Introduction

Goertzel, Ben; Iklé, Matthew; Goertzel, Izabela Freire; Heljakka, Ari

doi:10.1007/978-0-387-76872-4_1

Cited by 2 publications

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“…We use the "Bio-Atomspace" -an Atomspace filled with knowledge from multiple bio-ontologies, and with results from statistical and machine learning analysis of various genomics datasets from longevity-related studies (see [2] for prior work with an earlier version of Bio-Atomspace). Application of the Probabilistic Logic Networks (PLN) engine [1] to the Bio-Atomspace produces uncertain logical explanations for the connections found by machine learning algorithms between certain gene variations or expressions and combinations thereof and phenotypes such as longevity.…”

Section: Explorations With the Bio-atomspacementioning

confidence: 99%

Embedding Vector Differences Can Be Aligned With Uncertain Intensional Logic Differences

Goertzel¹,

Duncan²,

Duong³

et al. 2020

Preprint

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The DeepWalk algorithm is used to assign embedding vectors to nodes in the Atomspace weighted, labeled hypergraph that is used to represent knowledge in the OpenCog AGI system, in the context of an application to probabilistic inference regarding the causes of longevity based on data from biological ontologies and genomic analyses. It is shown that vector difference operations between embedding vectors are, in appropriate conditions, approximately alignable with "intensional difference" operations between the hypergraph nodes corresponding to the embedding vectors. This relationship hints at a broader functorial mapping between uncertain intensional logic and vector arithmetic, and opens the door for using embedding vector algebra to guide intensional inference control.

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Section: Explorations With the Bio-atomspacementioning

confidence: 99%

Embedding Vector Differences Can Be Aligned With Uncertain Intensional Logic Differences

Goertzel¹,

Duncan²,

Duong³

et al. 2020

Preprint

Self Cite

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“…published in 2006; Probabilistic Logic Networks (Goertzel et al 2008), a more technical work published in 2008; Real World Reasoning (Goertzel et al 2011), a sequel to Probabilistic Logic Networks published in 2011; and the two parts of this book.…”

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confidence: 99%

Engineering General Intelligence, Part 1

Goertzel¹,

Pennachin²,

Geisweiller³

2014

Atlantis Thinking Machines

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This is a large, two-part book with an even larger goal: To outline a practical approach to engineering software systems with general intelligence at the human level and ultimately beyond. Machines with flexible problem-solving ability, openended learning capability, creativity, and eventually their own kind of genius.Part 1 of the book (Volume 5 in the Atlantis Thinking Machines book series), reviews various critical conceptual issues related to the nature of intelligence and mind. It then sketches the broad outlines of a novel, integrative architecture for Artificial General Intelligence (AGI) called CogPrime… and describes an approach for giving a young AGI system (CogPrime or otherwise) appropriate experience, so that it can develop its own smarts, creativity, and wisdom through its own experience. Along the way a formal theory of general intelligence is sketched, and a broad roadmap leading from here to human-level artificial intelligence. Hints are also given regarding how to eventually, potentially create machines advancing beyond human level-including some frankly futuristic speculations about strongly self-modifying AGI architectures with flexibility far exceeding that of the human brain.Part 2 of the book (Volume 6 in the Atlantis Thinking Machines book series), then digs far deeper into the details of CogPrime's multiple structures, processes, and functions, culminating in a general argument as to why we believe CogPrime will be able to achieve general intelligence at the level of the smartest humans (and potentially greater), and a detailed discussion of how a CogPrime-powered virtual agent or robot would handle some simple practical tasks such as social play with blocks in a preschool context. It first describes the CogPrime software architecture and knowledge representation in detail; then reviews the cognitive cycle via which CogPrime perceives and acts in the world and reflects on itself; and next turns to various forms of learning: procedural, declarative (e.g., inference), simulative, and integrative. Methods of enabling natural language functionality in CogPrime are then discussed; and then the volume concludes with a chapter summarizing the argument that CogPrime can lead to human-level (and eventually perhaps greater) AGI, and a chapter giving a thought experiment describing the internal dynamics via which a completed CogPrime system might solve the problem of obeying the request ''Build me something with blocks that I haven't seen before.''The chapters here are written to be read in linear order-and if consumed thus, they tell a coherent story about how to get from here to advanced AGI. v However, we suggest the impatient reader may wish to take a quick look at the final chapter of Part 2, after reading Chaps. 1-3 of Part 1. This final chapter gives a broad overview of why we think the CogPrime design will work, in a way that depends on the technical details of the previous chapters, but (we believe) not so sensitively as to be incomprehensible without them. This is admittedly an unusual sor...

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Introduction

Cited by 2 publications

References 5 publications

Embedding Vector Differences Can Be Aligned With Uncertain Intensional Logic Differences

Embedding Vector Differences Can Be Aligned With Uncertain Intensional Logic Differences

Engineering General Intelligence, Part 1

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