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Towards a Logical Data Model for Genetics 2 of 4 - Genetic Software

How do you put intelligence in cloud infrastructure? It's all about the "software"

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Citation

Warren Jones, Lana Rubalsky (2010) "Genetic Software - Abstract", wJones Research, March 13, 2011

Abstract

One nature of the technology we call the Existence Model Architecture (Ema) is that it employs genetic software, specifically information that defines the Identity of a Self-aware information processing system. Genetic software, unlike procedural programs, defines the shape and nature of the Goals of an entity. Such systems, rather than run their software, align measured and predicted states in local contexts with platonic descriptions of goal states, as the entity traverses context. While this radical new approach to systems design may at first seem complex, it is something we may all have intimate familiarity with, as it is highly analogous to the bio-genetic information processing in our every cell.

Translation between Genetic Construction and Expression

Stored Purpose is a set of intelligent technologies based upon a design very similar to living cells. They implement a new information processing architecture that defines an entity’s Self and the technical means to sustain it.
Figure - Functional Breakdown of a System that “Exists”<br />Intelligent existence requires that an agent can exist.  This requires it process both a time invariant definition of its own meaning, i.e. “Self,” and the technical means to protect its own definition, i.e. “to be.”  The pairing of the two becomes its Identity, or genetic software.
Figure - Functional Breakdown of a System that “Exists”
Intelligent existence requires that an agent can exist. This requires it process both a time invariant definition of its own meaning, i.e. “Self,” and the technical means to protect its own definition, i.e. “to be.” The pairing of the two becomes its Identity, or genetic software.
The information that defines Self and Technology is called Identity. We developed a novel class of information types to capture the essence of a thing, and simplified it as format we call a Purpose Hypergraph. It defines the Purpose or “Construction” of an existential entity. It can be used for a wide variety of purposes, for example to define the essence of a garage door, a yogurt manufacturing plant or a hospital in such a way, that when loaded in a system with Stored Purpose “Translation” and “Expression” technology, will enable a system to “know” what it is and, what it must do to be itself and fulfill its designed Purpose.
Figure - Genetic Software<br />The “meaning” of an agent is stored as a Purpose hypergraph.  Through a process called Translation, states of technology in the Cloud are changed as the agent senses and predicts environmental changes that invoke Goal Pursuit.
Figure - Genetic Software
The “meaning” of an agent is stored as a Purpose hypergraph. Through a process called Translation, states of technology in the Cloud are changed as the agent senses and predicts environmental changes that invoke Goal Pursuit.

Timespaces and Existence

Systems that run genetic software implement a fundamental architecture that defines intelligent Existence as a three part system. In such a system, information from a timespace called “Construction” is converted by a process called “Translation” into a local timespace called “Expression.” According to Timespace Theory, this method of information processing is not subject to the information flow constraints (i.e. speed of light) typical in human made Computational systems. The genetic software that comprises Construction contains all future “answers,” making the only limiting factor of performance (Translation to Expression) the temporal (time) width of the Translation function, a constraint largely mitigated by the predictive features of the Stored Purpose translation function, called the General Intelligence Algorithm or Gia. Translation occurs at points in contextual space, where the genetic entity intersects with Reality.
Figure - Genetic Software Translated between Timespaces<br />Genetic software is non-procedural.  Instead of running programs that prescribe what a system will do, genetic systems defend a definition of “Self” stored in Construction by a process called Translation.  The result is called Expression.
Figure - Genetic Software Translated between Timespaces
Genetic software is non-procedural. Instead of running programs that prescribe what a system will do, genetic systems defend a definition of “Self” stored in Construction by a process called Translation. The result is called Expression.

Identity

The analog of a “program” in genetic software is called a Goal. It defines “right and wrong” states, as graphs of symbols, sequence, coincidence, linkages and other data. Goals are combined to create Purpose and augmented by regulatory Goals from Organizations to create a genetic software applications, called Identities. Regulation is based upon a theory of Organizational Ecology. Agent Identities can be Constructed to define any type of intelligence, from simple microbial class lighting systems to complex personal assistants with human-like levels of understanding.
Figure - Agent Identity<br />Key components of Identity in a multi-agent system such as one that might coordinate campus-wide intelligent cloud infrastructure.
Figure - Agent Identity
Key components of Identity in a multi-agent system such as one that might coordinate campus-wide intelligent cloud infrastructure.

Format

Genetic Software combines two principle constructs as a union of graphs, specifically Platonic Forms and Contextual Fabric. When the former is mapped onto the latter, it becomes something called a purpose hypergraph. This data is organized into Forms that define goals for a region of context. These Forms are special, because they define the essence of a Goal in a time invariant way that was theorized by Plato in the “Republic” (Plato 380BC). For this reason they are called Platonic Forms.

Platonic Forms can be used to define right and wrong states. They can also be used to define technology on a fundamental level, specifically sensory and controller transitions that employ available tools to accomplish goals, or create new tools from available matter and energy resources.
Figure - Genetic Information Format<br />Forms define the shapes, sequences, coincidences and linkages of knowledge in a pure Platonic format that can be translated directly to expression as the agent traverses contexts of reality.
Figure - Genetic Information Format
Forms define the shapes, sequences, coincidences and linkages of knowledge in a pure Platonic format that can be translated directly to expression as the agent traverses contexts of reality.
Although genetic software can communicate with and coordinate program software, it does not employ interpreted or compiled processor instructions, such as assembler, C++, Java or Python. Purpose hypergraphs are pure data with three basic components: nodes, connections, shapes and media.

Nodes and Linkages

All things stored in a hypergraph are nodes and linkages. Even shapes and media are nodes. For example, a linkage between the symbol of grass and a home’s lawn would be simply stored as the two nodes with a linkage, i.e. grass-lawn. Linkages are optional data as they are assumed to exist between each node on a row of data. They are required when describing change (such as the graph describing closure, near<-far).

Shape Graphs

Shapes are analog representations of Goal states. When linked to symbols, objects and locations in context, they describe the logic or flow for a context. Shape graphs are very much like music in that nodes can be represented as notes, with transitions and repeating cycles. Although a graph can be written in various formats such as delimited text or XML, sophisticated shape graphs currently require use of scientific music notation. Shapes can define subtle sequence, coincidence, amplitude and variation. A shape graph can be used to describe the growth of grass, molecular energy conversion, closure between a hand and apple, or anything that can be known.

A shape graph is composed of nodes and is a node. Since a single shape graph might include dozens of nodes which each might be a link to another shape graph or external Form, genetic software can be very complex to read without sophisticated context browsing software (that does not yet exist). That means it is currently more difficult to read genetic software than to write it.

Media

Notes, linkages and shapes in Platonic Forms can be used to construct and store any data knowable by an agent. A good example is video. An intelligent agent can store in memory three children at play Platonically as a Form with three nodes of children each linked to a context of play, location and temporal space, linked to the shapes of play for each child. This memory would be video-like, much like human memory. But sometimes it is impossible or unnecessary to know the nature of information, yet it may still be necessary for an agent to capture, relay or store it. That’s why genetic software has the ability to store “dumb” data, such as an mpeg data stream. The w3 multi-part message and mime data types are effective formats for storing media data.

Genetic Data Model

The combination of nodes, linkages, shapes and media combine as Platonic Forms which further combine as Purpose. The various information formats comprise the genetic data model for the Ema architecture. Ema is the most basic of all Stored Purpose existence architectures. One nature of the genetic data model is that as architectures become more complex, Purpose for higher levels can be superimposed within the same based data model. This makes it possible for a single Purpose Hypergraph to contain Purpose and regulatory Forms on cellular, multi-cellular and organizational levels (see Organizational Ecology). The construction of higher level regulatory Purpose is beyond the scope of this paper, but it is useful to note that these aspects of the genetic data model can be employed to both architect and understand resource-responsibility relationships and regulatory functions in natural and man-made organizations.

Systems Software

Genetic Software is the persistent information component of an intelligent cloud computing platform based upon the Existence Model Architecture (Ema). These include simple devices called instruments and more complex “multi-agent” systems called metacomputers. Genetic software can be made read-only, such as the Purpose of simple microbe-like instruments or the base regulatory Purpose of more complex entities. It can also be made read-write, Self improving or mediated externally. Genetic software can instantiated as a single entity (see Ema), copied and instantiated across many components of a multi-cellular entity (see Mica), or distributed across many agents of an organization such as a company, campus, forest or ecosystem (see Organizational Ecology). Genetic software is instantiated and run in a database-like “soma” hosting a translation algorithm such as Gia that propagates and gathers predicted states from connected sensors and controllers.

Bio-informatics Implications

Genetic Software is Genetic Software?

Bio-informatics is a set of technologies that makes discoveries about genetic information possible when little knowledge is available about the origins or specific methods used to employ genetic data, in life. It provides programmatic technologies to support the separation, sequencing and cataloging of genetic material. Literal methods include “shot-gunning” chromatin, re-assembling the pieces, sequencing data and finding pattern matches. This makes it possible to find patterns in expression and track and predict outcome relationships, minimally for protein expression.

Knowledge gained in the writing of genetic software for intelligent architectures such as Ema, Mica and Organizational Ecology offers a different route to understanding the construction of life’s software. Stored Purpose technology assumes a universe where there is no magic in the making of existential (intelligent) entities. That means that all the information necessary for an agent to achieve and sustain Purpose must exist within the genetic information that defines it. This applies to all agent types, whether as simple as a garage door that knows to open for family but not let the dog out, or as complex as a multi-agent metropolitan forest ... managing power, traffic, communication and security.

When developing Stored Purpose, we found strict information requirements for any genetic system, ... graph and context structures that must be satisfied for a system able to sustain existence of a Self. We theorize these requirements apply not only to the Stored Purpose machine architectures such as Ema and Mica, but to bio-genetic architectures, such as found in eukaryote, prokaryote, mitochondria and chloroplast systems.

This does not mean that genetic information defining similar Purpose cannot differ between different agent types. We found there can be significant differences in the formats chosen to write genetic information, such as the types and orientation of coordinate systems for storage of shapes and the methods of cataloging local and shared symbols and objects. We also know that agents can be designed with very different translation technologies and since these technologies are stored in the genetic software (of bio-genetic agents), will cause significant apparent differences between the genetic software of agents.

Protiomics Free Genomics

Stored Purpose genetic software focuses exclusively on Purpose expression and goal pursuit technologies using pre-existing tools. For example it can be used to create a garage door application that knows to open when a family member’s car approaches, and not to open if the family dog is in the garage (and may run into the oncoming vehicle). Its design assumes the tools employed by the garage as technology (i.e. door, motor, pulley system, proximity sensor, vehicle transponder, dog collar RFID) pre-exist and are maintained by parties other than the door’s “Self.” It is thus a pure form of genomics that by design includes no genetic software for re-creation of “Self” (i.e. no tools to build or repair its physical “Self” from available matter and energy resources).

Thus, in Stored Purpose genetic software, proteomics and genomics are exclusive approaches. Early generation smart machines will not be designed to self produce or genetically improve. Applications will be developed to understand responsibilities and resources and how to pursue goals using abstract and specific technologies, like living entities. They will also be designed to manipulate their environment, and their Selves, but only to a carefully limited extent by storing self repair information as programmatic media. In particular they will exist, but be curtailed in the repair and evolution of their own parts. The implementation of curtailment is non-trivial and the primary reason there are no “helloworld” examples of purpose hypergraphs published. This difference between man-made systems and life can be considered proteomic-free design, and must be expressly asserted and regulated as must controls on any proteomic-like software.

As such, for many generations, genetic software will be a tool best for understanding regulation beyond proteomics, genetic features that enable life to understand resources such as shapes and textures of foods and water and air, features that store sequence and coincidence and enable entities to understand consequence when employing technologies to pursue goals, including accelerating and stopping, closing on objects, traversing terrain, drinking, finding food and water to raise young and defending the Self. All these capabilities are genomic in nature.

Applications

When the Purpose (design) of any arbitrary entity can be written as a genetic data model independent of the particular bio or mechanical technology of an entity, it may be possible to create bio-informatics tools to more precisely understand, find and repair similar genetic information in bio-genetic software, such as chromatin. Designers will learn from authoring regulation in stored purpose intelligent systems, the meaning of regulatory data types and linkages, specific information formats such as shapes, sequence and linkage graphs. We will also learn to identify local and non-local symbol and object addressing, the algorithms of translation, universal and specific forms, contextual fabric and how it drives where translation is performed.

The following lists a few potential applications in bio-informatics:
  • Identify each of the data types in genetic data, sufficient to identify specific shape, color, layout and composition of symbols that enable all life to reproduce, convert energy, move, prioritize action, and close on objects of interest.
  • Identify coincidence data that enables all intelligent entities to determine which tasks should be performed coincident and which coincident groups should be performed in which order.
  • Identify structures that define the Identity of an entity i.e. the Purpose and regulatory Forms that are sustained and defended for the life of a living entity, data that curtails each cell of the entity to that Identity (keeps a skin cell a skin cell), data that segments the Identity by Purpose on multiple cellular levels, and Goals that comprise each Purpose.
  • Identify the stored state ranges, that define the shape and relative magnitude of “right and wrong” states that make possible the synthesis and design of a fly wing.
  • Identify details of biotechnology sensors and controllers, constructs that make a retina’s sight or fly wing’s operation possible.
  • Identify the underlying catalog and structure of a mind or genetic data, when remote Forms are used and when data is duplicated “nearby” … the contextual map of all symbolic information.
  • Determine not only the function of each code segment, but the shape of its intended result, the range of its accepted variation, its ancestry, its connections that make a goal that is a gene both resource and responsibility and the rules of the regulatory system that ensures all life in the ecosystem work cooperatively for billions of years.

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