Anecdote, but I live next to an elementary school and also on a route frequented by Waymos. Human drivers routinely cruise down the 25mph roads at 40+ and blow stop signs, even during school intake and release. Waymo vehicles always seem a lot more cautious.
When thinking about these things you have to factor in the prior probability that a driver is fully attentive, not just assume they are.
If you’ve ever been in a Waymo you quickly realize their field of view is pretty good. You often see the vehicle sensing small pets and children that are occluded to a passenger or driver. For this reason and my experience with humans near aforementioned school, I doubt a human would out perform the Waymo in this particular incident and it’s debatable they even have more context to inform their decisions.
All that said, despite having many hours in a Waymo, it’s not at all clear to me how they factor in sidewalk context. You get the sense that pedestrians movement vectors are accounted for near intersections, but I can’t say I’ve experienced something like a slow down when throngs of people are about.
A substance can have pharmacological effects and still not be recommended for therapeutic use. As a hyperbolic example, suppose a substance relieved all pain for 1% of the population but caused death in everyone else. Even with a highly precise screening process this substance likely would not be administered in medicinal contexts.
That's true, but I believe the authors' complaint here is efficacy rather than safety. (I also think they're using terms of art from evidence-based medicine to make a statement the general public is likely to misinterpret, per my other comment here.)
Safety is barely discussed in this paper, probably because the available RCT evidence is favorable to cannabis. I'm not sure that means it's actually safe, since RCTs of tobacco cigarettes over the same study periods probably wouldn't show signal either. This again shows the downside of ignoring all scientific knowledge except RCT outcomes, just in the other direction.
Who is thankful for sleep? It’s a biological necessity that robs us of a significant portion of our lives. I’d much rather be able to meditate for half an hour and reap the mental reset.
2016 already puts one far into the AI explosion. The current hype cycle, with LLMs as a service at the forefront, arguably makes python less relevant than in it was in the mid 2010’s. The current crop of “AI Engineers” can use whatever languages they want for the most part. In 2016 most practitioners were leveraging a lot more of the standard scientific computing frameworks afforded by python.
Python was the lingua franca of data science by 2016, but AI and data science was clearly not the reason startups were building in Django and Flask --- the data science teams were always a morass of Jupyter notebooks and pickle blobs.
I’ve never been able to switch since the feature set, whenever I’ve looked over the years, doesn’t seem geared toward serious mathematical exposition. I think I want to carry over habits from papers to blog pages though, which perhaps is misguided.
You’ll notice that most people arguing against DEI rarely perform meaningful analytics. Because when you do look at the data it tells the tale that the same centuries old systemic biases are still in play.
This is obvious to any adult in the room. But the benefactors of said biases do not want to acknowledge it since it lays bare their utter mediocrity.
While the scornful analogy there is dubious at best, I’d certainly reserve far more skepticism for the groups claiming AGI is within reach via the current crop of mathematically simplistic models
Might as well call advertising “fun programming breaks” while we are at it.