FAQ for Potential Investors and Corporate Partners

DLF Simulation Technology™ and Q-AL Assistant™ are an entirely new kind of computer simulation architecture that can add the capabilities of goal-directed and seemingly life-like behaviors to an ordinary medium power PC, behaviors that would enable a PC (or robot run by one) to act like it perceives the world in a manner similar to the way a person does, and to communicate with its users by means of simple sentences in English or other languages. Any PC or other computer running Q-AL Assistant simulation software as part of its operating system would be easier to use because one could simply talk to it like the computer on the Star Trek* TV series. In addition, a robot system using this technology would be capable of independent action based on its own Virtual Consciousness™, self-learned knowledge of the world, and so be able to act more like the Mr. Data™ character from Star Trek. The bottom line is that even a fairly early stage Q-AL Assistant simulation system would have about 5-10% of the "mental" capabilities of the Mr. Data character. Such a system, using the Quasi™ interface persona would make a valuable PC operating system assistant, PDA, or robot, depending on its form as a product.

These technologies are ready for programmers to write code for them now.

FAQ on DLF Simulation Technology

To help explain the business proposition for DLF technology and anticipate some questions that are likely to be asked in this regard, here are a few Frequently Asked Questions we have received from various people who have reviewed our Web site:

Questions:

What makes DLF Simulation Technology different from the state of the art Artificial Intelligence (AI) and Artificial Life (AL) technologies?

Why should I believe it works when 30 years of efforts by others from major companies and universities have not produced a system like the one just described?

The Short Answers:

1) A Digital Life-Form™(DLF™) is not a mechanistic automaton. Rather, it is essentially relational process; that is, a DLF is a virtual entity that is in continuous interaction with reality. The mechanisms of the computer it runs on simply serve to animate it.

2) Other workers in AI and AL have not taken the same approach, and that is why they have not produced a Star Trek like computer system to date. They study formal logical systems, computer programs, physical brain function, or psychology, to build systems with capabilities such as Doug Lenat's Cyc, but they do not study consciousness as an identification process. They do not study the mind and its relationship to reality as a limited, quasi-mathematical, proactive, teleological, volitional process with causal efficacy in reality. AI and AL workers in the state of the art do not practice reality-based computing so that a computer system has first-hand "information" about the world it can use to simulate identification, but rather they make up data, interpret data as human beings, or add so-called "human common sense" as some sort of text based calculus, with the net effect that the computer system always has second-hand information, never first-hand information about reality.

(Do not take our word for this. To prove to yourself that DLF technology works, you can read the description and explanation about its operating principles, and then you can mentally follow the links connecting all the data and processes involved in examples of its operation (just as you would to prove to yourself that any other kind of computer technology works). See the book How to Simulate Consciousness Using a Computer System for complete details.)

 

You may skip the longer answers, if you wish.

Longer Answers:

The longer answers to these questions are somewhat more complex because they cover a huge context consisting of several very abstract subjects, but they are quite understandable none the less, with some thought on the reader's part. The book just mentioned provides the detailed and specific explanations you need to understand DLF technology fully, but here is as short a summary of the key topics as can be provided for you to understand the basics of how and why DLF Simulation Technology works.

If you look at the history of the interactive PC and the Internet (see the Dec 2001 issue of Scientific American, page 85), you will find that these technologies did not start with Jobs, Wozniak, and Gates. Rather they depend on a basic conceptual and technological foundation that was created by defense industry work dating from 1948 through the 1960's.

Likewise, DLF Simulation Technology depends on a new conceptual and scientific framework in epistemology first published in detail 1966, and also on work in teleology (the study of goal-directed behavior) first published in 1976, and that is what distinguishes DLF technology from the state of the art in AI and AL.

The fundamental discovery work that is the foundation of DLF technology was done primarily by Ayn Rand®** in the 1950's. (Unfortunately, her discoveries have been ignored by the scientific community because she is generally considered to be merely a popular novelist, rather than the great scientist and philosopher she actually was, and that is why her non-fiction work is not as well known as her novels.)

Rand was the first person to discover and explain that: "Existence is Identity." and "Consciousness is Identification.," as she explained in her books Atlas Shrugged (1957) and For the New Intellectual (1961). Her work identifies the nature of conditional, goal-directed action as the essence of life (action that is supported by the mechanisms of physics and chemistry), human consciousness as a limited, identifiable, relational, causal process that must be maintained by continuous action, concept formation as a quasi&endash;mathematical process based on observation of the real world and psychological measurements, and that all knowledge must be linked to reality in clear cut, logical and mathematical terms, if it is to be knowledge at all. In addition, she integrated all these discoveries and identifications into a system of ideas and showed how that system is based on observations of reality. In other words, she validated that the system itself is objective and scientific, that is qualifies as knowledge, not arbitrary conjecture.

To understand why DLF technology works, requires a short digression into the history of philosophy. The relevant branches are metaphysics (the study of reality), epistemology (the study of knowledge, of what we know and how we know it), and teleology (the study of goal-directedness in life-forms). Leaving aside thousands of years of arguments by philosophers and adding to the work of Aristotle, what Ayn Rand did in the late 1950's was to observe and recognize that reality just is, and that it is primary to everything else, that reality is filled with objects with various attributes (each a property-measurement value pair (such as length, position, and so on) that form the identity of every object), that the very existence (survival) of life-forms is conditional on their own continuous self-generated, self-sustaining, self-regulated action (whereas the existence of a rock is not conditional on its own action), and that consciousness is an attribute of life-forms, a process which is used by them to identify objects in reality for the purpose of survival.

In other words, consciousness is neither supernatural nor mechanistic(which is a false alternative), but essentially a limited, causal, relational process with a specific identity. These are new ideas to both philosophy and science.

Rand further identified that reality is both the starting point of all knowing and the content or data of consciousness. According to her theory of consciousness, conscious identification of reality (by a human) is done at three levels: 1) Sensations (analogous to pixels), 2) Perceptions (the identification of objects as objects with attributes and measurement values), and 3) Concepts as open-ended groupings of objects and other concepts which share a range of measurement values (as contrasted against those that don't, making them a contextual rather than arbitrary datatype). When thousands of concepts are symbolized by words in coded form, the result is a natural human language such as English or Japanese. State of the art AI and AL technologies use pixels (bitmaps) and sometimes object attributes, but the latter, if used at all are not typically computed from reality; the are assigned by programmers, and therefore are arbitrary and dependent on the programmer, rather than being reality-based (from the point of view of the system). In fact, the date about "reality" most AI systems use, is nothing more than endless strings of characters stored in text files. Percepts and concepts (as defined by Ayn Rand's epistemology) are not used at all in state of the art AI and AL systems at all. These are a few reasons why extant systems are not good simulations of consciousness.

You can verify this assertion for yourself: (See the January 2002 issue of Scientific American, page 18, The World in a Box by Lamont Wood, or go to http://www.sciam.com/article.cfm?articleID=00063887-5C1E-1C6D-84A9809EC588EF21&pageNumber=1&catID=2 and also see a CNN article from April 11, 2002 at http://www.cnn.com/2002/TECH/industry/04/11/memome.project.idg/index.html ).

Goal-directed behavior and consciousness are largely ignored by state of the art AI and AL science. These subjects are ignored because of the implicit, underlying assumption by nearly all scientists that life and intelligence are inherently mechanical, rather than just based on mechanism, and that consciousness, if it exists at all, is not scientific. (Scientists make this assumption to avoid thinking life and consciousness are supernatural or magical, which is widely seen as the only alternative.) As a result of these assumptions, the teleology and the proactive behaviors of life-forms tend to be ignored as basis for control systems in favor of preprogrammed responses or the stasis seeking of negative feedback control systems (Cybernetics). From the standpoint of simulating a life-form, this approach would be like attempting to do all work with computers using only machine language because of the fear that "operating systems and software applications" were somehow supernatural or magical.

The AI community use what they call "concepts" in some systems, but only as arbitrary constructs that are part of a database of millions of "facts," not as open-ended data structures that are computed from measurements of reality by the systems that use them the way they are in DLF Simulation Technology and the Q-AL Assistant architecture.

There is a good reason for this latter state of affairs: The use of concepts and language have a history all their own that dates back to Plato and Aristotle, and are not well understood by most scientists, except as arbitrary, abstract, formal systems, rather than biological systems that interact with the real world. However, the fact is that there are only three ways to form concepts: 1) By intuition and guesswork, 2) By arbitrarily making them up, or 3) By using a reality based method, a specific, contextual, volitional, reality based process to produce them. (This brilliant identification was made by Dr. Leonard Peikoff in his book Objectivism: The Philosophy of Ayn Rand, Dutton 1991, ISBN# 0-525-93380-8.)

Since concepts deal with groupings of objects (and other concepts), the question that has been debated for more than two millennia is: "How does one find the "tree-ness" in individual trees, for example, since all trees are different, with no two being exactly alike?" This question is known in epistemology as the problem of universals, and what Ayn Rand did was to solve it. Prior to her solution, to form concepts everyone thought they either used their subconscious intuition to find intrinsic attributes that were supposedly in the objects themselves (also known as intrincism: divine inspiration or cultural "osmosis" and guesswork, depending on one's background), or people simply made up their concepts as needed (also known as subjectivism or nominalism), or they used some combination of these two means. Ayn Rand introduced a reality-based, quasi-mathematical method to form concepts, and thereby made concept formation an objective, scientific process for the first time in history.

The author of this web site discovered Rand's work while in college in 1967, and I recognized its potential for computer science, specifically for AI (AL didn't exist then). DLF technology is the result of my thought and effort since then, and it is technology that will work because it is based on sound, demonstrable scientific thinking and working computer code that interacts with reality more like a biological life-form does, rather than the way state of the art computer programs do.

As stated above, a Digital Life-Form is not a mechanistic automaton.

Rather, a DLF is essentially relational process between a computer system and reality. That is, a DLF is a virtual entity that is born of its continuous, cyclic interaction with reality. As a virtual entity, a DLF emerges from this relationship, just as the property of "rolling" emerges from two half spheres that have been glued together, but does not exist when the half spheres are separate. (This great example of what an emergent property is comes from the book The Biological Basis for Teleological Concepts by Dr. Harry Binswanger, ARI Press 1990, ISBN# 0-9625336-0-2.)

Likewise, the DLF is the relationship of the whole DLF simulation system to reality, and the mechanisms of the computer a DLF runs on simply serve to animate it. DLF Simulation Technology is an architecture that simulates the conditional relationship between an individual biological life&endash;form and reality. It is a simulation of goal-directed action that interacts with reality in a self-generated, self-sustaining, self-regulating fashion to cause its own continued existence (survival) in the future in a manner similar to the way biological life-forms do.

If you find this idea confusing, just think of how a business operates. The existence of a business is also conditional on its continued profitability, and by implication, its continuous, self-regulated actions to generate the cash it needs to survive. The only essential difference between a business and a life-form in this regard is that a life-form uses sugar for food, instead of money. Both are teleological entities. A successful business uses its own profits to cause its own future existence (survival). This is not a static situation, but it is dynamic. It is a continuous, cyclic process of complex causation which, like life processes, can be thought of as "spiraling" into the future in a self-generating, self-sustaining fashion to cause their own future existence.

In order to survive, a DLF must find and "eat" simulated food, and in order to do that, it must identify objects in reality, hence the need for its simulated consciousness. A DLF senses objects (inputs pixels or bitmaps), then it simulates the identification of these objects by calculating attribute lists from the sensed data to compute simulated percepts as lists of property-measurement value pairs (measurements) for the objects it senses. Once enough percepts are in its memory, a DLF can calculate its own simulated concepts (instead of having them preprogrammed) by comparing its percepts' attributes and measurements in order to group those with similar attributes, treat each group member as a distinct unit of the group, and finally symbolize the new concept with an English (or other language) word (provided by a human tutor) to complete the concept formation process. The meaning of the word as a symbol is the concept and all the calculations that link it to real world objects. (Freewill is also simulated, but that is beyond the scope of this description. How the simulation of freewill works is described in Chapter 4 of our free book. See link below)

Once simulated concepts of many types of objects are calculated, a hierarchy of abstract concepts can be formed (for example: bed, table, chair, bookcase >>> furniture >>> man-made object >>> object). Likewise, concepts of relationships and other intangibles such as grammar and syntax can be calculated. When sufficient concepts have been calculated, the result is that when a human user types in or asks a DLF or the Quasi persona the simple English sentence: "Is the chair next to the table?," Quasi can point his TV camera, identify the objects in his view perceptually (as objects instead of X, Y points or bitmaps), identify which concepts subsume the objects, and answer "Yes" or "No" to the question, depending on what he "sees" for himself. Quasi can do this because unlike state of the art systems, Quasi has his own internal motivation to do so (if he fails too often he "dies"). And Quasi has made his own identifications of reality (so he simply follows the logical chains of calculations linking his own actions, simulated sensations, percepts, and concepts in memory to what he perceives in the world to find the answer to the question, just as you or I might in a similar situation). Quasi 's simulated ideas are linked to reality as part of his continuous action relationship with it. Quasi thus simulates goal-directed, rational, conscious behavior.

(Do not take our word for this. To prove to yourself that DLF technology works, you can read the description and explanation about its operating principles, and then you can mentally follow the links connecting all the data and processes involved in examples of its operation (just as you would to prove to yourself that any other kind of computer technology works). See the book How to Simulate Consciousness Using a Computer System for complete details.)

 

Question:

What about goal-directed action? I've read your white paper, and I see what you're talking about with teleology. It seems to me that this is very closely associated with (1) cybernetic feedback mechanisms (monitor the self and adjust accordingly), and (2) cellular automata rules like the famous evolutionary 'game of life'. Isn't this a case of constantly monitoring one's state and then comparing to a pre-programmed set of values for 'well-being' or 'survival'? Maybe a constant comparison against a multi-dimensional matrix/data-structure with each axis being a parameter of well-being? It doesn't seem like such a difficult problem.

Answer:

Your suppositions are a common confusion that results from the implicit idea that all aspects of reality, including life, are mechanistic. That you would think this is no surprise because that is what we are all taught in school. The problem is, if you do not challenge that premise, you subconsciously rule out any other possible explanations and are left only with the false alternative of mechanism vs. the supernatural to explain how life works.

Though self monitoring is involved, teleologic behavior is different from the behavior of both cybernetic systems and cellular automata. Cybernetic control system is a simple, externally powered, mechanistic, system to regulate some output(s) to stasis (to attain a zero error between an input and a reference value). Cellular automata are also simple, mechanistic systems that are driven from the outside and automate some action or actions according to predefined, and arbitrary rules. Both of these types of systems are mechanistic embodiments of human ideas and goals that have been frozen into machines, so they do not simulate life very well. They are neither alive nor goal-directed, and essentially react to outside energy. Neither a cybernetic system nor cellular automatum needs to act to maintain its existence as a system. Both can be stopped and restarted ("Hit the reset button guys!") without ill effects. Leaving out human choice (which is a separate issue), these systems exist unconditionally (their actions are irrelevant to their existence), and the systems are essentially passive because they simply react to outside events.

Life is more complex causally than either of these systems. Life is "self-generated, self-sustaining action" (Ayn Rand). Implicit in that statement is that life is also self-regulating and self-caused. That is, life is proactive: Life must initiate action to keep its own processes going to stay in existence (survive), before it can cause any other actions. Life is therefore conditional, and the actions of life-forms are absolutely essential to its existence because it exists only on the condition of those actions success as it interacts with reality. If life's goal-directed processes are interrupted and the conditions required for survival are not maintained for only a short time, it dies, putrefies, and these changes are irreversible. There is no reset button for life-forms! The very existence of the process of life itself is caused by that process's own previous successes at maintaining the conditions its existence requires, and the successes of the life process at maintaining those conditions in the present cause all future successes, as well as the continued future existence of the process itself.

If you doubt that this is true, just recall any news reports or personal experiences of what happened to people who were unable to maintain the conditions necessary for human survival, and what happens to their remains afterward.

Philosophy provides the "architecture" for human thought, just as a "white paper" may provide the architecture for a technology. The philosophy of Materialism (in reaction to Idealism) has brainwashed science into thinking that everything that exists is reducible to simple mechanisms and that consciousness is just an empty word. This has happened largely because a mechanistic process is the only kind of process that is thought by most scientists to be rational and causal, as opposed to being supernatural or magic. As pointed out above, however, this is a false alternative.

Teleology is not vitalism (which asserts there is some mystical component to life), nor is life powered by magic. Teleology also does not deny mechanism. In fact, neither teleology nor life could not exist without mechanistic causality. The source of this confusion is a mistaken idea in the philosophy of science about what causality is: Mechanistic cause and effect is misunderstood as a mere sequence of events, but this idea is wrong: "Events are not causes." (Binswanger). As Ayn Rand so aptly pointed out, it is the identities of entities (things) that interact, not events. It is a thing's identity that determines how it acts, when it acts, and what it can interact with. Causality is not arbitrary action followed by arbitrary reaction. Many people trained as scientists have a hard time with the subtle distinction between mechanism and goal-directedness in the context of process operation. It took the author four years to understand it because no one in my educational experience taught me the difference.

Question:

I wonder if simulated sense perception is truly scalable? While the property lists of perceived objects are sufficient to identify simple geometric shapes, what about highly-complex, organic shapes like a tree? Surely the DLF won't measure every single leaf and branch and compare it to other previously-measured property lists of trees in order to identify this particular instance as 'tree'. Consequently, it seems that a large degree of data reduction and generalization must occur, somehow only extracting the data that is truly salient about 'tree'. If memory serves me correctly, this is a very hard problem, akin to the specialized code of facial recognition, speech recognition, signal-to-noise improvement algorithms, and so on. I wonder if the BLOB technique from Carnegie-Mellon could be applied? (BLOBs being the attempt at automating the systematic cataloging/retrieval of images without any human intervention.) Or maybe some sort of fractal compression/reduction of the image, then comparing the fractal equation to property lists of other fractals could be used? A very hard problem, I think. Don't you agree?

Answer:

We know the process of sense perception is scalable because we (humans) and animals do it all the time, and even state of the art computer face recognition programs have become very good in recent years. Granted, a lot of work needs to be done to deal with the simulated perception complex objects, but there are ways to deal with these problems by reverse engineering, by observing the biology and consciousness of real life-forms, and then creating technology that works in an analogous manner. (Critics said the same thing about the graphical user interface for PCs when it was first introduced, that is wasn't scalable!)

Humans do not "measure" everything we look at every time we look. It is well known that human perception "fills in the blanks" based on past experience and that babies spend years gaining that experience as they learn to use their perceptual systems. In addition, "measurement" in this context does not imply systematic, engineering type measurement of every aspect of perceived objects. Perceptual measurements require focusing on certain objects and only noting major attributes and ascertaining relative values for those attributes, as opposed to other objects in a scene. "Consciousness is a difference detector." (Binswanger), so it does not need every measurement. In addition, the level of focus (zoom) can be adjusted depending on a given goal for any simulated perceptual event. In the perception of a tiger example in our white paper, a person walking through the jungle would ignore almost all the stuff around except what was needed to not walk into something. The sound of a growl would quickly change the level of focus, however, in the direction of that sound, and the source of the sound would become the new object of perception (as driven by the need to survive). There are many studies regarding how adrenaline increases awareness and focus in animals and people, as well as many sense perception studies about selecting and differentiating foreground and background objects.

As far as the mechanisms for simulating sense perception, a lot of reverse engineering needs to be done to make simulated sense perception work effectively and efficiently. It is true that the author's DLF program uses only very simple examples and algorithms for extracting identity information from simulated sensations, however, it does so successfully. And, there are quite a number of state of the art techniques that may be useful for simulating perception currently in use for face recognition and other purposes. In addition to the analysis methods mentioned in the question above, there are fractals, eign functions, and others. Much work is needed in this area, but enough is known to get started and to build a good demonstration perceptual simulation system. Remember, it took 20 years for the PC to get where it is today. The first Macintosh® computers were thought by many people to be nothing more than an expensive "etch-a-sketch," and useless for any "serious" computing, which they thought required a command-line interface. Now nearly everyone uses windowing interfaces of one kind or another. Clearly, computer operating systems have come a long way since their early days.

The fact is, DLF Simulation Technology™ and the relational Virtual Consciousness™ identification process it uses to interact with reality are no more complex than modern computer operating systems. Over time, our technology will improve in a manner similar to the way operating systems have.

Question:

Exactly what do you mean by "calculating concepts?" There is a lot to discuss in this topic, but one immediate reaction I had relates to a classic problem from linguistics and semantics. To wit, if you teach a DLF that a stool has three legs and a flat top for standing on, then how does it automatically update its concepts to account for FUNCTIONAL adaptations like using a four-legged chair as a stool? That is to say, if a DLF is only familiar with tripod objects being stools, and then is asked if a chair is a stool, how does it account for the possibility of functional adaptation of the chair to a stool? And vice-versa: Can a stool be a chair? It seems to me that relying strictly on form to determine conceptual identity misses the slippery (but salient) world of functional identity. Furthermore, what about concepts that have no form, like 'commitment', 'volatile', or 'trustworthy'? Is this where Ayn Rand would ignore such things due to their lack of tangible, objectively-measured properties? Are emotions and mental states simply mystical because they lie purely in the realm of linguistic symbol-exchange? And what about possible higher-dimensional spaces in physics which we cannot measure but which may be real? Does Objectivism confine itself only to our ability to measure in 3 dimensions? Is measurable, binary existence/non-existence the fundamental measurement in Objectivism?

Answer:

Wow, that is a complicated question! To answer the parts of the question relating to Objectivism, you need to study the relevant Objectivist publications and the references to our book in detail. The questions you raise do all have good answers, but most of them are way beyond the scope of this FAQ.

To understand the the questions relating directly to life-form simulation, and how a DLF can form simulated concepts, you need to read the sections of Chapter 5 of our book that discuss concepts and how they are formed in detail, including how that process differentiates what we are doing from the state of the art. Then, if you are still not clear, purchase and listen very carefully to the taped lecture courses on concept formation listed in the references in Appendix A. The DLF simulation system's basic data comes from simulated percepts as per the previous question above and is explained in detail it our white paper and book. In the DLF simulation system, networks of concepts are calculated from this data with the help of simulated choice (free will) and a human tutor. Once calculated, it is the chains of simulated concepts and their contextual updates that would enable identifications such as the example of three legged vs. four legged stools that is cited in the question.

The reason linguists cannot solve problems like this one is that they insist on walking around with blinders on. They do not consider consciousness a process of identification, but rather an empty word that refers to a totally transparent (and causally impotent) phenomenon. And, linguists do not understand how objective concepts work or even know what they are in the context of consciousness and as a process of identification of reality. Such ideas are outside the scope of their studies. In fact, most linguists and AI researchers are either intrinsicists or subjectivists (see Chapter 5 of our book, where this point explained in detail). Most scientists today think of concepts as nothing more than a word with some arbitrary definition (not a quasi-mathematical data structure formed from percepts that identify real world objects). To them, definitions are simply made up as needed for pragmatic reasons, and they remain static (until someone decides to change them), rather than being dynamically dependent on an objective context identified in reality. Furthermore, most scientists have never heard about the inter-relationship of objective concepts as being formed in two, interacting directions that are both "wider integrations and more precise definitions," that are more general and more specific, as abstractions become more complex (Ayn Rand, "Introduction to Objectivist Epistemology", Chapter 3: "Abstractions from Abstractions").

Part of one's conceptual network consists of concepts that identify things by type (like the hierarchy example in our white paper), but these are the minority. There are thousands of other, related concepts (such as those about human nature and how people use objects, and their functions) that identify the relationship of objects such as 3 or 4 legged stools (as opposed to chairs or tables). The point is to enable a person to make the identifications (described above in the question) because their concepts give them the "wider integrations and more precise definitions" that they need to do so. And, let us not forget, they need to do so to survive. These ideas may seem obvious in some ways, but they are profound when you start to realize how objectively formed concepts really work, and then consider the implications.

But in order to understand how objective concepts work and grasp these implications, you need to recognize that life and consciousness are goal-directed, and more complex causally than machines are. You must understand how teleologic differs from the mechanistic causality it is based on, and recognize that consciousness is a limited, natural, process of identification. You must recognize that sense perception is an automatic (in the biological sense) form of consciousness that acquires the data that are its content from reality. You must recognize that humans have volition, recognize that first level concepts are neither intrinsic nor subjective, but objective identifications of relationships that are symbolized by words. Further, you must recognize that abstract concepts are formed by the additional processing of first level concepts, that they are formed by restricting or expanding the measurement ranges of first level concepts. You must recognize that grammar and syntax link words together into a code that produces meaning, and that the meaning is an abstract identification of something in reality, and that it is only meaning if there is a chain of concepts that link it to percepts and reality.

Question:

What Will It Take to Implement DLF Simulation Technology?

Answer:

Just to do so. The details of the simulation architecture that is DLF technology are detailed in a 494 page book as explained above, a patent application has been filed, and over 1 MB of computer code is already written to prove that the key processes of the architecture work. Both the book and the code have been favorably evaluated by expert third party reviewers. All that remains is to write the remaining code to build a product and market it.

In practical terms, this will require the following steps:

1) Complete the prototype system, debug it, and test the algorithms.

2) Identify design improvements, implement them, and rewrite the code in a more commonly used object oriented computer language such as Java or C++.

3) Train the production system to induce it to form whatever number of concepts are required for a basic level of functionality.

4) Make multiple copies of the system for training for specialized purposes.

Question:

What are the top 4 markets of DLF Simulation Technology?

Answer:

The top 4 markets for DLF technology are the industrial robotics companies, computer interface companies, computer games and hobbyist robotics companies, and the US Defense Department. These markets look promising for the following reasons:

Industrial Robotics: Companies like Honda are working on humanoid robots. Honda already has a walking robot called ASIMO, that can actually walk up and down stairs on its own. DLF Simulation Technology and Q-AL Assistant could make a product like this much more life-like by enabling it to see objects and use human languages to make ASIMO and even better simulation of a person, and therefore easier for users to communicate with.

Computer Interfaces: The interfaces for PCs and other computers have not changed much in the last ten years. DLF Simulation Technology and Q-AL Assistant would make it possible to add an intelligent assistant to PC interfaces that would be able to communicate with users in English and other common human languages, and this would make PCs orders of magnitude more powerful and useful.

Toys, Computer Games, and Hobbyist Robotics: Computer driven toys and games are becoming more and more sophisticated. DLF Simulation Technology and Q-AL Assistant could advance this process by making toys more life-like and give electronic game characters more human-like personalities. These new capabilities would make the toys and games much more interesting and fun to play. Companies like Sony are already marketing simulated pets in the form of robot dogs. In addition, there are many hobbyists interested in robots (as evidenced by the popularity of the Robotica TV show). Many robotics hobbyists would undoubtedly be interested in making their robots more life-like as well, and they would therefore buy an appropriately priced product, just as early PC users bought computer kits in the early days of personal computers.

U.S. Defense Department: Our government is currently looking for new technologies to help fight the war on terrorism. DLF Simulation Technology and Q-AL Assistant offers a number of advantages for building better military hardware and intelligence gathering tools. A specific example of why the Defense Department would be a good prospect for DLF technology is that the technology could be used in the war against terrorism and other areas to make many weapon systems smarter and easier to use.

The Defense Department could use DLF Simulation Technology and Q-AL Assistant to for reconnaissance, to make smarter weapons, or to augment the new computer network technology it is already providing to special forces infantrymen. DLF Simulation Technology and Q-AL Assistant could enable various recon vehicles and weapons to have the additional capabilities of simulated human intelligence for better operation and natural language for better communication with users. For infantryman, the technology could serve as a backup to a soldier's ability to sense what is going on around him, offer alternative solutions to problems, and call for help if he is injured. It could directly support the US ARMY PM Soldier Systems: the Land Warrior and Mounted Warrior programs.

At Blue Oak Mountain Technologies®, Inc., we are looking for "angel" investors and corporate partners to help us take DLF Simulation Technology to the next level. If you are interested, just mailto:greg@blueoakmountaintech.com and tell us how you want to help.

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