Second Edition · Web Edition 2.1
From DNA to AGI
An Undergraduate Research Guide to DOGMA
Start with four molecular letters. Build up through information theory, machine learning, the non-transformer DOGMA architecture, recursive evaluation, and the evidence an AGI research program would actually need.
19chapters
509PDF pages
166kwords
f397732source edition
Learning Path
Biology first. Claims last.
- 01Understand the substrate
Learn how DNA stores, copies, regulates, and expresses information.
- 02Build the computational bridge
Connect sequence biology to representation learning and modern foundation models.
- 03Study DOGMA as a hypothesis
Inspect each local stage, its formal contract, and the evidence still missing.
- 04Run falsifiable experiments
Use frozen evaluations and reproducible labs before making stronger intelligence claims.
25 reading units
Start Here
3 unitsPrefacePrefaceThis book begins with a research question: Can principles from molecular information processing help us design useful learning systems that do not rely on transformer self-attention? The answer is not known. The purpose of this book is to make the question precise enough to study.2 minRoadmapReader’s RoadmapSuggested one-semester course: Chapters 1, 2, 4, 5, 7, 8, 12, 14, 16, 18. Full two-semester sequence: All chapters in order, with Appendix A as prerequisite reading.1 minOne-hour guideDOGMA in One HourDOGMA is not DNA stored in a computer, not a wet-lab molecular computer, not a renamed transformer, and not AGI. It is an experimental architecture and evaluation program. “DNA-inspired” describes a source of hypotheses; it does not prove that the hypotheses work.28 min
The Biological Computer
3 unitsChapter 1The Code of Life — DNA as an Information SystemIn April 1953, James Watson and Francis Crick published a nine-hundred-word paper that changed every science it touched [ Watson and Crick , 1953 ]. The double-helical structure of deoxyribonucleic acid (DNA) revealed that hereditary information is encoded in a molecular sequence—a string over a four-letter alphabet...28 minChapter 2A History of DNA Computing — From Adleman to Molecular ProgrammingOn a November day in 1994, Leonard Adleman walked into his laboratory at the University of Southern California and solved an instance of the Hamiltonian path problem—not on a computer, but in a test tube. Using carefully designed DNA oligonucleotides, enzymatic reactions, and gel electrophoresis, he demonstrated tha...37 minChapter 3Gene Regulation as ComputationEvery cell in a human body contains the same genome—approximately 3.2 billion base pairs, encoding roughly 20,000 protein-coding genes. Yet a neuron looks and functions nothing like a liver cell, a muscle fiber, or an immune cell. The difference is not in what genes are present , but in which genes are expressed —an...27 min
The Intelligence Stack
3 unitsChapter 4Machine Learning Foundations for Biological AIParts I established that biology encodes, regulates, and evolves information through sophisticated computational mechanisms. This chapter begins Part II by building the machine learning foundations needed to translate those biological principles into working AI systems. We cover neural networks, optimization, repres...43 minChapter 5Transformers and Large Language ModelsChapter 4 established the foundational building blocks of machine learning: neural networks, optimization, embeddings, and evolutionary algorithms. This chapter focuses on the single most consequential architecture of the modern deep learning era: the transformer . We trace its development from the attention mechani...37 minChapter 6Biological Foundation ModelsIn the previous chapters we learned how neural networks work (Chapter 4 ) and how transformers power large language models (Chapter 5 ). Those tools were built for human language—English sentences, code, and web text. But in the last five years, researchers have taken the same ideas and pointed them at biology: at p...47 min
The DOGMA Architecture
5 unitsChapter 7DOGMA — The Six-Stage PipelineThis chapter presents the central contribution of this book: DOGMA , the DNA-Organized Genomic Model Architecture . DOGMA is not an incremental improvement to the transformer. It is a fundamentally different way of organizing computation in an AI system—one inspired by three and a half billion years of biological in...51 minChapter 8HelixHash — Structural Representation of Computational GenomesChapter 7 introduced the DOGMA pipeline and its genomic bases. But a genome is more than a sequence of bases—it has structure . In molecular biology, structure determines function: the double helix enables replication, the codon frame enables translation, and chromatin folding enables regulation. In DOGMA, the analo...35 minChapter 9Tera — The Hidden Regime StateIn Chapter 7 , we introduced the six-stage DOGMA pipeline and noted that the Evolutor maintains a persistent Tera regime state that guides regulation and synthesis. In Chapter 8 , we saw how HelixHash encodes structural novelty. This chapter reveals the hidden engine beneath both: Tera , DOGMA’s multi-objective evol...39 minChapter 10TetraData Structure and TetraMemoryComputer graphics has long relied on triangles as the fundamental rendering primitive. Triangle meshes approximate surfaces efficiently and map naturally to GPU hardware. But triangles are inherently two-dimensional : they describe surfaces, not volumes. For a computational architecture inspired by three-dimensional...30 minChapter 11CodonForge — The DNA Compiler and SDANSEvery sophisticated computational system needs a compiler: a principled mechanism for translating high-level specifications into executable low-level operations. In conventional computing, compilers translate C into assembly, or Python into bytecode. In DOGMA, CodonForge translates prompt bundles and genomic specifi...38 min
Building the Evolutor
3 unitsChapter 12Recursive, Evidence-Gated Training“Self-improving” is an appealing phrase and a dangerous specification. A model that trains on its own unchecked output can amplify errors, contaminate its evaluation set, forget rare cases, or optimize an easy proxy. Recursive learning therefore requires more structure than repeatedly launching a training script.8 minChapter 13Foundation Models on Genomic DataChapter 12 showed how to evolve prompt genomes through gradient-free optimization. But the evolutionary loop assumes an underlying language model capable of processing genomic data. Where does that model come from? How should it tokenize DNA, learn codon structure, and integrate biological sequences with natural lan...71 minChapter 14The Measured DOGMA BaselineThis chapter is the empirical boundary of the book. Earlier editions described a 224.7-million-parameter “DOGMA-Supreme” design and benchmark table as if they were a completed validation. That standard was not met. The material is retained in the companion design studies as a research proposal, but it is not reporte...8 min
Toward AGI
3 unitsChapter 15Scaling Laws and Post-Transformer ArchitecturesThe dominant paradigm in AI since 2020 has been simple: make the model bigger . Scaling laws revealed smooth power-law relationships between model size, training compute, dataset size, and loss. These laws enabled precise predictions of model capability and drove the largest engineering projects in AI history—GPT-4,...33 minChapter 16Paths Toward AGI and Post-Transformer IntelligenceThe preceding chapters established the architectural foundations of DOGMA: its six-stage pipeline (Chapter 7 ), the Tera regime dynamics (Chapter 9 ), the tetrahedral data substrate (Chapter 10 ), and the scaling arguments that position it beyond the transformer paradigm (Chapter 15 ). Each contribution addressed a...36 minChapter 17Ethics, Safety, and the Responsibility of IntelligenceThe preceding chapters have constructed a powerful computational paradigm. DOGMA’s six-stage pipeline (Chapter 7 ) organizes persistent genomic state under context-dependent regulation. HelixHash (Chapter 8 ) provides structural fingerprinting. Tera (Chapter 9 ) manages multi-objective regime dynamics. Chapter 16 ch...40 min
Practicum
2 unitsChapter 18Hands-On Labs — Building DOGMA from ScratchTheory without practice is inert. This chapter converts the preceding seventeen chapters of conceptual framework into running code through eight self-contained laboratory exercises. Each lab targets a specific layer of the DOGMA architecture, progressing from the molecular foundations of Watson-Crick base pairing th...53 minChapter 19Epilogue: The Future of IntelligenceThis book began with a molecule and ends with a vision. Between those two points lies an argument: that the deepest principles of biological information processing—storage, regulation, transcription, expression, evolution—are not mere metaphors for artificial intelligence but blueprints for it. We have traced the in...39 min
Reference
3 unitsAppendix AMathematical FoundationsThis appendix collects the minimum mathematics needed to read the DOGMA chapters and evaluate the experiments. It is a reference, not a substitute for a course in linear algebra, probability, or statistics.4 minAppendix BCanonical DOGMA Formal SpecificationThis appendix defines the minimum contract for the canonical non-transformer DOGMA research line. It is intentionally narrower than the full design space in Chapter 7 .3 minAppendix CGlossaryArtificial general intelligence. There is no universally accepted test. This book treats AGI as a research target requiring broad, transferable capability, not as a label for the current DOGMA model.18 min