But the things we try to make LLMs do post-pre-training are primarily achieved via reinforcement learning. Isn't reinforcement learning machine learning? Correct me if I'm misconstruing what you're trying to say here
You are still talking about training. Generative applications have always been fundamentally different from classification problems, and has now (in the form of transformers and diffusion models) taken on entirely new architectures.
If “machine learning” is taken to be so broad as to include any artificial neural network, all of which are trained with back propagation these days, then it is useless as a term.
The term “machine learning” was coined in the era of specialized classification agents that would learn how to segment inputs in some way. Thing email spam detection, or identifying cat pictures. These algorithms are still an essential part of both the pre-training and RLHF fine tuning of LLM models. But the generative architectures are new and very essential to the current interest in and hype surrounding AI at this point in time.
Just to follow up on the above replies, you could also just build a single package. For example, you could build asynchronous_union_find with `bazel build //in_memory/connected_components:asynchronous_union_find`. (This isn't very useful outside of the context of a cc_binary rule.)
This in turn allows you to only build and use the 'package' you care about without having to build the whole repo in other projects. Continuing on the above example, if you only wanted to use the asynchronous_union_find.h header file in your project, somewhere in your WORKSPACE file, you add the graph-mining library using a git_repository rule (see WORKSPACE.bazel for examples), and in a cc_library rule in a BUILD file inside your project, you can add a `@graph-mining//in_memory/connected_components:asynchronous_union_find`. Then you can include it as a header elsewhere. Building your project then only builds that package and its dependencies, and not the entire graph-mining library.
Fundamentals of Music Theory course from University of Edinburgh[1] and its accompanying textbook[2]. It's a centered around western music but certainly a good resource.
They did; perhaps not in ways that were immediately obvious to the general public. Back in 2014, they created attention to improve how RNNs handled long sequences, an effort driven by google translate. Same goes for when they created the transformer architecture. The first product to see an improvement was also translate. They simply never exposed the bare language models to the public a la OpenAI style with GPT3
