The human mind’s evolution owes much to its companion phenomena of intelligence, sapience, wisdom, awareness and consciousness. In this paper we take the concepts of intelligence and sa-pience as the starting point o...The human mind’s evolution owes much to its companion phenomena of intelligence, sapience, wisdom, awareness and consciousness. In this paper we take the concepts of intelligence and sa-pience as the starting point of a route towards elucidation of the conscious mind. There is much disagreement and confusion associated with the word intelligence. A lot of this results from its use in diverse contexts, where it is called upon to represent different ideas and to justify different ar-guments. Addition of the word sapience to the mix merely complicates matters, unless we can relate both of these words to different concepts in a way which acceptably crosses contextual boundaries. We have established a connection between information processing and processor “architecture” which provides just such a linguistic separation, and which is applicable in either a computational or conceptual form to any context. This paper reports the argumentation leading up to a distinction between intelligence and sapience, and relates this distinction to human “cognitive” activities. Information is always contextual. Information processing in a system always takes place between “architectural” scales: intelligence is the “tool” which permits an “overview” of the relevance of individual items of information. System unity presumes a degree of coherence across all the scales of a system: sapience is the “tool” which permits an evaluation of the relevance of both individual items and individual scales of information to a common purpose. This hyperscalar coherence is created through mutual inter-scalar observation, whose recursive nature generates the independence of high-level consciousness, making humans human. We conclude that intelligence and sapience are distinct and necessary properties of all information processing systems, and that the degree of their availability controls a system’s or a human’s cognitive capacity, if not its appli-cation. This establishes intelligence and sapience as prime ancestors of the conscious mind. How-ever, to our knowledge, there is no current mathematical approach which can satisfactorily deal with the native irrationalities of information integration across multiple scales, and therefore of formally modeling the mind.展开更多
With the increasing complexity and scale of hyperscale data centers,the requirement for intelligent,real-time power delivery has never been more critical to ensure uptime,energy efficiency,and sustainability.Those tec...With the increasing complexity and scale of hyperscale data centers,the requirement for intelligent,real-time power delivery has never been more critical to ensure uptime,energy efficiency,and sustainability.Those techniques are typically static,reactive(since CPU and workload scaling is applied to performance events that occur after a request has been submitted,and is thus can be classified as a reactive response.),and require manual operation,and cannot cope with the dynamic nature of the workloads,the distributed architectures as well as the non-uniform energy sources in today’s data centers.In this paper,we elaborate on how artificial intelligence(AI)is revolutionizing power distribution in hyperscale data centers,making predictive load forecasting,real-time fault detection,and autonomous power optimization possible.We explain how ML(machine learning)and RL(reinforcement learning)-based models have been introduced in PDN(power delivery networks)for load balancing in three-phase systems,overprovisioning reduction,and energy flow optimization from the grid to the rack.The paper considers the architectural pieces of the AI-led systems,such as data ingestion pipelines,anomaly detection frameworks,and control algorithms to manage the power switching,cooling synchronization,and grid/microgrid interaction.Practical use cases show the value of these systems on PUE,infrastructure reliability,and environmental footprint.Key implementation challenges,including data quality,legacy systemintegration,and AI decision-making governance,are also discussed.Last,the paper speculates on the future of autonomous DC power infrastructure where AI becomes not only an assistive resource to the operator but really takes control over infrastructure behavior end-to-end,from procuring energy,to phase balancing,to predicting maintenance.Integrating technology innovation with operational sustainability,AI-powered power distribution is emerging as a core competence for the Smart Digital Power Facility of the Future.展开更多
文摘The human mind’s evolution owes much to its companion phenomena of intelligence, sapience, wisdom, awareness and consciousness. In this paper we take the concepts of intelligence and sa-pience as the starting point of a route towards elucidation of the conscious mind. There is much disagreement and confusion associated with the word intelligence. A lot of this results from its use in diverse contexts, where it is called upon to represent different ideas and to justify different ar-guments. Addition of the word sapience to the mix merely complicates matters, unless we can relate both of these words to different concepts in a way which acceptably crosses contextual boundaries. We have established a connection between information processing and processor “architecture” which provides just such a linguistic separation, and which is applicable in either a computational or conceptual form to any context. This paper reports the argumentation leading up to a distinction between intelligence and sapience, and relates this distinction to human “cognitive” activities. Information is always contextual. Information processing in a system always takes place between “architectural” scales: intelligence is the “tool” which permits an “overview” of the relevance of individual items of information. System unity presumes a degree of coherence across all the scales of a system: sapience is the “tool” which permits an evaluation of the relevance of both individual items and individual scales of information to a common purpose. This hyperscalar coherence is created through mutual inter-scalar observation, whose recursive nature generates the independence of high-level consciousness, making humans human. We conclude that intelligence and sapience are distinct and necessary properties of all information processing systems, and that the degree of their availability controls a system’s or a human’s cognitive capacity, if not its appli-cation. This establishes intelligence and sapience as prime ancestors of the conscious mind. How-ever, to our knowledge, there is no current mathematical approach which can satisfactorily deal with the native irrationalities of information integration across multiple scales, and therefore of formally modeling the mind.
文摘With the increasing complexity and scale of hyperscale data centers,the requirement for intelligent,real-time power delivery has never been more critical to ensure uptime,energy efficiency,and sustainability.Those techniques are typically static,reactive(since CPU and workload scaling is applied to performance events that occur after a request has been submitted,and is thus can be classified as a reactive response.),and require manual operation,and cannot cope with the dynamic nature of the workloads,the distributed architectures as well as the non-uniform energy sources in today’s data centers.In this paper,we elaborate on how artificial intelligence(AI)is revolutionizing power distribution in hyperscale data centers,making predictive load forecasting,real-time fault detection,and autonomous power optimization possible.We explain how ML(machine learning)and RL(reinforcement learning)-based models have been introduced in PDN(power delivery networks)for load balancing in three-phase systems,overprovisioning reduction,and energy flow optimization from the grid to the rack.The paper considers the architectural pieces of the AI-led systems,such as data ingestion pipelines,anomaly detection frameworks,and control algorithms to manage the power switching,cooling synchronization,and grid/microgrid interaction.Practical use cases show the value of these systems on PUE,infrastructure reliability,and environmental footprint.Key implementation challenges,including data quality,legacy systemintegration,and AI decision-making governance,are also discussed.Last,the paper speculates on the future of autonomous DC power infrastructure where AI becomes not only an assistive resource to the operator but really takes control over infrastructure behavior end-to-end,from procuring energy,to phase balancing,to predicting maintenance.Integrating technology innovation with operational sustainability,AI-powered power distribution is emerging as a core competence for the Smart Digital Power Facility of the Future.
