Patterns that Connect: AI, Management, Metaverse, Quantum, Philosophy, and Physics

  The research paper  focuses on the classical simulation of a kicked Ising quantum system on the heavy hexagon lattice. It compares the accuracy and efficiency of this classical simulation with a recent quantum processor simulation. The authors employ a tensor network approach that reflects the lattice’s geometry and is contracted using belief propagation, resulting in significantly more accurate and precise results than those obtained from the quantum processor and other classical methods. They quantify the treelike correlations of the wave function to explain the accuracy of their belief propagation-based approach and demonstrate its ability to simulate the system to long times in the thermodynamic limit, equivalent to a quantum computer with an infinite number of qubits. The paper suggests that their tensor network approach has broader applications for simulating the dynamics of quantum systems with treelike correlations. The introduction of the research paper discusses th...

  I am traveling from January to March, attending and delivering at Oracle Data and AI forums hosted in various cities. I have already presented in Dallas and Houston while meeting with numerous Oracle customers in Seattle and Boston. The next speaking engagements are in Orlando, Atlanta, and Philly. While speaking at the Data and AI forums, I have already delivered the following Webinars at ODSC ( https://app.aiplus.training/bundles/odsc-webinars ) and presented a paper in NeurIPS (nips.cc) 2023: While preparing for upcoming presentations at NVIDIA GTC ( https://www.nvidia.com/gtc/ ) for both recorded and in-person, I am keeping an eye on the exciting developments in the field of AI and Quantum Computing. I would like to summarize two interesting developments in the field of Artificial Intelligence (AI) this week. I am going to cover the recent developments in Quantum next week. The first one is TaiPY. It is a tool designed to swiftly transform data and AI algorithms into producti...

  Last week, I came across a paper published by esteemed researchers at Seoul National University, South Korea. The paper is a comprehensive survey of compression algorithms for Language Models. The paper is 35 pages long. Here is a link to the paper. It took me a few days to read and make notes. My humble effort is to summarize the paper, focusing on the compression techniques. I have also added a few snippets of Python code as I am trying out these compression techniques on a GPU VM with Data Science image #oraclecloudinfrastructure #nvidia A100 node. The paper provides a thorough examination of various algorithms used to compress language models without reducing their accuracy. It covers methodologies like pruning, quantization, knowledge distillation, low-rank approximation, parameter sharing, and efficient architecture design. Each category is explored with in-depth analyses of representative algorithms, discussing their advantages, challenges, and applications. The goal is to...