I was on my daily walk with Steve, my husband, a software engineer who is all-knowing about all things tech, and we were discussing how AI might impact podcasting. He encouraged me to check out ChatGPT and after doing so, he may have just created a monster. I was researching Ep 69 “Habits” and gotta say, ChatGPT made it easy and effortless. I now understand why students love it!! I know I did!
The one thing I did ask and didn’t perform very well writing a podcast script. Oh well – Here’s what Steve had to say about that:
- Practice tuning my questions for better use of the ChatGPT format.
- Break up my search at different levels and see what you get.
- Use Google and see what it says about how to use ChatGPT to write a podcast script. So here’s what Google had to say (it was basically what Steve said to do):
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- Ask ChatGBT for video script ideas.
- Ask ChatGBT to write your video script giving direction on length, topic, characters, hook, and type of story.
- Refine script with more questions.
I know there’s a lot of fear or concern around AI but I’m thinking I was able to take a very complex topic like “Habits” and get a well-organized overview in a very short period time. Like a lot of things, what you put in is what you get out – so I quickly learned that refining the questions I was asking was important but I loved the interactivity and how I could follow-up easily with another question. So much easier than searching on Google. I could see they are both good for different things.
One other thing I should confess: I found myself wanting to confirm the research and still do it myself – make my own observations and still read the books myself. AI was helpful in pointing me in the direction but I still wanted to add my stamp of Yo-ness. In fact, I asked ChatGPT to write this blog. Can you tell what is in my voice and what’s AI?
Opportunities of Using AI in Podcasting:
Personalization: By using AI, podcasters may create personalized content that is tailored to the listener's interests and preferences – the problem now is that we don’t have access to very good metrics. If we did, this could help to increase engagement and listener retention, as well as improve the overall listening experience.
To create personalized content, several components are needed. These include:
Data Collection: The first step in creating personalized content is to collect data about the listener. This can be done using various methods, such as tracking the listener's listening habits, analyzing their social media activity, and collecting data from other sources.
Machine Learning Algorithms: Once the data is collected, machine learning algorithms can be used to analyze the data and identify patterns and trends in the listener's behavior. This could help us create a personalized profile of the listener which would be so very helpful in developing content.
Content Creation Tools: With the personalized profile of the listener in hand, content creation tools could be used to generate customized content. These tools could take the form of templates, scripts, or other forms of content creation software.
Delivery Platform: Finally, a delivery platform is needed to distribute the personalized content to the listener. This can take the form of a podcast platform, a social media platform, or other types of content delivery systems. We use a lot of these now.
Efficiency: AI can help podcasters create content more efficiently, as it can automate certain tasks, such as audio editing, transcription, and even content creation. This could save podcasters time and resources (and our mental health), allowing us to focus on other aspects of our podcast, such as marketing and promotion.
One example of AI-generated audio content is the startup called "Resemble AI." They have created a voice cloning platform that can generate synthetic voices that sound remarkably like real humans. The platform uses deep learning algorithms to analyze a person's voice and create a digital voice model that can be used to generate new audio content.
For example, Resemble AI's platform can be used to generate a podcast host's voice, even if the host is not available to record new content. This is done by analyzing the host's existing audio recordings, such as previous podcast episodes, and creating a digital voice model that can be used to generate new audio content in the host's voice. I can’t wait to hear my voice generated by AI – what a trippy experience that will be!
Another example is the creation of news articles in audio format. The Associated Press (AP) has partnered with a company called "Veritone" to create an AI-powered audio newsroom. The system uses machine learning algorithms to analyze written news articles and generate audio versions of the content. This allows the AP to distribute news content in both written and audio formats, increasing accessibility for listeners who prefer audio content.
Monetization: AI can help podcasters monetize their content by providing insights into listener behavior and preferences. This can help us create targeted advertising campaigns, sponsorships, and other revenue-generating opportunities.
Machine learning (ML) is not the only method to obtain listener behavior and preferences, but it is one of the most effective ways to do so. Other methods, such as surveys and focus groups, can also be used to obtain listener feedback and preferences, but they may not be as efficient or as accurate as ML algorithms.
ML algorithms can analyze vast amounts of data, such as listening history, search queries, and social media activity, to identify patterns and trends in listener behavior. This data can then be used to create personalized content that is tailored to the listener's interests and preferences.
However, it is worth noting that ML algorithms require a significant amount of data to be effective, and they must be properly trained and calibrated to ensure that the results are accurate and meaningful. Additionally, ethical considerations must be taken into account when collecting and analyzing listener data, as privacy concerns can arise.
Accessibility: AI can make podcast content more accessible to listeners with disabilities, such as those with hearing impairments. By transcribing podcasts in real-time or creating interactive transcripts, AI can make it easier for these listeners to enjoy podcast content.
Innovation: Finally, by using AI, podcasters can stay ahead of the curve and continue to innovate in the industry. As AI technology continues to develop, new and exciting opportunities for podcasters will emerge, allowing them to create even more engaging and interactive content. I’ll be watching closely!!
Challenges of Using AI in Podcasting:
Privacy Concerns: As with any technology that involves the collection and analysis of personal data, privacy concerns can arise when using AI in podcasting. Podcasters must be transparent about how they collect and use listener data, and take appropriate measures to ensure that the data is kept secure and confidential.
Ethical Considerations: The use of AI in podcasting raises ethical questions, particularly when it comes to creating synthetic voices or manipulating audio content. Podcasters must ensure that they are using AI in an ethical and responsible manner, and avoid creating content that is misleading or deceptive.
Accuracy and Bias: AI algorithms must be properly trained and calibrated to ensure that the results are accurate and unbiased. Podcasters must be aware of the potential for bias in their data sets and algorithms, and take steps to mitigate these issues.
Podcasts currently using AI:
The Daily from The New York Times - The Daily uses AI to create personalized audio snippets for each listener, based on their interests and listening history. These snippets are designed to give listeners a quick overview of the day's news and can be accessed through the podcast app.
BBC World Service - The BBC World Service uses AI to create automated news bulletins, which are generated in real-time using natural language processing (NLP) technology. This allows the BBC to provide up-to-date news coverage to its global audience, 24/7.
Wait, What? - Wait, What? is a podcast that explores the intersection of technology and culture. The podcast uses AI technology to create synthetic voices for its hosts, allowing them to deliver their content in a more engaging and immersive way.
The Vergecast - The Vergecast is a weekly podcast that covers the latest tech news and trends. The podcast uses AI to transcribe its episodes in real-time, making it easier for listeners to follow along and search for specific topics.
AI in Action - AI in Action is a podcast that explores the world of artificial intelligence and machine learning. The podcast uses AI technology to generate transcripts and summaries of its episodes, which are available on its website.
3 Takeaways:
- AI has the potential to transform the podcasting industry, providing new opportunities for podcasters to learn more about their listener.
- The use of AI also raises challenges and ethical considerations that must be carefully addressed – not sure I’m ready for my synthetic voice!
- Overall, AI-generated audio content is still in its early stages, but it's clear that the technology has the potential to revolutionize the way audio content is created and distributed. As the technology continues to develop, we can expect to see more and more AI-generated audio content in various forms, including podcasts, audiobooks, and news articles.