I share your aversion to modern marketing tactics, but by your logic, programmers that develop the addictive social media algorithms are the meth cooks. Everyone is complicit. Modern day "tech bros" get a significantly worse rep than marketing folks these days. No use in participating in this blame game.
I agree that software engineering isn't exempt from warranting serious introspection as to the world that a given project is enabling. I do not agree that we should simply throw up our collective hands and say "Oh well, everyone is complicit." Professional endeavours causing interpersonal harm and enabling exploitative behaviour should be called out and forced to bare the reputational cost wherever and whenever they occur.
I mean that kind of tracks? I had to take a computer science ethics course in college. It mainly focused on stuff like the Therac-25 case study, but I could easily see a more modern version of the course covering social media algorithms.
I wonder if marketing courses also have an ethics component taught in them?
For someone just learning about the Therac-25 incident, what more recent cases would've worked better to foster discussion that can also be read about?
Mass surveillance and face identification by private companies for law enforcement eg https://www.auror.co/role/loss-prevention
You can't appeal it and they don't respond to requests for your records (like a government department would).
Social media and addiction to it. How this should be managed with vulnerable groups, eg children?
I'm sure there are numerous better examples out there.
You don't have to be able to vibe code an entire business from scratch to know that the technology behind AI is significantly more impressive than VR, crypto, web3 etc. What the free version of ChatGPT can do right now, not just coding; would've been unimaginable to most people just 5 years ago.
Don't people and companies using AI lazily to put out low quality content blind you to its potential as well as the reality of what it can do right now. Look at Google's VO3, most people in the world right now won't be able to tell you that it's AI generated and not real.
AI development/research is far too modular and globalized to be regulated, or have regulation have any sizeable impact on its progress, assuming it's even possible to properly define clearly, "limits that shouldn't be crossed". Even needing to understand enough about AI to define those limits would ironically be made difficult through regulation. Just my 2 cents.
Aren't most interviews like this? Most dev openings I see posted mention the specific language who's expertise they're looking for and the number of years of experience needed working with said language as well.
It can be annoying, but manageable. I've never coded in Java for example, but knowing C#, C++ and Python I imagine it wouldn't be too hard to pick up.
Huh, okay. That's not how we run interviews but I guess it's at least a thing, even if not common around here that I've seen yet (I'm not super current on interview practices though)
Regarding the job ads, yes they'd describe the ideal candidate but I haven't the experience that the perfect candidate ever actually shows up. Like you say, knowing J, T and Z, the company is confident enough that you'll be able to quickly pick up dotting the Is and crossing the 7s
> It's amusing to me how people keep trying to apply engineering principles to an inherently unstable and unpredictable system in order to get a feeling of control.
Are you Insinuating that dealing with unstable and unpredictable systems isn't somewhere engineering principles are frequently applied to solve complex problems?
I think the point is that it's more about trial and error, and less about blindly winging it. When you don't know how a system seems to work, you latch on to whatever seems to initially work and proceed from there to find patterns. It's not an entire approach to engineering, just a small part of the process.
To be fair #3 and #4 are abilities I believe can only be learnt through actual work experience. Not much colleges can do in that regard. Sure, group projects, presentations, hosting/participating in workshops etc. did help a bit, but they were all fairly rudimentary in terms of developing those skills. Internships are key.
Couldn't agree more regarding taking English/History courses. I find that understanding and dissecting good English literature isn't any less challenging than any computer science problem.
