Thursday, May 31, 2007

Intelligent until proven otherwise.

Did you know there are different cultures in the Killer Whale species? They are genetically the same species but don't interbreed. The killer whales like Shamu at Sea World are from a fish eating culture; they would not even think of eating mammals. That is why it is perfectly safe for humans to swim along side of them. There are other cultures who are mammal eaters and hunt sea lions, porpoise, whales and probably would take a bite out of you . Their eating habits are not all that differ between the cultures. Their travel and recreational habits vary as do their proclivity to vocalize.

One is certainly on shaky ground when trying to draw conclusions from the behavior of animals. Its too easy to draw anthropomorphic conclusions about behavior that can be explained along simpler lines. However, I just watched a film showing a pack of killer whales hunt a gray whale calf. It took six hours to kill it. When they finally succeeded the whales barely ate the calf but seemed to celebrate the successful hunt. It was clearly a training exercise.

Many scientists and religious leaders alike refuse to attribute intelligence let alone consciousness or intentionality to anything non-human. I believe this philosophical stance is a hindrance to truly understanding these phenomenon. Rather, I propose we give every organism with a highly developed nervous system the benefit of the doubt and grant them intelligence (and dare I say a degree of consciousness) until proved otherwise.

Wednesday, May 30, 2007

What do networks of neurons do?

Our brains are a massive network of nodes communicating in a constant frenzy of electro-chemical stimulations and repressions. MRI and PET technology tells us that neuronal activity increase in various areas of the brain as we perform various tasks. What type of information processing is going on here? It is fairly certain to me that there is no simple mapping between what neurons do and what chips do. At least not the kind of chips that contain logic gates, flip flops and typical microprocessors.

If we can hope to find an analogue to neural processing in todays state of the art digital technology we probably would be better to look a specialized chips such as Digital Signal Processors (although the mapping onto brain circuits would still be of the coarsest kind).

I believe a good part of the function of biological neural networks is transformation. Specifically along the lines of the Fourier and Inverse Fourier Transforms. These sorts of transforms take signals in the spacial domain (for example an image) or the time domain (music or speech) and transform it into components in the frequency domain (and visa versa). These types of transformations are used extensively in digital signal processing to find patterns, remove noise and accentuate specific types of information. Applications for these transforms are ubiquitous as you can see here, here, and here. It would be an insult to mathematics and a travesty of nature if the brain did not exploit similar transformations even if they are not strictly Fourier transforms. (As it turns out the Fourier transform is just a special case of a much broader class of transform - for example, wavelet transforms [1, 2, 3] began stealing the limelight in the 90's). In fact, if you goggle "Fourier and Neural Networks" or "Transform and Neural Networks" you will find quite a bit of academic papers that explore joint applications of these technologies.

If you've read this earlier post, then you know that I am interested in applying more mathematically oriented models to semantic representation then have traditionally be employed in AI. This is not to say that first order logic (one of the mainstays of AI) is not mathematical. Rather, I am talking about branches of mathematics where numeric rather than symbolic computation is the focus (of course at its foundations all mathematics is symbolic but this is not the level that one typically operates when doing vector math or analysis).

If semantic knowledge can be modeled in vector form then it opens up many of the tools of mathematical analysis to AI. One of these tools is of course the Fourier transform and its friends. These are the kind of musings which give me goose bumps!

Life 2.0

In what field will the next technology billionaires emerge? I would not go far as to say the well dug by computer science has gone dry! However, if I was starting my career over today my college courses would have titles like "microbiology", "neurobiology" and "genetic engineering" rather than "operating systems", "programming languages" and "theory of computation".

See Life 2.0

Tuesday, May 29, 2007

Two Kinds of Minds

In The Conscious Mind: In Search of a Fundamental Theory, David J. Chalmers argues that there are two concepts of mind: the Phenomenal and the Psychological. The Phenomenal is primarily concerned with experience or how mental states feel. The Psychological refers to the casual basis of behavior or what mental states do.

When considering means of programming computers to think we are exclusively working in the realm of the psychological. I, for one, doubt that the present architecture of a computer can host the
Phenomenal (although some practitioners of Strong AI might differ).

It seems unlikely that computers will become intelligent in the human sense until we have an understanding of the phenomenal. What is the simplest machine that can feel? Can such a machine be formally specified as Turing did when he considered the simplest machine that could compute anything effectively computable?

Why is the phenomenal crucial for intelligence? If you ever had a hunch about something or felt that an answer was wrong or were awed by the elegance of a mathematical proof then you know what an important role feeling has in your own intelligence. In
The First Idea: How Symbols, Language, and Intelligence Evolved from our Primate Ancestors to Modern Humans, Stanley Greenspan and Stuart Hanker argue that emotions are the primary tools of intelligence and that more abstract and higher order modes of though rest on the foundations provided by our emotions. If this is the case then we have some interesting clues to consider. Emotions are often associated with hormones which act globally rather than locally. Neurotransmitters are more local but their relative concentrations have global effects. There is really no good counter part to the function of hormones and neurotransmitters in modern computers except if we somehow equate them to software.

Monday, May 28, 2007

Semantic Vectors

In mathematics a vector space is a collection of objects called vectors. For our purpose, the interesting thing about vectors is that that each vector has a number of linearly independent dimensions and that there is a notion of distance between vectors that relates to the distance between values of each independent dimension.

Quite a while ago I introduced the notion of a semantic vector. A semantic vector is a way to model objects in the world as vectors such that similarities between objects can be computed via a distance metric. Equally relevant, changes to objects, such as those imparted by adjectives or verbs, can be molded as transformations of vectors in a semantic space.

Unlike mathematical vectors, semantic vectors are most useful when organized in hierarchies that model concepts such as whole-part.

Another interesting aspect of semantic vectors is that they need not be organized into a rigid inheritance or classification hierarchies. Such hierarchies can be synthesized dynamically by concentrating on similarities and differences along specific dimensions.

Finally, the uniform mathematical representation across all dimensions is suggestive of a method for analogy, simile and metaphor.

A good portion of this blog will be dedicated to the elaboration and development of the idea of a semantic vector space.

The role of Language

It is of considerable importance to understand the degree in which an entity can have conscious experience without also having language. If there is a direct link between language and consciousness then we can make definite statements about consciousness in non-human organisms. Is there a relationship between the ability to deal with grammar and the ability to feel what it is like to experience something? On the surface the two seem as different as oil and water but at the same time oil and water are understood by the interplay of atoms and electric fields. Does consciousness require distinct machinery in the same way that the physicist most invoke distinct machinery to explain chemistry, gravity and radioactivity?

Sunday, May 27, 2007


Life is a necessary condition to consciousness. Therefore, before we can explain consciousness we must explain life. Before we can endow inanimate matter with consciousness we must endow it with life.

An entity is alive if it actively resists the Second Law of Thermodynamics. No non-living thing can resist the second law. This does not mean that living things violate the Second Law, but they resist it to the determent of other living and non-living entities.

When robots reach a level of sophistication whereby they autonomously repair themselves at the macro level (replace a broken motor) and the micro level (reprogram an EEPROM or deploy nano-repair bots internally) then they are resisting the 2nd law. When they take cover from a dangerous storm or seek out energy sources or even steal energy from lesser robots then they are actively resisting the 2nd law. When they achieve these things we must add them to the class of living things. We will have created life. We will have become what believers call God.