every connection and every neurotransmitter concentration represented in each synapse-thin layer. Human brain scanning has already started.
A condemned killer allowed his brain and body to be scanned and you can access all 10 billion bytes of him on the Internet http: He has a 25 billion byte female companion on the site as well in case he gets lonely.
But scanning a frozen brain is feasible today, albeit not yet at a sufficient speed or bandwidth, but again, the law of accelerating returns will provide the requisite speed of scanning, just as it did for the human genome scan.
We also have noninvasive scanning techniques today, including high-resolution magnetic resonance imaging MRI scans, optical imaging, near-infrared scanning, and other technologies which are capable in certain instances of resolving individual somas, or neuron cell bodies.
Brain scanning technologies are also increasing their resolution with each new generation, just what we would expect from the law of accelerating returns.
Future generations will enable us to resolve the connections between neurons and to peer inside the synapses and record the neurotransmitter concentrations. There are a number of technical challenges in accomplishing this, including achieving suitable resolution, bandwidth, lack of vibration, and safety. For a variety of reasons it is easier to scan the brain of someone recently deceased than of someone still living. It is easier to get someone deceased to sit still, for one thing.
But noninvasively scanning a living brain will ultimately become feasible as MRI, optical, and other scanning technologies continue to improve in resolution and speed.
Scanning from Inside Although noninvasive means of scanning the brain from outside the skull are rapidly improving, the most practical approach to capturing every salient neural detail will be to scan it from inside. Nanobots are robots that are the size of human blood cells, or even smaller. Billions of them could travel through every brain capillary and scan every relevant feature from up close. Using high speed wireless communication, the nanobots would communicate with each other, and with other computers that are compiling the brain scan data base in other words, the nanobots will all be on a wireless local area network.
This scenario involves only capabilities that we can touch and feel today. We already have technology capable of producing very high resolution scans, provided that the scanner is physically proximate to the neural features.
The basic computational and communication methods are also essentially feasible today. The primary features that are not yet practical are nanobot size and cost. As I discussed above, we can project the exponentially declining cost of computation, and the rapidly declining size of both electronic and mechanical technologies.
We can conservatively expect, therefore, the requisite nanobot technology by around Because of its ability to place each scanner in very close physical proximity to every neural feature, nanobot-based scanning will be more practical than scanning the brain from outside. How to Use Your Brain Scan How will we apply the thousands of trillions of bytes of information derived from each brain scan?
One approach is to use the results to design more intelligent parallel algorithms for our machines, particularly those based on one of the neural net paradigms. There is a great deal of repetition and redundancy within any particular brain region.
Although the information contained in a human brain would require thousands of trillions of bytes of information on the order of billion neurons times an average of 1, connections per neuron, each with multiple neurotransmitter concentrations and connection datathe design of the brain is characterized by a human genome of only about a billion bytes.
Furthermore, most of the genome is redundant, so the initial design of the brain is characterized by approximately one hundred million bytes, about the size of Microsoft Word. Of course, the complexity of our brains greatly increases as we interact with the world by a factor of more than ten million.
Because of the highly repetitive patterns found in each specific brain region, it is not necessary to capture each detail in order to reverse engineer the significant digital-analog algorithms. With this information, we can design simulated nets that operate similarly. There are already multiple efforts under way to scan the human brain and apply the insights derived to the design of intelligent machines. The pace of brain reverse engineering is only slightly behind the availability of the brain scanning and neuron structure information.
A contemporary example is a comprehensive model of a significant portion of the human auditory processing system that Lloyd Watts www.
Watts has implemented his model as real-time software which can locate and identify sounds with many of the same properties as human hearing. Although a work in progress, the model illustrates the feasibility of converting neurobiological models and brain connection data into working simulations. Also, as Hans Moravec and others have speculated, these efficient simulations require about 1, times less computation than the theoretical potential of the biological neurons being simulated.
Reverse Engineering the Human Brain: Sense organ of hearing. Relays spikes from the auditory nerve to the Lateral Superior. Encoding of timing and amplitude of signals for binaural comparison of level. Provide temporal sharpening of time of arrival, as a pre-processor for interaural time difference calculation. Detection of spectral edges and calibrating for noise levels. Ventral Nucleus of the Trapezoid Body. Feedback signals to modulate outer hair cell function in the cochlea. Processing transients from the Octopus Cells.
Computing inter-aural time difference difference in time of arrival between the two ears, used to tell where a sound is coming from. Also involved in computing inter-aural level difference. Central Nucleus of the Inferior Colliculus.
The site of major integration of multiple representations of sound. Exterior Nucleus of the Inferior Colliculus. Further refinement of sound localization.
The auditory portion of the thalamus. Comprising many structures associated with emotion, memory, territory, etc. As the requisite neuron models and brain interconnection data becomes available, detailed and implementable models such as the auditory example above will be developed for all brain regions.
After the algorithms of a region are understood, they can be refined and extended before being implemented in synthetic neural equivalents. For one thing, they can be run on a computational substrate that is already more than ten million times faster than neural circuitry. And we can also throw in the methods for building intelligent machines that we already understand. Downloading the Human Brain A more controversial application than this scanning-the-brain-to-understand-it scenario is scanning-the-brain-to-download-it.
Its entire organization can then be re-created on a neural computer of sufficient capacity, including the contents of its memory. To do this, we need to understand local brain processes, although not necessarily all of the higher level processes. Scanning a brain with sufficient detail to download it may sound daunting, but so did the human genome scan.
All of the basic technologies exist today, just not with the requisite speed, cost, and size, but these are the attributes that are improving at a double exponential pace.
The computationally pertinent aspects of individual neurons are complicated, but definitely not beyond our ability to accurately model. For example, Ted Berger and his colleagues at Hedco Neurosciences have built integrated circuits that precisely match the digital and analog information processing characteristics of neurons, including clusters with hundreds of neurons. Carver Mead and his colleagues at CalTech have built a variety of integrated circuits that emulate the digital-analog characteristics of mammalian neural circuits.
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When an entire network of neurons receives input from the outside world or from other networks of neuronsthe signaling amongst them appears at first to be frenzied and random.
Over time, typically a fraction of a second or so, the chaotic interplay of the neurons dies down, and a stable pattern emerges. If the neural network is performing a pattern recognition task which, incidentally, comprises the bulk of the activity in the human brainthen the emergent pattern represents the appropriate recognition. So the question addressed by the San Diego researchers was whether electronic neurons could engage in this chaotic dance alongside biological ones.
They hooked up their artificial neurons with those from spiney lobsters in a single network, and their hybrid biological-nonbiological network performed in the same way i. Essentially, the biological neurons accepted their electronic peers. It indicates that their mathematical model of these neurons was reasonably accurate.
There are many projects around the world which are creating nonbiological devices to recreate in great detail the functionality of human neuron clusters. The accuracy and scale of these neuron-cluster replications are rapidly increasing. We started with functionally equivalent recreations of single neurons, then clusters of tens, then hundreds, and now thousands. Scaling up technical processes at an exponential pace is what technology is good at. By the third decade of the twenty-first century, we will be in a position to create highly detailed and complete maps of all relevant features of all neurons, neural connections and synapses in the human brain, all of the neural details that play a role in the behavior and functionality of the brain, and to recreate these designs in suitably advanced neural computers.
Is the Human Brain Different from a Computer? Is the human brain different from a computer? Most computers today are all digital and perform one or perhaps a few computations at a time at extremely high speed.
In contrast, the human brain combines digital and analog methods with most computations performed in the analog domain. The brain is massively parallel, performing on the order of a hundred trillion computations at the same time, but at extremely slow speeds.
With regard to digital versus analog computing, we know that digital computing can be functionally equivalent to analog computing although the reverse is not trueso we can perform all of the capabilities of a hybrid digital—analog network with an all digital computer.
On the other hand, there is an engineering advantage to analog circuits in that analog computing is potentially thousands of times more efficient. An analog computation can be performed by a few transistors, or, in the case of mammalian neurons, specific electrochemical processes.
A digital computation, in contrast, requires thousands or tens of thousands of transistors. The massive parallelism of the human brain is the key to its pattern recognition abilities, which reflects the strength of human thinking. There is no reason why our nonbiological functionally equivalent recreations of biological neural networks cannot be built using these same principles, and indeed there are dozens of projects around the world that have succeeded in doing this.
My own technical field is pattern recognition, and the projects that I have been involved in for over thirty years use this form of chaotic computing. Objective and Subjective The Singularity envisions the emergence of human-like intelligent entities of astonishing diversity and scope.
To gain some insight as to why this is an extremely subtle question albeit an ultimately important one it is useful to consider some of the paradoxes that emerge from the concept of downloading specific human brains.
Although I anticipate that the most common application of the knowledge gained from reverse engineering the human brain will be creating more intelligent machines that are not necessarily modeled on specific biological human individuals, the scenario of scanning and reinstantiating all of the neural details of a specific person raises the most immediate questions of identity. We have to consider this question on both the objective and subjective levels.
That is, once the technology has been refined and perfected. But ultimately, the scans and recreations will be very accurate and realistic. Interacting with the newly instantiated person will feel like interacting with the original person. The new person will claim to be that same old person and will have a memory of having been that person. The new person will have all of the patterns of knowledge, skill, and personality of the original. We are already creating functionally equivalent recreations of neurons and neuron clusters with sufficient accuracy that biological neurons accept their nonbiological equivalents and work with them as if they were biological.
There are no natural limits that prevent us from doing the same with the hundred billion neuron cluster of clusters we call the human brain. Subjectively, the issue is more subtle and profound, but first we need to reflect on one additional objective issue: The Importance of Having a Body Consider how many of our thoughts and thinking are directed toward our body and its survival, security, nutrition, and image, not to mention affection, sexuality, and reproduction.
Many, if not most, of the goals we attempt to advance using our brains have to do with our bodies: Some philosophers maintain that achieving human level intelligence is impossible without a body. A disembodied mind will quickly get depressed. There are a variety of bodies that we will provide for our machines, and that they will provide for themselves: A detailed examination of twenty-first century bodies is beyond the scope of this essay, but recreating and enhancing our bodies will be and has been an easier task than recreating our minds.
To return to the issue of subjectivity, consider: Is this a mind or just a brain? Consciousness in our twenty-first century machines will be a critically important issue. But it is not easily resolved, or even readily understood. But at what point do we consider an entity, a process, to be conscious, to feel pain and discomfort, to have its own intentionality, its own free will? How do we determine if an entity is conscious; if it has subjective experience? How do we distinguish a process that is conscious from one that just acts as if it is conscious?
If we look inside its circuits, and see essentially the identical kinds of feedback loops and other mechanisms in its brain that we see in a human brain albeit implemented using nonbiological equivalentsdoes that settle the issue? And just who are these people in the machine, anyway?
The answer will depend on who you ask. If you ask the people in the machine, they will strenuously claim to be the original persons. Hey, this technology really works. Is this really me? Alas, I will have to sit back and watch the new Ray succeed in endeavors that I could only dream of.
First of all, am I the stuff in my brain and body? Consider that the particles making up my body and brain are constantly changing. We are not at all permanent collections of particles. The cells in our bodies turn over at different rates, but the particles e. I am just not the same collection of particles that I was even a month ago.
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It is the patterns of matter and energy that are semipermanent that is, changing only graduallybut our actual material content is changing constantly, and very quickly. We are rather like the patterns that water makes in a stream. The rushing water around a formation of rocks makes a particular, unique pattern. This pattern may remain relatively unchanged for hours, even years. Of course, the actual material constituting the pattern—the water—is replaced in milliseconds.
The same is true for Ray Kurzweil. Like the water in a stream, my particles are constantly changing, but the pattern that people recognize as Ray has a reasonable level of continuity. This argues that we should not associate our fundamental identity with a specific set of particles, but rather the pattern of matter and energy that we represent. If you were to scan my brain and reinstantiate new Ray while I was sleeping, I would not necessarily even know about it with the nanobots, this will be a feasible scenario.
How could he be me? After all, I would not necessarily know that he even existed. After I have this procedure performed, am I still the same person? My friends certainly think so. A new number associated with the no- virus computer software includes integrated firewalls though the majority with these people are just anti virus proper protection.
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Women always notice that man is presentable and who isn't. If you glance presentable only half the time, the odds of getting that date reduce noticeably. All the old saying is good quality that at the time you end looking for an item is if you commonly find it. Therefore, if you happen to go out, stop looking consequently desperately. The following makes you seem rather needy and that's commonly a frustrated for virtually every young lady.
Your goal as you move out ought to be to increase your social media and make the idea possible to get much more girls available. As outlined by one article I examine in Seventeen magazine, a single teen says she hopes her mom would change - although it isn't since it embarrasses her in front of her friends. My ex-boyfriend implemented to trick about getting and her!
Although I will never want my crushes crushing on my mommy. I feel like I have to help keep the girl's faraway from your new boyfriend to stay her from paying more consentrate on the girl's rather than all of us. As well as Let me provide The reason Designing and also running some sort of VPN service expenditures some sort of great option of money as some result for substance involving the techniques. Based at the kind of VPN company you aren't working with, the main speed definitely will fluctuate occasionally.
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Most mail choose brides websites talk about the dangers of online dating. There are certain ideas that every user really should follow. Here are many such tips compiled from different websites.
If the person who can be asking for this information is constant, consider reporting them how to a moderator. In the event that asked for expensive merchandise jewels, credit cards, cash, checks, expensive comfort itemsuse discernment. You are free to generate such gifts whenever you see fit, still always consider all the intentions of a man requesting them right from you.
If requested money for a jet ticket to visit you will, consider your options. Are you prepared to move this relationship to the next place? Are you ready for all the commitment to move ahead? Ask yourself whether associated with time has passed and whether you know this person as well as one thinks you do. Most times, honest ladies will not insist upon you paying for anything at all. If they break contact without providing you with any reason, tend not to escalate further.
Perhaps the person is not serious about you or can be busy with her daily life. Wait a week for a woman to get hold of you again. Your first procedures are to find out, through advertising or by using a friend, which website and service you need to use. Then, you will subscribe and get into your preferences.