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EAIGG: Ethical AI Governance Group

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Anik Bose discusses data collection, the role of unstructured data, and why it’s a key part of the AI industry that’s ripe for innovation. He shared these insights as a featured speaker at the #KPMGTechandInnovation24 Symposium in Utah, joining Tobias Yergin and Olivia Gambelin on stage. #innovation #machinelearning #artificialintelligence

Transcript

If I kind of go to the issue of data quality and data integration, this is the long pole in the tent. If you look at how much dollars go into that for an AI workflow, 6070% of the AI workflow is data cleansing. And what's interesting is when you look at that, there's different types of startups that are coming up. One is there's been a lot of focus on cleaning up, getting as much pre training data as possible. There's a race to get it all. But what's really interesting is lot of the quality work is being done on the post training data. So companies like Sergio I or Scale AI. It's all human in the loop, so it's a way to address data quality in a much different way. The second part of his data integration, I'm going back in the pasture a little bit when you think about. All Gen. AI is unstructured data. You're talking about video, audio. You know this is not your structured data. If you look at the IT industry, you know, in the early 2000 structured data, you know, people had ETL pipelines, data warehouses, all these things. You know, in the cloud, you know, era came, people said, well, how do we do this in the cloud era, managing structured data, you had Confluence. You had a God what? Yeah, yeah, yeah. All these guys came out. And what was kind of interesting is all dealing with structured data. You know when you look at unstructured data you don't have any front end ETL pipelines. So how you going to manage this? And we believe it's going to be a lot of innovation in that layer with using AI to try and solve that, whether you're dealing with multi modal models or different ways to do it. I think the last piece of it, which is interesting from an enterprise perspective is I think this is going to be AI innovation that we're seeing around LM model merging where you can actually work with multiple LM models in such a way that you don't have to use GPU, CPU is much more cost effective to basically, you know, do the data. Dancing and do the data processing. And then?

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