Monthly Archives: July 2007

“Let’s not make this into a Science Project”

This is a phrase I’ve heard with increasing frequency in my career as an optical engineer, usually coming from the mouth of a manager with an engineering background. The context is typically either the beginning of an investigation into the cause of a significant anomaly, or near the completion of the development of a new technique of manufacture or verification testing. My reaction has always been one of disgust, followed by confusion. The phrase is used almost as an apology, as the real intent of the manager is actually to do some limited experimentation, but is used to indicate that a strong limit is to be set on the extent of the experiments. My confusion stems from not at all understanding what the manager means by the term “science project”, and why such a person would be using “science” as a derogatory term. I now believe I have an answer to my confusion.

Engineering is the practical application of scientific knowledge to produce material items of value for Man’s use. Engineering presumes the existence of science, and each engineering field relies upon the existence of one or more fields of science containing an understanding of the phenomena which the engineers will create and control to Man’s advantage. Most engineers have a respect for the science upon which their careers rely, though most would admit to either not having the depth of knowledge, or the interest, in pursuing a scientific career.

What would cause an engineer to have a disinterest in science? One aspect of the difference typically between an engineer and a scientist is the level of education completed. The term “scientist” almost always applies only to individuals who have obtained a doctorate; most engineers are not doctorates. Although the resulting inequality in academic background can cause arrogance on the part of the scientist, and a latent envy in the engineer, I do not think this is the primary source of the engineer’s disinterest in most cases.

Rather, there appears to be a prevalent attitude that scientists are overly “theoretical”, and are therefore somehow removed from the “practical” concerns of the engineer. Rephrasing this, engineers may see scientists as Rationalists whose analytical constructions do not fully apply to the “real world”. Scientists may conversely see engineers as Pragmatists who fail to fully understand the science underlying their empirical experimentation and tinkerings. (Of course, I am speaking in gross generalizations here – not all scientists and engineers relate in this manner – but I do believe this is the general trend).

And so, what we see here is yet another manifestation of the analytic / synthetic dichotomy originally promoted by Kant. The engineer who has implicitly accepted the division of knowledge along these lines sees Science (and more fervently scientists) as impractical, and believes only in what he can observe through experiment. Analysis is generally rejected as insufficient “proof” (truth obtained by reason alone can contain no knowledge of “things-in-themselves”), and the engineer seeks confirmation in direct experience. However, he then faces the problem of empirical uncertainty (truth obtained by observation alone can never be certain). Experimentation can never be sufficient to establish certainty – there always seems to be another test that could be (should be?) done.

It is the engineering manager that needs to confront and solve this apparent paradox. The epistemology he has come to accept creates the paradox – he fails to see the certainty that comes (and can only come) from a proper union of the use of theory and experiment – the use of the Scientific Method. What is needed in this situation is precisely a “science project”, because only a project based in Scientific Method can lead to an understanding of the phenomenon under consideration, and can avoid an infinite progression of meaningless experimentation. Instead of declaring this need, the manager makes an irrational appeal to limit the effort arbitarily. Far too often, the results are at best exuberantlywasteful, and at worst insufficiently misleading.

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The Quest for a Thinking Assistant, Part II

The tools I discussed in my earlier post on this topic only attempt to deal with the storage of information. Organization of knowledge, attempts to integrate various facts or propositions, relies upon the human entirely. The toolsare designedto improve the efficiency with which information can be organized, while allowing for changes in the organizational structure. As previously noted, I have not found a viable solution yet which handles that challenge well.

Another of my long-term fascinations has been in the area of what is conventionally called “Artificial Intelligence”. For years, this interest was centered on the possibility- which fueled the majority of AI research – that a machine couldactually attain “intelligence”, and evenconsciousness. After a great many years of attempting to integrate this possibility with the tenets of Objectivism – and this only after becoming a serious student of Objectivism – it became clear to me that such a machine can indeed never exist. Specifically, it is impossible for a machine to attain a rational conciousness, as this requires a reasoning faculty, aware of its surroundings, operating with free will, performing goal-directed actions. Free will is impossible to devise in a machine, therefore values cannot be held by a machine (that connection requires additional comment, but not tonight), therefore goals cannot be developed, and consciousness cannot occur.

I’ll leave that direction of thought for a later discussion. My purpose tonight is to discuss my search for a thinking assistant using AI concepts. What can be achieved by a machine is a repository of information, combined with a logic processor. There are two approaches to combining these elements which AI research has developed to a level of sophistication sufficient to be considered as a possible route to eventually creating a useful tool for storage and maintenance of structured information. In this, and an ensuing post, I’ll discuss each in turn.

The first approach of interest is in the development of declarative programming languages. These languages, of which Prolog is the flagship, rely upon an interpreter in which is contained a generalized logic processor. This processor can determine the truth or falsity of statements of symbolic logic [the Objectivist reader cringes], once the truth-state of the parameters used by a symbolic logic statement are determined. The language harnesses this logic processor through definition of a grammar which allows a programmer to state a set of “facts”, and then construct a complex logical statement using these facts, which the logic processor will attempt to “prove”. In the process of solving the logical problem posed, the system achieves the programmer’s desired goal through various “side effects” generated as the system traverses the logical statement and facts in the quest for all possible “solutions” to the problem.

If this sounds like a simply bizarre way to create a software program, let me assure you, as a standard procedural programmer, I found this approach almost laughably alien to grasp. As a general-purpose programming language, the power of this approach is in the ability to create extraordinarily complex computational flows indirectly through the use of clever logical statements and a firm understanding of the algorithm underlying the logic processor. I reached the point of “seeing” this power, but certainly have not mastered the art of harnessing it effectively.

That being said, the underlying combination of a database of “facts” combined with a logical processor remains intriguing. A common “text book” use of Prolog is to create an expert system. An expert system is a set of encoded facts, which can be asked an encoded question (or series of questions)to receive an encoded answer. In the most trivial of expert systems, the question posed will have a True or False answer, and the system will respond with “True” or “False”. Slightly more advanced is a system structured as a diagnostic tool, which asks the usera series of questions, starting from the most general to increasingly specific, with the questions selected by the system based upon the previous answers of the user. Most of us have undoubtably dealt with various automated help systems that are poor implementations of this form of expert system.

Can one use such a declarative language system to encode a body of knowledge, and then examine its internal consistency? Or ask a question and receive an answer? The trick here is in the encoding, as is the case in any attempt to reduce knowledge to a symbolic logical form. Each “term” in the system needs to be exhaustively defined, along with all of its inter-relationships with other terms. A key problem is setting boundaries on the universe of terms introduced to the system – there will always be a practical limit to the number of terms and relationships that the system can manage, and the problem of precise definition of the terms themselves, andof all actual connections between terms rapidly becomes overwhelming. It is indeed quite likely that the task of creating a sufficient database of encoded factsis always impossible to accomplish.

I recall seeing one attempt to examine the possibilities of using such a system in this manner. In fact, the author of the software attempted to use Objectivism-based concepts as the terms in several of his examples. I have no idea if this system is still available, but if I find it on the Internet, I’ll be sure to post a link for interested parties. My recollection was that I could not at all understand the encoding scheme he was attempting to use, and made little or no headway in creating my own examples.

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I’ve owned a copy of Immanuel Kant’s Critique of Pure Reason for probably 20 years now. After an initial attempt at reading this, it has sat on my various bookshelves – usually far away from the Objectivist shelf – and tempted me with its evil. Finally, I picked it up a couple weeks ago and began trying to comprehend its pages, under the motivation of knowing one’s enemy. I am reading Kant gradually, as a challenge to my understanding of Objectivism, as an exercise in learning through contrast.

The basic premises of Kant are easily dismissed – this entire work rests on the falsedichotomy of Analytic versus Synthetic propositions, further supported by the presumption that the human mind comes pre-formed with a-priori knowledge. Nonetheless, it is equally important to resolve each of his detailed arguments into the categories of the completely incorrect in premise and argument, vs. the correct, but argued from erroneous assumptions. Where possible, I would like to be able to counter his erroneous conclusions with more than merely a negative evaluation,and providea positive explanation of an alternative conclusion.

In the opening sections of Kant’s text, after revealing his overall thesis in a lengthy introduction, he begins building evidence for the existence of a-priori synthetic knowledge by considering the concepts of Space and Time. The concept of space, he declares, is not arrived at through experience of the senses. The observation of individualized entities, separated in location, pre-supposes and invokes the concept of space. He places emphasis on the fact that while we can visualize objects being in space, we can also accept that space may exist devoid of objects. Because we can understand space in the absence of entities, we must therefore not need entities – and their observation – in order to comprehend the properties of space. Therefore, we must be able to intuit space without requiring experience. Space is then an a-priori synthetic concept.

Against this description, I offer the following. The newly born infant fails to comprehend the concept of space. His very first observations of the world through his sensory apparatus will not result immediately in an understanding of individuality of entities. All is a continuous blur of light, sound, smell, and touch.

In the very first moments of life, the infant begins the learning process. His limbs move about and make contact with objects. The brain begins to form connections between the motor movements of his various muscles and the sensations of touch from epidermal nerves. His other senses are also immediately active.The eyes at first are unfocussed. Through a rapid process of learning, the brain associates clarity of sight with levels of ciliary muscle contraction (the muscles controlling focus of the eye’s lens) for various content of his visual field. Both the sequence and magnitude of motor movements associated with various sensations of touch and sight, and the training of the focussing mechanism of the eye form the first yardsticks by which the concept of distance, and therefore of space, is learned. A third distance and orientation measuring sensation is hearing, which learns distance from the magnitude of a sound, and direction from the differential timing of the reception of sound between the two ears. (The use of differential timing to determine orientation is well-documented in neuroscience – I found this to be an amazing fact).

After a very short period of time – as little as a few hours, at most a day – the human infant will begin formulating an understanding of space from these sensual observations. The brain will accumulate remembered – trained – connections between motor movements and sensations. Space will be understood and measured by the muscular motions required to reach a nearby object. Sight and hearing will at first be secondary checks on the measurement of distance and orientation. As the space intuition matures, over weeks and months, sight and hearing will become increasingly the dominant source of spatial information. Finally, over a period of years, the spatial intuition will become increasingly generalized to account for distances beyond the child’s immediate experience.

As evidence that spatial intuition is gained through experience, and that the process of internalizing and learning the concept of space can last years, if not a lifetime, consider the following examples. A young child will only begin to understand the distance to a specific destination after having repeatedly travelled to that destination using the same means of transport.Say Iwalk my 2 year olddaughter to the end of our driveway repeatedly (a distance of about 400 ft), and then on other days walk her to a nearby park (a distance of about 1/4 mile). She will soon understand the relative length of time it takes us to cover these two distances, and since the mode of transportation is constant, she will begin to grasp the relative distances involved. If I then drive her past the end of the driveway, and past the park, and on into town (about 4 miles away), she will similarly begin to grasp those relative distances. Notably, there can result some confusion at first – specifically about the distance to the park – it will simultaneously seem far away at walking speed, and close at hand at driving speed. (As you can guess, I’ve heard thisconfusion in the form of a complaint – “Why does it take so long to get to the park, Dad?” “Because we’re walking there”).

In later years, it remains true that until an individual travels a given distance, or farther, it is hard to grasp that distance. Even though my geography is rather accurate, I didn’t fully understand the size of the United States until I flew from New York to San Diego a few years ago (and properly integrating that experience is definitely complicated by the changing of time zones – even though I abstractly understand the effect, I still “feel” that flying from California to New York takes more time than flying from New York to California). Saying that the Moon is 380,000 miles from Earth does not communicate its distance completely. Watching men travel on a spacecraft whichtravels100 miles to reacha lowEarth orbit in 30 minutes,forover threedays to reach the Moon brings clarity to the meaning of that distance (though this is a confused example physically, due to deceleration along the way,these flights still improvedour understanding of the distance). Similarly, it is undoubtably true that no one can claim an understanding of the distance to the nearest star, let alone the sizes of galaxies or distances between them.

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Categorization of Posts

I’ve just spent the past hour going through all of the posts and mapping them to appropriate Categories. In emphasizing that this space should not be read as a Blog, but as a collection of ideas, I recommend making use of the Category list found on the lower right side of this page, if you are not merely bringing yourself up to date on my latest additions to Thought Laboratory.

I also completed (I hope) removing those dang A-hat characters that magically appeared in some of the older posts. If you find any remaining, please post a comment so I can remove them.

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