Peter Norvig and Alfred Spector on their acclaimed new book on data science, and recent trends in AI.
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Peter Norvig (of Google and Stanford) and Alfred Spector (of MIT) are part of the team of authors behind the must-read book Data Science in Context: Foundations, Challenges, Opportunities. We discussed their recent book and their Data Science Analysis Rubric, and we also talked about a trending topics in AI including looming regulations, synthetic data, and Large Language and Foundation Models.
Make sure you read and share their new book! As I noted in a recent newsletter Data Science In Context is a valuable resource for anyone looking to use data science and AI in real-world applications. I encourage you to check it out and share it with your colleagues (more details below).
Interview highlights – key sections from the video version:
- UX and visualization, and data science
- Why they wrote the book “Data Science in Context”, and who is the target reader
- Data Science Analysis Rubric: Seven major considerations for determining data science’s applicability to a proposed solution
- Benefits of using their Data Science Analysis Rubric – relationship with software engineering best practices
- Unreasonable effectiveness of data – a status update
- Looming AI regulations: EU AI Act and more
- Automation in data science
- Synthetic Data: strengths and limitations
- Software Supply Chain attacks: the importance of PyPi
- Large Language Models and Foundation Models
- The nature of Data Science courses five years from now
- A video version of this conversation is available on our YouTube channel..
- Percy Liang: Evaluating Language Models
- Roy Schwartz: Efficient Methods for Natural Language Processing
- Barret Zoph and Liam Fedus: Efficient Scaling of Language Models
- Connor Leahy and Yoav Shoham: Large Language Models
- Foundation Models: A Primer for Investors and Builders
- FREE Report: 2023 Trends in Data, Machine Learning, and AI
- Machine Learning Trends You Need To Know
- Mark Chen of OpenAI: How DALL·E works
- Jack Clark: The 2022 AI Index
- Piotr Żelasko: The Unreasonable Effectiveness of Speech Data
- fastdup: Introducing a new free tool for curating image datasets at scale
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Featured Image: Data Science Analysis Rubric (from “Data Science In Context”); graphic by Ben Lorica. Seven major considerations for determining data science’s applicability to a proposed solution.