Archive for the 'Minds and Machines' Category

Is Faster Smarter? IBM’s Watson Search Engine Approach to Beat Humans

Friday, February 18th, 2011

IBM’s computer named “Watson” has beaten Jeopardy! (human) contestants in a series of games this month. IBM has a long history of innovations (watch this other 100th anniversary documentary featuring Greg Chaitin and Benoit Mandelbrot, among others here).

Not everybody was impressed by Watson though, according to Gavin C. Schmitt who interviewed Noam Chomsky, the recognized linguist:

  • Noam Chomsky: I’m not impressed by a bigger steamroller.
  • Interviewer: I assume that “a bigger steamroller” is a reference to Deep Blue. Watson understands spoken language and adapts its knowledge based on human interaction. What level of AI would be required to impress you?
  • Noam Chomsky: Watson understands nothing. It’s a bigger steamroller. Actually, I work in AI, and a lot of what is done impresses me, but not these devices to sell computers.

In some sense, I understand Chomsky’s view and he does well pointing out what may seem the clearest difference between Watson and a human being, but the point may be much more subtle and deeper. Wolfram’s Principle of Computational Equivalence (PCE) (see this previous post) may shed some light on this subject. According to this principle, memory is what makes a system intelligent, because basically any system that does not behave in an obviously trivial fashion will likely be as sophisticated as the most sophisticated system. So what is the difference between a system that is potentially intelligent and one that shows signs of actually being so? It is somehow a matter of scale in several directions. Take the example of weather. When Wolfram is asked whether clouds are intelligent according to his principle, his short answer is yes. Every time humans want to make a prediction about whether it will rain, it turns out to be incredibly difficult to do so for more than a couple of days, and often the weather forecast is wrong even for the next day. How is it that weather prediction is so hard despite our long experience at weather forecasting? Well, clouds are computing themselves, and as part of weather they are quite complex, as complex as systems like the human brain, says PCE.

Picture of Gregory Chaitin taken by Hector Zenil outside of the IBM Research center at Yorktown, N.Y. where Watson was designed.
(Picture of Gregory Chaitin taken by Hector Zenil outside of the IBM’s
Thomas J. Watson Research Lab at Yorktown, N.Y. where Watson was designed)

After all these years, IBM hasn’t come up with a theoretical breakthrough to meet the challenge but rather with a supercomputer fast enough to beat the other participants. Watson uses fairly sophisticated algorithms, but these aren’t that much more sophisticated than those used by search engines, which proceed by matching pieces of text and retrieving other pieces of text statistically related to the original. The IBM team has come up with a super-computer to challenge the Jeopardy! participants and not with a new algorithm. The main goal of machine learning labs is usually to perform about 1% to 3% better than the best benchmark of the top lab in a particular area (e.g. word tagging, word disambiguation, information retrieval, etc.). If Watson has achieved anything like a breakthrough, it may be credited with having advanced the current state of AI research. It takes Google’s kind of technology a couple steps further by having drawn together several technologies on steroids. The point is that one may not need to come up with the smartest algorithm– because one may not be able to, simply because it doesn’t make sense to engineer a super complicated algorithm to reach intelligence, it being more appropriate to start from a simple but potentially powerful system, and then extract the best of it by running it on a super large corpus of data on a super fast computer. The Watson- in-Jeopardy! experience tells us that even clouds may look intelligent when running on a supercomputer.

Watson confirms what I’d suspected, that we’re not that special after all (in this sense). Watson meets the challenge of beating a human on its own turf and at what it does best basically through the use of brute force. Achieving artificial intelligence (AI) is not, as I suspected (among other thinkers), a matter of science breakthrough but rather a matter of scale and technological achievement. Over time we’ll have faster and faster computers, and that means computers with intelligence resembling ours (of course there are other subtle issues here, such as the fact that the system should be allowed to interact with the intelligent forms it is meant to interact with, otherwise its intelligence may prove alien to ours, and look as strange as that of clouds).

Jean (Michel Jarré with his electronic harp. 
Picture by Hector Zenil, Paris concert 2010)
(Jean Michel Jarré with his electronic harp.
Picture by Hector Zenil, Paris concert 2010)

Wired published (when they used to publish interesting articles more often) an interesting article back in 2009, which reached the conclusion that one could exchange data for models: The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.

Some interesting inferences can be drawn from this milestone where IBM supercomputers have beaten humans at human tasks (remember this is the second time; at least with such a publicity, the first saw IBM’s Deep Blue beat Garry Kasparov, the reigning World Chess Champion at the time, in a series of chess matches). Among these we may single out the fact that we humans seem ready to call machines smart and intelligent even though they may not necessarily think like us (humans can only see ‘ahead’ a handful of selected chess movements while chess programs perform an exhaustive search only bounded by time), despite seeming as clever as we are. This is already a vindication of Turing’s proposal for a test of intelligence.

Yet Chomsky’s opinion seems to point in the opposite direction, that we may still think we are much more special than Watson, and he might be in some sense right. We definitely play very differently than machines, but is chess that different from natural language? It may be, but this time the question may be whether what Watson does at playing is that different from what we do.

At the end of the Deep Blue vs. Garry Kasparov, Kasparov pointed out that he felt that the machine actually had a strategy and something that made him think that the machine was actually playing like a human, somehow perhaps even teasing him. Ken Jennings (one of the two human participants against Watson) wrote a day after the match: “This is all an instant, intuitive process for a human Jeopardy! player, but I felt convinced that under the hood my brain was doing more or less the same thing.”

Some people think that Watson has absolutely no ability to understand what it did, or any awareness about it, and that still makes the difference. This might be only partially true. Watson may not understand or be yet aware of its achievement, but I wonder if the same processes that made Watson to win this match are not the same that may be in action for even more sophisticated human thinking, such as self-awareness. But the question can also be reversed and one may also ask whether we are really aware of what happened, and how much of our feeling of being aware is the result of the type of thinking that Watson just exhibited.

As many of my readers certainly know, Alan Turing came up with a basic test suggesting that if something looked intelligent then it was intelligent. So we have reached the point at which not only has a machine passed the Turing test but also humanity may be ready to accept the idea behind the test, that is that it doesn’t really matter how something thinks if it looks clever enough to fool us (or even beat us at something) then it is intelligent. For the machine, the milestone is located at a point in time that reflects the current state of technology, which basically amounts to the miniaturization and mastery of computing devices, as well as the management of very large quantities of data, not forgetting the current state of fields involved (basically machine learning and natural language processing or NLP). But it is by no means an isolated achievement. I see IBM as the standard bearer for a combination of several current technologies run on the best hardware available today, a pioneer in this sense.

Wolfram|Alpha computational engine control room. Lunch day picture by Hector Zenil.
(Wolfram|Alpha computational engine control room.
Launch day picture by Hector Zenil)

It seems we are now able to gauge the size and power of the human brain as against something that looks as sophisticated as we are, at least at playing a sophisticated game. Watson and humans may reach the same conclusions, whether or not they do so in different ways, but the fact that Watson requires a computer the size of 10 full-size fridges, 15-terabyte of memory (likely full of data and programs) and 2,880 microprocessors working in parallel tells us more about us than about Watson itself. We knew we carried a supercomputer in each of our heads but we didn’t know what its specs may be. We also thought that the were specially gifted with unprecedented intelligence but now a machine that is certainly not aware of it and hasn’t taken the same path is also able to exhibits key features of intelligence.

Jen Kennings adds after the last match: “…But unlike us, Watson cannot be intimidated. It never gets cocky or discouraged. It plays its game coldly, implacably, always offering a perfectly timed buzz when it’s confident about an answer.” “…I was already thinking about a possible consolation prize: a second-place finish ahead of the show’s other human contestant and my quiz-show archrival, undefeated Jeopardy! phenom Brad Rutter.” Read more here and here.

Watson specs will fit in a small box in the future–given the trend of the past several decades following Moore’s law– and in the future as it is the case today, faster will be smarter.

Aired on PBS, NOVA called their documentary The Smartest Machine On Earth:

Watch the full episode. See more NOVA.

Comments on Turing’s very first Universal machine approaching Turing’s 100th. birthday anniversary

Tuesday, May 18th, 2010

The idea that a machine could perform the tasks of any other machine is the description of a Universal (Turing) machine. Its invention is considered by many to have been one of the major landmarks giving rise to the field of computer science. ‘Universal’ means that one can ‘program’ a general-purpose machine to perform the tasks of any specific-purpose machine. Turing machines are to this day the central object of study in the theory of computation.

In an attempt to understand how the very first universal machine was described in Turing’s original 1936 paper “On Computable Numbers, with an Application to the Entscheidungsproblem,” I spent half a day re-reading the paper (and its corrected version by Donald Davies, published in The Essential Turing by Jack Copeland), trying to decode it, only to find that it is written in something like an anticipation of a (Turing-complete) subroutine-oriented programming language which is impossible to rewrite in a traditional transition table. So if one really tries hard, one ends up encoding an arbitrary universal Turing machine, not Alan Turing’s first universal Turing machine.

Although the paper has all the primary elements of a traditional description of a Turing machine (Turing’s ‘a-machine’ description), the fact that it used multiple conventions for describing increasingly complex machines was Emile Post’s strongest critique. In a letter to Church, Turing replied to Post’s appreciation, arguing that his use of more than one convention when building his universal machine did not affect the final result, though he did admit it made it hard to decipher.

The result is that not only would it be a giant machine in terms of states and symbols, but the number of actual colors that it may need seems to be unknown. It is only known to assume 18 states, and to simulate Turing’s second a-machine example with 23 instructions (here the product of states and colors does not necessarily lead to the number of instructions in Turing’s formalism, because his transitions are not total functions).

In the traditional 5-tuple form, Turing’s original universal machine could be written as (Mathematica notation):

{q1, “blank”} -> {q2, P0, R}
{DA, D} -> {DAA, DC, R}
{q2, “blank”} -> {q3, E, R}
{DAA, D} -> {DAAA, D, R}
{q3, “blank”} -> {q4, P1, R}
{DAAA, D} -> {DAAAA, DCC, R}
{q4, “blank”} -> {q1, E, R}
{DAAAA, D} -> {DA, D, R}

But notice that no state starting by q leads to a state starting by D because D (states are called m-configurations in Turing’s original jargon) are rather prefix subroutines defined in Turing’s paper while q’s are actually traditional Turing machine states. In Turing’s paper E is, for example, an erasing subroutine. Some other ‘m-configurations’ require scanning the whole tape several times (which is what one would do if one is asked to emulate another Turing machine), and so on. So most of the behavior description of the machine is encoded as strange strings of letters.

Nevertheless, Turing’s choice is somehow clever from a programmer perspective, he proceeded in the way one would do so today for designing an implementing a universal computer. One would hardly do so by describing the basic elements, but rather by constructing higher level subroutines describing a machine function and based itself in one or more other subroutines up to the level of states and symbols. Think of programming at the level of the machine language v. programming in an intermediate level language. Writing a universal Turing machine in detail in terms of states and symbols from the beginning leading to complete lost and misunderstanding, just as it would so if one pursuits the implementation of a complex piece of software writing binary code or even in a pure assembler language. Turing’s description provides a better understanding, not trivial though, of what a universal Turing machine does to carry out the computation of any other Turing machine by sketching and grouping intuitively the machine operations into these program subroutines.

Unlike today, that one can simply make Wolfram|Alpha to run any Turing machine simulation such as a random 2-state 5-color Turing machine or the 4-state 2-color Busy Beaver (for a list of other Turing machine examples one can type in WolfraAlpha click here), Turing had no computer to ran and test his code, it is not a surprise that his universal machine code came together with several glitches. It was quite amusing to see that the first ever program written for a digital computer was already bedeviled by bugs. And this program was the actual implementation of the very first universal Turing machine by Alan Turing himself.

If Turing had not died in 1954, at the age of only 41, next June 23 (2010) he would have 98 years old. As a tribute to his work I’ve set up the following webpage gathering most, if not all, the public images known of him

For his 100th birthday anniversary a series of events are being organized. 2012 will not only be the year of the Olympic Games that Turing would have particularly followed in his own country (UK) as an enthusiast long distance runner but also The Alan Turing Year to which I’m honored to be part of as a member of the advisory committee representing Wolfram’s Science Group.

The art of creating creatures from simple rules

Sunday, November 18th, 2007

Having quit his studies in physics, Theo Jansen became an artist. In this video he demonstrates his amazing life-like kinetic sculptures, built from plastic tubes and bottles. His Beach Creatures or Strandbeest are built to move and even survive on their own:

I’ve been in touch with Theo Jansen recently. For further details about his creations he referred me to his book (available at his web shop ) entitled The Great Pretender. Even more details are provided in Boris Ingram’s thesis on leg designs based on 12-bar linkages, in which he describes Jansen’s walker algorithm. Jansen’s designs are computer-generated using an evolutionary algorithm, and the animals, which are wind powered, are made out of PVC piping.


The valves essentially act like logic gates, allowing water to pass or not depending on the state of the other gates.


Jansen’s creations do not require engines, sensors or any other type of advanced technology in order to walk and react to the environment. As for Boris Ingram’s work, it would be greatly enriched if it were to incorporate a wider range of possible structures and algorithms.



More online references:

On the possible Computational Power of the Human Mind

Tuesday, March 13th, 2007

My paper On the possible Computational power of the Human Mind (co-authored with my BS thesis advisor Francisco Hernández-Quiroz of the Math Department of the National University of Mexico [UNAM], which I delivered as a lecture 2 years ago at the Complexity, Science & Society 2005 Conference at the University of Liverpool, U.K.) has been recently published by World Scientific as a book chapter. It is available from World Scientific at ; also as a paper or from Amazon.
The book is edited by Carlos Gershenson, Diederik Aerts (Brussels Free University, Belgium) & Bruce Edmonds (Manchester Metropolitan University Business School, UK).

Introduction: Scientific, technological, and cultural changes have always had an impact upon philosophy. They can force a change in the way we perceive the world, reveal new kinds of phenomena to be understood, and provide new ways of understanding phenomena. Complexity science, immersed in a culture of information, is having a diverse but particularly significant impact upon philosophy. Previous ideas do not necessarily sit comfortably with the new paradigm, resulting in new ideas or new interpretations of old ideas.In this unprecedented interdisciplinary volume, researchers from different backgrounds join efforts to update thinking upon philosophical questions with developments in the scientific study of complex systems. The paper contributions cover a wide range of topics, but share the common goal of increasing our understanding and improving our descriptions of our complex world. This revolutionary debate includes contributions from leading experts, as well as young researchers proposing fresh ideas.Contents:* Restricted Complexity, General Complexity (E Morin)* Complexity Science as an Aspect of the Complexity of Science (D Mikulecky)* On the Importance of a Certain Slowness (P Cilliers)* Simplicity Is Not Truth-Indicative (B Edmonds)* Why Diachronically Emergent Properties Must Also Be Salient (C Imbert)* Some Problems for an Ontology of Complexity (M McGuire)* Physical Complexity and Cognitive Evolution (P Jedlicka)* Informational Dynamic Systems: Autonomy, Information, Function (W Riofrio)* The Complexity of Information-Processing Tasks in Vision (J Symons)* On the possible Computational Power of the Human Mind (H Zenil & F Hernandez-Quiroz)and other papers

Daniel Dennett’s new thought experiment using Steve Pinker as subject…

Thursday, March 8th, 2007

An interesting thought experiment conceived by Daniel Dennet and recently published in the Time magazine.

The Mystery of Consciousness

Sunday, January 21st, 2007

New Steven Pinker article on Consciousness:,9171,1580394-1,00.html
with good links to related articles.

Steven Pinker recognizes in this article that the “I” problem has been over-estimated. However, despite a parenthetical moment of doubt, he does not recognize that the conscious/unconscious question is not a real problem. Science has long recognized that old problems can become meaningless over time. This is indeed the case with the Cartesian mind/body dichotomy, which for so long was the focus of  intense scholarly and scientific scrutiny. Nowadays no cognitive or neuroscientist will argue against the prevailing wisdom that every aspect of the  mind is nothing more than a consequence of brain activity.

Steven Pinker divides the problem of consciousness into two further problems –a first  “easy” problem and a  second “hard” problem.  I don’t think there is a second problem, and the first is neither easy nor hard, it’s just the core of neuroscience–or should be. New developments in the field, such as the discovery of mirror neurons, have shed light on problems that we did not understand before. As Pinker points out, a neuroscientist is capable of reading the mind of a person just by observing the blood flow in their brain. And  this can be done with a  degree of precision so high that it is possible to distinguish between someone thinking they are driving a car, someone thinking they are seated watching TV at home and someone pretending he is talking.  There are many real problems in the field that remain to be investigated.  For instance, there is the question of the matching of mental and physical representations, whether inside a single brain or involving several individuals—what is traditionally referred to as the type/type or token/token problem. Manipulating consciousness is another immense–and delicate– field of research and Pinker mentions  it in his article. Surgeons are able to stimulate a brain in such a way that a patient is incapable of distinguishing  between these induced hallucinations and reality. Neuroscience is capable of creating a whole new kind of “virtual” experience,  and also of improving existing reality by attacking mental problems such as memory loss or depression. Better human-machine interfaces could be created, thereby improving the communcation channel between humans and computers, which at present consists of someone typing on a keyboard, touching a screen or moving a mouse. What of the claims made on behalf of newly released  Operating Systems, claims that they are much more sophisticated because they use “new” old-fashioned windows, which look better just because they are now transparent or simulate a 3D environment embedded in a flat screen? Software enginneering could also take advantage of a new kind of interaction between the brain and software through better interdisciplinary research.

Meaning is in the word net: cyclic self-referential definitions, dictionaries and found in translation

Saturday, December 2nd, 2006

In the “Period 1 Cycle English Dictionary” published by “No way, Inc.” (it’s been said to be the most accurate dictionary ever) one can read:

dog (Noun) : a dog is a dog.

The lazy creators of this dictionary appear to have forgotten what is broadly accepted by common sense. A definition would not be a definition if it were cyclical and self-referential, simply because one would, theoretically, fall into an infinite loop trying to define an unknown word that has as part of its definition the word itself.

Let’s leave aside the fictional Period 1 Cycle English Dictionary, which I just made up, and consider how things work in a regular dictionary. Assume you want to know the definition of a word. Looking it up, you’ll encounter a set of ordered words in the language in question(if the dictionary is not a bilingual one). Let’s prove that in the end, any definition of a word w in a closed finite dictionary is cyclic with period p_w:

Proof sketch:

We may hypothesize that the dictionary (D) is closed under the operation “the definition of” (i.e. all words in the definition of a word have a definition in the dictionary). One can also safely assume that all words in D have a non-empty definition (given the definition of “dictionary” itself!) and that the dictionary is finite. Then if w is a word in D, the orbit of the successive definitions d_1,d_2,…d_n, … for w (listed as sets of words themselves) will end up eventually crossing the path d_w={d_1,d_2,…d_n, …} because d_w cannot be an infinite sequence without repeating elements in D– the finite nature of D would necessitate  coming back to the original word itself after a certain period p depending on the word, making every word w in the dictionary cyclic with period p_w. 

If all the definitions in a dictionary are then cyclic and enclosed in the dictionary itself, where does the analytic knowledge that a dictionary seems to provide come from? When one consults a dictionary in a given language, one already knows some words in that language, but assuming one doesn’t, would a dictionary be completely useless, as the issue of ultimately cyclic self-referential definitions seems to suggest?

One could conceive of a dictionary as a whole as a syntactical knowledge container with no actual knowledge in it– to someone who does not bring to it any knowledge of the relevant language. One may wonder how something so perfectly self-referential could actually be of use, as dictionaries seem to be. Is it indeed because you always know some, or indeed many, words of a language already when you consult a dictionary? For since every word is defined by other words in the same language, looking up the meaning of a given word would lead you to other words and yet others and eventually back to the first word, the word whose meaning you set out to learn. This would be the case even if  the dictionary were bilingual, and the meaning of the word you wished to check was given in a second language. Thus all dictionaries are perfectly circular, closed, self-referential sources.

However, the analytical knowledge in a dictionary does not come from the definitions as words, but from the word net underneath, where the words are connected in some, perhaps unique fashion(modulo exact synonyms) to each other. That’s what quite successful projects such as WordNet and functions like WordData in Mathematica are about. The power of being able to analyze the language as a net in a computable way is priceless for the progress of computational linguistics and linguistics in general.


2-level depth wordnet for the word "chair"

2-level depth wordnet for the word "chair"



For example, if “chair” is connected to “table”, “office”, “dining room”, etc. it should be easy to map it to its equivalent in any other language. Unless the target language doesn’t have the concept “chair” as referring to a seat placed at a table in a dining room (which was perhaps the case in some Asian countries before colonialism), together with an explicit cognitive representation of it.

Of course problems arise when considering the mappings between words having the same written form but different senses. The word “chair,” for example, is a seat, but may also mean the officer who presides at a meeting. Also posing a problem would be cases of words being mapped to or from words belonging to different parts of speech, such as “personne” in French, which  maps onto two completely different words in Spanish: “persona” and “nadie”,  a noun and an adjective respectively, with completely different connections and different supporting nets. So even when it seems that most relations might be surjective, the general case is certainly not bijective, and that applies to homonyms too, which often creates ambiguities in translation. However, the supporting network  would be able to uncover this fact and solve a possible ambiguity based on  context by extending the word network to encompass the ambiguity. In other words, if a subnet cannot be uniquely mapped, extending it should eventually solve the ambiguity. What one would need  is a corpus big enough to build such a network once and for all and then simply make comparisons at the network level. This could work even for completely new or unknown languages, either dead, living or artificial, assuming that they share a part of our actual reality and hence some part of our mental representations  (In a sense this is what Champollion did when he deciphered the Rosetta stone– he discovered a partial mapping of a subnetwork of words from an unknown language – Egyptian – to a subnetwork of a known one – Greek). In the final analysis, each language has a single unique network (changing slightly through time but remaining well connected and strong enough to make  it unique and recognizable while being isomorphic with that of any other language).  All languages could be identified by their fingerprints -their word net. This kind of analysis would identify the lack of a word net structure in hoax languages, such as perhaps the Voynich manuscript.

Having  established that, what about mining the world of all possible meanings, the world of all possible translations, and the world of all possible ideas? We wouldn’t have the problem of distinguishing between a coherent idea and a non-coherent one since the network would provide some minimal coherence. Thus the net-into-the -net approach would give us a way of translating from word to word and from phrase to phrase and from idea to idea as faithfully as possible in most cases, since in the end all of us as human beings share a single reality, though we perhaps approach it from different points of view.

Again, the analytical knowledge in a dictionary comes from the net connecting the words, so even if someone does not know English at all, I would say that he would be able, albeit with considerable difficulty, to learn English just by deducing the net connecting objects, in other words, by mapping his own mental  representations of objects onto words in the  English dictionary. In the process he could encounter some ambiguities, but the further he goes, the more of these he would be able to resolve. On the other hand,  speakers of those languages in which “chair” does not exist, both in the language itself and as a real object in the culture,   would be able to deduce what  a chair is by tracking its  relations with the objects they know and for which they do have mental representations and the phonemes to externalize them. So the problem of translation, which began with the mapping of word onto word and then phrase onto phrase  with statistical tools,  becomes, with this approach, a matter of  mapping net to net.  Indeed this seems to be the approach adopted  by Meaningful Machines.  

These ideas could be carried to the limit by taking the sum total of human languages and enquiring into the mapping between such a network and our cognitive representations. Such a move would provide grounds  for rebutting the Chinese room argument, since in the end it does not matter whether someone inside the room has no knowledge at all of a language; he would be able to map what he is mechanically translating onto his own mental representations, generating what, according to the argument, could not be generated: understanding. Because Searle’s  idea was, as I recall, to build up a case against A.I. in terms of the meaningless of the Turing test and true AI in general.

One may actually use a dictionary without knowing a single word in it! That is because there is a mapping between the word net in the dictionary and one’s own language, or even better, a mapping (not necessarily injective or surjective) between the word net of the dictionary and your cognitive personal word net.

Oversimplifying, translation might be reduced to the search for the homomorphism between algebraic groups, with each group G={W,d} being a language dictionary, with W the set of words in that language and d the closed operation “definition of”. One can then see each definition as an oversimplification of a directed, ordered graph g={w,s}, with w the set of vertex words involved in the definition and s the ordered (since definitions may be not commutative) set of edges for an ideally formal- enough language dictionary.

Bad practices of cyclic definitions in a dictionary should rather be expressed as the practice of period 1 cycles, i.e. words that have in their  immediate definition the word itself.

This post is  related to a previous post titled “Meaning against A.I.

Seth Lloyd’s quantum universe view

Wednesday, November 22nd, 2006


In an exchange of emails, Seth Lloyd and I discussed the topic I wrote about some posts ago. Here is some of it.

According to Lloyd, there is a perfectly good definition of a quantum Turing machine (basically, a Turing machine with qubits and extra instructions to put those qubits in superposition, as above). A universal quantum computer is a physical system that can be programmed (i.e., whose state can be prepared) to simulate any quantum Turing machine. The laws of physics support universal quantum computation in a straightforward way, which is why my colleagues and I can build quantum computers. So the universe is at least as powerful as a universal quantum computer. Conversely, he says, a number of years ago he proved that quantum computers could simulate any quantum system precisely, including one such as the universe that abides by the standard model. Accordingly, the universe is no more computationally powerful than a quantum computer.

The chain of reasoning, to jump to the quantum computer universe view, seems to be 1 and 2 implies 3 where 1, 2 premises and the conclusion 3 are:

1 the universe is completely describable by quantum mechanics
2 standard quantum computing completely captures quantum mechanics
3 therefore the universe is a quantum computer.

Seth Lloyd claims to have proved the connection between 1 and 2, which probably puts the standard (or some standard) theory of quantum mechanics and the standard quantum computing model in an isomorphic relation with each other.

Lloyd’s thesis adds to the conception of the Universe as a Turing computer an important and remarkable claim (albeit one that depends on the conception of the quantum computer), viz. that the Universe is not only Turing computable, but because it is constituted by quantum particles which behave according to quantum mechanics, it is a quantum computer.

In the end, the rigid definition of qubit together with the versatility of possible interpretations of quantum mechanics allows, makes difficult to establish the boundaries of the claim that the universe is a quantum computer. If one does assume that it is a standard quantum computer in the sense of the definition of a qubit then a description of the universe in these terms assumes that quantum particles encode only a finite amount of information as it does the qubit, and that the qubit can be used for a full description of the world.

Quantum computation may have, however, another property that may make it more powerful than Turing machines as Cristian Calude et al. have suggested. That is the production of indeterministic randomness for free. Nevertheless, no interpretation of quantum mechanics rules out the possibility of deterministic randomness even at the quantum level. Some colleagues, however, have some interesting results establishing that hidden variables theories may require many more resources in memory to keep up with known quantum phenomena. In other words hidden variable theories are more expensive to assume, and memory needed to simulate what happens in the quantum world grows as bad as it could be for certain deterministic machines. But still, that does not rule out other possibilities, not even the hidden variables theories, even if not efficient in traditional terms.

This is important because this means one does not actually need ‘true’ randomness, the kind of randomness assumed in quantum mechanics. So one does not really need quantum mechanics to explain the complexity of the world or to underly reality to explain it, one does require, however, computation, at least in this informational worldview. Unlike Lloyd and Deutsch, it is information that we think may explain some quantum phenomena and not quantum mechanics what explains computation (neither the structures in the world and how it seems to algorithmically unfold), so we put computation at the lowest level underlying physical reality.

Lloyd’s thesis adds to the conception of the Universe as a Turing computer an important and remarkable claim (albeit one that depends on the conception of the quantum computer), viz.  that the Universe is not only Turing computable, but because it is constituted by quantum particles which behave according to quantum mechanics, it is a quantum computer computing its future state from its current one. The better we understand and master such theories, the better prepared we would be to hack the universe in order to perform the kind of computations–quantum computations–we would like to perform.

I would agree with Rudy Rucker too as to why Seth Lloyd assigns such an important role to quantum mechanics in this story. Rudy Rucker basically says that being a subscriber to quantum mechanics, Lloyd doesn’t give enough consideration to the possibility of deterministic computations. Lloyd writes, “Without the laws of quantum mechanics, the universe would still be featureless and bare.” However, though I am one among many (including Stephen Wolfram) who agree  that it is unlikely that the universe is a cellular automaton, simply because cellular automata are unable to reproduce quantum behavior from empirical data (but note that Petri and Wolfram himself attempt explanations of quantum processes based on nets), there’s  absolutely no need to rush headlong into quantum mechanics. If you look at computer simulations of physical systems, they don’t use quantum mechanics as a randomizer, and they seem to be able to produce enough variations to feed a computational universe. Non-deterministic randomness is not neccesary; pseudorandomness or unpredictable computation seem to be enough.

Is the Universe a Computer? (Ist das Universum ein Computer?) Conference, Berlin, Germany, 6,7 November 2006

Sunday, November 12th, 2006


Ist das Universum ein Computer?
Germany, November 2006, Informatik Jahr
Deutschen Technikmuseum Berlin
From Konrad Zuse’s Invention of the Computer to his “Calculating Space” to Quantum Computing.

Lesson One: For someone with a hammer in his hand the world seems to be a  nail. Joseph Weizenbaun.

Lesson Two: Knowing the input and the transition function of a Turing machine we know everything about it. Marvin Minsky.

Zuse's drawing 2
Dr. Zuse’s futuristic drawing

– The first talk entitled  “What Can We Calculate With?” by Prof. Dr. Bernd Mahr from the Technische Universitat of Berlin was a very good introduction to the standard theory of computation based on Turing’s model and classical mathematical logic. His remarks on the time when computing arose from math because the Greeks discovered they were unable to compute the square root of 2 were interesting. He pointed out some evident but not always explicit facts: Calculation has a subject (the individual who calculates), an object (what is calculated),  a medium (how it is calculated), and a symbolic representation (the language -binary, for instance). His use of the Leibniz medallion for explaining   starting points, ending points and transitions in a calculation was elementary but interesting (transitions: intermediate calculations). Further explanations of reversibility and non-linearity using transition nets were also illuminating. The context of a calculation (or computation) and the strong relation between the computation itself and its context is  such that it is sometimes difficult to distinguish them. Since any process can be seen as an object in itself, the context can become the calculation and the context of the context too. In some way, as we know, the concept of calculation is a constraint of a part of a calculation, and then it is defined in terms of an input, an ouput and a transition. He pointed out too that behind many of these complicated systems there is a Turing machine. It  is no longer visible from the top, but it is there.

Konrad Zuse
Dr. Konrad Zuse

– Dr. Horst Zuse’s  talk titled “Konrad Zuse’s Calculating Space (Der Rechnende Raum)”:
Dr. Konrad Zuse’s son, Dr. Horst Zuse made some interesting remarks about his father’s book “Calcualting Space”,  in which  Dr. Zuse proposes studying  the universe as a digital system, specifically a cellular automaton. Dr. Horst Zuse is a professor at the Technische Universitat of Berlin and his personal webpage can be found at: and

Zuse's son
Dr. Konrad Zuse’s son, Dr. Horst Zuse

Dr. Zuse’s father’s main question was: “Is Nature Digital, Analog or Hybrid?” It seems that he tended to answer “Digital,” proposing a No-Yes value language (binary). His thoughts were published in the Nova Acta Leopoldina. He evidently did acknowledge that there could be problems attempting to reconcile an explanation of the Universe in terms of Cellular Automata with Quantum Mechanics and General Relativity.

According to Konrad Zuse, laws of physics could be explained in terms of laws of switches (in the computational sense). Physical laws are computing approximations captured by the formulas in our models. He saw that differential equations could be solved by digital (hence discrete) systems.

Dr. Petri talk
Dr. Carl Adam Petri at the Berlin conference

– Dr. Carl Adam Petri (yes, the creator of the Petri nets!) on “Rechnender Netzraum” or “Computing Net Universe”:
According to Dr. Petri, at the Planck length quantum computing can be described by digital systems using combinatorial models (net models, kennings, combinat), and therefore the universe can be studied using discrete nets which are even capable of explaining quantum and relativistic fundamentals like Bell’s theorem and Heisenberg’s uncertainty principle. That would mean that discrete systems (for instance those proposed by Stephen Wolfram) would suffice to explain even quantum and relativistic phenomena.

According to Petri, measurement is equivalent to counting. For instance in S.I. one second is 9192631770 Cs periods. In fact Norbert Wiener proposed some axiomatics of measurement.

The correspondence of Petri nets with the Heisenberg uncertainty principle arises from the limitations of our observational capacities when carrying out measurements. When two different types of observations are performed, -for example momentum p and position q- we can only see p or q in a chain of succesive events related by a causality net. His nets as well as his explanations of such phenomena are very neat and elegant. The relevant slides  on causality and linear logic may be found  at:
He also distributed a CD with his slide presentation at the conference.

For Petri, the Universe is a Petri Net.

Dr. Seth Lloyd’s presentation at the conference in Berlin, Germany

– Dr. Seth Lloyd’s talk entitled “The Universe is a Quantum Computer”:
Some researchers think of information as more fundamental than physics itself. And it is a common practice in science to find all the time more fundamental structures on which previous ones were lying on. Some others such as Seth Lloyd and David Deutsch, however, strongly stand in favor of putting a quantum reality at the lowest level of reality description as they stand for a physical universe where information only makes sense if it is carried and represented by a physical entity. So these authors have developed world theories based in the concept of quantum computation.

A quantum computer differs from a Turing machine in that its bits are quantum bits, and so can exist in a superposition. In addition, it can be instructed to put those bits in superpositions. A universal Turing machine can simulate any other Turing machine in polynomial time but while what a quantum computer does is still Turing computable, a typical quantum computation of T steps on N qubits requires $O(2^N)$ bits on a classical Turing machine, and $O(T 2^2N)$ logical operations. That is, it takes a Turing machine exponential amounts of time and space to simulate a quantum computer or quantum Turing machine.

The standard quantum computer as shaped by, among others, Feynman and Deutsch differs from the classical computer in the concept of the qubit. While the concept of qubit takes advantage of a clear asset that the quantum world provides, entanglement, it does not necessarily makes use of the full properties of quantum mechanics as interpreted under the Copenhagen interpretation. The convenient definition of a qubit makes a quantum computer not to compute more but much faster than classical digital computers.

Lloyd’s conclusion is that the universe is not only a computer but a quantum computer. However some questions arise:

1. What exactly is a quantum computer? Does the quantum computer definition actually captures all quantum phenomena? According to the standard model quantum computing is Turing computable (disregarding run time). If Lloyd is assuming the standard model then the universe is indeed a quantum computer, but even more remarkably (since we have scientific and philosophical hypotheses like the Turing thesis) it is Turing computable. However, if he is assuming the more general quantum mechanics model, let’s say the standard model in physics (which basically assumes the possibility of harmless continuity rather than inquiring into it) he is saying that the universe is not a computer (since the term derives  from what we mean by Turing computable and hence covers  digital computers too). So the assumptions made are significant and cannot be glossed over if one wishes  to argue convincingly that  the universe is  a computer in some standard sense. If what we  assume to be computing is something that seems to be deterministic or rule-based, the concept becomes fuzzy and additional remarks need to be made.

2. What if a quantum particle encodes more information than just a 1 or 0 for the spin or any other quantum property? Let’s say a third value, or even worse, a non-computable number of values. In quantum mechanics for example, the superposition of a particle assumes an infinite and non-countable number of places since it is in all the spectra at the same time. If space/time is a continuum then it is evidently in a non -countable number of positions, which leaves us with  a non-computable model, or at least with something that’s neither a Turing-computable model nor a standard quantum-computable model. And this is not a simple assumption since it requires another Turing-type thesis which in the final analysis does not answer the most fundamental  question, i.e. whether the universe is a computer or not and if it is, what kind of computer (in the computational power sense) it is.

I raised these questions with Seth Lloyd and I will be posting his answers soon.

Seth Lloyd lecture at Berlin
Seth Lloyd at the conference in Berlin

An interesting concept mentioned by Seth Lloyd is “hacking the universe”. As Charles Bennett used to say, a computer is a handicapped quantum computer. So if Lloyd is right, a computer is not only a handicapped quantum computer but it is not taking advantage of the full computational power of the universe and it is just patching the universe instead of hacking it, as it would be in its power to do. By contrast, a quantum computer uses some particles that are already computing “something” (nothing less and nothing more than the processes in the universe ) to perform the computation that we want it to perform. It can be said to be  “hacking the universe” in Lloyd’s terms.

On the other hand, if the notion of programmer monkeys is valid it should be possible to test it experimentally. Under the supervision of M. Jean-Paul Delahaye, computer science professor at the University of Lille I ( we are undertaking this task. We are exploring  Lloyd’s quantum computational universe (or at least a handicapped but representative part, the recursive computational universe), applying some complexity measures (universal distribution, average-case complexity or Levin’s measure) in order to uncover the monkeys behind the Universe, or in other terms, to analyze the average distribution of randomly discrete systems with random inputs.

Is Seth Lloyd falling into the carpenter’s problem of thinking that the universe is a nail and the moon made of wood?  Is it because he is a quantum computer scientist that he thinks the universe is a quantum computer? He argues of course that the charge is unfair, but then we have been told by Dr Petri  that the Universe is in fact a Petri Net which probably  needs neither strong randomness nor quantum mechanics!

Here  is a video online in which he explains much of this:

Zuse's drawing
Dr. Zuse’s futuristic drawing 2

– Jurgen Schmidhuber reprised his algorithmic approach to the theory of everything in his talk entitled   “The program that computes all computable universes”.
Jurgen Schmidhuber’s major contribution probably is his Speed Prior concept, a complexity measure similar to Algorithmic Information Complexity, except that it is based on computation speed and not program length. i.e. the fastest way of describing objects rather than the shortest.
There is more information on his website: (where he includes an unfavorable review of  NKS) and in his slide presentation on the Speed Prior at:
Of course Schmidhuber himself has identified a problem with the Prior measure: If every possible future exists, how can we predict anything?

Other interesting talks on philosophical issues: If the Universe is a computer, therefore the human mind should be a computer too.
Is “the Universe is a  computer” a metaphor?
My answer: The metaphor is “The Universe is not a Computer”

Lesson Three: Metaphors can be reversed.

Kovas Boguta
Kovas Boguta’s  talk was titled “Is the Computer a Universe?” In it he pointed out the richness of mining the computational universe of simple programs.

Because we were together during the gala dinner I had an interesting exchange with Dr. Konrad Zuse’s son, Dr. Horst Zuse (Also at our table were the Technikmuseum director Dr. Dirk Bondel and  my colleague Kovas Boguta from Wolfram Research, among others). He shed some light on his father’s interactions with Alan Turing ( none apparently),  with von Neumann (some interaction regarding the controversy over who first built a digital computer and concerning von Neumann’s architecture, which  our current digital computers do not conform to, the ALU being separated from the memory as it is in Zuse’s conception but not in  von Neumann’s original design).

Zuse’s Z1 first computer “the input device, something equivalent to the keyboard, at the Technikmuseum in Berlin”

Kurt Godel: The writings. Université de Lille III

Sunday, November 12th, 2006

Kurt Godel workshop for studying his legacy and writings. Lille, France, May 19-21, 2006

My thoughts, ideas, references, comments and informal notes:

– The wheel machine, a machine for real computation which I am proposing -as a thought experiment- in a forthcoming paper  on the Church-Turing thesis -Yes, one more paper on the CT thesis!- with comments on Wilfried Sieg’s paper entitled “Church Without Dogma: Axioms for Computability”

– “In case Cantor’s continuum problem should turn out to be undecidable from the accepted axioms of set theory, the question of its truth would loose its meaning, exactly as the question of the truth of Euclid’s fifth postulate in Euclidian geometry did”. Godel replies: “It has meaning anyway, as Euclid’s fifth postulate gave rise to other now accepted mathematical fields.”

– Godel Gibbs Lecture and his dicotomy on absolutely undecidable propositions and the computational power of the human mind (Turing did great work… but he was wrong when he proposed his formal theory as a model of human thought…)

– New contacts and references: Olivier Souan, Rudy Rucker, Karl Svozil

Mark van Atten’s “On Godel’s awareness of Skolem’s lecture”.
Rick Tieszen

– Herbrand on general recursive functions, letter to Godel.

– Leibniz’ influence on Godel’s arithmetization?

– Sources: Godel Editorial Project. Firestone Library, Princeton University. I.A.S. Marcia Tucker, librarian for Godel papers.

– Godel’s concept of finite procedure as the most satisfactory definition of computation. “A machine with a finite number of parts as Turing did” or “finite combinatorial procedure” as a definition of an algorithm, mechanical or computational procedure.

– Computation’s main constraints: boundness and locality (paper from Hernandez-Quiroz and Raymundo Morado).

– Aphorisms and autoreference (Gabriel Sandu and Hinttika)

– Feferman on Turing

– Is Sieg’s paper and the question of “finite machine=effective procedure” a tautology? In fact such an approach seems to be one of the most strict versions of the Turing Thesis, and even though both Church and Turing probably did propose it in such a strict sense, extensive versions of the thesis have traditionaly covered more content, but even when it is strictly stated that there is still space for a thesis, it is neither proved nor provable from my point of view, and most authors would concur, though some clearly would not. I will comment on this more extensively later, since this was one of my Master’s topics and merits a post by itself.

– Putnam’s thought experiment on cutting all sensorial inputs. Solution: It is impossible in practice. However, machines are an example in a sense, and that is why we do not recognize intelligence in them — they are deprived of  sensorial capabilities.

Yes, Godel found an inconsistency in the U.S. constitution. My answer: One? Certainly a bunch. That’s why we need lawyers, who make them even worse.

Meaning against A.I.

Thursday, October 12th, 2006

A significant number of researchers believe that there are sentences with semantic value that could never be understood by a machine. These researchers believe that the mind has a semantic component, unlike machines. Their’s is a  Chinese Room type argument a la Searle. Consider Chomsky’s example of two books in a library with the same title, and two readers, each taking out one of the books. Do they get the same book? These researchers argue that machines would be unable to answer correctly on the basis of context since the answer would depend on a cognitive understanding of the situation. My claim is that all  meaningful components of a situation are based on a hierarchy that can be artificially represented by categories capturing the essence and functionality of  human mental operations. The answer to Chomsky’s question would be “yes” if one is  referring to the information content of the books,  “no” if one is referring to the books as physical objects.