Saved by Ian Vanagas
How to Understand ML Papers Quickly
Lucas Kohorst and added
What you get from deep engagement with important papers is more significant than any single fact or technique: you get a sense for what a powerful result in the field looks like. It helps you imbibe the healthiest norms and standards of the field. It helps you internalize how to ask good questions in the field, and how to put techniques together. Y... See more
Michael Nielsen • Augmenting Long-Term Memory
One thing that is still confusing to me, is that we've been building products with machine learning pretty heavily for a decade now and somehow abandoned all that we have learned about the process now that we're building "AI".
The biggest thing any ML practitioner realizes when they step out of a research setting is that for most tasks accuracy has ... See more
The biggest thing any ML practitioner realizes when they step out of a research setting is that for most tasks accuracy has ... See more
Ask HN: What are some actual use cases of AI Agents right now? | Hacker News
Nicolay Gerold added
You are assuming that the probability of failure is independent, which couldn't be further from the truth. If a digit recogniser can recognise one of your "hard" handwritten digits, such as a 4 or a 9, it will likely be able to recognise all of them.
The same happens with AI agents. They are not good at some tasks, but really really food at others.
6. Clear up unfamiliar jargon.
Scott H. Young • 10 Best Ways To Use ChatGPT (With Examples)
It's worth deliberately practicing such switches, to avoid building a counter-productive habit of completionism in your reading. It's nearly always possible to read deeper into a paper, but that doesn't mean you can't easily be getting more value elsewhere. It's a failure mode to spend too long reading unimportant papers.
Michael Nielsen • Augmenting Long-Term Memory
Learning this type of complicated judgment — this instantaneous solution selection that happens to balance dozens of considerations against each other — this is what is valuable to learn. And it is almost impossible to learn it through explanation alone.
Commoncog • Why Tacit Knowledge Is More Important Than Deliberate Practice - Commonplace - The Commoncog Blog
Keely Adler added
they could try “switching to a different model, augmenting the training data in some way, collecting more or different kinds of data, post-processing outputs, changing the objective function, or something else.” Our interviewees recommended focusing on experiments that provided additional context to the model, typically via new features, to get the... See more
Shreya Shankar • "We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning.
Nicolay Gerold added