FAQ for Potential Licensees, Investors, and Strategic Partners

RICX Perceptual Simulation™ and DLF Simulation Technology™ architectures are an entirely new kind of computer simulation system in the field of Artificial Life (AL) that can add the new capabilities of sense perception, goal-directed and other seemingly life-like behaviors such as simulated consciousness to ordinary robots and PCs, behaviors that would enable a robot or PC to act like it perceives the world in a manner similar to the way an animal or person does, and to communicate with its users by means of simple sentences in English or other ordinary human languages. Any robot, android, PC, or other computer running RICX and DLF simulation software would be capable of many new things because it would interact with the world more like people do, and one could simply talk to it like a human assistant. We believe that these new technologies will eventually lead to an entire new industry, just as the first PCs did in the 1970s and 80s.

 

FAQ on Our Business Proposition and Simulation Technology Architectures

To help better explain the Blue Oak business proposition for RICX and DLF technology architectures and anticipate some of the questions that are likely to be asked in this regard, here are a few Frequently Asked Questions we have already received from various potential licensees and strategic partners who have reviewed our Web site:

Question: What are the applications for your technologies, and what patents have you applied for to protect them?

Answer: The company CEO and inventor of Blue Oak technologies has applied for two patents in the field of Artificial Life (AL) and robotics. The most obvious applications of Blue Oak technology architectures are as an enabler for more human-like computer and robot systems. Their main purpose is to enable the simulation of consciousness for robots, computer systems, and other as yet unknown uses in a more human-like manner than state of the art technology designs. The Blue Oak CEO and Founder has been researching and developing these technologies and the ideas they depend on for over 42 years. And in 2001 we  published our on-line book entitled:  How to Simulate Consciousness Using a Computer System to help explain these new ideas to others in the fields of AI and AL.

Our patent for a simulated sense perception system was filed in April 2008 and is still pending, but we are hopeful it will soon be issued. A design document is also nearly completed for our sense perception simulation system called Reality Identification Cortex (RICX)™ Technology. In addition, there is a confidential 40 page white paper that describes RICX Technology in detail. (Excerpts from the white paper are published on this website: RICX Perceptual Simulation)

Our DLF Simulation Technology patent is issuing now.

 

Question: Are there any products available yet that are based on your technologies?

Answer: No, not yet, and we are not offering products for sale directly to customers. Our business model is to offer licence's to individuals and select corporate strategic partners for our patented and patent pending technology architectures. We will also offer training and support services in order to help our licensees and strategic partners develop and sell their own products that are based on our technologies. It is our opinion that people who are already in the business of product development, marketing, sales, and support are much better qualified for these roles than Blue Oak is, so after much consideration of this issue, we have set up our business to follow this model of operation.

 

Question: What are the main differences between the RICX and DLF technology architectures?

Answer: RICX Technology enables computer systems and robots to simulate perceptual consciousness and our patent was filed in April 2008. That is, RICX simulates sense perception in a more human-like way than is possible with state of the art computer and robot systems. RICX enables robots to see a world of 3D objects in action, instead of a world of bitmaps and pixels. Our other DLF patent was filed in 2005 and was allowed by the US Patent Office in October 2008. It is currently being issued and is for our Digital Life-Form (DLF)™ Technology. The main use for DLF technology is to simulate human-like conceptual consciousness. That is, to further process the simulated sense perception output from a RICX Technology system in order to simulate higher levels of consciousness, such as concept formation, logic, and simple sentence understanding in ordinary human languages. We believe these technologies together will revolutionize the robotics industry, the Semantic Web, software development for large organizations, and will have many other applications as yet unrecognized.

 

Question: What company is the nearest competitor to Blue Oak in the fields of Artificial Intelligence and Artificial Life, and how does your technology differ from theirs?

Answer: The nearest competitor to Blue Oak is Numenta, Inc., a relatively new company founded by Jeff Hawkins (the author of the wonderful book On Intelligence ) and Donna Dubinsky (a former Apple Computer, Inc. executive). The essential difference between Numenta technology and Blue Oak technology is as follows:

Numenta technology is a mathematical modeling system that names causes it detects. That is, the system assigns causes a name and an identity based only on statistics. This means that there is no causal connection (in the sense of direct measurement) between the real world and system memory contents in the Numenta design. There is simply a name and an identity assigned on the basis of Bayesian statistical calculations, instead of direct identification of reality by differentiation and relative measurement on the part of an animated entity such as a DLF. The resulting object identities are, therefore, arbitrary by definition, and subjective because they are totally dependent on the statistical algorithms and whatever human goals may have been anticipated and coded by a programmer. True, Numenta technology is designed to be self-tuning and can improve over time because it similar to neural networks, but that does not make the content it produces objective and factual, as opposed to statistical.

With Blue Oak RICX technology, on the other hand, Digital Life-Forms are goal oriented and self-regulating from their own independent, internal perspective. The DLFs use RICX technology to sense, perceive, and identify the world by simulating human conscious processes (analogous to the way a robot shape is designed to simulate human form). This means DLFs cause certain relational processes that directly differentiate and measure the properties of objects in reality, thereby identifying real objects in the world mathematically and objectively, not arbitrarily and subjectively (as may have been anticipated and prescribed by a human programmer and statistics).

Once objects are identified by RICX technology, in order to simulate human concept formation, DLFs further processes these data, using the simulated percepts to methodically conceptualize the objects detected in the world, and to form abstractions from abstractions to build ontologies. Ontologies are hierarchies consisting of unbroken chains of context oriented, objective concepts. A human tutor assists this process to ensure every word is correctly connected through these unbroken conceptual chains to actual objects in reality. The identities of the perceived objects are the objective meaning of the words, words that symbolize both the conceptual chains and the objects existing in the world at the other end of the chains (their meaning). Finally, combined with additional observations, a DLF performs logical inductions to produce premises (simple sentences) about causal interactions and other relationships in reality. Once a significant number of conceptual chains and inductions are in place, simulated human-like language understanding and generation is possible for DLFs using simple sentences. This process operates by means of the DLF following its unbroken conceptual chains from the words it “knows” back and forth between the objects in reality and those words. All of these relationships internal to the DLF, have been defined by the DLF as it gains experience, and are relationships between the DLF and reality. These relationships are its simulated objective knowledge, "knowledge" that is held from the perspective of the DLF itself (as opposed to the arbitrary statistical and subjective "knowledge" in competing systems).

 

Question: What is the value proposition that Numenta technology offers?

Answer: Numenta has a radical new and workable Hierarchical Temporal Memory (HTM)™ technology. But, in our opinion, their idea that HTM technology can be used to simulate all brain intelligence functions is flawed. Their idea will work well for some applications, including some object recognition applications and many standard model building applications, but not for real world 3D simulated sense perception of a world in action, simulated concept formation, simulated premise formation, and simulated language. One reason their technology works is because Numenta are correct in their view that the world is hierarchical, that hierarchical systems must be self-consistent to operate effectively, and their ideas about mimicry of the layered functionality of the neo-cortex are excellent. Bayesian statistics are powerful and work well for many applications, but they are not a substitute for the relational process of reality-based consciousness, for reality based relational identification. Recent research shows that consciousness is essentially a quasi- mathematical process of methodical, relational identification using differentiation and integration. Numenta technology simulates some aspects of that process (which is why their process works), but their system does not measure and identify relationships in reality directly the way ours does. Their system uses statistics (unidentified "causes" that persist, then are assigned statistical values) to name objects. This technique can be effective in some areas and for some purposes, but is, in our opinion, a less accurate and effective method than the ones Blue Oak technology uses to identify objects in reality in a firsthand way.

 

Question: What is the value proposition that Blue Oak technology offers licensees and strategic partners?

Answer: It is not possible to solve a problem you do not know exists. Virtually all the competitors to Blue Oak, except possibly Numenta, believe life is mechanistic. That is the working assumption of many others in this field for whatever reason. In other words, they think life operates exactly like machines do and that living cells are little machines. This is a false conclusion. It is an over simplification of life's true complexity. Life is a real, causal, earthbound process, but life's many interacting processes are more complex than that of machinery, which man-made and requires outside help to function. It is not clear to us to what degree Numenta understands this, but certainly not to the degree or in the same way that Blue Oak does. Life is self-generated, self-sustaining, and self-regulating action. Life is also conditional, and must act continuously and for its own sake to remain in existence, to survive. To the degree some machines seem to have life-like capacity, it is only because they are man-made for that purpose, and they therefore got their life-like attributes from other life---from the people who designed an built them. Such machines are simulations of life, but not life itself. Machines do not spring up on their own, and their existence is unconditional. Nor do machines operate for very long on their own, without human attention. If a machine fails, it just sits there. It does not decay and disappear from existence like a life-form does. Consciousness is primarily a self-generated relational process that is an attribute of life-forms. Consciousness establishes relationships by using relative measurement comparison of differences it detects to methodically identify and predict the nature of reality. Blue Oak technology measures reality by continuously detecting and relating differences as change occurs. It does so for the purpose of identifying the 3D objects that exist there to predict their future behavior, then methodically classifies those objects into abstractions, and forms hierarchies (unbroken chains) of abstractions from earlier formed abstractions. Human tutors supply words and context boundaries to assist the system (and to enable users to relate the abstractions to their own language and knowledge). Consciousness is one of many survival processes possessed by some life-forms. It is relational, conditional, and not essentially mechanical. (That does not mean life and consciousness cannot be simulated using computer machinery. Life itself requires and “runs on” the unconditional “mechanisms” of physics and chemistry.)

 

Question: What are the barriers to entry for more competitors to Blue Oak in the Artificial Life field?

Answer: It is well known that successful ideas in the fields of AI and AL are not easy to develop, and many systems have not been successful, though millions in R&D dollars has been spent. However, the problems in these fields are not impossible to solve. Blue Oak has found and developed the newest and most radical, innovative, and unique new ideas, ideas that are almost unknown to others in the field. We have one of these ideas patented already, and another that works on the same principle is patent pending. So we are now ready to work with our licensees and strategic partners to create the demonstrations and new products necessary to profit from them. The ideas on which Blue Oak technologies rest are a completely integrated system of ideas and operating principles, a system which stands alone without contradiction, and in which all aspects of the system are causally connected in unbroken conceptual chains, unbroken chains that run from the most abstract levels to the level of computer code to real objects in the world outside the system. There are no other ideas like this that we know of. So we believe the barriers are high, and that potential competitors will have a difficult time duplicating what we have done.

Question: Are smarter Artificial Life robots in demand?

Answer: Many of the application areas for Blue Oak technology already use robots. Our new technology architectures can make these robots better and more effective at what they do already. There are two new markets that may surprise you. The Semantic Web, for example, depends on ontologies. That is, hierarchies of Web objects. Today, these must all be hand programmed with tools like Owl (see Google or wikipedia). Imagine if robots could do a large part of that work semi-automatcally. Many large corporations have realized that if they could capture and reuse the wealth of conceptual knowledge that is locked in the minds of their employees, they could save billions of dollars in new software development alone, not to mention product development and many other areas of their businesses. Blue Oak has also been working in the new field of computational epistemology in order to discover easier ways to capture and conserve business knowledge that is now routinely lost. We call this new field Business Knowledge Engineering or BKE for short, and it will be especially helpful to large organizations that routinely spend millions to recapture lost company knowledge. Computers that can simulate consciousness will make knowledge capture and reuse much faster and easier. Such systems will be able to work quietly in the background much of the time, after being tutored by human experts and by requesting on-going interactive sessions with human experts as necessary.

 

Question: What makes RICX and DLF Simulation Technology architectures different from the state of the art Artificial Intelligence (AI) and Artificial Life (AL) technology system designs?

Answer: Well, the short explanation is that Blue Oak technologies work on entirely different ideas and operational principles from the state of the art systems. The details of what, why, and how are provided in our book and white papers. But in a few sentences: Blue Oak technologies are different from state of the art systems because we have added the complexity necessary to better simulate certain life functions, such as goal-directed behavior and consciousness. Both of these processes are conditional in nature, and more complex than the simple mechanistic automatons used in state of the art systems. And though goal-directed behavior and consciousness are limited and identifiable, and both depend or "run on" the simpler, unconditional mechanistic systems, both have been largely ignored by AI and AL system designers. Goal-directed behavior is a more complex causal process that is performed by every life-form, and the consciousness is a relational, reality based identification process that is performed by many highly evolved life-forms (mostly animals). Except for the type of mimicry necessary for ecological experimentation or entertainment using AL systems, both of these crucial process are simply ignored by state of the art AI and AL system designers. Blue Oak simulation system designs do include the conditional, relational, complex causal processes necessary to better simulate goal-directed behavior and consciousness in artificial life-forms.

 

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

Answer: There are two parts to this:

1) As pointed out in the previous answer: A Digital Life-Form, a DLF is not a mechanistic automaton. Rather, a DLF is essentially logical entity, a conditional and relational process identification process; that is, a DLF is a logical entity, a virtual entity that is in continuous interaction with reality. That means, in other words, that a DLF is entirely new kind of computer based entity that runs on systems that are mechanistic automations, but with special software that allow life-like processes to be simulated as emergent properties. The mechanisms of the computer a DLF runs on simply serve to animate it the way physics and chemistry animate biological life. A DLF simulates more of the complex internal life processes than state of the art robots or software bots do, and that is why they are unique.

2) Other workers in AI and AL have not taken the same approach, and that is why they have not produced a "Star Trek" or "Hal" like computer system to date. Other workers in AI and AL study formal logical systems, computer programs, physical brain function, or psychology, to build systems with capabilities such as Asimo by Honda, Doug Lenat's Cyc or Jeff Hawkin's HTMs, but they do not study consciousness itself as a relational identification process. They do not study the mind and its relationship to reality as a real, limited, quasi-mathematical, proactive, teleological, volitional process with its own causal efficacy in reality. AI and AL workers in the state of the art do not practice reality-based computing, so that their robot or computer systems could have first-hand "information" about the world they can use to simulate identification, but rather they operate from assumptions intended only to satisfy human goals, make up statistical data disconnected from its causes in reality, interpret data as human beings (not from the internal perspective of a simulated life-form), or add so-called "human common sense" as some sort of text based calculus or system of complex computational linguistic formulae. All of this has the net effect that their robot and computer systems always have second-hand information, rarely first-hand information about reality. DLF technology is unique, and it works in an entirely different way.

 

Question: Why have the AI and AL workers you mention in your documentation not embraced the same ideas and operational principles Blue Oak has? Why do I not find even one university or major company on board with your theories and technologies?

Answer: The main reason is that the system of ideas that is the basis of Blue Oak technology is little known by most scientists and intellectuals and in many ways, it goes against the prevailing thinking in the fields of AI and AL. If you study the references to our book and white papers, you will find our work is based on the very clear thinking of a number of intellectuals with doctorates in philosophy, psychology, biology, and many other subjects. But these intellectuals are not widely read by workers in the AI and AL fields, for whatever reasons. Philosophy in particular has a terrible reputation among many of the so-called "serious" scientists in mathematics and computer science, though not all of them. At least one serious scientist has reviewed our book and white papers and given us high marks. But the bottom line is that the ideas which support Blue Oak technologies are not currently in favor in the minds of most intellectuals, and especially in the AI and AL fields. That fact, however, does not make our ideas and the body of knowledge that supports them wrong. This is one of those cases where, if you as the reader of our documentation are interested in our technology architectures as a licensee, investor, or strategic partner, you will have to learn something about them for yourself, and then use your own independent judgment to decide whether we are right or wrong about the architectures we have developed.

 

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 most people have that all aspects of reality, including life, are simple mechanistic causality (instead of being animated by it). 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 automatically and subconsciously rule out any other possible explanations. So you 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 automaton needs to act to maintain its existence as a system---it exists unconditionally. Both can be stopped and restarted without ill effects. Leaving aside human choice (which is a separate issue), these systems exist unconditionally, which means that their actions are causally irrelevant to their existence. 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, self-regulating action. Implicit in that statement is the fact that life is also 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, life's causal chain broken, and the conditions required for survival are not maintained for only a short time, it dies, disintegrates, 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.

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 of science about what causality is: Mechanistic cause and effect is misunderstood as a mere sequence of events, but this idea is an over simplification and wrong: "Events are not causes." (Binswanger). As he and others have 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. Identity determines action capacity*. That is a very important new principle. 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. (*Note: This is an example of one of the new and little know ideas mentioned in the previous answer above, ideas that have huge implications for the fields of AI and AL. If you think carefully about this, we believe you will see what we mean.)

 

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, sense perception is a hard idea and a lot of work needs to be done to deal with the simulated perception of 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, and that is wasn't scalable!)

Humans do not "measure" everything we look at every time we look or hear (in the sense of formal measurement with a ruler). It is well known that human perception "fills in the blanks" as Jeff Hawkins and Gibson have aptly pointed out, and do so based on past experience. It is also known that babies spend years gaining such 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 as described in our book uses only very simple examples and algorithms for extracting identity information from simulated sensations, however, it does so successfully. 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 to do all kinds of serious computing. Clearly, computer operating systems have come a long way since their early days.

The fact is, DLF Simulation Technology and the relational identification process it uses to interact with reality are no more complex than modern computer operating systems, just based on different principles. Over time, our technology will improve in a manner similar to the way operating systems have. In the years since our book was published, we have been busy working on the problem of simulating sense perception is a scalable way. We believe our patent pending RICX Perceptual Simulation Technology Architecture does in fact provide a scalable solution identifying and recognizing 3D objects in real world settings.

 

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 one 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?

Answer:

Wow, that is a complicated question! The sub-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. We also suggest you study our white papers. 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 and in the white papers. The DLF simulation system's basic data comes from simulated percepts as per a previous question above and is explained in detail it our white papers 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 do not consider consciousness a process of relational 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. Most scientists today think of concepts as nothing more than a word with some arbitrary definition (not a quasi-mathematical data structure formed methodically and firsthand 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. Yet these new ideas are what make our DLF system work.

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 causal interactions, human nature and how people use objects, and the object's 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 how they work as survival tools, 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 the objects in reality used to form the concepts in the first place. None of this will be found in state of the art AI and AL system designs.

 

Question:

What Will It Take to Implement DLF Simulation Technology?

Answer:

Only for some people do decide to do so and become licensees for Blue Oak technologies. The details of the Blue Oak simulation architectures are detailed in a 494 page book and our white papers, as explained above, DLF technology is patented, a patent application has been filed for RICX technology, and over 1 MB of computer code is already written to prove that the key processes of the DLF architecture work. Both the book and the code have been favorably evaluated by expert third party reviewers. As pointed out, Blue Oak will assist, train, and support our licensees as they move forward. All that remains is for them do their design work and to write the remaining code to build a product and market it.

 

Question:

What are the top potential markets for RICX and DLF Simulation Technology?

Answer:

The top potential markets for RICX and DLF technology are robotics companies, the Semantic Web, computer software development companies, computer games and hobbyist robotics companies, and defense industries. These markets look promising for the following reasons:

Robotics: Today there are many robotics companies that could use Blue Oak technologies to make their robots behave in more life-like ways. This applies to both hardware and software robots. In addition, companies like Honda are working on humanoid robots that would be much more impressive if it could use simple sentences. Honda already has a walking robot called ASIMO that can actually walk up and down stairs on its own, and even run.

Semantic Web : The Semantic Web requires hierarchies of Web objects be constructed for it to reach its full potential. Currently, this is done solely with manual tools. DLF technology can offer a semi-automatic solution to speed up this process.

Computer Software Development: There are many aspects of computer software development that could be semi-automated using Blue Oak technologies. This is especially true in the area of Business Knowledge Engineering™, where knowledge in the minds of the employees of organizations is captured for later reuse.

Toys, Computer Games, and Hobbyist Robotics: Computer driven toys and games are becoming more and more sophisticated. RICX and DLF Simulation Technology architectures 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. 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 and helped fuel an entire industry.

Defense Industries: Our government's defense contractors are currently looking for new technologies to help fight the war on terrorism. RICX and DLF Simulation Technology architectures can offer a number of advantages for building better military hardware and intelligence gathering tools. A specific example of why the defense industries would be good prospects for RICX and DLF technology is that the technologies could be used to solve difficult problems, such as detecting malicious code built into computer and network hardware components.

 

At Blue Oak Mountain Technologies, Inc., we are looking for licensees and strategic partners to help us take our RICX Perceptual Simulation and DLF Simulation technology architectures 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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