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Season 2 Trailer: Mastering Search
Manage episode 433135289 series 3585930
Today we are launching the season 2 of How AI Is Built.
The last few weeks, we spoke to a lot of regular listeners and past guests and collected feedback. Analyzed our episode data. And we will be applying the learnings to season 2.
This season will be all about search.
We are trying to make it better, more actionable, and more in-depth. The goal is that at the end of this season, you have a full-fleshed course on search in podcast form, which mini-courses on specific elements like RAG.
We will be talking to experts from information retrieval, information architecture, recommendation systems, and RAG; from academia and industry. Fields that do not really talk to each other.
We will try to unify and transfer the knowledge and give you a full tour of search, so you can build your next search application or feature with confidence.
We will be talking to Charlie Hull on how to systematically improve search systems, with Nils Reimers on the fundamental flaws of embeddings and how to fix them, with Daniel Tunkelang on how to actually understand the queries of the user, and many more.
We will try to bridge the gaps. How to use decades of research and practice in iteratively improving traditional search and apply it to RAG. How to take new methods from recommendation systems and vector databases and bring it into traditional search systems. How to use all of the different methods as search signals and combine them to deliver the results your user actually wants.
We will be using two types of episodes:
- Traditional deep dives, like we have done them so far. Each one will dive into one specific topic within search interviewing an expert on that topic.
- Supplementary episodes, which answer one additional question; often either complementary or precursory knowledge for the episode, which we did not get to in the deep dive.
We will be starting with episodes next week, looking at the first, last, and overarching action in search: understanding user intent and understanding the queries with Daniel Tunkelang.
I am really excited to kick this off.
I would love to hear from you:
- What would you love to learn in this season?
- What guest should I have on?
- What topics should I make a deep dive on (try to be specific)?
Yeah, let me know in the comments or just slide into my DMs on Twitter or LinkedIn.
I am looking forward to hearing from you guys.
I want to try to be more interactive. So anytime you encounter anything unclear or any question pops up in one of the episode, give me a shout and I will try to answer it to you and to everyone.
Enough of me rambling. Let’s kick this off. I will see you next Thursday, when we start with query understanding.
Shoot me a message and stay up to date:
34 episode
Manage episode 433135289 series 3585930
Today we are launching the season 2 of How AI Is Built.
The last few weeks, we spoke to a lot of regular listeners and past guests and collected feedback. Analyzed our episode data. And we will be applying the learnings to season 2.
This season will be all about search.
We are trying to make it better, more actionable, and more in-depth. The goal is that at the end of this season, you have a full-fleshed course on search in podcast form, which mini-courses on specific elements like RAG.
We will be talking to experts from information retrieval, information architecture, recommendation systems, and RAG; from academia and industry. Fields that do not really talk to each other.
We will try to unify and transfer the knowledge and give you a full tour of search, so you can build your next search application or feature with confidence.
We will be talking to Charlie Hull on how to systematically improve search systems, with Nils Reimers on the fundamental flaws of embeddings and how to fix them, with Daniel Tunkelang on how to actually understand the queries of the user, and many more.
We will try to bridge the gaps. How to use decades of research and practice in iteratively improving traditional search and apply it to RAG. How to take new methods from recommendation systems and vector databases and bring it into traditional search systems. How to use all of the different methods as search signals and combine them to deliver the results your user actually wants.
We will be using two types of episodes:
- Traditional deep dives, like we have done them so far. Each one will dive into one specific topic within search interviewing an expert on that topic.
- Supplementary episodes, which answer one additional question; often either complementary or precursory knowledge for the episode, which we did not get to in the deep dive.
We will be starting with episodes next week, looking at the first, last, and overarching action in search: understanding user intent and understanding the queries with Daniel Tunkelang.
I am really excited to kick this off.
I would love to hear from you:
- What would you love to learn in this season?
- What guest should I have on?
- What topics should I make a deep dive on (try to be specific)?
Yeah, let me know in the comments or just slide into my DMs on Twitter or LinkedIn.
I am looking forward to hearing from you guys.
I want to try to be more interactive. So anytime you encounter anything unclear or any question pops up in one of the episode, give me a shout and I will try to answer it to you and to everyone.
Enough of me rambling. Let’s kick this off. I will see you next Thursday, when we start with query understanding.
Shoot me a message and stay up to date:
34 episode
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