ISC23 keynote, Intel introduced the Aurora genAI model, which will undergo training using diverse domains such as General text, Scientific texts, Scientific data, and codes. This upcoming generative AI model is specifically focused on scientific applications, with potential uses in various fields including Systems Biology, Cancer Research, Climate Science, Cosmology, Polymer Chemistry, and Materials Science.

At the core of the Intel Aurora genAI model lie Megatron and DeepSpeed frameworks. Notably, the target parameter size for this new model is set at an impressive 1 trillion parameters, in stark contrast to the free and public versions of ChatGPT, which have a parameter size of only 175 million. This represents a remarkable 5.7x increase in the number of parameters.

The generative AI models for scientific purposes will be trained on a wide range of data, encompassing general text, code, scientific texts, and structured scientific data from fields such as biology, chemistry, materials science, physics, and medicine, among others.

The resulting models, boasting up to 1 trillion parameters, will find applications in numerous scientific endeavors. They will contribute to designing molecules and materials, synthesizing knowledge from millions of sources to propose new and intriguing experiments in fields like systems biology, polymer chemistry, energy materials, climate science, and cosmology. Moreover, the model will accelerate the discovery of biological processes associated with cancer and other diseases, as well as aid in identifying potential targets for drug design.

Final Thoughts

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