He doesn't go into this in the book, but I am fairly sure that Harris would agree with your consequentialist take of "acting as if they had free will". I have heard him speak on this matter in a few of his podcasts around "the hard problem of consciousness" with Dennett, Chalmers and a neurosurgeon that I can't find the name of (I remember him being british).
As I understand him, his view is to not view criminals (or anyone) as "morally bad" for whatever they have done, but to move directly on to figuring out the best possible way to avoid bad things happening again, to their future potential victims and to themselves. I think he sees this is as an important starting point in order to be able to be consequentialist about it at all.
For example, if the best way to avoid criminals re-offending turns out to be to put them into a cushy, luxurious rehabilitation program, then in order to even consider this as an option, we must remove our sense of needing to punish them for being morally reprehensible.
Helpful resource for whoever ends up doing this: Contraceptive Technology. It's a huge book that summarises almost all effectiveness studies that have been done on contraceptives, including the definitions of perfect and typical use (very important when comparing contraceptives). It also has detailed summaries of side effects, medical interactions, description of method of action and well researched "advantages" and "disadvantages" sections — it's basically what doctors use to decide how to prescribe birth control.
Source: I have used this book myself in research, I work for a birth control app company.
Good points! Yes this snippet is particularly nonsensical to me
an AI system could be“superintelligent” without any basic humanlike common sense, yet while seamlessly preserving the speed, precision and programmability of a computer
It sounds like their experience with computers has involved them having a lot of "basic humanlike common sense" which is a pretty crazy experience in this case. When I explain what programming is like to kids, I usually say something like "The computer will do exactly exactly exactly what you tell it to, extremely fast. You can't rely on any basic sense checking or common sense, or understanding from it, if you can't define what you want specifically enough, the computer will fail in a (to you) very stupid way, very quickly."
Great and fair critique of this paper! I also enjoyed reading it and would recommend it also just for the history write up.
What do you think is the underlying reason for the bad reasoning in fallacy 4? Is the orthogonal it thesis particularly hard to understand intuitively or has it been covered so badly by media so often that the broad consensus of what it means is now wrong?
Great write-up! I work at another femtech company and will share this with some of my colleagues.
Three thoughts/comments:1. Clue is very focused on period dates (being a period tracker) and doesn't have very accurate ovulation data. Therefore, cyclical changes in metrics that are affected by ovulation rather than by period will look dimmer due to differences between the length of peoples' Luteal phase. E.g. the BBT signal would probably be much sharper if the x axis was "days relative to ovulation" rather than "days relative to period", since the temperature change is being caused by hormonal changes at ovulation rather than at the period.
2. On sleep tracking, many of my colleagues track their sleep with the Oura ring and have noticed that their sleep is reported in the Oura app as being significantly worse during their Luteal phase (the phase between Ovulation and Period). Turns out this was not due to actual worse sleep, but due to Oura measuring their temperature and picking up on the BBT rise after ovulation. Oura assumed that higher temperatures = worse sleep (in general probably true, but only useful for people without menstrual cycles). Just something to watch out for — surprisingly many products still aren't designed to work properly for 50% of their potential users.
3. Echoing what remizidae says: although these patterns show up with enough data, only a few of them are usable on individuals. I've looked at trying to apply predictive models for several of these metrics on individuals and it's very rare that you get anything that feels more accurate than pure noise. The only exceptions are the really physical ones such as heart rate and temperature.
I don't menstruate, but I work at Natural Cycles (a data-driven birth control app) with data science, and look for these types of patterns a lot — our users are not using Hormonal Birth Control though, so the sample is biased in that way.
Clue (a popular period tracker app) recently released one of the best studies I've seen on the mood changes over the menstrual cycle, but unfortunately it is not open access. The authors shared a pdf with me after I emailed them though, DM me if you would like a copy (sci-hub doesn't seem to have the paper yet), or email the authors directly, they are very helpful.
True, circulatory diseases would be a big win, but do you think the marginal buck there is likely to do as much as a marginal buck focused on aging giving the amount of funding allocated to each? If we add the R&D budgets focused on circulatory diseases to the treatment cost of circulatory diseases (potential profit pool for pharma companies), my intuition says that the number would be ~20-100x the total amount of funding to aging-stopping or -reversing technology. What do you think the ratio would be?
Definitely a bug! It was my first and only foray into D3.js so there are a lot of bad states you can get into fairly easily. Might rebuild it in something else one day.
Love this article.
After reading the The Fable of the Dragon-Tyrant a few years ago after my father died, I went into a deep dive on this and ended up making a calculator, comparing the impact of eliminating various causes of death on average / median lifespan. It's very simplistic, but I found it interesting to use to illustrate how ageing contributes to death: