Of course now it's old news I guess since it is being trumpeted daily across all the major news outlets, both left and right leaning... BUT... that certainly wasn't the case when I wrote this post back in mid-November now was it? I really should get a check for all I give you all for free before it happens.
US looks at 8% defense budget cut in each of next 5 years, Washington Post reports By Reuters February 19, 2025 Why I don't get a paycheck here... one of life's great mysteries.
Tick, tick, tick..... They better go full blown Hamas... and put those meth labs in elementary schools. Doesn't matter. We'll waste a vehicle a vehicle or two when they leave the compound. Bye bye Pedro the pusher.
I think there could be value in finding a company which specializes in building RAG’s. This would be highly specialized LLM’s which have a controlled datafeed, instead of public data. If you can/want guide me a bit in the stockpicking, I can do the work. Just an idea
LOL... I had to look up the acronym and look what I got.Now that's irony. It sounds like a brilliant idea Z. ________________________________________________ Why RAG Systems Struggle with Acronyms – And How to Fix It Acronyms allow us to compact a wealth of information into a few letters. The goal of such a linguistic shortcut is obvious – quicker and more efficient communication, saving time and reducing complexity in both spoken and written language. But it comes at a price – due to their condensed nature and the variability in their meanings depending on the context, acronyms pose a unique challenge for AI. Retrieval-Augmented Generation (RAG) systems, which blend search retrieval with generative AI, are especially challenged by the daunting task of deciphering these, often ambiguous, clusters of letters. The inherent brevity of acronyms, while beneficial for human efficiency, demands a high level of precision and adaptability from AI systems tasked with interpreting them accurately. As AI models encounter acronyms across different domains and disciplines, they must consider all the potential interpretations, each tied to specific professional jargon, regional overtones, or industry-specific terminologies. This complex variability tests the limits of current AI technologies and shows us just how important sophisticated context-aware processing is to ensure reliable and coherent communication. The Ambiguity and Context-Dependence of Acronyms Even though there are 17,576 possible combinations of three letters, it’s estimated that about 70% of three-letter acronyms carry more than one meaning. What follows is that, in most cases, when a RAG system encounters an acronym, it faces a decision: should it assume the most statistically common meaning, or should it analyze the surrounding context to deduce the correct interpretation? Opting for the most common meaning might speed up processing and suit general cases, but can lead to inaccuracies in specialized or more complex discussions. Risks of Misinterpretation – Nonsensical or Irrelevant Outputs Disambiguation, the process of resolving the meanings of ambiguous terms in a given context, can make a difference between a helpful and unreliable RAG system. Some of the risks associated with misinterpretation and the generation of nonsensical or irrelevant outputs include: Compounding Errors Errors in initial retrieval due to ambiguous queries can lead to a chain reaction of errors in the generative process. Scalability Issues As the volume and variety of data increase, the challenge of accurately disambiguating terms scales accordingly. RAG models operating in domains with a high rate of ambiguous terms or jargon (such as legal, medical, or technical fields) are particularly at risk of generating inaccurate outputs if they cannot effectively disambiguate. Impact on User Experience Users expect coherent and contextually appropriate responses from AI systems. Outputs that are nonsensical, irrelevant, or factually incorrect can frustrate them, leading to reduced trust and reliance. Difficulty in Traceability When RAG systems generate incorrect or irrelevant information, tracing the error back to its source (whether in the retrieval or generation phase) can be a demanding task. This makes debugging and improving the system more difficult, as it’s not always clear whether the fault lies in the retrieval of information or its subsequent interpretation and use. Risk of Misinformation In accuracy-dependent scenarios, such as news dissemination, financial advice, or medical information, the risks of spreading misinformation due to poor disambiguation can cause significant trust, financial, or health-related consequences. Human-written contexts Even with human-written contexts, acronyms remain a challenge due to their condensed nature and the assumption of prior knowledge. People are not afraid of using acronyms without further clarification, expecting the reader to understand the meaning based on the context. For a RAG system, this presents a challenge in mimicking human-like understanding, requiring sophisticated linguistic models that can decipher subtle contextual hints and adjust their interpretations.
You've sent me on a mission. I have a good one, but we have to wait for the IPO I guess. Best thing about it, it has a goofy name that you know the WSB's children will jump on just for the name and run it 200% ipo day. Hugging Face. https://huggingface.co/ Lots of biggies invested in it. Looks like the latest round put it a about $4B. I'll find another one. A lot of the good ones are private... but there's ways to get in pre-ipo so... It's a great idea Z.
Ya know that's how I found Sports Radar before it went ipo. Stoney and I were talking about sports betting and it's potential years ago (before it was all the rage) and I went on a search for an under the radar play (no pun) and ferreted it out. I knew it was a good company. Took forever to take off. I only have about 100 posts here about it. It finally doubled and I of course got out around $12.
Yeah HF was one the first, delivering an eco-system. I’m also going to dive more into it, update after the weekend when I first heard the name 2 years back I thought it was nonsense. I thought the same with Kraken (btc exchange) back in the days(2013)