> they were not following the certification requirements before...
Those requirements didn't yet exist. The multitude of new safety analysis requirements enacted by the Aircraft Certification, Safety, and Accountability Act that was passed in 2020 just overlapped the program's (then) planned introduction, which also slid.
This is the new reality for all airliners now that there are much more rigorous mandatory safety analyses in place. The 777-9 just happened to be on the edge of certification when they came along.
Like some others have said, couldn't this be just another "look how amazing AI is" marketing test from OpenAI with the goal of hyping up AI's capabilities in an attempt to make people regard it as God-like, thereby keeping it from falling into the been-there, done-that category that all new tech eventually occupies?
I may not be a "typical" engineer as much anymore, in that I don't seem to be excited by knowing the intricate details of how things work as much as I enjoy knowing the capabilities of the systems that enable the projects I work on, and working at a higher level that might be more abstract and conceptual. AI enables that very well in that it enables jumping between paradigms and enabling creativity by removing a lot of the grind that was necessary in gathering technical details. I find it less appealing now to get into the weeds of how a thing works, even though at 10 I very much did. Like the author, I dug into the details of exactly which bits needed to be activated to make a particular program work. But as I aged, I was drawn to higher levels of abstraction, I think because I realized I could do more overall by piecing together bigger blocks. Yet I know colleagues who like to be involved in a very granular level of detail. I have many friends who almost revel in their knowledge that they are the ones who know precisely why something works the way it does. I think about the story of the NASA engineer on the Apollo 11 program who understood the assembly so well that he could confidently answer on the spot the moment the radar overload "1201 alarm" error flashed that the mission was still good to go. Some folks get so deep in this perspective that it almost seems like an affront to suggest they may be wrong about one of those details. And well they should, because precise people are very much needed to understand intricate systems.
But it makes me wonder how they feel with AI being an indeterminate system - one where you can never really know what the answer will be every time. Likely still correct, and getting better every day, but also possibly different every time. Added to that, it's exponential spread across the software engineering domain. For me, even as someone who likes high-level systems, I still relate to the feeling that the detailed part of me that still likes some of the internals may be less focused on in the future. Even for me that's kind of sad. But I am also curious about my colleagues who are super detail oriented - how do you feel about AI and how it changes the focus on that detail aspect?
Very true, and all I am basing my comment on is the improvement in speed AI has demonstrated when applied to software development, and inferring it might enable a similar 10X or 100X improvement in both hardware architecture as well LLM structure and/or interface methods. If that speed improvement applies to performance of AI, that could mean the 70 years it took for people to improve storage technology might be able to be compressed to achieve a step change in AI performance in a drastically shorter timeframe.
With the pace of AI, and with AI helping to pave the way for faster/better AI, I keep wondering if hardware like this will become obsolete well before it has a meaningful ROI. Huge AI models can be run with less resources already through quantization and offloading, but that's just the beginning.
One day, maybe not far from now, a breakthrough will allow huge LLMs (say 200B in size) to run well on an old 5 year old Dell desktop. Think that's crazy? Look at the size of the first hard drives. The IBM 350 was a disk with 50 platters, 24 inches in diameter, that held 3.5Mb, and was leased for today's equivalent of $35K.
Compare that to a multi-terabyte ssd. Now apply that improvement to how an LLM is architected and run now. With AI assisting, it won't be long before a leap occurs and these data centers with all their current ultra-cutting edge Nvidia cards are nearly obsolete overnight.
This is true, and I'm sure AI cuts will continue, but it's obvious that the ones who went "all in" at AI's mass introduction were drinking a special kind of Kool-Aid reserved for the truly sycophantic Wall Street lap dogs, not the CEOs who think about risk and are cautious about betting the farm on a relatively new and mostly untested technology. GM is over 100 years old, and no doubt released improvements that were well-tested and predictable, because you don't take massive chances with a company that well established. It was a couple years into the mass AI deployment that studies on the minimal overall productivity gains of AI even started to come out(!) This was "get on the bandwagon" thinking at a massive scale, which shows you how many CEOs are not independent thinkers at all, but are really just followers. Yes, use AI, but do it responsibly, never forgetting that your investors aren't your only stakeholders - so are your people.
I know a lot of level-headed engineers here may not side with me, but I say let the companies who abandoned their people at the drop of a hat, with CEOs who waved their flag around on social media, proudly declaring how they'd now run their companies with 75% fewer employees wither and die. If I had been let go, there's no way I'd go back to a company like that, and there should be a black list of CEOs who acted this way established and kept public. These CEOs are not holistic thinkers, and are too susceptible to mass hysteria and too irresponsible to real people and their lives to be trusted with the vision for any company ever again.
What was really behind the push to get everything browser based in the first place? Is this all to make everything cloud based, software as a service, or did some exec see a demo of Windows 8 and think "web is the future" and over-rotate?
How is having local AI going to produce a result that's any better than using OpenAI or Anthropic? Isn't what we really need programmers who rely on themselves more than AI so they avoid technical debt accumulation?
This is what I have been thinking. Business will always try to do more with less because their only true goal is figuring out how to make more money. They will sacrifice giving those juniors time to learn from their mistakes for the sake of making more widgets (code). From the wider generational view, they will rob today's juniors from the chance to learn and thereby keep the talent pipeline full so they can profit today, the future (and the developers who will arrive there) be damned. The economic game is flawed because it only ever comes down to a single output that is optimized for: money. One solution? I think software people might consider forming unions. I know that's antithetical to the lone coder ethos, but if what this comment reflects is true, the industry needs a check and balance to prevent it from destroying its foundation from the inside.
I always wanted to get into RF design, but couldn't find it within the mega company I work for (we integrate more than we design at the component level). RF design has always been a bit of black magic, even as an EE. Other than some really great books from ARRL in the amateur radio arena, I haven't found too many good "as it really works in the working world" references. Can anyone point at any good books and/or sites that go into detail about this fascinating field?
Those requirements didn't yet exist. The multitude of new safety analysis requirements enacted by the Aircraft Certification, Safety, and Accountability Act that was passed in 2020 just overlapped the program's (then) planned introduction, which also slid.