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Silicon Valley doesn’t have to help. At this point a regular nerd like me can do AI in my basement as long as the Pentagon provides a labeled dataset. Data is t

Silicon Valley doesn’t have to help. At this point a regular nerd like me can do AI in my basement as long as the Pentagon provides a labeled dataset. Data is the hard part, but Pentagon can handle it: just have a bunch of soldiers label stuff.

BucketSort 8y ago
You are memeing AI. There are many applications in AI that require heavy R&D. For example, DARPA has their XAI project[1] which calls for developing methods to build systems for interpreting the results produced by opaque ML methods. As a long time lurker on HN, and doing independent research in this area for a few years now, I will tell you that most of HN has no clue what modern AI research really is, even though it is talked about a lot. Try creating something non-trivial and you will see how little you know about the subject. [1]:https://www.darpa.mil/program/explainable-artificial-intelligence https://www.darpa.mil/program/explainable-artificial-intelli...
- h4b4n3r0 8y ago
  Sure, but that’s DARPA, it’s by definition for stuff that won’t be practical for the next 20 years, and it’s orthogonal to what they are pursuing in the near future. In the near future the goal is pretty simple: do the same things as they do now, but with more drones and a lot fewer people. A lot of off the shelf stuff is ready to go now: classification, object detection, segmentation, etc. And it’s not even that hard to get off the ground if you have a large, high quality dataset.
  - tsomctl 8y ago
    Ok. I've spent the last several years working on neural networks for audio and music, and trying to reuse off the shelf classification and segmentation networks. It is ridiculously hard to get to work. I'm going to agree with BucketSort; I get the feeling that a lot of AI cheerleaders here have never actually trained a neural network.
    - h4b4n3r0 8y ago
      What are you talking about? It’s ridiculously easy, to the point where my 14 year old can train a classifier. For classification in particular you don’t even need to do anything: just clone, point it to your dataset in one of the established formats (eg imagenet) and let it train for a few days. Object detection is quite a bit harder (and takes way longer to train even from a pretrained net), but again, totally doable half a dozen different ways using existing code you can get from GitHub. If you’ve spent years doing this stuff and training a classifier is an insurmountable obstacle, you should consider changing your field of work.
      - deleted 8y ago
        [deleted]
      - tsomctl 8y ago
        Some of us are working on more complicated data than just handwritten digits. Part of the problem is that existing networks are tuned for the dataset that the original authors were working on. If you want to use it on completely different problems, you have to change the sizes of the layers, convolution size, max pooling, etc. The other problem is figuring out how to preprocess your data to make it as easy as possible for the network to digest. Then, to make it harder, changing the preprocessing means you have to change the network architecture, and vice versa. Fun times!
        ericd 8y ago
        It can certainly be tricky. That said, if you've never used it, I highly recommend trying out adaptive max pooling.
        deleted 8y ago
        [deleted]
      - newen 8y ago
        Try training a classifier that can detect a person holding weapon aiming to kill someone vs a person not. Make it a 99.9999% accuracy classfier under different weather conditions. Now make one for night vision images. Even seemingly straighforward classification problems can be hard. Not to mention the vast array of other problems out there.
- oh-kumudo 8y ago
  They OP isn't wrong in regard, the kind of research you mentioned here, you are unlikely to find too many engineers from Silicon Valley that can handle it. AI for majority of the engineers, is just neural network/trial and error.
stephengillie 8y ago
Where's the AI that labels stuff?

BucketSort 8y ago

You are memeing AI. There are many applications in AI that require heavy R&D. For example, DARPA has their XAI project[1] which calls for developing methods to build systems for interpreting the results produced by opaque ML methods. As a long time lurker on HN, and doing independent research in this area for a few years now, I will tell you that most of HN has no clue what modern AI research really is, even though it is talked about a lot. Try creating something non-trivial and you will see how little you know about the subject. [1]:https://www.darpa.mil/program/explainable-artificial-intelligence https://www.darpa.mil/program/explainable-artificial-intelli...

h4b4n3r0 8y ago
Sure, but that’s DARPA, it’s by definition for stuff that won’t be practical for the next 20 years, and it’s orthogonal to what they are pursuing in the near future. In the near future the goal is pretty simple: do the same things as they do now, but with more drones and a lot fewer people. A lot of off the shelf stuff is ready to go now: classification, object detection, segmentation, etc. And it’s not even that hard to get off the ground if you have a large, high quality dataset.
- tsomctl 8y ago
  Ok. I've spent the last several years working on neural networks for audio and music, and trying to reuse off the shelf classification and segmentation networks. It is ridiculously hard to get to work. I'm going to agree with BucketSort; I get the feeling that a lot of AI cheerleaders here have never actually trained a neural network.
  - h4b4n3r0 8y ago
    What are you talking about? It’s ridiculously easy, to the point where my 14 year old can train a classifier. For classification in particular you don’t even need to do anything: just clone, point it to your dataset in one of the established formats (eg imagenet) and let it train for a few days. Object detection is quite a bit harder (and takes way longer to train even from a pretrained net), but again, totally doable half a dozen different ways using existing code you can get from GitHub. If you’ve spent years doing this stuff and training a classifier is an insurmountable obstacle, you should consider changing your field of work.
    - deleted 8y ago
      [deleted]
    - tsomctl 8y ago
      Some of us are working on more complicated data than just handwritten digits. Part of the problem is that existing networks are tuned for the dataset that the original authors were working on. If you want to use it on completely different problems, you have to change the sizes of the layers, convolution size, max pooling, etc. The other problem is figuring out how to preprocess your data to make it as easy as possible for the network to digest. Then, to make it harder, changing the preprocessing means you have to change the network architecture, and vice versa. Fun times!
      - ericd 8y ago
        It can certainly be tricky. That said, if you've never used it, I highly recommend trying out adaptive max pooling.
      - deleted 8y ago
        [deleted]
    - newen 8y ago
      Try training a classifier that can detect a person holding weapon aiming to kill someone vs a person not. Make it a 99.9999% accuracy classfier under different weather conditions. Now make one for night vision images. Even seemingly straighforward classification problems can be hard. Not to mention the vast array of other problems out there.
oh-kumudo 8y ago
They OP isn't wrong in regard, the kind of research you mentioned here, you are unlikely to find too many engineers from Silicon Valley that can handle it. AI for majority of the engineers, is just neural network/trial and error.

stephengillie 8y ago

Where's the AI that labels stuff?