Thanksgiving dinner

Dad is about to put the turkey in the oven. There's a lot of food yet to prepare. Three guests wander into the kitchen and ask how they can help. The last thing Dad needs is a distraction!

Dad figures it will take him an hour to finish preparing the meal. The guests don't know the kitchen, so they can only do what is needed half as fast, and Dad won't be able to do anything at all if he's showing them what to do.

Dad's choices:

1. Do it himself.
   60 minutes.
2. Step away from the knives and focus entirely on showing them what to do. The guests do the work.
   40 minutes.

Bonus: Dad gets to tell sea stories while they prepare the feast, making it a social activity.

Platforms and User-Generated Content

Jack Musckerberg wants to start a website where they can sell ads. The problem: He needs content to attract visitors, and he needs new content continually to keep them coming.

Jack's choice:

1. Hire content creators to generate content.
   Marginal cost per page of content: $25.
2. Create a platform where the users themselves post pictures of cats.
   Marginal cost per page of content: < $0.01.

Bonus: Safe Harbor (17 U.S. Code § 512) means he's less liable for some problematic content.

Software and plugins/extensions

The new application FaceHugger is a tool for aliens using machine learning models. ML moves fast, and it's difficult to keep the software up-to-date with the latest algorithms.

The proprietor from another world has to choose:

1. Bring the entire hive to Earth to implement new models.
   Cost per model: One meal for the alien modeler.
2. Make the software support plugins and give credit to hooman contributors.
   Cost per model: < $0.01.

Moving Mt. Honeycrisp

Shia is interviewing at Aleph, a startup in search. Aleph hires strictly based on IQ, so their interview process asks some interesting questions.

"How would you move Mt. Honeycrisp?"

(Two of) Shia's choices:

1. Bulldozers.
2. Start a religion, where the highest ritual is a pilgrimage to Mt. Honeycrisp. Adherents are to take a piece of the mountain home with them.

Bonus: Shia doesn't have to worry about where to put the mountain.

Assorted sorting

Sally is applying for a job as a machine learning engineer. Having met with the charismatic and empathetic hiring manager, the next step is the Python test. The examiner directs, "Write code to sort this DataFrame on the second column."

She could:

1. Implement a sorting algorithm from scratch. This is a coding test, right? She can show off her familiarity with bubble sort.
2. Use a provided library: df_sorted = df.sort_values(by=1).

Bonuses:

• It's more Pythonic.
• Total of (development time) + (runtime) is guaranteed to be faster, and the interview is only an hour.
• There will be fewer bugs and better handling of edge cases.

But wait! Doesn't that mean someone else is doing more work? Not if things are set up right.

Data science team collaboration

Chris and Barinder are experimenting with ML models. Each model has many similar, complicated parts (data splits without leakage, training, tuning, calibration, testing, etc.)

They could:

1. Work separately, each creating their own code tuning and calibrating the models.
2. Split the work, Chris building a generic tuning capability and Barinder building a generic calibration capability. Even with the extra work of generalizing the code, there will be more time to refine, test, optimize, and harden the code.

Bonuses include:

• Given enough eyeballs, all bugs shallow.
• Collaboration is part of the fun of being on a team.