Showing posts with label Deep Blue. Show all posts
Showing posts with label Deep Blue. Show all posts

Artificial Intelligence - What Is The Mac Hack IV Program?


Mac Hack IV, a 1967 chess software built by Richard Greenblatt, gained notoriety for being the first computer chess program to engage in a chess tournament and to play adequately against humans, obtaining a USCF rating of 1,400 to 1,500.

Greenblatt's software, written in the macro assembly language MIDAS, operated on a DEC PDP-6 computer with a clock speed of 200 kilohertz.

While a graduate student at MIT's Artificial Intelligence Laboratory, he built the software as part of Project MAC.

"Chess is the drosophila [fruit fly] of artificial intelligence," according to Russian mathematician Alexander Kronrod, the field's chosen experimental organ ism (Quoted in McCarthy 1990, 227).

Creating a champion chess software has been a cherished goal in artificial intelligence since 1950, when Claude Shan ley first described chess play as a task for computer programmers.

Chess and games in general involve difficult but well-defined issues with well-defined rules and objectives.

Chess has long been seen as a prime illustration of human-like intelligence.

Chess is a well-defined example of human decision-making in which movements must be chosen with a specific purpose in mind, with limited knowledge and uncertainty about the result.

The processing capability of computers in the mid-1960s severely restricted the depth to which a chess move and its alternative answers could be studied since the number of different configurations rises exponentially with each consecutive reply.

The greatest human players have been proven to examine a small number of moves in greater detail rather than a large number of moves in lower depth.

Greenblatt aimed to recreate the methods used by good players to locate significant game tree branches.

He created Mac Hack to reduce the number of nodes analyzed while choosing moves by using a minimax search of the game tree along with alpha-beta pruning and heuristic components.

In this regard, Mac Hack's style of play was more human-like than that of more current chess computers (such as Deep Thought and Deep Blue), which use the sheer force of high processing rates to study tens of millions of branches of the game tree before making moves.

In a contest hosted by MIT mathematician Seymour Papert in 1967, Mac Hack defeated MIT philosopher Hubert Dreyfus and gained substantial renown among artificial intelligence researchers.

The RAND Corporation published a mimeographed version of Dreyfus's paper, Alchemy and Artificial Intelligence, in 1965, which criticized artificial intelligence researchers' claims and aspirations.

Dreyfus claimed that no computer could ever acquire intelligence since human reason and intelligence are not totally rule-bound, and hence a computer's data processing could not imitate or represent human cognition.

In a part of the paper titled "Signs of Stagnation," Dreyfus highlighted attempts to construct chess-playing computers, among his many critiques of AI.

Mac Hack's victory against Dreyfus was first seen as vindication by the AI community.

Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Alchemy and Artificial Intelligence; Deep Blue.

Further Reading:

Crevier, Daniel. 1993. AI: The Tumultuous History of the Search for Artificial Intelligence. New York: Basic Books.

Greenblatt, Richard D., Donald E. Eastlake III, and Stephen D. Crocker. 1967. “The Greenblatt Chess Program.” In AFIPS ’67: Proceedings of the November 14–16, 1967, Fall Joint Computer Conference, 801–10. Washington, DC: Thomson Book Company.

Marsland, T. Anthony. 1990. “A Short History of Computer Chess.” In Computers, Chess, and Cognition, edited by T. Anthony Marsland and Jonathan Schaeffer, 3–7. New York: Springer-Verlag.

McCarthy, John. 1990. “Chess as the Drosophila of AI.” In Computers, Chess, and Cognition, edited by T. Anthony Marsland and Jonathan Schaeffer, 227–37. New York: Springer-Verlag.

McCorduck, Pamela. 1979. Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. San Francisco: W. H. Freeman.

Artificial Intelligence - What Is The Deep Blue Computer?

The color deep blue Since the 1950s, artificial intelligence has been utilized to play chess.

Chess has been studied for a variety of reasons.

First, since there are a limited number of pieces that may occupy distinct spots on the board, the game is simple to represent in computers.

The game is quite challenging to play.

There are a tremendous number of alternative states (piece configurations), and exceptional chess players evaluate both their own and their opponents' actions, which means they must predict what could happen many turns in the future.

Finally, chess is a competitive sport.

When a human competes against a computer, they are comparing intellect.

Deep Blue, the first computer to beat a reigning chess world champion, demonstrated that machine intelligence was catching up to humans in 1997.

Deep Blue was first released in 1985.

Feng-Hsiung Hsu, Thomas Anantharaman, and Murray Campbell created ChipTest, a chess-playing computer, while at Carnegie Mellon University.

The computer used brute force, generating and comparing move sequences using the alpha-beta search technique in order to determine the best one.

The generated positions would be scored by an evaluation function, enabling several locations to be compared.

Furthermore, the algorithm was adversarial, anticipating the opponent's movements in order to discover a means to defeat them.

If a computer has enough time and memory to execute the calculations, it can theoretically produce and evaluate an unlimited number of moves.

When employed in tournament play, however, the machine is restricted in both directions.

ChipTest was able to generate and assess 50,000 movements per second because to the usage of a single special-purpose chip.

The search process was enhanced in 1988 to add single extensions, which may rapidly discover a move that is superior to all other options.

ChipTest could construct bigger sequences and see farther ahead in the game by swiftly deciding superior actions, testing human players' foresight.

Mike Browne and Andreas Nowatzyk joined the team as ChipTest developed into Deep Thought.

Deep Thought was able to process about 700,000 chess moves per second because to two upgraded move genera tor chips.

Deep Thought defeated Bent Larsen in 1988, becoming the first computer to defeat a chess grandmaster.

After IBM recruited the majority of the development team, work on Deep Thought continued.

The squad has now set its sights on defeating the world's finest chess player.

Garry Kasparov was the finest chess player in the world at the time, as well as one of the best in his generation.

Kasparov, who was born in Baku, Azerbaijan, in 1963, won the Soviet Junior Championship when he was twelve years old.

He was the youngest player to qualify for the Soviet Chess Championship at the age of fifteen.

He won the under-twenty world championship when he was seventeen years old.

Kasparov was also the world's youngest chess champion, having won the championship at the age of twenty-two in 1985.

He held the championship until 1993, when he was forced to relinquish it after quitting the International Chess Federation.

He instantly won the Classical World Champion, which he held from 1993 to 2000.

Kasparov was the best chess player in the world for the majority of 1986 to 2005 (when he retired).

Deep Thought faced off against Kasparov in a two-game match in 1989.

Kasparov easily overcame Deep Thought by winning both games.

Deep Thought evolved into Deep Blue, which only appeared in two bouts, both of which were versus Kasparov.

When it came to the matches, Kasparov was at a disadvantage since he was up against Deep Blue.

He would scout his opponents before matches, as do many chess players, by watching them play or reading records of tournament matches to obtain insight into their play style and methods.

Deep Blue, on the other hand, has no prior match experience, having only played in private matches against the developers before to facing Kasparov.

As a result, Kasparov was unable to scout Deep Blue.

The developers, on the other hand, had access to Kasparov's match history, allowing them to tailor Deep Blue to his playing style.

Despite this, Kasparov remained confident, claiming that no machine would ever be able to defeat him.

On February 10, 1996, Deep Blue and Kasparov played their first six-game match in Philadelphia.

Deep Blue was the first machine to defeat a reigning world champion in a single game, winning the opening game.

After two draws and three victories, Kasparov would go on to win the match.

The contest drew international notice, and a rematch was planned.

Deep Blue and Kasparov faced off in another six-game contest on May 11, 1997, at the Equitable Center in New York City, after a series of improvements.

The match had a crowd and was broadcast.

At this point, Deep Blue was com posed of 400 special-purpose chips capable of searching through 200,000,000 chess moves per second.

Kasparov won the first game, while Deep Blue won the second.

The following three games were draws.

The final game would determine the match.

In this final game, Deep Blue capitalized on a mistake by Kasparov, causing the champion to concede after nineteen moves.

Deep Blue became the first machine ever to defeat a reigning world champion in a match.

Kasparov believed that a human had interfered with the match, providing Deep Blue with winning moves.

The claim was based on a move made in the second match, where Deep Blue made a sacrifice that (to many) hinted at a different strat egy than the machine had used in prior games.

The move made a significant impact on Kasparov, upsetting him for the remainder of the match and affecting his play.

Two factors may have combined to generate the move.

First, Deep Blue underwent modifications between the first and second game to correct strategic flaws, thereby influencing its strategy.

Second, designer Murray Campbell men tioned in an interview that if the machine could not decide which move to make, it would select one at random; thus there was a chance that surprising moves would be made.

Kasparov requested a rematch and was denied.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Demis Hassabis.

Further Reading:

Campbell, Murray, A. Joseph Hoane Jr., and Feng-Hsiung Hsu. 2002. “Deep Blue.” Artificial Intelligence 134, no. 1–2 (January): 57–83.

Hsu, Feng-Hsiung. 2004. Behind Deep Blue: Building the Computer That Defeated the World Chess Champion. Princeton, NJ: Princeton University Press.

Kasparov, Garry. 2018. Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins. London: John Murray.

Levy, Steven. 2017. “What Deep Blue Tells Us about AI in 2017.” Wired, May 23, 2017.

Artificial Intelligence - What Is Computational Creativity?


Computational Creativity is a term used to describe a kind of creativity that is based on Computer-generated art is connected to computational creativity, although it is not reducible to it.

According to Margaret Boden, "CG-art" is an artwork that "results from some computer program being allowed to operate on its own, with zero input from the human artist" (Boden 2010, 141).

This definition is both severe and limiting, since it is confined to the creation of "art works" as defined by human observers.

Computational creativity, on the other hand, is a broader phrase that encompasses a broader range of actions, equipment, and outputs.

"Computational creativity is an area of Artificial Intelligence (AI) study... where we construct and engage with computational systems that produce products and ideas," said Simon Colton and Geraint A. Wiggins.

Those "artefacts and ideas" might be works of art, as well as other things, discoveries, and/or performances (Colton and Wiggins 2012, 21).

Games, narrative, music composition and performance, and visual arts are examples of computational creativity applications and implementations.

Games and other cognitive skill competitions are often used to evaluate and assess machine skills.

The fundamental criterion of machine intelligence, in fact, was established via a game, which Alan Turing dubbed "The Game of Imitation" (1950).

Since then, AI progress and accomplishment have been monitored and evaluated via games and other human-machine contests.

Chess has had a special status and privileged position among all the games in which computers have been involved, to the point where critics such as Douglas Hofstadter (1979, 674) and Hubert Dreyfus (1992) confidently asserted that championship-level AI chess would forever remain out of reach and unattainable.

After beating Garry Kasparov in 1997, IBM's Deep Blue modified the game's rules.

But chess was just the start.

In 2015, AlphaGo, a Go-playing algorithm built by Google DeepMind, defeated Lee Sedol, one of the most famous human players of this notoriously tough board game, in four out of five games.

Human observers, including as Fan Hui (2016), have praised AlphaGo's nimble play as "beautiful," "intuitive," and "innovative." 'Automated Insights' is a service provided by Automated Insights Natural Language Generation (NLG) techniques such as Wordsmith and Narrative Science's Quill are used to create human-readable tales from machine-readable data.

Unlike basic news aggregators or template NLG systems, these computers "write" (or "produce," as the case may be) unique tales that are almost indistinguishable from human-created material in many cases.

Christer Clerwall, for example, performed a small-scale research in 2014 in which human test subjects were asked to assess news pieces written by Wordsmith and a professional writer from the Los Angeles Times.

The study's findings reveal that, although software-generated information is often seen as descriptive and dull, it is also regarded as more impartial and trustworthy (Clerwall 2014, 519).

"Within 10 years, a digital computer would produce music regarded by critics as holding great artistic merit," Herbert Simon and Allen Newell predicted in their famous article "Heuristic Problem Solving" (1958). (Simon and Newell 1958, 7).

This prediction has come true.

Experiments in Musical Intelligence (EMI, or "Emmy") by David Cope is one of the most well-known works in the subject of "algorithmic composition." 

Emmy is a computer-based algorithmic composer capable of analyzing existing musical compositions, rearranging their fundamental components, and then creating new, unique scores that sound like and, in some circumstances, are indistinguishable from Mozart, Bach, and Chopin's iconic masterpieces (Cope 2001).

There are robotic systems in music performance, such as Shimon, a marimba-playing jazz-bot from Georgia Tech University, that can not only improvise with human musicians in real time, but also "is designed to create meaningful and inspiring musical interactions with humans, leading to novel musical experiences and outcomes" (Hoffman and Weinberg 2011).

Cope's method, which he refers to as "recombinacy," is not restricted to music.

It may be used and applied to any creative technique in which new works are created by reorganizing or recombining a set of finite parts, such as the alphabet's twenty-six letters, the musical scale's twelve tones, the human eye's sixteen million colors, and so on.

As a result, other creative undertakings, like as painting, have adopted similar computational creativity method.

The Painting Fool is an automated painter created by Simon Colton that seeks to be "considered seriously as a creative artist in its own right" (Colton 2012, 16).

To far, the algorithm has generated thousands of "original" artworks, which have been shown in both online and physical art exhibitions.

Obvious, a Paris-based collaboration comprised of the artists Hugo Caselles-Dupré, Pierre Fautrel, and Gauthier Vernie, uses a generative adversarial network (GAN) to create portraits of a fictitious family (the Belamys) in the manner of the European masters.

Christies auctioned one of these pictures, "Portrait of Edmond Belamy," for $432,500 in October 2018.

Designing ostensibly creative systems instantly runs into semantic and conceptual issues.

Creativity is an enigmatic phenomena that is difficult to pinpoint or quantify.

Are these programs, algorithms, and systems really "creative," or are they merely a sort of "imitation," as some detractors have labeled them? This issue is similar to John Searle's (1984, 32–38) Chinese Room thought experiment, which aimed to highlight the distinction between genuine cognitive activity, such as creative expression, and simple simulation or imitation.

Researchers in the field of computational creativity have introduced and operationalized a rather specific formulation to characterize their efforts: "The philosophy, science, and engineering of computational systems that, by taking on specific responsibilities, exhibit behaviors that unbiased observers would deem creative" (Colton and Wig gins 2012, 21).

The key word in this description is "responsibility." 

"The term responsibilities highlights the difference between the systems we build and creativity support tools studied in the HCI [human-computer interaction] community and embedded in tools like Adobe's Photoshop, to which most observers would probably not attribute creative intent or behavior," Colton and Wiggins explain (Colton and Wiggins 2012, 21).

"The program is only a tool to improve human creativity" (Colton 2012, 3–4) using a software application like Photoshop; it is an instrument utilized by a human artist who is and remains responsible for the creative choices and output created by the instrument.

Computational creativity research, on the other hand, "seeks to develop software that is creative in and of itself" (Colton 2012, 4).

On the one hand, one might react as we have in the past, dismissing contemporary technological advancements as simply another instrument or tool of human action—or what technology philosophers such as Martin Heidegger (1977) and Andrew Feenberg (1991) refer to as "the instrumental theory of technology." 

This is, in fact, the explanation supplied by David Cope in his own appraisal of his work's influence and relevance.

Emmy and other algorithmic composition systems, according to Cope, do not compete with or threaten to replace human composition.

They are just instruments used in and for musical creation.

"Computers represent just instruments with which we stretch our ideas and bodies," writes Cope.

Computers, programs, and the data utilized to generate their output were all developed by humanity.

Our algorithms make music that is just as much ours as music made by our greatest human inspirations" (Cope 2001, 139).

According to Cope, no matter how much algorithmic mediation is invented and used, the musical composition generated by these advanced digital tools is ultimately the responsibility of the human person.

The similar argument may be made for other supposedly creative programs, such as AlphaGo, a Go-playing algorithm, or The Painting Fool, a painting software.

When AlphaGo wins a big tournament or The Painting Fool creates a spectacular piece of visual art that is presented in a gallery, there is still a human person (or individuals) who is (or can reply or answer for) what has been created, according to the argument.

The attribution lines may get more intricate and drawn out, but there is always someone in a position of power behind the scenes, it might be claimed.

In circumstances where efforts have been made to transfer responsibility to the computer, evidence of this already exists.

Consider AlphaGo's game-winning move 37 versus Lee Sedol in game two.

If someone wants to learn more about the move and its significance, AlphaGo is the one to ask.

The algorithm, on the other hand, will remain silent.

In actuality, it was up to the human programmers and spectators to answer on AlphaGo's behalf and explain the importance and effect of the move.

As a result, as Colton (2012) and Colton et al. (2015) point out, if the mission of computational creativity is to succeed, the software will have to do more than create objects and behaviors that humans interpret as creative output.

It must also take ownership of the task by accounting for what it accomplished and how it did it.

"The software," Colton and Wiggins argue, "should be available for questioning about its motivations, processes, and products," eventually capable of not only generating titles for and explanations and narratives about the work but also responding to questions by engaging in critical dialogue with its audience (Colton and Wiggins 2012, 25). (Colton et al. 2015, 15).

At the same time, these algorithmic incursions into what had previously been a protected and solely human realm have created possibilities.

It's not only a question of whether computers, machine learning algorithms, or other applications can or cannot be held accountable for what they do or don't do; it's also a question of how we define, explain, and define creative responsibility in the first place.

This suggests that there is a strong and weak component to this endeavor, which Mohammad Majid al-Rifaie and Mark Bishop refer to as strong and weak forms of computational creativity, reflecting Searle's initial difference on AI initiatives (Majid al-Rifaie and Bishop 2015, 37).

The types of application development and demonstrations presented by people and companies such as DeepMind, David Cope, and Simon Colton are examples of the "strong" sort.

However, these efforts have a "weak AI" component in that they simulate, operationalize, and stress test various conceptualizations of artistic responsibility and creative expression, resulting in critical and potentially insightful reevaluations of how we have defined these concepts in our own thinking.

Nothing has made Douglas Hofstadter reexamine his own thinking about thinking more than the endeavor to cope with and make sense of David Cope's Emmy nomination (Hofstadter 2001, 38).

To put it another way, developing and experimenting with new algorithmic capabilities does not necessarily detract from human beings and what (hopefully) makes us unique, but it does provide new opportunities to be more precise and scientific about these distinguishing characteristics and their limits.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

AARON; Automatic Film Editing; Deep Blue; Emily Howell; Generative Design; Generative Music and Algorithmic Composition.

Further Reading

Boden, Margaret. 2010. Creativity and Art: Three Roads to Surprise. Oxford, UK: Oxford University Press.

Clerwall, Christer. 2014. “Enter the Robot Journalist: Users’ Perceptions of Automated Content.” Journalism Practice 8, no. 5: 519–31.

Colton, Simon. 2012. “The Painting Fool: Stories from Building an Automated Painter.” In Computers and Creativity, edited by Jon McCormack and Mark d’Inverno, 3–38. Berlin: Springer Verlag.

Colton, Simon, Alison Pease, Joseph Corneli, Michael Cook, Rose Hepworth, and Dan Ventura. 2015. “Stakeholder Groups in Computational Creativity Research and Practice.” In Computational Creativity Research: Towards Creative Machines, edited by Tarek R. Besold, Marco Schorlemmer, and Alan Smaill, 3–36. Amster￾dam: Atlantis Press.

Colton, Simon, and Geraint A. Wiggins. 2012. “Computational Creativity: The Final Frontier.” In Frontiers in Artificial Intelligence and Applications, vol. 242, edited by Luc De Raedt et al., 21–26. Amsterdam: IOS Press.

Cope, David. 2001. Virtual Music: Computer Synthesis of Musical Style. Cambridge, MA: MIT Press.

Dreyfus, Hubert L. 1992. What Computers Still Can’t Do: A Critique of Artificial Reason. Cambridge, MA: MIT Press.

Feenberg, Andrew. 1991. Critical Theory of Technology. Oxford, UK: Oxford University Press.

Heidegger, Martin. 1977. The Question Concerning Technology, and Other Essays. Translated by William Lovitt. New York: Harper & Row.

Hoffman, Guy, and Gil Weinberg. 2011. “Interactive Improvisation with a Robotic Marimba Player.” Autonomous Robots 31, no. 2–3: 133–53.

Hofstadter, Douglas R. 1979. Gödel, Escher, Bach: An Eternal Golden Braid. New York: Basic Books.

Hofstadter, Douglas R. 2001. “Staring Emmy Straight in the Eye—And Doing My Best Not to Flinch.” In Virtual Music: Computer Synthesis of Musical Style, edited by David Cope, 33–82. Cambridge, MA: MIT Press.

Hui, Fan. 2016. “AlphaGo Games—English. DeepMind.”

Majid al-Rifaie, Mohammad, and Mark Bishop. 2015. “Weak and Strong Computational Creativity.” In Computational Creativity Research: Towards Creative Machines, edited by Tarek R. Besold, Marco Schorlemmer, and Alan Smaill, 37–50. Amsterdam: Atlantis Press.

Searle, John. 1984. Mind, Brains and Science. Cambridge, MA: Harvard University Press.

What Is Artificial General Intelligence?

Artificial General Intelligence (AGI) is defined as the software representation of generalized human cognitive capacities that enables the ...