In fast evolving fields it’s always all about sociology, not canon or pedagogy. Meaning in new fields is created in community (constructionism).
You need to plug into the community and overhear what people are talking about (HN is such a community). You’ll also get a sense of the linguistic subculture (acronyms, lingo etc) much like you learn to talk hip hop if you’re into the hip hop subculture. Much of it will be noise but overall you’ll get a sense of what the community cares about, which helps you narrow what you need to focus on. The subreddit r/localllama is the watering hole for hobbyists right now.
In this particular case, I find it helpful to do syntopical reading (per Mortimer Adler) around LLMs not AI in general. Mamba is interesting to me because I have a background in optimal control and state space models are my bread an butter and it’s fascinating to see them applied in this way.
Side: I’m in my 40s and this isn’t my first rodeo. There will always be new fields and trends emerging — I’ve been through several waves of this (cloud, big data, ML, data science etc) where posts like yours are commonplace. But there is no need to be frustrated. Overhearing conversations is one way to make sense of them instead of feeling lost and waiting for someone to summarize and explain everything to you.
The same applies to academic fields.
Ps also consider you might not need to be on the cutting edge. If you’re not trying to build leading edge stuff, it’s good to wait for the dust to settle — you’ll waste less time following dead ends while the community is figuring out what’s good.
Perhaps the community at r/localllama could train an LLM that knows about the latest developments and explains jargon and papers, updated weekly. Free idea for karma.
1. Select abstract or select all text then copy/paste.
2. Save the PDF and upload with ChatGPT’s document feature.
3. Ask for it, “what’s that well known LLM paper about context and getting lost in the middle?”. It will web search as needed.
You can also do more than summarize. Ask about equations, ask it to make analogies, challenge the key findings as devil’s advocate to learn from different angles. Propose your own ideas.
Use voice to digest topics during your commute and ask tons of questions until you understand.
You need to plug into the community and overhear what people are talking about (HN is such a community). You’ll also get a sense of the linguistic subculture (acronyms, lingo etc) much like you learn to talk hip hop if you’re into the hip hop subculture. Much of it will be noise but overall you’ll get a sense of what the community cares about, which helps you narrow what you need to focus on. The subreddit r/localllama is the watering hole for hobbyists right now.
If you need a primer, this is a good guide.
https://flyte.org/blog/getting-started-with-large-language-m...
In this particular case, I find it helpful to do syntopical reading (per Mortimer Adler) around LLMs not AI in general. Mamba is interesting to me because I have a background in optimal control and state space models are my bread an butter and it’s fascinating to see them applied in this way.
Side: I’m in my 40s and this isn’t my first rodeo. There will always be new fields and trends emerging — I’ve been through several waves of this (cloud, big data, ML, data science etc) where posts like yours are commonplace. But there is no need to be frustrated. Overhearing conversations is one way to make sense of them instead of feeling lost and waiting for someone to summarize and explain everything to you.
The same applies to academic fields.
Ps also consider you might not need to be on the cutting edge. If you’re not trying to build leading edge stuff, it’s good to wait for the dust to settle — you’ll waste less time following dead ends while the community is figuring out what’s good.