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Were they not using AI and ML techniques before? Or is this announcement just indicating that they are going to put a greater emphasis on deep learning? I have

by briga 9y ago

Were they not using AI and ML techniques before? Or is this announcement just indicating that they are going to put a greater emphasis on deep learning? I have trouble seeing how the NSA would function without ML techniques, for example.

fnbr 9y ago
I suspect that they were using classical ML techniques, and a lot of traditional scientific computing techniques, but not a lot of deep learning, given how new it is, and how slow government operates. It's also not clear how a lot of the classical ML can be translated into a DL setting- a lot of graph analysis [1] is traditionally solved using matrix factorisation, for instance, which doesn't directly translate into deep learning. You can find different ways of doing it with DL, but it's not as transferable. [1]: e.g. analysing an individual's social graph. I believe that Facebook uses a lot of classical techniques, like PCA. I've heard that Facebook has been pouring a lot of money into research that uses DL on arbitrary graphs.
- deepnotderp 9y ago
  Random walks or spectral embeddings for graph convolutions already work exceptionally well.
  - visarga 9y ago
    I've been following the domain of Graph Neural Nets with interest for a while. It seems to be picking up.
- danmaz74 9y ago
  > how slow government operates Government pioneered, or pushed contractors to pioneer, lots of technologies - just think about NASA. I'm pretty sure that, with their budget, the NSA is at the forefront of technologies that can be used in their field, but they obviously don't want to disclose what they do. Adopting a publicly available technology shouldn't be difficult at all.
afarrell 9y ago
They were definitely putting money into AI and Data Infrastructure projects. See: https://opencatalog.darpa.mil/MEMEX.html https://opencatalog.darpa.mil/MEMEX.html
mirimir 9y ago
Sure they were. But they have so much data. Potentially all Internet (and other telecommunications) data. There are limitations in private network bandwidth and processing, of course. But they get around that using distributed clusters. Analysis, and particularly extraction of actionable intelligence, is the key challenge. They have lots of analysts, for sure. But organizing and integrating their output has been a slow process. Reports, summaries of reports, summaries of summaries, etc. So yeah, they want godlike AI ;)
- etiam 9y ago
  This is going to be a dark pseudogod indeed...
  - jonnybgood 9y ago
    To play devil's advocate: why is it dark? If an AI can process significantly more data than us humans to find actionable intelligence is that not a good thing? Imagine a building is on a strike list and the intelligence on it needs to be revetted. An analyst pulls in SIGINT, GEOINT, OSINT, HUMINT, etc. The analyst has 48 hours to get his products uploaded for battlefield commanders to make an air strike or not. The analyst can't possibly process all the data in time. The analyst assesses it's a weapons cache based on what they can process. In reality, it was a makeshift hospital. This is how mistakes happen in war.
    - FLUX-YOU 9y ago
      >why is it dark? I doubt the training data/AI will be accountable or visible to the citizens (yes, the analyst gets fired/van'd, but someone else takes his place). So we're trusting the intel agencies, but they're trusting an AI to some extent. They're plainly not dumb enough to let the AI drive, but it still plays a part. I'm not worried about an AI being able to sift data given a training set to some degree of accuracy. I'm more worried about the training set as a dark way of generating actionable intelligence, for good or bad purposes.
      - 9y ago
nether 9y ago
I've heard that most federal agencies are just starting to fix basic problems in their data management. Like vast troves of inconsistent spreadsheets being ETL'd into actual databases now. ML wasn't feasible because everything was such a mess.
zitterbewegung 9y ago
Just because they used the old way doesn’t mean they are easily able to translate it to new systems .

by briga 9y ago

fnbr 9y ago

I suspect that they were using classical ML techniques, and a lot of traditional scientific computing techniques, but not a lot of deep learning, given how new it is, and how slow government operates. It's also not clear how a lot of the classical ML can be translated into a DL setting- a lot of graph analysis [1] is traditionally solved using matrix factorisation, for instance, which doesn't directly translate into deep learning. You can find different ways of doing it with DL, but it's not as transferable. [1]: e.g. analysing an individual's social graph. I believe that Facebook uses a lot of classical techniques, like PCA. I've heard that Facebook has been pouring a lot of money into research that uses DL on arbitrary graphs.

deepnotderp 9y ago
Random walks or spectral embeddings for graph convolutions already work exceptionally well.
- visarga 9y ago
  I've been following the domain of Graph Neural Nets with interest for a while. It seems to be picking up.
danmaz74 9y ago
> how slow government operates Government pioneered, or pushed contractors to pioneer, lots of technologies - just think about NASA. I'm pretty sure that, with their budget, the NSA is at the forefront of technologies that can be used in their field, but they obviously don't want to disclose what they do. Adopting a publicly available technology shouldn't be difficult at all.

afarrell 9y ago

They were definitely putting money into AI and Data Infrastructure projects. See: https://opencatalog.darpa.mil/MEMEX.html https://opencatalog.darpa.mil/MEMEX.html

mirimir 9y ago

Sure they were. But they have so much data. Potentially all Internet (and other telecommunications) data. There are limitations in private network bandwidth and processing, of course. But they get around that using distributed clusters. Analysis, and particularly extraction of actionable intelligence, is the key challenge. They have lots of analysts, for sure. But organizing and integrating their output has been a slow process. Reports, summaries of reports, summaries of summaries, etc. So yeah, they want godlike AI ;)

etiam 9y ago
This is going to be a dark pseudogod indeed...
- jonnybgood 9y ago
  To play devil's advocate: why is it dark? If an AI can process significantly more data than us humans to find actionable intelligence is that not a good thing? Imagine a building is on a strike list and the intelligence on it needs to be revetted. An analyst pulls in SIGINT, GEOINT, OSINT, HUMINT, etc. The analyst has 48 hours to get his products uploaded for battlefield commanders to make an air strike or not. The analyst can't possibly process all the data in time. The analyst assesses it's a weapons cache based on what they can process. In reality, it was a makeshift hospital. This is how mistakes happen in war.
  - FLUX-YOU 9y ago
    >why is it dark? I doubt the training data/AI will be accountable or visible to the citizens (yes, the analyst gets fired/van'd, but someone else takes his place). So we're trusting the intel agencies, but they're trusting an AI to some extent. They're plainly not dumb enough to let the AI drive, but it still plays a part. I'm not worried about an AI being able to sift data given a training set to some degree of accuracy. I'm more worried about the training set as a dark way of generating actionable intelligence, for good or bad purposes.
    - 9y ago

nether 9y ago

I've heard that most federal agencies are just starting to fix basic problems in their data management. Like vast troves of inconsistent spreadsheets being ETL'd into actual databases now. ML wasn't feasible because everything was such a mess.

zitterbewegung 9y ago

Just because they used the old way doesn’t mean they are easily able to translate it to new systems .