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they do not attempt to give precise logical answers but give results that are only probably correct this allowed them to solve problems that precise symbolic methods could not handle press accounts often claimed these tools could think like a human 201 202 judea pearl s probabilistic reasoning in intelligent systems networks of plausible inference an influential 1988 book 203 brought probability and decision theory into ai 204 fuzzy logic developed by lofti zadeh in the 60s began to be more widely used in ai and robotics evolutionary computation and artificial neural networks also handle imprecise information and are classified as soft in the 90s and early 2000s many other soft computing tools were developed and put into use including bayesian networks 204 hidden markov models 204 information theory and stochastic modeling these tools in turn depended on advanced mathematical techniques such as classical optimization for a time in the 1990s and early 2000s these soft tools were studied by a subfield of ai called computational intelligence 205 reinforcement learning edit reinforcement learning 206 rewards an agent every time it performs a desired action well and may give negative rewards or punishments when it performs poorly it was described in the first half of the twentieth century by psychologists using animal models such as thorndike 207 208 pavlov 209 and skinner 210 in the 1950s alan turing 208 211 and arthur samuel 208 foresaw the role of reinforcement learning in ai a successful and influential research program was led by richard sutton and andrew barto beginning in 1972 their collaboration revolutionized the study of reinforcement learning and decision making over the past four decades 212 213 in 1988 sutton described machine learning in terms of decision theory i e the markov decision process this gave the subject a solid theoretical foundation and access to a large body of theoretical results developed in the field of operations research 213 also in 1988 sutton and barto developed the temporal difference td learning algorithm where the agent is rewarded only when its predictions show improvement it significantly outperformed previous algorithms 214 td learning was used by gerald tesauro in 1992 in the program td gammon which played backgammon as well as the best human players the program learned the game by playing against itself with zero prior knowledge 215 in an interesting case of interdisciplinary convergence neurologists discovered in 1997 that the dopamine reward system in brains also uses a version of the td learning algorithm 216 217 218 td learning would be become highly influential in the 21st century used in both alphago and alphazero 219 second ai winter 1990s edit the business community s fascination with ai rose and fell in the 1980s in the classic pattern of an economic bubble as dozens of companies failed the perception in the business world was that the technology was not viable 220 the damage to ai s reputation would last into the 21st century 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 221 over the next 20 years ai consistently delivered working solutions to specific isolated problems by the late 1990s it was being used throughout the technology industry although somewhat behind the scenes the success was due to increasing computer power by collaboration with other fields such as mathematical optimization and statistics and using higher standards of scientific accountability ai winter edit the term ai winter was coined by researchers who had survived the funding cuts of 1974 and had become concerned that enthusiasm for expert systems had spiraled out of control and that disappointment would certainly follow ae their fears were well founded in the late 1980s and early 1990s ai suffered a series of financial setbacks 114 the first indication of a change in weather was the sudden collapse of the market for specialized ai hardware in 1987 desktop computers from apple and ibm had been steadily gaining speed and power and in 1987 they became more powerful than the more expensive lisp machines made by symbolics and others there was no longer a good reason to buy them an entire industry worth half a billion dollars was demolished overnight 223 eventually the earliest successful expert systems such as r1 proved too expensive to maintain they were difficult to update they could not learn and they were brittle i e they could make grotesque mistakes when given unusual inputs expert systems proved useful but only in a few special contexts 224 in the late 1980s the strategic computing initiative cut funding to ai deeply and brutally new leadership at darpa had decided that ai was not the next wave and directed funds towards projects that seemed more likely to produce immediate results 225 by 1991 the impressive list of goals penned in 1981 for japan s fifth generation project had not been met some of them like carry on a casual conversation would not be accomplished for another 30 years as with other ai projects expectations had run much higher than what was actually possible 226 af over 300 ai companies had shut down gone bankrupt or been acquired by the end of 1993 effectively ending the first commercial wave of ai 228 in 1994 hp newquist stated in the brain makers that the immediate future of artificial intelligence in its commercial form seems to rest in part on the continued success of neural networks 228 ai behind the scenes edit in the 1990s algorithms originally developed by ai researchers began to appear as parts of larger systems ai had solved a lot of very difficult problems ag and their solutions proved to be useful throughout the technology industry 229 230 such as data mining industrial robotics logistics speech recognition 231 banking software 232 medical diagnosis 232 and google s search engine 233 234 the field of ai received little or no credit for these successes in the 1990s and early 2000s many of ai s greatest innovations have been reduced to the status of just another item in the tool chest of computer science 235 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 232 many researchers in ai in the 1990s deliberately called their work by other names such as informatics knowledge based systems cognitive systems or computational intelligence in part this may have been because they considered their field to be fundamentally different from ai but also the new names helped to procure funding 231 236 237 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 238 mathematical rigor greater collaboration and a narrow focus edit ai researchers began to develop and use sophisticated mathematical tools more than they ever had in the past 239 240 most of the new directions in ai relied heavily on mathematical models including artificial neural networks probabilistic reasoning soft computing and reinforcement learning in the 90s and 2000s many other highly mathematical tools were adapted for ai these tools were applied to machine learning perception and mobility 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 statistics mathematics electrical engineering 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 that were measurable and provable ai had become a more rigorous scientific discipline 240 another key reason for the success in the 90s was that ai researchers focused on specific problems with verifiable solutions an approach later derided as narrow ai this provided useful tools in the present rather than speculation about the future intelligent agents edit a new paradigm called intelligent agents became widely accepted during the 1990s 241 242 ah although earlier researchers had proposed modular divide and conquer approaches to ai ai the intelligent agent did not reach its modern form until judea pearl allen newell leslie p kaelbling and others brought concepts from decision theory and economics into the study of ai 243 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 an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success by this definition simple programs that solve specific problems are intelligent agents as are human beings and organizations of human beings such as firms the intelligent agent paradigm defines ai research as the study of intelligent agents aj this is a generalization of some earlier definitions of ai it goes beyond studying human intelligence it studies all kinds of intelligence the paradigm gave researchers license to study isolated problems and to disagree about methods but still retain hope that their work could be combined into an agent architecture that would be capable of general intelligence 244 milestones and moore s law edit on 11 may 1997 deep blue became the first computer to beat a reigning world chess champion garry kasparov in a match 245 in 2005 a stanford robot won the darpa grand challenge by driving autonomously for 131 miles along an unrehearsed desert trail two years later a team from cmu won the darpa urban challenge by autonomously navigating 55 miles in an urban environment while responding to traffic hazards and adhering to traffic laws 246 these successes were not due to some revolutionary new paradigm but mostly to the application of engineering skill and to the tremendous increase in the speed and capacity of computers by the 90s ak in fact deep blue was 10 million times faster than the ferranti mark 1 that christopher strachey programmed to play chess in 1951 al this dramatic increase is measured by moore s law which predicts that the speed and memory capacity of computers doubles every two years the fundamental problem of computer power was slowly being overcome arts and literature influenced by ai edit electronic literature experiments such as the impermanence agent 1998 2002 and digital art such as agent ruby used ai in their art and literature laying bare the bias accompanying forms of technology that feign objectivity 250 big data deep learning agi 2005 2017 edit in the first decades of the 21st century access to large amounts of data known as big data cheaper and faster computers and advanced machine learning techniques were successfully applied to many problems throughout the economy a turning point was the success of deep learning around 2012 which improved the performance of machine learning on many tasks including image and video processing text analysis and speech recognition 251 investment in ai increased along with its capabilities and by 2016 the market for ai related products hardware and software reached more than 8 billion and the new york times reported that interest in ai had reached a frenzy 252 in 2002 ben goertzel and others became concerned that ai had largely abandoned its original goal of producing versatile fully intelligent machines and argued in favor of more direct research into artificial general intelligence agi by the mid 2010s several companies and institutions had been founded to pursue artificial general intelligence such as openai and google s deepmind during the same period new insights into superintelligence raised concerns that ai was an existential threat the risks and unintended consequences of ai technology became an area of serious academic research after 2016 big data and big machines edit see also list of datasets for machine learning research the success of machine learning in the 2000s depended on the availability of vast amounts of training data and faster computers 253 russell and norvig wrote that the improvement in performance obtained by increasing the size of the data set by two or three orders of magnitude outweighs any improvement that can be made by tweaking the algorithm 193 geoffrey hinton recalled that back in the 80s and 90s the problem was that our labeled datasets were thousands of times too small and our computers were millions of times too slow 254 in 2007 a group at umass amherst released labeled faces in the wild an annotated set of images of faces that was widely used to train and test face recognition systems for the next several decades 255 fei fei li developed imagenet a database of three million images captioned by volunteers using the amazon mechanical turk released in 2009 it was a useful body of training data and a benchmark for testing for the next generation of image processing systems 256 193 in 2013 tomáš mikolov and colleagues at google introduced word2vec as an open source resource it used large amounts of data text scraped from the internet and word embedding to create a numeric vector to represent each word users were surprised at how well it was able to capture word meanings for example ordinary vector addition would give equivalences like china river yangtze or london england france paris 257 the internet gave machine learning programs access to billions of pages of text and images that could be scraped large privately held databases also contained relevant data mckinsey global institute reported that by 2009 nearly all sectors in the us economy had at least an average of 200 terabytes of stored data 258 this collection of information was known in the 2000s as big data in a jeopardy exhibition match in february 2011 ibm s question answering system watson defeated the two best jeopardy champions brad rutter and ken jennings by a significant margin 259 watson was trained on information available on the internet 193 deep learning edit main article deep learning in 2012 alexnet a deep learning model am developed by alex krizhevsky won the imagenet large scale visual recognition challenge with significantly fewer errors than the second place winner 261 193 krizhevsky worked with geoffrey hinton at the university of toronto an this was a turning point in machine learning over the next few years dozens of other approaches to image recognition were abandoned in favor of deep learning 253 deep learning uses a multi layer perceptron ...