4 ms·

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 indu

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.

4 ms·

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 indu

by 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.