awe inspiring machines

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Awe-Inspiring Machines: A History of Artificial Intelligence

In 1950, Alan Turing published his seminal paper “Computing Machinery andIntelligence” in the British philosophical journal Mind , and this sparked the beginning of what

we know as the field of Artificial Intelligence. Turing proposed a test as to whether a machine

can imitate a human in conversation in order to fool a human into thinking the machine is humanas a testament to the abilities of the machine. Turing wrote: “The original question, 'Can

machines think?’ I believe to be too meaningless to deserve discussion. Nevertheless, I believe

that at the end of the century the use of words and general educated opinion will have altered so

much that one will be able to speak of machines thinking without expecting to be contradicted”(442). Obviously, Turing was not quite correct in his predictions but he provided the foundation

for what became the field of AI.

The history of Turing’s ideas of a machine that can do calculations are much older than the1950s. In 1822, an Englishman by the name of Charles Babbage came up with the designs for

two different machines; one was called the Analytical Engine, a machine designed to solve math

problems by using tabulated cards as instructions. In 1843, a woman named Ada Loveless had

read Babbage’s writings and designed her own cards for the machine to calculate Bernoullinumbers. She is generally recognized as the world’s first computer programmer. However,

Babbage’s machine was never built. In 1910, his son constructed a partial implementationof the device and it has been suggested by historians and researchers that the machine would

have worked had the political and scientific funding been stronger for Babbage. Babbage also

designed a machine called the difference engine, which computed logarithms, but this machinewas never constructed in Babbage’s lifetime either. However, from 1989 to 1991 in honor of

his birth, the London science museum built a working machine seemingly cementing Babbage’s

place in history as the father of the machines and ideas that would become today’s digital

computers.Alan Turing’s papers ignited a firestorm of debate among the newly formed computer science

community of the 1950s. In addition to being the father of computer science—even before there

were digital or analog computers—in 1936, Turing published a paper called “On Computable Numbers, with an Application to the Entscheidungsproblem,” in which Turing formulated

the ideas for what are now known as Turing machines. The article not only specified the

philosophical underpinnings of Turing machines, but also the notion of what can be computed.The Entscheidungsproblem, or “Decision Problem” is a mathematical challenged posed by the

mathematician David Hilbert in 1928. The problem was formulated as is there an algorithm that

will produce a true or false answer about a mathematical statement in a formal language alongwith a description of that formal language? What Turing’s paper did was establish that there

can be no general solution to such a problem. The problem in Turing’s case, that is, as applied

to computer systems is that there is no algorithm that can be applied to a program in order todetermine when it will end. For example, if a program has a loop where the program is supposed

to do something repeatedly until some condition is true. While (1) { DoSomething();} as in the

C programming language, this will cause the program to loop forever until it is manually shut

down by the user of the machine. The halting problem cannot tell us if those sorts of loops existin bits of code or not, it is up to the programmers to find those bugs and fix them, whereas if

all the program does is print “Hello, World” onto the screen, then those sorts of programs halt

very soon. It is not true that the halting problem exists for specific cases of specific problems but in the general sense, it does exist. In 1931, the German logician Kurt Gödel proved that a



complete formal system that describes all properties in mathematics is impossible to construct,

which was a basis for many of the computing ideas of the mathematicians of the time including

not only Turing, but also Alonzo Church whose lambda (ƛ) calculus was proven an equivalent

computational system to the Turing machines. Naturally, many computer scientists published papers against Turing’s ideas of AI, and several

schools of thought developed with regard to the philosophy of mind as well as the technicalchallenges to AI. In the 1955, John McCarthy of (at the time) Princeton University coinedthe term “Artificial Intelligence” as well as helped created the LISP programming language,

frequently used in AI research at that time. The philosophy of mind was beginning to be debated

with researchers such as Paul Ziff saying, “Robots are mechanisms, not organisms, not livingcreatures. There could be a broken-down robot but not a dead one. Only living creatures can

literally have feelings.” (64) Many researchers of this era echoed this sentiment, such as Michael

Schriven, that only a human mind can have feelings, express emotion, have a soul, and other similar attributes people associate with being uniquely human. There are those of the modern era,

such as Ray Kurzweil, a futurist who believes that a technological singularity or quantum leap

is coming within the next 25 years that suggests that computers will have the power to rival a

human brain, but this view is not shared by most AI researchers.However, the 1950s and 1960s were dominated by feelings of huge potential in the AI field.

People such as Marvin Minsky of MIT said in 1967 that: “within a generation, the problem of creating artificial intelligence will be mostly solved.” Obviously, that prediction did not come

true, but it is representative of the spirit of hope residing in the culture of the era. Great strides

were made in areas such as Machine Learning, and Machine Vision that surprised and delighted

researchers. Researchers predicted in 1958 that people would have a chess champion that could beat a grand master in 10 years. This did not happen until the match between the computer

Deep Blue and chess champion Gerry Kasparov in 1997. As the 1970s dawned, problems such

as searching and machine translation were beginning to make improvements. However by themid- to late-70s, much of the funding that AI research had enjoyed dried up as grant givers,

such as the National Science Foundation, became disillusioned with the promises and hopes of

researchers did not come to fruition as was promised.As the 1980s came, an article by the professor John Searle of Berkley, called “Minds, Brains,

and Programs”, in the journal Behavioral and Brain Sciences constructs an argument that came

to be known as the “Chinese Room” argument. In the paper, Searle suggests a man in a room,with a book written in English. The man would manipulate Chinese symbols according to

instructions in the book, that is, he would take one set of Chinese symbols as input and produce

another set of Chinese symbols as output. Searle argues that just because an accurate symbolgroup appears as output does not mean the man in the room understands Chinese as people

would think of what it means to understand a language. Searle says that all the man is doing

is following a formula and that syntax and semantics are different and cannot be combined,

similarly, programs themselves are not syntactically capable of generating semantics. Accordingto Searle, no matter the program, machines cannot gain a mind simply by executing a computer

program: “programs are neither constitutive of nor sufficient for minds.” (26)

Naturally, the article touched off a big debate in the AI community, just as Alan Turing’s paper had done 30 years earlier; most of the replies to Searle were made up of attempts to refute his

ideas. In 2002, Searle published a paper called “Twenty-One Years in the Chinese Room” in

which he takes aim at his critics and tried to show how his critics rebuttals and arguments wereincorrect. One reply is known as the Systems Reply, which says that although semantic content



does not reside in the person alone, it does reside somewhere else inside the system as a whole.

Searle says that this is false because the system as a whole still does not understand Chinese, the

man in the room has no way of knowing what the words mean and neither does the whole system

and that the very idea of such an argument is contradictory. Various people have tried to modifythe constraints of the Chinese Room, but Searle rejects these. Searle does not disagree that the

operations of the brain can be simulated, but that that is all they are, simulations, they do notreflect a real, physical thing in the way that an artificial heart does.This debate continues today with articles published in journals such as Minds and Machines.

Articles with titles such as, “Why Computers Can’t Feel Pain” in which the author states that

if the basis of Searle’s Strong AI which is that a properly programmed computer with theright input and output has a mind just as humans have minds, would lead to Panpsychism.

Panpsychism is the philosophy that all things are sentient and that either many separate minds, or

a single mind that unifies everything.At the beginning of the 1990s, computers and the software that powered them were getting even

more complex than people would have thought possible 20 years before. There were new ways

of communicating; the Internet was becoming more available. Computer scientists discovered

new ways of programming computers to translate natural languages and to use computers toimprove the lives of others in physical ways, such as the use of PC tablets for the disabled.

Scientists programmed an AI to drive a car, driving it for almost 3000 miles. Robotics alsogained big advantages; in 1996, scientists programmed a robot to perform surgery for the first

time.

At the beginning of the 2000s, NASA started using more and more robotics in their space

voyages. Futurists, who see huge leaps in artificial intelligence capabilities, were coming moreand more into the popular forefront. Google came to prominence, having been founded in 1998;

they use a large chunk of AI in their search engine algorithms. Speech recognition was and is

becoming increasingly popular, with many people using it to dictate memos or whole papers andarticles. Computing power is getting ever faster, but chip designers are running into some of the

physical limits of Moore’s law1. Such as, computer engineers are running out of space to store

ever more transistors on a chip so what they have to do is take and shove those transistors thatare used in CPUs into two or more physical spaces within a single chip.

Obviously the predictions made by the futurists are still to pass but many are skeptical that AI

will become so advanced that it will rival a human brain; however if it does, humanity probablywill not be having to deal with any machine invasion any time soon. It is not impossible that

this may happen, but there are whole hosts of issues associated with the idea, such as computers

would need to be able to portray negative human emotion for them to attack us or they wouldhave to see us as inefficient. Time will tell.

Works Cited

Russell, Stuart J., and Peter Norvig. Artificial Intelligence: a Modern Approach. Upper Saddle

River, NJ: Prentice Hall/Pearson Education, 2003. Print.

Searle, John. "Minds, Brains and Programs." Behavioral and Brain Sciences 3 (1980): 417-24.Print.

1 Moore’s law being a trend predicted by Intel co-founder Gordon Moore who said that computing power will

double every 2 years. This trend is not just limited to CPUs, but also includes computer memory and disk storage

space.



Searle, John. "Is the Brain's Mind a Computer Program?" Scientific American 262.6 (1990): 26-

31. Print.

Searle, John. "Twenty-One Years in the Chinese Room." Ed. Mark Bishop. Views into theChinese Room: New Essays on Searle and Artificial Intelligence. Ed. John Preston.

Oxford: Clarendon, 2002. 51-69. Print.

Turing, Alan. “Computing Machinery and Intelligence” Mind 59.236 (1950) : 442. Print.Ziff, Paul. “The Feelings of Robots” Analysis 19.3 (1959) : 64. Print.

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