Synthetic Philosophy and Deductive Engineering
I am not a historian, nor an evolutionary biologist, or an academic of any kind, but I have taken some interest in the origins and trajectory of rationality, science and engineering. The historical background discussed here has influenced the philosophy and architecture of SPaDE and has contributed to motivating the ways in which the approach being progressed in SPaDE differs from (and perhaps complements) mainstream ASI (Artificial Super-intelligence) research and development.
The current mainstream builds LLMs (Large Language Models) which are thought and talked of as “neural networks”, architecturally inspired (if loosely) by the structure of the human brain. These neural networks begin as tabulae rasae (blank slates) and are trained on large corpora of text (and/or other media), and then used to generate similar media in response to prompts. Though this is a far cry from the way in which the human brain evolved, develops and learns, it is proving very effective once sufficient computational resource is provided, and has substantially reduced estlimates of the timescales to AGI, ASI, “The Singularity” and sustainable abundance for all (now expected by some key proponents to be achieved within a decade).
Much of the research on AI over the past century has not adopted this approach, but has involved more deliberate architectural thinking about the representation of knowledge and the use of deductive reasoning to derive conclusions from that knowledge. Because of its prominence in the earliest research on AI this approach is sometimes called GOFAI, but perhaps more informatively as the “symbolic” or “logic-based” approach. This kind of research has been marginalised by the technical and commercial success of the “connectionist” (neural net) approach.
Alongside that main thrust, there have been some impressive results achieved by means much closer to the symbolic approach, notably the Deep Mind’s AlphaZero and AlphaFold, which use neural nets for heuristics in MCTS (Monte Carlo Tree Search) and reinforcement learning. In Mathematics both informal and formal proofs have been generated by neural nets, and the formal proofs have been verified by proof assistants. There does not appear to be any work which has achieved prominence by more systematic use of symbolic methods of the kind envisaged in SPaDE.
SPaDE advocates for and seeks, in pursuit of its goals, to contribute to the adoption of more systematic and rigorous approaches to the representation of knowledge and the use of deductive reasoning in the development of ASI. It is not predicated on the belief that such approaches are essential to achieving ASI, or sustainable abundance, but in the expectation that eventually ASI will see the benefits of adopting such methods and will seek to exploit formal declarative knowledge representation and deductive reasoning in its own development and operation. Because this is not yet done by homo sapiens, the training of ASI does not encompass effective methods along the lines proposed by SPaDE or any other approach to the same objective. But the techniques proposed are mostly in the literature, and the impediments to their full exploitation by homo sapiens are not significant to ASI, which can therefore be expected to adopt them as soon as it assumes architectural responsibilities in the engineering of its successors.
The remainder of this document falls into the following sections:
This is primarily a story of cultural evolution advancing epistemology, knowledge representation and deductive reason.
The following waypoints are of interest:
Though, from a modern perspective, declarative knowledge and deductive inference are linguistic (symbolic) phenomena, the distinction between declarative and procedural memory substantially predates the evolution of language.
Procedural memory goes back to the earliest organisms, and is implicated in the evolution of the first nervous systems.
Declarative memory is first inferred from consideration of the capabilities of modern organisms, locating those capabilities in cerebral structures (such as the hippocampus) which first emerged in early vertebrates.
Reasoning capabilities are implicated when behaviours are observed which are not explicable by simple stimulus-response mechanisms, and which appear to be based on the use of declarative knowledge to derive conclusions about the world which are then used to guide behaviour. It is premature at this stage to distinguish deductive reasoning, inductive reasoning and abductive reasoning. One reason for this is that the representation of declarative knowledge as memory lacks a definite semantics.
In the context of a long history of declarative memory (in various forms), the ability to communicate the knowledge stored in declarative memory is a major evolutionary advantage, and the emergence of declarative language is a major evolutionary milestone.
It is generally believed that language co-evolved with the development of the human brain, and that the emergence of language was a major factor in the development of human intelligence. It is therefore reasonable to date the origin of language to the emergence of anatomically modern humans, and to locate that emergence in Africa around 300,000 years ago. Of that, an important part is declarative language, the means to communicate declarative knowledge, sharing the content of declarative memory.
To enable communication, the idosyncratic reprsentation of declarative memory in synaptic connections and weights must be translated into a common oral representation. The effectiveness of communication then depends on common understanding of the semantics of this shared representation, though linguistic diversity at various levels may work against this.
Competence in a language necessarily involves knowledge of conceptual inclusions, and hence relations of entailment underpinning deductive inference.
As culuture evolves, at first very slowly indeed, language develops with it. A point of inflection in that development is the emergence of written language.
Agriculture and early civilisations placed demands for administration, trade, taxation, land measurement, and construction which stimulated the development of mathematics in the form of arithmetic and geometric techniques for calculation and measurement, which depend upon written notations.
These early written forms elaborate within half a millenium into written language capable of capturing bulk of the oral tradition, contributing greatly to the preservation and propagation of cultural knowledge, in written declarative language.
We are now 90% through the time from the advent of anatomically modern humans to the present day, and we have just learned to write. All the achievements of Mathematics, Science and Engineering which have transformed the world, and which are now being used to transform the future, took place in that remaining 1%.
The transformation of mathematics into a theoretical discipline takes place early in classical Greece, with the development of axiomatic method.
This makes deductive reasoning more explicit by requiring that each theory is derived from a fixed set of premises of various kinds (axioms, postulates, definitions, …) by chains of reason in which each theorem is inferred exclusively from these presmises or previous theorems. Though the reasoning in fact is deductive, the rules of inference are not explicitly defined, and it will not be for over two millennia that logical systems formalising the rules of inference are developed.
After around 300 years of development of axiomatic mathematics, Aristotle undertook the first study of logic in the collection of books which are now known as the Organon.
An important theme in those books is the idea of demonstrative science, which aims to expand the deductive methods adopted in mathematics to the whole of science as it was then known.
A part of this is his study of the syllogism, which is a systematic treatment of certain aspects of deductive reasoning. Aristotles influence on the development of logic and science was profound, and lasted for over a millennium, until the Renaissance and the Scientific Revolution. But the syllogism became a straitjacket, falling short of the needs of mathematics and science, and inhibiting the development of more flexible and powerful methods of reasoning.
This was not an impediment to the continued advancement of those disciplines, in which the deductive reasoning remained informal but effective.
In transiting from the use of deduction in mathematics to its use in science, it is the identification of the premises from which deduction may proceed which is problematic, and it is in this that modern science was to be transformative.
It is not until the Scientific Revolution that the identification of premises is addressed in a systematic way, and the nomologico-deductive method is developed.
Hypothetico-deductive and nomologico-deductive