Computers are Logic based devices. We used to believe implementing Intelligence in computers would be easy because we thought brains were built on Logic. AI researchers and programmers were especially skilled at logical, step-by-step Reasoning and analysis and when thinking about thinking, all they saw was Logic and Reasoning. But by the end of the 20th century it became clear that there was another component to Intelligence that had been severely neglected – intuitive Understanding.

Reasoning requires Understanding

Reasoning is a conscious, goal-directed and Logic-based step-by-step process that takes seconds to years. In contrast, Understanding is a subconscious, aggregating, Intuition-based and virtually instantaneous recognition of objects, agents, and concepts.

We call this process “Intuition” becase that is the word traditionally used for insights that appear from our opaque subconscious without us being able to retrace any reasoning steps to reach that insight. But note that there is nothing mystical about Intuition. It is a straightforward process of recalling past experiences and matching them to the current situation. If you see a chair, your Intuition tells you it’s a chair by matching your sensory input patterns to those of past occasions when you have seen chairs. You don’t have to reason about it.

Our Intuition also allows us to understand relationships, complex patterns, trends, abstractions, contexts, meaning (semantics), and the big picture. With little effort we recognize the image of a chair, the flavor of an apple, the meaning of a sentence, or the face of a friend. Neither requires Reasoning, and humans can recognize and understand all of these without effort, and within milliseconds. Animals can do this; higher animals can evaluate “situation semantics” - they can understand what is going on. We build on this ability to achieve our ability to understand language semantics. Current computers and AI systems cannot understand any of these things.

More than 99.99% of the processing in the brain is subconscious, which makes conscious Logic based Reasoning look like a paint-thin layer on top of our subconscious and Intuition-based Understanding.

In the 20th century, AI research was overmuch concerned with this thin layer of Reasoning. In the 21st century we must focus our AI research and resources on Understanding. Our computers need Artificial Intuition in order to recognize sensory input data and understand concepts at low levels. Only after they understand something will they have something to reason about. The Understanding part could well be straightforward and easy to implement in computers compared to our attempts so far to automate Reasoning; we just need to implement these two in the correct order.

Reductionist stance versus holistic stance

Computer Science and a few other scientific disciplines such as Mathematics and Physics are permeated by a problem-solving strategy and philosophy called “The Reductionist Stance”. In this Reductionist paradigm we solve problems by extracting them from their environment and by splitting them into smaller problems. Once we understand enough of the problems we create Logic-based simplifications called “models” such as theories, equations, and computer algorithms. Models describe simplified and context-free, reusable and portable slices of reality that can economically solve entire classes of similar problems; this gain in problem solving power for simple problems is the main reason to use models. But before models can be created, chosen, and applied, and the results interpreted in the current context we must Understand the problem domain since we have to do a reduction, an analysis of what is relevant and then to discard the details of the situation that are not used by the model.

In contrast, when adopting a “Holistic Stance” we solve the actual current instance of the problem directly, without using models. We solve it in its actual context without attempting to bring it into the laboratory or discarding any details. We don’t attempt to subdivide it into smaller problems. We don’t look for known models that might fit the problem. Instead, we attempt to match a large set of simple and self-assembling patterns (and patterns of patterns) against anything and everything in the problem including its current context. The final assembly of patterns that matches is our Understanding of the problem. Our brains use sets of neurons to represent these patterns. The set of activated neurons is our Understanding. What else could it be? Note that this provides us with a very specific and useful definition of what Understanding means in humans which is also implementable in computers.

This application of patterns may seem like much more effort than the use of models and Reductionist methods. But it can be done effectively and mindlessly, without requiring a high level of Intelligence, by both brains and computers. It just requires a very large database of patterns; we call this database “experience”. Brains gather experience over a lifetime, and stores it as these self-assembling patterns. Computers could to the same.

Note the difference: models require Understanding (“Intelligence”) for creation and use. Patterns don’t. Instead, self-assembling patterns provide the Understanding that is required for all Reasoning, including model creation and use. Only after recognition, abstraction, and Understanding will you have something to reason about.

And if you are building an AGI you should realize that building intelligent machines out of “intelligent components” just pushes the problem around. Intelligent machines must be built out of unintelligent, mindless parts and trivial algorithms that don’t require understanding in order to work.

Scientific versus unscientific phenomena

Some people believe the world splits cleanly into the Scientific and the Unscientific. This is incorrect. Worse, this belief causes a blindness to entire spaces of approaches and solutions that has hindered progress in AI.

The majority of phenomena in the world, including almost everything in our mundane everyday lives, are neither scientific nor unscientific. Consider the task of walking a mile into town to buy a newspaper. People can easily do it, so it is not mystical, but it is also out of reach of science to reliably duplicate this task, for instance by building a robot.

The Mundane world is deeply complex and changes too rapidly. And the Mundane is exactly the domain of Artificial Intelligence. How to navigate the aisles of a grocery store it if you are a robot; how to understand the slurred speech of the cashier; how to understand written language in a newspaper; how to understand a complex world with its many layers of meaning in nested contexts; how to deal with many other intelligent agents with goals often at odds with your own.

Brains solve all these little context-rich problems every day seemingly without effort. Brains can do it because they don’t operate logically and don’t use Reasoning for the majority of their functionality. They do it using a much simpler algorithm that instantly recognizes nested patterns. When we employ Holistic thinking, context isn’t a distraction to be removed. In fact, if a problem is too hard, we search for more context. More context means more patterns can be matched which means more powerful higher level patterns can be brought into play.

AI systems have to be able to deal with context in the same way. This means Understanding the mundane world Holistically. If we restrict our AI implementation to Logic based Reasoning then we will only be able to operate in the thin slice of rational problem domains. Such systems may be able to reason about math and Logic but they will not be able to deal with the world at large. Real world concepts cannot be “defined”, they must be learned.

Any sufficiently Reductionist AI effort is indistinguishable from programming. If a programmer attempts to describe the world to a Logic based AI, for instance by creating ontologies, he’ll never finish the task. The world is too rich. The Cyc project – the largest and most famous AI project ever undertaken – has been trying to describe the world using predicate calculus for decades; it is the poster project for Reductionist approaches to AI. But Cyc will never approach anything worthy of the term “Intelligence”. It has been told many things and can recite many definitions but Understands nothing. This is the difference between “Instructionist” top down education and “Constructionist” bottom up learning – a distinction poorly understood even in human education.

One of the biggest hurdles in the transition from a Reductionist to a Holistic stance is that the Reductionist stance works so well for simple problems, and thus is very seductive to beginners. But we must learn to fully understand the value of context in solving complex problems so that we can stay our hand before attempting futile reductions when facing an irreducible problem. This is a tradeoff. Let’s look at what we are giving up and what we might gain, if we are AI researchers trying to adopt a Holistic stance:

Virtues of a Reductionist Stance

Optimality	The best answer
Completeness	All the answers
Repeatability	Same answer every time
Efficiency	No waste of resources
Transparency	Understand the process of getting the answer
Scrutability	Understand the answer

These are some of the virtues we learn to appreciate as scientists. These are what we learn in school, especially as part of a Ph. D. education and especially in Computer Science. We internalize these virtues as obviously desirable properties of any investigative process. But this is also where our blindness begins.

At the lowest levels of the brain, scientific principles like Logic, Reductionist methods, proof, falsifiability, P-vs-NP, or the six virtues above simply don’t enter into the picture. Human minds neither provide nor need any of these values; human problem solving in our mundane everyday life is nothing like science. When pulling up to a stoplight, you don’t compute a differential equation to determine how hard to push the brake pedal. You use your Intuition and experience. The deceleration may be a bit jerky (not optimal), maybe you should have started braking earlier (incomplete), you did it slightly differently yesterday (not repeatable), and you have no idea why you did it the way you did (inscrutable); your brain activated untold neurons unnecessarily that did not contribute to the result (inefficient), and so on. But it is good enough to keep you alive, which is why Intuition-based Intelligence evolved in the first place.

We all use the Holistic stance when we are young. Through education (and by independently discovering in childhood how to build naive models) we learn the virtues of model use, of Reductionism, and science. We want to be solid Reductionists because our education always rewarded us for using Reductionist methods. But Understanding is more important than Reasoning. Enlightenment is the ability to see both kinds of solutions and to know which one is the most appropriate. We in the AI research community have repeatedly chosen poorly and we have paid the price. I have estimated that one million man-years may have been wasted on Reductionist AI.

Disadvantages of a Reductionist Stance

Limited applicability	Only works in simple problem domains where models can be created and used
Understanding	Requires Understanding the problem, the problem domain, and candidate models
Correct input data	Requires correct and unambiguous input data for correct results
Brittleness	Models may fail catastrophically if used in situations where they do not apply

Building robust and useful Reductionist models of complex dynamic systems such as physiology, drug effects and interactions in the body, of people, societies, industries, economies, stock markets, brains, minds, or human languages is impossible. These are “Bizarre” problem domains – defined as domains where models cannot be created or used and are discussed in detail at http://artificial-intuition.com.

Reductionist model-based attempts at AI will typically fail spectacularly at the edges of their competence but there may be no easy way to detect this failure since the borders of the model’s applicability can be difficult to determine mechanistically. Humans know what they don’t know and will at worst make human-like mistakes. Computer systems using inapplicable models make mistakes that make headlines and have shown us time and again how little computers really “know”. The AI community calls this “Brittleness” and it is a major problem.

The alternative is to get by without models, or to use weaker models with lesser requirements. The life sciences often do this since they may have no choice; life scientists may not be aware of the distinction. In a paper published 1935, Dr. Lionel S. Penrose, a pioneer geneticist working in the field decades before DNA was described (and incidentally, father of Sir Roger Penrose) first encouraged use of what he called “Model Free Methods”. If we examine various methods used in the life sciences we will find that many of these are Model Free or use Weak Models. Trial and error, pattern matching, table lookup, adaptation and other kinds of learning, search, evolutionary computation, markets, statistics, and even language use can be viewed as either Model Free Methods or Weak Models, depending on details, definitions, and circumstances. I discuss some of these issues in talks available as videos at http://videos.syntience.com. All truly Model Free Methods are Holistic, and all Holistic Methods are Model Free. AI research in the 21st century needs to learn from the life sciences:

Benefits of a Holistic Stance

Applicability	Can be used anywhere, including in Bizarre problem domains
Ignorance	No need to understand the problem or the problem domain
Directness	Problem is solved directly. No need to interpret model output in current context
Learning	Many Model Free Methods provide learning and gathering of experience
Abduction	The ability to jump to conclusions (often correctly) based on incomplete input data
Novelty	Holistic Methods can provide true novelty; some depend on it for their function
Robustness	Graceful degradation. Failures are non-catastrophic, “human-like” errors.
Skepticism	Robustness against internal errors also provides ability to deal with erroneous input
Disambiguation	Provides context based ability to handle ambiguities in problem statement and input
Understanding	Provides saliency, abstractions, situation semantics, and language semantics

The last four benefits (in boldface) in the table above may be available as emergent effects when using Artificial Intuition or other advanced MFMs. The others are often available even in simpler MFMs.

Disadvantages of a Holistic Stance

Fallible	No guarantees of reaching a solution, or that found solutions are correct
Non-portable	Solving a problem may not be much help on other problems or in other domains
Wasteful	May be wasteful of computational resources and may require parallel processing
Inscrutable	Solutions don’t necessarily provide any clues to how to create models for problem
Opaque	Solutions emerge. We cannot always analyze the details of how this happens.

There is no conflict between emergent Understanding and the inscrutability of the result. Direct application of simpler Model Free Methods such as evolutionary computation may well provide working but inscrutable solutions using an opaque process. Advanced Model Free Methods like Artificial Intuition can provide, as emergent effects, Robustness, Skepticism, Disambiguation, and Understanding. This is how MFMs can provide the foundations required for Reasoning and the ability to use Reductionist stance and Models. We could call this Emergent Reductionism. We may be able to create an AI that Understands and Reasons, but we may not understand exactly how it Understands.

Fallibility looks like a deal-breaker to a Reductionist. But given a Bizarre Mundane Real World, where Reductionist Models cannot be built and a Reductionist approach could not even get started, we have no choice. Humans in the Bizarre Mundane Real World operate Holistically, and as our experience grows we find that we fail very rarely in common everyday situations. Graceful degradation and emergent robustness allows the Holistic Methods to continue to work as the details of the mundane tasks change. The normal road to town may be blocked by construction. We add some more matching patterns to our Understanding of the situation and we spontaneously deal with the new context, adjust to it, and learn from it. The same kind of robustness will be available to our computer based AIs if we program them to use Holistic Methods.

AI should really have been a life science

Not all scientific disciplines are dominated by Reductionism. In the life sciences such as Biology, Ecology, and Psychology it has long been recognized that Reductionist Methods are insufficient. A few decades ago there was a kind of “Physics envy” in the life sciences. Every discipline was measured the way Physics was measured – by how well you could make long term predictions. This is easy when you are predicting movements of pendulums but very difficult when dealing with the population of muskrats in New England. The life sciences have achieved impressive results and have shaken off this inferiority complex by using alternatives to Reductionist Methods, including many Model Free Methods. Here is a clue to what has been wrong with AI research.

There has been a fatal mismatch between the properties of the problem domain and the properties of the attempted solutions. In the above I have argued (like so many others) that Intelligence is the ability to solve problems in their context. In contrast, programming deals with discarding context and breaking down complex problems into simple subproblems that can then be programmed as portable and reusable subroutines in the computer. Programming is the most Reductionist profession there is and has therefore mainly attracted the kinds of minds that favor Reductionist approaches.

That means that one of the most Holistic phenomena in the world – Intelligence – was being attacked by the most Reductionist researchers in the world. This is the mismatch; the biggest problem with AI research in the 20th century was that it was done by programmers.

Since the beginning in the early 1950s we’ve gone through dozens of paradigms and thousands of approaches trying to build software systems and robots that would behave “more intelligently”, more like humans do. This research has given us many advanced programming tricks and a few amazing consumer products, but nothing has come of all this effort that truly deserves the label “intelligence”.

But what if all these failed paradigms and approaches shared a common fatal flaw? Could it be that AI was actually a much easier problem than most people working in the field believe it is?

How would we go about discovering such a flaw? We could look at what all these failing attempts and paradigms have had in common and scrutinize that. Many approaches map to other approaches, which simplifies the task, but we’re left with a very small kernel that’s common to the majority of the failing approaches:

Reductionism.

Reductionism is the basis of western science; the most outstanding performer in problem solving since 1650, by a wide margin. It’s the method that says “Simplify the problem, and then solve the simpler problem” in so many different ways. It is aptly named for several reasons since most of the simplifications imply a reduction of something:

• Reduce complex systems to straightforward collections of simpler components. A frog can be split into a skeleton, a circulatory system, a nervous system, a digestive system, etc. and if you understand all of these, then you understand the frog.
• Reduce the number of free variables by separating a core of the problem away from its open environment into a closed and controlled laboratory environment. In science textbooks this is expressed as “all else being constant”. The simplified view of the original problem is called “a model” and is, in the best cases, formalized into some number of manageable equations that describe the most important aspects of the original problem.
• Reduce the problem to a more fundamental discipline. Problems in Biology should be attacked at the level of Biochemistry, which are merely problems of Chemistry, which are really just problems involving atoms, which is what we deal with in Physics.
• Reduce the number of types of matter by finding even lower level components. Chemicals are combinations of atoms which are made of electrons, protons, etc. which are made of quarks, which may be “made of strings”.
• Reduce the number of models, equations and formulas to a single great Theory of Everything that explains everything in the entire universe.
• Reduce the complexity of a description of a system by only considering interactions from components to wholes (only consider upward causation)
• Reduce the number of grad student hours wasted by only attacking problems that we already almost know how to solve (a form of hill climbing).

The so-called “Reductionist Stance” and these meta-methods have served us extremely well ever since Newton, Bacon, Descartes, John Stuart Mill, and others collectively discovered the efficiency of these methods in the seventeenth century. But much of it has origins in antiquity. Aristotle said “The whole equals the sum of its parts”, implying that if you understood the parts, then you fully understood the whole.

If this is the way Science is done, do we really have an alternative? Wouldn’t any alternative to Science have to be UNSCIENTIFIC?

To many people’s surprise, the answer is NO. This was established decades ago (and will be the topic of a future blog post). There are several Non-Reductionist ways to attack problems, including problems that “require intelligence”. The Life Sciences (such as Biology, Genetics, Psychology etc) have always been dealing with problem domains that resist Reductionist methods and have had to find other ways to make progress.

The criticisms targeting these reductions have always been variations on the theme that “this reduction discards something important so the answer you get is incomplete or incorrect”. The alternative to reduction is to consider wholes rather than parts:

Holism

The opposite stance is called “The Holistic Stance” and here the battle-cry is “The whole is more than the sum of its parts”. This stance also goes back to antiquity, which makes the Reductionism/Holism debate thousands of years old. From 1650 to the 1920’s the Holistic viewpoint was largely suppressed by the barrage of successes produced by people using Reductionist methods. The hapless Holists supposedly had no methods. A Holistic stance was useful in getting an overview, to see what problems existed and which ones would be appropriate to attack, but once you were working on a problem, the only tools available were the tools of Reductionism. “Holists had the superior ontology while the Reductionists were the masters of method” (2). In this manner, we solved thousands of scientific and engineering problems over the past three centuries.

The simpler problems.

Certain problem domains have refused to yield. Some problems were simple, and some complicated which meant they would yield eventually, but many problems exhibited a “deep complexity” that went beyond the “merely complicated”. Other kinds of difficulties were identified. An amazing number of problems were found to be at once deeply complex, irreducible, riddled with ambiguities, and to exhibit emergent effects. We call these kinds of problem domains “Bizarre”; they are described elsewhere in quite some detail but I will summarize the highlights:

The thing that makes certain problem domains deserve the “Bizarre Domain” label is that no useful models can be built of these domains.

• The complexity and nonlinearity of the domain prevents accurate and precise longer term predictions (Chaotic systems)
• Any simplification you attempt to make obviously discards something of vital importance (Irreducibility).
• The input data available is incomplete, ambiguous, and inconsistent. (Ambiguity)
• Emergent behavior makes the whole behave different than a collection of its parts (Emergence or Downward Causation).

Some examples: The laws of Thermodynamics only apply to closed systems, but the world is open and time-variant, and hence “irreducible” which simply means “Reductionist models cannot be used”. Many Scientific methods require correct input data for correct results. In the real world, input may be ambiguous, incomplete, and self-contradictory. Chaos and emergence can be found all around us, once you learn to
recognize them. Intelligence in humans emerges from unintelligent neurons. Meaning of language emerges from mere words and letters.

After solving so many relatively simple problems using the Reductionist stance we’re left with a number of really hard ones. They are all problems in Bizarre Domains:

• The world is Bizarre. Any attempt to model the world, completely or in significant part, will fail. Models cannot be made of the global economy, or stock markets. (3) Any partial model will be bleeding at the edges where it was cut loose from its context.
• Life is Bizarre. All life sciences deal with the complexity of life. Organisms, human physiology, drug design and drug interactions cannot be completely modeled. If you take a frog apart, it is no longer alive.
• The Mind is Bizarre. The Brain is too complex to be modeled. Intelligence is Bizarre.
• Language is Bizarre. All attempts to model human languages using grammars etc. to date have failed and will continue to fail. The meaning of language cannot be retrieved from a grammatical analysis.

It is possible that certain domains are regarded as Bizarre today but with advances in theory, computing power, etc. we might someday find ways to build models and to attack the domain using Reductionist methods. And it is possible that certain sub-problems in Bizarre domains can be partially solved, for instance using statistical methods. Recognizing you have such a borderline case is harder than recognizing you are in a Bizarre Domain to start with; we will leave it to those who insist on using Reductionist methods to identify and tackle these borderline cases.

But real progress at the core of any Bizarre Domain requires adopting Holistic Methods. Yes, they actually do exist. They are also called “Model Free Methods” and deserve to be discussed in a separate post.

(1) This is my own estimate. Some large AI conferences have had attendance numbers in the 10-50,000 people range; but while working in an AI department at a university it was clear that most researchers that labeled their work as “AI” would only attend a small fraction of the conferences that might have been useful to them. Add to this all “AI related” (by some suitable definition) work in the industry. Multiply this fan-out factor with the number of subdisciplines, and over 60 years of history this starts looking like a clear underestimate.

(2) Attributed to Robert Brandom, but I have not been able to verify this

(3) Friedrich Hayek received the Nobel Prize for telling us things like this

Before we even get into the AGI (Artificial General Intelligence) field there are two major decisions that have been made for us. First is the question about whether AGI is possible at all. Those who claim it is not, have made arguments that are basically variations of Dualist thinking, claiming that there is a soul, or something much like it, that computers just cannot have. Everyone in the AGI field is, by actively working on the problem, demonstrating that they believe the opposite.

Then there is what I would call “Weak AI” or “Practical AI”. This is the kind of AI that works today. It’s largely the legacy of the AI research of the seventies and early eighties, and consists to a large extent of a bag of tricks, a catalog of programming techniques, that can be used to make computers exhibit surface behaviors that mimic what humans would do in specific circumstances. Examples would be expert systems that approve bank loans or detect credit card fraud, and AI in games that computes how a non-player character should behave in a battle.

But the true battle in AI itself is in the field of “Strong” AI, in the quest for Artificial General Intelligence.  This is what I’ll be talking about henceforth.

The Dichotomies that Define AGI

Philosophy is full of dichotomies, which is why philosophers never run out of things to talk about; for every major idea, there seems to exist an equal and opposite idea. Philosophers have given some of these ideas names, and in each case they form pairs that define the distinction under debate. If you want to work in AI, then you need to make at least a half-dozen of these important decisions.

The most important dichotomies are the Reductionist / Holist split, the Symbolic / Subsymbolic split, the Essentialist / Nominalist split, the Instructionist / Selectionist split, The Infallible / Fallible split, and the Logic  / Intuition split (which could also be called the Reasoning / Understanding split). We’ll examine some of those in more detail later (in later blog entries).  Let’s pick one of these, say the Essentialist / Nominalist dichotomy, to use as an example.

If you are an Essentialist, you believe that there is such a thing as a dog, and that most dogs share the properties of “dogness”, if you will. Nominalists in turn say there is no such thing as a dog, it’s just a label that we apply to things that look enough like dogs. Essentialists get in trouble at the borders - Is a statue of a dog a dog? How about a wolf-dog half-breed? A dead dog? Nominalists on the other hand cannot really say much about dogs, since they don’t believe in a prototype dog with specific properties that you could use for logical reasoning about what dogs are or what they can do.

For now, let’s treat these dichotomies as opaque labels for our binary dimensions of choice. If we take one dichotomy we can imagine two squares side by side. You can stand in one or the other - You can be an Essentialist or a Nominalist. If we add another dichotomy, we can make a two-by-two checkerboard of four combinations.

If we want to add a third dichotomy we need two copies of that entire two-by-two checkerboard, which gives us a 4 x 2 board. As we add more dimensions, each a binary choice, we can illustrate those with larger and larger boards. At 6 dichotomies we have a checkerboard of 64 squares. Let’s say that this is enough for now.

These decisions are very important. If you believe one thing and then change your mind halfway through an AI project, you would have to throw away what you had created so far and start over.

When we examine the largest existing theories for AI, and largest active AI projects in the world, we get a minor surprise. Most popular theories and projects are standing in the same square. Almost all of them are

• Reductionist
• Symbolic
• Essentialist
• Instructionist
• Reason/Logic-based
• Infallible

and they agree in many of the other existing dichotomies that I have decided not to discuss here.

This should not have been a surprise. If you select any one of the above, then you pretty much have to select the others since doing otherwise would give rise to internal conflicts - both in your mind and in the AI systems you are creating.  If you want create a symbolic world model, say an Ontology, then you’d better be an Essentialist since Nominalists wouldn’t want to attach properties - essences - to symbols. And if you believe your symbol for “dog” captures all aspects of dogness, then you’d view a borderline or corner case as a problem requiring attention and refinement of the concept.

Surely there may have existed AGI efforts that chose differently in one or two of these dimensions. I imagine working on these projects involved repeated and heated design discussions as the conflicts inherent in the mixed mode theory manifested themselves as impossible choices at the code level.

So most AI theories, and most popular AI projects all start from the same handful of assumptions and therefore stand on the same square on our checkerboard. Fair enough, there’s room for all. We can safely call this the Majority View of AI.

But what is this? Over in the very opposite corner, there are some other theories and a few projects. Granted, not that many.

It turns out that by selecting the opposite answer for each dichotomy gives you a second set of theories, that are internally just as consistent as the majority view AI theories are. These theories and projects are almost all

• Holistic
• Subsymbolic
• Nominalist
• Selectionist
• Understanding/Intuition-based
• Fallible

This is the minority view corner. As long as we’ve had AI research, we’ve had activity in both corners.

But why is there so much more activity in the Majority View corner? There are several reasons. One is that if you are a programmer, then this is where you start out. In order to build, say, ontology based systems you need to learn very little; you can immediately start programming. And the mere fact that you are a programmer makes it likely that you prefer Reductionist methods. In order to move to the other corner you’d have to study quite a lot of Philosophy and Epistemology, some Neuroscience, etc., which constitutes a barrier that keeps most people from shifting viewpoint.

People in the Minority View corner may have started as Biologists, Psychologists or even Philosophers. They are less inclined to start AI projects since they may not have sufficient background and interest in programming and computer science. Proponents of the Minority View can claim that their theories are more Biologically Plausible than the Majority view theories. If you build a system according to the Minority View, it will have more similarities with brains than Majority View systems.

When I discuss my minority view AI theory with people from the majority camp I have to explain everything down to the basics of holistic thinking and I never seem to get through to them; they are often very skeptical. My “unproven theory of AI” is about as consistent as any Reductionist/majority view “unproven theory of AI” so I rarely get any outright arguments against my ideas; a typical response is “show me”.

In contrast, when I discuss my theories with competent people from other fields, such as Biology, they will nod their heads vigorously and say, “Of course, all along I’ve been thinking it has to be something like this”. This is also true for many people without any science education, since these theories are quite congruent with naive ideas of how the mind works.

I think that aversion to Holistic thought and Model Free Methods is an occupational hazard for people working in AI. Holistic thinking is regarded as unscientific fluff and it is more difficult to get funded; better to tow the party line. You need to get the Holistic Thinking meme early in your AI career since once the Reductionist meme package takes hold it will very effectively block these competing ideas.

It is time for this to change. Systems based on Model Free Methods and Holistic Patterns are starting to show results. The Four Color Theorem proof and Google’s breakthrough performance machine translation systems are early signs of this. I’ll talk more about this in another blog.