r/MachineLearning - Reddit
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r/MachineLearning - Reddit
First of all, I'd say you have a bigger problem where your company is trying to find nails with a hammer. That is where your sentiment comes from, and could be an obstacle for both you and the company. It's the same deal when I see people keep on talking about RAG, and nowadays "modular RAG", when really, you could treat everything as a software co... See more
r/MachineLearning - Reddit
Nicolay Gerold added
Like all machine learning, LLMs turn a logic problem into a statistics problem: instead of people writing the pattern for each possible question by hand, which doesn’t scale, you give the machine a meaningful sample of all the text and data that there is and it works out the patterns for itself, and that does scale (or should do). You get the machi... See more
Benedict Evans • Unbundling AI
sari and added
a couple of the top of my head:
- LLM in the loop with preference optimization
- synthetic data generation
- cross modality "distillation" / dictionary remapping
- constrained decoding
r/MachineLearning - Reddit
Nicolay Gerold added
Additional LLM paradigms beyond RAG
Andrea Badia and added
When I worked in machine learning every day, I found a close analogue of this to be very true. A slightly better architecture tended to matter much less than getting better data. Or, at least, once I hit on a reasonable model architecture, I tended to do much better by cleaning and gathering more data than by optimizing the architecture.
Nate Meyvis • Nate Meyvis
sari added
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.
We generally lean towards picking more advanced commercial LLMs to quickly validate our ideas and obtain early feedback from users. Although they may be expensive, the general idea is that if problems can't be adequately solved with state-of-the-art foundational models like GPT-4, then more often than not, those problems may not be addressable usin... See more
Developing Rapidly with Generative AI
Nicolay Gerold added