Sublime

Your primary responsibility now is to have a deep understanding of how platform algorithms work and to guide the machine in achieving your client’s goals.

So simple models, made from hand-selected high-level variables, perform about as well as more complex models—sometimes better

We could train a machine-learning system up to a certain level of competence—by normal imitation learning, say—and then, from that point forward, we could use it to help evaluate

pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems,

Livingston: Who did you learn things from at Google? Did you have mentors? Buchheit: I didn't know anything about building these large systems before working at Google. So I'd look at how different parts of Google work and sort of say, "Does that apply to us? Can we reuse that technique?"—since there was already a successful model of how

Learning requires two structures: an immense set of potential models and an efficient algorithm to adjust them to reality.

Human beings will also do better in chaotic environments where what the problem is, what our goals are, and how people are thinking about things keeps changing. This quote made me think a lot about working at Meta:

"In addition, there are problems that humans rather than computers will have to solve for purely practical reasons. It isn’t becaus

“I am busy