> Rarely(never?) have I found new knowledge on youtube, however its a great source of joy/emotions/slop.
I suspect you are not looking very hard. I have learned a tremendous amount about everything from stone cutting to metalworking to welding to Kalman filters to linear algebra. There is a lot out there. The main annoyance I have is keeping AI slop out of my feed so that I can instead learn from genuine experts. There is a huge amount out there.
The gestation period of a cow is approximately 9 months, similar to humans, by coincidence. Only a cow that has given birth to a calf will produce milk. The normal lactation period is 305 days before the cow is "dried up" before giving birth again. 10,000 pounds of milk is considered a good lactation total. Typically, cows are bred to calve once per year. Typically going through 10 lactations before that one way trip to MacDonald's.
Dairy bulls are notoriously nasty creatures, so artificial insemination is almost universal in the dairy industry. The "tract" that you speak of is the cow's colon. The technician is careful to guide the pipette so as not to injure the animal, and the colon provides convenient access to feel what is going on inside.
If you are squeamish about such things as cow's colons, then vet school is not for you.
I was speaking from the perspective of the people in my opening sentence. How commonly known would you suspect those facts are in your comment?
e.g. "[They might assume] cows simply produce milk like chickens lay eggs."
It's normal to never really think about it -- our society is set up so that you never have to. The secretion comes in a jug, the meat comes in cellophane, and that's it.
> e.g. "[They might assume] cows simply produce milk like chickens lay eggs."
You may have a point that many have no idea how chickens work. Egg laying being like giving birth isn't an unreasonable explanation if you had to come up with one on the spot while completely in the dark. But most understand how milk is produced because even if they've never seen a cattlebeast, they deal with milk-producing humans daily.
That will vary by person. My father-in-law bred and milked pedigreed Holsteins. They had a 1 gallon pasteurizer and would just dip a gallon out of the bulk tank for household use when needed. So, most of the time they had pasteurized, non-homogenized. On occasion, the pasteurizer would break, so for a while they would drink raw milk. But of course understood the risk, and also knew darn well where the milk had come from and how clean the milking facility was.
The thing about LR parsers is that since it is parsing bottom-up, you have no idea what larger syntactic structure is being built, so error recovery is ugly, and giving the user a sensible error message is a fool’s errand.
In the end, all the hard work in a compiler is in the back-end optimization phases. Put your mental energy there.
I have worked on compilers (mostly) for high-performance computing for over 40 years, writing every part of a production compiler twice or more. Optimization and code generation and register allocation/scheduling are definitely the most fun -- but the hardest work is in parsing and semantics, where "hardest" means it takes the most work to get things right for the language and to deal with user errors in the most graceful and informative manner. This is especially true for badly specified legacy languages like Fortran.
I was just going into the second quarter of compiler design when the dragon book came out. My copy was still literally “hot of the press” — still warm from the ink baking ovens. It was worlds better that anything else available at the time.
It seems to me that compared to your phone, a power brick dangling off a charging cable is much more likely to slip off your lap unnoticed and get wedged in the seat hinge only to get subsequently punctured.
I recently took a flight where I had a laptop, my phone, a power brick, a new power brick for my wife, a second phone (for reasons) and a battery for a piece of ham radio equipment in my backpack. As I got on the plane, I was thinking I was probably one of the risker passengers on board :) Anyway, when I use the brick, I keep it zipped in a jacket pocket with just the charing cable coming out in an effort to keep it from finding its way to a place that it shouldn't.
he he... is that the equivalent of when I was a kid we differentiated by "drive-in", "paper-napkin restaurant" and "cloth-napkin restaurant" in order of how much trouble you would be in if you embarrassed your parents.
It is easy to underestimate how much one relies on senses other than vision. You hear many kinds of noises that indicate road surface, traffic, etc. You feel road surface imperfections telegraphed through the steering wheel. You feel accelerations in your butt, and conclude loss of traction from response of the accelerator and motion of the vehicle. Secondly, the human eye has much more dynamic range than any camera. And is mounted on an exquisite PTZ platform. Then turning to the model -- you are classifying obstacles and agents at a furious rate, and making predictions about the behavior of the agents. So, in part I agree that the models need work, but the models need to be fed, and IMHO computer vision is not a sufficient sensor feed.
Consider an exhaust condensation cloud coming from a vehicle's tail pipe -- it could be opaque to a camera/computer-vision system. Can you model your way out of that? Or is it also useful to do sensor fusion of vision data with radar data (cloud is transparent) and others like lidar, etc. A multi-modal sensor feed is going to simplify the model, which in the end translates into compute load.
No, I don't think that will be successful. Consider a day where the temperature and humidity is just right to make tail pipe exhaust form dense fog clouds. That will be opaque or nearly so to a camera, transparent to a radar, and I would assume something in between to a lidar. Multi-modal sensor fusion is always going to be more reliable at classifying some kinds of challenging scene segments. It doesn't take long to imagine many other scenarios where fusing the returns of multiple sensors is going to greatly increase classification accuracy.
The goal is not to drive in all conditions; it is to drive in all drivable conditions. Human eyeballs also cannot see through dense fog clouds. Operating in these environments is extra credit with marginal utility in real life.
But humans react to this extremely differently than a self driving car.
Humans take responsability, and the self-driving disengages and say : WELP.
Oh sorry were you "enjoying your travel time to do something useful" as we very explicitely marketed ? Well now your wife is dead and it's your fault (legally). Kisses, Elon.
I suspect you are not looking very hard. I have learned a tremendous amount about everything from stone cutting to metalworking to welding to Kalman filters to linear algebra. There is a lot out there. The main annoyance I have is keeping AI slop out of my feed so that I can instead learn from genuine experts. There is a huge amount out there.
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