AI News, The History of Artificial Intelligence artificial intelligence
History of artificial intelligence
In 1973, in response to the criticism from James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence, and the difficult years that followed would later be known as an 'AI winter'.
Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned by the absence of the needed computer power (hardware) and withdrew funding again.
Investment and interest in AI boomed in the first decades of the 21st century, when machine learning was successfully applied to many problems in academia and industry due to the presence of powerful computer hardware.
The dream of artificial intelligence was first thought of in Indian philosophies like those of Charvaka, a famous philosophy tradition dating back to 1500 BCE and surviving documents around 600 BCE.
McCorduck (2004) writes 'artificial intelligence in one form or another is an idea that has pervaded intellectual history, a dream in urgent need of being realized,' expressed in humanity's myths, legends, stories, speculation and clockwork automatons.
The faithful believed that craftsman had imbued these figures with very real minds, capable of wisdom and emotion—Hermes Trismegistus wrote that 'by discovering the true nature of the gods, man has been able to reproduce it.'
Their ideas were developed over the centuries by philosophers such as Aristotle (who gave a formal analysis of the syllogism), Euclid (whose Elements was a model of formal reasoning), al-Khwārizmī (who developed algebra and gave his name to 'algorithm') and European scholastic philosophers such as William of Ockham and Duns Scotus.
Llull described his machines as mechanical entities that could combine basic and undeniable truths by simple logical operations, produced by the machine by mechanical meanings, in such ways as to produce all the possible knowledge.
The Church-Turing thesis implied that a mechanical device, shuffling symbols as simple as 0 and 1, could imitate any conceivable process of mathematical deduction.
In the 1940s and 50s, a handful of scientists from a variety of fields (mathematics, psychology, engineering, economics and political science) began to discuss the possibility of creating an artificial brain.
The earliest research into thinking machines was inspired by a confluence of ideas that became prevalent in the late 1930s, 1940s, and early 1950s.
This simplified version of the problem allowed Turing to argue convincingly that a 'thinking machine' was at least plausible and the paper answered all the most common objections to the proposition.
Arthur Samuel's checkers program, developed in the middle 50s and early 60s, eventually achieved sufficient skill to challenge a respectable amateur.
When access to digital computers became possible in the middle fifties, a few scientists instinctively recognized that a machine that could manipulate numbers could also manipulate symbols and that the manipulation of symbols could well be the essence of human thought.
To achieve some goal (like winning a game or proving a theorem), they proceeded step by step towards it (by making a move or a deduction) as if searching through a maze, backtracking whenever they reached a dead end.
Other 'searching' programs were able to accomplish impressive tasks like solving problems in geometry and algebra, such as Herbert Gelernter's Geometry Theorem Prover (1958) and SAINT, written by Minsky's student James Slagle (1961).
They pointed out that in successful sciences like physics, basic principles were often best understood using simplified models like frictionless planes or perfectly rigid bodies.
Its limb control system allowed it to walk with the lower limbs, and to grip and transport objects with hands, using tactile sensors.
One of the earliest was John Lucas, who argued that Gödel's incompleteness theorem showed that a formal system (such as a computer program) could never see the truth of certain statements, while a human being could.
Hubert Dreyfus ridiculed the broken promises of the 1960s and critiqued the assumptions of AI, arguing that human reasoning actually involved very little 'symbol processing' and a great deal of embodied, instinctive, unconscious 'know how'.
However, straightforward implementations, like those attempted by McCarthy and his students in the late 1960s, were especially intractable: the programs required astronomical numbers of steps to prove simple theorems.
A more fruitful approach to logic was developed in the 1970s by Robert Kowalski at the University of Edinburgh, and soon this led to the collaboration with French researchers Alain Colmerauer and Philippe Roussel who created the successful logic programming language Prolog. Prolog
In 1975, in a seminal paper, Minsky noted that many of his fellow 'scruffy' researchers were using the same kind of tool: a framework that captures all our common sense assumptions about something.
We know these facts are not always true and that deductions using these facts will not be 'logical', but these structured sets of assumptions are part of the context of everything we say and think.
In the 1980s a form of AI program called 'expert systems' was adopted by corporations around the world and knowledge became the focus of mainstream AI research.
An expert system is a program that answers questions or solves problems about a specific domain of knowledge, using logical rules that are derived from the knowledge of experts.
Expert systems restricted themselves to a small domain of specific knowledge (thus avoiding the commonsense knowledge problem) and their simple design made it relatively easy for programs to be built and then modified once they were in place.
'AI researchers were beginning to suspect—reluctantly, for it violated the scientific canon of parsimony—that intelligence might very well be based on the ability to use large amounts of diverse knowledge in different ways,'
'[T]he great lesson from the 1970s was that intelligent behavior depended very much on dealing with knowledge, sometimes quite detailed knowledge, of a domain where a given task lay'.
The 1980s also saw the birth of Cyc, the first attempt to attack the commonsense knowledge problem directly, by creating a massive database that would contain all the mundane facts that the average person knows.
Douglas Lenat, who started and led the project, argued that there is no shortcut ― the only way for machines to know the meaning of human concepts is to teach them, one concept at a time, by hand.
Their objectives were to write programs and build machines that could carry on conversations, translate languages, interpret pictures, and reason like human beings.
In 1982, physicist John Hopfield was able to prove that a form of neural network (now called a 'Hopfield net') could learn and process information in a completely new way.
Around the same time, Geoffrey Hinton and David Rumelhart popularized a method for training neural networks called 'backpropagation', also known as the reverse mode of automatic differentiation published by Seppo Linnainmaa (1970) and applied to neural networks by Paul Werbos.
Neural networks would become commercially successful in the 1990s, when they began to be used as the engines driving programs like optical character recognition and speech recognition.
The term 'AI winter' was coined by researchers who had survived the funding cuts of 1974 when they became concerned that enthusiasm for expert systems had spiraled out of control and that disappointment would certainly follow.
They were difficult to update, they could not learn, they were 'brittle' (i.e., they could make grotesque mistakes when given unusual inputs), and they fell prey to problems (such as the qualification problem) that had been identified years earlier.
They believed that, to show real intelligence, a machine needs to have a body — it needs to perceive, move, survive and deal with the world.
They argued that these sensorimotor skills are essential to higher level skills like commonsense reasoning and that abstract reasoning was actually the least interesting or important human skill (see Moravec's paradox).
He rejected all symbolic approaches (both McCarthy's logic and Minsky's frames), arguing that AI needed to understand the physical machinery of vision from the bottom up before any symbolic processing took place.
robotics researcher Rodney Brooks took direct aim at the physical symbol system hypothesis, arguing that symbols are not always necessary since 'the world is its own best model.
In the 1980s and 1990s, many cognitive scientists also rejected the symbol processing model of the mind and argued that the body was essential for reasoning, a theory called the embodied mind thesis.
Some of the success was due to increasing computer power and some was achieved by focusing on specific isolated problems and pursuing them with the highest standards of scientific accountability.
Inside the field there was little agreement on the reasons for AI's failure to fulfill the dream of human level intelligence that had captured the imagination of the world in the 1960s.
Together, all these factors helped to fragment AI into competing subfields focused on particular problems or approaches, sometimes even under new names that disguised the tarnished pedigree of 'artificial intelligence'.
The super computer was a specialized version of a framework produced by IBM, and was capable of processing twice as many moves per second as it had during the first match (which Deep Blue had lost), reportedly 200,000,000 moves per second.
Two years later, a team from CMU won the DARPA Urban Challenge by autonomously navigating 55 miles in an Urban environment while adhering to traffic hazards and all traffic laws.
These successes were not due to some revolutionary new paradigm, but mostly on the tedious application of engineering skill and on the tremendous increase in the speed and capacity of computer by the 90s.
When the economist's definition of a rational agent was married to computer science's definition of an object or module, the intelligent agent paradigm was complete.
By this definition, simple programs that solve specific problems are 'intelligent agents', as are human beings and organizations of human beings, such as firms.
It provided a common language to describe problems and share their solutions with each other, and with other fields that also used concepts of abstract agents, like economics and control theory.
It was hoped that a complete agent architecture (like Newell's SOAR) would one day allow researchers to build more versatile and intelligent systems out of interacting intelligent agents.
There was a widespread realization that many of the problems that AI needed to solve were already being worked on by researchers in fields like mathematics, economics or operations research.
The shared mathematical language allowed both a higher level of collaboration with more established and successful fields and the achievement of results which were measurable and provable;
Nick Bostrom explains 'A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore.'
In the commercial world at least, the failed promises of the AI Winter continued to haunt AI research into the 2000s, as the New York Times reported in 2005: 'Computer scientists and software engineers avoided the term artificial intelligence for fear of being viewed as wild-eyed dreamers.'
Minsky believed that the answer is that the central problems, like commonsense reasoning, were being neglected, while most researchers pursued things like commercial applications of neural nets or genetic algorithms.
Jeff Hawkins argued that neural net research ignores the essential properties of the human cortex, preferring simple models that have been successful at solving simple problems.
In the first decades of the 21st century, access to large amounts of data (known as 'big data'), faster computers and advanced machine learning techniques were successfully applied to many problems throughout the economy.
In fact, McKinsey Global Institute estimated in their famous paper 'Big data: The next frontier for innovation, competition, and productivity' that 'by 2009, nearly all sectors in the US economy had at least an average of 200 terabytes of stored data'.
Advances in deep learning (particularly deep convolutional neural networks and recurrent neural networks) drove progress and research in image and video processing, text analysis, and even speech recognition.
Deep learning is a branch of machine learning that models high level abstractions in data by using a deep graph with many processing layers.
A common problem for recurrent neural networks is the vanishing gradient problem, which is where gradients passed between layers gradually shrink and literally disappear as they are rounded off to zero.
State-of-the-art deep neural network architectures can sometimes even rival human accuracy in fields like computer vision, specifically on things like the MNIST database, and traffic sign recognition.
Language processing engines powered by smart search engines can easily beat humans at answering general trivia questions (such as IBM Watson), and recent developments in deep learning have produced astounding results in competing with humans, in things like Go and Doom (which, being a First-Person Shooter game, has sparked some controversy).
Big data refers to a collection of data that cannot be captured, managed, and processed by conventional software tools within a certain time frame.
In other words, if big data is likened to an industry, the key to realizing profitability in this industry is to increase the “Process capability” of the data and realize the “Value added” of the data through “Processing”.
Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that responds in a manner similar to human intelligence.
The History of Artificial Intelligence
by Rockwell Anyoha In the first half of the 20th century, science fiction familiarized the world with the concept of artificially intelligent robots.
A proof of concept as well as advocacy from high profile people were needed to persuade funding sources that machine intelligence was worth pursuing.
In this historic conference, McCarthy, imagining a great collaborative effort, brought together top researchers from various fields for an open ended discussion on artificial intelligence, the term which he coined at the very event.
These successes, as well as the advocacy of leading researchers (namely the attendees of the DSRPAI) convinced government agencies such as the Defense Advanced Research Projects Agency (DARPA) to fund AI research at several institutions.
The government was particularly interested in a machine that could transcribe and translate spoken language as well as high throughput data processing.
In 1970 Marvin Minsky told Life Magazine, “from three to eight years we will have a machine with the general intelligence of an average human being.” However, while the basic proof of principle was there, there was still a long way to go before the end goals of natural language processing, abstract thinking, and self-recognition could be achieved.
The biggest was the lack of computational power to do anything substantial: computers simply couldn’t store enough information or process it fast enough.
Hans Moravec, a doctoral student of McCarthy at the time, stated that “computers were still millions of times too weak to exhibit intelligence.” As patience dwindled so did the funding, and research came to a slow roll for ten years.
The program would ask an expert in a field how to respond in a given situation, and once this was learned for virtually every situation, non-experts could receive advice from that program.
From 1982-1990, they invested $400 million dollars with the goals of revolutionizing computer processing, implementing logic programming, and improving artificial intelligence.
In 1997, reigning world chess champion and grand master Gary Kasparov was defeated by IBM’s Deep Blue, a chess playing computer program.
This highly publicized match was the first time a reigning world chess champion loss to a computer and served as a huge step towards an artificially intelligent decision making program.
It turns out, the fundamental limit of computer storage that was holding us back 30 years ago was no longer a problem.
We now live in the age of “big data,” an age in which we have the capacity to collect huge sums of information too cumbersome for a person to process.
We’ve seen that even if algorithms don’t improve much, big data and massive computing simply allow artificial intelligence to learn through brute force.
When that time comes (but better even before the time comes), we will need to have a serious conversation about machine policy and ethics (ironically both fundamentally human subjects), but for now, we’ll allow AI to steadily improve and run amok in society.
A Very Short History Of Artificial Intelligence (AI)
Catalan poet and theologian Ramon Llull publishes Ars generalis ultima (The Ultimate General Art), further perfecting his method of using paper-based mechanical means to create new knowledge from combinations of concepts.
Mathematician and philosopher Gottfried Leibniz publishes Dissertatio de arte combinatoria (On the Combinatorial Art), following Ramon Llull in proposing an alphabet of human thought and arguing that all ideas are nothing but combinations of a relatively small number of simple concepts.
This influential paper, in which they discussed networks of idealized and simplified artificial “neurons” and how they might perform simple logical functions, will become the inspiration for computer-based “neural networks” (and later “deep learning”) and their popular description as “mimicking the brain.” 1949
Edmund Berkeley publishes Giant Brains: Or Machines That Think in which he writes: “Recently there have been a good deal of news about strange giant machines that can handle information with vast speed and skill….These machines are similar to what a brain would be if it were made of hardware and wire instead of flesh and nerves… A machine can handle information;
Arthur Samuel coins the term “machine learning,” reporting on programming a computer “so that it will learn to play a better game of checkers than can be played by the person who wrote the program.” 1959
John McCarthy publishes “Programs with Common Sense” in the Proceedings of the Symposium on Mechanization of Thought Processes, in which he describes the Advice Taker, a program for solving problems by manipulating sentences in formal languages with the ultimate objective of making programs “that learn from their experience as effectively as humans do.” 1961
The first expert system, it automated the decision-making process and problem-solving behavior of organic chemists, with the general aim of studying hypothesis formation and constructing models of empirical induction in science.
In a Life magazine 1970 article about this “first electronic person,” Marvin Minsky is quoted saying with “certitude”: “In from three to eight years we will have a machine with the general intelligence of an average human being.” 1968
A learning algorithm for multi-layer artificial neural networks, it has contributed significantly to the success of deep learning in the 2000s and 2010s, once computing power has sufficiently advanced to accommodate the training of large networks.
In an expanded edition published in 1988, they responded to claims that their 1969 conclusions significantly reduced funding for neural network research: “Our version is that progress had already come to a virtual halt because of the lack of adequate basic theories… by the mid-1960s there had been a great many experiments with perceptrons, but no one had been able to explain why they were able to recognize certain kinds of patterns and not others.” 1970
James Lighthill reports to the British Science Research Council on the state artificial intelligence research, concluding that 'in no part of the field have discoveries made so far produced the major impact that was then promised,' leading to drastically reduced government support for AI research.
Wabot-2 is built at Waseda University in Japan, a musician humanoid robot able to communicate with a person, read a musical score and play tunes of average difficulty on an electronic organ.
At the annual meeting of AAAI, Roger Schank and Marvin Minsky warn of the coming “AI Winter,” predicting an immanent bursting of the AI bubble (which did happen three years later), similar to the reduction in AI investment and research funding in the mid-1970s.
The video Knowledge Navigator, accompanying Apple CEO John Sculley’s keynote speech at Educom, envisions a future in which “knowledge applications would be accessed by smart agents working over networks connected to massive amounts of digitized information.” 1988
Watson Research Center publish “A statistical approach to language translation,” heralding the shift from rule-based to probabilistic methods of machine translation, and reflecting a broader shift to “machine learning” based on statistical analysis of known examples, not comprehension and “understanding” of the task at hand (IBM’s project Candide, successfully translating between English and French, was based on 2.2 million pairs of sentences, mostly from the bilingual proceedings of the Canadian parliament).
Rodney Brooks publishes “Elephants Don’t Play Chess,” proposing a new approach to AI—building intelligent systems, specifically robots, from the ground up and on the basis of ongoing physical interaction with the environment: “The world is its own best model… The trick is to sense it appropriately and often enough.” 1993
Geoffrey Hinton publishes “Learning Multiple Layers of Representation,” summarizing the ideas that have led to “multilayer neural networks that contain top-down connections and training them to generate sensory data rather than to classify it,” i.e., the new approaches to deep learning.
Rajat Raina, Anand Madhavan and Andrew Ng publish “Large-scale Deep Unsupervised Learning using Graphics Processors,” arguing that “modern graphics processors far surpass the computational capabilities of multicore CPUs, and have the potential to revolutionize the applicability of deep unsupervised learning methods.” 2009
Jeff Dean and Andrew Ng report on an experiment in which they showed a very large neural network 10 million unlabeled images randomly taken from YouTube videos, and “to our amusement, one of our artificial neurons learned to respond strongly to pictures of...
A convolutional neural network designed by researchers at the University of Toronto achieve an error rate of only 16% in the ImageNet Large Scale Visual Recognition Challenge, a significant improvement over the 25% error rate achieved by the best entry the year before.
- On 5. marts 2021
The History of Artificial Intelligence
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