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mbohu last won the day on November 23 2019

mbohu had the most liked content!

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  1. Best 4-way competition exit ever. I definitely want to try to duplicate it: http://www.omniskore.com/comp/2019/2019uspanspc/media/3_221_4222_1.mp4
  2. Yup. That sounds like AFF for many people. As long as the fun/excitement outweighs the rest, just stay with it. The plane ride and door fear definitely does go away eventually, but for some people it takes 10-20 jumps, for some up to 100 to completely disappear. If you stick with it, it WILL go away, at least in my experience.
  3. I have them and I do like them, but since it's the only booty suit I had in the last 2 years I can't say for sure how they compare to the regular cordura booties. Essentially they have a couple extra pieces of fabric that create an air channel, which inflates them when you put your legs fully into the airflow (see red circle). I probably notice it most, when tracking away. I sure get a lot of forward push and lift from my legs. My measurements were also slightly off and the booties are a bit longer than they should have been, so they don't fully stretch out until I point my toes to the max, and I think that air channel helps to counteract that, inflating them a bit more, even when I don't have them stretched to the max. In the picture you can also see that this is an extra area where the stitching can come off (bottom left of the fabric--so in that picture they are actually not quite doing their job), but that took about 2 years of regular usage and was extremely easy to fix. A rigger sowed it back on within 5 minutes.
  4. I'm not a DZO or airplane expert, so I'm not sure what the definition of a "short" Caravan is, but Orange Skies Skydiving in Colorado is flying this one:
  5. Somebody's got to disagree: (not about the equipment--definitely don't try to buy equipment too early and no reason to worry about it now. I waited a year and just over 100 solo jumps, because I wanted to try out a few different canopies before deciding, and am super-happy I did.) But I did read this book right as I was doing my first AFF jumps. When you can't jump and are itching to do so, this is a little bit of a substitute, and it won't send you down the wrong road, in my opinion: https://www.amazon.com/Parachute-Its-Pilot-Ultimate-Ram-Air/dp/0977627721
  6. mbohu


    I wish I remembered more about probability theory, so I'd know how to create the actual formula, but my guess is that this is not unlikely at all.: You currently have a base probability of about 0.08% of anyone being part of the group of confirmed infections (total US infections/total US population)--let's call it i/p Then you have the group of active members in this community: m Then you have the average group-size of family/close friends of each active member (maybe 20-30?): f So the sample size is f*m (maybe 1000-2000?) This is extremely small as a percentage of p BUT the likelihood of at least 1 member of (f*m) to also be a member of i is probably not that small But of course, the infection rate MUST BE much higher than the reported cases, simply because the percentage of how many people are tested, is so low. If testing was random, you'd have to assume that the true rate of infection (I) is: (i/t) * p where "t"=number of tested individuals. That would come out to a HUGE number. Of course it is to be assumed that it isn't quite as high, because we are mostly testing individuals, who we think have a high likelihood of being infected, but STILL: Of course, the real rate of infection is much higher than what's being reported as confirmed cases. Anyway: kallend, I hope your son recovers and everything will be well.
  7. I don't know. Us getting some even marginally better ones is pretty essential, I think. On the other hand, we'd never vote for them, so it's probably just a waste of resources.
  8. It doesn't really matter what the exact scenarios or rules are. No matter how you slice it, somewhere at the center of the program has to be some sort of decision making algorithm, which is fed the data from the sensors or even from multiple cars and road sensors. It sounds like billvon was mostly talking about using neural networks in the pattern recognition algorithms. These would be the algorithms feeding the decision making. It's quite possible that the decision making algorithms themselves are extremely limited right now, and operate on extremely simple instructions (always try to break, never drive faster than your ability to break immediately--but as you pointed out with the jaywalker, this is really not realistic--same with the curve, if you drive around a blind corner, ready for your own lane being completely blocked, you'd have to come to a virtual stop) The more data is being fed into the decision making algorithm though, the more options it will have available, and the more it will have some kind of morally relevant preferences. This would actually be much more the case in a centralized system that is aware of multiple vehicles (via vehicles sending it data and/or road sensors) It now has the ability to consider consequences for ALL vehicles and in situations that have no perfect outcome it will have to prioritize between all vehicles. Again, the more data it has, the less likely such situations will be, but the likelihood will never be zero.
  9. Well, I gave an example from skydiving already. But I guess the easiest one I would come up with for a car would be a mountain road: Say, you drive around the curve and find a truck stalled straight across the 2 lanes (=big object to avoid); on the right side is the mountain wall (bigger object to avoid), and the left side is completely free. With the rules we have in place right now ("avoid objects") we'll have some fun on the way down. Now, if you say, the response is ALWAYS just to break as hard as possible, I could not imagine that to be true, because there would be many easy (and more common) scenarios where veering to the left or right would produce much better outcomes.
  10. I think it may have been the original 2016 one that is referenced in link a) (but not the one the article is about)
  11. But aren't there clearly cases where hitting something is preferable to the alternatives? Along the lines of: better to fly into the tree than execute an extreme low turn and hit the ground full speed?
  12. Oh, I didn't know that. Very cool. So that sounds like the decision algorithm is then just based on very simple fixed parameters like "don't hit anything that is considered a threat" and the neural network and learning algorithm is only used in the determination and pattern recognition phase to determine what is a threat. That is different from what I was told and had discussed with people that were involved in other AI efforts. I would still think that in the end a more powerful system would be more sophisticated, and be trained based on outcomes, not such a simple instruction, so it would choose its decisions based on learned experience from millions of prior outcomes. This is most definitely done in other fields of AI.
  13. And again, then it is possible to conscioulsly decide to go no further with any more detailed determinations. But that is a conscious choice as well--and I can tell you the people I talked to that are in related fields are not indicating that they are stopping with such simple determinations. They do want to figure out if the thing is a person, a vehicle, an inanimate object, etc.
  14. That isn't true for sure. As I said above, the car needs to know not to brake full force for a tumbleweed; it needs to determine if a pothole is too deep to just speed over; And as I said, I am pretty sure that the fatal Tesla accident was caused by the program interpreting an object it should have avoided as one that was irrelevant.