Artificial intelligence is gaining popularity and ChatGPT is leading the trend. However, there are many applications of AI beyond language-based models and chatbots.
ChatGPT was asked to name the top 4 major AI protocols everyone should know about. The AI responded with well-known names, none of which are crypto-specific. However, these protocols have broad applications and are commonly used by companies in the cryptocurrency field. If you’re interested in the top 5 AI coins, we have a special guide for you to check out.
Let’s dive into the top 4 AI protocols:
TensorFlow: Google’s Deep Learning Framework
TensorFlow is an end-to-end open-source platform for machine learning (ML) developed by Google. It can be used for preparing large sets of data, building machine learning (ML) models, deploying ML models, implementing MLOps, and much more. Its ecosystem of tools, libraries, and resources for developing AI applications is broad and comprehensive.
PyTorch: Meta’s Stab at Deep Learning
PyTorch is another open-source machine learning framework developed by Meta (formerly known as Facebook). It aims to accelerate the path from research prototyping to production deployment. The torch.distributed backend offers both scalable and distributed training and performance optimization for delivering research and production. PyTorch is well-supported on major cloud platforms, providing frictionless development and easy scaling. With TorchScript, the transition between eager and graph modes is seamless. Teams can also accelerate their paths to production using TorchServe.
ONNX: The Open Neural Network Exchange
ONNX is an intermediary machine learning framework used to convert between various ML frameworks. It’s useful for converting models from one framework to another, such as from TensorFlow to TensorRT. The team has implemented a range of different neural network functions and functionalities.
Keras: Google at it Once Again
Keras is a high-level, open-source neural network API written in Python. It’s used to make the implementation of various neural networks easy. Keras also supports multiple backend neural network computations and provides a user-friendly interface for building and training deep-learning models. Keras is often used in conjunction with TensorFlow as a higher-level abstraction, according to ChatGPT.