Ben Goertzel’s research while affiliated with Caritas Hong Kong and other places

In a previous article, the authors described a series of experiments combining probabilisitic logical inference with an artificial economics based attention allocation system. In those experiments, the authors compared their results with those from two standard examples chosen from the Markov Logic Networks literature. The examples were insufficient to determine the full usefulness of the integrated system, as the information provided in the examples was precisely the information required for inference. Due to limitations of the test suite, any attention allocation system would be unable to provide additional direction. In this current followup article, the authors describe a new series of experiments and tests intended to demonstrate the effective utilization of attention allocation for inference control. The authors conclude by demonstrating the success, on the first of these experiments, of the cog-nitive synergy provided via an integration of attention control and prob-abilisitic inference mechanisms.

We conducted a series of experiments within the ECAN system governing the attentional cognitive dynamics for Hanson Robotics Sophia robot. We logged short-term importance values (STI) within Sophia's "Economic Attention Network" attentional system and then empirically derived Tononi Phi values from the resulting STI time series. We compared the Phi value time-series with Sophia's current cognitive actions to determine the degree of connectedness in the system, which has been hypothesized to be a neural correlate of consciousness (NCC.)

Safeguarding GenAI models against threats and aligning them with security requirements is imperative yet challenging. This chapter provides an overview of the security landscape for generative models. It begins by elucidating common vulnerabilities and attack vectors, including adversarial attacks, model inversion, backdoors, data extraction, and algorithmic bias. The practical implications of these threats are discussed, spanning domains like finance, healthcare, and content creation. The narrative then shifts to exploring mitigation strategies and innovative security paradigms. Differential privacy, blockchain-based provenance, quantum-resistant algorithms, and human-guided reinforcement learning are analyzed as potential techniques to harden generative models. Broader ethical concerns surrounding transparency, accountability, deepfakes, and model interpretability are also addressed. The chapter aims to establish a conceptual foundation encompassing both the technical and ethical dimensions of security for generative AI. It highlights open challenges and lays the groundwork for developing robust, trustworthy, and human-centric solutions. The multifaceted perspective spanning vulnerabilities, implications, and solutions is intended to further discourse on securing society’s growing reliance on generative models. Frontier model security is discussed using Anthropic proposed approach.

This chapter discusses the synergistic intersection of ChatGPT and Web3 applications, exploring the transformative potential and challenges within this emerging digital landscape. We first introduce Web3 and decentralized networks, detailing ChatGPT’s pivotal role in these applications. The chapter further illustrates how ChatGPT can invigorate decentralized applications (dApps), decentralized finance (DeFi) platforms, and digital asset management. We explore how the Web3 ecosystem can be leveraged to revolutionize AI data governance, model validation, and the democratization of computational power in ChatGPT applications. We also examine the role of tokenization and ChatGPT in incentivizing user engagement, governing applications, and devising novel monetization strategies. As we anticipate future technological advancements, the chapter underscores the importance of interdisciplinary collaboration and robust integration strategies for navigating the evolving AI and decentralized technology landscape. This analysis aids businesses in harnessing next-gen AI technology in a decentralized ecosystem.

Aging is best understood as a gradual, sometimes punctuated, change in the dynamical regime of a self-organizing network composed of heterogeneous complex processes interacting via complex nonlinear spatiotemporal interactions. Key easily observable aspects such as the “hallmarks of aging” represent specific manifestations of underlying holistic network dynamics. Different aspects of this self-organizing network are apt to be best understood by reference to different datasets and by means of different analytical approaches. However, it seems likely that to create therapies substantially increasing maximum human lifespan in a reliable way, ultimately a holistic understanding of the dynamics of aging across the organism will be required. This leads naturally to a network-based approach to data-analytics and hypothesis-formation and -evaluation, in which holistic models of aging in the organism are automatically assembled from multiple datasets and models addressing various aspects. One key issue in realizing a network-based approach to aging is the process of mathematically combining multiple models; toward this end we propose an assemblage of techniques beginning with a relatively simple quadratic programming based approach, and culminating in a “computational social science” approach in which multiple models collectively form a social network of models. Within that network, symbols and cultures, reflecting complex holistic patterns in the underlying data, may emerge. Another key issue is how to incentivize an appropriately diverse and capable community of individuals or organizations to contribute data and models to the holistic “Generative Cooperative Network” of models. We propose a tokenomics approach, in which a variety of cryptographic token types are used to incentivize contribution to the GCN. These issues comprise much of the inspiration for the Rejuve.AI project, which is building general mechanisms for GCN and tokenomic incentivization. Rejuve.AI is also creating a set of relatively simple aging-related models to seed the GCN, including: A hand-crafted Bayes Net model assessing aspects of an individual’s path to healthy longevity, via using the logic of the hallmarks of aging to interpret data from questionnaire answers and biosignals (as gathered from users of the Rejuve.AI longevity app). A model of longevity-related pathways and networks identified by pattern mining in the BioAtomspace, an integrated genomic and medical knowledge base created within the OpenCog Atomspace knowledge-metagraph framework. A model indicating genes, pathways and networks involved in the longevity of the Methuselah flies, long-lived Drosophila Melanogaster created via experimental evolution over multiple decades. Automated integration of insights from these diverse models, based on diverse datasets, will enable prototyping of the overall GCN framework and will serve as a seed for broader growth of the GCN based on contributions from the research and Rejuve.AI app user communities. Growth of our holistic network will enable the formation of a dynamic, multiresolutional mechanistic simulation of the human body that will shed new light on the causes of aging and its treatment.KeywordsHybrid artificial intelligenceComplex adaptive systemsMultiresolutional simulationBayesian networksSystems biologyAgingLongevityCrowdsourcingCoevolutionBlockchain

We present an operational semantics for the language MeTTa.

Multiple cognitive algorithms posited to play a key role in AGI (forward and backward chaining inference, clustering and concept formation, evolutionary and reinforcement learning, probabilistic programming, etc.) are given a common formulation as recursive discrete decision processes involving optimizing functions defined over metagraphs, in which the key decisions involve sampling from probability distributions over metagraphs and enacting sets of combinatory operations on selected sub-metagraphs. This forms a bridge between abstract conceptions of general intelligence founded on notions of algorithmic information and complex systems theory, and the practical design of multi-paradigm AGI systems.

We introduce a formal meta-language for probabilistic programming, capable of expressing both programs and the type systems in which they are embedded. We are motivated here by the desire to allow an AGI to learn not only relevant knowledge (programs/proofs), but also appropriate ways of reasoning (logics/type systems). We draw on the frameworks of cubical type theory and dependent typed metagraphs to formalize our approach. In doing so, we show that specific constructions within the meta-language can be related via bisimulation (implying path equivalence) to the type systems they correspond. This allows our approach to provide a convenient means of deriving synthetic denotational semantics for various type systems. Particularly, we derive bisimulations for pure type systems (PTS), and probabilistic dependent type systems (PDTS). We discuss further the relationship of PTS to non-well-founded set theory, and demonstrate the feasibility of our approach with an implementation of a bisimulation proof in a Guarded Cubical Type Theory type checker.

Problem of reliable democratic governance is critical for survival of any community, and it will be critical for communities powered with Artificial Intelligence (AI) systems upon developments of the latter. Apparently, it will be getting more and more critical because of increasing speeds and scales of electronic communications and decreasing latencies in system responses. In order to address this need, we present design and implementation of a reputation system supporting "liquid democracy" principle. The system is based on "weighted liquid rank" algorithm employing different sorts of explicit and implicit ratings being exchanged by members of the society as well as implicit assessments of of the members based on measures of their activity in the society. The system is evaluated against live social network data with help of simulation modelling for an online marketplace case.