Human IT - tidskrift för studier av IT ur ett humanvetenskapligt perspektiv

ITH - Centrum för studier av IT ur ett humanvetenskapligt perspektiv
vid Högskolan i Borås


Artificial Intelligence

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Dear reader, 

This issue of Human IT presents five articles proper, four of which in the peer reviewed section, and three of which are designated to a particular theme, namely that of artificial intelligence (AI) and in particular machine learning. In addition, our guest editor for the theme, Ulf Johansson (department of business and informatics, UC of Borås), has written a commendable introductory piece to the issue and to the topics of AI and machine learning in general. As he there goes on to introduce and comment on the three thematic articles, I refer the reader to his text for further presentation. Suffice it to say that all three provide fascinating journeys into quite diverse domains of research: computer game studies, papyrology, and data mining. I think this already suggests just how the methodology and theory of AI are able to cut across disciplines. And to an extent, this is of course what Human IT tries to do on a broader scale: selecting cross-disciplinary themes, topics and articles.

There are also two extrathematic articles in this issue:

Olof Sundin continues his discussion of user education for information seeking from the last issue of Human IT with a study of how user education is presented and conducted in 31 web-based guides to information seeking made available by Nordic university libraries. In the guides he identifies four different approaches to user education, approaches that will also have consequences for how such central concepts, such as information and user, are understood. The guides are viewed as forums in which the shaping of librarians' professional expertise is negotiated, and the different approaches also involve different views of what constitutes this professional expertise.

The second piece is an article by Patrik Svensson that portraits a well-known MIT researcher, Henry Jenkins, and his work on fan fiction and computer games. Earlier this year, Jenkins visited Sweden and “The Technological Texture” (quite an Ihdea for a title!), a two-day conference at HumLab, Umeå University. He there gave not only a funny and thought-through talk on media convergence, but also participated in a debate on game studies with Espen Aarseth. Both Jenkins’ talk and the debate were filmed and are accessible online through HumLab’s web site at <>.

Svensson’s article brings me to present a new section in Human IT, “People and Opinions” (P&O). In previous editorials, we have addressed the need to open up a new section in the journal for miscellaneous pieces of text that for various reasons do not fit in the two journal sections so far launched (the open section and the refereed section). In P&O, we hope to make room for material in the outskirts or even outside of traditional scholarly journal genres but which we still feel is of value and interest to the readers of Human IT. The section will contain e.g. opinionated and argumentative pieces, interviews and portraits, popular science articles, minor classics and classical minors, reports from conferences and other events, or other texts of significant intellectual value that have found no permanent place in print or on the Web.

We aim to publish at least two more issues of Human IT this year, of which one has a theme, “Dynamic Maps”, while the other does not. Looking further ahead, we’re currently planning for upcoming issues and novel themes and warmly welcome article contributions as well as reader suggestions for future themes and journal improvements. As for the latter, finally, work has been going on during the winter to provide rich Dublin Core metadata to all material published in Human IT, and to create new navigational, metadata-based aids for the readers, including keyword and author indices as well as a list of abstracts for the entire volumes and issues of the journal. Clicking an item in each index will direct the reader to the full-text article referenced to. These aids will be made available on the journal web site later this spring.

Borås in March 2005
Mats Dahlström, Editor


From Ding to Überding

An introduction to the artificial intelligence issue theme

Last February I went to a blistering cold Stockholm to finally see my long-time heroes Kraftwerk perform live. The concert was a massive experience, full of contrasts. For most of the time, the four German gentlemen (all well over 50 years old) stood completely motionless, while suggestive, state-of-the-art graphics were projected on large screens behind the stage. Already in the first song, the legendary “Man Machine”, the overall theme of technology and mankind was well established. It suddenly became very obvious to me, being an AI researcher, that these guys were able as early as 1978, when Man Machine was written, to (maybe accidentally) pinpoint the goal of AI.

The lyrics of “Man Machine”, as in most Kraftwerk songs, are tiny and repetitive. More specifically, the words ‘man machine’ are repeated almost endlessly, but sometimes the lines

halb Wesen – halb Ding

halb Wesen – halb Überding

are interleaved.

And there it is; an artifact partly human, with the other part being better than a machine, a supermachine. To me this says in four words what AI researchers have tried to achieve from its beginnings. This is what Good (1965) referred to as “the ultraintelligent computer”. Obtaining mechanical intelligence on a human level is not the ultimate goal. Unfortunately the goals have nearly always been either to mechanically mimic human intelligence or to create a machine capable of performing tasks that would require intelligence, if performed by a human.

Two months later I attend an AI conference in the always hot and sunny city of Miami, Florida. The keynote speaker, professor Edward Feigenbaum from Stanford, is one of the most famous and respected pioneers in the entire field. The topic of his talk is, loosely put, “Some Grand Challenges for Computational Intelligence”, and is based on a highly interesting paper (Feigenbaum 2003). To set the stage, professor Feigenbaum starts by reasoning about the Turing Test (TT) (Turing 1950).

In a nutshell the TT is a test determining if an artifact is capable of showing a behavior so similar to human behavior that the term intelligence would be appropriate. More specifically, the test is an imitation game where an interrogator is allowed to ask any question both to the artifact being tested and to a human. The restrictions of the game are normally that the interrogator, the artifact and the human are placed in separate rooms, and that the communication is in written text, transformed electronically. If, after the questioning, the interrogator is unable to distinguish the artifact from the human, the artifact is deemed intelligent. The TT thus clearly focuses behavior only, and disregards the question of whether or not the interior processes of the artifact resembles human processes.

Although Turing himself thought that the imitation game would be beaten before the year 2000, we are still nowhere near an artifact being able to pass even a few minutes of a TT. As a matter of fact, most AI researchers would reluctantly regard the TT as a long term vision rather than as an operative goal worth striving for. The simple truth is that the TT is so overwhelming that only rarely does one even discuss what kind of skills it would require. However, Feigenbaum does list several high-level skills, taken from a paper by Gentner (2003), as definite parts of human intelligence, such as:

  • the ability to draw abstractions from particulars
  • the ability to reason outside the current context
  • the ability to reason analogically
  • the ability to learn and use external symbols to represent numerical, spatial or conceptual information,
  • the ability to invent and learn terms for abstractions as well as concrete entities.

According to Feigenbaum, substantial progress has been made in most of the skills listed, although the level necessary to pass a TT is still way out of reach. For example, no artifact is able to “read and understand as well as a human”. More specifically it is the understanding part that fails, while computer linguists have come up with very clever methods for the basic “reading”.

Feigenbaum goes on to discuss one of his favorite subjects, Expert Systems (ES), i.e. reasoning systems tailor-made for specific difficult tasks limited to very narrow domains. In a way, ES are at the opposite end of AI compared to efforts ultimately trying to pass the TT. ES are focused on specific and well specified tasks, something tremendously different from the extremely wide and unpredictable TT. In addition, the performance of ES is totally dependent on the (human) knowledge encoded in the system. Nevertheless, Feigenbaum calls ES partially intelligent artifacts, and claims that the major lesson learned from the golden age of ES is that artifacts must have extensive knowledge of the domain to be able to compete with humans on complex tasks. According to Feigenbaum, the power of an intelligent artifact must lie in the knowledge base and not in the reasoning methods. This is a strong claim, and it strikes directly at one of the major disputes within the AI community. During Feigenbaum’s talk in Miami this is further stressed when he debates the subject with professor Tom Mitchell from Carnegie Mellon. Professor Mitchell, the author of the standard textbook Machine Learning (1997), is a well-known representative of the field of machine learning. Although the tone of the argument is polite and humorous, it is obvious that this really is a heated topic. In his paper, Feigenbaum calmly states that “we now have an overabundance of logically powerful and elegant methods”. When returning to the TT, Feigenbaum concludes that the major obstacle for an artifact being able to pass the test is the span of the huge knowledge base needed. “Acquiring such a large computer-useable knowledge base is a Very Grand Challenge”.

In accordance, professor Feigenbaum introduces another test, “a more manageable task”, which he calls challenge #1 - the Feigenbaum test (FT). The FT is similar to the TT but now the human player is an elite scientist (member of the National Academy) in a preselected field, while the interrogator is another member of the National Academy in the chosen domain. Obviously the questioning is now limited to the specific domain, but may include problem solving, explanations and theories.

It is hard to say whether the FT is really that much easier than the original TT. It is nevertheless an interesting idea, and professor Feigenbaum does indeed think that an artifact will pass the FT within 25 years. Arguably, even more interesting are the two “extra” grand challenges he proposes. Since the key to any intelligent system according to Feigenbaum is the knowledge base, efficient knowledge acquisition becomes crucial. Therefore, challenge #2 is to build a large knowledge base by reading text (reducing the knowledge engineering effort by one order of magnitude) and challenge #3 is to distill a large knowledge base from the WWW, reducing the effort by many orders of magnitude.

Is the AI field up to these challenges? I do not know. 25 years seem to be a very short time, but then again, AI has constantly produced amazing results, in very diverse domains. A computer vision steering system can be trained to steer a car just by “observing” a human driver. Medical ES are able to perform at the level of expert physicians in several areas of medicine. There are programs capable of language understanding and problem solving, making them able to outperform most humans on crossword puzzles. Nettalk (Sejnowski & Rosenberg 1987) used neural network technology and only 1024 more or less randomly selected words to train on when learning to pronounce written English text. Nettalk performed on a level comparable to specialized programs that had required several man years to build.

Many of my favorite results come from the field of game play. The best chess players in the world are now regularly beaten by software opponents. Perhaps the most fantastic result is still when Gerald Tesauro (1995) trained an agent (TD-Gammon) to play backgammon, starting with the agent only knowing the rules of the game and the concepts of winning and loosing. Using a variant of reinforcement learning, TD-Gammon learned to play at world champion level using only self-play (i.e. playing a copy of itself). In fact, human players of all ranks now learn from how the agent plays. Is not that another twist to machine learning?

In the introductory AI course at the University College of Borås we normally assign the students to select a game (such as Othello, Four-in-a-row or Dots-and-Boxes) and train an agent using techniques similar to that used by TD-Gammon. Every year the students become equally surprised when the agent, after sufficient training, turns more or less unbeatable. Sometimes we discuss this result in connection to “Lady Lovelace’s objection” to the TT, i.e. that machines are only able to do what they are programmed to. If that is the case, how can the students produce an agent obviously playing much better than its creator? To me learning is probably the most important key to successful intelligent agents in the future. Only if an artifact is able to actually learn (from experience or other sources) and generalize from the knowledge learned can we talk about machine intelligence.  So when Kraftwerk in their perhaps most popular song “The robots”1 say “We are programmed just to do anything you want us to”, it means just that. Robots, machines, agents, artifacts or whatever we choose to name them will eventually be able to do things unimaginable today. From that perspective both the TT and the FT are just milestones on the way to the Überding.

So where is AI today and in what direction should we move? Personally I think that we must balance the immediate need for many intelligent applications against more distant goals. Today, AI techniques are tools in many practitioners’ toolboxes. This is an important factor for AI project funding, but also for generating respect for the field in general. Data mining, strong computer opponents in games, more or less intelligent robots and ES in all kinds of domains are only four examples where AI techniques are already integral parts of systems in use. At the same time I think that also in the future, AI will need the “hype” and coolness it has enjoyed over the years. If nothing else, some of the sharpest brains have been attracted to AI just because of the overall ambition to create intelligence. From this perspective, it is important to maintain some of the magic and science fiction around AI. It isn’t simply a mix of mathematics, engineering and logic. It is the ultimate goal for computer science. Professor Feigenbaum calls CI (computational intelligence) the destiny for computer science. He even compares it to the “manifest destiny” i.e. when the vision of a United States spanning from the Atlantic all the way to the Pacific inspired generations of settlers to embark on adventurous journeys to move the frontier forward, closer to the ultimate goal. Professor Feigenbaum concludes his paper with the following paragraph:

Computational Intelligence is the manifest destiny of computer science, the goal, the destination, the final frontier. More than any other field of science, our computer science concepts and methods are central to the quest to unravel and understand one of the grandest mysteries of our existence, the nature of intelligence. Generations of computer scientists to come must be inspired by the challenges and grand challenges of this great quest.

This special volume of Human IT focuses on AI and machine learning. The call for papers was intentionally very broad to encourage a mixture of submissions, showing the fascinating diversity of AI. Judging by the three papers accepted for publication this goal has indeed been met. The first paper by Terras and Robertson describes an impressive application where sophisticated AI techniques are brought to assist in reading ancient Roman tablets which have only small fragments of text extant and are therefore notoriously hard to decipher. Can machine intelligence help us make these so far dumb artifacts speak? The work described by Terras and Robertson blends cutting edge research from both the humanities and the technology sciences, making it a fine example of the kind of cross-disciplinary work Human IT strives to present. The second paper by Smed and Hakonen thoroughly describes (computer) games focusing on synthetic players; i.e. all computer-controlled actors in the game. Especially interesting is the concluding prediction that the behavior aspects of the synthetic player will be even more important in the future. Is not that an interesting task for AI researchers? The third paper, written by Löfström and myself, is a meta-learning study, focusing on whether some interesting properties of a data mining problem can be predicted from characteristics of the data set. The motivation for this study is that data miners often have to take vital decisions early in the process and that some of these design decision are better left to the (intelligent) machine.

When I ride the subway after the Kraftwerk concert I read the Metro newspaper. In an article I find that a major Swedish retailer is accused of gathering excessive data from customers using the retailer’s credit card to pay. Again it strikes me that Kraftwerk wrote songs about this more than 20 years ago. In “Computer World” they recognize that data can and will be transformed to knowledge, and express the accompanying anxiety with the lines: “Interpol and Deutsche bank, FBI and Scotland Yard, CIA and KGB control the data memory”. Even more fascinating is their prediction in the same song that the “computer world” will really consist of “Business, numbers, money, people, communication, time, medicine, entertainment”. From this it must be very obvious that AI will only increase in significance for the years to come.

After the talk by professor Feigenbaum I feel extremely excited and proud to work in the field of AI. Maybe AI really is the holy grail of computer science. President Kennedy said, when establishing another manifest destiny: “We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard”. Isn’t that the perfect motivation?  Let's strive for the Überding, just because it is so hard. Feigenbaum ended his speech by saying something like “AI is a wonderful thing because it can do wonderful things”. Actually I would like to add to that and conclude this introduction by saying that AI is indeed a wonderful thing but not only because it can do wonderful things but also because it is done in wonderful ways!

Borås in March 2005
Ulf Johansson


Feigenbaum, Edward A. (2003). “Some Challenges and Grand Challenges for Computational Intelligence.” Journal of the ACM 50.1: 32-40.
Gentner, Dedre (2003). “Why We’re So Smart.” Language in Mind: Advances in the Study of Language and Thought. Eds. Dedre Gentner & Susan Goldin-Meadow. Cambridge, MA: MIT Press. 195-235.
Good, Irving J. (1965). “Speculations Concerning the First Ultraintelligent Machine.” Advance Computation 6: 31-38.
Mitchell, Tom M. (1997). Machine Learning. New York: McGraw-Hill.
Sejnowski, Terence J. & Charles R. Rosenberg (1987). “Parallel Networks that Learn to Pronounce English Text.” Complex Systems 1: 145-168.
Tesauro, Gerald (1995). “Temporal Difference Learning and TD-Gammon.” Communications of the ACM 38.3: 58-68.
Turing, Alan M. (1950). “Computing Machinery and Intelligence.” Mind 59: 433-460.

1 The group is actually replaced on stage (by robots of course) for this specific song. [Return to text]

Högskolan i Borås
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ISSN 1402-151X

Senast uppdaterad: 2008-02-14
Jonas Söderholm

University College of Borås
Human IT / ITH
SE-501 90 Borås, Sweden
Phone. +46 33 435 44 21 (editor)
Fax. +46 33 435 40 05
ISSN  1402-151X
Published with support from
University College of Borås and
Nordic board for periodicals in the
humanities and social sciences