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Found 26 papers, 4 papers with code
An Evaluation of GPT-4 on the ETHICS Dataset
This report summarizes a short study of the performance of GPT-4 on the ETHICS dataset.
Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs
A moderately detailed consideration of interactive LLMs as cognitive systems is given, focusing on LLMs circa mid-2023 such as ChatGPT, GPT-4, Bard, Llama, etc.. Cognitive strengths of these systems are reviewed, and then careful attention is paid to the substantial differences between the sort of cognitive system these LLMs are, and the sort of cognitive systems human beings are.
OpenCog Hyperon: A Framework for AGI at the Human Level and Beyond
An introduction to the OpenCog Hyperon framework for Artificiai General Intelligence is presented.
A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems
We introduce a formal meta-language for probabilistic programming, capable of expressing both programs and the type systems in which they are embedded.
Architecture of Automated Crypto-Finance Agent
We present the cognitive architecture of an autonomous agent for active portfolio management in decentralized finance, involving activities such as asset selection, portfolio balancing, liquidity provision, and trading.
The General Theory of General Intelligence: A Pragmatic Patternist Perspective
A multi-decade exploration into the theoretical foundations of artificial and natural general intelligence, which has been expressed in a series of books and papers and used to guide a series of practical and research-prototype software systems, is reviewed at a moderate level of detail.
Patterns of Cognition: Cognitive Algorithms as Galois Connections Fulfilled by Chronomorphisms On Probabilistically Typed Metagraphs
It is argued that a broad class of AGI-relevant algorithms can be expressed in a common formal framework, via specifying Galois connections linking search and optimization processes on directed metagraphs whose edge targets are labeled with probabilistic dependent types, and then showing these connections are fulfilled by processes involving metagraph chronomorphisms.
Info-Evo: Using Information Geometry to Guide Evolutionary Program Learning
A novel optimization strategy, Info-Evo, is described, in which natural gradient search using nonparametric Fisher information is used to provide ongoing guidance to an evolutionary learning algorithm, so that the evolutionary process preferentially moves in the directions identified as "shortest paths" according to the natural gradient.
Paraconsistent Foundations for Quantum Probability
It is argued that a fuzzy version of 4-truth-valued paraconsistent logic (with truth values corresponding to True, False, Both and Neither) can be approximately isomorphically mapped into the complex-number algebra of quantum probabilities.
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation
It is argued that 4-valued paraconsistent truth values (called here "p-bits") can serve as a conceptual, mathematical and practical foundation for highly AI-relevant forms of probabilistic logic and probabilistic programming and concept formation.
Folding and Unfolding on Metagraphs
Typed metagraphs are defined as hypergraphs with types assigned to hyperedges and their targets, and the potential to have targets of hyperedges connect to whole links as well as targets.
Uncertain Linear Logic via Fibring of Probabilistic and Fuzzy Logic
Beginning with a simple semantics for propositions, based on counting observations, it is shown that probabilistic and fuzzy logic correspond to two different heuristic assumptions regarding the combination of propositions whose evidence bases are not currently available.
Embedding Vector Differences Can Be Aligned With Uncertain Intensional Logic Differences
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.
Guiding Symbolic Natural Language Grammar Induction via Transformer-Based Sequence Probabilities
A novel approach to automated learning of syntactic rules governing natural languages is proposed, based on using probabilities assigned to sentences (and potentially longer word sequences) by transformer neural network language models to guide symbolic learning processes like clustering and rule induction.
Maximal Algorithmic Caliber and Algorithmic Causal Network Inference: General Principles of Real-World General Intelligence?
Ideas and formalisms from far-from-equilibrium thermodynamics are ported to the context of stochastic computational processes, via following and extending Tadaki's algorithmic thermodynamics.
Grounding Occam's Razor in a Formal Theory of Simplicity
CoSMs and CoSMOS are then used as a foundation for a theory of pattern and multipattern, and a theory of hierarchy and heterarchy in systems of patterns.
What Kind of Programming Language Best Suits Integrative AGI?
This framework should support concurrent rewriting of the metagraph according to rules that are labeled with various sorts of uncertainty-quantifications, and that are labeled with various sorts of types associated with various type systems.
Combinatorial Decision Dags: A Natural Computational Model for General Intelligence
A novel computational model (CoDD) utilizing combinatory logic to create higher-order decision trees is presented.
Distinction Graphs and Graphtropy: A Formalized Phenomenological Layer Underlying Classical and Quantum Entropy, Observational Semantics and Cognitive Computation
A new conceptual foundation for the notion of "information" is proposed, based on the concept of a "distinction graph": a graph in which two nodes are connected iff they cannot be distinguished by a particular observer.
A Reputation System for Artificial Societies
One approach to achieving artificial general intelligence (AGI) is through the emergence of complex structures and dynamic properties arising from decentralized networks of interacting artificial intelligence (AI) agents.
Humanoid Robots as Agents of Human Consciousness Expansion
The "Loving AI" project involves developing software enabling humanoid robots to interact with people in loving and compassionate ways, and to promote people' self-understanding and self-transcendence.
Metalearning for Feature Selection
A general formulation of optimization problems in which various candidate solutions may use different feature-sets is presented, encompassing supervised classification, automated program learning and other cases.
Symbol Grounding via Chaining of Morphisms
A new model of symbol grounding is presented, in which the structures of natural language, logical semantics, perception and action are represented categorically, and symbol grounding is modeled via the composition of morphisms between the relevant categories.
Toward a Formal Model of Cognitive Synergy
"Cognitive synergy" refers to a dynamic in which multiple cognitive processes, cooperating to control the same cognitive system, assist each other in overcoming bottlenecks encountered during their internal processing.
Cost-Based Intuitionist Probabilities on Spaces of Graphs, Hypergraphs and Theorems
A novel partial order is defined on the space of digraphs or hypergraphs, based on assessing the cost of producing a graph via a sequence of elementary transformations.
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of dependency grammars and associated syntax-to-semantic-relationship mappings from large text corpora is described.
