If Tesla never rose, the world EV market outside of Tesla would have seen precisely the same rise.
would have seen the same rise _eventually_. I know from a friend that worked R&D at a major car company that Tesla really lit a fire under then and 'forced' them to push their own EV experiments from proof of concepts to commercial product much faster than they where originally thinking about doing it.
The EV ventures of most automakers are massive money losers (just as it always has been for Tesla outside of selling green credits and subsidies). But for sure they all rushed to get there not because the EVs themselves were valuable, but because of the insanity of the capital markets where Tesla is valued at a trillion dollars at a 200x P/E, while the rest of the market is at like a 7-14x ratio. Everyone wanted some of that irrational hype.
Only if the camera is angled in such a way that it only sees your property. A door bell camera that can also see the public road in front your house for example is technically not allowed, even if most people ignore that rule.
The big problem is that universities basically never hire or promote based on a persons teaching ability. One of the best lecturers I had at university was a postdoc who didn't get hired and ended up teaching at a 'third rate' university. One of the worst lecturers I had got head hunted by MIT.
>The big problem is that universities basically never hire or promote based on a persons teaching ability.
Because they aren't intended to be educational. Universities (as they are run today) are primarily grant-revenue capture organizations, secondarily research organizations (at least to the degree necessary that grant money doesn't dry up because of fraudulent spending accusations), and finally after that, a begrudged effort is made at education for optics. If they could ditch the education angle entirely, they'd send the students home tomorrow.
That's not necessarily a problem. There are different options in the marketplace. If you attend an R1 research university then of course hiring decisions will heavily weight research productivity. But many other smaller schools absolutely do look at teaching ability.
Pop in to publish your article, return to whatever else you had been doing after.
Nothing is stopping you. I've published papers and presented at academic conferences while working in industry. Both in collaboration with academics and without.
The working is the bulk of available exam marks but the calculator only gives answers
Knowing what the answer is supposed to be makes it much easier to reverse engineer the workings, and it lets you double check that your workings are correct. Also many of the more advanced graphing calculators (don't know the TI-89 specifically) have build in CAS systems and can do symbolic differentiation and as such help with showing your workings as well.
Plus the fact that these calculators let you store arbitrary data, so you can have your entire textbook stored in memory if you wanted.
I want to say something like "who learns advanced calculator functions solo but not the taught subject?" but of course shortcuts-by-rote spread like lice. I suppose that's the thrust of the problem with AI in schools
if one looks at completion rates of any online courses (Udemy/Coursera - under 4%)
As someone with a 96+% 'failure' rate on Udemy/Coursera I honestly don't see the relevance of this statistic. Most people going to University are there primarily because they want/need the degree. That piece of paper is really valuable, perhaps even more so than the knowledge gained. The piece of 'paper' offered by Coursera/Udemy etc. has basically zero value, so the people taking those courses are doing it almost exclusively for the knowledge they offer. Once you've learned what you wanted to learn from the course there is very little incentive to go the extra mile and go for the 'completion'.
The piece of paper is valuable because it represents a sustained effort of learning over an extended period of time.
I understand how from an individual's pov what you said makes sense. Similarly I hope you understand why from the system's perspective: it's the effort that's mandated and not just the proficiency.
Employers and others (higher education orgs, etc) care a lot about sustained effort, alongside proficiency. Only proficiency-focused systems (like Udemy/Coursera/Youtube) are not respected as credentials, since they do not showcase this.
Civil engineering uses a lot of C#, mainly since it is the main language for writing plugins and extension for most of the big software packages. Python is probably a close second.
For what it's worth I know several people in various roles at various aviation companies big and small, and everybody uses C++. Second most common language is probably Matlab for simulation and modelling. While a lot of Ada code exists I've never heard of anyone writing new Ada code.
I write or test new Ada code everyday at my current $DAYJOB. I used to do the same at an avionics company. I expect this to continue since its the best language for this type of software (high-integrity, embedded, real-time, in use for decades).
Is there a lot of programming in civil engineering? I would have imagined a lot of it is about using ready made software (from auto desk or whoever). I don’t know that much about either (civil engineering or programming) so I am just guessing here.
Is there a lot of programming in civil engineering?
Most civil engineers don't do a lot of programming, but virtually all civil engineering companies have people on staff that program. Mostly for developing custom and project specific tools and plugins for the software they use.
My core memory of SGI workstations was not that they necessarily were super fast when it came to pure flops (especially towards the end of their life), but how smooth and solid they were. Even if our Nvidia/Wintel machines were faster on paper and faster at things like rendering, the SGI machines would run buttery smooth no matter what we threw at them. Whether scrubbing through complex composition shots, doing real time lighting preview, or manipulating large 3D models, the frame rate and latency on the SGI machines was rock solid.
would have seen the same rise _eventually_. I know from a friend that worked R&D at a major car company that Tesla really lit a fire under then and 'forced' them to push their own EV experiments from proof of concepts to commercial product much faster than they where originally thinking about doing it.