We Asked ChatGPT: Which Are the Top 4 AI Protocols You Should Know About

We Asked ChatGPT: Which Are the Top 4 AI Protocols You Should Know About


Artificial intelligence is growing in popularity, and ChatGPT is at the trend’s forefront. However, there are many applications of AI beyond language-based models and chatbots.

We decided to ask ChatGPT itself to tell us which are the top 4 major AI protocols that everyone should know about.

The AI ​​came back with some well-known names, but it’s worth noting that none of them are crypto-specific. However, they have broad applications and are also commonly used by companies in the cryptocurrency field.

Nevertheless, we have a special guide that you can take a look at in regard to the top 5 AI coins.

That said, let’s dive in.

TensorFlow: Google’s Deep Learning Framework

TensorFlow is an end-to-end open-source platform for machine learning (ML) developed by Google.

In essence, the tool can be used to:

  • Prepare large sets of data
  • Build machine learning (ML) models
  • Deploy ML models
  • Implement 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, and it’s aimed at accelerating the path from research prototyping to production deployment.

It was developed by Meta (formerly known as Facebook), and it brings forward the following features:

To deliver research and production, the torch.distributed backend offers both scalable and distributed training and performance optimization.

PyTorch is well-supported on some of the major cloud platforms, which in turn provides for frictionless development and easy scaling.

The transition between eager and graph modes with TorchScript is seamless. In addition, teams can also accelerate the path to production using TorchServe

ONNX: The Open Neural Network Exchange

ONNX brings forward an intermediary machine learning framework. It is used to convert between various ML frameworks.

For example, if you’re using TensorFlow and you want to get to TensorRT, ONNX will provide a good intermediary to convert your model while you are actually going through the various ML frameworks.

The team has worked hard to implement a range of different neural network functions and functionalities.

Keras: Google at it Once Again

You can tell that Google is pushing a lot of resources in this direction. Keras is another high-level, deep-learning API that’s developed by the tech behemoth.

Keras is written in Python (one of the most comprehensive programming languages) and is used to make the implementation of various neural networks easy.

In addition, Keras also supports multiple backend neural network computations. Per ChatGPT:

It porvides a user-friendly interface for building and training deep-learning models. Keras is often used in conjunction with TensorFlow as a higher-level abstraction.


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