I suppose so. But the leading line before their 5 points made it sound like they were trying to explain why the fall deaths were increasing regardless of age.
> Why have deaths from falls, on an age-adjusted basis, risen? There are at least five possible reasons.
It's a tricky term, but the definition given towards the top where "age-adjusted" is clickable helps clarify. It's introduced in "But an aging population only partially explains the rise in these deaths. Deaths by falls have risen 2.4-fold on an age-<adjusted basis>. " The <clickable> part in brackets (easy to miss) expands to:
> Age-adjusted data helps to compare health data over time or between groups more fairly by accounting for the age differences in populations. For example, suppose Population A has a higher average age than Population B. In that case, age-adjusting ensures that Population A's naturally higher death rate due to age doesn't skew comparisons of overall health between the two. This measurement makes death statistic comparisons more accurate than crude death rates.
An "age group" might be "45-54" or "85+" but a "population" would be "all age groups in 2000" or "all age groups in 2023". Age-adjusted here means the differences in the number of people in each population (2000 vs 2023) are normalized to each so we can compare the absolute numbers from each age group directly, not the other way around.
There is some immediate follow-up text which helps clarify they do not mean to normalize the age groups themselves together within a single population for comparison:
> While they [population age-adjusted fall deaths] have fallen among younger people and only risen slightly among the middle aged, they have risen substantially within every age bracket of the elderly.
This all gave me flashbacks to stats class, and I now need to go relax :).
This is interesting, do all major airports have the ability to set up this cable system if needed? Or is this unique to PDX, or perhaps only those airports which are near where fighters train.
This reads like a fluff piece for Goldman Sachs and the startup Cognition. It’s running in a bunch of outlets concurrently.
Goldman gets to appear as being on the cutting edge by incorporating AI. The startup behind the agent GS is using, Cognition (who is seeking a $2B valuation), gets to be seen as effective and bolster their name recognition.
The “AI Software Engineers” at my company, whose applications and workflows I often support as an SRE, by and large build things with LLMs rather than train or create them.
What they build ranges from product features to internal tools. They make heavy use of LLM vendor inference APIs, vector databases, etc. They end up writing a lot of glue code and software to manage the context of the LLM, query for data, integrate with other systems and so on. They also develop front-end interfaces for their applications.
Only recently have they started to, lightly, explore the idea of training LLMs with managed services like AWS SageMaker.
All this is to say that, the “AI Engineer/SWE” title will probably represent vastly different things depending on the technical sophistication of the organization.
If someone told me they were an AI Engineer at OpenAI I’d be more inclined to expect their role to be more fundamental, elsewhere, not so much.
FWIW, eBay no longer works like this. When you put up a listing there’s now an option, enabled by default, to require payment at checkout.
This situation may still occur if you are taking offers on an item. In that case the buyer has to pay within 48 hours of the offer being accepted or the listing will become eligible for re-posting.
Our national buy/sell platform (aquired by ebay twenty or so years ago) has a similar system now where they will handle the payments and exchange of currency on confirmation of reception of items etc, I believe it's very successful, but they had to do something because nobody was trusting the site anymore for trading anything valuable.
This is basically the point of PSLF[0]. The cost to participants is not $0, but it can ultimately be very low if they only make income adjusted payments during their 10 years of service.
The second paragraph is discussing drinking habits for all US adults.
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