We’re entering the AI twilight zone between narrow and general AI

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With recent advances, the tech industry is leaving the confines of narrow artificial intelligence (AI) and entering a twilight zone, an ill-defined area between narrow and general AI.

To date, all the capabilities attributed to machine learning and AI have been in the category of narrow AI. No matter how sophisticated – from insurance rating to fraud detection to manufacturing quality control and aerial dogfights or even aiding with nuclear fission research – each algorithm has only been able to meet a single purpose. This means a couple of things: 1) an algorithm designed to do one thing (say, identify objects) cannot be used for anything else (play a video game, for example), and 2) anything one algorithm “learns” cannot be effectively transferred to another algorithm designed to fulfill a different specific purpose. For example, AlphaGO, the algorithm that outperformed the human world champion at the game of Go, cannot play other games, despite those games being much simpler.

Many of the leading examples of AI today use deep learning models implemented using artificial neural networks. By emulating connected brain neurons, these networks run on graphics processing units (GPUs) – very advanced microprocessors designed to run hundreds or thousands of computing operations in parallel, millions of times every second. The numerous layers in the neural network are meant to emulate synapses, reflecting the number of parameters that the algorithm must evaluate. Large neural networks today may have 10 billion parameters. The model functions simulate the brain, cascading information from layer-to-layer in the network – each layer evaluating another parameter – to refine the algorithmic output. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human, such as digits or faces.