The text below is an auto generated transcript from my podcast episode 4
Hey everyone! It’s Joe, and I’ve got a quick podcast break between the top three episodes I recently posted. I’m in the process of setting up the website and configuring podcast catchers. Exciting news – I’m exploring video content, and YouTube, being budget-friendly, fits perfectly into my $1.00 budget. Stay tuned for post notifications, especially if you haven’t subscribed on the podcast catchers or visited the website. Subscribing via email on the website is the best way to stay in the loop and know what’s happening.
Now, let’s jump into our fireside chat about AI. I want to discuss the hype surrounding it, steering clear of vendor talk from big names like Salesforce or Microsoft. This isn’t about criticizing them; they provide valuable tools for consultants and engineers. My focus is on the AI landscape and the concerns that have been on my mind.
Firstly, I’m pondering the term “AI expert.” I got ChatGPT on day one, built a bot in February, and now, in November, I see people claiming two years of experience as AI experts. How did they become experts in 6 to 9 months? It raises questions about the definition of expertise, and is 11 months enough to be considered an expert? It’s a question that deserves careful consideration.
Moving on to caution #1: Choosing your AI partners wisely, whether you’re an enterprise player or a hobbyist. As Ronald Reagan wisely said, “Trust but verify.” It’s crucial to be cautious when selecting AI partners.
Caution #2 is about data sources. I’m not an AI expert, but I’ve been playing with it for nearly a year, both as a hobbyist and in proof of concepts within the enterprise. Think of feeding data into AI systems without proper governance as hiring someone off the street and letting them recommend things in your organization. Guardrails and well-defined parameters are essential.
Lastly, caution #3 is directed at the large language model (LLM) itself. Understanding how these models function and contribute to training is crucial. If you’re deploying AI, especially in Generative AI, be aware of the possibility of unintentionally contributing to the training of the LLM. This raises security concerns, particularly if your confidential data is involved.
These are just a few cautions in the AI landscape. Check out my corresponding blog post for more details. Don’t forget to subscribe, share, and hit that thumbs up if you found this content valuable. Your support means a lot. I’ll catch you later!
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