What are some examples of a nerfed response? I just asked Gpt4 to help me write a python program to analyze the sentiment and determine if biases are present in mathematical research papers in a PDF format.
Sure, it needs some love and there were some abstractions. For instance it assumed we had a labeled dataset for the text and the associated sentiment, but beyond that it worked fine.
ChatGPTs strength isn't in solving new problems, but in helping you understand things that are already solved. There's a lot more developers out there using these tools to create react apps and python scripts then there are solving race conditions with USB 2.0.
I just asked it to explain a problem that actually has been acknowledged to exist in USB errata from 2002 - not to solve anything.
It took me a while to realize that what I was experiencing was this particular problem, but I already did all the hard work there and only asked it to explain how it fails.
I also recently tried to use it to write a code for drawing wrapped text formatted a'la HTML (just paragraphs, bolds and italics) in C, again, just to see how it does. It took me about 2 hours to make it output something that could be easily fixed to work without essentially rewriting it from scratch (it didn't work without edits, but at that point they were small enough that I considered it a "pass" already) - and only because I already knew how to tackle such task. I can't imagine it being helpful for someone who doesn't know how to do it. It can help you come up with some mindless boilerplate faster (which is something I used it for too - it did well when asked to "write a code that sends these bytes to this I2C device and then reads from this address back"), but that's about it.
1) Those in power tend to not give up said power. There are exceptions of course. Take for example the contracting of the British empire. However, as the outcomes of significant societal changes become less clear, those in power and comfort are less likely to forfeit those advantages.
2) In my own little bubble, no one I know is particularly fulfilled by creating crud API's and gluing together microservices. I'd bet that with guaranteed financial stability finding meaning in life would become easier, not harder.
How many presidents have held onto power in the US history?
Monarchies have a history of getting taken down (in terms of power). They held an absolute power and a tight grip.
You could see money as a proxy for power, but that's only true as long as it's scarce and you can buy real power with it.
I've been a follower of your thinking patterns regarding 2) for a long time.
Life will be easier, no questions. But that's different question from the mental health one. Feeling useless is a pretty shitty feeling and a primer for depression.
> How many presidents have held onto power in the US history?
I see the point you're driving at but the democratic institution of the US has explicit guard rails against this. We force presidents to step down because we _know_ that voluntary relinquishing of power is unlikely.
I do see money as a proxy for power. If you make enough of it you can influence public opinion, enrich or destroy education, buy twitter, etc.
Interesting data. Looks like postings are still up nearly 100% since the bottom of the covid curve.
I'd be interested in seeing this compared to other non-tech job postings. I'd expect the trends wouldn't match entirely but that software development jobs are still in higher demand.
Interesting idea, but feels incomplete. It more or less just asks you repeatedly what job you'd like, or suggests you to try exploring the job you're already in.
I wonder if the world is trending in this direction. If anything, the moat between the two is growing larger, highlighting the differences. Everyone in your bubble may seem to be growing and learning, but you're also the type who spends their off time browsing highly technical message boards.
Adding to what was said about high quality customer feedback, a downturn will tend to more quickly expose weak product market fit. In lean times, companies are less likely to experiment with new tools or processes that don't have strong and clear value propositions. If you're targeting an emerging or growing market a downturn may be a good time to test that markets' staying power.
Soft skills rely heavily on connecting with the people you're interfacing with in meaningful ways. For example: discerning what is being said be a speaker from what the speaker is actually trying to convey. Tone, subtle facial expressions, body language, or otherwise deeply human characteristics all contribute to every day basic interactions. I'm not sure drilling social interactions with an algorithm will provide much value here.
Sure, it needs some love and there were some abstractions. For instance it assumed we had a labeled dataset for the text and the associated sentiment, but beyond that it worked fine.