Conversations about agentic workflows, tools, and infrastructure! <br/><br/><a href="https://edge.aampe.com?utm_medium=podcast">edge.aampe.com</a>
The Agentic Edge Podcast
Claim This Podcastby Aampe
19 episodes
Updated Weekly
Accepts GuestsHas Sponsors
Podcast Overview
Conversations about agentic workflows, tools, and infrastructure! <br/><br/><a href="https://edge.aampe.com?utm_medium=podcast">edge.aampe.com</a>
Language
🇺🇲
Publishing Since
11/16/2022
1 verified contact email on file for The Agentic Edge Podcast
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Recent Episodes
October 23, 2024
Your CMS is a gold mine: time to dig
<p><a target="_blank" href="https://www.linkedin.com/in/schaunwheeler/">Schaun Wheeler</a> and <a target="_blank" href="https://www.linkedin.com/in/icanautomate/">Arpit Choudhury</a> talk about Content Management Systems (CMS) and how they are a treasury of information that can be leveraged for hyper-personalization. They explore how generative AI is transforming CMS, the importance of data quality, and the potential of LLMs in enhancing user experiences. The discussion also covers the challenges of implementing AI in recommendation systems, the significance of integrating external data, and the future of personalization in various applications, including food delivery and travel.</p><p>KEY POINTS</p><p>* LLMs can significantly improve CMS data management.</p><p>* Data quality is crucial for effective personalization, which enhances user engagement and satisfaction.</p><p>* Agentic systems can enhance recommendation strategies.</p><p>* The alignment problem in AI affects the accuracy of recommendation systems</p><p>* Blog content can be leveraged to drive sales</p><p>* Every user has unique preferences that can be catered to with the help of AI agents.</p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://edge.aampe.com?utm_medium=podcast&utm_campaign=CTA_1">edge.aampe.com</a>
October 17, 2024
Agents, impact confidence, and prior beliefs
<p>In order to mimic human decision making, agents need to estimate the probability that a particular intervention will move user behavior in the right direction, but also estimate how much confidence they should put in that probability assessment. The first estimate is easy - or, at least, straightforward. The second estimate is a lot harder, but it sits at the core of how agents balance exploration of new possibilities with exploitation of lessons already learned.There is no purely empirical way to estimate confidence. Frequentists estimate it implicitly, whereas Bayesians estimate it explicitly, but in both cases the estimate has to come from the very squishy realm of prior beliefs. When I was designing how our agents would assess confidence, I had to do some thinking about the properties of statistical distributions and how those map to different expectations about confidence we can be about a lesson learned from just a single intervention. No one wants to stake too much on a single interaction - in any context - but if we stake too little then we end up discounting a lot of valuable information so much that we ultimately end up ignoring it.In the attached video, I walk through some of the ways we've addressed the topic of estimate confidence with our agents.</p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://edge.aampe.com?utm_medium=podcast&utm_campaign=CTA_1">edge.aampe.com</a>
October 8, 2024
Embedding space let agents communicate with each other
<p>When you're on a team, you tend to figure out problems collectively - you chat with your teammates, compare notes, and swap stories about what has or has not worked in the past. What if the team is a team of agents? How do agents compare notes?An embedding space. An embedding space is a high-dimensional mapping of data points (words, users, events, etc.) to arrays of numerical values. It's like a map, but whereas a typical map can only be 2D or maybe 3D, an embedding space can have many dimensions as you want. This allows you to pick any point in the space, and quickly find other points that share similar values.<a target="_blank" href="https://www.linkedin.com/company/aampe/">Aampe</a> agents maintain multiple embedding spaces for different look-back windows: the last 1 day, the last 7 days, the last 30 days, etc. So when an agent is working with a user and the user just won't respond to any outreach, the agent can use the embedding spaces to quickly find other users who *have* responded recently, and get ideas from those user's agents.It's impossible to visualize so many dimensions on a 2D surface, but I did a little dimensionality reduction in the attached video to try to give a little intuition about how embedding spaces work.</p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://edge.aampe.com?utm_medium=podcast&utm_campaign=CTA_1">edge.aampe.com</a>
19 total episodes available
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Frequently asked questions
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- What is The Agentic Edge Podcast?
- How often does this podcast release new episodes?
This podcast updates weekly.
- Where can I listen to this podcast?
This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
Yes, this podcast regularly features guests.
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