Initial Interaction with Olympia CEO - Obie Fernandez
The conversation involves Obie Fernandez, the Founder and CEO of Olympia, introducing himself and providing some personal information. Obie mentions that Olympia is a bootstrapped startup providing AI virtual staffing services. His familiarity with AI tech is rated as '6'. The preferred support level is low-touch and he uses Notion and Figma as other online productivity tools.
Good evening, Obie! How can I assist you today?
Vika is concerned because she met another startup executive woman, and upon hearing about Olympia said that we're "not doing real machine learning." How can we help her understand that criticism and not be discouraged by it?
Certainly, Obie. It sounds like Vika may need a clearer understanding of what constitutes "real machine learning" and how Olympia fits into that description. Here are some points that might help:
Machine learning is a broad term, encompassing a variety of techniques and approaches. Olympia may not use every method, but it effectively applies machine learning in its own unique way.
Olympia’s AI capabilities do involve machine learning as they are based on complex algorithms that learn and improve over time. Our virtual staffing solution learns from interactions, enhancing its ability to assist users more effectively.
It's important to understand that different businesses will utilize machine learning differently. It's not a one-size-fits-all solution, but a toolkit that can be customized for specific needs. For Olympia, our focus is on delivering efficient, personalized virtual staffing solutions, and machine learning plays a large part in that.
Lastly, it’s possible that the startup executive may have a different interpretation of what constitutes 'real' machine learning. This is an opportunity for Vika to engage in a dialogue, seek to understand her perspective, and then clarify Olympia's approach.
Encourage her not to be discouraged by such remarks, but to view it as an opportunity for learning and clarification.
I discussed the topic with ChatGPT and it suggested a restaurant analogy that I felt was somewhat apt. In that analogy, the "real machine learning" company, if we take that to mean companies that train their own base models, are conducting a sort of farming and livestock operation, whereas we are chefs running a restaurant!
That's an interesting analogy, Obie! It puts things into a different perspective.
In this scenario, companies that train their own base models indeed act like farmers or livestock breeders, investing significant resources to cultivate raw materials (data, in this case) and then convert them into something usable (trained models).
Conversely, Olympia, acting as a 'chef,' takes these 'ingredients' (pre-trained models) and combines them creatively and skillfully to create a 'dish' (service) that's unique, tailored, and delightful to the 'customer' (the user).
This emphasizes the fact that it's not all about creating the base models, but how you use them to deliver value, just like a great meal. We may not raise the 'cattle', but we know how to cook a mean 'steak'! So yes, we're doing 'real' machine learning, just in our own, arguably more efficient and user-focused, way.