Rick Wallace is the CEO of KLA.

a ring is… something; its kind of a field but it doesn’t have inverses for multiplication.

Wealth is a much more complex utility than others because given the different levels of wealth you have the marginal benefit of having that wealth decreases.

That is, let \(A\) be the fact that you are given $50, and let \(B\) be there being \(0.5\) chance of winning $100.

• risk neutral: the utility is linear—therefore \(A \sim B\)
• risk seeking: utility is convex (derivative increases as reward increases), so \(A \prec B\)
• risk averse: utility is concave (derivate decreases as reward decreases), so \(A \succ B\)

Make PACE better: no need to check the bacteriophage population in PACE yourself; just check it automatically! https://github.com/dgretton/pyhamilton https://www.chorylab.com/

1. take a constant plate measurement of the culture
2. check the growth grade
3. use the grid of materials to test the environmental combinations; checking if certain factors worked better
4. PyLabRobotic to automatically handle the materials

“run PACE sweeps, adjust parameters as needed to promote mutation replication”

“cheaters”

some molecules create specific increases in population without the need of any mutation at all, “cheating” the evolutionary process. We don’t know why, and the lab seem to have given up on them until an in vivo test is needed.

Four key points of robustness of machine learning models

1. Well-Possessedness: a problem is ill-posed if small changes in the inputs lead to large changes in the outputs, implying that any source of error would dominate the results; you can well-posedness
2. Condition Number: an algorithm is ill-conditioned if small changes in the inputs lead to large changes in the output; large condition number is bad (our system is “sensitive”) and small condition number are good (insensitive); if the relative input/output change is identical, then condition number is 1
3. Stability: an algorithm is stable if it could complete itself in a meaningful way—i.e. don’t create numerically explosive results
4. Accuracy: we want low size of error (i.e. our answer should be close to the solution)