And I learned how to keep sub microsecond time accuracy.
Then I put the two together and made a raspberry pi sound localizer.
Anyway, the goal now is to find physical evidence that the localization was correct. In my area, there is lot of test data from the kind people that are setting off these large explosions. For that I am thankful :) Real life forensics can be fun :)
If I can find evidence that the indicated location was indeed where the explosion was set off then I will have verified that the localization can indeed be accurate over distances of almost 5km away, which is where one of the recorders is. Currently, I'm seeing a recurring pattern of explosions that appear to being set off in parking lot of a shopping area (Outside of a local Gamma in Limburg).
However, these fireworks do seem to destroy themselves pretty well, I haven't found physical evidence yet except for one that was very close (The next street), for that I found bits of the fireworks paper.
In any case, it will be quite a feat as right now the recorders are based 4.7km, 3k, > 2km sort of distances away from where they indicate that the most explosions are happening.
Does it do any kind of sound identification or matching? So if I sneeze around the device around the same time another system picks up a firework sound, will it be able to figure that out or is that a post process thing?
Would be interesting for you and your friends to pick a very exact point on GPS somewhere in the middle of all you and light off a big firework, then go back and see how close the system thought it was to the actual location. I know that kind of defeats the purpose of not launching illegal fireworks, but it’s better than shooting a gun in the air I guess.
I was so much happier painting houses. There is something about the constant mental engagement and frustration of software engineering, plus the fact that you are tethered to a laptop all day every day, that is just completely draining.
Currently enjoying a two week fermented Carolina Reaper hot sauce!
And is a little addictive like ex. running.
Background: I've been swimming for the last few years for about 10-20 minutes twice a week and I've experienced the changes.
Switching few months ago to more intense sessions led to dramatically higher rate of the change and I start to think about the ways of sustaining this. For now I've bought the swimming paddles and this works.
After it happens, you’ll have more time to play with AI :D
For the RISC CPU I couldn't tell you exactly what resources I used, it was kind of a blur lol. Basically it was Wikipedia, what I remember from computer architecture class 10 years ago, and a lot of googling, mostly lecture slides from classes I didn't get to take in person. I used ChipVerify for Verilog reference a lot. I can't remember what tutorial I used, but there are plenty out there.
At some time T, that grid is going to have a new values in every cell through the physics of air and turbulence.
The main question is, can an AI learn a compressed equation to where the inputs are the grid initial values and time T, and the output is the accurate final grid values for every cell?And by compressed, I mean the entire network takes up less space than a computer program that simulates the interactions at every boundary.
Or the more general way to put it, could we simulate reality faster than reality is happening, and have AI learn some compressed version of it to where you can ask it 2 "states" of reality (like the initial conditions of today, and then some time in the future where you have more money in your bank account and no criminal record and are still alive), and it can output a set of actions for you to take.
The answer, while not 100% definite, is very highly likely to be no.
I have worked with C++, Python, Node, React and compared to all this Java seems so much fun to write and understand
2. How do self-driving cars work
and
there's never enough time.
