Getting into egocentric data collection. Need suggestions.(reddit.com)

Background: I confounded a startup last year on Execution as a Service model. We're two confounders, and a core team of 5 guys. 4 of us used to be at xAI human data. And collectively we've worked for most of the leading genAI companies in the human data space. We started off as a managed outsourcing platform where we assign a frac COO to handle your entire outsourcing ops across functionalities which also included AI annotation and labelling. The problem: We were trying to secure contracts all over the place. Though we had 150+ registered fulfilment partners, and we secured some sizable contracts, I was genuinely confused about the growth and the direction of the company, specially with the kind of developments happening in the ops domain. I just brokered a deal valued at over 100k just for sharing internal ops data for AI training. We can't predict exactly how would the space look like. The present: The outsourcing business isn't fully justified to the kind of profiles the core team has. We were being reduced a software and marketing firm. We figured out that we need to stay relevant in the data industry. With the logistical edge that I have, and the trial run I did, I am very confident about working on physical data. We collected over 10 hours sample dataset spanning across household, industrial, construction, and electrical egocentric data. The question: Before we jump into physical data, I am genuinely looking for researchers' perspectives on ego-exocentric vs synthetic data. I understand that the upfront cost is high for synthetic, but long term cost is significantly cheaper, but how does the difference play out in the actual training workplace. TIA submitted by /u/Low_Can_4600 [link] [Kommentare]