Shownotes Transcript
Let’s talk through some of the challenges that Enterprises will have with AI - from data location to GPU location, to model biases, to data privacy to training vs. execution.
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SHOW NOTES:
- An Interview with Daniel Gross and Nat Friedman on the AI Hype Cycle) (Stratechery)
ARE THERE EXPECTATIONS OF “OLD AI” vs. “NEW AI”?
- Are business leaders thinking about unique AI applications and use-cases, or just “ChatGPT-everything”?
- Formal data scientists vs. citizen data scientists?
- Will this just be an application, or have an impact on every aspect of a business and the IT industry?
**WILL ENTERPRISE AI BE DIFFERENT THAN CONSUMER AI? **
- The industry is actively working on a broad set of models that can be used for different use-cases.
- It's commonly accepted that AI models need to be trained near the sources of data.
- Many businesses are concerned about including their company data into these public models
- Many businesses will want to deploy tuned models and applications in data center, public cloud and edge environments.
- New AI applications will be required to meet security, regulatory and compliance standards, like other business applications.
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