Part 10 a series of articles describing the elements of Lawrence Livermore National Laboratory’s fusion breakthrough.

For hundreds of Lawrence Livermore National Laboratory (LLNL) scientists on the design, experimental, and modeling and simulation teams behind inertial confinement fusion (ICF) experiments at the National Ignition Facility (NIF), the results of the now-famous Dec. 5, 2022, ignition shot didn’t come as a complete surprise.

The “crystal ball” that gave them increased pre-shot confidence in a breakthrough involved a combination of detailed high-performance computing (HPC) design and a suite of methods combining physics-based simulation with machine learning. LLNL calls this “cognitive simulation,” or CogSim.

The above considerations have to be carefully factored in while selecting the approach to model different subsystems and modules, hardware or physics-based twins in the digital twin.

Data recording and logging are crucial components of any digital twin project. This data not only serves as the basis for simulation and testing but also facilitates debugging, system optimization and performance analysis. Effective data recording strategies can also assist in the validation of model assumptions, further enhancing system accuracy and reliability.

Digital twins are not merely simulation tools; they represent a fundamental shift in the way we can plan, design, deploy and optimize robotic automation systems in warehouses. A well-designed digital twin, factoring in the aspects outlined in this article, empowers reliable, predictable and efficient order fulfillment, catalyzing innovation and progress in customer satisfaction.

Freely accessible large language models have accelerated the pace of innovation, computer scientists say.

Open-source AI can be defined as software engineers collaborating on various artificial intelligence projects that are open to the public to develop. The goal is to better integrate computing with humanity. In early March, the open source community got their hands on Meta’s LLaMA which was leaked to the public. In barely a month, there are very innovative OpenSource AI model variants with instruction tuning, quantization, quality improvements, human evals, multimodality, RLHF, etc.

Open-source models are faster, more customizable, more private, and capable. They are doing things with $100 and 13B params that even market leaders are struggling with. One open-source solution, Vicuna, is an… More.

This article explores AI in the context of open-sourced alternatives and highlights market dynamics in play.