She may not have been in direct contact with Apollo, but she did contribute to difficult decision-making

The scope of human intuition is ubiquitous and all-pervasive; virtually every discovery made in science and engineering, and the whole of the arts, are products of intuition. This “leap” of the human mind when we “intuitively” see the whole picture by connecting seemingly unconnected dots – when we get inspired to write a novel or invent a new machine – cannot be mapped in any formal mathematical notation (or computing language, which is the “computer age equivalent”).

Furthermore, problems that need “intuition” to be solved (such as proving mathematical theorems) cannot be known in advance and thus fall under the spectre of the “halting problem” in computation as defined by Alan Turing: a Turing machine may compute forever and thus never arrive at the solution (i.e. it will never “halt”). This notion is another way of saying that the computer may never find the answer. Computers, which are Turing machines operating with formal algorithms, cannot be intuitive. Therefore, Artificial Intelligence based on such computers will never be really “intelligent” in any general sense, but always confined to addressing specific problems within a narrow space of possible solutions.

Alan Turing was well aware of this limitation in computing machines. In 1939 he wrote in his ordinal logics paper a short statement about “oracle machines” (or “o-machines”).

“Let us suppose we are supplied with some unspecified means of solving number-theoretic problems; a kind of oracle as it were.. . . this oracle . . . cannot be a machine. With the help of the oracle we could form a new kind of machine (call them o-machines), having as one of its fundamental processes that of solving a given number-theoretic problem.”

This is virtually all Turing said of oracle machines. His description was only a page and a half long of that was devoted to the insolvability of related problems such as whether an o-machine will output an infinite number of 0′s or not. Since then, Turing left the topic never to return.

Not quite what Alan had in mind...

Oracle machines are “super Turing machines”: they are machines encompassing a classic Turing machine connected to an “oracle”, a black box that answers “yes” or “no” to a decision problem that the Turing machine cannot solve. Obviously, every o-machine has its own limitations. The oracle may not be able to answer either “yes” or “no” to a given problem; in which case another, “higher-order”, oracle is necessary. Oracle machines thus tend to cluster one-within-another, in infinite nests, like Russian dolls: as their numbers tend to infinity, incomputability tends to zero.

Oracle machines solve the problem of incomputability by means of an infinite series. In a replay of Zeno’s paradox super Turing machines forever get closer to intuition without ever reaching it. And although mathematicians like Turing would have been content with such a mathematical description philosophers are a tough bunch to convince that this is anything more than a Pyrrhic victory .

Evidently, computational scientists are the children of mathematics than philosophy. In the current issue of Neural Computation Hava Siegelmann of the University of Massachusetts Amherst and post-doctoral research colleague Jeremie Cabessa, describe a “super-Turing machine” that, they claim, will increase artificial intelligence by many orders of magnitude.

Each step in Siegelmann’s model starts with a new Turing machine that computes once and then adapts. The size of the set of natural numbers is represented by the notation aleph-zero, א₀, representing also the number of infinite calculations possible by classical Turing machines in a real-world environment on continuously arriving inputs. By contrast, Siegelmann’s most recent analysis demonstrates that Super-Turing computation has 2 ^{א 0}, possible behaviours. “If the Turing machine had 300 behaviours, the Super-Turing would have 2³⁰⁰, more than the number of atoms in the observable universe,” she explains in “Daily Science“.

According to Siegelmann, the Super-Turing framework allows a stimulus to actually change the computer at each computational step, behaving in a way closer to that of the constantly adapting and evolving brain.

This approach closely resembles an oracle machine. The machine will not try to forcefully calculate all possible outcomes before deciding, but will adapt to the problem’s parameters by “connecting the dots”, i.e. asking an “oracle” about “yes” or “no” at each step before proceeding any further. The scientists at Amherst intend to implement their theoretical model on analogue recurrent neural networks. It will be interesting to see the results.

Reference: A. M. Turing (1939), Systems of logic based on ordinals, Proc. London Math. Soc. 45 Part 3, 161{228}.

Primates practicing love and tenderness

Observing primates in the wild, as well in the lab, has given scientists new insights of the way neurology and behavior connect. For example, chimps in a sanctuary in Uganda have been seen moving their hands up and down in synchrony with a chimp cracking a nut with a stone, and so acquiring the same skill.

Learning by imitation in the animal world should inform designers of machine leaning systems in robots. Robots of the future will watch your actions and “ape” them; and thus learn how to do the same thing on their own.

Cross-fertilization between primatology and engineering may be happening already. Karlsruhe Institute of Technology and the FZI Research Center for Information Technology have developed ARMAR, a humanoid robot, who can understand commands and execute them independently. It designers claim that it reacts to gestures and learns by watching a human do something, e.g. how to empty a dishwasher or clean the counter; then does it too.

The challenge for robot designers is to add contextual background to human actions. For example, if a human pushes a button with his head because his hands are occupied should not make the robot imitate the same action exactly. The robot must “understand” the ultimate objective of the action. Thus the robot must push the button with his hands. Such contextual hermeneutics seem to be hard-wired in the primate brain, where the action is closely linked to the goal. If we could decode and reproduce this hard-wiring unto our robots, then we would have developed the “next” generation of primates, mechanical creatures build in our image that would learn our ways like our children do.

Reference: The Primate Mind, Edited by Frans B.M. de Waal and Pier Francesco Ferrari, Harvard University Press, 2012

This is not how we do it

 Unlike computer memory which is designed human memory is the product of millions of years of evolution. Mammalian brains such as ours do not use fixed-address systems, but store memories in a very haphazard fashion; memories tend to overlap, combine or simply disappear. Neuroscience has not yet cracked the code of human memory but it does give us some first clues: our memories live in the hippocampus and the prefrontal cortex. Whenever we “remember” a rich set of data is retrieved which is contextually intertwined with emotions. Human memories are never like a video or a photograph or a text file; they are never “objective”. They are always “subjective”, i.e. value-laden. The plasticity of our brains might be the cause for our memories changing over time, or under a variety of emotional conditions (such as stress, excitement, sadness, etc.).

Jill Price (photo by Bryce Duffy)

An interesting case made news several years ago of an American lady named Jill Price who could remember virtually everything. Ms Price had a perfect recollection of every single event in her life since she was 12 years old. Her case has been studied by neuroscientist James McGaugh of UC Irvine. McCaugh and his collaborators named Ms Price’s syndrome as “hyperthymetic”, a Greek word meaning “supermemoriser”. Although, understanding what exactly happens in Ms Price’s brain is beyond the capabilities of current brain scan technologies, current observations indicate that her brain shares many characteristics of people with obsessive-compulsive syndrome. Ms Price is obsessive in “collecting” items (e.g. puffy toys) that remind her of things that happen to her; she is also going over and over again thinking about things that happen to her (she keeps a detailed diary), something that tends to reinforce neural pathways. Nevertheless these observations explain almost nothing. Her capability of remembering everything is truly “super-human”.

Rachel remembered having a mother

Imagining intelligent androids of the future has failed to deal satisfactorily with the issue of memory. In Blade Runner, for example, the android Rachel has been programmed with false memories; a childhood she never had. Tyler Corporation have given her photographs of her “parents” which Rachel treasures, since they convince her that she had a human past. Such emotional reaction to memories requires a human-like brain. Androids that can hold memories in a human-like fashion will be prone to all the problems that we face with our memories; ultimately we lose them, or they mutate into a subjective narrative that reflects our inner wishes rather than the facts that actually took place.  Like humans, androids must be able to lie about their past, without necessarily intending to. But that seems like a waste. Unless human programmers decide to install faulty memories in their creations, intelligent androids will be more like the hyperthymetic Ms Price.

The Enigma Machine

During WW2 he led in Bletchley Park (a secret location of British Naval counter-intelligence) a team of mathematicians and cryptanalysts in breaking the Nazi “Enigma” code. He did so by developing a “mirror” coding machine, a precursor of modern computers.

The Universal Machine, by Jin Wicked

His seminal paper on uncomputability expanded Gödel’s incompleteness theorem by introducing the concept of a “universal machine” (often called “Universal Turing Machine”), thereby foretelling the modern, digital computer. A Universal Machine is a machine that can be programmed to perform any operation. Turing showed that there will always be mathematical truths (theorems) which the Universal Machine will never be able, not only to solve but, – most importantly – to know a priori if they are solvable. In such cases the machine would never “halt”, i.e. end its operation, but would continue forever.

The Imitation Game: Guess who is what

Turing believed that computing machines will one day become as intelligent as humans, perhaps more so. He suggested the “imitation game”, in order to demonstrate a way to tell whether a machine was intelligent or not: if a human interrogator on the basis of a conversation between himself and some “unknown person” (an intelligent machine “imitating” a human) could not tell whether that “person” was human or not, then one had to accept that the intelligent machine was as intelligent as a human being. Although this “behaviourist” approach to “Artificial Intelligence” has been challenged, Turing’s Imitation Game remains the best available concept for assessing the degree of machine intelligence.

2012: The Alan Turing Year

This year 2012 marks 100 years since the birth of  Turing, and a number of celebrations are being organized around the world. You can find more by visiting this website.

Your author has written a play on the last days of Turing, which is currently on submission. You may read a summary of the play here, and some notes here.

 Turing Dreams will be celebrating the Alan Turing Centenary throughout 2012, by reporting regularly on events that are taking place. Stay tuned!

Here’s an excerpt:

A San Francisco cable car holds 60 people. This blog was viewed about 2,800 times in 2011. If it were a cable car, it would take about 47 trips to carry that many people.

Click here to see the complete report.

The blissful mind of a golden shiner

The story circulated in the media with much journalistic spin as “evidence” of how people take decisions in democratic societies. According to the spin, and extrapolating wildly from fish, uninformed human voters tend to side with the informed majority, therefore counterbalancing extremist minorities. I am obviously very sceptical of this extrapolation which makes a number of impossible assumptions, the most obvious ones being (a) that we do not live in idealistic direct democracies, (b) that an “uninformed” human does not exit anymore; “misinformed” would be a better word. Mathematical models of human behaviour, however sophisticated they might be, are run on the basis of assumptions that usually are too simplified to reflect the complexities of human societies.

A society of intelligent agents

Nevertheless, the experiment, and the models, are interesting for societies of intelligent agents. Such societies are by design democratic. Decisions aimed at problem-solving are taken by dynamics of interaction between agents. No agent has all the information or the complete solution. As the system (or society) evolves a solution various lines of reasoning come to productive conflict. The experiment suggests that we may get better solutions if we keep a number of agents initially uninvolved in the problem-solving process. Bringing them on at a later stage, where there is a minority and a majority, could safeguard the correct decision.

Journal Reference: D. Couzin, C. C. Ioannou, G. Demirel, T. Gross, C. J. Torney, A. Hartnett, L. Conradt, S. A. Levin, N. E. Leonard. Uninformed Individuals Promote Democratic Consensus in Animal Groups. Science, 2011; 334 (6062): 1578 DOI: 10.1126/science.1210280

Loving the mecha has haunted European literature ever since. Rousseau, Goethe, Shakespeare borrowed the theme in writings of their own. Bernard Shaw’s Pygmalion is a reinterpretation of the myth whereby the girl is brought to life by two men in speech, the goal for their masterpiece is for her to marry and become a duchess. The stories of Frankenstein, as well as Pinocchio, also feed from the ancient concept of the metamorphosis of dead matter into a living, feeling, thinking creature.

The advent of cinema, coinciding with the expansion of the industrial revolution, saw Pygmalion’s myth in a new light. No need for divine intervention anymore. Simulacra could now be constructed using machines and machine tools. In the classic 1927 “Metropolis” by Fritz Lang, Galatea  is now a mechanical woman, a simulacrum of living Maria. Her fist task is – what else? – to seduce.  Here’s the classic “dance of Babylon” scene from the film:

In 1982 Ridley Scott introduced us to a future where mechas are part of society. The scene of Zhora the stripper, hunted down by the Blade Runner, and dying by smashing though successive glass windows is an unforgettable ode to human self-destruction.

Zhora on the run

Steven Spielberg, taking up where Stanley Kubrick left, directs and produces AI in 2001. Here too, mechas are used as sex objects. Gigolo Joe, played by Jude Law, is a male prostitute mecha with the ability to mimic love.

Mecha sex workers

Alan Turing would have approved, for how can we really tell if someone loves us? What subtle messages lovers exchange during lovemaking that cannot be copied in a machine? Isn’t it the commonest experience in life the “betrayal” of love? In the imitation game of love there comes a time when all the words and actions shared vanish like dew under a scorching sun; when all that is left is the feeling of being fooled by a lie.

In the future, sex and love would be the first to bind humans and mechas, for only the latter will be able to mimic love so perfectly well as to make it look eternal. And that is exactly what we humans have been seeking all along.

History must have seemed to be taking a whole new course, unimagined by people who lived only a few years earlier. Writers such as Samuel Butler, H.G. Wells, William Morris, and others pondered upon the question of progress, and a new literary genre was created that mixed fantasy, satire and allegory: the scientific romance. A few notable books of this genre are “The Time Machine” (1895) by Wells, “News from Nowhere” (1890) by Morris and “Erewhon” (1872) by Butler.

Samuel Butler (1835-1902)

In Erewhon (an anagram of “nowhere”) Butler describes a utopian society that had become industrialized long before Europe and had opted to banish machines. This was because in Erewhon machines were deemed to be dangerous. Butler expanded on the idea in his “Book of Machines” where he claimed that Darwinism applied to machine evolution, and therefore it was inevitable that machines will ultimately develop consciousness. Butler claimed that  ”it was the race of the intelligent machines and not the race of men which would be the next step in evolution.” Frank Herbert, the author of “Dune”, as a back-story coined the term “Butlerian jihad” to describe an event 10,000 years before the events of Dune where thinking machines were outlawed.

Butlerian Jihad: calling the faithful

Perhaps there is a cautionary take to be found in Victorian scientific romance, something that could possibly resonate in our own age too. The 21^st century arrived in an awkward fashion. The events of 9/11 and the Afghanistan and Iraq wars that followed, colored the first decade of our century with the shades of two unnecessary wars that polarized politics. The economic crises of 2008 and the current one in the eurozone have shifted the public debate towards doubting capitalism. Whilst all this take place in the forefront of public awareness an immense technological revolution is quietly brewing in the background. This revolution is all about intelligent machines. They may not have arrived at the level of consciousness yet (but who is to really tell?) but they do control our planet. Our financial and commodity markets, our defense systems, our industries, our infrastructures are all controlled to a greater or lesser degree by autonomous computer programs.

Last month a major military exercise took place across NATO countries in preparation for future cyberwar.  NATO scenarios assumed a cyber attack from a hostile country or terrorist organization. But, what if the “attack” comes as a rebellion of our “mechanical slaves”?  How could we tell the difference?  And what could we possibly do to defend ourselves then?

John McCarthy (1927-2011)

LISP (the language I coded during my PhD) made use of lambda calculus to express relations between objects, subjects and ideas. It was “programming without programming”, at least on the high-level of the computer language itself. This means that the programmer did not tell the computer how to execute a specific algorithm. Instead, it coded a picture of the world – a “knowledge base”. The computer was then asked questions about that world. The response from the machine was produced by building inferences based on the coded knowledge base. The syntax of the language reflected the aspirations of McCarthy that machines should be able to “think for themselves” in a manner similar to humans: we have an idea of the world based on knowledge and experience; when a new problem comes along we solve it by accessing our knowledge and experience and producing new ideas and solutions.

Early dreams of machines...

McCarthy belonged to a long tradition of engineers that dreamt of machines and their relationship to humans and human society. When in 1962 he moved from MIT to Stanford in order to set up the legendary Artificial Intelligence Lab he declared that he was going to produce an intelligent machine within the next 10 years. The promise was never fulfilled, and later McCarthy conceded that he had been rather hubristic in his predictions. He acknowledged that we know too little about the human thinking processes in order to reproduce them in a machine. Nevertheless, he was confident that given enough resources, both in money and brainpower, a truly artificial intelligent system was feasible. An intelligent computer, he half-joked, would require “1.8 Einsteins and one-tenth of the resources of the Manhattan project”.

He will remain an inspiration to us all.

Zeus commanded Hephaestus, the god-engineer, to make the first woman (please note that according to Theogony there were only men living on earth until then). Hephaestus knew the art of making androids (or “gynaekoids” to be more exact) well, because he had already built several of them, beautiful maidens that obediently served him at his lab-cum-workshop. But Pandora had to be special. So after the basic hardware was constructed by Hephaestus, and the operating system was put in place, Zeus invited the other gods in Olympus to give Pandora “gifts”, i.e. special functions and properties.

The all-gifted (by Jules Joseph Lefebvre, 1882)

What made Pandora different from the other robots in Hephaestus lab was a gift given to her by Hermes, the god of thieves and traders: “the gift of deceit”. Pandora was furnished with “theory of mind”; she could tell what other people thought or thought that they thought and use this knowledge to manipulate them. Thus she was named the “all-gifted” and duly dispatched to the middleworld of humans.

The Greek myth of Pandora is one of many in the ancient world where gods, and sometimes talented humans, build artificial beings – usually women. One can read much in stories such as these.

A historian of technology may recognize the roots of imagining artificial life and intelligence. A feminist may read the obsessive will of men to subjugate women taken to an extreme: why not create one according to specification? An ethicist may diagnose a precautionary tale: Pandora with her insatiable curiosity ultimately brings about the fall of humankind. Finally, a philosopher of science may notice a disconnect with evolution: artificial intelligence is created, not evolved. Pandora, the all gifted, is a design.

You may argue that this is just another creation myth from a tribe of white people who lived in the Balkans many thousands of years ago. Yes, it is exactly that, a tale from a non-scientific past. Nevertheless, some of us may see a disturbing symmetry arising – another Pandora being born into a not-so-distant, scientific future.