#26 Algorithms to Live By – Recipes for success

We go about our lives trying to make the best decisions, often without knowing all the variables that influence different situations. In “Algorithms to Live by” by Brian Christian & Tom Griffiths we learn how some of the greatest algorithms used in computer science tackle uncertainty and how they can be transferred to and exploited in daily life. In our discussion we go through a number of practical real life examples to make the takeways truly useful.

Time stamps:
0:30 Today’s topic: Algorithms to Live By (And yes, we say ep. 25 when it should be 26 here.)
0:50 What is an algorithm?
3:55 Optimal stopping problem
8:55 Explore vs expoit problem
11:30 Making order – sorting algorithms
14:00 Caching – having at hand what you need the most
16:10 Caching algos: Least recently used, First in first out, The Noguchi filing system
17:45 Time management – how to get the most out of your time
19:00 TM algos: Earliest due date, Moore’s algorithm, shortest processing time (risk: priority inversion), Weighted shortest processing time, Hard work (focus!)
21:00 Predictions and Bayesian updating
22:30 Normal distribution events
23:45 Power-law distributions
24:35 Over-fitting – drawing too much on historic events
28:15 Approaches to intractable problems
30:35 Constraint relaxation
33:35 Example: Lagrangian constraint relaxation
34:35 Using your imagination to find creative solutions
36:15 Sampling from the observed reality
37:30 Summary and takeaways
37:45 There are optimal algos for (almost) every problem
38:55 Have confidence in (and optimize) the process
40:30 There are ways to tackle even intractable problems
42:30 Knowing the complexity of problems and their solutions can help us choose challenges and algorithms
44:00 Computational kindness – lessen the cognitive burden on people around you
49:55 Thanks and until next week!

13:50 Sorting algorithms:

Understanding scaling challenges in different algorithms (a.k.a. big O notation):


Software engineering Daily on Algorithms to live by:

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