ML for Beginners GitHub (opens in new tab)

Maybe it's me reading too deep into it, but that article sounds like the author had strongly held predetermination that simplistic ML translations that generate malignant deformations of English are "obviously objectively better", and could not accept the evaluations from actual first language speakers that they don't even make sense. IOW the fight had already happened and the author won by power. Two points that stuck up to me:

> [x] Try some more sentences. Which is better, ML or human translation? In which cases?

Any English second language speaker just knows this: There are basically zero cases where ML translation is in any way better or more accurate. That includes DeepL and ChatGPT w/GPT-4 proper. It's just tap water at restaurants. Which is a fantastic choice to pair with actual drinks. It's weird this sentence appears here at all.

> [...]and why is TextBlob so good at translation? Well, behind the scenes, it's using Google translate, a sophisticated AI able to parse millions of[...]

And this part. Maybe it's just me, but I think it might be showing that the author first tried to hand-roll translation, as seemingly needlessly lengthily elaborated up to this part. It could be that they then either faced technical challenges or failed validation by certified Frenchmen, and had to rewrite the section as a guide to use Google Translate API.

There seems to be an endemic misconception, mostly seen among but not limited to American people, that American English is a perfect language that is also completely disconnected from internal thoughts and intents and monologues, that simplifies language translation problem to a simple matter of "convert[ing] the formal grammar rules for one language, such as English, into a non-language dependent structure, and then translate it by converting back to another language" as the author claims, while in reality even the possibility of "non-language dependent structure" is still under debate. This kinds of attitude always existed, but now it's borderline beyond annoying.

Unless something I can’t predict changes, using LLMs for EVERYTHING smacks of bloat. They’re just not very efficient at many tasks. I don’t know why in a world where people complain relentlessly about bloat in web development, how it could possibly be right for everybody to just use something as heavyweight as LLMs.

I don’t know, it’s not my field, but the applicability of an LLM in a lot of current AI fields such as motion control, vision, planning, etc. That is classic AI stuff, I don’t think LLMs are appropriate yet.

Too big, too slow, too much resources etc. And it’s not even clear to me (mind, who is ignorant) that the LLM is some generic model suitable for all AI like tasks.

Making a big splash right now to be sure, but seems to me there’s still room for the core concepts folks have been working on for a long time.

It's almost like asking whether it's more valuable to learn about networking, http, html, js, databases or start building apps on top of nextjs.

i dont really understand what an LLM is going to do for you in probably 99% of all problems that have an ML solution except maybe write a block of code using sklearn or whatever to get you started. There are several reasons for this:

1. there is no reason to pay the api costs for an LLM to ingest data for you and do something with it when basically all it will be doing is writing the python codes for you that you will eventually be using

2. the LLM doesn't represent some sort of conceptual understanding of whatever you are trying to do to solve your ML problem, so you can't rely on it to be clever and answer questions or brain storm new ideas

3. even if you have a reason to use an LLM in some data processing pipeline it will only be one stop on the information super highway you are trying to create. you probably are going to use it to do something, but you probably also are going to be doing other things (e.g., image segmentation, time series analysis, etc.).

LLMs are great. but they are really just like, one more tool to have, they aren't the only tool.

I just have a hobbyist curiosity in the area, I think learning ml basics removed alot of the magic of ai hype, I at least like to think I'm less susceptible to hype and bs. I feel like I have a good idea what is going on and what the limits are of models and how to use them. There is a bigger world of ai besides llm

Depends on your goals though, using llm just seems like using any other api to me.

Definitely read up on ML basics if you are going to build anything. And by ML basics I mean the concepts of supervised/unsupervised /reinforcement learning - and in particular model evaluation. It is absolutely critical for any ML-based system to have a structured way to judge how well a system works: whether it works well enough (according to some target), or which of two alternatives is better, and that the nature of the errors are not critical or maybe some of them can be mitigated etc.

My personal approach, as someone not in the field but who has tangential interest (perhaps intellectual and not industrial interest) in it, my approach is to start from scratch, implementing the basic ideas in my favorite languages with no supporting libraries. I personally like this approach, but I doubt it gets anyone a job. So it just depends on your goals.

LLMs are simply too overkill and resource-intensive for many tasks.

I think it is more relevant to learn how the technology that will take over most junior tasks works, before it takes over everything and the only job left is being LLM Architect, like in those offshoring projects, the difference being those offshore folks aren't there any longer.

The answer to your question depends on whether you think LLMs will remain the state-of-the art solution in the domains you are interested over the next few years/decades/timescale you care about.

It's like asking if it's better to learn to code or to write on MS words. It entirely depends on your use case

If you need motivation, build an app. If not, study the theory, and implement basic models like linear regression yourself.

Depends. What is your goal?

Thanks for letting us know. I plan to read the article when I get home from work.

It's just a big ad for Azure. There are probably way better things out there that really teach you machine learning, instead of just the product palette of microsoft.