April 2024
34m
English
audiobook
Generative AI in the Real World: Democratizing AI with Gwendolyn Stripling
by Gwendolyn Stripling, Ben Lorica
Overview
Gwendolyn Stripling, author of Low-Code AI, talks about the democratization of AI, the primacy of data, the future of data science, and the coming of agents. It’s easy to think that AI is all about algorithms and models but it’s not; it’s really about understanding the business use case and the data that can be applied to that use case. We’re only beginning to have tools for the rest of the job: collecting, preparing, and exploring the data to find out what’s relevant to your business. Looking ahead, Gwendolyn sees generative AI automating even more of the workload. But focusing on the data, and collecting, understanding, and interpreting it, will always be the human part of the job.
About the Generative AI in the Real World podcast: In 2023, ChatGPT put AI on everyone’s agenda. In 2024, the challenge will be turning those agendas into reality. In Generative AI in the Real World, Ben Lorica interviews leaders who are building with AI. Learn from their experience to help put AI to work in your enterprise.
Points of interest
- 0:57: What’s the boundary between no-code and low-code?
- 3:10: Using the minimum amount of code necessary to achieve your goal.
- 4:09: Low-code reduces the heavy lifting. But what if you want to learn about AI and ML?
- 6:35: Learning ML isn’t about the tools; it’s about the business case and the data.
- 7:55: What made you think about exposing more people to low-code AI?
- 11:21: The key to all of this is the use case and then the data.
- 14:32: What if I primarily use SQL?
- 15:30: Is there an equivalent of AutoML for data collection and preparation?
- 16:50: Generative AI looks like it will be able to help prepare data.
- 19:22: How did the release of ChatGPT and other LLMs affect your book?
- 24:00: Is there a low-code or no-code approach to RAG?
- 26:30: The GenAI pipeline is becoming completely automated.
- 26:49: The word of 2024 is agents. A lot of what can be automated will be automated.
- 28:00: A lot of people are sharing lessons and best practices. That makes this an exciting time.
- 29:17: Looking ahead five years, what will data scientists and ML Engineers do?
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