I have found myself legit using this instead of duolingo for the past couple of days, in spite of the speech to text issues. It feels like a better way to learn. The inline suggestions, in particular, seem like they will help me quickly move through bad habits and mistakes in order of severity.
The tutor review at the end of the lesson doesn't feel as useful currently; its content isn't particularly actionable. Maybe if it gave me a couple of exercises or something?
The auto submit slider doesn't work for me (on Android) but I can imagine what it would be like. I don't think the Portuguese speech to text is good enough for the app to be usable hands free even if the slider did work. For example, it just transcribed
"Eu quero ir a assistir ao filme." (which is almost correct)
Penalizing a crime might make sense for the purpose of deterrence even if there is no conscious free will. The size of the deterrent effect is a scientific question, and how to weigh the benefits of deterrence to society vs. the harm the punishment inflicts on the criminal is a moral question (where by moral I mean "should we do this").
However, penalizing a crime for the purpose of retribution makes no sense if there is no conscious free will. The criminal's consciousness experiences the harm of the punishment, but didn't cause the actions that constituted the crime. This is patently unfair.
I don't think conscious free will exists. One of the reasons I care is that, if most people shared that belief, we would talk and think about crime so differently that our approach to crime would change in the direction of becoming more humane.
For example, given the news of SBF's conviction for fraud, no one would be happy about the fact that he will likely experience many years in prison. We might be happy that the harm his actions caused has been stopped, and we may feel a kind of dutiful satisfaction in the knowledge that the system is working to deter and prevent this kind of harm. However, these emotions would be tempered by regret that a young man is going to lose part of his life, and we would be questioning whether there's any way to achieve the same level of harm prevention without inflicting such serious harm on one person.
I find it plausible that it's often a good idea in medicine to be conservative before launching interventions that may have adverse side effects, but have difficulty swallowing the idea that this conservatism should be implemented by reducing visibility (eg by not applying tests too broadly if they have a nontrivial false positive rate and the condition is rare). It seems like the right thing to do would be to maximize visibility, but then to try to corroborate and not overreact to apparent positives.
I heard about this video from an astronomer friend a few months ago. It was mostly about how Loeb's social and professional behavior matches with that of past crackpots. He does seem to leave himself open to that.
There wasn't really a material argument against his controversial hypotheses about interstellar objects. I counted an ad hominem (45 minutes of analysis of his awkward public persona), an argument from ignorance ('Omuamua is out of view so it's not possible to collect evidence for the lightsail hypothesis) and an appeal to the authority of the broader astronomical community.
He is going out and looking for evidence about IM1, and is seeking cross-validation from other labs, in a very transparent way. He's making lots of possibly inappropriately optimistic public speculations in the meantime, but I haven't seen him make any actual claims that aren't justified, which seems like the defining feature of a crackpot.
In hindsight this comment was too short. Clarifying some points:
By "This makes sense", I meant that this kind of thing can happen; as more data are gathered, the Bayesian probability of a candidate value can increase and then suddenly decrease. Here's a Colab notebook demonstrating the general phenomenon: https://colab.research.google.com/drive/1Eb1_humiGPdKb0c3qr_...
"Calibration" in this context means "statistical consistency between distributional forecasts and observations" in the words of https://sites.stat.washington.edu/raftery/Research/PDF/Gneit... . If the model's early forecasts predict impact with probability >3% for a class of objects that end up impacting with frequency much less than 3%, then the model is not well calibrated with respect to its early forecasts for those objects.
Based on the GP, it sounds like these early impact "probabilities" are no one's subjective (Bayesian) probability of impact because people who are closely familiar with this model know it is not well calibrated. The reported probabilities may still be useful to them as indicators or flags. However, those of us who are _not_ closely familiar with the model have found it confusing to see things that are not really probabilities reported as probabilities.
This makes sense, thanks. However, it doesn't mean that the 3% estimate of the chance of impact was wrong at the time of initial observation given data available, and that still makes it a huge deal at that time. At minimum, it would seem to justify using the best available instruments to characterize the asteroid more precisely as soon as possible.
If these large numbers happen so often that asteroids with initial impact probabilities of 3% are known to actually impact much less frequently than that, then the model is poorly calibrated, no? In other words, the reported probabilities aren't really probabilities and that is what has caused the confusion and anxiety in these comments.
The tutor review at the end of the lesson doesn't feel as useful currently; its content isn't particularly actionable. Maybe if it gave me a couple of exercises or something?