How the Media Covers Gen AI (w/ Matthew Lynley, Supervised)
Mar 24, 2024
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Journalist/data practitioner Matthew Lynley discusses the rise of Gen AI in the media, his journey from mathematics to journalism, encountering Chat GPT, challenges of monetizing an AI newsletter, AI content creation, and the convergence of fields in AI development.
The rise of Gen AI in popular media reflects the evolving landscape of AI coverage and public interest.
Optimization challenges persist in AI models, pushing towards exploration of alternative architectures and mid-layer models for performance enhancement.
Deep dives
The Intersection of Journalism and Data Practitioners: A Unique Perspective on AI
Matt Lindley, a journalist with a background in applied mathematics, shares his journey transitioning from journalism to hands-on data practitioner roles. Having covered early AI developments at TechCrunch, he returned to journalism with a technical perspective, focusing on tools like Snowflake and Weights and Biases. The conversation delves into the rise of AI in popular media and how Matt's hybrid experience brings a distinct viewpoint to the AI landscape.
Evolution of AI Models and Technological Optimization
As AI models like GPT-3 and Grok advance, questions arise about performance scalability and optimization. With the exponential curve of transformer architecture quality enhancing models, there's exploration into alternative architectures and heavy-duty optimizations. The competitive battle shifts towards the mid-layer models like Mistral, Cloud Haiku, and GPT-3. Optimization and commercialization challenges persist, hinting at the need for innovation and robust scaling solutions.
Metadata's Role in AI and Analytics Governance
Metadata companies and platforms play a vital role in AI integration, especially with lineage metadata providing governance and observability layers. The values of structured data analytics can be enhanced by incorporating metadata to drive AI efficiency and value creation. The convergence of AI and analytics tools leads to the adoption of metadata for robust governance, lineage tracing, and optimizing AI workflows for real-world applications.
Future Trends: Infrastructural Convergence and Value Creation in AI
As AI and infrastructure converge, there's a shift towards downstream applications benefiting from robust infrastructure tools. The infrastructure space sees AI integration as essential for creating value on top of existing framework. The evolving landscape hints at transforming AI applications into valuable assets built upon structured data analytics and metadata, shaping future trends in AI and analytics governance.
Matthew Lynley is a bit of a hybrid. He's been a long-time journalist covering enterprise tech, currently in his fantastic AI and data newsletter Supervised, and he's also been a hands-on data practitioner.
Matthew has covered the analytics tech stack, but this time Tristan turns the tables to get Matthew’s perspective on the rise of Gen AI as a topic in the popular press, what's going on in the space today, and where AI is headed.
For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.
The Analytics Engineering Podcast is sponsored by dbt Labs.
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