Readit NewsSo, to calibrate a sleep tracking device, you have a person wear the device, while also doing the sleep study. You do this a bunch of times. You train some ML models to try and make the outputs from the sensor data, after processing, the same as the study data.
After some degree of accuracy you declare success.
Now, does it work? In broad strokes, yes. You can (easily!!) see the effect of alcohol on sleep quality. If you have a crap night vs a good night, sure, a wrist based consumer device can figure that out.
Actual details? Eh. I wouldn't trust the devices for anything but directional data.
The more sensors devices get, the better than ML model can be trained.
Now it has been awhile since I last worked on this stuff (I actually just sat next to the people doing the work), so maybe there is some revolutionary new technique out there, but if not, it is still ML models trying to correlate things and match them up to what a bunch of fancier sensors said during studies.
I have very little wrist pain any more.
I also have a kinesis pro keyboard and a MX master mouse. They both add to the improvement.
I’ve found that when you’re going from (weak, sedentary) => (strong, active) it can sometimes be difficult to discern what activities are good or bad for your pain. Sometimes you need to work through pain to find relief and strength on the other side, but sometimes working through pain just leads to more pain. The boundaries aren’t always clear at the time.
Also, for what it's worth, it's hard to imagine a better executor than Christopher Tolkien. He basically spent his entire life serving his father's artistic interests.
These software giants never take a responsibility of wrongful automated result for decades but we keep using it.
Meta doesn't have an incentive to fix this case. It costs them manual labor of many manhours that outweigh the ad revenue from this person.
It probably keep this way.
Task Allocation: Using real-time data on each engineer's strengths, past performance, learning curve, and even their preferred working hours, EMAI allocates tasks from the backlog. It uses predictive modeling to optimize for both efficiency and team satisfaction.
Conflict Resolution: If two engineers have a disagreement or are blocked by each other, EMAI steps in. Using its vast knowledge base and understanding of human psychology (aided by its training data), it mediates discussions, ensuring a harmonious team environment.
Training & Upgradation: EMAI monitors the latest tech trends. If a new tool or technology emerges in the market, it identifies which team members would benefit most from training and automatically schedules online courses or tutorials for them.
End-of-Day Reports: Every team member receives a personalized report detailing their accomplishments, areas of improvement, and resources for further learning. These reports aren't just data-driven and include motivational feedback designed to boost morale and foster continuous learning."
It'll be a cold day in hell before I work 5 minutes under those conditions.
I remember writing a super simple wrapper around Jasmine to do the same kind of thing, and was glad when I could ditch it for Storybook many moons ago. But if I was starting a project now I would probably rawdog it again, Storybook just causes too much pain.
Is anyone aware of any similar projects that are more developer friendly?
Erm, Itanium never had any kind of market leadership. It was a failure.
Chips being cheap makes sense at the lithography / wafer level because sure, you can stamp out thousands of them at once. But once you need to dice them up, bond wires to them, and package them... how on earth do you do that so efficiently that each chip can be sold for fractions of a cent?