In it ISC23 keynote address, Intel announced Aurora genAI – a science-focused generative AI model with a trillion parameters, almost six times more than in the free and public versions of ChatGPT. This news has sparked conversations about all the possibilities this model can unlock.
It has always been well understood that in order to train and build models close to human standards, enterprises require an enormous amount of computational power, where fine-tuning begins at the hardware level.
Intel’s bold vision makes a strong case backed by one of the largest chipmakers. It has already proven its ability to produce a matching chip and is often treated as a gold standard in compatible hardware for AI enhancement.
Backed by the 2 Exaflops Intel Aurora supercomputer, with Megatron and DeepSpeed models as its base, the Aurora-GenAI model promises to train scientific data, general data, scientific and machine codes, and other texts related primarily to the scientific domain with 1 billion parameters which is almost six times the parameters we see in the open and publicly accessible versions of ChatGPT.
Intel is focusing on building this model to serve the scientific community and accelerate advancement in systems biology, cancer research, climate science, cosmology, polymer chemistry, and materials science.
The deep learning models we use today are well trained to solve systematic problems. These systems can translate everything you can type step by step. You can enhance it and use it on the fly to solve real-world problems. In addition to the obvious existing use cases, people now expect to find patterns among complex data such as molecular biology and formulation. Things like molecular binding patterns and compatibility disclosures in a way that takes a lot of work to understand with conventional methods.
What’s more interesting is that Intel aims to predict the patterns and bottlenecks that we miss due to a lack of vision and understanding of the use case at hand, especially when combined with time. To understand it, this model will aim to predict problem scenarios that we can’t estimate or see yet, but are likely to emerge at some point, given the data on that particular problem.
People are taking this announcement with a lot of excitement and positivity in the AI community. People are more interested in knowing how you would perceive and understand topics that are by nature more sensitive and challenging, for example, political scenarios and policy making, prevailing social issues, climate changes, cosmological predictions and their interpretation. to solve them at some level.
It’s interesting to understand here that this project is a work in progress right now, and you’re talking about a future commitment. Actually, it is still, in fact, an advertisement. The project will be developed in collaboration with Argonne National Laboratory and HPE.
In conclusion, this news brings a lot of hope not only to the AI community but also to retail investors. This news generates positive sentiments for Intel, making it a promising stock option to explore, which certainly puts Intel in a good spot. It would be interesting to see how Intel will fare against some of its closest competitors in the market, such as Nvidia, and how well its model will fare against the commitments made.
review the Advertisement. Don’t forget to join our 22k+ ML SubReddit, discord channel, and electronic newsletter, where we share the latest AI research news, exciting AI projects, and more. If you have any questions about the article above or if we missed anything, feel free to email us at [email protected]
🚀 Check out 100 AI tools at AI Tools Club
Anant is a Computer Science Engineer currently working as a Data Scientist with a background in Finance and AI-as-a-Service products. He is interested in creating AI-powered solutions that create better data points and solve everyday problems in powerful and efficient ways.
