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By Sandra Aamodt and Sam Wang

Inventor Ray Kurzweil，in his 2005 futurist manifesto“The Singularity Is Near，”extrapolates current trends in computer technology to conclude that machines will be able to out-think people within a few decades.In his eagerness to salute our robotic overlords，he neglects some key differences between brains and computers that make his prediction unlikely to Come true.

Brains have long been compared to the most advanced existing technology—including，at one point，telephone switchboards.Today people talk about brains as if they were a sort of biological computer，with pink mushy“hardware”and“software”generated by life experiences.

However，any Comparison with computers misses a messy truth.Because the brain arose through natural selection，it contains layers of systems that arose for one function and then were adopted for another，even though they don't work perfectly.An engineer with time to get it right would have started over，but it's easier for evolution to adapt an old system to a new purpose than to Come up with an entirely new structure.Our colleague David Linden has compared the evolutionary history of the brain to the task of building a modern car by adding parts to a 1925 Model T that never stops running.As a result，brains differ from computers in many ways，from their highly efficient use of energy to their tremendous adaptability.

One striking feature of brain tissue is its compactness.In the brain's wiring，space is at a premium，and is more tightly packed than even the most condensed computer architecture.One cubic centimeter of human brain tissue，which would fill a thimble，contains 50 million neurons； several hundred miles of axons，the wires over which neurons send signals；and close to a trillion(that's a million million)synapses，the connections between neurons.

The memory capacity in this small volume is potentially immense.Electrical impulses that arrive at a synapse give the recipient neuron a small chemical kick that can vary in size.Variation in synaptic strength is thought to be a means of memory formation.Sam's lab has shown that synaptic strength flips between extreme high and low states，a flip that is reminiscent of a computer storing a“one”or a“zero”—a single bit of information.

But unlike a computer，connections between neurons can form and break too，a process that continues throughout life and can store even more information because of the potential for creating new paths for activity.Although we're forced to guess because the neural basis of memory isn't understood at this level，let's say that one movable synapse could store one byte(8 bits)of memory.That thimble would then contain 1，000 gigabytes(1 terabyte)of information.A thousand thimblefuls make up a whole brain，giving us a million gigabytes—a petabyte—of information.To put this in perspective，the entire archived contents of the Internet fill just three petabytes.

To address this challenge，Kurzweil invokes Moore's Law，the principle that for the last four decades，engineers have managed to double the capacity of chips(and hard drives)every year or two.If we imagine that the trend will continue，it's possible to guess when a single computer the size of a brain could contain a petabyte.That would be about 2025 to 2030，just 15 or 20 years from now.

This projection overlooks the dark，hot underbelly of Moore's Law: power consumption per chip，which has also exploded since 1985.By 2025，the memory of an artificial brain would use nearly a gigawatt of power，the amount currently consumed by all of Washington，D.C.So brute-force escalation of current computer technology would give us an artificial brain that is far too costly to operate.

Campare this with your brain，which uses about 12 watts，an amount that supports not only memory but all your thought processes.This is less than the energy consumed by a typical refrigerator light，and half the typical needs of a laptop computer.Cutting power consumption by half while increasing computing power many times over is a pretty challenging design standard.As smart as we are，in this sense we are all dim bulbs.

A persistent problem in artificial computing is the sensitivity of the system to component failure.Yet biological synapses are remarkably flaky devices even in normal，healthy conditions.They release neurotransmitter only a small fraction of the time when their parent neuron fires an electrical impulse.This unreliability may arise because individual synapses are so small that they contain barely enough machinery to function.This may be a trade-off that stuffs the most function into the smallest possible space.

In any case，a brain's success is not measured by its ability to process information in precisely repeatable ways.Instead，it has evolved to guide behaviors that allow us to survive and reproduce，which often requires fast responses to complex situations.As a result，we constantly make approximations and find“good-enough”solutions.This leads to mistakes and biases.We think that when two events occur at the same time，one must have caused the other.We make inaccurate snap judgments such as racial prejudice.We fail to plan rationally for the future，as explored in the field of neuroeconomics.

Still，engineers could learn a thing or two from brain strategies.For example，even the most advanced computers have difficulty telling a dog from a cat，something that can be done at a glance by a toddler—or a cat.We use emotions，the brain's steersman，to assign value to our experiences and to future possibilities，often allowing us to evaluate potential outComes efficiently and rapidly when information is uncertain.In general，we bring an extraordinary amount of background information to bear on seemingly simple tasks，allowing us to make inferences that are difficult for machines.

If engineers can understand how to apply these shortcuts and tricks，computer performance could begin to emulate some of the more impressive feats of human brains.However，this route may lead to computers that share our imperfections.This may not be exactly what we want from robot overlords，but it could lead to better“soft”judgments from our computers.

As neuroscientists，we'reexcitedaboutthepotentialofusing computational models to test our understanding of how the brain works.On the other hand，although it eventually may be possible to design sophisticated computing devices that imitate what we do，the capability to make such a device is already here.All you need is a fertile man and woman with the resources to nurture their child to adulthood.With luck，by 2030 you'll have a full-grown，college-educated，walking petabyte.A drawback is that it may be difficult to get this computing device to do what you ask.

1.What are the differences and similarities between computers and human brains?

2.Why does the writer say that we human beings“are all dim bulbs”?In what way?

3.What is the purpose of the discussion of“unreliability”and“trade-off”of human brains?

4.According to the article，in what way is racial prejudice related to human brain?What do you think?

5.What does the fact that even the most sophisticated computers have difficulty telling a dog from a cat tell us?

“The Singularity Is Near”:When Humans Transcend Biology is a 2005 update of Raymond Kurzweil's 1999 book，The Age of Spiritual Machines and his 1987 book The Age of Intelligent Machines.In it，as in the two previous versions，Kurzweil attempts to give us a glimpse of what awaits us in the near future.His reasoning rests on the combination of four postulates:

1.That a technological-evolutionary point known as“the singularity“exists as an achievable goal for humanity(the exact nature of the point is an arbitrarily high level of technology).

2.That through a law of accelerating returns，technology is progressing toward the singularity at an exponential rate.

3.That the functionality of the human brain is quantifiable in terms of technology that we can build in the near future.

4.That medical advanComents could keep a significant number of his generation(Baby Boomers)alive long enough for the exponential growth of technology to intersect and surpass the processing of the human brain.

Model T:Automobile built by the Ford Motor Co.from 1908 until 1927，the first widely affordable mass-produced car.Assembly-line production methods introduced by Henry Ford in 1913 enabled the price of this five-seat touring car to drop from$850 in 1908 to$300 in 1925.Over 15 million Model T's were built.The car was offered in several body styles，all mounted on a standard chassis.Various colors were initially available，but after 1913 its sole color was black.It was replaced by the popular Model T in 1928.

Byte字节vs.Bit比特:Byte is a sequence of 8 bits(enough to represent one character of alphanumeric data)processed as a single unit of information.In the computer，electronics，and communications fields，“bit”is generally understood as a shortened form of“binary digit.“In a numerical binary system，a bit is either a 0 or 1.Bits are generally used to indicate situations that can take one of two values or one of two states，for example，on and off，true or false，or yes or no.If，by convention，1 represents a particular state，then 0 represents the other state.For example，if 1 stands for“yes，”then 0 stands for“no.“

Underbelly:下腹部，易受攻击的地带，薄弱部分，弱点　The underbelly of something is the part of it that can be most easily attacked or criticized.

Natural selection:“物竞天择，优胜劣汰。适者生存，不适者淘汰。”Process that results in adaptation of an organism to its environment by means of selectively reproducing changes in its genotype.Variations that increase an organism's chances of survival and procreation are preserved and multiplied from generation to generation at the expense of less advantageous variations.As proposed by Charles Darwin，natural selection is the mechanism by which evolution occurs.It may arise from differences in survival，fertility，rate of development，mating success，or any other aspect of the lifecycle.Mutation，gene flow，and genetic drift，all of which are random processes，also alter gene abundance.Natural selection moderates the effects of these processes because it multiplies the incidence of beneficial mutations over generations and eliminates harmful ones，since the organisms that carry them leave few or no descendants.

The concept of natural selection sometimes is rendered popularly as the“survival of the fittest.”Scientists are less likely to use this phrase for several reasons，including the fact that it has been associated with distasteful social philosophies or murderous political ideologies—for example，Nazism.Additionally，the word fittest is a bit confusing，because it implies“fitness，”or the quality of being physically fit.

This implication，in turn，might lead a person to believe that natural selection entails the survival of the strongest，which is not the case.Yet this is precisely what proponents of a loosely defined philosophy known as social Darwinism claimed.Popular among a wide range of groups and people in the late nineteenth and early twentieth centuries，social Darwinism could be used in the service of almost any belief.Industrialists and men of wealth asserted that those who succeeded financially did so because they were the fittest，while Marxists claimed that the working class ultimately would triumph for the same reason.Across the political spectrum，social Darwinism confused the meaning of“fittest”with that of other concepts:“strongest，”“most advanced，”or even“most moral.”All of this，it need hardly be said，is misguided，not least because evolutionary theory has nothing to do with race，ethnicity，or social class.

In fact，“survival of the fittest，”in a more accurate interpretation，means the individuals that“fit，”or“fit in with，”their environments are those most likely to survive.This is a far cry from any implication of strength or superiority.

Evolution:Biological theory that animals and plants have their origin in other preexisting types and that the distinguishable differences are due to modifications in successive generations.It is one of the keystones of modern biological theory.In 1858 Charles Darwin and Alfred Russel Wallace jointly published a paper on evolution.The next year Darwin presented his major treatise On the Origin of Species by Means of Natural Selection，which revolutionized all later biological study.The heart of Darwinian evolution is the mechanism of natural selection.Surviving individuals，which vary in some way that enables them to live longer and reproduce，pass on their advantage to succeeding generations.In 1937 Theodosius Dobzhansky applied Mendelian genetics to Darwinian theory，contributing to a new understanding of evolution as the cumulative action of natural selection on small genetic variations in whole populations.Part of the proof of evolution is in the fossil record，which shows a succession of gradually changing forms leading up to those known today.Structural similarities and similarities in embryonic development among living forms also point to common ancestry.Molecular biology(especially the study of genes and proteins)provides the most detailed evidence of evolutionary change.Though the theory of evolution is accepted by nearly the entire scientific community，it has sparked much controversy from Darwin's time to the present；many of the objections have Come from religious leaders and thinkers who believe that elements of the theory conflict with literal interpretations of the Bible.

Moor's Law:“The number of transistors and resistors on a chip doubles every 18 months”by Intel(英特尔公司)co-founder Gordon Moore regarding the pace of semiconductor technology.He made this famous comment in 1965 when there were approximately 60 devices on a chip.Proving Moore's Law to be rather accurate，four decades later，Intel placed 1.7 billion transistors on its Itanium chip.In 1975，Moore extended the 18 months to 24 months.More recently，he said that the cost of a semiconductor manufacturing plant doubles with each generation of microprocessor.

http://www.washingtonpost.com/wp-dyn/content/article/2007/05/11/ AR2007051102050_pf.html

Out-think:前面读到John-Bolton-like distaste of multilateralism中的John-Bolton-like在词典中是查不到的。其实，新闻英语这种灵活构词有时作一时应景之用，有时则是轻松利用前缀、后缀构建较稳定的词汇，如本文中的out-think。out此处是“胜出、超过”之义，这种“out+及物动词”结构表示主语在某方面胜过宾语。同结构的词不下50个，如outbid，outdate，outdo，outgrow，outguess，outlast，outlive，outmaneuver，outperform，outtake，outwash等。同时，out还可以和大量的名词结合构成类似的动词。

I.According to the article，determine which statements are true and which are false.

1.Ray Kurzweil predicts that computers will be able to out-think by 2015.

2.In the Comparison of brains to computers，the“pink mushy hardware”probably refers to brain cells.

3.In this article，“natural selection”is used synonymously with“evolution.”

4.Model T is a car model probably built in the early 20th century.

5.Three whole brains of information contains about the entire archived contents of the Internet.

1.Moor's Law________.

A.means that the capacity of computer chips doubles periodically

B.does not state that there is a limit to the capacity growth of computer chips and hard drives

Comeans by inference that by year 2030 a single computer could have a human brain in information storage

D.all of the above

2.The“underbelly”of Moor's Law________.

A.is mentioned but not explained in this article

B.figuratively refers to the overlooked possibility of huge power consumption by an artificial brain with a storage capacity equivalent to that of a brain

C.exaggerates the power consumed by the District of Columbia

D.none of the above

3.Which of the following is true about the human brain?

A.It is in an extremely compact architecture.

B.Its tissue contains neurons，axons and synapses.

C.It could contain a million gigabytes of information.

D.All of the above.

4.Which of the following is true about the computer?

A.It's more difficult for the computer to make inferences based on the extent of background information.

B.It's impossible for the computer to emulate the more impressive feats of the human brain.

C.The computer can only make hard，not soft，judgments.

D.None of the above.

5.Which of the following best summarizes this passage?

A.Computers can surely out-smart human brains.

B.There are some imperfections with brains and lots of glitches with computers.

C.Human brains are a lot more energy-efficient and situationresponsive than computers.

D.None of the above.