Is our conception of human neuron functioning and our estimates of the number of neurons and connections in the human brain consistent of what we know about the brain’s capabilities Perhaps human neurons are far less capable than we think they are. If so, building a machine with human level capabilities might bring longer than expected. We find that estimates of the number of concepts-"chunks" of knowledge-that a human expert in a particular field has mastered is remarkably consistent: about 50,000 to 100,000. This approximate range appears to be valid over wide range of human endeavors: the number of board positions mastered by a chess grand master, the concepts mastered by an expert in a technical field, such like a physician, the vocabulary of a writer. This type of professional knowledge is, of course, only a small subset of the knowledge we need to functioning as human beings. Basic knowledge of the world, including so-called common sense, is more extensive. We also have an ability to recognize patterns: spoken language, written language, objects, faces. And we have our skills: walking, talking, catching balls. I believe that a reasonable conservative estimate of the general knowledge of a typical human is a thousand times greater than the knowledge of an expert in her professional field. This provides us a rough estimate of 200 million chunks-bits of understanding, concepts, patterns, specific skills-per human. As we will see below, if this estimate is low (by a factor of up to a thousand), the brain is still big enough. The number of neurons in the human brain is estimated at approximately 100 billion, with an average of 1,000 connections per neuron, for a total of 100 trillion connections. With 100 trillion connections and 100 million chunks of knowledge (including patterns and skills), we get an estimate of about a million connections per chunk. Our computer simulations of neural nets use a variety of different type of neuron models, all of which are relatively simple. Efforts to provide detailed electronic models of real mammalian neurons appear to show that while animal neurons are more complicated than typical computer models, the difference in complexity is modest.