• Home
  • Technology
  • Gaming
  • Entertainment
  • World & Business
  • Science
  • Sports
  • AI
HomeTechnologyGamingEntertainmentWorld & BusinessScienceSportsAI
    • HomeTechnologyGamingEntertainmentWorld & BusinessScienceSportsAI
    • Home
    • Technology
    • Gaming
    • Entertainment
    • World & Business
    • Science
    • Sports
    • AI
    AI

    Eight Sleep App Shows Bryan Johnson's Biological Age as 31

    The app now reports the biohacker's age as 31 using only Pod data and AI.

    BJ
    TM
    MF
    5 Sources, 27d ago, first seen 27d ago

    TLDR

    Matteo Franceschetti, co-founder and CEO of Eight Sleep, posted that Bryan Johnson is 49 yet his Pod reports a biological age of 31. The feature was developed in collaboration with Johnson and is now live in the Eight Sleep app. Franceschetti said the AI model was trained on data the Pod already captures, so no separate wearable or lab panel is needed.

    Combined views

    152.5K

    5 Sources, first seen 27d ago

    Combined views

    152.5K

    5 Sources, first seen 27d ago

    1.5K likes
    Today's Rank

    —

    Not ranked yet

    Today's Rank

    —

    Not ranked yet

    1.5K likes
    118 comments
    750 saves
    121 reposts
    118 comments
    750 saves
    121 reposts

    5 Sources

    @m_franceschettiBryan Johnson is 49, but his Pod tells him he's actually 31. That's his Biological Age, developed in collaboration with @bryan_johnson, now live on the Eight Sleep app. Most biological age tools need a wearable and a lab panel. Ours doesn't. We built it by training an AI model on the data the Pod already captures every night, heart rate, HRV, and sleep, continuously, instead of a one-time wearable reading. Connect Apple Health and it adds your workouts. Connect HealthEx (US) and it adds your medical history. Go find yours now.
    @bryan_johnsonI just launched an AI model based on sleep data… and it accurately predicts your age. I teamed up with @m_franceschetti, and it's now available on their platforms. It turns out you have a sleep fingerprint. This research can identify you out of thousands of users from one night's signal with 92.5% accuracy. It also detects… + biological age within 3.3 years + diabetes better than Apple’s model + speed of aging + heart failure at 0.822 This Eight Sleep model is possibly the most accurate contactless bioage estimate ever reported. What we did: #1 What data was it built on? This model was built on the largest raw biosignal dataset ever used to train an AI, from any device, including every wearable on the market. > 2.04 million hours > 136,575 participants > 498k sessions > 122 million segments #2 What can it predict? It can predict your biological age (the age your body acts like) within 3.3 years. It also predicts heart-related and metabolic conditions. Here are the detection scores (AUROC): > diabetes (0.852) > heart failure (0.82) > hypertension (0.810) > sleep apnea (0.792) > snoring (0.751) > general heart conditions (0.734) > cancer (0.678) > hot flashes (0.671) > migraines (0.673) #3 How was it built? Interestingly, the pretraining task was not “predict someone’s age”. The model was tasked with comparing two 60 second windows across different nights to figure out if the nights belonged to the same person. To do that, it had to find someone’s ‘sleep fingerprint’. These are biological signals that the data is coming from the same person. Things like how forcefully your heart contracts, your breathing depth and rhythm, and the timing of the recoil wave each heartbeat sends through your body. Those signals are age-predictive. It learned to estimate age, detect diabetes, and flag heart failure as a downstream readout. The whole pretraining run was ~four days. #4 Why is it good at age? The reason aging prediction is accurate is mechanical. Aging stiffens arteries, reduces cardiac compliance, changes autonomic tone, HRV declines. Aging also alters sleep architecture. Deep sleep shrinks and fragmentation rises. Every one of those changes the recoil waveform and its overnight dynamics. Said differently, the heart of a 65 year old mechanically pushes the body differently than a 25 year old's. #5 Why a bed vs wearables? A bed is an elegant solution. It makes a high fidelity uninterrupted 5 to 10 hour recording every single night possible. And session-level sequence modeling becomes viable. Whereas wearables get fragmented data: battery limits, sparse snippets, people taking the watch off, adherence dropping over weeks. #6 More data, better prediction The bigger the training batches (the more people the model compares at once) the better it got, log-linearly (R²=0.982). That means the recipe is predictable: you can forecast improvement with more compute, the same way scaling laws work for language models. The current model only ever compares two nights at a time, and the average training user contributed under 4 nights. The team's stated next step is modeling 30+ consecutive nights per person. You can imagine how this will improve with the constant stream of data Eight Sleep gets every night. — It’s worth noting some limitations. Internal labels are self-reported and external cohorts are small, and that this is a research milestone, not a diagnostic device. What makes this exciting: a passive, daily activity like sleep can now provide meaningful insight into your well-being. I speak often of Autonomous Health, a world where the things around us take care of us without our knowing or asking. Eight Sleep is a great example of this in practice and a major reason I maintain so much optimism for the future of health.
    @iScienceLuvrA self-supervised model trained on hundreds of thousands of hours of sleep data.. Already deployed in Eight Sleep app... Yet another example of how underrated self-supervised learning in medical AI can be!

    Sentiment

    Positive43.2%56.8%Negative

    Summary

    Sentiment

    Positive43.2%56.8%Negative

    Positive replies welcomed Eight Sleep's AI model for predicting biological age from sleep data as an exciting advance for health insights, while negative replies called the term unscientific snake oil and objected to high product costs.

    Based on 39 sentiment-bearing replies from 37 accounts across 3 conversations.

    Summary

    Positive replies welcomed Eight Sleep's AI model for predicting biological age from sleep data as an exciting advance for health insights, while negative replies called the term unscientific snake oil and objected to high product costs.

    Based on 39 sentiment-bearing replies from 37 accounts across 3 conversations.

    5 Sources

    @m_franceschettiBryan Johnson is 49, but his Pod tells him he's actually 31. That's his Biological Age, developed in collaboration with @bryan_johnson, now live on the Eight Sleep app. Most biological age tools need a wearable and a lab panel. Ours doesn't. We built it by training an AI model on the data the Pod already captures every night, heart rate, HRV, and sleep, continuously, instead of a one-time wearable reading. Connect Apple Health and it adds your workouts. Connect HealthEx (US) and it adds your medical history. Go find yours now.
    @bryan_johnsonI just launched an AI model based on sleep data… and it accurately predicts your age. I teamed up with @m_franceschetti, and it's now available on their platforms. It turns out you have a sleep fingerprint. This research can identify you out of thousands of users from one night's signal with 92.5% accuracy. It also detects… + biological age within 3.3 years + diabetes better than Apple’s model + speed of aging + heart failure at 0.822 This Eight Sleep model is possibly the most accurate contactless bioage estimate ever reported. What we did: #1 What data was it built on? This model was built on the largest raw biosignal dataset ever used to train an AI, from any device, including every wearable on the market. > 2.04 million hours > 136,575 participants > 498k sessions > 122 million segments #2 What can it predict? It can predict your biological age (the age your body acts like) within 3.3 years. It also predicts heart-related and metabolic conditions. Here are the detection scores (AUROC): > diabetes (0.852) > heart failure (0.82) > hypertension (0.810) > sleep apnea (0.792) > snoring (0.751) > general heart conditions (0.734) > cancer (0.678) > hot flashes (0.671) > migraines (0.673) #3 How was it built? Interestingly, the pretraining task was not “predict someone’s age”. The model was tasked with comparing two 60 second windows across different nights to figure out if the nights belonged to the same person. To do that, it had to find someone’s ‘sleep fingerprint’. These are biological signals that the data is coming from the same person. Things like how forcefully your heart contracts, your breathing depth and rhythm, and the timing of the recoil wave each heartbeat sends through your body. Those signals are age-predictive. It learned to estimate age, detect diabetes, and flag heart failure as a downstream readout. The whole pretraining run was ~four days. #4 Why is it good at age? The reason aging prediction is accurate is mechanical. Aging stiffens arteries, reduces cardiac compliance, changes autonomic tone, HRV declines. Aging also alters sleep architecture. Deep sleep shrinks and fragmentation rises. Every one of those changes the recoil waveform and its overnight dynamics. Said differently, the heart of a 65 year old mechanically pushes the body differently than a 25 year old's. #5 Why a bed vs wearables? A bed is an elegant solution. It makes a high fidelity uninterrupted 5 to 10 hour recording every single night possible. And session-level sequence modeling becomes viable. Whereas wearables get fragmented data: battery limits, sparse snippets, people taking the watch off, adherence dropping over weeks. #6 More data, better prediction The bigger the training batches (the more people the model compares at once) the better it got, log-linearly (R²=0.982). That means the recipe is predictable: you can forecast improvement with more compute, the same way scaling laws work for language models. The current model only ever compares two nights at a time, and the average training user contributed under 4 nights. The team's stated next step is modeling 30+ consecutive nights per person. You can imagine how this will improve with the constant stream of data Eight Sleep gets every night. — It’s worth noting some limitations. Internal labels are self-reported and external cohorts are small, and that this is a research milestone, not a diagnostic device. What makes this exciting: a passive, daily activity like sleep can now provide meaningful insight into your well-being. I speak often of Autonomous Health, a world where the things around us take care of us without our knowing or asking. Eight Sleep is a great example of this in practice and a major reason I maintain so much optimism for the future of health.
    @iScienceLuvrA self-supervised model trained on hundreds of thousands of hours of sleep data.. Already deployed in Eight Sleep app... Yet another example of how underrated self-supervised learning in medical AI can be!