I'd suggest commercial aircraft as an even better analogy than cars.
Most of the ongoing costs you mention for cars still apply--but there are also the occasional (possibly dramatic) changes to the interior 'cabin product' like new seats and entertainment systems, new fabrics/branding, new business class seats/pods, changes in seat layouts, etc in order to remain competitive in their market segment.
Cars rarely have such significant refreshes, but software products often have analogous design and UX overhauls that are also intended to try to keep the software competitive in its market segment. And again airlines don't need to engage the specific airframe manufacturer like Boeing or Airbus for these, but they do need some semblance of a tech team that have certain domain expertise in aircraft engineering constraints.
Airframes also have major overhauls called MROs (Maintenance, Repair, and Overhaul) about every 6-10 years, which again does not require the original manufacturer but does require significant engineering expertise. To me this is akin to certain ongoing software maintenance activities like updating a codebase to use newer library versions, major database version updates, API or SDK version compatibility, etc.
You are right. This puzzle started my search into this type of puzzle. But in the end my puzzle is harder, because it also contains the day of the week and has the most "irregular" puzzle pieces possible.
Yes, very dependent on the market.
In some East African markets that don't have much history of recycling, Kubik [0] has a neat model of employing folks to recover plastic waste from landfills which are then used to creating building materials
While there are some similarities, yes, our brains definitely don't have anything akin to backpropagation, which is the critical mechanism for how current AI models learn.
Hinton has some research on a forward-forward learning paradigm [1], which might be closer to how our brains learn (but the artificial implementations are not great yet). He also posits that maybe the purpose of humans' dreams are generating negative data for such a contrastive forward-forward learning mechanism.
Fun fact: Hans Albert Einstein, son of Albert Einstein, helped with the design of the Old River Control Structure.
According to Wunderground's three-part series about the ORCS[1], there is some uncertainty as to whether it will be able to keep the Mississippi River on its current route without failure.
Maybe? The Scaling Hypothesis[1] suggests that greater capabilities of intelligence may emerge from scaling up 'scalable architectures' to large sizes. GPT-3 exhibits 'meta-learning' capabilities that GPT-2 did not (like learning how to sum numbers)--probably just because its a 100x larger version of GPT-2.
Uganda is home to a large regional hub for UN logistics, a state-of-the-art CDC infectious disease laboratory, and similar outposts--presumably due to Uganda's relative stability over the past few decades and strategic proximity to several less-stable neighbors.
Most of the ongoing costs you mention for cars still apply--but there are also the occasional (possibly dramatic) changes to the interior 'cabin product' like new seats and entertainment systems, new fabrics/branding, new business class seats/pods, changes in seat layouts, etc in order to remain competitive in their market segment. Cars rarely have such significant refreshes, but software products often have analogous design and UX overhauls that are also intended to try to keep the software competitive in its market segment. And again airlines don't need to engage the specific airframe manufacturer like Boeing or Airbus for these, but they do need some semblance of a tech team that have certain domain expertise in aircraft engineering constraints.
Airframes also have major overhauls called MROs (Maintenance, Repair, and Overhaul) about every 6-10 years, which again does not require the original manufacturer but does require significant engineering expertise. To me this is akin to certain ongoing software maintenance activities like updating a codebase to use newer library versions, major database version updates, API or SDK version compatibility, etc.