.Converging human languages on natures maths
There are thousands of human languages and culturally rich as they often are- we need to have deeeper common sense of how nature's systems work- von neumann saw this as one of main reasons for developing ai's mathematical brainpower
I think using chats without contexts is at best a waste of time
lets look at some llms which have not been marketed as chats
start with nvidia megatron
AI Overview
The main use of NVIDIA's Megatron framework has been to train and develop very large-scale language models (LLMs) and other generative AI models
. The framework was created to overcome the significant computational challenges of training models with billions or trillions of parameters by allowing the work to be split across thousands of GPUs. Core capabilities of Megatron
Megatron provides a comprehensive, high-performance training library for large transformer models. Its most critical uses include:
- Massive model training: The framework uses advanced parallelism strategies—including tensor, pipeline, and sequence parallelism—that enable the training of enormous models like GPT, BERT, and T5 architectures.
- Distributed training: By efficiently distributing the training workload across multiple GPUs and compute nodes, Megatron enables high-speed training of models that would be computationally impossible on a single machine.
- Foundation for other frameworks: The techniques pioneered in Megatron have inspired and influenced many other popular LLM development frameworks, such as Hugging Face Accelerate, Colossal-AI, and NVIDIA's own NeMo.
Landmark Megatron projects
The Megatron framework has been instrumental in the development of several notable AI models and systems:
- Megatron-Turing NLG 530B: A 530-billion-parameter language model developed in collaboration with Microsoft, which pushed the boundaries of natural language generation at the time of its release in 2021.
- GatorTron: The largest clinical language model, developed by the University of Florida to understand medical texts.
- BioMegatron: A biomedical language model trained on PubMed and other biomedical datasets to improve performance on specialized tasks.
- LLM-jp: A large language model with strong Japanese-language capabilities, developed for Japan's Ministry of Economy, Trade and Industry (METI).
Applications of Megatron-trained models
The language models trained using Megatron have been applied to various natural language processing (NLP) tasks, such as:
- Text generation
- Question answering
- Text summarization
- Machine translation
- Sentiment analysis
- Conversational agents and chatbots
Megatron has been repurposed for use within NVIDIA's Earth-2 platform, though not in its original form as a large language model (LLM). Instead, NVIDIA leveraged the underlying architecture of Megatron to create a version specifically designed for physics-based AI, called PhysicsNeMo, for large-scale scientific simulations.
Here is a breakdown of Megatron's role in NVIDIA's climate and energy projects:
How Megatron technology is used for Earth-2
- Physics-informed AI: The Megatron framework, with its massive scaling capabilities, was adapted for NVIDIA's Earth-2 platform, a "digital twin" cloud for simulating climate and weather. The resulting tool, PhysicsNeMo, combines the strengths of large-scale AI with physics-based models to create highly accurate and fast weather and climate simulations.
- Massive parallel training: Megatron's core innovation was in tensor and pipeline parallelism, which allows training huge models across thousands of GPUs. NVIDIA has extended this to Earth-2 to train models like FourCastNet, which can accelerate weather forecasting with greater energy efficiency than traditional methods.
- High-resolution simulations: By combining Megatron-based AI with the NVIDIA Modulus framework, Earth-2 can produce high-resolution, kilometer-scale climate simulations. This allows for detailed visualization and prediction of phenomena like storms, floods, and wildfires.
- Enabling scientific collaboration: The infrastructure, including the Earth-2 platform and its AI tools, has been used in collaborative projects with research institutions like the Max Planck Institute for Meteorology.
Key differences between traditional Megatron and its climate application
It's important to distinguish the specific type of technology involved:
- Megatron-LM: The original Megatron was a large language model framework, trained on text data to advance the state-of-the-art in LLMs.
- Earth-2 and PhysicsNeMo: In this context, the name "Megatron" refers to the underlying distributed training techniques used to train large-scale neural network models, which are then applied to climate and weather data rather than text. The models are physics-informed, meaning they are built with the underlying laws of physics incorporated into their design.
