Podcasts have become daily companions for half a billion users. Given the enormous amount of podcast content available, highlights provide a valuable signal that helps viewers get the gist of an episode and decide if they want to invest in listening to it in its entirety. However, identifying highlights automatically is challenging due to the unstructured and long-form nature of the content. We introduce Rhapsody, a dataset of 13K podcast episodes paired with segment-level highlight scores derived from YouTube's 'most replayed' feature. We frame the podcast highlight detection as a segment-level binary classification task. We explore various baseline approaches, including zero-shot prompting of language models and lightweight fine-tuned language models using segment-level classification heads. Our experimental results indicate that even state-of-the-art language models like GPT-4o and Gemini struggle with this task, while models fine-tuned with in-domain data significantly outperform their zero-shot performance. The fine-tuned model benefits from leveraging both speech signal features and transcripts. These findings highlight the challenges for fine-grained information access in long-form spoken media.

An AI-powered technology called Podium is intended to speed up the post-production of podcasts significantly. It lets you quickly create transcripts, highlights, chapters, and show notes with episode summaries. The application is simple to use and doesn't need to create an account; all required is to submit an audio file. The AI in Podium will swiftly find quotable passages, develop chapters and titles, and provide a summary of the episode that can be easily shared on social media. Also, it offers a superb transcript for enhanced accessibility and search engine optimization. The application is initially free but will soon change to a cheap pay-per-use fee or bespoke pricing if you need to handle a big volume of episodes at once. AI tool is intended to improve the post-production of podcasts via the creation of AI-powered show notes, titles, and descriptions.

The modern internet is powered by recommendation algorithms. These systems track your online consumption and use that data to suggest the next piece of content for you to absorb. Their goal is to keep users on a platform by presenting them with things they'll spend more time engaging with. Trouble is, those link chains can lead to some weird places, occasionally taking users down dark internet rabbit holes or showing harmful content. Lawmakers and researchers have criticized recommendation systems before, but these methods are under renewed scrutiny now that Google and Twitter are going before the US Supreme Court to defend their algorithmic practices.

What can marketers bring to the mix when AI is so powerful? Don't miss a MarketingProfs podcast, subscribe to our free newsletter! Passion, for one thing, says Peter Prodromou of Boathouse. "If you're in the upper right-hand corner with passion, chances are people are going to want to work with you or buy your product," he says on the latest episode of Marketing Smarts. "Think about Apple and Tesla; those are two brands that are very much about passion. Your ability to convey that is critically important." AI is just an algorithm, after all. "Everybody is going to shop at Amazon because they have the best algorithm, and there may or may not be passion for it," Peter says.

Podcasting has emerged as a major billion-dollar industry, with ad revenue in the U.S. alone expected to hit $2 billion this year -- a figure that's set to double by 2024. Against that backdrop, major players in the field are bolstering their podcasting armory, with Spotify recently doling out around $85 million for two companies specializing in podcast measurement and analytics, while Acast recently snapped up Podchaser -- an "IMDb for podcasts" that gives advertiser deeper data insights -- in a $27 million deal. But as the big platforms lock horns in the hunt for podcasting riches, smaller players continue to arrive on the scene with their own ideas on how they can advance the podcast medium for creators and consumers alike. One of these is Snipd, a Swiss startup building a podcast app that uses AI to transcribe content and synchronize with note-taking apps; automatically generate book-style "chapters"; and, as of this week, deliver podcast highlights in a TikTok-style personalized feed. Similar to other so-called "podcatcher" apps, Snipd works by users searching and subscribing to podcasts that are of interest to them -- this could be anything from true crime to history and sport.

How has technology and AI impacted society and what's around the corner? Based on our AI Operations podcast episode, we look at what might happen next in our post-pandemic world, and how AI could help shape it. We can't discuss technology's impact on our society without reflecting on the COVID-19 pandemic, which has so profoundly affected our lives. People across the globe found themselves in a completely new environment and had to rapidly adjust to them in completely new ways. Digitalization at home and in the workplace were fast-forwarded like never before.

This is a Podcast by WiselyWise, from our series " Impact of Education and Knowledge podcast series". This series will cover the Impact of education and knowledge in day-to-day life. Our 1st Podcast Episode is on the Importance of Python in Artificial Intelligence with our guest Sathya Dayanithi. This is a Podcast by WiselyWise, from our series " Impact of Education and Knowledge podcast series". This series will cover the Impact of education and knowledge in day-to-day life.

Prifina is thrilled to welcome Dr. Peter Cotton as our special guest in the fifth episode of the "Liberty. Peter currently serves as the Senior Vice President and Chief Data Scientist at Intech Investment Management LLC. D. degree in Mathematics from Stanford, he held leadership roles at major U.S. financial institutions. Peter has led data science projects at Morgan Stanley, J.P. Morgan Chase, and several major hedge funds, where he built solutions solving complex data problems. He has extensive experience with crowdsourcing models and helped build one of the first in the world privacy-preserving computation mechanisms at J.P. Morgan Chase. In this podcast we talk about algorithms and innovation, with a focus on financial data. How does a hedge fund normalize messy financial data to build bespoke predictive models? What are the current trends and challenges related to machine learning in the financial services industry? From the innovation point of view, what happens when we cut down the cost of building algorithms to minimum functionality? Is it possible to build personal AI systems for small and mid-cap companies as well as individuals? We also delve into geeky topics, such as how to build a financial probability model for the pricing of vanilla bonds. You can find this podcast on Spotify, Apple Podcasts, Google Podcasts, and SoundCloud. He noted that one of the main areas of focus in his career was to level the playing field in the machine learning (ML) space. He notes that one of the first things to do before building financial prediction, ML, and AI models is create a system that helps clean and normalize data. "It is the most interesting mathematical problem of all because the cleaning of data implies that you understand the market itself.

Episode 167 - Kevin Craine and Billee Howard discuss the use of nuero-powered technology to quantify, measure and understand human thought. Explore how to use artificial intelligence and sentiment analysis to connect customer emotion directly to improved business performance. Understand the convergence of'big emotion' and'big data' and how it is valuable from a strategic and marketing perspective. Stay tuned for three action items in the second half. Host, Kevin Craine Do you want to be a guest?

Episode 106 - Edward Hess discusses The New Smart Machine Age and where humans fit into the convergence of business, technology and automation. Will smart machines and robots start taking our jobs? What can we do...us humans...to prepare for the inevitable transformation? Ed is a Professor of Business Administration and Batten Executive-in-Residence at the Darden Graduate School of Business at the University of Virginia. You've seen and heard him in places like WSJ Radio, CNBC, NPR, and Investor's Business Daily, among many others.