Evolution processes of complex networked systems in biology and social sciences, and their underlying mechanisms, still need better understanding. The authors propose a machine learning approach to reconstruct the evolution history of complex networks.
In recent years, artificial intelligence technologies, especially machine learning algorithms, have made great strides. These technologies have enabled unprecedented efficiency in tasks such as image recognition, natural language generation and processing, and object detection, but such outstanding functionality requires substantial computational power as a foundation.
In the burgeoning field of AI and cybernetics, we stand at the cusp of a paradigm shift—a reimagining of the foundational principles that underpin our understanding of reality itself. This article delves into the tenets of the Cybernetic Theory, of Mind (CTM), a model that amalgamates the rigor of science with the vast potentialities of consciousness (observer-dependence, causality, teleology, phenomenality), offering a novel lens through which to view the mechanisms of mind and matter. As we explore these principles, we uncover a framework that transcends traditional boundaries, positioning consciousness as the bedrock of existence and viewing the universe not merely as a collection of separate entities but as an interconnected web of information processing and exchange. This new ontological model invites us to reconsider not just the nature of human thought and machine intelligence but also the very essence of what it means to be, heralding an era where the cybernetic fusion of technology and human mind shapes our future.
Year 2023 face_with_colon_three
The new manufacturing method deals with the packaging substrate, the material to which chip dies are bonded. Intel and others have long used plastic (also known as organic) substrates, but the material can shrink or warp during the chip-making process, leading to defects.
Intel notes the warping risk grows as more silicon is placed on the substrate. “As the demand for data-centric, AI-centric compute increases, we are seeing an increasing amount of silicon being packed onto the package substrate, which organic packages have come to some kind of limitation in terms of handling it,” Manepalli added.
The company found a solution in glass, a homogenous substance that can remain rigid under a higher chip load. “Compared to today’s organic substrates, glass offers distinctive properties such as ultra-low flatness and better thermal and mechanical stability, resulting in much higher interconnect density in a substrate,” Intel said in its announcement.
The recent development of AI presents challenges, but also great opportunities. In this clip I will discuss the topiv in general.
… whar hasn’t worked perfectly is AI sound post-production, I apologize smile
Mind also my backup channel:
https://odysee.com/@TheMachian: c.
My books: www.amazon.com/Alexander-Unzicker/e/B00DQCRYYY/
Apple researchers have developed a new artificial intelligence system that can understand ambiguous references to on-screen entities as well as conversational and background context, enabling more natural interactions with voice assistants, according to a paper published on Friday.
The system, called ReALM (Reference Resolution As Language Modeling), leverages large language models to convert the complex task of reference resolution — including understanding references to visual elements on a screen — into a pure language modeling problem. This allows ReALM to achieve substantial performance gains compared to existing methods.
AI companies are swiftly running into a massive problem: there isn’t enough data on the internet to train the next generation of models.
The allure of machinic life cybernetics artificial life and thd new AI.
Shared with Dropbox.
Posted in robotics/AI
The current state of artificial intelligence generative language models is more creative in every devergent thinking test of humans.
Shared with Dropbox.
Aurora-M
The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order https://huggingface.co/papers/2404.
Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility.
Join the discussion on this paper page.
