This PhD mostly uses it to summarize emails from the administration. It does a shit job, but it frees up time for more science so who cares.

The real irony is that the administration probably used AI to write the emails in the first place. The mails have gotten significantly longer, less dense and the grammar has gotten better.

Begun this AI arms race has.

Out of context, and I didn't read the rest, that sounds reasonable.

"If my dumbass is learning and finding, what about actual pros?!"

Or vibe build a deep sea submarine, cause well you know.

If only. They'll fill it with sycophants until the explosions stop. They're just more resources for them to use.

Just listen to Cramer and then... don't do that.

in the dotcom days I worked for an "interactive agency". We took people that had product ideas or website ideas brought them in charge them and exorbitant amount of money, made them a professional flash website, got them some awards from whoever would give away awards for ideas and hope them up with venture capitalists. The one thing I can say about all those venture capitalists is they Will throw cash at anything that might make the money. If it fails it's tax abatement. If one in 10 succeeds they make a s*** ton of money off of it.

AI doesn't even need to be good it just needs to be perceived as worth something and they make money.

I unironically started to dislike the bullet point presentation format, even the axios smart brevity format. I feel like it's treating me like I'm too dumb to read a news report or a normal text. That why it matters feels like being instructed on how to think. It's honestly bullocks and it was turning me into a worse reader.

Fair warning, though, might get scratched😬

Neural Networks, which are the base technology of what nowadays gets called AI, are just great automated pattern detection systems, which in the last couple of years with the invention of things like adversarial training can also be made to output content that match those patterns.

The simpler stuff that just does pattern recognition without the fancy outputting stuff that matches the pattern was already, way back 3 decades ago, recognized at being able to process large datasets and spot patterns which humans hadn't been able to spot: for example there was this NN trained to find tumors in photos which seemed to work perfectly in testing but didn't work at all in practice, and it turned out that the NN had been trained with pictures were all those with tumors had a ruler next to it showing its size and those without tumors did not, so the pattern derived in training by the NN for "tumor present" was actually the presence of the ruler.

Anyways, it's mainly this simpler and older stuff that can be used to help with scientific discovery by spotting in large datasets patterns which we humans have not, mainly because they can much faster and more easily trawl through an entire haystack to find the needles than we humans can, but like in the "tumor detection NN" example above, sometimes the patterns aren't in the data but in the way the data was obtained.

The fancy stuff that actually outputs content that matches patterns detected in the data, such as LLMs and image generation, and which is fueling the current AI bubble, is totally irrelevant for this kind of use.

Drink verification can or be violently ripped atom from atom in an unplanned hyperspace exit.

And it would be like that, I picture a ton of seemingly pointless steps and then the effect.

And ever worse is it would ether not work unless every silly step was done or (possibly even more dark) we remove steps and it still works to the point that all the steps are gone, and its just a button.