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Summary

In this conversation, Mike, Jon and Sam Smith discuss various topics including the Power Platform, Microsoft certifications, AI, and the future of generative AI. Sam shares his journey into the Power Platform space, highlighting the transferable skills he gained as an opera singer. They also discuss the new applied certification exams and the need for a balance between practical skills and knowledge of the platform. The conversation then shifts to the topic of AI, with a focus on Google's Gemini and the ethical concerns surrounding fabricated videos. They also touch on the environmental impact of large language models and the future of generative AI. The conversation explores the future of language models and their impact on various industries. The guests discuss the potential for larger and more productized language models, the importance of data security and privacy, and the need for transparency in AI technology. They also touch on the challenges of retaining knowledge when using language models and the importance of understanding the underlying principles rather than just relying on the technology. The conversation concludes with a discussion on the need to contextualize the use of technology and focus on the specific needs and goals of each situation.


Takeaways

• The Power Platform offers opportunities for individuals from diverse backgrounds to enter the technology field.
• Applied certification exams provide a practical assessment of skills, but there is a need for a balance with knowledge-based exams.
• Ethical concerns arise when companies fabricate videos to showcase the capabilities of AI models.
• The environmental impact of large language models and the power requirements for running them are important considerations.
• The future of generative AI may involve smaller models that can run on local hardware, but there are challenges to overcome. Language models are expected to become larger and more productized in the future.
• Data security and privacy concerns will drive enterprises to host language models within their own networks.
• Retaining knowledge when using language models can be challenging, and it's important to take a slower and more considered approach.
• Understanding the underlying principles is crucial for effectively using language models.
• Contextualizing the use of technology and focusing on specific needs and goals is essential.

Special Thanks to

Sam Smith for joining us for this episode! You can find their socials at:

LinkedIn Samuel Smith | LinkedIn

KenLikeFit for providing music for this episode - [Find out more about him and listen to his music here](https://linktr.ee/kenlikefit)

Resources mentioned in this episode

• LM Studio LM Studio - Discover, download, and run local LLMs
• Chat RTX Build a Custom LLM with ChatRTX | NVIDIA

Thanks to:
KenLikeFit for providing music for this episode - [Find out more about him and listen to his music here](https://linktr.ee/kenlikefit)

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Love you,
Mike Gowland,
Jon Russell

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