INTERVIEW 🎙️ Robert Ciemniak of Robotic Online Intelligence discussing AI and LLM
Channel: Beryl Elites
Date: 2023-12-18

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INTERVIEW 🎙️ featuring:
Robert Ciemniak - Founder & CEO, Robotic Online Intelligence
My Hong - The Beryl Consulting Group

What Robotics Online Intelligence does?
❖ I'm Robert Ciemniak, the founder of Robotics Online Intelligence or ROI in short. We are in the business of automation of research. If you can think of it on one hand as building AI agents and robo analysts that can augment all the human analysts. It's kind of one of the things we pursue and on the other hand for us it's about automation of data extraction. Many companies have an issue where they have a lot of unstructured content and they want to build structured databases. We help achieve that.

Could you describe to us a few use cases of your service?
❖ In those two categories, we have clients who are data companies and
typically, the challenge there is that there's a lot of manual work done by the human analysts in the collection, filtering of unstructured information on one hand and then extraction that extraction of unstructured text into specific data points. So, that's one
group and then on the other hand for research firms or investment firms we would help with automation of market intelligence. For example, if you think of a sector like China
property, we would build what we call robo analysts who would scan through news announcements about new policies and then digital junior analysts would summarize and synthesize that very quickly from all kinds of local sources.  

How do you apply large language models in your work? Could you give us more details on that?
❖ Historically, we've been doing the whole business for now almost six years and we started writing our own software for our own use as a research firm even before. So, build for yourself first, then bring to clients. Originally it was much more about the workflow, about automation the more classic tools like web scraping classification, NLP. But in the last year what the large language models allow us to do is more direct extraction of that information. If you imagine very specific use case where you have an article or PDF documents or SEC filing on one hand and on the other hand you have 20 fields you want to fill in the LLM, you can actually take that unstructured text and populate those fields and it's a beautiful case. It works quite well but it needs to be augmented with quite a lot of code and fine-tuning and then less structured when you think of good old summarization. So, if somebody is monitoring companies that do stuff in AI now and you want to have a daily summary of the latest news announcements from them, synthesize, summarize - that's what LLMs can now easily do.


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