Amazon is an example of data-driven experimentalist companies constantly reinventing algorithms. Exploring industries like transportation, health, and manufacturing and reinventing the electric car industry by applying first principle thinking.
The speaker discusses the benefits and drawbacks of having a variety of programming languages for optimization and data science, while also highlighting AMPL, a language for mathematical programming.
This episode explores the importance of data in our daily lives. It looks at how the human brain processes data, how loss of data can affect our stability, and how data is important for us to function effectively.
Intelligence in data science goes beyond coding and algorithms; humans are needed to creatively and ethically analyze data and understand user behavior. This includes theorizing about data generation and understanding the impact of company policy and incentives on user behavior.
Learning how to fit a curve properly is a valuable skill, especially when dealing with multi-dimensional problems or problems that can only be solved by AI. It teaches a mode of thinking that can be applied to various fields and scenarios, and is crucial in understanding how things may change in the future.
