Trust and Validation in AI

Cloud Native Compass

Here are 5 key takeaways from this episode that you don't want to miss:

1️⃣ The People Problem: Laura Santamaria raises an important concern about verifying AI-generated outputs and tackling the challenge of the "people problem" in AI development.

2️⃣ Verifying Data Authenticity: JJ discusses the challenge of proving that a data blob originated from a specific model and how this issue is being addressed by companies like IBM through pile cleaning and legal penalties.

3️⃣ AI Misconceptions: We debunk some common misconceptions about AI, including the belief that it is an all-knowing fact machine.

4️⃣ Trusted AI: IBM's approach to building trusted models, with dedicated engineers responsible for cleaning and verifying data, is explained. Plus, we discover IBM's partnerships with Hugging Face to leverage the open-source ecosystem.

5️⃣ The Impact of AI: We delve into the potential positive and negative implications of AI, and how the rapid advancement of this technology presents challenges with trust and validation.

💡 Fun Fact: Did you know that 95% of open-source language models are trained on a data set called "the pile," which contains pirated and copyrighted material? Discover why this has implications for copyright and patent laws!

As always, the conversation in this episode is engaging and eye-opening. JJ Asghar provides insightful perspectives and sheds light on the future of AI development. Don't miss out on the valuable information shared!

Questions We Covered

1. How can the problem of untrusted data in AI models be effectively addressed?
2. Should companies like OpenAI and Microsoft be required to provide their data sets for verification purposes? Why or why not?
3. What are the potential risks and challenges associated with using AI technology without proper regulation?
4. Should AI creations be eligible for copyright protection? Why or why not?
5. How can we ensure the accuracy and trustworthiness of AI-generated data, especially when it comes to extracting information from sources like PDFs?
6. What are some potential positive impacts of AI technology, and how can we maximize its benefits while minimizing its negative implications?
7. How can the rapid advancement of AI technology be balanced with the need for trust and validation?
8. In what ways do copyright and patent laws need to evolve to accommodate AI technology?
9. What are the implications of China having its own set of laws and approaches to technology that may differ from other countries?
10. How can individuals navigate and better understand the AI space in order to make informed decisions and contributions?