Machine Learning
Machine Learning
[Cueball Prime holds a canoe paddle at his side and stands on top of a "big pile of linear algebra" containing a funnel labeled "data" and box labeled "answers". Cueball II stands to the left side of the panel.)]
Cueball II: This is your machine learning system?
Cueball Prime: Yup! You pour the data into this big pile of linear algebra, then collect the answers on the other side.
Cueball II: What if the answers are wrong?
Cueball Prime: Just stir the pile until they start looking right.

Machine learning is a method employed in automation of complex tasks. It usually involves creation of algorithms that deal with statistical analysis of data and pattern recognition to generate output. The validity/accuracy of the output can be used to give feedback to make changes to the system, usually making future results statistically better.
Cueball stands next to what looks like a pile of garbage (or compost), with a Cueball-like friend standing atop it. The pile has a funnel (labelled "data") at one end and a box labelled "answers" at the other. Here and there mathematical matrices stick out of the pile. As the friend explains to the incredulous Cueball, data enters through the funnel, undergoes an incomprehensible process of linear algebra , and comes out as answers. The friend appears to be a functional part of this system himself, as he stands atop the pile stirring it with a paddle. His machine learning system is probably very inefficient, as he is integral to both the mechanical part (repeated stirring) and the learning part (making the answers look "right").
The main joke is that, despite this description being too vague and giving no intuition or details into the system, it is close to the level of understanding most machine learning experts have of the many techniques in machine learning. 'Machine learning' algorithms that can be reasonably described as pouring data into linear algebra and stirring until the output looks right include support vector machines , linear regressors , logistic regressors , and neural networks . Major recent advances in machine learning often amount to 'stacking' the linear algebra up differently, or varying stirring techniques for the compost.
This comic compares a machine learning system to a compost pile. Composting is the process of taking organic matter, such as food and yard waste, and allowing it to decompose into a form that serves as fertilizer. A common method of composting is to mound the organic matter in a pile with a certain amount of moisture, then "stirring" the pile occasionally to move the less-decomposed material from the top to the interior of the pile, where it will decompose faster.
In large-scale composting operations, the raw organic matter added to the pile is referred to as "input". This cartoon implies a play on the term "input", comparing a compost input to a data input.
A recurrent neural network is a neural network where the nodes affect one another in cycles, creating feedback loops in the network that allow it to change over time. To put it another way, the neural network has 'state', with the results of previous inputs affecting how each successive input is processed. In the title text, Randall is saying that the machine learning system is technically recurrent because it "changes" (i.e. gets mushy) over time.
[Cueball Prime holds a canoe paddle at his side and stands on top of a "big pile of linear algebra" containing a funnel labeled "data" and box labeled "answers". Cueball II stands to the left side of the panel.)]
Cueball II: This is your machine learning system?
Cueball Prime: Yup! You pour the data into this big pile of linear algebra, then collect the answers on the other side.
Cueball II: What if the answers are wrong?
Cueball Prime: Just stir the pile until they start looking right.
