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GTC 2024 Announcements

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  I had two sessions  At the GTC 2024 event, a significant announcement was the unveiling of the new Blackwell GPU architecture, designed to usher in a new era of AI and high-performance computing. This architecture introduces several key innovations aimed at dramatically improving AI model training and inference speeds and energy efficiency. One of the highlights of Blackwell is its design, which comprises two “reticle-sized dies” connected via NVIDIA’s proprietary high-bandwidth interface, NV-HBI. This connection operates at up to 10 TB/second, ensuring full performance with no compromises. NVIDIA claims this architecture can train AI models four times faster than its predecessor, Hopper, and perform AI inference thirty times faster. Moreover, it reportedly improves energy efficiency by 2500%, albeit with a significant increase in power consumption, up to 1,200 watts per chip. The Blackwell architecture brings forward six primary innovations: an extraordinarily high transistor count,
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Thoughts on Sir Roger Penrose’s Conformal Cyclical Cosmology (CCC)

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  I finally collected all my notes, and in this article, I am writing down my thoughts around CCC, comparing Capra and Penrose’s worldview and parallels with Eastern mysticism and creation stories. This is the first part. In the second part, I will briefly explain scientific works to reconcile CCC with modern quantum information theory and gravitational entropy. Abstract While CCC offers a thought-provoking alternative to the standard cosmological model and attempts to resolve some outstanding issues in quantum information theory, it is not as widely accepted as the standard model. The standard model of cosmology, based on the Big Bang theory and inflationary cosmology, remains the most well-supported and extensively tested framework for understanding the universe’s evolution. CCC faces challenges in terms of both theoretical consistency and observational evidence, and more research is needed to determine its viability as a cosmological model. Background Reading Sir Roger Penrose’s “Th
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Are Hidden Attractors Shaping the Trajectory of AI Development?

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  A hidden attractor is a type of attractor in the study of dynamical systems, including those found in mathematics, physics, engineering, and economics, that cannot be reached by trajectories originating from a neighborhood of the system's equilibrium points. Classical attractors can be identified by following trajectories from conditions near equilibrium points. Hidden attractors require specific initial conditions for their discovery and are not associated with any unstable equilibrium. Significance of Hidden Attractors Across Disciplines Nonlinear Dynamics and Chaos Theory:  Hidden attractors play a crucial role in understanding the complex behavior of nonlinear dynamical systems. They are essential for analyzing systems that exhibit chaotic behavior, helping researchers to uncover patterns and predictabilities within systems that initially appear to be completely random. Hidden attractors enable a deeper understanding of the underlying mechanisms driving chaotic behavior, pote
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Large Language Models: Analyzing the Hope and Hype

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  Large Language Models (LLMs) like GPT have taken the AI world by storm in recent years. Powered by massive datasets, advanced neural network architectures, and tremendous compute power, these models can understand, generate, and translate human language with unprecedented sophistication. LLMs are unlocking amazing new capabilities — but they also come with significant risks and challenges that must be carefully navigated. To start, it’s important to understand the breakthroughs (to name a few) that have enabled the rise of LLMs: - Transformer architectures allow models to process text in parallel and learn relationships between distant words - Self-supervised learning on web-scale data corpuses teaches LLMs about language from vast real-world examples - Increasing model size (billions/trillions of parameters) and compute power allow more knowledge to be encoded - Techniques like few-shot learning enable LLMs to perform new tasks from just a handful of examples The capabilities unlock
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