The device generates a podcast referred to as Deep Dive, which contains a male and a feminine voice discussing no matter you uploaded. The voices are breathtakingly real looking—the episodes are laced with little human-sounding phrases like “Man” and “Wow” and “Oh proper” and “Maintain on, let me get this proper.” The “hosts” even interrupt one another.
To try it out, I copied each story from MIT Expertise Assessment’s One hundred and twenty fifth-anniversary concern 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 a terrific job at conveying the final, high-level gist of what the problem was about. Have a hear.
MIT Expertise Assessment One hundred and twenty fifth Anniversary concern
The AI system is designed to create “magic in trade for a bit of little bit of content material,” Raiza Martin, the product lead for NotebookLM, mentioned on X. The voice mannequin is supposed to create emotive and interesting audio, which is conveyed in an “upbeat hyper-interested tone,” Martin mentioned.
NotebookLM, which was initially marketed as a research device, has taken a lifetime of its personal amongst customers. The corporate is now engaged on including extra customization choices, resembling altering the size, format, voices, and languages, Martin mentioned. Presently it’s purported to generate podcasts solely in English, however some customers on Reddit managed to get the device to create audio in French and Hungarian.
Sure, it’s cool—bordering on pleasant, even—however additionally it is not immune from the issues that plague generative AI, resembling hallucinations and bias.
Listed below are among the essential methods individuals are utilizing NotebookLM up to now.
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 sequence referred to as Histories of Mysteries, which goals to “uncover historical past’s most intriguing mysteries.” He says he researched matters utilizing 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 entire podcast sequence took him two hours to create, he says.