Like. What is it?
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In a sentence, it’s the study of taking data and automatically, with computers, learning attributes, facts, distributions about it, usually for the purposes of classification, prediction, compression, etc. Usually accomplishing those goals well can lead to a variety of applications, including facial recognition, content recommendation, automated content generation, chat bots, etc.
Classical machine learning is closely related to statistics in that there’s usually some underlying human-curated model which encodes some understanding/belief about the data. These days the focus/hype is (deservedly so) around deep learning, where a one line sales pitch may be “throw enough data and hardware at the problem and even with minimal modeling assumptions the technology will still learn properly”. This is great as it allows for: a common vocabulary and end-to-end platform to perform a variety of real world tasks; a tool that rounds the edges around where human-curated modeling makes sharp simplifying assumptions; a system based on relatively simple compute units that is massively scalable (think GPUs instead of CPUs).
An example of the above: ChatGPT is a design that is relatively simple in terms of architectural complexity, but it’s creators trained it with tons of texts, conversations, etc. and now it can behave and converse very closely to a real human can.
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