>Being able to apply statistics is like having a secret superpower.
I totally with this sentence. BUT If you ask for my opinion, merely knowing a list of statistical formulas is not very helpful. Most of the time, people don’t remember the underlying assumptions, so there is a fair chance they will use them in inappropriate situations.
I recommend watching these two YouTube videos. The presenters advocate using simulation/bootstrapping/shuffling methods instead of memorizing formulas.
You can compute the max-flow of an undirected graph. The edges have capacities and in the undirected case you assume that capacity can be used in both 'directions'.
> General-purpose language models can be fine-tuned to achieve several common tasks such as sentiment analysis and named entity recognition. These tasks generally don't require additional background knowledge.
> For more complex and knowledge-intensive tasks, it's possible to build a language model-based system that accesses external knowledge sources to complete tasks. This enables more factual consistency, improves reliability of the generated responses, and helps to mitigate the problem of "hallucination".
> Meta AI researchers introduced a method called Retrieval Augmented Generation (RAG) to address such knowledge-intensive tasks. RAG combines an information retrieval component with a text generator model. RAG can be fine-tuned and its internal knowledge can be modified in an efficient manner and without needing retraining of the entire model.
Very good and important observation. In his talk "Simple made easy" [1] Rich Hickey defines simple as opposite of complex and easy as opposite of hard.
The easiness is relative (as you described) and depends on the things you are familiar with. For example, Docker containers and k8s stuff is easy (for you), and GraphQL is hard (for you).
The simplicity should be assessed (somehow) more objectively.