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I think Arthur Samuel would agree. This approach has a loss function, parameters, and inputs that feed in to a model to optimise the parameters. The big differ

by mjlee 6y ago

I think Arthur Samuel would agree. This approach has a loss function, parameters, and inputs that feed in to a model to optimise the parameters. The big difference between this and the other approach mentioned in the article is the model is a simple one that's easy to understand instead of a many layered neural network which is rather opaque. I think the article may be better titled "You might not need neural networks."

0-_-0 6y ago
> I think the article may be better titled "You might not need neural networks." Since a linear model is essentially a single layer neural network with linear activation, we can't even say that. The athor was using a neural network without realising it :)
- SomeoneFromCA 6y ago
  I'd argue that a NN cannot have linear activation. I mean, if the activation function is linear, it is not an NN anymore.
username90 6y ago
Machine learning is a set of techniques developed to attack modeling problems that traditional algorithms couldn't solve. So if the algorithm was developed and used before computers it definitely isn't machine learning. Everything done in this article was known and used before computers existed hence not machine learning. Doing automatically controlled systems was still possible before computers just that it required a bit more creative use of analog components.
- disgruntledphd2 6y ago
  > Machine learning is a set of techniques developed to attack modeling problems that traditional algorithms couldn't solve. So if the algorithm was developed and used before computers it definitely isn't machine learning I think that it's important to note here that this was the set of problems that computer science researchers didn't know how to solve. In the early days, they mostly ended up re-inventing statistical methods. And to be fair, a neural net is just a bunch of linear models joined by a non-linearity. In that case, it's essentially stacked logistic regression, which was invented before computers.
  - username90 6y ago
    > And to be fair, a neural net is just a bunch of linear models joined by a non-linearity. Nobody did this before computers though. > . In that case, it's essentially stacked logistic regression, which was invented before computers. It isn't "basically logistic regression", it is just a technique which uses logistic regressions. The full technique is ML. If you remove the ML parts it is basically just logistic regression left though.
    - dryrunes 6y ago
      Is logistic regression not ml? Or maybe only if it's fit with gd?
    - disgruntledphd2 6y ago
      Like, logistic regression uses a non-linearity to convert the outputs to the 0-1 scale. How does that differ from a one layer neural network? I think this would probably be a more profitable discussion if you could define Machine Learning for me.
- RyanGoosling 6y ago
  > Machine learning is a set of techniques developed to attack modeling problems that traditional algorithms couldn't solve. So if the algorithm was developed and used before computers it definitely isn't machine learning. How does your evidence support your claim?

by mjlee 6y ago

0-_-0 6y ago

> I think the article may be better titled "You might not need neural networks." Since a linear model is essentially a single layer neural network with linear activation, we can't even say that. The athor was using a neural network without realising it :)

SomeoneFromCA 6y ago
I'd argue that a NN cannot have linear activation. I mean, if the activation function is linear, it is not an NN anymore.

username90 6y ago

Machine learning is a set of techniques developed to attack modeling problems that traditional algorithms couldn't solve. So if the algorithm was developed and used before computers it definitely isn't machine learning. Everything done in this article was known and used before computers existed hence not machine learning. Doing automatically controlled systems was still possible before computers just that it required a bit more creative use of analog components.

disgruntledphd2 6y ago
> Machine learning is a set of techniques developed to attack modeling problems that traditional algorithms couldn't solve. So if the algorithm was developed and used before computers it definitely isn't machine learning I think that it's important to note here that this was the set of problems that computer science researchers didn't know how to solve. In the early days, they mostly ended up re-inventing statistical methods. And to be fair, a neural net is just a bunch of linear models joined by a non-linearity. In that case, it's essentially stacked logistic regression, which was invented before computers.
- username90 6y ago
  > And to be fair, a neural net is just a bunch of linear models joined by a non-linearity. Nobody did this before computers though. > . In that case, it's essentially stacked logistic regression, which was invented before computers. It isn't "basically logistic regression", it is just a technique which uses logistic regressions. The full technique is ML. If you remove the ML parts it is basically just logistic regression left though.
  - dryrunes 6y ago
    Is logistic regression not ml? Or maybe only if it's fit with gd?
  - disgruntledphd2 6y ago
    Like, logistic regression uses a non-linearity to convert the outputs to the 0-1 scale. How does that differ from a one layer neural network? I think this would probably be a more profitable discussion if you could define Machine Learning for me.
RyanGoosling 6y ago
> Machine learning is a set of techniques developed to attack modeling problems that traditional algorithms couldn't solve. So if the algorithm was developed and used before computers it definitely isn't machine learning. How does your evidence support your claim?