MIT's D-Lab https://d-lab.mit.edu/research does a lot of similar work on fuels/cooking, evaporative cooling, and design with locally available materials/techniques
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.
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.
For those looking for a modern take on improving on the CSV format, I'd recommend Frictionless Data's Datapackage specification[1] which basically consists of a json file of metadata that accompanies a CSV file that describes column types, versions, sources, and how to validate correctness of the CSV's data. This allows for quite a lot of tooling and workflow improvements to CSV files without mucking with the CSV itself
Another hack to improve CSV workflows is OCHA's HXL[2] that is used by humanitarian organizations. Basically adding a row of hashtags in addition to column names, which is surprisingly useful considering the ease of adding these to a file.
I'd recommend reading more about the 'information fiduciary' approach[1] promoted by Prof Jonathan Zittrain (and others) which I find to be among the most realistic steps in a better direction, as it offers a 'grand bargain' in the spirit of DCMA's 'safe harbor' for companies that choose to commit to putting consumers and their rights above maximizing profit.
The 'Retail jobs growth by state' chart in this article (approx 2/3 down the page) is lovely: one square line chart for each US state, positioned roughly according to geography, all equal in size.
I find it very easy to grok compared to choropleth maps and similar charts that attempt to stay true to geography.
Unlike other non-map presentations of US state data, this layout preserves ability to spot regional similarities visually.
Same with phone numbers. In most parts of the world, local numbers (not fully-qualified with country code), are written/dialed with a leading zero.
Excel eats these and/or uses scientific notation!
Its not just desire to avoid spending the money to provide chip+pin systems
Most Americans have many credit cards, unlike consumers in Europe, for example, where chip+pin is the norm.
US banks and credit card companies worried that Americans wouldn't be able to remember 6 different PINs for 6 different cards. So, they prefer eating the overhead of fraudulent charges rather than potentially reducing the number of credit cards (and profit) each consumer uses
Technically, The Expanse is financed and produced by Alcon Television Group, which is a division of Alcon Entertainment.
Certainly having SyFy on board for US broadcast distribution is a big boon for the series, but I suspect a lot of the series' excellence is due to Alcon and not SyFy