Trends to watch: How shifts in data structure and volume demand new approaches to AI compute
Demand for AI compute is doubling every three months. Why? Taking full advantage of data means using more of it, leading to larger, increasingly complex models with billions of hyperparameters requiring massive clusters of compute nodes, all while meeting ever-stricter latency and power requirements. Alexis Crowell Helzer explains why the way we compute AI has to be completely rethought so it can evolve to enable the promise of global business transformation.
What you'll learn
- Understand how volume and complexity in data is shifting algorithmic development
- Learn why current ways of computing are being rethought to keep up with these demands
- Get a look at new AI chips that are being designed specifically for the next wave of AI
Alexis Crowell Helzer
Alexis Crowell Helzer is senior director of artificial intelligence product marketing at Intel, where she and her team are responsible for technical positioning and messaging as well as outbound content and campaigns for Intel AI products. Alexis and her team partner with AI adopters across the industry from small device implementations to HPC clusters to launch products, showcase innovative use cases, and help other companies find their own AI path. She has an unyielding passion to deliver technology solutions that help businesses thrive. Over her rich career, she has run a cloud software engineering team focused on distributed computing and microservices integration, led the open source marketing efforts from Intel, and worked with many of the Fortune 100 companies to help incubate service offerings and deliver innovative products.
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