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In his book Algorithms Are Not Enough, data scientist Herbert Roitblat provides an in-depth review of different branches of AI and describes why each of them falls short of the dream of creating general intelligence.

Here’s how Roitblat describes supervised learning: ML involves a representation of the problem it is set to solve as three sets of numbers.

Therefore, while supervised ML is not tightly bound to rules like symbolic AI, it still requires strict representations created by human intelligence.

At the heart of deep learning is the deep neural network, which stacks layers upon layers of simple computational units to create machine learning models that can perform very complicated tasks.

Most deep learning models needs labeled data, and there is no universal neural network architecture that can solve every possible problem.

But it still needs machine learning engineers to decide the number and type of layers, learning rate, optimization function, loss function, and other unlearnable aspects of the neural network.

They need a lot of help from human intelligence to design the right rewards, simplify the problem, and choose the right architecture.

Here’s how Roitblat summarizes the shortcomings of current AI systems in Algorithms Are Not Enough: “Current approaches to AI work because their designers have figured out how to structure and simplify problems so that existing computers and processes can address them.

To have a truly general intelligence, computers will need the capability to define and structure their own problems.”

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