Sema4 CTO Ram Venkatesh
from How AI Happens Hosted by Sama
Key Points From This Episode: Ram Venkatesh describes his career journey to founding Sema4.ai. The pain points he was trying to ease with Sema4.ai. How our general approach to big data is becoming more streamlined, albeit rather slowly. The ins and outs of Sema4.ai and how it serves its clients. What Ram means by “agent” and “agent agency” when referring to machine learning copilots. The difference between writing a program to execute versus an agent reasoning with it. Understanding the contextual work training method for agents. The relationship between an LLM and an agent and the risks of training LLMs on agent data. Exploring the next generation of LLM training protocols in the hopes of improving efficiency. The requirements of an LLM if you’re not training it and unpacking modality improvements. Why agent input and feedback are major disruptions to SaaS and beyond. Our guest shares his hopes for the future of AI. Quotes: “I’ve spent the last 30 years in data. So, if there’s a database out there, whether it’s relational or object or XML or JSON, I’ve done something unspeakable to it at some point.” — @ramvzz [0:01:46] “As people are getting more experienced with how they could apply GenAI to solve their problems, then they’re realizing that they do need to organize their data and that data is really important.” — @ramvzz [0:18:58] “Following the technology and where it can go, there’s a lot of fun to be had with that.” — @ramvzz [0:23:29] “Now that we can see how software development itself is evolving, I think that 12-year-old me would’ve built so many more cooler things than I did with all the tech that’s out here now.” — @ramvzz [0:29:14] Links Mentioned in Today’s Episode: Ram Venkatesh on LinkedIn Ram Venkatesh on X Sema4.ai Cloudera How AI Happens Sama
Embed this episode
What this episode covers
Today, we are joined by Ram Venkatesh, the founder of Sema4.ai – an enterprise AI agent platform that enables businesses to build, operate, and scale AI agents. Ram shares the risks of training LLMs from agent data, and the contextual work training protocol for agents. We also unpack the requirements of a large language model when you’re not responsible for training it, the various modalities and how they can be improved, the threat that agents pose to SaaS, and Ram’s vision of the future of AI.
No transcript yet.