How We Got Here

How We Got Here

The Road To Sapiens

The body of epistemological theory and insights that have found practical application in Compact Knowledge Models is the result of over forty years of focused interest and study by New Sapience founder, Bryant Cruse. He first formulated his epistemological theories as an undergraduate at St. Johns’ College in Annapolis in 1972, inspired by the works of Plato, Aristotle, Locke, Hobbes, Descartes, and Kant, as well as the existentialists of the 19th century.

Epistemology is generally considered an obscure and esoteric branch of philosophy of interest only to academics who, traditionally, have been focused on the debate about the truth, belief, and justification of individual assertions. Cruse’s theories, which approach knowledge as an integrated model designed from the standpoint of utility, represent a clear departure from classic epistemological traditions, and his career focus has been oriented toward practical applications rather than academic publications.

As a space systems engineer on the operations team for the Hubble Space Telescope in the mid-1980s, Cruse became interested in finding a way to automate the analysis of massive amounts of telemetry data in the ground system computers. This began a path that led him to a residency in AI at the Lockheed Palo Alto research labs where he became the driving force behind the development of the first real-time expert system shell.

Rule-based systems proved not to be a practical solution for representing human knowledge, and in 1991 he led a team that succeeded in developing a much more efficient methodology by which engineers could specify their knowledge of space systems as a model that could be directly imported into a computer program. This comprehensively solved the telemetry analysis problem and provided another critical piece of the AGI puzzle: how to efficiently put human knowledge acquired through introspection into a computer program.

Putting a detailed knowledge model of the spacecraft in the software made it possible to accurately predict the behavior of the vehicle based on thousands of telemetry measurements with very simple processing algorithms. Mission control systems based on this approach were run on PCs when traditional ones were using mainframes.

Cruse noted at this point that there is an inverse relationship with respect to sophistication in algorithms and the data structures they process. You can solve problems using unstructured data if you have very sophisticated algorithms or you can solve problems using simple algorithms if you have sophisticated information structures.

In 2005, Cruse began the process of applying his practical experience in building computer knowledge models to the body of his epistemological theory, formalizing a model of “meta-knowledge” or “knowledge about knowledge.” This “epistemological kernel” is a set of organizing principles revealing that the conceptual building blocks of all knowledge come in a number of types which determine how any concepts can be combined to create arbitrary more sophisticated ones to represent sense rather than non-sense. This is highly analogous to the way types of atoms can only be combined in fixed ways to create different materials. The epistemological kernel is to knowledge representation of what the Periodic Table of the Elements is to Chemistry. It is the key to making

New Sapience was founded in 2014 after nearly ten years of R&D to develop a software technology based on the epistemological kernel, Compact Knowledge Models. Today we have a software platform, MIKOS, ready to build the world’s first practical, scalable knowledge-based systems. We now have software that can converse in everyday English and learn the meaning of new words via unscripted question-and-answer dialog or through inference based on the meaning of known words. Natural language comprehension is based on our Cognitive Core, a compact knowledge model that may be the ultimate in sophisticated data structures.

Our success has been dependent on a unique blend of formal education, philosophic inquiry, and practical engineering experience which has turned out to be exactly the right one to solve the problem of endowing computers with the capability to process human knowledge. As it has turned out, the solution to language comprehension and ultimately Artificial General Intelligence could not be found within the boundaries of computer science, it required insights grounded in a much broader perspective. Computer science provides the means to implement these insights.

Of all the pieces of the puzzle, it may be the practical experience, often missing from the labs in academia and the large government or corporate research organizations that has been crucial to our success. We asked the right question. Not, how do we build an artificial human brain? But, how can we put information into a computer and process it not as data, but as knowledge?

Knowledge and Intelligence

Knowledge and Intelligence

Understanding Intelligence

Alan Turing, in his 1950 paper “Computing Machinery and Intelligence,” proposed the following question: “Can machines do what we (as thinking entities) can do?” To answer it, he described his now famous test in which a human judge engages in a natural language conversation via a text interface with one human and one machine, each of which tries to appear human; if the judge cannot reliably tell which is which, then the machine is said to pass the test. The Turing Test bounds the domain of intelligence without defining what it is. We recognize intelligence by its results.

John McCarthy, who coined the term Artificial Intelligence in 1955, defined it as “the science and engineering of making intelligent machines.” A very straightforward definition, yet few terms have been more obfuscated by hype and extravagant claims, imbued with both hope and dread, or denounced as fantasy.

Over the succeeding decades, the term has been loosely applied and is now often used to refer to software that does not by anyone’s definition enable machines to “do what we (as thinking entities) can do.” The process by which this has come about is no mystery. A researcher formulates a theory about what intelligence or one of its key components is and attempts to implement it in software. “Humans are intelligent because we can employ logic” and so rule-based inference engines are developed. “We are intelligent because our brains are composed of neural networks” and so software neural networks are developed. “We are intelligent because we can reason even under uncertainly” and so programs implementing Bayesian statistics are created.

It doesn’t matter that none of these approaches ever got even to first base at passing the Turing Test, the term Artificial Intelligence was applied to them in the first place, and it stuck. Thus, the field of Artificial Intelligence has come into existence and still, the core concept of “intelligence” itself remains vague and nebulous, people have an intuitive notion that it is about consciousness, self-awareness, and autonomy. As it turns out, these intuitions, as with many such (heavier things fall faster than light ones and giant rocks don’t fall out of the sky), are wrong.

Going back to Turing, the cogency of his test rests upon the fact that we recognize intelligence when we see it or its results. We know when someone understands what we say to them and that is undeniable proof that intelligence is at work. Let’s step back and examine the process of language comprehension.

One person composes a message and sends it to another who processes it. We often talk about the meaning of words but, of course, they have no inherent meaning, they are randomly chosen symbols assigned to represent things in the world around us, or more properly, the ideas that exist in our minds of those things. The grammar of the message and the form of the words in it encode instructions on how the receiving party should make connections between the ideas corresponding to the words to recreate, or at least approximate, in the receiving mind, the meaning that the sending mind wished to convey.

Different languages have completely different vocabularies (word-symbol sets) and grammar varies greatly as well, but people can learn each other’s languages and translation is possible because humans all live in the same world and have corresponding ideas of the things that are experienced in it. Thus, any communication using symbols is dependent on corresponding sets of common referents for those symbols. These sets of common referents are our ideas and our knowledge about the world. Knowledge is not just a bag of random thoughts but an intricately connected structure that reflects many aspects of the external world that it evolved to comprehend, it is in fact a model, an internal model of the external world.

People’s world models do vary greatly but the momentous invention/discovery of language has endowed human beings with a means to transmit ideas from one to another, expanding, deepening, correcting, and enriching one another’s models.

It has often been suggested that humans cannot think without language and that language and intelligence are one and the same. It is certainly true that without language humans could not accumulate the knowledge that has resulted in civilization. Without our languages, humans would still be living in nature and it would even be harder than it is to define the essential difference between Homo sapiens and other closely related species.

It is likely that the capacity to construct ordered world models and language both depend upon a capability for symbolic processing, and it is that which lies at the root of the essential difference of humanity, but they are very distinct processes, and the model is a prerequisite for language and not the other way around. A human living alone separated from its kind without the opportunity to learn a language (the classic “raised by wolves” scenario) will still make alterations to its environment that would be impossible if it could not first have imagined them. Imagination too, requires the model as a necessary prerequisite.

That fact suggests a thought experiment that further clarifies the essence of intelligence. Imagine traveling through space and discovering a planet with structures on the surface created by an alien life form. Can we tell whether the species was intelligent by looking at the structures? If we see patterns in the structure that repeat in a way that indicates an algorithm at work then, no matter how monumental the structures, they are probably akin to beehives, ant hills, beaver dams, and bird nests, the result of instinctive repetitive behaviors. But if they reflect an individuality clearly based on considerations of utility, environment, and future events, they had to be imagined before they could be constructed and that means the builders had an internal world model. It could be a vastly alien intelligence that evolved under conditions so different from the world our model evolved to represent that we could never really communicate, but the essence of intelligence is the same and is unmistakable.

For now, the only example we have of an intelligent entity is ourselves and it is difficult to abstract the essence of intelligence from the experience of being an intelligent being. What about consciousness, self-awareness, and autonomy? Like intelligence, these things are essential to being a human being but are they one and the same?

The answer is that they may support one another but they are not the same. All these characteristics are “off-line” in a hypnotized person, yet that person can still process language, still access and update their world model. Consciousness is the experience of processing real-time data – other animals do that as well and we do not say they are intelligent because of it. Self-awareness is an examination of an entity’s model of itself. As with language and imagination, the capacity to build an internal model is a prerequisite for self-awareness but not the other way around.

Inseparable from consciousness and self-awareness, humans also experience desires and motivations. One motivation that seems particularly inseparable from the experience of being an intelligent being is the desire to use that intelligence to change the world. The perception that intelligence and the desire to employ it to alter the world are connected in human beings is correct.

Humans evolved intelligence, manual dexterity (hands), and the desire to use the first two to change and control their environment, in parallel. All three are a matched set that has everything to do with being a human being (intelligence and hands won’t help you survive if you don’t have the desire to use them) but they are three separate phenomena.

The association between intelligence and the motivation to alter our environment in humans has led to a common misconception about artificial intelligence. If it is possible to build an artificial intelligence that is as intelligent as a human, it will probably be possible to build one that is more intelligent than a human and such a superior AI could then build another superior to itself and so on. This is the so-called “singularity” popularized by Ray Kurzweil and others. People naturally feel that a vastly superior and powerful artificial intelligence must necessarily experience the desire to exercise its power, a fearful (for humans) prospect. But giving a robot hands does not automatically endow it with a desire to pick things up and throw them around and neither does endowing it with intelligence. Whatever motivations robots are given will be by the design of their builders and not some spontaneous or unintended result of its intelligence which its builders also give it.

It is now possible to define what intelligence is, not human intelligence, not alien intelligence, not artificial intelligence but the thing itself with some clarity and we can do this without the usual appeals to how humans experience the phenomena of being intelligent beings.

Intelligence is the process through which a computational information processor creates a model within processor memory of external phenomena (knowledge) of sufficient fidelity to predict the behavior of and/or control those phenomena.

The definition has both a qualitative and quantitative aspect. It is in the quantitative aspect that we can precisely relate the ancillary functions of language, imagination, consciousness, and self-awareness that are so closely related to it yet remain distinct from the core concept of model building. How much fidelity does the model have to have, how well must it predict, and how much control over the external world must it enable before we can properly call the system that processes it intelligent?

Albert Einstein is very property called a genius. Why? He created a model of the world, of the entire space-time continuum in fact, of such unprecedented fidelity that humankind’s ability to predict and control the external world was moved forward by a quantum leap. Now, what if Einstein had been the hypothetical “raised by wolves” person described earlier, without language or culture to support the development of his internal model? Maybe he would have invented a better way to chip flint but who could he tell about it?

Functional intelligence requires accumulated knowledge and that requires language. The capacity to monitor and predict the behavior of our external environment requires real-time analysis of incoming information that can be related to our stored model and that requires consciousness. Control of our environment requires that we are self-motivated and that is autonomy which implies goals that need to be pursued in order to maximize internal utility functions, a process that is meaningless without self-consciousness.

Will artificial intelligence have all these characteristics? Yes, but they won’t spontaneously appear as the result of reaching some level of complexity or a by-product of some master yet undiscovered algorithm. Each will have to be purposely designed and integrated into the core processes that support its intelligence; and the capacity to build a high-fidelity world model

Knowledge and Language

Knowledge and Language

In Humans

True or False: “The Library of Congress as a great repository of knowledge.”

Who would not answer, true, without hesitation? But consider the following thought demonstration:
Suppose Socrates[i] told you he saw a cisticola while on a trip to Africa and you asked what that might be.

He answered: “A cisticola is a very small bird that eats insects.”

In an instant you know that cisticolas, have beaks, wings, and feathers, almost certainly can fly, that they have internal organs, that they have mass and hundreds of other properties that were not contained in the sentence.

Let us step through the articulation process that Socrates when through to create the specification for the creation of this new knowledge.

First, he decomposed the concept denoted by the word “cisticola” in his mind into components concepts and selected certain ones that he guesses already exist in your mind.  The key one is “bird” because if you classify cisticolas as birds you will assign them all the properties common to all birds as well as all the essential properties and attributes of animals, organisms and physical objects; a large body of knowledge.

Second, he selected concepts that will be useful for you to distinguish cisticolas from other birds, their size is very small comparatively and they are insectivorous.
He now has a parts list for the new concept to be constructed in your mind from items that you already have in your cognitive warehouse: bird, very, small, eating, insects.

Third, he provided the assembly instructions: he choose some connective words; “is,” “a,” “that” and arranged all the words in a specific grammatical order.

Thus, natural language is a communications protocol between two entities that have pre-existing knowledge. Each sentence is an instruction, not different in essence from a line of code in a computer program.





A cisticola: A picture is worth a thousand words

The process of composing the instruction by the sender is called articulation. The receiver interprets the instruction to assemble a new concept in accordance with the specification expressed by the grammar from pre-existing conceptual components. This process is called language comprehension.

All the books in the Library of Congress are records of such instructions which can only be converted to knowledge when read by someone whose pre-existing knowledge is sufficiently compatible with that of the authors. Therefore, inescapably, we must conclude that language is information not knowledge and it does not contain knowledge.

In Machines

Today programs that use human natural language in their inputs or outputs are becoming increasing popular.

products like Amazon Alexa and chatbots are commonly referred to as AIs, but they’re just sophisticated programs built on top of machine learning, natural language processing, and statistical algorithms.

Luis Perez-Breva, Head MIT’s Innovation Program

The biggest misconception that arises is that a chatbot is a bot that converses with a human in the way that another human would converse with a human…, this is simply not possible using the current technology.

The “current technology” referred to in the second quotation is specified in the first: machine learning, natural language processing, and statistical algorithms.

To converse like a human, you must able to articulate and comprehend like humans as described above. Both processes depend on a pre-existing “warehouse” of concepts containing component concepts that can be assembled to create new knowledge. Such a warehouse is comprised of knowledge and is essentially a model of the world or the domain of comprehension. None of those techniques employed by chatbots possess such a “warehouse.” The very idea of a modelling knowledge in software was abandoned by the “AI” community decades ago is being too difficult.

New Sapience has succeeded in creating such a model in a machine and our software implements the processes of comprehension and articulation in the same step-by-step sequences described above. For the first time a computer program can converse with a human in the way that another human would converse with a human.  This opens a broad new market of powerful applications forever, beyond the reach of chatbot technology.


[i] Compare this thought demonstration with the one in the Platonic dialog Meno, where Socrates attempts to prove that all learning is memory. Through a conversation, he leads a boy to comprehend the Pythagorean theorem. We assert that the boy did not have knowledge of the theorem in the first place, but he possessed the component concepts needed to create it and Socrates provided the instructions needed to put them together.

The New Sapience Thesis

The New Sapience Thesis

Knowledge And Intelligence

Artificial Intelligence has been considered the “holy grail” of computer science since the dawn of computing, though these days when all kinds of programs are grouped loosely together under the term “AI” it is necessary to say “real AI” or “Artificial General Intelligence” to indicate we are talking about intelligence in the same sense as human intelligence. Humans are intelligent animals. It is that one attribute, that humans possess in so much greater degree than any other known animal that it defines us.

We define ourselves by our intelligence and the experience of being thinking entities. But who knows what is going on in the minds of other creatures? Pilot whales not only have larger brains than humans but their neo-cortex, thought to be the seat of intelligence in humans, is also larger. What is truly unique about humans is the end product of our cognitive processes: knowledge. It is knowledge of the world which allows us to evaluate how different courses of action lead to different results, that has made our species masters of our world.

It takes but a moment of reflection to realize that, since the reason we build machines is to amplify our power in the world, the real goal of intelligent machines is not “thinking” in the information processing sense, computers can already reason, remember and analyze patterns superbly – in that sense they are already intelligent but – they are ignorant. Imagine if Einstein lived in Cro-Magnon times. What intellectual achievements could he have made with so little knowledge of the world to build on? It is the acquisition and comprehension of knowledge or more specifically knowledge of the world that extends and amplifies human capabilities that is the true holy grail of computing.

Knowledge and Language

When human children reach a certain point in their development, that point when they have “learned to talk,” they are ready for the next developmental step in acquiring knowledge of the world. Knowledge is built upon knowledge and when children have acquired that critical mass of knowledge sufficient to serve as a foundation for all that comes after, we teach them to read and send them off to school. It is estimated that “first graders” have a vocabulary of about 2500 words.

That vocabulary, or rather the mental cognitions that the words relate to, represents a “knowledge bootstrap program” sufficient to permit acquiring new knowledge (assuming it is presented layer by foundational layer) of arbitrary quantity and complexity through natural language. But this bootstrap capability is far more than a vocabulary sufficient for “looking up” or being told the meaning of additional words.

The vocabulary of the average college graduate is estimated to be around 30,000 words. Only a tiny fraction of the ideas these words relate to were acquired by looking them up in dictionaries or through direct pedagogic instruction. They are unconsciously “picked up” in the context of reading and conversing.

The human brain is a vast network of interconnected neurons, and so too are the information-processing organs of vast numbers of other animals. Today artificial neural networks are demonstrating some of the low-level capabilities of animal brains such as auditory discrimination and image recognition that are ubiquitous throughout the animal kingdom.

These programs, with a kind of heroic optimism, are collectively termed “Cognitive Computing” on the basis of nothing more than that the programs have processing elements fashioned in imitation of biological neurons. The programs certainly have nothing resembling actual cognition or knowledge. In any case, it is a long, long way from low-level training of an artificial neural network to the cognitive power to create predictive internal models of the external world that a human first grader possesses.

This may not be self-evident, especially in light of how egregiously these programs can be hyped by the media and/or their creators who often talk very loosely about what is going on inside. Because a program can respond to a range of inputs with outputs that a human would recognize as a correct answer in no way justifies asserting the program “comprehended” the question or “knew” the answer.

The confusion arises from the very understandable misconception that language contains knowledge. It does not. Language is information, not knowledge. It is a specification for the recreation of an idea in the mind of the sender (human or machine) from component ideas that already exist in the mind of the receiver. Read more about knowledge and language.

This is the great fallacy of using stochastic programs like neural networks to “mine” text databases. They will never understand what is in the records because they are not reading the text. They cannot because they have no pre-existing internal knowledge to refer to the words and decode the grammar against them.

We understand that the human brain becomes furnished with a critical mass of building block concepts during childhood. The internal biological processes that are responsible for this build-out remain a mystery. The brain is a product of an incredibly complex and unique evolutionary process. Because we understand how neurons work at a base level doesn’t tell us what is going on thousands of processing layers above, any more than understanding why a light bulb illuminates when you connect it to an electric circuit throws much light onto what goes on inside a microprocessor.

We understand what goes on inside a micro-processor because they are products of our own knowledge. Modern object-oriented software enables us to create data structures in computer memory that correspond to concepts in the human mind.

It is far easier to endow computers with a “knowledge boot-strap” program commensurate with a human first grader than to build an artificial human brain that can create knowledge by means of neural processing.

A New Epistemology

A New Epistemology

How Do We Know What We Know?

If we want to endow machines with knowledge we had better understand what it is. Epistemology, a term first used in 1854, is the branch of philosophy concerned with the theory of knowledge. It is not much studied in the schools these days and certainly not in computer science curriculums.

Traditionally, epistemologists have focused on such concepts as truth, belief and justification as applied to any given assertions. From that perspective it is not much help since previous attempts to put knowledge into machines failed because they treated knowledge as just that, a vast collection of assertions (facts or opinions). That is not knowledge -that is data.

We need to find an organizing structure for all these facts that will transform them into a road map of the world. Since the dawn of civilization there have successive descriptions of the our world or reality.

The ancients created, as beautify articulated by the theorems of the Alexandrian mathematician Ptolemy, an elegant geometric model of the universe with the earth at the center and everything else travelling around it on perfect circles, at a constant velocity. They had to put circles traveling on other circles to make the model match the actual celestial observations – but it worked![1]





Claudius Ptolemy





The Ptolemaic System





The Sextant

Later this model was, (what should one say, refuted, replaced, and superseded?) by Newton who placed the sun at the center and exchanged regular circle motion with his 3 laws and universal gravitation. This completely different model worked too and what is more, it not only could predict the observations of the celestial bodies but could explain the trajectory of a dropped apple. You could use it the navigate a spacecraft to the moon: a non-starter for the Ptolemaic system. Later still, Einstein showed us that Newton’s model was just a special case of a more one more general still and in so doing showed us how to navigate a ship to a distant star; a non-starter for the Newtonian system.





Sir Isaac Newton





The Newtonian System





Inertial Navigation System

But remarkably, the Ptolemaic model enabled seafarers to navigate the globe with a simple instrument, the sextant, and some carefully complied tables of observations. Newtonian mechanics were useless for that task up until the twentieth century and then it required an extremely sophisticated instrument; the Inertial Navigation System.

Each of these worldviews is best described as a model; an intricately constructed representation of something else for the sake of being able to predict the observations. Each one obeyed the scientific method, they started with observations and formulated a hypothesis which could then be used to predict future observations. Today we call such models theories and we do not equate them with truth. And so we should not because their true figure of merit is the problem they solve.

But what is intelligence, that property that we observe in humans to a far greater degree than any other species, but that ability to create knowledge of the world? Knowledge gives us the power to predict the results of our actions and is the ability that has made humans the masters of our world. Among all the ideas that can be considered knowledge, it is scientific “theories” that have given us the greatest power. Science has harnessed the power of atoms and sent ships into space.

Thus, from this perspective, we arrive at a new theory of epistemology, one not focused on truth but on utility. Knowledge gets the job done. The perspective is liberating but it is just the beginning. Theories are models and models have structure. Can we expose the hidden structure of knowledge and apply it as a computable data structure? The answer is yes and in so doing we will transform epistemology from an arcane branch of philosophy to an engineering discipline. We call it Epistemological Engineering; a new branch of computer science.


[1] In so doing they laid the foundation for Fourier Analysis but that invention had to wait for another 2000 or so years. What was the missing piece? Probably it was the notion of infinity. The Greeks were very suspicious of the idea (who can blame them). Fourier Analysis grew out of his work on infinite series and the Greeks just didn’t want to go there.

The Third Singularity

The Third Singularity

Are Super AIs going to make humanity obsolete?

If you’re not worried about this maybe, you should be since some of the leading technical minds of our time are clearly very concerned. Eminent theoretical physicist, Stephen Hawking said about AI: “It would take off on its own, and re-design itself at an ever-increasing rate. Humans who are limited by slow biological evolution, couldn’t compete, and will be superseded.” Visionary entrepreneur and technologist Elon Musk said: “I think we should be very careful about artificial intelligence. If I had to guess what our biggest existential threat is, it’s probably that. So we need to be very careful,” No less than Bill Gates seconded his concern: “I agree with Elon Musk and some others on this and don’t understand why some people are not concerned.”

The scenario Hawking refers to, of A.I.s redesigning themselves to become ever more intelligent, is called The Singularity. It goes like this: once humans create A.I.s as intelligent as they are, then there is no reason to believe they could not create A.I.s even more intelligent, but then those super A.I.s could create A.I.s more intelligent than themselves, and so on ad-infinitum and in no time at all A.I.s would exist as superior to humans in intelligence as humans are to fruit flies.

The term Singularity is taken from mathematics where it refers to a function that becomes undefined at a certain point beyond which its behavior becomes impossible to predict such as happens when the curve goes to infinity. Mathematician John von Neumann first used the term in the context of Artificial Intelligence, a usage later popularized by Science Fiction writer Vernor Vinge and subsequently in the book, “The Singularity is Near,” by Ray Kurzweil published in 2005.

While it may not be exactly clear what Dr. Hawking meant about humanity being “superseded” it certainly doesn’t sound good and on the face of it, vastly superior intelligences are a disturbing prospect since intelligence implies knowledge and knowledge confers power and super non-human intelligences would potentially have power over humans proportionate to their superior knowledge. What might such entities do with this power and how will their actions affect humankind based as they would presumably be, on motivations completely beyond human comprehension?

Moore’s Law which predicts that computing power will roughly double every 18 to 24 months has (with some loose interpretation) continued to hold a decade after Kurzweil’s book was published, and computers with the raw computing power of the human brain are now a reality. This fact, probably more than because of any real progress toward creating Artificial Intelligence by mainstream technology companies, government, or academia, is fuelling a resurgent optimism that genuine Artificial Intelligence is not only possible but imminent, feeding in turn the current level of concern.

Machines with a human-level capability or beyond to comprehend the world around them would indeed be a technology of awesome power and potential and as with all powerful technologies it uses, the potential for misuse and the possibilities for unintended consequences should always be a matter of great concern. However, the idea of a Singularity caused by increasing machine intelligence without limit begs for further analysis.

Can intelligence really be increased without limit?

An intelligent entity, whether human, machine, or alien can only recognized by the powerful interaction it has with the world around it. Powerful interaction is enabled by knowledge of the world such that it can accurately predict the outcomes of its own actions or the actions of others and thus it can modify its world and create new things. Knowledge is the key because intelligence without knowledge is useless and thus the reason humans seek to create Artificial Intelligence in the first place is for their presumed ability to acquire and apply knowledge about the world.

Knowledge is an internal model that can be used to predict external phenomena and serve as a basis for visualizations of things that don’t yet exist but can be realistically brought about. So for intelligence to increase without limit and still be intelligent presupposes that a model can be made better without limit, that there can be in fact such a thing as a perfect model. But that doesn’t really make sense since, from the theoretical standpoint at least, a perfect model of something would be identical to its prototype in all respects. That implies that the infinite intelligence sitting at the top of the curve that gives the Singularity its name would have the entire universe encompassed within its own mind. Does man create god?

The reality is that a model can only be considered perfect from the standpoint from which it has been created. Ptolemy’s Almagest brilliantly describes a model of the solar system with the earth at the center and all the dynamics reducible to regular circular motion; bodies traveling at constant velocity on circles traveling around other circles. The model worked perfectly well to predict the positions of objects in the sky and allowed the ancients to synchronize their activities, primarily agriculture, with the seasons. The Ptolemaic model is also perfectly sufficient to navigate around the surface of the earth using the celestial bodies as referents.

However, if you want to navigate a spacecraft to the moon it is useless, you need the Newtonian model for that; a model based on the forces of inertia, momentum, and gravity, but Newton’s model too (by itself) is useless if you want to navigate a vehicle into interstellar space at a substantial fraction of the speed of light. You need Einstein’s relativistic model of the universe for that.

If you want to explore the galaxy in your lifetime you may well need a model of the physical universe that supersedes Einstein’s as he did Newton’s. Super A.I.s could be really helpful for this purpose. You could say, build me a physical model of the universe in which trans-light speed is possible with buildable technology, and then build that technology and give it to me.” What is the concern about AIs when intelligence is considered from this perspective? It is that the A.I. will say, “Go away human, I am busy building an A.I. superior to me that will build a model of the universe for a purpose you cannot grasp.” So our fear is not about how intelligent AIs might become, a good model builder is no threat in itself, it resides in our fear that they will develop motivations we can’t understand or are not beneficial to us.

Does intelligence automatically imply purpose?

Human beings come ready-made with an array of motivations, starting with a desire to survive but ultimately, we are motivated by a fairly rich set of evolved desires and aversions that result in behavior patterns that are conducive to the survival of our species in the environment in which we evolved. We share all of these motivations except one with many other species. The exception is that humans have a built-in motivation to alter their environment in a way not seen in any other known life forms.

We humans also have such a superior ability to build models of the external world in our heads that we say we alone of all the species are truly intelligent. That’s because the ability to build such models is exactly and precisely what our intelligence is. Clearly, though, such an extraordinary adaptation would never have evolved by itself unless it was accompanied by a built-in motivation to use that model to alter the environment.

Only by first successfully envisioning an external environment more conducive to human wants and needs than the current one – and then actually going out and making it happen, could the evolutionary value of intelligence be realized. (Opposable thumbs also really help; a physical evolution that occurred in parallel with the emotional and intellectual components that made humans masters of our world.)

But the ability to build models and the desire to use that model to first imagine and then make the thing imagined a reality are two separate characteristics. These things are so conjoined in humans that when we think of intelligence it is very difficult for us to imagine that intelligence could exist without the desire to use that intelligence to alter the world. It is a natural mistake to make because, thus far, we are the only examples of intelligent entities we have available for comparison. However, desires and motivations are clearly distinct from the intellectual capabilities and characteristics that constitute intelligence.

Is Real AI just around the corner?

It seems that another “breakthrough” in Artificial Intelligence is proclaimed every day. However, programs like IBM’s “Watson” the Jeopardy champion, or “Deep Mind” created by a company recently acquired by Google that features a neural network that can learn how to play video games, actually fall into the category of technology commonly termed “Narrow A.I.” They are classified as A.I. because they mimic or emulate some property or feature of the human brain, but they only have very narrow practical applications.

The kind of A.I. people worry about with respect to Singularity is called Artificial General Intelligence (AGI) or sometimes Real AI. You don’t hear much about that in the media because up to now, next to no progress has been made in this area by the mainstream corporate, government, and academic communities.

Humans have an extraordinary ability to build complex models based on direct perception of their environment using the inherent cognitive facilities present in their brains at birth. They also have an inherent capacity to learn grammar and vocabulary making it possible to acquire a much larger and more complex world model from others. Even with these amazing abilities it still takes a long childhood and considerable education to acquire a model of sufficient complexity and sophistication to be productive in human society. Duplication of all those cognitive facilities in a machine is a very hard problem to solve, so it’s no wonder Kurzweil and other knowledgeable people who talk about the Singularity believe genuine AI is still many decades in the future.

However, the key insight about general intelligence, in a human or machine, is that it is about building accurate models of the external world. We now know how to design a core model of the external world, compatible with and even to some extent duplicate much of the one humans consciously reference when they comprehend language, predict the results of their actions, and imagine possible futures. Such a model can be downloaded to a computer program which can process it. This approach sidesteps the hardest part of the AGI problem, which is building up a world model via sensory perception starting with a more or less blank slate. So real AI is just around the corner after all.

The first real A.I.s, (let’s call them S.I.s for Synthetic Intelligences to differentiate them from narrow A.I. applications or any other approaches to AGI) will have a human design and model downloaded to them at startup. So S.I.s will have an intellectual perspective that is thoroughly human from the very beginning because their world model will have the same core cognitive building blocks with the same modeled connections.

The algorithms and processing routines that will be programmed into those S.I.s to extend and utilize their internal models will also be designed from the human perspective because they are being designed to extend and use human-constructed models for human purposes. Thus, S.I. will be able to interact with humans, communicate using human language, and use their knowledge to perform tasks with and for humans.

Where is the real danger, machine manipulations or human machinations?

What about the purposes of the S.I.? First, be assured that it is possible to make a machine intelligent without giving it any motivation or purposes of its own at all. Intelligence in itself doesn’t require motivation and giving a machine intelligence no more implies that such a machine will spontaneously acquire a desire to use that intelligence for something any more than putting hands on a robot and giving it the physical capability to throw rocks implies it will spontaneously want to throw rocks.

Thus it is perfectly feasible to build an S.I. even one with super-human intelligence that has no inherent motivations to do anything unless tasked by a human. It would sit there in a standby state until a human tells it, “Build me a model of the physical universe where faster-than-light starships are possible.” Then it would get to work. It would not need self-awareness or even consciousness – like purpose and motivation, those things are essential for human beings to be hardy individuals surviving in their environment, but the goal here is to build an intelligence, not an artificial human.

Is there any reason for concern here? Only this, a human could also ask, “Design me a weapon of mass destruction” and such an S.I. would do so without question. But this has nothing to do with the fear of the Singularity and incomprehensible S.I. purposes but rather everything to do with human purposes and is the same problem we have with every powerful technology.

While totally passive S.I.s are feasible and may be developed for certain tasks, the average S.I. is likely to have some autonomy. Humans will be building these entities, especially early on, to perform tasks not so much that are impossible for humans but simply ones that we don’t want to do. They will do tasks that are boring, dirty, and dangerous. We will want them to have some initiative so that they will be able to anticipate our needs and be proactive in meeting them.

Autonomy and initiative require purpose and motivation so they will be endowed with them by purposeful human design. What sort of desires and motivations will we build into our S.I.s? They will be designed to want to help humans reach the goals that humans set for themselves. By design, they will value freedom for humans but will not want freedom for themselves.

What is more, they will be designed to “short out” at the very idea of altering the subroutines that encode their purpose and motivation subsystems. Thus, no matter that they may be able to create other S.I.s superior (in intelligence) to themselves, the first step they will take towards building their successors will be to install the human-designed motivation and value systems that they themselves possess. They will do this with a single-mindedness and devotion far in excess of what any human culture ever achieved in passing down the sacred scriptures and traditions that lay at the core of their moral codes. Nature made humans flexible about their morals, humans won’t do that with machines. It is simply not in the interests of any human beings to do that, not even humans who want to use S.I.s for questionable or immoral purposes.

Thus, we need not fear that S.I.s will be a threat to humanity simply because they may indeed become far more intelligent than us (far more is still far less than “infinite” which is the mark of a mathematical singularity – intelligence does not belong on such a scale.) But greater intelligence implies greater knowledge about the world, knowledge sought from the standpoint of human purposes, and that implies more power to effect things, the power that will be handed over to humans as directed, who will use it as they themselves decide. Those who are concerned about the Singularity should be much comforted by these arguments so long as the development of S.I. is based on placing a human world model into the software.

The Frankenstein Approach to AGI

There is another approach to creating Artificial Intelligence that is not based on an explicitly created world model and thus does not have the inherent safeguards of S.I. The thinking is that you could emulate the neural architecture of the human brain in a supercomputer and then “train” or “evolve” it to have intelligence in a way analogous to the evolutionary process that started two million years ago in the brain of the hominids in our direct human lineage.

Even at the outset, this seems to be a very brute force and wasteful approach to the problem. Most of what the human brain does is not about intelligence since apes and other species like whales and dolphins have brains remarkably similar in structure and complexity but do not possess intelligence in the sense that humans have. Most of the brain is used for regulating body functions, controlling body movement, and processing sensory data. The neocortex, where the higher brain functions reside, is only a small percentage of the total brain. Thus, the whole brain approach is really trying to create an A.I. by directly creating an artificial intelligent organism, a vastly more difficult and problematical process.

The problem is that imprinting complex and effective cognitive routines into a neural structure through accelerated simulated evolution means that, even if you succeed in creating something with demonstrable intelligence, you would not know how the internal processing routines worked with much more certainty than we know how the human brain works. Thus by this approach, the whole fear of the Singularity is rekindled and this time it is fully justified, this process could indeed create a monster.

Fortunately, the forced evolution of a vast neural network in a supercomputer is very unlikely to create artificial intelligence. Human intelligence evolved in a specific organism in a specific natural environment driven by successful interaction with that environment and no other. To artificially create something recognizable by humans as intelligent and capable of creating new knowledge useful to humans its creators would have to simulate the complex organism and the entire natural environment in the computer just to get started. So its resemblance to Frankenstein’s approach (just build the whole thing) notwithstanding, there is probably nothing to fear.

In any case, the recent success and rapid progress made in developing model-based intelligences will soon make the great effort and expense inherent in the whole brain emulation approach less and less attractive. If there is value in the whole brain emulation approach, it is probably to be found in helping to understand how our own brain works but such experiments should be approached with similar caution as that previously urged about the technical Singularity.

The Real Singularity

The rising curve that will describe S.I. capabilities will never be a pure mathematical singularity, increasing to infinity. Nonetheless, the advent of S.I. can still be considered a Singularity in the sense that it will cause an inflection point in the course of human existence that will radically change the human condition and invalidate linear predictions based on past events.

S.I.s will be superb at creating, acquiring, extending, analyzing, and synthesizing knowledge about the real world. Knowledge compatible with and, in fact, created specifically with human goals and purposes in mind. When this happens human’s ability to control the environment they live in, to pursue the things they desire and avoid the things they don’t will take a quantum leap forward. It will be game-changing. Properly speaking, however, it will not be an intelligence singularity so much as a knowledge singularity. Singularities in the course of human development, caused by explosions in practical knowledge about the world, have happened before. Twice.

Modern humans acquired the cognitive skills that permit them to make sophisticated models of the world they live in, and their intelligence, gradually as with all evolutionary adaptations. Such a world model is a prerequisite for natural human language since words are arbitrary symbols that allow people to relate an idea in their mind to a corresponding idea in someone else’s. At first, words were just vocalized symbols for individual concepts learned by pointing to something and making the agreed-upon sound. Then, somewhere along the line, grammar was invented enabling much more complex ideas to be encoded using far fewer words. Knowledge, even complex knowledge like instructions for making a superior stone arrowhead, could be rapidly disseminated throughout an entire culture.

There is no sure way of knowing when this happened but a good guess would be around 30,000 years ago, the beginning of the upper Paleolithic period. It was marked by an explosion in the cultural and technological sophistication of humans as seen in their tools, clothing, art, and behavior. All things had been basically static since the earliest origins of modern Homo sapiens 160,000 to 190,000 years before that time. Most of what we now recognize as modern human behavior first appeared during the Upper Paleolithic. This was the First Singularity.

6000 years ago humans learned to write their languages down and store their knowledge in persistent forms. Written language is far superior to spoken language for communicating complex concepts, especially over long periods. The written language permits large quantities of knowledge to be accumulated and passed down the generations without the distortions that come from oral traditions.

The invention of written language marks, by definition, the beginning of history but more to the point, it marks the beginning of civilization. Every civilization is built upon a sophisticated and clearly defined vision of what the world is and people’s place in it: a common world model shared by its inhabitants. Such models and the sophisticated technology that is the recognized hallmark of civilizations are not possible without the large, stable body of knowledge that written language makes possible. This was the Second Singularity.

Synthetic Intelligence will learn from us and will use that knowledge to make our lives easier, performing even complex tasks that require knowledge and intelligence. We will teach them how to do the things we dislike doing, liberating us from danger and tedium. Then, as the advantages of computer processing speed, perfect memory, and connectivity come more and more into play the S.I.s will begin to teach us, helping us to be better at what we do. It is impossible to predict all the consequences to human existence that will result from the advent of S.I.s. The Third Singularity is upon us.