Thursday, October 11, 2018

Only the Big Ideas #5 — Quantum computing, Bias, Plasticity, Genomics

Surfacing mental models, weekly
Read 4 articles to learn 10+ mental models this week, including:
  • Quantum Suprememcy
  • Qubit
  • Quantum Superposition
  • Regression to the Mean
  • Loss Aversion
  • Emergent Behavior
  • Self-Organization
  • Theory of Minimal Surfaces
  • Moore’s Law
  • Network Effects
  • Winner-Takes-All Market Dynamics

Wired — The Wired Guide to Quantum Computing

The potential ability of quantum computing devices to solve problems that classical computers practically cannot (Google AI BlogWikipedia)
The basic unit of quantum information. Whereas the state of a bit can only be either 0 or 1, the general state of a qubit according to quantum mechanics can be a coherent superposition of both states (Wikipedia)
Another Take: Quantum computers aren’t limited to two states; they encode information as quantum bits, or qubits, which can exist in superposition…Because a quantum computer can contain these multiple states simultaneously, it has the potential to be millions of times more powerful than today’s most powerful supercomputers. (How Stuff Works)
“The math of superposition describes the probability of discovering either a 0 or 1 when a qubit is read out…A superposition is in an intuition-defying mathematical combination of both 0 and 1. Quantum algorithms can use a group of qubits in a superposition to shortcut through calculations.”


Extreme outcomes tend to be followed by more moderate ones (Farnam Street)
Regression to the mean occurs when unusually large or small measurements tend to be followed by measurements that are closer to the mean. It happens because values are observed with random error. (Regression to the mean: what it is and how to deal with it)
The idea that losses generally have a much larger psychological impact than gains of the same size (Scientific American)
Another take: The key idea behind the bias is that people react differently to positive and negative changes of their status-quo. More specifically, losses are said to be twice as powerful compared to equivalent gains. (The Decisions Lab)

Quanta Magazine— The Physics of Glass Opens a Window Into Biology

Jordana Cepelewicz with physicist Lisa Manning
A process by which a system of interacting subunits acquires qualitatively new properties that cannot be understood as the simple addition of their individual contributions (Sante Fe Institute — Complexity Explorer)
“The whole is other than the sum of its parts” (Nicky Case)
Self-organization can be defined as the spontaneous emergence of global structure out of local interactions.
“Spontaneous” means that no internal or external agent is in control of the process: for a large enough system, any individual agent can be eliminated or replaced without damaging the resulting structure.
The process is truly collective, i.e. parallel and distributed over all the agents. This makes the resulting organization intrinsically robust and resistant to damage and perturbations (Complexity and Self-organization,6)
A surface that locally minimizes its area subject to some constraint. Of all possible surfaces, it is the one with minimal energy (WikipediaMath.Berkely)
(this was a tough one to nail down…I’m not satisfied with this explanation, but do believe the mental model has enough utility to include)

Neo.Life — Who Will Be the Google of Genomics?

Moore’s law is the observation that the number of transistors in a dense integrated circuit doubles about every two years. Moore’s law is an observation and projection of a historical trend and not a physical or natural law (Wikipedia)
Moore’s law suggests exponential growth. Thus, it is unlikely to continue indefinitely (Investopedia)
A network effect (a.k.a. demand-side economies of scale) occurs when a product or a service becomes more valuable to its users as more people use it (a16z)
A winner-takes-all market is a market in which the best performers are able to capture a very large share of the rewards, and the remaining competitors are left with very little (Investopedia)