Synthetic Intelligence

Our Technology

For more than 60 years computer scientists believed that the path to thinking machines was to imitate the human brain, an approach that has culminated in “artificial neural networks” and the rise of data science over the last decade.

But even as the hype and investment bubble engendered by “Generative AI” is peaking, the technology that will make it obsolete and the age of thinking machines is here, but not from data science.

For more than 60 years computer scientists believed that the path to thinking machines was to imitate the human brain, an approach that has culminated in “artificial neural networks” and the rise of data science over the last decade.

But even as the hype and investment bubble engendered by “Generative AI” is peaking, the technology that will make it obsolete and the age of thinking machines is here, but not from data science.

“What is needed is nothing less than a breakthrough in philosophy, a new epistemological theory…”

David Deutsch

David Deutsch is a quantum computation physicist at Oxford, not an AI scientist but often the most prescient observations come from outside the mainstream. He is correct, at New Sapience we are more about epistemology than neuroscience.

David Deutsch

David Deutsch is a quantum computation physicist at Oxford, not an AI scientist but often the most prescient observations come from outside the mainstream. He is correct, at New Sapience we are more about epistemology than neuroscience.

Cognitive Chemistry

Rather than attempt to emulate natural human intelligence, we studied what human intelligence creates – knowledge – in order to engineer it, to synthesize it. Our journey has been a stunning recapitulation of the transformation of Alchemy into Chemistry. We too began with the conjecture of Democritus, but transposed it from the material realm to the intellectual. Complex ideas are composed of simpler ones, and if you keep breaking them down, eventually you must get to the “atoms.”

Rather than attempt to emulate natural human intelligence, we studied what human intelligence creates – knowledge – in order to engineer it, to synthesize it. Our journey has been a stunning recapitulation of the transformation of Alchemy into Chemistry. We too began with the conjecture of Democritus, but transposed it from the material realm to the intellectual. Complex ideas are composed of simpler ones, and if you keep breaking them down, eventually you must get to the “atoms.”

We have identified and classified about 150 “atoms of thought”

into a multi-dimensional array by identifying their properties of connection. We know which concepts are elemental and which are composites, and we understand how they combine to make sense rather than nonsense. This elegant classification schema has given us the key to a science of knowledge, and the solid foundation needed to engineer synthetic intelligences.

We have identified and classified about 150 “atoms of thought”

into a multi-dimensional array by identifying their properties of connection. We know which concepts are elemental and which are composites, and we understand how they combine to make sense rather than nonsense. This elegant classification schema has given us the key to a science of knowledge, and the solid foundation needed to engineer synthetic intelligences.

The Cognitive Core: Digital DNA

Our cognitive core controls the assembly of incoming information from multiple sources to automatically extend sapiens’ internal model, analogous to how DNA is a compact specification for assembling protein molecules into organisms.

Our cognitive core controls the assembly of incoming information from multiple sources to automatically extend sapiens’ internal model, analogous to how DNA is a compact specification for assembling protein molecules into organisms.

This non-symbolic, non-semantic kernel functions as the controlling schema

for an amazingly compact AI operating system capable of common-sense reasoning and problem-solving as well as converting incoming information into knowledge: extensions of the original model structure, which continuously increase its scope and power. Incoming information streams such as statements in human language are first transcribed, by applying our meta-knowledge categories, into intermediate input graphs which in turn are transformed, according to the logic and organizing principles in the kernel, into extensions of the original structure.

This process is stunningly analogous to the way information in DNA is transcribed first into RNA and then used to transform raw materials in the cell into proteins that are the building blocks of organisms. Our synthetic intelligence cognitive core is the DNA of machine knowledge, a compact specification that enables a well-bounded and stable  body of code to transform unlimited quantities of information into functional models of the world.

This non-symbolic, non-semantic kernel functions as the controlling schema

for an amazingly compact AI operating system capable of common-sense reasoning and problem-solving as well as converting incoming information into knowledge: extensions of the original model structure, which continuously increase its scope and power. Incoming information streams such as statements in human language are first transcribed, by applying our meta-knowledge categories, into intermediate input graphs which in turn are transformed, according to the logic and organizing principles in the kernel, into extensions of the original structure.

This process is stunningly analogous to the way information in DNA is transcribed first into RNA and then used to transform raw materials in the cell into proteins that are the building blocks of organisms. Our synthetic intelligence cognitive core is the DNA of machine knowledge, a compact specification that enables a well-bounded and stable  body of code to transform unlimited quantities of information into functional models of the world.

The World Model

A compact model of reality, initially modeling the commonsense world, with sufficient scope to decode and encode natural language while providing the foundation for any expert, scientific or academic knowledge

A compact model of reality, initially modeling the commonsense world, with sufficient scope to decode and encode natural language while providing the foundation for any expert, scientific or academic knowledge

All models are designed from a standpoint of utility,

that is, a problem or a class of problems to be solved, or a capability we want sapiens to have. A foundational capability for the first sapiens and almost all that follow is the comprehension of human language.

Language is a communication protocol for transferring information encoded in symbols. First we model concepts common to all languages and then those common to particular languages. Then we build the processing routines needed to perform encoding (articulation) and decoding (comprehension).

But language only works as a communication protocol by suggesting how pre-existing ideas or concepts in the mind (or computer memory) should be combined to create the thoughts being communicated. Thus language comprehension – learning through language – is directly dependent on the quality and scope of pre-existing world knowledge.

The quality (how well ideas are integrated, with clearly defined relationships) and scope (how much of the world is in the model) are what we mean by education. A well-educated mind has better language comprehension and can learn more easily.

The model of the commonsense world being developed for first generation sapiens is comparable in scope to that a human child that has learned to read and is ready to commence formal education.

All models are designed from a standpoint of utility,

that is, a problem or a class of problems to be solved, or a capability we want sapiens to have. A foundational capability for the first sapiens and almost all that follow is the comprehension of human language.

Language is a communication protocol for transferring information encoded in symbols. First we model concepts common to all languages and then those common to particular languages. Then we build the processing routines needed to perform encoding (articulation) and decoding (comprehension).

But language only works as a communication protocol by suggesting how pre-existing ideas or concepts in the mind (or computer memory) should be combined to create the thoughts being communicated. Thus language comprehension – learning through language – is directly dependent on the quality and scope of pre-existing world knowledge.

The quality (how well ideas are integrated, with clearly defined relationships) and scope (how much of the world is in the model) are what we mean by education. A well-educated mind has better language comprehension and can learn more easily.

The model of the commonsense world being developed for first generation sapiens is comparable in scope to that a human child that has learned to read and is ready to commence formal education.

Applied Epistemology

At initial product release, sapiens will have a model of the commonsense world roughly comparable with that of a first grader. But their “education” will not be acquired though learning or training of any kind. Essentially, they are “born” with it.

At initial product release, sapiens will have a model of the commonsense world roughly comparable with that of a first grader. But their “education” will not be acquired though learning or training of any kind. Essentially, they are “born” with it.

This bootstrap model is being meticulously developed by human modellers

using a set tools and methodologies we call epistemological engineering or Applied Epistemology (AE).

From the functional standpoint, AE can be thought of as a new way to program computers, with knowledge rather than with data and information. But it is unlike traditional programming in that is consists primarily of curating existing knowledge from human minds and converting it into synthetic information structures.

With a sufficient bootstrap model in place to support cognitive learning through language, all future learning for sapiens could be accomplished through language-based education as it is with humans. But even with sapiens’ obvious advantages of perfect memory, focus, and unlimited processing capacity, learning through language is still very “lossy” and time consuming.

AE provides a better way, creating models of far greater precision with far greater efficiency.

This bootstrap model is being meticulously developed by human modellers

using a set tools and methodologies we call epistemological engineering or Applied Epistemology (AE).

From the functional standpoint, AE can be thought of as a new way to program computers, with knowledge rather than with data and information. But it is unlike traditional programming in that is consists primarily of curating existing knowledge from human minds and converting it into synthetic information structures.

With a sufficient bootstrap model in place to support cognitive learning through language, all future learning for sapiens could be accomplished through language-based education as it is with humans. But even with sapiens’ obvious advantages of perfect memory, focus, and unlimited processing capacity, learning through language is still very “lossy” and time consuming.

AE provides a better way, creating models of far greater precision with far greater efficiency.

First Principles

Deep thinkers and the philosophical minded should start here. Discover how New Sapience is laying the foundations for a new science of machine intelligence.

Cutting Through the AI Hype

Skeptical that ChatGPT and the other chatbots are anything more than “statistical parrots” or “autocomplete on steroids?” Start here for a step-by-step walkthrough of imitative AI today, what it is, what it is not, and what it will never be.

Our Technology

You don’t need to be deeply technical to grasp the power and plausibility of our approach but if you already know something about traditional approaches to AI, you will need the flexibility of mind to “unlearn” some of it.

Products and Applications

How we intend to build our technology into multiple product lines to create what we anticipate will become an extraordinary business with the potential for explosive growth.

Origins

How we got here. Learn how, by taking a contrarian approach, New Sapience solved the problem that no one else could.

Delivering on the Promise

Pass through this portal to experience a vison of human life in a world where everyone can be partnered with a selfless companion of unlimited knowledge and deep intelligence.

By investing in New Sapience, you’re not just supporting innovation. You’re unlocking transformative potential for humanity.

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