Great counter post. An off-the-top-of-my-head summary of some key points: - Don't dismiss diminishing returns. Even if takes 10^6 times more computations to d

Great counter post. An off-the-top-of-my-head summary of some key points: - Don't dismiss diminishing returns. Even if takes 10^6 times more computations to double intelligence, we may very well build a machine powerful enough. - Units of computing per dollar, in contrast to moore's law, continues to double consistently and shows no sign of slowing down. - Computational complexity is a theoretical model that makes certain assumptions that don't always matter in the real world, such as optimality and worst-case performance. Approximate solutions can be far cheaper and plenty sufficient. Average case performance is often more important. - Impassable barriers can sometimes be entirely avoided by solving the root problem in a different way. Self-driving cars don't need human level vision, they just need a way of sensing the immediate environment, which LIDAR allows, despite being technically inferior to the human eye. Of course none of this says we will achieve strong AI or a singularity. It is primarily a response to the type of arguments found in the linked article above.