3 ms·

I think machine learning coupled with increase in sensitivity of the signals could make a difference.

by harigov 9y ago

sillysaurus3 9y ago
Why would machine learning be able to overcome a lack of statistical power?
- cperciva 9y ago
  It's not clear whether there's actually a lack of statistical power, or simply a lack of sufficient modelling of the noise.
- TeMPOraL 9y ago
  It wouldn't, but maybe there is enough statistical power, but it's just tricky to process?
- harigov 9y ago
  Did you miss the part about sensitive sensors? A single sensor may not be enough to classify accurately but multiple sensors measuring different things can sometimes work. Such sensor fusion has worked incredibly well for IMU's and also in self-driving cars.
  - lj3 9y ago
    That's how the Glucotrack claims to work. From their site: GlucoTrack uses ultrasonic, electromagnetic and thermal technologies to non-invasively measure glucose levels in the blood. GlucoTrack is intended for use by Type 2 and pre-diabetics. GlucoTrack combines the following technologies: Ultrasound (speed of sound change within the tissue); Electromagnetic (conductivity of the tissue); Thermal (heat capacity of the tissue). It's not approved by the FDA, but it appears to be selling in Australia, New Zealand, Spain, China, Korea, Italy and some others.
weego 9y ago
Yeah let's get some AI in there to help out, maybe building the front end in react native will solve some problems no one though of. We could get the data with graphql to solve some issues.
- deleted 9y ago
  [deleted]
- harigov 9y ago
  Except, it actually does. ML is just advanced statistical modeling which performs complex computation over large amounts of data to give out better results. Because Apple can afford to do things at scale and use their marketing prowess to buy sensors at an economical price, they can afford to use multiple sensors to give out a reasonably accurate metric. All the technologies that you mentioned do solve problems they are intended to solve. Why so much negativity?
rubidium 9y ago
hahaha. Siri can't even parse "What time does the Indianapolis 500 start?" It gives me movie start times.
- harigov 9y ago
  They are totally different things. Sure, Siri can't parse that and yet we have working* self-driving cars and autopilots for planes.

I think machine learning coupled with increase in sensitivity of the signals could make a difference.

sillysaurus3 9y ago

Why would machine learning be able to overcome a lack of statistical power?

cperciva 9y ago
It's not clear whether there's actually a lack of statistical power, or simply a lack of sufficient modelling of the noise.
TeMPOraL 9y ago
It wouldn't, but maybe there is enough statistical power, but it's just tricky to process?
harigov 9y ago
Did you miss the part about sensitive sensors? A single sensor may not be enough to classify accurately but multiple sensors measuring different things can sometimes work. Such sensor fusion has worked incredibly well for IMU's and also in self-driving cars.
- lj3 9y ago
  That's how the Glucotrack claims to work. From their site: GlucoTrack uses ultrasonic, electromagnetic and thermal technologies to non-invasively measure glucose levels in the blood. GlucoTrack is intended for use by Type 2 and pre-diabetics. GlucoTrack combines the following technologies: Ultrasound (speed of sound change within the tissue); Electromagnetic (conductivity of the tissue); Thermal (heat capacity of the tissue). It's not approved by the FDA, but it appears to be selling in Australia, New Zealand, Spain, China, Korea, Italy and some others.

weego 9y ago

Yeah let's get some AI in there to help out, maybe building the front end in react native will solve some problems no one though of. We could get the data with graphql to solve some issues.

deleted 9y ago
[deleted]
harigov 9y ago
Except, it actually does. ML is just advanced statistical modeling which performs complex computation over large amounts of data to give out better results. Because Apple can afford to do things at scale and use their marketing prowess to buy sensors at an economical price, they can afford to use multiple sensors to give out a reasonably accurate metric. All the technologies that you mentioned do solve problems they are intended to solve. Why so much negativity?

rubidium 9y ago

hahaha. Siri can't even parse "What time does the Indianapolis 500 start?" It gives me movie start times.

harigov 9y ago
They are totally different things. Sure, Siri can't parse that and yet we have working* self-driving cars and autopilots for planes.