LLMs
.png?table=block&id=e222d02f-1d78-4887-8972-a958b1fbca65&spaceId=996f2b3b-deaa-4214-aedb-cbc914a1833e&width=1250&userId=&cache=v2)
The context size of the input is too small for when you want to analyse CSV's with 1000's of rows and embedding doesn't really work because it loses context.
r/LLMDevs - Reddit
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
Additional LLM paradigms beyond RAG
We went to OpenAI's office in San Francisco yesterday to ask them all the questions we had on Quivr (YC W24), here is what we learned:
1. Their office is super nice & you can eat damn good croissant in SF!
2. We can expect GPT 3.5 & 4 price to keep going down
3. A lot of people are using the Assistants API to build their use cases
4. It costs 2M$ to... See more
1. Their office is super nice & you can eat damn good croissant in SF!
2. We can expect GPT 3.5 & 4 price to keep going down
3. A lot of people are using the Assistants API to build their use cases
4. It costs 2M$ to... See more
Paul Venuto • feed updates
core components of Deep RL that enabled success like AlphaGo: self-play and look-ahead planning.
Self-play is the idea that an agent can improve its gameplay by playing against slightly different versions of itself because it’ll progressively encounter more challenging situations. In the space of LLMs, it is almost certain that the largest portion... See more
Self-play is the idea that an agent can improve its gameplay by playing against slightly different versions of itself because it’ll progressively encounter more challenging situations. In the space of LLMs, it is almost certain that the largest portion... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
These two components might be some of the most important ideas to improve all of AI.
Humans are bad at coming up with search queries. Humans are good at incrementally narrowing down options with a series of filters, and pointing where they want to go next. This seems obvious, but we keep building interfaces for finding information that look more like Google Search and less like a map.
All information tools have to give users some... See more
All information tools have to give users some... See more
thesephist.com • Navigate, don't search
Memory Considerations
Since co-occurrence matrices are square, they grow exponential with the number of entities being embedded. For 50k entities and a 32-bit data format, a dense matrix will already be at 10GB. 100k entities puts it at 40GB.
If you are trying to embed even more entities than that or have limited RAM available, you may need to use a... See more
Since co-occurrence matrices are square, they grow exponential with the number of entities being embedded. For 50k entities and a 32-bit data format, a dense matrix will already be at 10GB. 100k entities puts it at 40GB.
If you are trying to embed even more entities than that or have limited RAM available, you may need to use a... See more
What I've Learned Building Interactive Embedding Visualizations
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
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.