Machine learning
Machine learning is the idea of letting algorithms write (or update) their own codes. It is at the heart of recent breakthroughs in computer science, especially in image analysis, speech recognition, natural language processing, but even problem solving [cite AlphaFold, deep learning for symbolic math]. Perhaps most crucially, there are strong arguments to suggest that it will allow further spectacular progress in the coming years.
Contents
Turing's argument
The most compelling case for machine learning is arguably given in Section 7 of Turing 1950 paper.
Empirical observations
Sutton19 argues that this is indeed what we have been observing over the last few decades. Cleverly crafted algorithms for numerous tasks have been spectacularly outperformed by machine learning algorithms.
More data beats clever algorithms, but better data beats more data. (Peter Norvig)
When a large language model is trained on a sufficiently large and diverse dataset it is able to perform well across many domains and datasets [...] high-capacity models trained to maximize the likelihood of a sufficiently varied text corpus begin to learn how to perform a surprising amount of tasks without the need for explicit supervision. RWCLAS19
Supervised, unsupervised and reinforced
There are 3 main forms of learning.
What makes machine learning safety hard
Can't apply formal verification!
Machine learning is data and goal-driven!
This means that we should care about quality data collection, but also on objective function design. It has been argued that the latter should follow the principle of alignment.
