You can now make money from your AI chatbot
AI can diagnose type 2 diabetes in 10 seconds from your voice
Quora's AI chatbot platform, Poe, is now compensating bot creators, marking one of the first initiatives to financially reward AI bot builders. The monetization scheme offers two revenue streams for creators: a share of revenue when a bot leads to a Poe subscription, and soon, a per-message fee paid by Quora. The Poe app, launched in February, acts as a hub where users can interact with AI chatbots from various companies, including OpenAI. If the chatbot content on Poe meets specific quality standards, it might also be featured on Quora's main Q&A site, which boasts 400 million monthly visitors.
Poe has seen impressive growth, with over 18.4 million installs since its public launch and 1.22 million monthly active users. However, its in-app purchase revenue is estimated to be under $250,000 monthly. The monetization program for creators, currently available only for U.S. users, can pay up to $20 per user who subscribes to Poe due to a creator's bot. The company allocates funds from subscriptions to pay the creators based on specific metrics. Quora uses Stripe for these payments.
The CEO of Quora, Adam D’Angelo, expressed optimism about the diverse range of bots that could thrive on the platform and believes this move will not only empower smaller entities to build bots but also bolster Poe's position in the competitive AI chatbot market.
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Canadian researchers have trained an AI model to diagnose type 2 diabetes by listening to a patient's voice for six to 10 seconds. This machine-learning AI identified 14 vocal differences between individuals with and without the condition, including subtle changes in pitch and intensity that are undetectable to the human ear. By also considering basic health data like age, sex, height, and weight, the researchers believe this method will significantly reduce diagnosis costs.
The technology, developed by Klick Labs in collaboration with Ontario Tech University, was trained using 18,000 voice recordings from 267 participants in India. The study revealed that the AI diagnosed women with 89% accuracy and men with 86%. Specific vocal features were more indicative depending on gender: for instance, 'relative average perturbation jitter' was more diagnostic in women, while 'intensity' and '11-point amplitude perturbation quotient shimmer' were more diagnostic in men.
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