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My sense is that AI begins with automation. Originally I believed water clocks were the first embodied algorithms, but now I think the first simple traps and snares: Are simple animal snares and traps a form of automation? Of computation? (Mechanism, in and of itself, is not understood to be intelligence, but when mechanism is selected for fitness, it ...


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A good way of looking at it would be understanding neural networks mathematically, i.e. purely on the basis of the fact that you're just trying to fit a function and solve an optimisation problem (apart from looking at it as multiple units of logistic regression). Say we want to approximate a function $y =f_w(x)$ with $x \in D$, where $D$ is our domain-...


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One way to view a neural network is as a series of linear transformations. You take a bunch of data points and look at it from a different perspective from a different space. You apply some non linear function on the data points like, ReLU, sigmoid etc. Now you repeat the same process of looking from a different space. Our goal is to look at it from a ...


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My reading of AI development (somewhat simplified here) is that the availability of large data sets, increased computing power, and the introduction of new machine learning algorithms (which require large data sets and massive computing power) contributed to the resurgence of AI. However, as witnessed on this site, there has been a paradigm shift within the ...


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tl;dr I always like to think of Neural Networks as a generalization of logistic regression. I too don't like that, traditionally, when introducing Neural Networks, books start with biological neurons and synapses, etc. I think its more beneficial to start from statistics and linear regression, then logistic regression and then neural networks. A ...


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