291–296 of 296 posts
If what you're trying to predict can't be represented as some combination of your existing data, it breaks immediately. Data drives everything; all models are wrong, but some are useful. (George Box)
As a chemical engineer who started learning deep learning after learning regular old regression-based empirical modeling, my interpretation of deep learning is that it's just high-dimensional non-linear interpolation. If what you're trying to predict can't be represented as some combination of your existing data, it breaks immediately. Data drives everything; all models are wrong, but some are useful. (George Box)
Earlier quoted context omitted.
> The quality of the algo and I assume the deep learning model lies in the quality (breadth and depth) of the data, and how honest with himself the person choose to model it. I've only dabbled with machine-learning here and there for the past 10 years or so, but if there's one thing I've learned so far is that the data behind your ML code (and the way it is structured) is responsible for almost all the success or fai…
> focus a lot of his time on the data itself... from where he intends to collect it? how is it structured? is it reliable? is it "enough"? What's the best books on this subject? I suppose it's a very broad topic and thus more difficult to talk about than a single "neural network" algorithm.
Earlier quoted context omitted.
> The quality of the algo and I assume the deep learning model lies in the quality (breadth and depth) of the data, and how honest with himself the person choose to model it. I've only dabbled with machine-learning here and there for the past 10 years or so, but if there's one thing I've learned so far is that the data behind your ML code (and the way it is structured) is responsible for almost all the success or fai…
> focus a lot of his time on the data itself... from where he intends to collect it? how is it structured? is it reliable? is it "enough"? What's the best books on this subject? I suppose it's a very broad topic and thus more difficult to talk about than a single "neural network" algorithm.
Earlier quoted context omitted.
I like your comment. The real question is whether they are conscious. The analogy between deep neural networks and the brain has proven to be very fruitful. Other analogies may as well. See our upcoming paper for more info. https://grey.colorado.edu/mediawiki/sites/mingus/images/3/3a...
I think a lot of people end up mixing being alive with being conscious. Is a tree conscious? Is a self driving car conscious? If we use the definition "Aware of its surroundings, responding and acting towards a certain goal" then a lot of things fit that definition. When an AI plays the atari games, learns from it and plays at a human level, I would call it conscious. It's not a human level conscious agent but consci…
On the limitations of machine learning as in the OP, the OP is correct. So, right, current approaches to "machine learning* as in the OP have some serious "limitations". But this point is a small, tiny special case of something else much larger and more important: Current approaches to "machine learning" as in the OP are essentially some applied math, and applied math is commonly much more powerful than machine learn…
BTW the 'machine learning' term is Russian coinage and its genesis lies in non-paramteric statistics, the key result that sparked it all off was Vapnik and Chervonenkis's result that is essentially a much generalized and non-asymptotic version of Glivenko Cantelli. The other result was that of Stone that showed universal algorithms that can achieve the Bayes error in the limit not only exist but also constructed such an algorithm. This was the first time it was established that 'learning' is possible.