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Let's DiscussWhile artificial intelligence (AI) is among today’s most popular topics, a commonly forgotten fact is that it was actually born in 1950 and went through a hype cycle between 1956 and 1982. The purpose of this article is to
highlight some of the achievements that took place during the boom phase of this cycle and explain what led
to its bust phase. The lessons to be learned from this hype cycle should not be overlooked – its successes
formed the archetypes for machine learning algorithms used today, and its shortcomings indicated the
dangers of overenthusiasm in promising fields of research and development.
Although the first computers were developed during World War II [1,2], what seemed to truly spark the field of
AI was a question proposed by Alan Turing in 1950 [3]: can a machine imitate human intelligence? In his
seminal paper, “Computing Machinery and Intelligence,” he formulated a game, called the imitation game, in
which a human, a computer, and a (human) interrogator are in three different rooms. The interrogator’s goal is
to distinguish the human from the computer by asking them a series of questions and reading their
typewritten responses; the computer’s goal is to convince the interrogator that it is the human [3]. In a 1952
BBC interview, Turing suggested that, by the year 2000, the average interrogator would have less than a 70%
chance of correctly identifying the human after a five-minute session [4].
Turing was not the only one to ask whether a machine could model intelligent life. In 1951, Marvin Minsky, a
graduate student inspired by earlier neuroscience research indicating that the brain was composed of an
electrical network of neurons firing with all-or-nothing pulses, attempted to computationally model the
behavior of a rat. In collaboration with physics graduate student Dean Edmonds, he built the first neural
network machine called Stochastic Neural Analogy Reinforcement Computer (SNARC) [5]. Although primitive
(consisting of about 300 vacuum tubes and motors), it was successful in modeling the behavior of a rat in a
small maze searching for food [5].
The notion that it might be possible to create an intelligent machine was an alluring one indeed, and it led to several subsequent developments. For instance, Arthur Samuel built a Checkers-playing program in 1952 that
was the world’s first self-learning program [12]. Later, in 1955, Newell, Simon and Shaw built Logic Theorist,
which was the first program to mimic the problem-solving skills of a human and would eventually prove 38 of
the first 52 theorems in Whitehead and Russell’s Principia Mathematica [6].
Inspired by these successes, young Dartmouth professor John McCarthy organized a conference in 1956 to
gather twenty pioneering researchers and, “explore ways to make a machine that could reason like a human,
was capable of abstract thought, problem-solving and self-improvement” [7]. It was in his 1955 proposal for
this conference where the term, “artificial intelligence,” was coined [7,40,41,42], and it was at this conference
where AI gained its vision, mission, and hype.
Researchers soon began making audacious claims about the incipience of powerful machine intelligence, and
many anticipated that a machine as intelligent as a human would exist in no more than a generation [40, 41,
42]. For instance:
AI had even caught Hollywood’s attention. In 1968, Arthur Clarke and Stanley Kubrick produced the movie,
2001: A Space Odyssey, whose antagonist was an artificially intelligent computer, HAL 9000 exhibiting
creativity, a sense of humor, and the ability to scheme against anyone who threatened its survival. This was
based on the belief held by Turing, Minsky, McCarthy and many others that such a machine would exist by
2000; in fact, Minsky served as an adviser for this film and one of its characters, Victor Kaminski, was named in
his honor.
Between 1956 and 1982, the unabated enthusiasm in AI led to seminal work, which gave birth to several
subfields of AI that are explained below. Much of this work led to the first prototypes for the modern theory of
AI.
Rule based expert systems try to solve complex problems by implementing series of “if-then-else” rules. One
advantage to such systems is that their instructions (what the program should do when it sees “if” or “else”)
are flexible and can be modified either by the coder, user or program itself. Such expert systems were created
and used in the 1970s by Feigenbaum and his colleagues [13], and many of them constitute the foundation
blocks for AI systems today.
The field of machine learning was coined by Arthur Samuel in 1959 as, “the field of study that gives computers
the ability to learn without being explicitly programmed” [14]. Machine learning is a vast field and its detailed
explanation is beyond the scope of this article. The second article in this series – see Prologue on the first page and [57] – will briefly discuss its subfields and applications. However, below we give one example of a machine learning program, known as the perceptron network.
Single and Multilayer Perceptron Networks
Inspired by the work of McCulloch and Pitts in 1943 and of Hebb in 1949 [15,16], Rosenblatt in 1957 introduced
the perceptron network as an artificial model of communicating neurons [17]. This model is shown in Figure 5
and can be briefly described as follows. One layer of vertices, where input variables are entered, is connected
to a hidden layer of vertices (also called perceptrons), which in turn is connected to an output layer of
perceptrons. A signal coming via a connection from an input vertex to a perceptron in the hidden layer is
calibrated by a “weight” associated with that connection, and this weight is assigned during a “learning
process”. Signals from hidden layer perceptrons to output layer perceptrons are calibrated in an analogous
way. Like a human neuron, a perceptron “fires” if the total weight of all incoming signals exceeds a specified
potential. However, unlike for humans, signals in this model are only transmitted towards the output layer,
which is why these networks are often called “feed-forward.” Perceptron networks with only one hidden layer
of perceptrons (i.e., with two layers of weighted edge connections) later became known as “shallow” artificial
neural networks. Although shallow networks were limited in power, Rosenblatt managed to create a one-layer
perceptron network, which he called created Mark 1, that was able to recognize basic images [17].
Today, the excitement is about “deep” (two or more hidden layers) neural networks, which were also studied in
the 1960s. Indeed, the first general learning algorithm for deep networks goes back to the work of Ivakhnenko
and Lapa in 1965 [18,19]. Networks as deep as eight layers were considered by Ivakhnenko in 1971, when he
also provided a technique for training them [20].
In 1957 Chomsky revolutionized linguistics with universal grammar, a rule based system for understanding
syntax [21]. This formed the first model that researchers could use to create successful NLP systems in the
1960s, including SHRDLU, a program which worked with small vocabularies and was partially able to
understand textual documents in specific domains [22]. During the early 1970s, researchers started writing
conceptual ontologies, which are data structures that allow computers to interpret relationships between
words, phrases and concepts; these ontologies widely remain in use today [23].
The question of whether a computer could recognize speech was first proposed by a group of three
researchers at AT&T Bell Labs in 1952, when they built a system for isolated digit recognition for a single
speaker [24]. This system was vastly improved upon during the late 1960s, when Reddy created the Hearsay I, a program which had low accuracy but was one of the first to convert large vocabulary continuous speech into text. In
1975, his students Baker and Baker created the Dragon System [25], which further improved upon Hearsay I by
using the Hidden Markov Model (HMM), a unified probabilistic model that allowed them to combine various
sources such as acoustics, language, and syntax. Today, the HMM remains an effective framework for speech
recognition [26].
In the summer of 1966, Minsky hired a first-year undergraduate student at MIT and asked him to solve the
following problem: connect a television camera to a computer and get the machine to describe what it sees
[27]. The aim was to extract three-dimensional structure from images, thereby enabling robotic sensory
systems to partially mimic the human visual system. Research in computer vision in the early 1970s formed the
foundation for many algorithms that exist today, including extracting edges from images, labeling lines and
circles, and estimating motion in videos [28].
The above theoretical advances led to several applications, most of which fell short of being used in practice at
that time but set the stage for their derivatives to be used commercially later. Some of these applications are
discussed below.
Between 1964 and 1966, Weizenbaum created the first chat-bot, ELIZA, named after Eliza Doolittle who was
taught to speak properly in Bernard Shaw’s novel, Pygmalion (later adapted into the movie, My Fair Lady).
ELIZA could carry out conversations that would sometimes fool users into believing that they were
communicating with a human but, as it happens, ELIZA only gave standard responses that were often
meaningless [29]. Later in 1972, medical researcher Colby created a “paranoid” chatbot, PARRY, which was also
a mindless program. Still, in short imitation games, psychiatrists were unable to distinguish PARRY’s ramblings
from those of a paranoid human’s [30].
In 1954, Devol built the first programmable robot called, Unimate, which was one of the few AI inventions of its
time to be commercialized; it was bought by General Motors in 1961 for use in automobile assembly lines [31].
Significantly improving on Unimate, in 1972, researchers at Waseda University in 1972 built the world’s first full-
scale intelligent humanoid robot, WABOT-1 [32]. Although it was almost a toy, its limb system allowed it to walk
and grip as well as transport objects with hands; its vision system (consisting of its artificial eyes and ears)
allowed it to measure distances and directions to objects; and its artificial mouth allowed it to converse in
Japanese [32]. This gradually led to innovative work in machine vision, including the creation of robots that
could stack blocks [33].
Despite some successes, by 1975 AI programs were largely limited to solving rudimentary problems. In
hindsight, researchers realized two fundamental issues with their approach.
In 1976, the world’s fastest supercomputer (which would have cost over five million US Dollars) was only
capable of performing about 100 million instructions per second [34]. In contrast, the 1976 study by Moravec
indicated that even the edge-matching and motion detection capabilities alone of a human retina would
require a computer to execute such instructions ten times faster [35]. Likewise, a human has about 86 billion
neurons and one trillion synapses; basic computations using the figures provided in [36,37] indicate that
creating a perceptron network of that size would have cost over 1.6 trillion USD, consuming the entire U.S.
GDP in 1974.
Scientists did not understand how the human brain functions and remained especially unaware of the
neurological mechanisms behind creativity, reasoning and humor. The lack of an understanding as to what
precisely machine learning programs should be trying to imitate posed a significant obstacle to moving the
theory of artificial intelligence forward. In fact, in the 1970s, scientists in other fields even began to question
the notion of, ‘imitating a human brain,’ proposed by AI researchers. For example, some argued that if
symbols have no ‘meaning’ for the machine, then the machine could not be described as ‘thinking’ [38].
Eventually it became obvious to the pioneers that they had grossly underestimated the difficulty of creating an
AI computer capable of winning the imitation game. For example, in 1969, Minsky and Papert published the
book, Perceptrons [39], in which they indicated severe limitations of Rosenblatt’s one-hidden layer perceptron.
Coauthored by one of the founders of artificial intelligence while attesting to the shortcomings of perceptrons,
this book served as a serious deterrent towards research in neural networks for almost a decade [40,41,42].
In the following years, other researchers began to share Minsky’s doubts in the incipient future of strong AI.
For example, in a 1977 conference, a now much more circumspect John McCarthy noted that creating such a
machine would require ‘conceptual breakthroughs,’ because ‘what you want is 1.7 Einsteins and 0.3 of the
Manhattan Project, and you want the Einsteins first. I believe it’ll take five to 500 years’ [43].
The hype of the 1950s had raised expectations to such audacious heights that, when the results did not
materialize by 1973, the U.S. and British governments withdrew research funding in AI [41]. Although the
Japanese government temporarily provided additional funding in 1980, it quickly became disillusioned by the
late 1980s and withdrew its investments again [42, 40,]. This bust phase (particularly between 1974 and 1982)
is commonly referred to as the “AI winter,” as it was when research in artificial intelligence almost stopped
completely. Indeed, during this time and the subsequent years, “some computer scientists and software
engineers would avoid the term artificial intelligence for fear of being viewed as wild-eyed dreamers”
[44].
The prevailing attitude during the 1974-1982 period was highly unfortunate, as the few substantial advances
that took place during this period essentially went unnoticed, and significant effort was undertaken to recreate
them. Two such advances are the following:
The defining characteristics of a hype cycle are a boom phase, when researchers, developers and investors
become overly optimistic and enormous growth takes place, and a bust phase, when investments are
withdrawn, and growth reduces substantially. From the story presented in this article, we can see that AI went
through such a cycle during 1956 and 1982.
Born from the vision of Turing and Minsky that a machine could imitate intelligent life, AI received its name,
mission, and hype from the conference organized by McCarthy at Dartmouth University in 1956. This marked
the beginning of the boom phase of the AI hype cycle. Between 1956 and 1973, many penetrating theoretical
and practical advances were discovered in the field of AI, including rule-based systems; shallow and deep
neural networks; natural language processing; speech processing; and image recognition. The achievements
that took place during this time formed the initial archetypes for current AI systems.
What also took place during this boom phase was “irrational exuberance” [52]. The pioneers of AI were quick
to make exaggerated predictions about the future of strong artificially intelligent machines. By 1974, these
predictions did not come to pass, and researchers realized that their promises had been inflated. By this point,
investors had also become skeptical and withdrew funding. This resulted in a bust phase, also called the AI
winter, when research in AI was slow and even the term, “artificial intelligence,” was spurned. Most of the few
inventions during this period, such as backpropagation and recurrent neural networks, went largely
overlooked, and substantial effort was spent to rediscover them in the subsequent decades.
Most of the few inventions during this period, such as backpropagation and recurrent neural networks, went
largely overlooked, and substantial effort was spent to rediscover them in the subsequent decades.
Nevertheless, like most hype cycles, “green shoots” start appearing again in mid 1980s and there was a
gradual resurgence of AI research during 1983 and 2010; we will discuss these and related developments in
our next article, “Resurgence of Artificial Intelligence During 1983-2010” [57].
In general hype cycles are double-ended swords, and the one exhibited by AI between 1956 and 1982 was no
different. Care must be taken to learn from it: the successes of its boom phase should be remembered and
appreciated, but its overenthusiasm should be viewed with at least some skepticism to avoid the full penalties
of the bust phase.
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We have the right, in our sole and absolute discretion, (1) to edit, redact, or otherwise change any Content; (2) to recategorize any Content to place them in more appropriate locations in the Website; and (3) to prescreen or delete any Content at any time and for any reason, without notice. We have no obligation to monitor your Content. Any use of the Website in violation of these Terms of Service may result in, among other things, termination or suspension of your right to use the Website.
These Submission Standards apply to any and all User Submissions. User Submissions must in their entirety comply with all the applicable federal, state, local, and international laws and regulations. Without limiting the foregoing, User Submissions must not:
We have the right, without provision of notice to:
You waive and hold harmless company and its parent, subsidiaries, affiliates, and their respective directors, officers, employees, agents, service providers, contractors, licensors, licensees, suppliers, and successors from any and all claims resulting from any action taken by the company and any of the foregoing parties relating to any, investigations by either the company or by law enforcement authorities.
For your convenience, this Website may provide links or pointers to third-party sites or third-party content. We make no representations about any other websites or third-party content that may be accessed from this Website. If you choose to access any such sites, you do so at your own risk. We have no control over the third-party content or any such third-party sites and accept no responsibility for such sites or for any loss or damage that may arise from your use of them. You are subject to any terms and conditions of such third-party sites.
This Website may provide certain social media features that enable you to:
You may use these features solely as they are provided by us and solely with respect to the content they are displayed with. Subject to the foregoing, you must not:
The Website from which you are linking, or on which you make certain content accessible, must comply in all respects with the Submission Standards set out in these Terms of Service.
You agree to cooperate with us in causing any unauthorized framing or linking immediately to stop.
We reserve the right to withdraw linking permission without notice.
We may disable all or any social media features and any links at any time without notice in our discretion.
You understand and agree that your use of the website, its content, and any goods, digital products, services, information or items found or attained through the website is at your own risk. The website, its content, and any goods, services, digital products, information or items found or attained through the website are provided on an "as is" and "as available" basis, without any warranties or conditions of any kind, either express or implied including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. The foregoing does not affect any warranties that cannot be excluded or limited under applicable law.
You acknowledge and agree that company or its respective directors, officers, employees, agents, service providers, contractors, licensors, licensees, suppliers, or successors make no warranty, representation, or endorsement with respect to the completeness, security, reliability, suitability, accuracy, currency, or availability of the website or its contents or that any goods, services, digital products, information or items found or attained through the website will be accurate, reliable, error-free, or uninterrupted, that defects will be corrected, that our website or the server that makes it available or content are free of viruses or other harmful components or destructive code.
Except where such exclusions are prohibited by law, in no event shall the company nor its respective directors, officers, employees, agents, service providers, contractors, licensors, licensees, suppliers, or successors be liable under these terms of service to you or any third-party for any consequential, indirect, incidental, exemplary, special, or punitive damages whatsoever, including any damages for business interruption, loss of use, data, revenue or profit, cost of capital, loss of business opportunity, loss of goodwill, whether arising out of breach of contract, tort (including negligence), any other theory of liability, or otherwise, regardless of whether such damages were foreseeable and whether or not the company was advised of the possibility of such damages.
To the maximum extent permitted by applicable law, you agree to defend, indemnify, and hold harmless Company, its parent, subsidiaries, affiliates, and their respective directors, officers, employees, agents, service providers, contractors, licensors, suppliers, successors, and assigns from and against any claims, liabilities, damages, judgments, awards, losses, costs, expenses, or fees (including reasonable attorneys' fees) arising out of or relating to your breach of these Terms of Service or your use of the Website including, but not limited to, third-party sites and content, any use of the Website's content and services other than as expressly authorized in these Terms of Service or any use of any goods, digital products and information purchased from this Website.
At Company’s sole discretion, it may require you to submit any disputes arising from these Terms of Service or use of the Website, including disputes arising from or concerning their interpretation, violation, invalidity, non-performance, or termination, to final and binding arbitration under the Rules of Arbitration of the American Arbitration Association applying Ontario law. (If multiple jurisdictions, under applicable laws).
Any cause of action or claim you may have arising out of or relating to these terms of use or the website must be commenced within 1 year(s) after the cause of action accrues; otherwise, such cause of action or claim is permanently barred.
Your provision of personal information through the Website is governed by our privacy policy located at the "Privacy Policy".
The Website and these Terms of Service will be governed by and construed in accordance with the laws of the Province of Ontario and any applicable federal laws applicable therein, without giving effect to any choice or conflict of law provision, principle, or rule and notwithstanding your domicile, residence, or physical location. Any action or proceeding arising out of or relating to this Website and/or under these Terms of Service will be instituted in the courts of the Province of Ontario, and each party irrevocably submits to the exclusive jurisdiction of such courts in any such action or proceeding. You waive any and all objections to the exercise of jurisdiction over you by such courts and to the venue of such courts.
If you are a citizen of any European Union country or Switzerland, Norway or Iceland, the governing law and forum shall be the laws and courts of your usual place of residence.
The parties agree that the United Nations Convention on Contracts for the International Sale of Goods will not govern these Terms of Service or the rights and obligations of the parties under these Terms of Service.
If any provision of these Terms of Service is illegal or unenforceable under applicable law, the remainder of the provision will be amended to achieve as closely as possible the effect of the original term and all other provisions of these Terms of Service will continue in full force and effect.
These Terms of Service constitute the entire and only Terms of Service between the parties in relation to its subject matter and replaces and extinguishes all prior or simultaneous Terms of Services, undertakings, arrangements, understandings or statements of any nature made by the parties or any of them whether oral or written (and, if written, whether or not in draft form) with respect to such subject matter. Each of the parties acknowledges that they are not relying on any statements, warranties or representations given or made by any of them in relation to the subject matter of these Terms of Service, save those expressly set out in these Terms of Service, and that they shall have no rights or remedies with respect to such subject matter otherwise than under these Terms of Service save to the extent that they arise out of the fraud or fraudulent misrepresentation of another party. No variation of these Terms of Service shall be effective unless it is in writing and signed by or on behalf of Company.
No failure to exercise, and no delay in exercising, on the part of either party, any right or any power hereunder shall operate as a waiver thereof, nor shall any single or partial exercise of any right or power hereunder preclude further exercise of that or any other right hereunder.
We may provide any notice to you under these Terms of Service by: (i) sending a message to the email address you provide to us and consent to us using; or (ii) by posting to the Website. Notices sent by email will be effective when we send the email and notices we provide by posting will be effective upon posting. It is your responsibility to keep your email address current.
To give us notice under these Terms of Service, you must contact us as follows: (i) by personal delivery, overnight courier or registered or certified mail to Scry Analytics Inc. 2635 North 1st Street, Suite 200 San Jose, CA 95134, USA. We may update the address for notices to us by posting a notice on this Website. Notices provided by personal delivery will be effective immediately once personally received by an authorized representative of Company. Notices provided by overnight courier or registered or certified mail will be effective once received and where confirmation has been provided to evidence the receipt of the notice.
To request a copy for your information, unsubscribe from our email list, request for your data to be deleted, or ask a question about your data privacy, we've made the process simple: