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 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.
I actually read papers with the help of ChatGPT-4 and Claude. It helps me quickly understand papers that I don’t have a background in.
For instance when I see something I don’t understand I ask it “can you break that down for me?” Or “is this similar to (concept I know)?”
It’s the new way of doing syntopical reading — but faster and more efficient.
(For the uninitiated, it’s a technique from Mortimer Adler’s How to read a book)
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.
Only the "everybody who knows what mamba is" are the ones upvoting and commenting. Think of all the people who ignore it. For me, Mamba is the faster version of Conda [1], and that's why I clicked on the article.
Well played.
That is actually accurate but probably sounds just as outlandish.
The approachable version is: Mamba is a proof of concept language model which showcases a new LLM architecture called S6 which is a competitor to the Transformer architecture (the 'T' in ChatGPT) and it is better in every measurable way.
try talking about stacked open linear dynamical system for more than three times and you're bound to figure out a token that conveys the same but is quicker to produce
it's turtles all the way down with LLM And your comment. people are just trying to maximize their token conversations
Just came out a few days ago. It's new for everyone.
Just to make it a little extra confusing :)
If you mean the "branding" that is common in ML, which is often criticized, I much prefer it over the jargon used in other fields, e.g. Mathematics. It is nice to have distinguished words to talk about different concepts.
Sorry for getting semantical here, but isn't ML a subfield of AI? In other words, I would have expected "... in the field of machine learning and AI in general"
But I did notice the "References" section in the bottom of the README, which does explain what Mamba is by linking to the original paper: "Mamba: Linear-Time Sequence Modeling with Selective State Spaces" https://arxiv.org/abs/2312.00752
I don't work in AI at all (and don't plan to), but it's fun to know about stuff a little before they become mainstream.
They literally get paid to read papers, and implement models on a day-to-day basis.
I wouldn't worry too much not being up to date or things sounding a bit foreign. The names themselves are just that, names, the models themselves tend to be incremental versions of some previous model.
Researcher is more for highly funded organizations. Starrups can get by with off the shelf models.
I really struggle with dozens and dozens of vocabulary that is being used in the field of machine learning and especially AI. I'm not a beginner at all, but I wonder if there is a comprehensive guide for all those terms that not necessarily explains the technology behind them in detail, but shows their position and relation to each other. like some kind of landscape.
"everyone" seems to know Mamba. I never heard of Mamba. There are constantly new kind of llm popping up, talking about stuff that seems to be obvious.
So, is there some kind of resource like that, not aiming at beginners, but experienced users, coming from other fields of IT?