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 disagree any of the bloat you are talking about exists because puffying paper numbers is basically required to justify your work. Its because they were distrusted extensively so they have to ritually say their work is useful. Also I think its very challenging because most extra committees and stuff exist because people complained about how streamlined science use to be. Those committees exist because science got wrongfully accused of wasting money in the 80/90s with the golden fleece awards among other things, where republican's claimed someone's basic science research was a total waste of government money. Ironically many of the things that won a golden fleece ended up saving the country billions if not trillions of dollars overtime.
I think the major struggle with basic research is there is no way to conduct it in which results are guarenteed. If you could do that you wouldn't need basic research. But there are a ton of questions whose outcomes are not really valuable at all but you simply don't know. On net science dispite those many useless questions answered still is extremely net posititve because some of those apparently meaningless questions ended up being the right question to drive research to useful good answers.
> 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.
I have a feeling we would all be terrified if we knew how much AI had a role in building bridges at the moment.
TBD if they stay up, I suppose.
The stories I hear from various white collar professions not related to tech are... interesting, to say the least. There is a whole lot of unsanctioned shadow IT going on regardless of policy.
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.
