Social sampling, very simply, is looking around you at the actions of people who are like you, finding what’s popular, and then copying it if it seems like a good idea to you. Idea propagation has this popularity function driving it, but individual adoption also is about figuring out how the idea works for the individual—a reflective attitude. When you combine social sampling and personal judgment, you get superior decision making. That’s amazing, because now we have a mathematical recipe for doing with humans what all those AI techniques are doing with dumb computer neurons. We have a way of putting people together to make better decisions, given more and more experience. So, what happens in the real world? Why don’t we do this all the time? Well, people are good at it, but there are ways it can run amok. One of these is through advertising, propaganda, or “fake news.” There are many ways to get people to think something 1s popular when it’s not, and this destroys the usefulness of social sampling. The way you can make groups of people smarter, the way you can make human ATI, will work only if you can get feedback to them that’s truthful. It must be grounded on whether each person’s actions worked for them or not. That’s the key to AI mechanisms, too. What they do is analyze whether they performed correctly. If so, plus one; if not, minus one. We need that truthful feedback to make this human mechanism work well, and we need good ways of knowing about what other people are doing so that we can correctly assess popularity and the likelihood of this being a good choice. The next step is to build this credit-assignment function, this feedback function, for people, so that we can make a good human-artificial ecosystem—a smart organization and a smart culture. In a way, we need to duplicate some of the early insights that resulted in, for instance, the U.S. census—trying to find basic facts that everybody can agree on and understand so that the transmission of knowle