Energy-efficient, deep-learning processors are what’s needed to make smart phones, wearables, and other consumer electronics smarter.
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Category: wearables – Page 63
This article does try to highlight what and where we are going with the merge of bio and technology. However, what has been shown to date is all very invasive as Quantum Biology has remained a gap in this development work until recently. Thanks to DARPA and others in the private sector who are working on technologies that leverages Quantum Biology principles to develop new integrated Biosystem technologies; we will see amazing work in cell circuitry and connectivity in areas of bio-security, BMI, prosthetics, immunology, anti-disease, reverse aging, etc.
These might sound like outlandish predictions, but DARPA’s Sanchez said it’s not as crazy as it might have sounded several years ago.
“Advancement of A.I. is making machines more powerful in the way they can understand everything from scientific papers to interpreting them and helping us solve big problems,” said Sanchez. “Another aspect to consider is our society [is] embracing things like wearables that… allow algorithms to analyze our physiology. Great examples of that are being able to monitor your sleep patterns and provide feedback on if you should change the time you go to bed or wake up in the morning.”
Sanchez said we’re at the point where wearables could easily be made to communicate with smart thermostats so heat could be turned up or the AC turned on automatically, depending on the user’s needs.
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Many have asked me what does this DARPA announcement on their project (RadioBio) mean. Well, imagine a world in the next 10 to 15 years where you no longer need any devices (no smartphone, no AR contacts, no smartwatch, no wearables, no external BMIs or invasive implants, etc.) of any kind as Quantum Bio technology uses (in DARPA’s case) connected cell technology to connect people to people and information online (private and publically available. This approach is the least invasive method of turning cells into connected technology.
Military will mean no more lugging of devices and certain types of equipment around on the battlefield plus lower risk of stolen intelligence as no device or equipment left behind or stolen.
What does it mean to consumers? Means no more losing phones and other devices as well as broken down equipment be replaced every 2years and no more insurance and extra-warranty payments for devices; and no more devices stolen with your information on it. And, it means my doctors and body (AI and non-AI methods) can monitor my health and activate pain relief, etc. through biosystem treatments such as pain can be suppressed via the readings or before the pain is felt. It also empowers the immune system to proactively prevent diseases as the biosystem technology will monitor and treat as needed.
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The graphene temporary tattoo seen here is the thinnest epidermal electronic device ever and according to the University of Texas at Austin researchers who developed it, the device can take some medical measurements as accurately as bulky wearable sensors like EKG monitors. From IEEE Spectrum:
Graphene’s conformity to the skin might be what enables the high-quality measurements. Air gaps between the skin and the relatively large, rigid electrodes used in conventional medical devices degrade these instruments’ signal quality. Newer sensors that stick to the skin and stretch and wrinkle with it have fewer airgaps, but because they’re still a few micrometers thick, and use gold electrodes hundreds of nanometers thick, they can lose contact with the skin when it wrinkles. The graphene in the Texas researchers’ device is 0.3-nm thick. Most of the tattoo’s bulk comes from the 463-nm-thick polymer support.
The next step is to add an antenna to the design so that signals can be beamed off the device to a phone or computer, says (electrical engineer Deji) Akinwande.
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Apple’s first paper on artificial intelligence, published Dec. 22 on arXiv (open access), describes a method for improving the ability of a deep neural network to recognize images.
To train neural networks to recognize images, AI researchers have typically labeled (identified or described) each image in a dataset. For example, last year, Georgia Institute of Technology researchers developed a deep-learning method to recognize images taken at regular intervals on a person’s wearable smartphone camera.
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