![But more often than not, these fictional representations
misrepresent the Turing Test, turning it into a measure of
whether a robot can pass for human. The original Turing Test
wasn’t intended for that, but rather, for deciding whether a
machine can be considered to think in a manner
indistinguishable from a human - and that, even Turing himself
discerned, depends on which questions you ask.
What’s more, there are many other aspects of humanity that the
test neglects – and that’s why several researchers have devised
new variants of the Turing Test that aren’t about the capacity to
hold a plausible conversation.
13
Take game-playing, for example. To rival or surpass human
cognitive powers in something more sophisticated than mere
number-crunching, Turing thought that chess might be a good
place to start – a game that seems to be characterised by
strategic thinking, perhaps even invention.
Deep Blue won its first game against a world champion on 10
February 1996, when it defeated Garry Kasparov in game one of
a six-game match. However, Kasparov won three and drew two
of the following five games, defeating Deep Blue by a score of
4–2. Deep Blue was then heavily upgraded, and played
Kasparov again in May 1997.[1] Deep Blue won game six,
therefore winning the six-game rematch 3½–2½ and becoming
the first computer system to defeat a reigning world champion](https://image.slidesharecdn.com/aiartificialintelligence1readingresponsepeter-221206031739-453e26d9/85/AI-Artificial-Intelligence1Reading-responsePeter-docx-5-320.jpg)
![in a match under standard chess tournament time
controls.[2] Kasparov accused IBM of cheating and demanded a
rematch. IBM refused and retired Deep Blue.
The “44th move” per se represents the moment when a human
being (Kasparov) realised he was facing a superior intellect
(Deep Blue).
The IBM vs. Kasparov game taught us not to be naïve about the
advancements in brute force (calculative) computing or
artificial intelligence. Kasparov’s frustration and anger
following the loss against Deep Blue almost feels cute today (I
say this as a huge fan of Garry, it almost pains me to write that
sentence). It’s likely that we underestimate advancements in a
similar manner, due to sheer disbelief or ignorance, the
incapacity of imagining a future where we work in a different
way all-together. It’s a pity.
And we now have algorithms that are all but invincible (in the
long term) for bluffing games like poker – although this turns
out to be less psychological than you might think, and more a
matter of hard maths.
14
What about something more creative and ineffable, like music?
Machines can fool us there too. There is now a music-
composing computer called Iamus, which produces work
sophisticated enough to be deemed worthy of attention by
professional musicians. Iamus’s developer Francisco Vico of the
University of Malaga and his colleagues carried out a kind of
Turing Test by asking 250 subjects – half of them professional](https://image.slidesharecdn.com/aiartificialintelligence1readingresponsepeter-221206031739-453e26d9/85/AI-Artificial-Intelligence1Reading-responsePeter-docx-6-320.jpg)
AI Artificial Intelligence1Reading responsePeter .docx
- 1. AI: Artificial Intelligence 1 Reading response Peter Dormer, “Craft and the Turing Test for Practical Thinking,” in The Challenge of Technology. What is personal know-how? What is distributed knowledge? How do they relate to the Turing test? Give one example of your own how these concepts matter today to artists and makers, or better yet, in your own experience? Journal homework Keep a record (text and drawings) of events in daily life where human and machine intersect and interact. Fill at least two pages with your observations. Mary Shelley, Frankenstein, or The Modern Prometheus, 1818 Boris Karloff in Frankenstein in 1931 directed by James Whale Mary Shelley first published Frankenstein, or the Modern Prometheus 1818. the novel allegorizes the Romantic obsession with discovering the power or principle of life. Ideas about a
- 2. life power were consistent with the scientific understanding of the day. Darwin himself spoke of an organizing “spirit of animation” in his Zoonomia; or, The Laws of Organic Life, in which he stated “the world itself might have been generated, rather than created.” Dr. Frankenstein picked all the parts for his monster based on their beauty, but when it comes to life, the monster is unbearably ugly. “I had worked hard for nearly two years, for the sole purpose of infusing life into an inanimate body…the beauty of the dream vanished, and breathless horror and disgust filled my heart. Unable to endure the aspect of the being I had created, I rushed out of the room”. 4 Two definitions of AI: “The use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. --Margaret Boden “The science of making machines do things that would require intelligence if done by humans.” -Marvin Minsky BOTH OF THESE STATEMENTS ORIGINATE IN ALAN
- 3. TURING’S FIRST COMPUTER SCIENCE ARTICLE Working assumption: all cognition is computable Question: Is what’s not yet known to be computable actually computable? if so, then what? if not, why not, and what does that tell us about cognition? 7 Who was Alan Turing? B. 1912 London, attended King’s College, Cambridge and Princeton University. He studied mathematics and logic (he hadn’t invented computer science yet) At 23, he invented the “Turing machine” and published “On Computable Numbers in 1936, the first and most important paper in comp. sci. During WWII, solved the German Enigma code by use of electromechanical devices—a precursor to the computer Laid the foundation for major subfields of comp sci: theory of computation, design of hardware and software, and the study of artificial intelligence “The Imitation Game,” aka “The Turing Test” In 1950, Turing posited a way to test machine intelligence: a person in a room before a screen. S/he would correspond with two agents and based on their responses, decide which was a machine and which was human. If the machine can pass for human, the machine is intelligent. This is still a question. Is passing the Turing Test necessary for AI? Or desirable? Stuart Watt (1996) has proposed an “inverted
- 4. Turing Test”: have the computer as the interrogator, distinguishing between a machine and human. This would prove a theory of mind for the computer. Currently, “reverse Turing Tests” are used when contacting companies or signing up for email services to filter out bots (spell a word out of deformed letters, or click on images with signs in them) Turing hypothesized that in fifty years (year 2000), it would be “pointless” to asking if machines can think==we can think of this in the same way we say planes “fly” and submarines “swim.” 9 The idea of putting a computer through a test already implies some agency on the part of the machine. It’s the same process that Descartes recommended for determining if other beings have a mind. 11 blade runner What's more, the Turing Test has been referenced many times in popular-culture depictions of robots and artificial life – perhaps most notably inspiring the polygraph-like Voight-Kampff Test that opened the movie Blade Runner. 12
- 5. But more often than not, these fictional representations misrepresent the Turing Test, turning it into a measure of whether a robot can pass for human. The original Turing Test wasn’t intended for that, but rather, for deciding whether a machine can be considered to think in a manner indistinguishable from a human - and that, even Turing himself discerned, depends on which questions you ask. What’s more, there are many other aspects of humanity that the test neglects – and that’s why several researchers have devised new variants of the Turing Test that aren’t about the capacity to hold a plausible conversation. 13 Take game-playing, for example. To rival or surpass human cognitive powers in something more sophisticated than mere number-crunching, Turing thought that chess might be a good place to start – a game that seems to be characterised by strategic thinking, perhaps even invention. Deep Blue won its first game against a world champion on 10 February 1996, when it defeated Garry Kasparov in game one of a six-game match. However, Kasparov won three and drew two of the following five games, defeating Deep Blue by a score of 4–2. Deep Blue was then heavily upgraded, and played Kasparov again in May 1997.[1] Deep Blue won game six, therefore winning the six-game rematch 3½–2½ and becoming the first computer system to defeat a reigning world champion
- 6. in a match under standard chess tournament time controls.[2] Kasparov accused IBM of cheating and demanded a rematch. IBM refused and retired Deep Blue. The “44th move” per se represents the moment when a human being (Kasparov) realised he was facing a superior intellect (Deep Blue). The IBM vs. Kasparov game taught us not to be naïve about the advancements in brute force (calculative) computing or artificial intelligence. Kasparov’s frustration and anger following the loss against Deep Blue almost feels cute today (I say this as a huge fan of Garry, it almost pains me to write that sentence). It’s likely that we underestimate advancements in a similar manner, due to sheer disbelief or ignorance, the incapacity of imagining a future where we work in a different way all-together. It’s a pity. And we now have algorithms that are all but invincible (in the long term) for bluffing games like poker – although this turns out to be less psychological than you might think, and more a matter of hard maths. 14 What about something more creative and ineffable, like music? Machines can fool us there too. There is now a music- composing computer called Iamus, which produces work sophisticated enough to be deemed worthy of attention by professional musicians. Iamus’s developer Francisco Vico of the University of Malaga and his colleagues carried out a kind of Turing Test by asking 250 subjects – half of them professional
- 7. musicians – to listen to one of Iamus’s compositions and music in a comparable style by human composers, and decide which is which. “The computer piece raises the same feelings and emotions as the human one, and participants can’t distinguish them”, says Vico. “We would have obtained similar results by flipping coins.” 15 Then there’s the “Turing touch test”. Turing himself claimed that even if a material were ever to be found that mimicked human skin perfectly, there was little reason to try to make a machine more human by giving it artificial flesh. Our current motivation is a little different: We know that prosthetic limbs that can pass for the real thing may lessen the psychological and emotional impact that wearers report. To this end, mechanical engineer John-John Cabibihan at Qatar University and his colleagues are creating materials that look and feel indistinguishable from human skin. Earlier this year, he and his coworkers reported that they had created a soft silicone polymer that, when heated close to body temperature with sub- surface electronic heaters, closely resembled real skin. The researchers created an artificial hand by coating a 3D-printed resin skeleton with the electrically warmed polymer and used it to touch the forearms of people while the hand itself was concealed. The participants proved unable to make any reliable distinction between the touch of the artificial hand and a real one. 16
- 8. 2014 a “supercomputer” program called “Eugene Goostman”—an impersonation of a wisecracking, thirteen-year-old Ukranian boy—had become the first machine to pass the Turing Test. Kevin Warwick, a professor of cybernetics at the University of Reading, who administered the test, wrote, “In the field of Artificial Intelligence there is no more iconic and controversial milestone than the Turing Test, when a computer convinces a sufficient number of interrogators into believing that it is not a machine but rather is a human.” Warwick went on to call Goostman’s victory “ a milestone” that “would go down in history as one of the most exciting” moments in the field of artificial intelligence. Developed by PrincetonAI (a small team of programmers and technologists not affiliated with Princeton University) and backed by a computer and some gee-whiz algorithms, "Eugene Goostman" was able to fool the Turing Test 2014 judges 33% of the time — good enough to surpass the threshold set by computer scientist Alan Turing in 1950. Turing believed that by 2000, computers would be able to, through five-minute text- based conversations, fool humans into believing that they were flesh and blood, at least 30% of the time. Depending on whom you talk to, Goostman's achievement is either a huge turning point for technology, or just another blip. 17 Scott: … Do you understand why I’m asking such basic questions? Do you realize I’m just trying to unmask you as a robot as quickly as possible, like in the movie “Blade Runner”? Eugene: … wait
- 9. Scott: Do you think your ability to fool unsophisticated judges indicates a flaw with the Turing Test itself, or merely with the way people have interpreted the test? Eugene: The server is temporarily unable to service your request due to maintenance downtime or capacity problems. Please try again later. certainly it doesn’t obviously justify claims that the Turing Test has been passed. As computer scientist Scott Aaronson of the Massachusetts Institute of Technology has said, “Turing’s famous example dialogue, involving Mr. Pickwick and Christmas, clearly shows that the kind of conversation Turing had in mind was at a vastly higher level than what any chatbot, including Goostman, has ever been able to achieve.” More to the point, Aaronson’s splendid conversation with Eugene, after he decided to probe further into all the publicity surrounding “him”, demonstrates the limitations rather graphically: Scott: … Do you understand why I’m asking such basic questions? Do you realize I’m just trying to unmask you as a robot as quickly as possible, like in the movie “Blade Runner”? Eugene: … wait Scott: Do you think your ability to fool unsophisticated judges indicates a flaw with the Turing Test itself, or merely with the way people have interpreted the test? Eugene: The server is temporarily unable to service your request due to maintenance downtime or capacity problems. Please try again later. 18 Two theories of AI: Base of knowledge
- 10. Neural networks The base of knowledge idea—basically filling a machine with encyclopedic knowledge is the “bottom-up” method. It establishes a base of knowledge from which the machine can operate The neural net idea constructs a system that will analyze huge amounts of data. This is the “top-down” method. For example, through analyzing millions of images of cats, a neural net will “learn” to recognize a cat. 19 Google’s Deep Dream Google’s Deep Dream is an example of a neural net. Given an input image, it analyzes and classifies the image according to millions of images it’s seen before. The results so far have been these kaleidoscopic/psychedelic outputs that cram as much information into one space as possible. It’s job is essentially to find the sound in noise https://www.youtube.com/watch?v=egk683bKJYU see esp min 18:00—25:00 for google dream architecture; 32:30—36:00 “shore of portraits” 20 Asked to find bananas, Google’s Deep Dream will find bananas within a set of noise 21
- 11. SketchRNN (recurrent neural network) Google’s Project Magenta includes SketchRNN, in which the network has “learned” to draw. It is interactive—the user starts a drawing and the network will finish it 22 What is the most important difference between humans and artificial intelligence-- what makes us human? Is it thinking? Learning? Creativity? Emotion? Would it be possible for machines to achieve this function? How would it be tested? Is there a good reason to create machines that can perform in this way? Conversely, is there a reason to prevent this technology? 23 Terence Broad, Blade Runner-Autoencoded and Koyaanisqatsi Autoencoded Through Blade Runner Is this a machine “memory”? Blade Runner Autoencoded was a research project for Broad’s dissertation in the Creative Computing program at Goldsmiths. He trained a type of artificial neural network called an autoencoder to reconstruct individual frames from Blade Runner, which he then re-sequenced into a video. The technique
- 12. was first proposed in 2015 by Larsen et al at the International Conference on machine Learning (ICML). Running Koyaanisqatsi through the nerual net trained on Blade Runner results in a strange merging of the two. https://arxiv.org/pdf/1512.09300.pdf 24 From Larsen et all, “Autoencoding beyond pixels using a learned similarity metric,” 2015 Image illustrating an autoencoding process of reconstructing dataset samples with visual attribute vectors added to latent representations 25 Terence Broad, Topological Visualization of Convolutional Neural Network Open in safari: http://terencebroad.com/convnetvis/vis.html This is a simplification of the connections between nodes in a neural network. The algorithm is a “recursive depth first tree search.” Starting with an input value, the network classifies the input along each layer. Convolutional networks are what made the generation of images possible. 26 Trevor Paglen and Kronos Quartet, Sight Machine,2017
- 13. Paglan is interested in machine vision and surveillance. These are images not meant for us, but for computers. He explores the mechanics and the implications for aesthetics but also their sociological impact. In Sight Machine, Paglen worked with Obscura Digital to track the Kronos quartet in real-time with technology sourced from open source software that runs neural nets. Paglen wanted to reveal how the networks “see” and process images. https://www.youtube.com/watch?v=HEI8cuGKiNk https://www.wired.com/2017/04/unsettling-performance- showed-world-ais-eyes/ 27 Paglen, Machine-Readable Hito, 2017, part of “A Study of Invisible Images” https://qz.com/1103545/macarthur-genius-trevor-paglen- reveals-what-ai-sees-in-the-human-world/ Paglen turns a face-analyzing algorithm on fellow artist Hito Steyerl. In hundreds of snapshots, she grimaces, laughs, yawns, shouts, rages, and smiles. Each picture is annotated with the AI’s earnest guesstimate of Steyerl’s age, gender, and emotional state. In one instance, she is evaluated as 74% female. It’s an absurd but simple way to raise a complicated question: Should computers even attempt to measure existentially indivisible characteristics like sex, gender, and personality— and without asking their subject? (Secondarily, what does 100% female even look like?) Computers already and increasingly make decisions about you—
- 14. which advertisement to serve, whether or not you’ve committed a prior crime—based on vast banks of training data and image libraries basically inaccessible to anyone not already literate in machine-vision research. That could soon complicate traditional ideas of accountability: In the future, humans working with computer-vision technologies in corporations and law enforcement agencies may not themselves be capable of tracing back how an AI made its decision, much less be able to make that process transparent to consumers and citizens. 28 William Latham, Mutator, 2014 William Latham calls this series of computer animation Organic Art. He builds generative algorithms based on geometric patterns in life to create “living” forms Latham trained as an artist and became a Research Felow at the IBM UK Scientific Centre 29