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I believe the contribution here is overstated. None of the tech used for today's AVs relies on anything developed for grand challenge, including any principles

I believe the contribution here is overstated. None of the tech used for today's AVs relies on anything developed for grand challenge, including any principles they derived. The leading tech today was derived purely from industry outside the dod space and cifar.

whiplash451 2y ago
That's not the point. The point is to put it front and center for the tech companies and the general community (think of it as a call for proposal). Would Google have invested into self-driving if DGC had not happened? Very unclear. Note that the initial Waymo team was a direct draft of the winning Stanford team at the DARPA Urban Challenge'07.
- ipunchghosts 2y ago
  I think googles could care less about what darpa is doing. How would I know? I'm a darpa PM and google isn't reaching out to me or my team. I think what you are seeing is correlation but not causation. Stanford was working on self driving long before darpa, same with cmu. The darpa money was just a bonus but I don't think it pushed anything ahead. After the first few years of the darpa challenge, many teams were able to finish. Yet here we are in 2025 and I cam get bounding box detections on video to not jitter or drop between frames because of some unlucky pixel combination. Humans have zero issues and aren't even able to perceive these minute differences. My guess is vision will be solve in about 18 months using methods developed purely in industry.
kelipso 2y ago
You need an entire community of researchers (both academic and non-academics) working on a field for industry to produce tech. It was definitely not a pure industry thing, almost no tech is.
- ipunchghosts 2y ago
  Sure, but none of that was developed by dod in any real way. A litmus test to consider is that the dod still halls belig ml problems but you don't see phds funded by dod going to the dod. The good ones all go to industry because they know thier reseaech stops as soon as they start working for a contractor or warfare center. The money excuse is just a patsy. The government has plenty of money to pay these ppl if they can produce which they do well in industry. It's simply not a focus of darpa to do basic research in learning and intelligence in a meaningful way.
  - kelipso 2y ago
    Government agencies have been behind in ML/AI for a long time now and they are still playing catch up, yes. But they do fund academia in the ML space, which is very important since industry goes nowhere without the ML PhDs who did their research with DoD funding. And they are ahead of industry in other areas, sensors being one of them.
    - ipunchghosts 2y ago
      Have u seen the work coming out of phd students on dod grants? It's generally poor. There is a lot of grant money from the big tech firms where the slide ts have access to incredible brain and compute resources.
      - kelipso 2y ago
        Yes, that's basically all university research. And it probably follows the 80/20 bad/good rule like pretty much everything. Focusing on the 80% and ignoring the 20% is flawed thinking. Research where large compute resources are required or used is a small part of all research, and a small part of ML and AI research for that matter. You're just focusing on the positives in industry research and the negatives in university research.

whiplash451 2y ago

That's not the point. The point is to put it front and center for the tech companies and the general community (think of it as a call for proposal). Would Google have invested into self-driving if DGC had not happened? Very unclear. Note that the initial Waymo team was a direct draft of the winning Stanford team at the DARPA Urban Challenge'07.

ipunchghosts 2y ago
I think googles could care less about what darpa is doing. How would I know? I'm a darpa PM and google isn't reaching out to me or my team. I think what you are seeing is correlation but not causation. Stanford was working on self driving long before darpa, same with cmu. The darpa money was just a bonus but I don't think it pushed anything ahead. After the first few years of the darpa challenge, many teams were able to finish. Yet here we are in 2025 and I cam get bounding box detections on video to not jitter or drop between frames because of some unlucky pixel combination. Humans have zero issues and aren't even able to perceive these minute differences. My guess is vision will be solve in about 18 months using methods developed purely in industry.

kelipso 2y ago

You need an entire community of researchers (both academic and non-academics) working on a field for industry to produce tech. It was definitely not a pure industry thing, almost no tech is.

ipunchghosts 2y ago
Sure, but none of that was developed by dod in any real way. A litmus test to consider is that the dod still halls belig ml problems but you don't see phds funded by dod going to the dod. The good ones all go to industry because they know thier reseaech stops as soon as they start working for a contractor or warfare center. The money excuse is just a patsy. The government has plenty of money to pay these ppl if they can produce which they do well in industry. It's simply not a focus of darpa to do basic research in learning and intelligence in a meaningful way.
- kelipso 2y ago
  Government agencies have been behind in ML/AI for a long time now and they are still playing catch up, yes. But they do fund academia in the ML space, which is very important since industry goes nowhere without the ML PhDs who did their research with DoD funding. And they are ahead of industry in other areas, sensors being one of them.
  - ipunchghosts 2y ago
    Have u seen the work coming out of phd students on dod grants? It's generally poor. There is a lot of grant money from the big tech firms where the slide ts have access to incredible brain and compute resources.
    - kelipso 2y ago
      Yes, that's basically all university research. And it probably follows the 80/20 bad/good rule like pretty much everything. Focusing on the 80% and ignoring the 20% is flawed thinking. Research where large compute resources are required or used is a small part of all research, and a small part of ML and AI research for that matter. You're just focusing on the positives in industry research and the negatives in university research.