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Your brain is an interconnected network of 86 billion neurons — a neural net, if you will. Artificial neural nets are inspired by this design, and while the simplest construct — a perceptron — is just a single neuron, modern neural nets (NNs) can reach up to a billion weights and millions of neurons.
But is that really the case — or are some companies “cheating”? As it turns out, several companies have been caught in the act, claiming to use AI while actually outsourcing menial tasks. As Forbes reports, these are just some of the companies guilty of “pseudo-AI”:
Building data pipelines isn’t easy. To build data products, you need to be able to collect data from potentially millions of users and process the results in near real-time. Your pipeline needs to be robust, scalable, efficient, and with monitoring capabilities.
By training a neural network — or passing training data through a composite function many times, so as to learn patterns — we can predict new data. If you don’t train long enough, your model will “underfit,” or simply not have learned patterns in the data.
For all the progress we’ve made on AI, relatively little has been done in the way of explainability. While the idea of “black box” AI is a bit of a myth — as there are ways to interpret the results — there isn’t complete transparency or intuition into how most AI models, especially deep learning, really work under the hood.
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