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xAI is a startup that aims to discover the answers to life, the universe, and everything with AI. It offers Grok, a multimodal conversational AI that connects the digital and physical worlds.
- Open Release of Grok-1
We are releasing the base model weights and network...
- About
xAI is a company working on building artificial intelligence...
- Open Release of Grok-1
Explainable artificial intelligence (XAI) allows human users to comprehend and trust the results and output created by machine learning algorithms.
Mar 17, 2024 · We are releasing the base model weights and network architecture of Grok-1, our large language model. Grok-1 is a 314 billion parameter Mixture-of-Experts model trained from scratch by xAI. This is the raw base model checkpoint from the Grok-1 pre-training phase, which concluded in October 2023.
Jul 12, 2023 · E lon Musk wants to “understand the true nature of the universe.”. At least that’s what his new AI company, xAI, said on its website as he announced its formation on Wednesday. Announcing ...
- XAI - An eXplainability toolbox for machine learning
- 0.1.0
- What do we mean by eXplainable AI?
- Installation
- Usage
XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by The Institute for Ethical AI & ML, and it was developed based on the 8 principles for Responsible Machine Learning.
You can find the documentation at https://ethicalml.github.io/xai/index.html. You can also check out our talk at Tensorflow London where the idea was first conceived - the talk also contains an insight on the definitions and principles in this library.
If you want to see a fully functional demo in action clone this repo and run the Example Jupyter Notebook in the Examples folder.
We see the challenge of explainability as more than just an algorithmic challenge, which requires a combination of data science best practices with domain-specific knowledge. The XAI library is designed to empower machine learning engineers and relevant domain experts to analyse the end-to-end solution and identify discrepancies that may result in sub-optimal performance relative to the objectives required. More broadly, the XAI library is designed using the 3-steps of explainable machine learning, which involve 1) data analysis, 2) model evaluation, and 3) production monitoring.
We provide a visual overview of these three steps mentioned above in this diagram:
The XAI package is on PyPI. To install you can run:
Alternatively you can install from source by cloning the repo and running:
1) Data Analysis
With XAI you can identify imbalances in the data. For this, we will load the census dataset from the XAI library.
2) Model Evaluation
We are able to also analyse the interaction between inference results and input features. For this, we will train a single layer deep learning model.
xAI is a company working on building artificial intelligence to accelerate human scientific discovery. We are guided by our mission to advance our collective understanding of the universe. Our team is led by Elon Musk, CEO of Tesla and SpaceX.
Explainable AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), either refers to an artificial intelligence (AI) system over which it is possible for humans to retain intellectual oversight, or refers to the methods to achieve this.
