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Honestly I think a large part of the value add of machine learning is going to be the ability for huge entities to launder intellectual property violations. As

Honestly I think a large part of the value add of machine learning is going to be the ability for huge entities to launder intellectual property violations. As an example, my grandfather (an old school EE who got his start on radar systems in the 50s, who then got his radiology MD when my jewish grandmother berated him enough with "engineer's not doctor though...") has some really cool patents around highlighting interesting parts of the frequency domain in MRIs that should make detection of cancer a whole lot easier. As an implementation he did a bunch of tensor calculus by hand to extract and highlight those features because he's an incredibly smart old school EE with 70 years experience cranking that kind of thing out with only his trusty slide rule. He hasn't gotten any uptake from MRI manufacturers, but they're all suddenly really into recurrent machine learning models to highlight the same sorts of stuff. Part of me wants to tell him to try selling it as a machine learning model and just obfuscate the fact that the model was carefully hand written rather than back propagated. I'm personally pretty anti intellectual property (at least how it's implemented in the states), but a system where large entities that have the capital investment to compute the large ML models can launder IP violations, but little guys get stuck to the letter of the law certainly seems like the worst of both worlds to me.

908B64B197 5y ago
> Part of me wants to tell him to try selling it as a machine learning model and just obfuscate the fact that the model was carefully hand written rather than back propagated. How many models are back-propagated first and then hand-tuned?
- monocasa 5y ago
  That's a great question. I had assumed that the workflow of an ML engineer consisted of managing the data and a relatively high level set of parameters around a search space of layers and connectivity, as the whole shtick of ML is that the hyperparameter space of the tensors themselves is too complex to grok or tweak when generated from training. But I only have a passing knowledge of the subject, pretty much just enough to get myself in trouble in these kinds of discussions. Any chance some fantastic HNer could chime in there?
  - srcreigh 5y ago
    I'm no data scientist but many statistical methods rely on prior knowledge and even computed inputs. Two examples I can think of are doing linear regression on the square of your input. For deep learning, people have improved visual representation by taking samples of the colors at various frequencies. [1] [1]: https://arxiv.org/pdf/2003.08934.pdf https://arxiv.org/pdf/2003.08934.pdf
    - monocasa 5y ago
      Yeah, that's a better way of saying what I meant by managing the data. Mentally projecting data through, massaging said data, and building reproducible pipelines rather than manually tweaking the learned weights after the fact.
ipsum2 5y ago
I don't understand how your example relates to "launder intellectual property violations". What you're saying is that your grandfather hand wrote some feature extractors that look similar to the neurons that ML models have learned from backpropagation. There's no stealing of IP there at all.
- monocasa 5y ago
  He has a set of patents on certain types of highlighting frequency domain patterns in MRIs. In a lot of ways recurrent neural networks can be frequency domain feature extractors as the backwards data flows create sort of delay line memories tapped at interesting periods. The MRI manufacturers after refusing to license his patents, heavily invested in ML models that focus on using recurrent networks for frequency domain feature extraction. Patents aren't like copyright where independent reinvention is a way out; he has a monopoly on the concepts regardless of how someone came about them, even by growing them a bit organically like how ML works.
  - otterley 5y ago
    You can't patent a concept. You can only patent a process, a machine, an article of manufacture, or a composition of matter. And the invention must be described sufficiently such that a practitioner skilled in the relevant art can reproduce the subject matter.
    - monocasa 5y ago
      You can patent a software process or machine running classes of software in the US as long as it doesn't conflict with Alice Corp's test which is analogous to what I mean by concept in this case. And his patents are extremely well documented in the patents so that they can be reproduced. I guess if someone manually cranked through the math for each video's set of pixels they wouldn't be infringing, but oncologists aren't really ok with waiting a year for results. Any practical implementation would be infringing. And like I said, I'm pretty anti US structures around intellectual property (including software patents), but I'm not for the only ones being able to circumvent the legal process being entities with large banks of capital.

908B64B197 5y ago

> Part of me wants to tell him to try selling it as a machine learning model and just obfuscate the fact that the model was carefully hand written rather than back propagated. How many models are back-propagated first and then hand-tuned?

monocasa 5y ago
That's a great question. I had assumed that the workflow of an ML engineer consisted of managing the data and a relatively high level set of parameters around a search space of layers and connectivity, as the whole shtick of ML is that the hyperparameter space of the tensors themselves is too complex to grok or tweak when generated from training. But I only have a passing knowledge of the subject, pretty much just enough to get myself in trouble in these kinds of discussions. Any chance some fantastic HNer could chime in there?
- srcreigh 5y ago
  I'm no data scientist but many statistical methods rely on prior knowledge and even computed inputs. Two examples I can think of are doing linear regression on the square of your input. For deep learning, people have improved visual representation by taking samples of the colors at various frequencies. [1] [1]: https://arxiv.org/pdf/2003.08934.pdf https://arxiv.org/pdf/2003.08934.pdf
  - monocasa 5y ago
    Yeah, that's a better way of saying what I meant by managing the data. Mentally projecting data through, massaging said data, and building reproducible pipelines rather than manually tweaking the learned weights after the fact.

ipsum2 5y ago

I don't understand how your example relates to "launder intellectual property violations". What you're saying is that your grandfather hand wrote some feature extractors that look similar to the neurons that ML models have learned from backpropagation. There's no stealing of IP there at all.

monocasa 5y ago
He has a set of patents on certain types of highlighting frequency domain patterns in MRIs. In a lot of ways recurrent neural networks can be frequency domain feature extractors as the backwards data flows create sort of delay line memories tapped at interesting periods. The MRI manufacturers after refusing to license his patents, heavily invested in ML models that focus on using recurrent networks for frequency domain feature extraction. Patents aren't like copyright where independent reinvention is a way out; he has a monopoly on the concepts regardless of how someone came about them, even by growing them a bit organically like how ML works.
- otterley 5y ago
  You can't patent a concept. You can only patent a process, a machine, an article of manufacture, or a composition of matter. And the invention must be described sufficiently such that a practitioner skilled in the relevant art can reproduce the subject matter.
  - monocasa 5y ago
    You can patent a software process or machine running classes of software in the US as long as it doesn't conflict with Alice Corp's test which is analogous to what I mean by concept in this case. And his patents are extremely well documented in the patents so that they can be reproduced. I guess if someone manually cranked through the math for each video's set of pixels they wouldn't be infringing, but oncologists aren't really ok with waiting a year for results. Any practical implementation would be infringing. And like I said, I'm pretty anti US structures around intellectual property (including software patents), but I'm not for the only ones being able to circumvent the legal process being entities with large banks of capital.