Homo Faber Scapegoated, or Apocalyptic Artificial Intelligence: Rethinking the Technological Singularity Concept From the Synergetic Historicism Position

The article is focused on discussing a new methodological approach to the study on specifics of transferring human beings to the posthuman cyber society.The approach in question assists in rethinking interconnected problems both of human origins in the universe and mankind’s digital future.And,besides,such an approach allows to deal with self-organising interconversions between the poles of the cardinal dual opposition of the Global Noosphere Brain and the Artificial General Intelligence.Herewith such phenomena of digital social life as Global Digitalisation,Digital Immortality,Mindcloning,and Technological Zombification being the constituents of Technological Singularity Concept,are rethought as paving the way for oncoming Posthuman Digital Era.This concept is evidently exemplified by a bifurcation resulting in two alternatives to be chosen by human beings,to wit,either to be undergone Mindcloning and become digitally immortal or being destroyed by powerful intelligent machines.The investigation in question is based on such a progressive methodology as the Law of Self-Organizing Ideals,as well as on the Method of Dual Oppositions.Rethinking interrelationships between the problem of a sense of social history and the meaning-of-life of local societies members which any intelligent machine is devoid of permits to substantiate specific regularities of Self-Transforming Homo Faber into Homo Digitalis and Technological Zombies ready to be transferred to posthuman cyberspace.

Generative pre-trained transformers(GPT)-based automated data mining for building energy management:Advantages,limitations and the future

Advanced data mining methods have shown a promising capacity in building energy management.However,in the past decade,such methods are rarely applied in practice,since they highly rely on users to customize solutions according to the characteristics of target building energy systems.Hence,the major barrier is that the practical applications of such methods remain laborious.It is necessary to enable computers to have the human-like ability to solve data mining tasks.Generative pre-trained transformers(GPT)might be capable of addressing this issue,as some GPT models such as GPT-3.5 and GPT-4 have shown powerful abilities on interaction with humans,code generation,and inference with common sense and domain knowledge.This study explores the potential of the most advanced GPT model(GPT-4)in three data mining scenarios of building energy management,i.e.,energy load prediction,fault diagnosis,and anomaly detection.A performance evaluation framework is proposed to verify the capabilities of GPT-4 on generating energy load prediction codes,diagnosing device faults,and detecting abnormal system operation patterns.It is demonstrated that GPT-4 can automatically solve most of the data mining tasks in this domain,which overcomes the barrier of practical applications of data mining methods in this domain.In the exploration of GPT-4,its advantages and limitations are also discussed comprehensively for revealing future research directions in this domain.

How We Will Discover Sentience in AI

This paper explores the question of how we can know if Artificial Intelligence(AI)systems have become or are becoming sentient.After an overview of some arguments regarding AI sentience,it proceeds to an outline of the notion of negation in the philosophy of Josiah Royce,which is then applied to the arguments already presented.Royce’s notion of the primitive dyadic and symmetric negation relation is shown to bypass such arguments.The negation relation and its expansion into higher types of order are then considered with regard to how,in small variations of active negation,they would disclose sentience in AI systems.Finally,I argue that the much-hyped arguments and apocalyptic speculations regarding Artificial General Intelligence(AGI)takeover and similar scenarios,abetted by the notion of unlimited data,are based on a fundamental misunderstanding of how entities engage their experience.Namely,limitation,proceeding from the symmetric negation relation,expands outward into higher types of order in polyadic relations,wherein the entity self-limits and creatively moves toward uniqueness.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsufficient precondition of the downstream in a new notion of Space Economy 4.0-Part 2:Software developments

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysis Ready Data(ARD)products and processes are critically compared,to overcome their lack of harmonization/standardization/interoperability and suitability in a new notion of Space Economy 4.0.In the present Part 2,original contributions comprise,at the Marr five levels of system understanding:(1)an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification.First,in the pursuit of third-level semantic/ontological interoperability,a novel ARD symbolic(categorical and semantic)co-product,known as Scene Classification Map(SCM),adopts an augmented Cloud versus Not-Cloud taxonomy,whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization.Second,a novel ARD subsymbolic numerical co-product,specifically,a panchromatic or multispectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure,ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values,in a five-stage radiometric correction sequence.(2)An original ARD process requirements specification.(3)An innovative ARD processing system design(architecture),where stepwise SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence.(4)An original modular hierarchical hybrid(combined deductive and inductive)computer vision subsystem design,provided with feedback loops,where software solutions at the Marr two shallowest levels of system understanding,specifically,algorithm and implementation,are selected from the scientific literature,to benefit from their technology readiness level as proof of feasibility,required in addition to proven suitability.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.