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One of the greatest common misconceptions about many of today's Artificial Intelligence (AI) systems is that they possess something called "Generalised intelligence," in other words, many people today can be forgiven for thinking that today's AI's are good at lots of things, hence "general," when in matter of fact, like humans, different AI's are good at different tasks. While there are a couple of companies trying to create Artificial General Intelligence (AGI) platforms, like Google's DeepMind who last year published the world's first AGI architecture, which they based on hierarchies of interconnected neural networks, Facebook is coming at the problem from a different angle. And in a nod to the growing sub-field of reinforcement learning, which is one of the DeepMind team's favourite training methods, the Facebook team also notes that AGI should resemble a human's ability to master new tasks with "decreasing explicit rewards," and that these new AGI's should be able to communicate and express themselves in a variety of ways – depending on the situation they find themselves in at the time. Facebook considers these capabilities to be more of a prerequisite to assess whether or not a platform has in fact achieved "true" AGI than the Turing test, which was designed in the 1950's by Alan Turing and which is still today's preferred, and only, method of comparing machine intelligence with human intelligence.