IEEE Rolls Out Large Language Models Training Course

>>Relying on such LLMs without understanding their internal logic creates a significant reliability risk. To build tools that work consistently, developers must understand the core principles that govern how the models process information and generate results. By mastering how a model processes information and how its internal settings influence the result, developers can move away from a trial-and-error approach toward a more precise one to ensure the AI tool handles complex data reliably.

This is staggering bullshitp. In what way does understanding a transformer allow you to solve the core problem of LLM's that no frontier lab has managed to resolve?

>>To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a company’s database.

This also is bullshit. Yes, RAG helps and reduces errors, but NOOOO! it does not fix hallucinations...

>>Prioritizing data security. When using AI with proprietary code, security is a major concern. Engineers must learn how to set up “private” instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions.

This is somewhat true, but really the motive is providing a soverign instance that cannot be withdrawn for arbitary reasons. Fundamentally the big providers are not going to steal your data, they may change the license to allow them to use it in the future, but then all their big customers will leave. So, they won't be able to, probably. What might well happen (and has happened) is that the USA might withdraw access with no notice leaving you high and dry.

I want to learn to build a real LLM so I looked at https://allenai.org/olmo where there are instructions and ingredients. But, unfortunately I can't afford the required compute resource so I will have to wait for a bit I guess.

Personally, I think understanding deeply how a transformer works helps a lot to understand what's probably the result of specific choices in the RL process vs what's architecture. A lot of the "We asked 30 LLMs and they all said the same thing" type analyses of how LLMs work often bump into what's being prioritized in the name of alignment right now, as opposed to architectural insights.

LLM training courses may have some valuable tips and tricks behind them, but the platforms change so often and no two personalized LLMs look the same. It feels like prompting isn't a science you can capture with step-by-step tutorials, but rather it's an art form you compose. Can start from the same place and get two completely different outcomes.

> if you don't know it you are left behind

This simply isn't true. Given that the whole promise of AI is accessibility, there isn't an obstacle being raised by other people adopting it faster. You can always pick up the latest trick quickly, and if you struggle at all, an LLM can explain. There is no evidence of people falling behind.

The idea that you need specialised knowledge to compete with the tool that is designed to let you do things without specialised knowledge is trivially nonsensical.

I really want to read a bunch of old papers from the IEE library but it looks very hard to get access to at any reasonable price for personal usage.

Anyone got any hints or tips for me?