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Trance (2013 film) – Wikipedia, the free encyclopedia

Posted: September 10, 2016 at 5:30 am

Trance is a 2013 British psychological thriller film directed by Danny Boyle with a screenplay by Joe Ahearne and John Hodge from a story by Ahearne. The film stars James McAvoy, Vincent Cassel, and Rosario Dawson. The world premiere of the film was held in London on 19 March 2013.

Simon (McAvoy), an art auctioneer, becomes an accessory to the theft of a paintingGoya’s Witches in the Airfrom his own auction house. When a gang attacks during an auction, Simon follows the house emergency protocol by packaging the painting. The gang’s leader Franck (Cassel) then takes the package from him at gunpoint. Simon attacks Franck, who delivers him a blow to the head that leaves him with amnesia. When Franck gets home, he discovers that the package contains only an empty frame. After ransacking Simon’s apartment and trashing his car, the gang kidnaps and unsuccessfully tortures him. But he has no memory of where he has hidden the painting. Franck decides to hire a hypnotherapist to try to help him remember.

Franck makes Simon choose a hypnotist from a directory, and he chooses a woman named Elizabeth Lamb (Dawson). As a first hypnotic exercise, Simon recalls where he put some car keys. Elizabeth exposes the gang’s plan to have her hypnotize him, and demands partnership. In a next hypnotic episode, under gang supervision, Simon remembers that, shortly after the blow to his head, he awoke alone. On finding the stolen painting hidden in his suit, he left the art gallery. Distracted by a phone text message, while crossing the road, he was hit by a red car. The female driver tried to take him to hospital. Simon, in a kind of memory fugue, believed the woman was Elizabeth, recalling that she had made him forget her. The gang tries violently to force Simon to remember where he put the painting, and that ends the hypnotic episode. When asked by Elizabeth about how he met Franck, Simon confesses that he has a gambling problem. Franck helped him pay his debts in exchange for his help in stealing the painting.

To help Simon recover from the violence, Elizabeth stays overnight in his apartment. In the morning, Simon dreams of Elizabeth’s having used a brain-scan behaviour-conditioning technique to erase an obsession that he had for her. Elizabeth tells Franck about that.

For the next step to recover the painting, Elizabeth tells Franck that she will sexually seduce Simon. Simon’s feelings for Elizabeth recur, gently this time. At the same time, Franck and Elizabeth have unanticipated sex, and she steals his pistol from his bedside drawer. Nate, a gang member, sees them together and warns Simon, who confronts Elizabeth with it. She responds by touching his erotic mindspot, related to Goya’s Nude Maja.

Remembering where the painting is, Simon goes with Franck and his associates to reveal the location, but overhears their plan to kill him. He calls Elizabeth and tells her that the painting is in a red car in a certain car park, and unable to leave Franck’s apartment, he returns and kills the gang members instead. After shooting Franck, Simon wakes up: this was all dream, and he still is in Elizabeth’s apartment. Elizabeth takes the car keys and goes to get the painting, leaving Franck’s pistol for Simon. While searching for Elizabeth, Simon finds Franck in her apartment. Nate and his associates intercept Elizabeth and bring her there. Franck takes Simon to get the painting, and as he kisses Elizabeth, she secretly passes three bullets into his mouth. On the elevator, Simon stops Franck with a fire extinguisher, and then with the pistol, now loaded with the three bullets. In the apartment, Nate prepares to rape Elizabeth. Heading back into the apartment, Simon shoots the three gang members. He takes the car keys from Elizabeth, and loads the pistol with the remaining three bullets. He takes Elizabeth to get the painting, and she tells him to let Franck come with them. With Franck driving, Simon leads them to a parking garage where the painting is.

They collect the red car and drive it to a safe warehouse. During the trip, Elizabeth reveals that Simon was previously a client of hers. He had a gambling addiction he wanted to fix. They started an affair, and she found his erotic mindspot. However, he became obsessed with her, and eventually abusive. Fearing for her life, she re-directed the hypnosis to make him forget her. This led him back into his gambling addiction, which as previously stated, caused him to go in debt and to try to pay it off by stealing a painting, with the help of Franck. Simon recalls that, after the heist, when he was hit by the red car and mistook the female driver for Elizabeth, he strangled her.

At the warehouse, in the car’s trunk, Elizabeth finds the painting and the body of the female driver. Simon, having at last remembered his past and wanting to forget, douses the car in fuel with Franck zip-tied to the steering wheel, sets it on fire and tells Elizabeth to run away with the painting. She runs away but promptly returns driving a truck which she drives into Simon, pinning him against the other car, and ultimately sending Simon, and the car Franck is trapped in, into the river.

Franck manages to escape, while it is implied that Simon is killed. The scene cuts to Franck swimming in his apartment while thinking of the event. He gets out of the pool and receives a package. He opens the package and finds an iPad that plays a video of Elizabeth talking about the painting, which is now hanging in her apartment. She reveals that when she hypnotized Simon to make him forget her, she also hypnotized him to go back into his gambling addiction and then try to steal a painting to pay off his debt. When this happened, he would instead give the painting over to Elizabeth. This explains why Simon took the painting away from Franck at the beginning and the text message he received before being hit by the car, which is revealed to be from Elizabeth telling Simon to deliver the painting to her. Elizabeth tells Franck that he can search for her and try to find her, but also gives him the option to forget the entire ordeal, and a button for an app called “Trance” appears as the video ends. Franck is shown debating whether to press the button just as the screen cuts to black.

After director Danny Boyle filmed Shallow Grave in 1994, Joe Ahearne sent the director his screenplay for Trance, seeking Boyle’s encouragement. Boyle thought that the project would be “quite difficult” for a beginning screenwriter. Ahearne later turned the script into a 2001 television movie.[5][6] Boyle never forgot it, and almost two decades after their original conversation he contacted Ahearne about turning it into a feature film.[7] Partially based on Ahearne’s 2001 British television film of the same name, Trance underwent script doctoring by screenwriter John Hodge marking the fifth motion picture collaboration between Hodge and Boyle.[8]

In May 2011, Michael Fassbender was cast as Franck but dropped out due to scheduling conflicts.[9][10]Colin Firth was considered for the part before Cassel was cast.[11][12]Scarlett Johansson, Melanie Thierry, and Zoe Saldana were considered for the role that went to Dawson.[12][13]

McAvoy, who accepted the role in 2011, said that he almost turned down the part, while reading the script, because Simon seemed to be a victim, which didn’t interest him. He told NPR’s reporter Laura Sullivan, “And then I got about 15 or 20 pages in, and I started to sense that something else was coming in the character. And then something else did come. And then about every 10 pages, something else came. Until at the end, I was hunching at the bit, as we say in Scotland… It just means I was desperate…I was hungry to play this part.”[14]

Principal photography began in September 2011. After filming wrapped up, the film was placed on hold in order for Boyle to work on the opening ceremony of the 2012 Summer Olympics in London. Post-production was then picked up again in August 2012.[15]

Boyle said that this is “the first time I put a woman at the heart of a movie.”[8] He also said that he originally intended to set the movie in New York City,[16] but it was filmed in London and in Kent instead, as Boyle’s Olympic ceremony duties meant he had to stay in the UK.[17]

On 4 January 2013, it was announced that Rick Smith of the band Underworld would be composing the music for the film.[18] Underworld previously contributed tracks to other Danny Boyle films, including Trainspotting (1996), A Life Less Ordinary (1997), The Beach (2000), and Sunshine (2007). About the collaboration, Smith said, “After finishing the Opening Ceremony, I hardly knew what day of the week it was. I took a month off work, off music, off everything. Exactly one month and three days after we said goodbye in the stadium, I received a text from Danny that said, ‘Do you ever want to hear from me again workwise and would that go as far as having a chat about Trance… Questions, questions.’ Two Minutes later I was on board.”[19] The soundtrack album for Trance was released in the United Kingdom on 25 March and in the United States on 2 April 2013.[19][20]

When asked by an interviewer about the secret of their 17-year-old creative partnership, Boyle joked, “He’s cheap.” Then, answering seriously, he said that they both like electronic music and that he doesn’t prescribe a sound for a scene, but lets Smith follow his own instincts.[21]

Boyle showed a teaser trailer and an extended version of an alternate ending at South by Southwest on 9 March 2013.[22][23] The entire film could not be screened at the festival, as is usually done, because the producing studio Path owned the rights to the world premire.[24] The world premire of the film was held in London on 19 March 2013.[25] The film saw general release on 27 March 2013 in the United Kingdom,[26] with a United States release date on 5 April 2013.[27]

The film received mostly positive reviews from critics. Rotten Tomatoes gives a score of 68% based on reviews from 160 critics; the site’s consensus is: “As stylish as ever, director Danny Boyle seems to be treading water with the surprisingly thinly written Trance — but for fans of Boyle’s work, it should still prove a trippily entertaining distraction”.[28]Washington Post writer Michael O’Sullivan describes Boyle as “playing fast and loose with reality.”[29]

On Metacritic, which assigns a weighted mean rating out of 100 based on reviews from film critics, the film has a rating score of 61% based on 37 reviews.[30]

Empire magazine in its review gave the film 4 out of 5 and called the film “a dazzling, absorbing entertainment which shows off Danny Boyle’s mastery of complex storytelling and black, black humour.”[31]Empire also ranked it 27 in its top 50 films of 2013.[32]

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Trance (2013 film) – Wikipedia, the free encyclopedia

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History of artificial intelligence – Wikipedia, the free …

Posted: August 30, 2016 at 11:03 pm

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with “an ancient wish to forge the gods.”

The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.

The Turing test was proposed by British mathematician Alan Turing in his 1950 paper Computing Machinery and Intelligence, which opens with the words: “I propose to consider the question, ‘Can machines think?'” The term ‘Artificial Intelligence’ was created at a conference held at Dartmouth College in 1956.[2]Allen Newell, J. C. Shaw, and Herbert A. Simon pioneered the newly created artificial intelligence field with the Logic Theory Machine (1956), and the General Problem Solver in 1957.[3] In 1958, John McCarthy and Marvin Minsky started the MIT Artificial Intelligence lab with $50,000.[4] John McCarthy also created LISP in the summer of 1958, a programming language still important in artificial intelligence research.[5]

In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence. 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 and withdrew funding again.

McCorduck (2004) writes “artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized,” expressed in humanity’s myths, legends, stories, speculation and clockwork automatons.

Mechanical men and artificial beings appear in Greek myths, such as the golden robots of Hephaestus and Pygmalion’s Galatea.[7] In the Middle Ages, there were rumors of secret mystical or alchemical means of placing mind into matter, such as Jbir ibn Hayyn’s Takwin, Paracelsus’ homunculus and Rabbi Judah Loew’s Golem.[8] By the 19th century, ideas about artificial men and thinking machines were developed in fiction, as in Mary Shelley’s Frankenstein or Karel apek’s R.U.R. (Rossum’s Universal Robots), and speculation, such as Samuel Butler’s “Darwin among the Machines.” AI has continued to be an important element of science fiction into the present.

Realistic humanoid automatons were built by craftsman from every civilization, including Yan Shi,[11]Hero of Alexandria,[12]Al-Jazari and Wolfgang von Kempelen.[14] The oldest known automatons were the sacred statues of ancient Egypt and Greece. The faithful believed that craftsman had imbued these figures with very real minds, capable of wisdom and emotionHermes Trismegistus wrote that “by discovering the true nature of the gods, man has been able to reproduce it.”[15][16]

Artificial intelligence is based on the assumption that the process of human thought can be mechanized. The study of mechanicalor “formal”reasoning has a long history. Chinese, Indian and Greek philosophers all developed structured methods of formal deduction in the first millennium BCE. 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), Muslim mathematician al-Khwrizm (who developed algebra and gave his name to “algorithm”) and European scholastic philosophers such as William of Ockham and Duns Scotus.[17]

Majorcan philosopher Ramon Llull (12321315) developed several logical machines devoted to the production of knowledge by logical means;[18] 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.[19] Llull’s work had a great influence on Gottfried Leibniz, who redeveloped his ideas.[20]

In the 17th century, Leibniz, Thomas Hobbes and Ren Descartes explored the possibility that all rational thought could be made as systematic as algebra or geometry.[21]Hobbes famously wrote in Leviathan: “reason is nothing but reckoning”.[22]Leibniz envisioned a universal language of reasoning (his characteristica universalis) which would reduce argumentation to calculation, so that “there would be no more need of disputation between two philosophers than between two accountants. For it would suffice to take their pencils in hand, down to their slates, and to say each other (with a friend as witness, if they liked): Let us calculate.”[23] These philosophers had begun to articulate the physical symbol system hypothesis that would become the guiding faith of AI research.

In the 20th century, the study of mathematical logic provided the essential breakthrough that made artificial intelligence seem plausible. The foundations had been set by such works as Boole’s The Laws of Thought and Frege’s Begriffsschrift. Building on Frege’s system, Russell and Whitehead presented a formal treatment of the foundations of mathematics in their masterpiece, the Principia Mathematica in 1913. Inspired by Russell’s success, David Hilbert challenged mathematicians of the 1920s and 30s to answer this fundamental question: “can all of mathematical reasoning be formalized?”[17] His question was answered by Gdel’s incompleteness proof, Turing’s machine and Church’s Lambda calculus.[17][24] Their answer was surprising in two ways.

First, they proved that there were, in fact, limits to what mathematical logic could accomplish. But second (and more important for AI) their work suggested that, within these limits, any form of mathematical reasoning could be mechanized. 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. The key insight was the Turing machinea simple theoretical construct that captured the essence of abstract symbol manipulation. This invention would inspire a handful of scientists to begin discussing the possibility of thinking machines.[17][26]

Calculating machines were built in antiquity and improved throughout history by many mathematicians, including (once again) philosopher Gottfried Leibniz. In the early 19th century, Charles Babbage designed a programmable computer (the Analytical Engine), although it was never built. Ada Lovelace speculated that the machine “might compose elaborate and scientific pieces of music of any degree of complexity or extent”.[27] (She is often credited as the first programmer because of a set of notes she wrote that completely detail a method for calculating Bernoulli numbers with the Engine.)

The first modern computers were the massive code breaking machines of the Second World War (such as Z3, ENIAC and Colossus). The latter two of these machines were based on the theoretical foundation laid by Alan Turing[28] and developed by John von Neumann.[29]

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 field of artificial intelligence research was founded as an academic discipline in 1956.

The earliest research into thinking machines was inspired by a confluence of ideas that became prevalent in the late 30s, 40s and early 50s. Recent research in neurology had shown that the brain was an electrical network of neurons that fired in all-or-nothing pulses. Norbert Wiener’s cybernetics described control and stability in electrical networks. Claude Shannon’s information theory described digital signals (i.e., all-or-nothing signals). Alan Turing’s theory of computation showed that any form of computation could be described digitally. The close relationship between these ideas suggested that it might be possible to construct an electronic brain.[30]

Examples of work in this vein includes robots such as W. Grey Walter’s turtles and the Johns Hopkins Beast. These machines did not use computers, digital electronics or symbolic reasoning; they were controlled entirely by analog circuitry.[31]

Walter Pitts and Warren McCulloch analyzed networks of idealized artificial neurons and showed how they might perform simple logical functions. They were the first to describe what later researchers would call a neural network.[32] One of the students inspired by Pitts and McCulloch was a young Marvin Minsky, then a 24-year-old graduate student. In 1951 (with Dean Edmonds) he built the first neural net machine, the SNARC.[33]Minsky was to become one of the most important leaders and innovators in AI for the next 50 years.

In 1950 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think.[34] He noted that “thinking” is difficult to define and devised his famous Turing Test. If a machine could carry on a conversation (over a teleprinter) that was indistinguishable from a conversation with a human being, then it was reasonable to say that the machine was “thinking”. 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.[35] The Turing Test was the first serious proposal in the philosophy of artificial intelligence.

In 1951, using the Ferranti Mark 1 machine of the University of Manchester, Christopher Strachey wrote a checkers program and Dietrich Prinz wrote one for chess.[36]Arthur Samuel’s checkers program, developed in the middle 50s and early 60s, eventually achieved sufficient skill to challenge a respectable amateur.[37]Game AI would continue to be used as a measure of progress in AI throughout its history.

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. This was a new approach to creating thinking machines.[38]

In 1955, Allen Newell and (future Nobel Laureate) Herbert A. Simon created the “Logic Theorist” (with help from J. C. Shaw). The program would eventually prove 38 of the first 52 theorems in Russell and Whitehead’s Principia Mathematica, and find new and more elegant proofs for some.[39] Simon said that they had “solved the venerable mind/body problem, explaining how a system composed of matter can have the properties of mind.”[40] (This was an early statement of the philosophical position John Searle would later call “Strong AI”: that machines can contain minds just as human bodies do.)[41]

The Dartmouth Conference of 1956[42] was organized by Marvin Minsky, John McCarthy and two senior scientists: Claude Shannon and Nathan Rochester of IBM. The proposal for the conference included this assertion: “every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it”.[43] The participants included Ray Solomonoff, Oliver Selfridge, Trenchard More, Arthur Samuel, Allen Newell and Herbert A. Simon, all of whom would create important programs during the first decades of AI research.[44] At the conference Newell and Simon debuted the “Logic Theorist” and McCarthy persuaded the attendees to accept “Artificial Intelligence” as the name of the field.[45] The 1956 Dartmouth conference was the moment that AI gained its name, its mission, its first success and its major players, and is widely considered the birth of AI.[46]

The years after the Dartmouth conference were an era of discovery, of sprinting across new ground. The programs that were developed during this time were, to most people, simply “astonishing”:[47] computers were solving algebra word problems, proving theorems in geometry and learning to speak English. Few at the time would have believed that such “intelligent” behavior by machines was possible at all.[48] Researchers expressed an intense optimism in private and in print, predicting that a fully intelligent machine would be built in less than 20 years.[49] Government agencies like ARPA poured money into the new field.[50]

There were many successful programs and new directions in the late 50s and 1960s. Among the most influential were these:

Many early AI programs used the same basic algorithm. 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. This paradigm was called “reasoning as search”.[51]

The principal difficulty was that, for many problems, the number of possible paths through the “maze” was simply astronomical (a situation known as a “combinatorial explosion”). Researchers would reduce the search space by using heuristics or “rules of thumb” that would eliminate those paths that were unlikely to lead to a solution.[52]

Newell and Simon tried to capture a general version of this algorithm in a program called the “General Problem Solver”.[53] 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).[54] Other programs searched through goals and subgoals to plan actions, like the STRIPS system developed at Stanford to control the behavior of their robot Shakey.[55]

An important goal of AI research is to allow computers to communicate in natural languages like English. An early success was Daniel Bobrow’s program STUDENT, which could solve high school algebra word problems.[56]

A semantic net represents concepts (e.g. “house”,”door”) as nodes and relations among concepts (e.g. “has-a”) as links between the nodes. The first AI program to use a semantic net was written by Ross Quillian[57] and the most successful (and controversial) version was Roger Schank’s Conceptual dependency theory.[58]

Joseph Weizenbaum’s ELIZA could carry out conversations that were so realistic that users occasionally were fooled into thinking they were communicating with a human being and not a program. But in fact, ELIZA had no idea what she was talking about. She simply gave a canned response or repeated back what was said to her, rephrasing her response with a few grammar rules. ELIZA was the first chatterbot.[59]

In the late 60s, Marvin Minsky and Seymour Papert of the MIT AI Laboratory proposed that AI research should focus on artificially simple situations known as micro-worlds. 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. Much of the research focused on a “blocks world,” which consists of colored blocks of various shapes and sizes arrayed on a flat surface.[60]

This paradigm led to innovative work in machine vision by Gerald Sussman (who led the team), Adolfo Guzman, David Waltz (who invented “constraint propagation”), and especially Patrick Winston. At the same time, Minsky and Papert built a robot arm that could stack blocks, bringing the blocks world to life. The crowning achievement of the micro-world program was Terry Winograd’s SHRDLU. It could communicate in ordinary English sentences, plan operations and execute them.[61]

The first generation of AI researchers made these predictions about their work:

In June 1963, MIT received a $2.2 million grant from the newly created Advanced Research Projects Agency (later known as DARPA). The money was used to fund project MAC which subsumed the “AI Group” founded by Minsky and McCarthy five years earlier. DARPA continued to provide three million dollars a year until the 70s.[66]DARPA made similar grants to Newell and Simon’s program at CMU and to the Stanford AI Project (founded by John McCarthy in 1963).[67] Another important AI laboratory was established at Edinburgh University by Donald Michie in 1965.[68] These four institutions would continue to be the main centers of AI research (and funding) in academia for many years.[69]

The money was proffered with few strings attached: J. C. R. Licklider, then the director of ARPA, believed that his organization should “fund people, not projects!” and allowed researchers to pursue whatever directions might interest them.[70] This created a freewheeling atmosphere at MIT that gave birth to the hacker culture,[71] but this “hands off” approach would not last.

In the 70s, AI was subject to critiques and financial setbacks. AI researchers had failed to appreciate the difficulty of the problems they faced. Their tremendous optimism had raised expectations impossibly high, and when the promised results failed to materialize, funding for AI disappeared.[72] At the same time, the field of connectionism (or neural nets) was shut down almost completely for 10 years by Marvin Minsky’s devastating criticism of perceptrons.[73] Despite the difficulties with public perception of AI in the late 70s, new ideas were explored in logic programming, commonsense reasoning and many other areas.[74]

In the early seventies, the capabilities of AI programs were limited. Even the most impressive could only handle trivial versions of the problems they were supposed to solve; all the programs were, in some sense, “toys”.[75] AI researchers had begun to run into several fundamental limits that could not be overcome in the 1970s. Although some of these limits would be conquered in later decades, others still stymie the field to this day.[76]

The agencies which funded AI research (such as the British government, DARPA and NRC) became frustrated with the lack of progress and eventually cut off almost all funding for undirected research into AI. The pattern began as early as 1966 when the ALPAC report appeared criticizing machine translation efforts. After spending 20 million dollars, the NRC ended all support.[84] In 1973, the Lighthill report on the state of AI research in England criticized the utter failure of AI to achieve its “grandiose objectives” and led to the dismantling of AI research in that country.[85] (The report specifically mentioned the combinatorial explosion problem as a reason for AI’s failings.)[86]DARPA was deeply disappointed with researchers working on the Speech Understanding Research program at CMU and canceled an annual grant of three million dollars.[87] By 1974, funding for AI projects was hard to find.

Hans Moravec blamed the crisis on the unrealistic predictions of his colleagues. “Many researchers were caught up in a web of increasing exaggeration.”[88] However, there was another issue: since the passage of the Mansfield Amendment in 1969, DARPA had been under increasing pressure to fund “mission-oriented direct research, rather than basic undirected research”. Funding for the creative, freewheeling exploration that had gone on in the 60s would not come from DARPA. Instead, the money was directed at specific projects with clear objectives, such as autonomous tanks and battle management systems.[89]

Several philosophers had strong objections to the claims being made by AI researchers. One of the earliest was John Lucas, who argued that Gdel’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.[90]Hubert Dreyfus ridiculed the broken promises of the 60s 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”.[91][92]John Searle’s Chinese Room argument, presented in 1980, attempted to show that a program could not be said to “understand” the symbols that it uses (a quality called “intentionality”). If the symbols have no meaning for the machine, Searle argued, then the machine can not be described as “thinking”.[93]

These critiques were not taken seriously by AI researchers, often because they seemed so far off the point. Problems like intractability and commonsense knowledge seemed much more immediate and serious. It was unclear what difference “know how” or “intentionality” made to an actual computer program. Minsky said of Dreyfus and Searle “they misunderstand, and should be ignored.”[94] Dreyfus, who taught at MIT, was given a cold shoulder: he later said that AI researchers “dared not be seen having lunch with me.”[95]Joseph Weizenbaum, the author of ELIZA, felt his colleagues’ treatment of Dreyfus was unprofessional and childish. Although he was an outspoken critic of Dreyfus’ positions, he “deliberately made it plain that theirs was not the way to treat a human being.”[96]

Weizenbaum began to have serious ethical doubts about AI when Kenneth Colby wrote DOCTOR, a chatterbot therapist. Weizenbaum was disturbed that Colby saw his mindless program as a serious therapeutic tool. A feud began, and the situation was not helped when Colby did not credit Weizenbaum for his contribution to the program. In 1976, Weizenbaum published Computer Power and Human Reason which argued that the misuse of artificial intelligence has the potential to devalue human life.[97]

A perceptron was a form of neural network introduced in 1958 by Frank Rosenblatt, who had been a schoolmate of Marvin Minsky at the Bronx High School of Science. Like most AI researchers, he was optimistic about their power, predicting that “perceptron may eventually be able to learn, make decisions, and translate languages.” An active research program into the paradigm was carried out throughout the 60s but came to a sudden halt with the publication of Minsky and Papert’s 1969 book Perceptrons. It suggested that there were severe limitations to what perceptrons could do and that Frank Rosenblatt’s predictions had been grossly exaggerated. The effect of the book was devastating: virtually no research at all was done in connectionism for 10 years. Eventually, a new generation of researchers would revive the field and thereafter it would become a vital and useful part of artificial intelligence. Rosenblatt would not live to see this, as he died in a boating accident shortly after the book was published.[73]

Logic was introduced into AI research as early as 1958, by John McCarthy in his Advice Taker proposal.[98] In 1963, J. Alan Robinson had discovered a simple method to implement deduction on computers, the resolution and unification algorithm. However, straightforward implementations, like those attempted by McCarthy and his students in the late 60s, were especially intractable: the programs required astronomical numbers of steps to prove simple theorems.[99] 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.[100] Prolog uses a subset of logic (Horn clauses, closely related to “rules” and “production rules”) that permit tractable computation. Rules would continue to be influential, providing a foundation for Edward Feigenbaum’s expert systems and the continuing work by Allen Newell and Herbert A. Simon that would lead to Soar and their unified theories of cognition.[101]

Critics of the logical approach noted, as Dreyfus had, that human beings rarely used logic when they solved problems. Experiments by psychologists like Peter Wason, Eleanor Rosch, Amos Tversky, Daniel Kahneman and others provided proof.[102] McCarthy responded that what people do is irrelevant. He argued that what is really needed are machines that can solve problemsnot machines that think as people do.[103]

Among the critics of McCarthy’s approach were his colleagues across the country at MIT. Marvin Minsky, Seymour Papert and Roger Schank were trying to solve problems like “story understanding” and “object recognition” that required a machine to think like a person. In order to use ordinary concepts like “chair” or “restaurant” they had to make all the same illogical assumptions that people normally made. Unfortunately, imprecise concepts like these are hard to represent in logic. Gerald Sussman observed that “using precise language to describe essentially imprecise concepts doesn’t make them any more precise.”[104]Schank described their “anti-logic” approaches as “scruffy”, as opposed to the “neat” paradigms used by McCarthy, Kowalski, Feigenbaum, Newell and Simon.[105]

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. For example, if we use the concept of a bird, there is a constellation of facts that immediately come to mind: we might assume that it flies, eats worms and so on. 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. He called these structures “frames”. Schank used a version of frames he called “scripts” to successfully answer questions about short stories in English.[106] Many years later object-oriented programming would adopt the essential idea of “inheritance” from AI research on frames.

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. In those same years, the Japanese government aggressively funded AI with its fifth generation computer project. Another encouraging event in the early 1980s was the revival of connectionism in the work of John Hopfield and David Rumelhart. Once again, AI had achieved success.

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. The earliest examples were developed by Edward Feigenbaum and his students. Dendral, begun in 1965, identified compounds from spectrometer readings. MYCIN, developed in 1972, diagnosed infectious blood diseases. They demonstrated the feasibility of the approach.[107]

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. All in all, the programs proved to be useful: something that AI had not been able to achieve up to this point.[108]

In 1980, an expert system called XCON was completed at CMU for the Digital Equipment Corporation. It was an enormous success: it was saving the company 40 million dollars annually by 1986.[109] Corporations around the world began to develop and deploy expert systems and by 1985 they were spending over a billion dollars on AI, most of it to in-house AI departments. An industry grew up to support them, including hardware companies like Symbolics and Lisp Machines and software companies such as IntelliCorp and Aion.[110]

The power of expert systems came from the expert knowledge they contained. They were part of a new direction in AI research that had been gaining ground throughout the 70s. “AI researchers were beginning to suspectreluctantly, for it violated the scientific canon of parsimonythat intelligence might very well be based on the ability to use large amounts of diverse knowledge in different ways,”[111] writes Pamela McCorduck. “[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”.[112]Knowledge based systems and knowledge engineering became a major focus of AI research in the 1980s.[113]

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. The project was not expected to be completed for many decades.[114]

Chess playing programs HiTech and Deep Thought defeated chess masters in 1989. Both were developed by Carnegie Mellon University; Deep Thought development paved the way for the Deep Blue.[115]

In 1981, the Japanese Ministry of International Trade and Industry set aside $850 million for the Fifth generation computer project. Their objectives were to write programs and build machines that could carry on conversations, translate languages, interpret pictures, and reason like human beings.[116] Much to the chagrin of scruffies, they chose Prolog as the primary computer language for the project.[117]

Other countries responded with new programs of their own. The UK began the 350 million Alvey project. A consortium of American companies formed the Microelectronics and Computer Technology Corporation (or “MCC”) to fund large scale projects in AI and information technology.[118][119]DARPA responded as well, founding the Strategic Computing Initiative and tripling its investment in AI between 1984 and 1988.[120]

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, David Rumelhart popularized a new method for training neural networks called “backpropagation” (discovered years earlier by Paul Werbos). These two discoveries revived the field of connectionism which had been largely abandoned since 1970.[119][121]

The new field was unified and inspired by the appearance of Parallel Distributed Processing in 1986a two volume collection of papers edited by Rumelhart and psychologist James McClelland. 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.[119][122]

The business community’s fascination with AI rose and fell in the 80s in the classic pattern of an economic bubble. The collapse was in the perception of AI by government agencies and investors the field continued to make advances despite the criticism. Rodney Brooks and Hans Moravec, researchers from the related field of robotics, argued for an entirely new approach to artificial intelligence.

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.[123] Their fears were well founded: in the late 80s and early 90s, AI suffered a series of financial setbacks.

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.[124]

Eventually the earliest successful expert systems, such as XCON, proved too expensive to maintain. 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. Expert systems proved useful, but only in a few special contexts.[125]

In the late 80s, 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.[126]

By 1991, the impressive list of goals penned in 1981 for Japan’s Fifth Generation Project had not been met. Indeed, some of them, like “carry on a casual conversation” had not been met by 2010.[127] As with other AI projects, expectations had run much higher than what was actually possible.[127]

In the late 80s, several researchers advocated a completely new approach to artificial intelligence, based on robotics.[128] 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). They advocated building intelligence “from the bottom up.”[129]

The approach revived ideas from cybernetics and control theory that had been unpopular since the sixties. Another precursor was David Marr, who had come to MIT in the late 70s from a successful background in theoretical neuroscience to lead the group studying vision. 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. (Marr’s work would be cut short by leukemia in 1980.)[130]

In a 1990 paper, “Elephants Don’t Play Chess,”[131] 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. It is always exactly up to date. It always has every detail there is to be known. The trick is to sense it appropriately and often enough.”[132] In the 80s and 90s, 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.[133]

The field of AI, now more than a half a century old, finally achieved some of its oldest goals. It began to be used successfully throughout the technology industry, although somewhat behind the scenes. 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. Still, the reputation of AI, in the business world at least, was less than pristine. 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”.[134] AI was both more cautious and more successful than it had ever been.

On 11 May 1997, Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov.[135] 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. The event was broadcast live over the internet and received over 74 million hits.[136]

In 2005, a Stanford robot won the DARPA Grand Challenge by driving autonomously for 131 miles along an unrehearsed desert trail.[137] 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.[138] In February 2011, in a Jeopardy! quiz show exhibition match, IBM’s question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin.[139]

These successes were not due to some revolutionary new paradigm, but mostly on the tedious application of engineering skill and on the tremendous power of computers today.[140] In fact, Deep Blue’s computer was 10 million times faster than the Ferranti Mark 1 that Christopher Strachey taught to play chess in 1951.[141] 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 “raw computer power” was slowly being overcome.

A new paradigm called “intelligent agents” became widely accepted during the 90s.[142] Although earlier researchers had proposed modular “divide and conquer” approaches to AI,[143] the intelligent agent did not reach its modern form until Judea Pearl, Allen Newell and others brought concepts from decision theory and economics into the study of AI.[144] 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”. This is a generalization of some earlier definitions of AI: it goes beyond studying human intelligence; it studies all kinds of intelligence.[145]

The paradigm gave researchers license to study isolated problems and find solutions that were both verifiable and useful. 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.[144][146]

AI researchers began to develop and use sophisticated mathematical tools more than they ever had in the past.[147] 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; AI had become a more rigorous “scientific” discipline. Russell & Norvig (2003) describe this as nothing less than a “revolution” and “the victory of the neats”.[148][149]

Judea Pearl’s highly influential 1988 book[150] brought probability and decision theory into AI. Among the many new tools in use were Bayesian networks, hidden Markov models, information theory, stochastic modeling and classical optimization. Precise mathematical descriptions were also developed for “computational intelligence” paradigms like neural networks and evolutionary algorithms.[148]

Algorithms originally developed by AI researchers began to appear as parts of larger systems. AI had solved a lot of very difficult problems[151] and their solutions proved to be useful throughout the technology industry,[152] such as data mining, industrial robotics, logistics,[153]speech recognition,[154] banking software,[155] medical diagnosis[155] and Google’s search engine.[156]

The field of AI receives little or no credit for these successes. Many of AI’s greatest innovations have been reduced to the status of just another item in the tool chest of computer science.[157]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.”[158]

Many researchers in AI in 1990s deliberately called their work by other names, such as informatics, knowledge-based systems, cognitive systems or computational intelligence. In part, this may be because they considered their field to be fundamentally different from AI, but also the new names help to procure funding. In the commercial world at least, the failed promises of the AI Winter continue to haunt AI research, 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.”[159][160][161]

In 1968, Arthur C. Clarke and Stanley Kubrick had imagined that by the year 2001, a machine would exist with an intelligence that matched or exceeded the capability of human beings. The character they created, HAL 9000, was based on a belief shared by many leading AI researchers that such a machine would exist by the year 2001.[162]

Marvin Minsky asks “So the question is why didn’t we get HAL in 2001?”[163] Minsky believes 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. John McCarthy, on the other hand, still blames the qualification problem.[164] For Ray Kurzweil, the issue is computer power and, using Moore’s Law, he predicts that machines with human-level intelligence will appear by 2029.[165]Jeff Hawkins argues that neural net research ignores the essential properties of the human cortex, preferring simple models that have been successful at solving simple problems.[166] There are many other explanations and for each there is a corresponding research program underway.

.

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Intro to Artificial Intelligence Course and Training Online …

Posted: July 5, 2016 at 11:42 pm

Featured Celebrate our 5th birthday with a 55% discount! Enroll through this link by 7/10/16 & the discount will automatically apply.

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Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, youll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

Note: Parts of this course are featured in the Machine Learning Engineer Nanodegree and the Data Analyst Nanodegree programs. If you are interested in AI, be sure to check out those programs as well!

Artificial Intelligence (AI) technology is increasingly prevalent in our everyday lives. It has uses in a variety of industries from gaming, journalism/media, to finance, as well as in the state-of-the-art research fields from robotics, medical diagnosis, and quantum science. In this course youll learn the basics and applications of AI, including: machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.

Some of the topics in Introduction to Artificial Intelligence will build on probability theory and linear algebra. You should have understanding of probability theory comparable to that covered in our Intro to Statistics course.

See the Technology Requirements for using Udacity.

Peter Norvig is Director of Research at Google Inc. He is also a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Norvig is co-author of the popular textbook Artificial Intelligence: A Modern Approach. Prior to joining Google he was the head of the Computation Sciences Division at NASA Ames Research Center.

Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning, specifically his work with self-driving cars.

This class is self paced. You can begin whenever you like and then follow your own pace. Its a good idea to set goals for yourself to make sure you stick with the course.

This class will always be available!

Take a look at the Class Summary, What Should I Know, and What Will I Learn sections above. If you want to know more, just enroll in the course and start exploring.

Yes! The point is for you to learn what YOU need (or want) to learn. If you already know something, feel free to skip ahead. If you ever find that youre confused, you can always go back and watch something that you skipped.

Its completely free! If youre feeling generous, we would love to have you contribute your thoughts, questions, and answers to the course discussion forum.

Collaboration is a great way to learn. You should do it! The key is to use collaboration as a way to enhance learning, not as a way of sharing answers without understanding them.

Udacity classes are a little different from traditional courses. We intersperse our video segments with interactive questions. There are many reasons for including these questions: to get you thinking, to check your understanding, for fun, etc… But really, they are there to help you learn. They are NOT there to evaluate your intelligence, so try not to let them stress you out.

Learn actively! You will retain more of what you learn if you take notes, draw diagrams, make notecards, and actively try to make sense of the material.

Nanodegree is a trademark of Udacity 20112016 Udacity, Inc.

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Intro to Artificial Intelligence Course and Training Online …

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Intro to Artificial Intelligence Course and Training …

Posted: June 28, 2016 at 2:46 am

Watch Video

Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, youll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

Note: Parts of this course are featured in the Machine Learning Engineer Nanodegree and the Data Analyst Nanodegree programs. If you are interested in AI, be sure to check out those programs as well!

Artificial Intelligence (AI) technology is increasingly prevalent in our everyday lives. It has uses in a variety of industries from gaming, journalism/media, to finance, as well as in the state-of-the-art research fields from robotics, medical diagnosis, and quantum science. In this course youll learn the basics and applications of AI, including: machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.

Some of the topics in Introduction to Artificial Intelligence will build on probability theory and linear algebra. You should have understanding of probability theory comparable to that covered in our Intro to Statistics course.

See the Technology Requirements for using Udacity.

Peter Norvig is Director of Research at Google Inc. He is also a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Norvig is co-author of the popular textbook Artificial Intelligence: A Modern Approach. Prior to joining Google he was the head of the Computation Sciences Division at NASA Ames Research Center.

Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning, specifically his work with self-driving cars.

This class is self paced. You can begin whenever you like and then follow your own pace. Its a good idea to set goals for yourself to make sure you stick with the course.

This class will always be available!

Take a look at the Class Summary, What Should I Know, and What Will I Learn sections above. If you want to know more, just enroll in the course and start exploring.

Yes! The point is for you to learn what YOU need (or want) to learn. If you already know something, feel free to skip ahead. If you ever find that youre confused, you can always go back and watch something that you skipped.

Its completely free! If youre feeling generous, we would love to have you contribute your thoughts, questions, and answers to the course discussion forum.

Collaboration is a great way to learn. You should do it! The key is to use collaboration as a way to enhance learning, not as a way of sharing answers without understanding them.

Udacity classes are a little different from traditional courses. We intersperse our video segments with interactive questions. There are many reasons for including these questions: to get you thinking, to check your understanding, for fun, etc… But really, they are there to help you learn. They are NOT there to evaluate your intelligence, so try not to let them stress you out.

Learn actively! You will retain more of what you learn if you take notes, draw diagrams, make notecards, and actively try to make sense of the material.

Nanodegree is a trademark of Udacity 20112016 Udacity, Inc.

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Intro to Artificial Intelligence Course and Training …

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NanoTech Institute – The University of Texas at Dallas

Posted: June 12, 2016 at 8:22 pm

Guided by theory and enabled by synthesis, the NanoTech Institute develops new science and technology exploiting the nanoscale.

Our researchers inspire students by creating an atmosphere of excitement, fun, and creativity.

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The University of Texas at Dallas [ Recipient’s Name ] * The Alan G MacDiarmid NanoTech Institute, BE 26 800 West Campbell Road Richardson, TX 75080-3021

Phone: 972-883-6530 Fax: 972-883-6529

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On August 20th, the 2013 class of NanoExplorers will presenting their research that they conducted along the researchers of the NanoTech Institute. See this flyer for more information. See the schedule here.

An article covering Ali Aliev’s and his collegues work on carbon nanotube thermoacustic transducers has been put online. You can read the whole article here.

The faculty, staff, and students of the Alan G. MacDiarmid NanoTech Institute at The University of Texas at Dallas welcome the 2013 class of NanoExplorers. We had over 200 highly qualified applicants this year. (see more)

The talk is devoted to recent achievements made by our Russian (NUST MISiS, Moscow) and French (G2Elab, Grenoble) groups in application of original shape memory composites for both microactuation and thermal energy harvesting. Novel prestrained scheme of shape memory composite allows creating actuators able to giant reversible bending deformation. (see more)

The faculty, staff, and students of the Alan G. MacDiarmid NanoTech Institute at The University of Texas at Dallas welcome the 2012 class of NanoExplorers. We had over 200 highly qualified applicants this year. (see more)

Read about former NanoExplorer Amy Chyao and her work at UT Dallas

Experience the collaboration of the NanoTech Institute with the University of Guanajuato (Guanajuato, Mexico) through the eyes of Raquel Ovalle Robles.

Discover the NanoTech Institute’s work through its library of publications.

Use the NanoTech Institute’s facilities to conduct cutting-edge research.

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NanoTech Institute – The University of Texas at Dallas

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'Space-age' research looks to provide new human health insights

Posted: April 11, 2015 at 7:46 am

IMAGE:NASA’s Rodent Habitat module is shown with both access doors open. view more

Credit: NASA/Dominic Hart

Imagine if all of your physiological changes were hyper accelerated so that you passed through life cycles in weeks as opposed to decades. You’d be able to grow a beard overnight or your hair might begin graying in a matter of days or maybe menopause would come knocking by next weekend. This may seem like a far stretch from reality, but spaceflight causes significant physiological changes including an accelerated loss of muscle and bone density, and immune system dysfunction that parallel the effects of natural aging here on Earth. This makes the International Space Station (ISS) is an ideal place for scientists to conduct research on aging at a “space-aged” pace.

One of the several investigations that are part of the second rodent research mission, Rodent Research-2 will focus on the age-old phenomenon of aging. This research, sponsored by Novartis Biomedical Research Institute, the U.S. Department of Defense and the Center for the Advancement of Science in Space (CASIS) is slated to launch to the space station via SpaceX-6. Researchers will use the study to analyze how spaceflight and exposure to the microgravity environment impacts a model organism’s musculoskeletal system.

Studying the disease mechanisms of muscle wasting and bone loss, which are associated with both spaceflight and aging, can provide greater insight into these processes and help to identify potential new drug targets and develop new therapeutics for other conditions as well. Advanced treatments for diseases like osteoporosis, muscular dystrophy, cancer, spinal cord injury, and kidney failure could all be developed through valuable data gained through this investigation and subsequent studies.

The musculoskeletal system is made up of bones, muscles, ligaments, tendons, cartilage and joints. This biological system is greatly affected by the aging process and certain diseases, as well as spaceflight. Researchers anticipate that the science conducted on the space station will provide insight into molecular variations induced by spaceflight, and can applied to our knowledge of similar changes on Earth.

The ISS National Lab is now equipped with specialized hardware, like the Bone Densitometer Locker. This facility, which launched with the previous rodent research mission, allows scientists to gather data in real time. Researchers will track numerous data points from the mice test subjects, including gene expression, various biomarkers from several biological systems, and molecular changes within musculoskeletal tissues.

A second part of the Rodent Research-2 study is scheduled to launch aboard SpaceX-7 and will include three separate investigations sponsored by NASA. Michael Pecaut, Ph.D., of Loma Linda University is the principal investigator for a study of the effects of spaceflight on immune system function. Michael Delp, Ph.D., of Florida State University, is the principal investigator for a study of spaceflight-induced changes in the structure of the blood-brain barrier. Data from a third study of the effect of spaceflight on liver metabolism and gene expression will be shared with the scientific community via GeneLab, NASA’s new open access system for sharing of genomics data gained from research in space. NASA’s Ames Research Center is responsible for carrying out all of the CASIS and NASA-funded science on this mission.

Collaboration between other government agencies and commercial entities, facilitated by CASIS and NASA, are helping to maximize the research capabilities of the ISS National Lab for the benefit of Earth.

We may still have to wait years to experience our own aging–which is likely a good thing–but thanks to space station research we may have help for treating those age-related challenges through the accelerated knowledge gained through studies like Rodent Research-2.

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'Space-age' research looks to provide new human health insights

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Broad, Bayer expand partnership to develop therapies for cardiovascular disease

Posted: March 31, 2015 at 10:45 pm

The Broad Institute of MIT and Harvard have expanded their collaboration with Bayer HealthCare to include cardiovascular genomics and drug discovery. The goal of this new part of the alliance is to leverage insights from human genetics to help create new cardiovascular therapies.

“It is exciting to be expanding on our ongoing, successful partnership with Bayer in oncology,” said Professor Eric Lander, President and Director of Broad Institute. “We are looking forward to a fruitful collaboration combining Bayer’s expertise in the cardiovascular therapeutic area with Broad’s deep knowledge of genomics and biology”.

Cardiovascular genomics is an emerging field of cardiology that uses genomic information to characterize disease risk and identify new therapeutic targets for drug discovery. Cardiovascular disease is responsible for approximately one-third of all deaths worldwide each year. While a majority of cardiovascular disease can be associated with lifestyle factors such as tobacco consumption, diet, and level of physical activity, risk genes can influence the predisposition to cardiovascular disease, age of onset, and severity.

“We are excited to broaden our collaboration with the Broad Institute to the area of cardiovascular genomics to discover genes and mutational changes underlying cardiovascular disorders in order to develop new therapies and diagnostic options for these diseases,” said Prof. Andreas Busch, Head of Global Drug Discovery and member of the Executive Committee of Bayer HealthCare. “We have been collaborating already for the last two years and have developed a very constructive partnership during this time.”

As part of this strategic alliance, Broad Institute and Bayer HealthCare will collaborate on genetic discovery, target validation, and drug discovery activities. Governance for this alliance will be comprised of a joint steering committee and joint research committee that will oversee research progress and direction. Financial terms of the agreement were not disclosed.

###

About the Broad Institute of MIT and Harvard

The Eli and Edythe L. Broad Institute of MIT and Harvard was launched in 2004 to empower this generation of creative scientists to transform medicine. The Broad Institute seeks to describe all the molecular components of life and their connections; discover the molecular basis of major human diseases; develop effective new approaches to diagnostics and therapeutics; and disseminate discoveries, tools, methods and data openly to the entire scientific community.

Founded by MIT, Harvard and its affiliated hospitals, and the visionary Los Angeles philanthropists Eli and Edythe L. Broad, the Broad Institute includes faculty, professional staff and students from throughout the MIT and Harvard biomedical research communities and beyond, with collaborations spanning over a hundred private and public institutions in more than 40 countries worldwide. For further information about the Broad Institute, go to broadinstitute.org.

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Broad, Bayer expand partnership to develop therapies for cardiovascular disease

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PRESS RELEASE: Second Genome and Evotec to collaborate in microbiome discovery and development

Posted: March 13, 2015 at 3:48 pm

PRESS RELEASE: Second Genome and Evotec to collaborate in microbiome discovery and development

DGAP-News: Evotec AG / Key word(s): Miscellaneous Second Genome and Evotec to collaborate in microbiome discovery and development

2015-03-13 / 07:31

=——————————————————————–

Hamburg, Germany – 13 March 2015: Evotec AG (Frankfurt Stock Exchange: EVT, TecDAX, ISIN: DE0005664809) and Second Genome, Inc. today announced a collaboration in small molecule-based discovery and development activities for the treatment of microbiome-mediated diseases. The collaboration comprises the identification and optimisation of novel compounds as well as licence agreements for already existing assets developed by Evotec. Second Genome’s unique approach to identify and modulate microbiome-mediated pathways will be further enhanced by the use and the results of Evotec’s integrated drug discovery platform.

As part of the collaboration, Second Genome and Evotec will work together to screen microbiome-mediated targets of interest identified by the Second Genome microbiome discovery platform with Evotec’s technology platform, chemical libraries and other pre-clinical capabilities. The agreement between Evotec and Second Genome triggers an undisclosed upfront payment. Evotec is also eligible for pre-clinical, clinical and regulatory milestones as well as royalty payments related to commercialisation.

Dr Cord Dohrmann, Chief Scientific Officer of Evotec, commented: “We are pleased to contribute to Second Genome’s unique approach to treat microbiome-mediated diseases in the body with a particular emphasis on the gut. The enrichment and maturation of Second Genome’s project portfolio through our contributions will enhance the Company’s clinical pipeline in the near future.”

Mohan Iyer, Chief Business Officer of Second Genome, added: “The partnership with Evotec allows us efficiently to translate our unique microbiome discovery platform efficiently into tangible drug molecules for clinical development. Our enriched pipeline offers new treatment approaches for patients across a wide range of diseases with an initial focus on inflammatory conditions. We look forward to a sustained partnership with Evotec.”

Further financial terms were not disclosed.

ABOUT EVOTEC AG Evotec is a drug discovery alliance and development partnership company focused on rapidly progressing innovative product approaches with leading pharmaceutical and biotechnology companies, academics, patient advocacy groups and venture capitalists. We operate worldwide providing the highest quality stand-alone and integrated drug discovery solutions, covering all activities from target-to-clinic. The Company has established a unique position by assembling top-class scientific experts and integrating state-of-the-art technologies as well as substantial experience and expertise in key therapeutic areas including neuroscience, pain, metabolic diseases as well as oncology, inflammation and infectious diseases. Evotec has long-term discovery alliances with partners including Bayer, Boehringer Ingelheim, CHDI, Genentech, Janssen Pharmaceuticals, MedImmune/AstraZeneca, Roche and UCB. In addition, the Company has existing development partnerships and product candidates both in clinical and pre-clinical development. These include partnerships with Boehringer Ingelheim and MedImmune in the field of diabetes, with Janssen Pharmaceuticals in the field of depression and with Roche in the field of Alzheimer’s disease. For additional information please go to http://www.evotec.com.

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PRESS RELEASE: Second Genome and Evotec to collaborate in microbiome discovery and development

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Cancer centers facing new challenges, NM doctor says

Posted: March 5, 2015 at 8:43 pm

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Cancer medicine is entering a new era, using drugs that target specific mutations identified by gene sequencing of cancerous tumors.

WILLMAN: A different way of delivering medicine

The new techniques pose special challenges for cancer centers in small states like New Mexico, where patient numbers are small, said Dr. Cheryl Willman, director and CEO of the University of New Mexico Cancer Center.

The real challenge for our patients is, how do you get your hands on those drugs, because you are going to need a whole cabinet of targeted agents, Willman said.

Part of the answer involves collaborating with other institutions to pool the genetic data from large numbers of patients, she said.

UNM Cancer Center announced recently that it has joined a research collaboration with five other U.S. cancer centers to pool genetic data of cancerous tumors and more quickly match patients to targeted treatments and drug trials.

For New Mexico, the collaboration is expected to help attract partnerships with drug companies that require large numbers of cancer patients to validate the results of drug trials, Willman said.

Cancer medicine is going through a huge transformation, which is to do comprehensive sequencing of each patients tumor, identify the mutations that are present, then pick the drug that really is targeting those mutations, she said.

UNM Cancer Center also plans this year to begin a nationwide study of leukemia patients. UNM will genetically sequence cancerous tumors for each of some 4,000 U.S. patients diagnosed with the blood cancer each year in search of mutations that can be targeted for drug therapies, Willman said.

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Cancer centers facing new challenges, NM doctor says

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Building Tailor-Made DNA Nanotubes Step by Step

Posted: February 23, 2015 at 10:44 pm

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Newswise Researchers at McGill University have developed a new, low-cost method to build DNA nanotubes block by block a breakthrough that could help pave the way for scaffolds made from DNA strands to be used in applications such as optical and electronic devices or smart drug-delivery systems.

Many researchers, including the McGill team, have previously constructed nanotubes using a method that relies on spontaneous assembly of DNA in solution. The new technique, reported today in Nature Chemistry, promises to yield fewer structural flaws than the spontaneous-assembly method. The building-block approach also makes it possible to better control the size and patterns of the DNA structures, the scientists report.

Just like a Tetris game, where we manipulate the game pieces with the aim of creating a horizontal line of several blocks, we can now build long nanotubes block by block, said Amani Hariri, a PhD student in McGills Department of Chemistry and lead author of the study. By using a fluorescence microscope we can further visualize the formation of the tubes at each stage of assembly, as each block is tagged with a fluorescent compound that serves as a beacon. We can then count the number of blocks incorporated in each tube as it is constructed.

This new technique was made possible by the development in recent years of single-molecule microscopy, which enables scientists to peer into the nano-world by turning the fluorescence of individual molecules on and off. (That groundbreaking work won three U.S.- and German-based scientists the 2014 Nobel Prize in Chemistry.)

Hariris research is jointly supervised by chemistry professors Gonzalo Cosa and Hanadi Sleiman, who co-authored the new study. Cosas research group specializes in single-molecule fluorescence techniques, while Sleimans uses DNA chemistry to design new materials for drug delivery and diagnostic tools.

The custom-built assembly technique developed through this collaboration gives us the ability to monitor the nanotubes as were building them, and see their structure, robustness and morphology, Cosa said.

We wanted to control the nanotubes lengths and features one-by-one, said Sleiman, who holds the Canada Research Chair in DNA Nanoscience. The resulting designer nanotubes, she adds, promise to be far cheaper to produce on a large scale than those created with so-called DNA origami, another innovative technique for using DNA as a nanoscale construction material.

Funding for the research was provided by the Natural Sciences and Engineering Research Council of Canada, the Canada Foundation for Innovation, NanoQubec, the Canadian Institutes of Health Research and the Fonds de recherch du Qubec Nature et technologies. —————————————————————————-

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Building Tailor-Made DNA Nanotubes Step by Step

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