Deep Learning at NVIDIA

Learn about the disruptive language, vision, and multimodal technologies and how it is making us more productive and effective.

With the rise of large language models like GPT-3, NLP is producing awe-inspiring results. In this article, we discuss how NLP is driving the future of data science and machine learning, its future applications, risks, and how to mitigate them.

In this episode, Hugo speaks with Hilary Mason about the past, present, and future of data science.

Daniel Situnayake talks about his work with EdgeML, the biggest challenges in embedded machine learning, potential use cases of machine learning models in edge devices, and the best tips for aspiring machine learning engineers and data science practices.

Sandra Kublik and Shubham Saboo, authors of GPT-3: Building Innovative NLP Products Using Large Language Models shares insights about what makes GPT-3 unique, the transformative use-cases it has ushered in, the technology powering GPT-3, its risks and limits.

Will the future of data science and machine learning be automated entirely? Which verticals will experience the largest disruption? What will the role of data science become?