AI can replace management roles in scientific research.
New research from ESMT Berlin shows how AI can manage human participants in large-scale research projects, taking over functions such as task allocation, coordination, and motivation.
Machine learning revolutionizes secure quantum communication, pushing its boundaries to unprecedented frontiers.
Agriculture is a cornerstone of human civilization, a testament to our ability to harness nature for sustenance. Yet, this age-old industry faces many challenges that hamper productivity, impact livelihoods, and threaten global food security.
By 2050, we must produce 60 percent more food to feed a world population of 9.3 billion, reports the Food and Agriculture Organization. Given the current industry challenges, doing that with a farming-as-usual approach could be tricky. Moreover, this would extend the heavy toll we already place on our natural resources.
This is where Artificial Intelligence can come to our rescue. The AI in Agriculture Market is projected to grow from $1.7 billion in 2023 to $4.7 billion by 2028, highlighting the pivotal role of advanced technologies in this sector. This article explores three significant issues agriculture faces today and shows how AI is helping tackle them using real-world examples.
People like the veteran computer scientist Ray Kurzweil had anticipated that humanity would reach the technological singularity (where an AI agent is just as smart as a human) for yonks, outlining his thesis in ‘The Singularity is Near’ (2005) – with a projection for 2029.
Disciples like Ben Goertzel have claimed it can come as soon as 2027. Nvidia’s CEO Jensen Huang says it’s “five years away”, joining the likes of OpenAI CEO Sam Altman and others in predicting an aggressive and exponential escalation. Should these predictions be true, they will also introduce a whole cluster bomb of ethical, moral, and existential anxieties that we will have to confront. So as The Matrix turns 25, maybe it wasn’t so far-fetched after all?
Artificial intelligence (AI) systems like ChatGPT have taken the world by storm. There isn’t much in which they’re not involved, from recommending the next binge-worthy TV show to helping navigate through traffic. But can AI systems learn the language of life and help biologists reveal exciting breakthroughs in science?
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.
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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.
Continue reading “Intel to Build Next-Generation Chips Using ‘Glass Substrate’” »
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/
