Advances still need to prove themselves safer. Deep investigation of each incident has vastly improved air & sea travel, for example. So while blame is the catalyst, the money is actually moving to pay for the deep statistics to be gathered. Lawyers want details, and the advance needs to die by debt if it's the wrong direction.

Capitalizing on convenience is how capitalism works. If you have a better system, why isn't it more popular?

You would not be upset, or anything else, dead. And dying from self-driving car vs a speeding distracted driver really doesn't matter. If the general numbers go down, even if nonzero and have complications, then society will absorb it. Every single advance in civilization follows this path. Electricty, Petroleum, Pasturization, Power tools, Skyscapers, etc. In fact, defly navigating the new dangers of a technological advance are considered a sign of ability by the young generation. Over time, accepting the management of those dangers can cause a plateau, but luddites eventually die down to a few when tech of their youth is replaced. Still spinning your own textiles? Still firing those clay pots? Still jiggling that old carburator? This is exactly how society moves to a new technology. Obstacle-avoiding moving machines are no different. Get in and be dazzled like any World's Fair attendee of the last 120 years.

These effects could be real and irrefutable, and yet time and again we see that bright sunlight has equal or greater effects to these levels. Indeed, many environmental exposures, natural or not (perfumes, dyes, exhaust, smoke, nearly every volatile compound) can disrupt cellular behavior. Plastics and plasticizers can disrupt hormonal reactions, especially in a fetus. These new correlations in the article do not include cellular microwaves because they don't rise to a level that exceeds other influences. One must read the details. But hey if you think TV EMF, Cellular Microwaves, appliances or even transformer coils and other electromagnetic sources are the largest culprit, publish some quantified measurements and you'll have your day in debate. I dont need to refute anything because there's no quantifiable claim you make.

"good coding" was in service to concepts like bug fixing, feature enhancement, transferrability or documentation. What if all that was tossed, as the code was no longer even read? Just results-oriented output, checked against other solution paths? It may be that foundational work continues while LLM's generate the software that nobody reads directly, just like we don't check the solder joints on the parts, we exoect them to work and review only the results. "Coding" might be a narrower band of training models to encapsulate discrete functions that serve a purpose, while Product Development is orchestrating code generators to create a machine hardware+software, without looking under the hood at all.

Which aspect? Hardware drivers? OS schedulers? Language designers? Shader code? I think you might be underestimating the actual scope of what digital machines cover. From examples like SCADA to FPGA to Kernal Routine to Office macro to Game Engine - computer science has an incredibly large discipline space. Think of the science as so young that we're jumping from wheelbarrow to Cat 798 each decade. A decade from now perhaps we'll simply be schooling LLM's on the standards of your personal project, not really coding. The issues will be reigning in the models' erratic tendencies, not checking indents and variable names. They won't need to write in a "language" you recognize.

What skills does that cover in your opinion?

Problem-splitting, and solution splitting are good skills, yes. But the choice of division-lines in those splits - and when - become more and more important. Parameterization, DRY, Optimization, can all become a ruthless taskmaster for no important reason, way too early in solutions. Test cases can be drafted too late, too myopically. Documentation and transferrability never even arrive at the table. Decomposition should begin with the Why and What Problem Are We Solving. "To build anything, one must first make a universe" isn't entirely wrong, so modelling the problem & solution space just enough to get a result is also necessary. I find devs are typically thinking of a conveyor belt of narrow-field mini problems, and pulling LLM python code snippets together to achieve a too-quickly-decided overall solution. That overall somution is the big think, really. It can be much more illustrative to contemplate a realm of Userland in V.amazing, then scale it backward to a few simple cases. No need to even design for future concepts, as wholesale refactors are a great cleanse. Once a Computer Scientist sees the real-world arc of a productized system, they (can) discern the useful work vs the deck-chair-arranging that wastes time. And accept that everyone will get a few things wrong, so that wrong isnt the problem, just moving too slowly to correct and keep going.

Several issues, although I support further research to overcome them: (1) integrated barttery structure with vehicle structure is a weight savings, worth quite a bit. (2) standardization of any sort is not there (3) a single mishap isn't just an EV fire, its a conflagration of an unpredictable size of battery stacks, more akin to a chemical factory.

The stories come from prior stories, with new prompts to re-order the words essentially. This is enshitification. It will grow until the LLM's can coin new terms, build analogies, research the principals of a story, and even call people close to the story for their opinion and summarize it. Then LLM's will have to associate good journalism practices with prompt guidelines given by trainer models.

Those missing parts are ultimately solvable by even more LLM API's and trickery, but it's still not intelligent. In fact, the guardrails of most public LLM's are so narrow for divisive issues that most newsworthy issues would be dry-as-a-bone recaps. The arc of time that makes previously non-controversial phrases turn into a dogwhistle to a social agenda would make LLM's just agree with the accusation and move on. They have no agenda, including any to dodge embarrassment.

LLM's that could write in an acerbic, critical form like some great writers of social commentary (Twain, Vonnegut, Hitchens) are a far way off. Those would be able to build a cohesive worldview using a mostly-sensible value system. As it is now, the Transforms don't really have a way of teasing out a contextually-generic moral system, because there isn't one. So we're creating the best savant possible in the field of reading everything, summarizing what's its read. This covers a lot of daily human thought, but it cannot cross over to feeling something, and it seems absurd when a machine tries to fake it.

Thanks for doing some math, but the point is: Massive human disruption is far easier than geoengineering ocean flood levels.

You may be applying a niave perspective of the scale involved. A scaled-up wall doesn't account for water rising put of the ground. And one cannot pump even a centimeter of an ocean somewhere else. These average sea level effects create massive flood stages during powerful storms, leaving behind a soggy landfill. The only solution is moving to higher ground.

Sea walls stop wave damage, not water levels. The ground is porous. Desalination is not a massive pumping effect for the level of an ocean.

The primates are smart, but gullible and predictable. So, each decacde we should expect to see ever-larger forced movements of people from lowland coastal areas. The early signs would be: flooding, massive erosion, catastrophic pollution events, rising insurance premiums, real estate price drops and ultimately lower valuations then a reduced tax base. I suspect there's a map tracking these events and amounts already. Perhaps there'll be a hand-scrawled sign offshore somewhere saying "we will rebuild!"

They'll never know what it gets wrong, but a lucky customer will. And I am curious who will weave in bug fix, new features, or a breaking change upgrade. The AI? The vibe may be quite solemn indeed when the LLM cannot generate symbol sets on something it never trained for.