Is Your Pancake Batter Than a Robot’s? AI Reveals The Perfect Pancake Recipe

Nothing’s quite as versatile as the humble pancake. We all have our go-to toppings and preferences, from an American stack to a French crepe, but what is key to the perfect pancake recipe?

This year Monolith, the leading engineering AI software company, has discovered the fluffiest pancake recipe which will please even the fussiest of eaters.

Usually working with the likes of Airbus and Honda to find the best engine or car prototype, Monolith applied their AI model to compare the most popular recipes to create the perfect pancake.

The perfect pancake

From the optimum butter-to-milk ratio, to the cooking time & heat, Monolith identified the ultimate combination of ingredients:

Flour – 210 g

Sugar – 48 g

Baking powder – 14 g

Salt – 6 g

Eggs – 2

Milk – 256 g

Butter – 25g

Number of flips – 2

Pan temp – Medium heat (Electric) / Low heat (Gas)


How was it worked out?

Monolith is a machine learning software that allows the user to test the impact of variables to create prototypes for cars, rockets and much more.

Using machine learning technology, Monolith tests millions of variants in minutes identifying the cause and effect of deliberate changes, and the subsequent result of the test. For this experiment, Monolith conducted hundreds of thousands of calculations.

The process was broken down into three stages:

  1. Ingredients – the manufacturing materials
  2. Pancake Mixture – desired production output and initial design conception
  3. Frying – the manufacturing process

Monolith used a Random Forest Regression algorithm.

Random forest is essentially a forest of decision trees. Decision trees are similar to how you decide what to take with you on a rainy day – Is it raining? yes, take an umbrella. Is it windy? No, then don’t take a coat.

It’s called a Random Forest algorithm because a lot of decision trees work together, instead of just one. They are given 80% of the recipes (called the training set) and then a recipe from the ‘test set’ to see if it can predict what the texture would be. The 1000s of trees take a majority vote and decide what the likely outcome will be.

From this, the AI can predict the output (texture of the pancake) based on a single or multiple changes to the ingredients.

So there you have it, the perfect AI pancake…how did yours compare?