6 ms·

I remember 2018-2020, when everyone was saying that in 1-2 years, all cars would be autonomous vehicles and we won't need drivers anymore. Guess what.

jimsimmons 1y ago
And how solving the trolley problem is the key to make it all work
pera 1y ago
I still remember this one from 2016 very well: A Driverless Tesla Will Travel From L.A. to NYC by 2017, Says Musk https://www.nbcnews.com/business/autos/driverless-tesla-will-travel-l-nyc-2017-says-musk-n670206 https://www.nbcnews.com/business/autos/driverless-tesla-will...
- bboygravity 1y ago
  Really not that far off in the grand scheme of things IMO. Ray Kurzweil was even more on the money in his 2006 book "The singularity is near". I remember reading some of the stuff in there that is now happening. From the top of my head he predicted 2030 for human-level AI hardware and software that could be bought for 1000 USD by anyone. I feel he's going to be very close.
  - crinkly 1y ago
    https://en.wikipedia.org/wiki/List_of_Tesla_Autopilot_crashes https://en.wikipedia.org/wiki/List_of_Tesla_Autopilot_crashe...
    - bufferoverflow 1y ago
      [dead]
    - mettamage 1y ago
      Hey, this is perhaps a bit random. But do you want to chat a bit about your math journey? I saw it here [1]. I'm curious about it. If you're (potentially) up for it, my email is in my profile. Cheers! [1] https://news.ycombinator.com/item?id=44899552 https://news.ycombinator.com/item?id=44899552
      - crinkly 1y ago
        Not much to it. Was bored, had some spare cash, was fed up of being an imposter at a stats/mathematics org and decided to get a degree. Did all the modules, got the paper. Took 6 years.
    - bboygravity 1y ago
      k, now do a list of drunk driver crashes.
  - card_zero 1y ago
    2020. "Based on the above analyses, it is reasonable to expect the hardware that can emulate human-brain functionality to be available for approximately one thousand dollars by around 2020."
flohofwoe 1y ago
Same for UML in the 90s: "in a few years programmers will be obsolete because a handful system architects will draw UML diagrams which will be synthesized into code", in the end the opposite happened, and the dedicated system architect role pretty much disappeared. And UML even made a lot more sense to precisely describe a problem compared to human language prompts.
- glimshe 1y ago
  XML was going to end all other data formats.
- voxleone 1y ago
  And using UML selectively and intentionally can build the habit of thinking before coding. It's a mental exercise, much like test-driven development or writing pseudocode, that strengthens your design intuition.
  - flohofwoe 1y ago
    The question is if (too much) upfront planning is actually better. IME it's more useful to treat a source file (or new project) like a sketchbook, you start with an empty sheet of paper but already have a rough idea what you want, then you quickly sketch out the outlines, try out different ideas, explore different solutions (all without going into too much detail yet), you step through the code, explore some different paths to get an idea what "feels right", delete things, shuffle them around, rewrite them and slowly filling out the details and that way incrementally get towards the first working version. Which such an approach of incremental "micro-feedback-loops" you already eliminated a lot of dead ends that will appear anyway despite the best plans - but identifying such dead ends early is much better than late. This incremental approach also forces you to keep the code small, tidy and malleable. The planning stage basically already happens in source code, and it's not one long planning stage, but many micro-planning-stages. ...this is also my problem with the current state of LLMs, they're pretty good at creating a first initial sketch from a very general problem statement (but only for things that have been done thousands of times by other people - but that's a different issue...) - but the more you'll need to go into the details and discover and fix problems in your initial 'mental design' (e.g. discovering what you actually wanted in the first place), the more the differences to traditional programming disappear, and at some tipping point it gets even more complex because you need to steer the LLM with a language (the human language) that simply isn't useful enough for detailed problem descriptions - that's why lawyers, mathematicians, engineers and scientists all invented their own precise 'DSLs'. After a few iterations your prompts need to be just as detailed as writing source code in the first place - so what's the point of writing prompts in human language again? I feel like productivity could be improved much more by improving programming tools (yeah - boring old-school incremental maintenance work) instead of betting on some weird AI future which will just move the focus from writing source code to writing human-language prompts (which IMHO is a definitive step backwards because human language lacks precision - and adding that precision is how you end up with programming languages). Why are editing, compilation and debugging/testing still separate steps, why do debuggers still have those 60s style bare variable panels instead of realtime visualizations of the internal program state? Why do I still need to wait for a build to finish? Why is version control still such a PITA to work with? Instead everybody is jumping on the AI hype train while everything around them is crumbling into a post-apocalyptic wasteland.
bufferoverflow 1y ago
[dead]

jimsimmons 1y ago

And how solving the trolley problem is the key to make it all work

pera 1y ago

I still remember this one from 2016 very well: A Driverless Tesla Will Travel From L.A. to NYC by 2017, Says Musk https://www.nbcnews.com/business/autos/driverless-tesla-will-travel-l-nyc-2017-says-musk-n670206 https://www.nbcnews.com/business/autos/driverless-tesla-will...

bboygravity 1y ago
Really not that far off in the grand scheme of things IMO. Ray Kurzweil was even more on the money in his 2006 book "The singularity is near". I remember reading some of the stuff in there that is now happening. From the top of my head he predicted 2030 for human-level AI hardware and software that could be bought for 1000 USD by anyone. I feel he's going to be very close.
- crinkly 1y ago
  https://en.wikipedia.org/wiki/List_of_Tesla_Autopilot_crashes https://en.wikipedia.org/wiki/List_of_Tesla_Autopilot_crashe...
  - bufferoverflow 1y ago
    [dead]
  - mettamage 1y ago
    Hey, this is perhaps a bit random. But do you want to chat a bit about your math journey? I saw it here [1]. I'm curious about it. If you're (potentially) up for it, my email is in my profile. Cheers! [1] https://news.ycombinator.com/item?id=44899552 https://news.ycombinator.com/item?id=44899552
    - crinkly 1y ago
      Not much to it. Was bored, had some spare cash, was fed up of being an imposter at a stats/mathematics org and decided to get a degree. Did all the modules, got the paper. Took 6 years.
  - bboygravity 1y ago
    k, now do a list of drunk driver crashes.
- card_zero 1y ago
  2020. "Based on the above analyses, it is reasonable to expect the hardware that can emulate human-brain functionality to be available for approximately one thousand dollars by around 2020."

flohofwoe 1y ago

Same for UML in the 90s: "in a few years programmers will be obsolete because a handful system architects will draw UML diagrams which will be synthesized into code", in the end the opposite happened, and the dedicated system architect role pretty much disappeared. And UML even made a lot more sense to precisely describe a problem compared to human language prompts.

glimshe 1y ago
XML was going to end all other data formats.
voxleone 1y ago
And using UML selectively and intentionally can build the habit of thinking before coding. It's a mental exercise, much like test-driven development or writing pseudocode, that strengthens your design intuition.
- flohofwoe 1y ago
  The question is if (too much) upfront planning is actually better. IME it's more useful to treat a source file (or new project) like a sketchbook, you start with an empty sheet of paper but already have a rough idea what you want, then you quickly sketch out the outlines, try out different ideas, explore different solutions (all without going into too much detail yet), you step through the code, explore some different paths to get an idea what "feels right", delete things, shuffle them around, rewrite them and slowly filling out the details and that way incrementally get towards the first working version. Which such an approach of incremental "micro-feedback-loops" you already eliminated a lot of dead ends that will appear anyway despite the best plans - but identifying such dead ends early is much better than late. This incremental approach also forces you to keep the code small, tidy and malleable. The planning stage basically already happens in source code, and it's not one long planning stage, but many micro-planning-stages. ...this is also my problem with the current state of LLMs, they're pretty good at creating a first initial sketch from a very general problem statement (but only for things that have been done thousands of times by other people - but that's a different issue...) - but the more you'll need to go into the details and discover and fix problems in your initial 'mental design' (e.g. discovering what you actually wanted in the first place), the more the differences to traditional programming disappear, and at some tipping point it gets even more complex because you need to steer the LLM with a language (the human language) that simply isn't useful enough for detailed problem descriptions - that's why lawyers, mathematicians, engineers and scientists all invented their own precise 'DSLs'. After a few iterations your prompts need to be just as detailed as writing source code in the first place - so what's the point of writing prompts in human language again? I feel like productivity could be improved much more by improving programming tools (yeah - boring old-school incremental maintenance work) instead of betting on some weird AI future which will just move the focus from writing source code to writing human-language prompts (which IMHO is a definitive step backwards because human language lacks precision - and adding that precision is how you end up with programming languages). Why are editing, compilation and debugging/testing still separate steps, why do debuggers still have those 60s style bare variable panels instead of realtime visualizations of the internal program state? Why do I still need to wait for a build to finish? Why is version control still such a PITA to work with? Instead everybody is jumping on the AI hype train while everything around them is crumbling into a post-apocalyptic wasteland.

bufferoverflow 1y ago

[dead]