I'm personally really looking forward to general hardware availability for SteamOS 3. I'd love to have a small but capable machine sitting behind the TV running Linux with Steam
> Marijuana smokers.—Cases were identified by searching for the terms marijuana and cannabis in The Ottawa Hospital picture archiving and communications system, and results were filtered to include only those in which chest CT was performed. Charts were reviewed to assess the frequency and duration of marijuana use, as well as for concomitant tobacco use. A total of 56 marijuana smokers were identified with chest CT performed between October 2005 and July 2020. Patient ages were sorted into 5-year age blocks (15–19 years, 20–24 years, 25–30 years, etc), and the number of men and women in each age category was determined. Marijuana consumption was quantified using the conversion of 0.32 g of marijuana per joint, as described by Ridgeway et al (14).
> Our study had limitations. First, the small sample size precluded us from drawing strong conclusions. Second, the retrospective nature of the study had its own inherent limitations. Third, there was inconsistent quantification of patient marijuana use, due in part to the previous illegal nature of marijuana possession, which led to a lack of patient reporting. Accurate quantification is further complicated by the fact that users often share joints, use different inhalation techniques, and use marijuana of varying potency. Fourth, given that most marijuana smokers also smoke tobacco, the synergistic effects of these two substances cannot be effectively evaluated. Fifth, only a portion of patients could be age matched, since the tobacco-only cohort was taken from the lung cancer screening study and the patients were aged at least 50 years. Due to the age mismatch in the larger cohort, there are differences in the duration of smoking. Lastly, variable interobserver agreement limits our ability to draw strong conclusions about bronchial wall thickening and bronchiectasis.
So it reads like marijuana only smokers were not distinguished from marijuana + tobacco smokers. Aka the comparison was really between a) non-smokers b) tobacco only smokers c) marijuana only smokers d) marijuana and tobacco smokers. With c) and d) combined into the same group. Meaning there could be many interpretations like:
1) “People that smoke more have more adverse effects” - since it’s possible that some people in “marijuana smokers” group consumed the same amount of tobacco as did people in “tobacco-only” group in addition to also smoking marijuana
2) Marijuana when combined with tobacco results in a synergetic effect that causes more morbidity
Smaller sample size + not having a “marijuana only” group makes it difficult to attribute specific effects to marijuana, to tobacco or to combination thereof.
The only “definitive” conclusion is that not smoking at all is certainly better than being a smoker of any kind - which is not very surprising.
Overall I do agree with you, it does make reporting seem very “sponsored“ and disingenuous.
However, an argument could me made that “We have the first look at the new technology. We’re obviously very limited at what we can legally say or show, but nevertheless here’s a very limited sneak peak” is still genuine journalism. They’ve reported as much as they could and did not hide obvious Intel participation, etc.
The video was very underwhelming and cringey imho. But to informed audience it probably wasn’t misleading or false - that’s how I felt after watching it.
I would very much recommend Johann Gottlieb Fichte’s “Foundations of the Science of Knowledge”. It’s a dense read, nevertheless an interesting insight on consciousness.
Apologies in advance if my terminology is not entirely correct - no formal CS background.
I wonder what implications, if any, this method has on the way science is done.
Perhaps I’m out of date, but I have always thought that with machine learning, with have little insight into how the algorithm does exactly what it does. We know how to train it, how it works - in terms of expected output given a certain input. But is not knowing exactly how the result is achieved a problem?
For modelling airplane air resistance, etc - it may not matter much. For fundamental physics research, I am not so sure. If replicating and verifying experimental results is so important to the “scientific method” - how would one approach confirming the results obtained via ML? Does independently building another ML algorithm count?
Yep! Years ago while in school, I kindly asked Roche to send me the posters. These are great, both in terms of the form - as in room decoration, and in terms of function - great when studying biochem, etc.
Same sentiment here. I could come up with one "advantage" of OpenBSD over Linux - smaller user base. In a simplistic kind of a view that should make it less compelling to put in as much effort into developing exploits vs more common OS'. I couldn't back this up with any concrete data though.
Security through obscurity isn’t foolproof, but it isn’t entirely foolish, either. It can reduce risk if part of a layered defense strategy.
It is important to remember though that it is not the number of users but but the value of the compromise that determines how compelling the O/S is. There are like vintage mainframe O/Ses for which folks are actively developing compromises because some government or telco hasn’t migrated off. Then again, those teams aren’t going to waste a exploit on somebody’s home Usenet archive.
TL;DR: nevermind. I am too busy arguing with myself to be coherent.
