I hate that it happened because of a political reason, and many topics affected were unnecessarily targeted, but it’s 1000% true that many labs were overfunded, and accumulated resources which were essentially spent on ego bullshit. There need to be more cuts and selective funding of research labs, in general. Sadly, funding R1 does not guarantee that you’re going to get anything meaningful from that research as a non-trivial number of PIs just used excessive funding to bloat up their numbers to appear politically important, like middle managers at FAANG. So, essentially creating an adult daycare with no regards to output or impact. This needs to stop, and spending needs to be allocated responsibly. Lab impact needs to be assessed on regular (2-yr seems reasonable) basis, and then funding needs to be diverted to new or better players.
> I agree with you take the there isn’t a lot of specialist work for data scientists to do with using off-the-shelf LLMs that can’t be done by an engineer.
Conversely, data scientists are doing software engineering, including webdev. It’s an interesting time. I think it’s less about the job title demarcation now, and more about output.
Where are all the production issues that have been created because of AI? Are there more incidences than before now? What’s the rate of production failures pre and post AI?
Only reason humans need to be in the loop is so there is someone to blame or hold accountable in a legal sense.
What’s important? That bridges get built and stay up, or that they’re built only after toiling X amounts of hours. AI will change the nature of work, it’s going to make a lot of people uncomfortable. But more importantly, it’s going to let people who understand things faster get the info they need to be productive.
There aren’t different definitions of consciousness, rather different conditions which result in an emergent property. The field has generally accepted sentience as a level of consciousness, which needs further examination.
One key reason you’re wrong is that many interesting things aren’t even getting published, they’re on the DL for years and eventually make it to public spheres and products.
Academia is just a daycare at this point, and many labs shouldn’t exists or get funding. The people who move the field aren’t necessarily the ones with the most citations, they’re usually hard at work in places that don’t publish at all.
I was in a programming class when ChatGPT/CoPilot first came out. I hadn’t started using it yet for classes because I was under the impression that “my work should be my own”. I was the only one in the class who would get 80+ average on quizzes, everyone else got nearly perfect scores. Oh well.
Something else is afoot in the markets, I wouldn’t take rando tweets at face value, especially if they’re confirming a narrative you’re biased to accept.
The problem is that inefficient systems will cost even more as you scale their use, but gains from such systems are not guaranteed, and profits even less so.