The software generates a podcast referred to as Deep Dive, which encompasses a male and a feminine voice discussing no matter you uploaded. The voices are breathtakingly practical—the episodes are laced with little human-sounding phrases like “Man” and “Wow” and “Oh right” and “Hold on, let me get this right.” The “hosts” even interrupt one another.
To try it out, I copied each story from MIT Technology Review’s One hundred and twenty fifth-anniversary situation into NotebookLM and made the system generate a 10-minute podcast with the outcomes. The system picked a few tales to concentrate on, and the AI hosts did an amazing job at conveying the overall, high-level gist of what the difficulty was about. Have a hear.
MIT Technology Review One hundred and twenty fifth Anniversary situation
The AI system is designed to create “magic in exchange for a little bit of content,” Raiza Martin, the product lead for NotebookLM, mentioned on X. The voice mannequin is supposed to create emotive and partaking audio, which is conveyed in an “upbeat hyper-interested tone,” Martin mentioned.
NotebookLM, which was initially marketed as a study software, has taken a lifetime of its personal amongst customers. The firm is now engaged on including extra customization choices, resembling altering the size, format, voices, and languages, Martin mentioned. Currently it’s supposed to generate podcasts solely in English, however some customers on Reddit managed to get the software to create audio in French and Hungarian.
Yes, it’s cool—bordering on pleasant, even—however additionally it is not immune from the issues that plague generative AI, resembling hallucinations and bias.
Here are among the principal methods folks are using NotebookLM thus far.
On-demand podcasts
Andrej Karpathy, a member of OpenAI’s founding workforce and beforehand the director of AI at Tesla, mentioned on X that Deep Dive is now his favourite podcast. Karpathy created his personal AI podcast collection referred to as Histories of Mysteries, which goals to “uncover history’s most intriguing mysteries.” He says he researched matters using ChatGPT, Claude, and Google, and used a Wikipedia hyperlink from every matter because the supply materials in NotebookLM to generate audio. He then used NotebookLM to generate the episode descriptions. The complete podcast collection took him two hours to create, he says.