K.S. Pua’s Post

This is a helpful animated article in the FT explaining how LLMs like GPT-4 work in simple terms. However, one of the first comments was someone who posted the article to GPT-4 which was quick to highlight two main problems with LLMs... "...the article fails to address the issue of bias present in generative AI models... ...which can have harmful consequences when applied in real-world contexts such as finance, law, and public services." "...the article briefly mentions that LLMs can fabricate information and engage in "hallucination." However, it does not adequately delve into the potential ethical implications of this capability. Fabricated information generated by LLMs can mislead users and have serious consequences, especially in areas such as legal cases or journalistic reporting where accuracy and factuality are crucial." This is why Rainbird exists. Article here 👉 https://lnkd.in/evnm7uEQ Rainbird video here 👉 https://lnkd.in/eVwudi4X #GPT4, #LLM #GenerativeAI #Explainable AI #Trust #Rainbird

One of my tests for open-source large language models is evaluating how well they can convert meeting transcripts into detailed meeting notes. Cohere recently released their Command-R+ model, which performed exceptionally well. The downside is that this model is 58GB, requiring significant RAM to operate effectively, but it is really good. https://lnkd.in/g7qbXHwf Ollama #ai #ossllm #cohere

Join the 324 Technology and Business Leaders already signed up – and Growing! 🌟 Don't miss out on this AI Workshop – secure your spot today and be part of the industry's top minds. 🚀 #innovation #technology #business #SonatafyTechnology

How aligned should AI be to human limitations? Human annotators routinely answer “don’t know” when checking AI’s output. Not to mention how often humans fail Captcha and can’t even point out all the bikes in a photo. So are humans qualified to check and evaluate the work — after all, we can’t ingest and process billions of data points. That’s just one of the topics we explored at the recent OpenAI’s session at our LLM Reading Group. I guess the answer is… 42. #ai #ml #artificialintelligence #machinelearning #responsibleAI #LLM

Companies are spending a fortune on AI solutions and tech, DBMT just published a new White Paper on how to get the most out of AI/LLM solutions with a Knowledge Domain - bridging the gap between Language and Knowledge. This is a must read: https://lnkd.in/efXC37b5

Deepset-Mxbai-Embed-de-Large-v1 Released: A New Open Source German/English Embedding Model Deepset and Mixedbread have taken a bold step toward addressing the imbalance in the AI landscape that predominantly favors English-speaking markets. They have introduced a groundbreaking open-source German/English embedding model, deepset-mxbai-emb... https://lnkd.in/eWMer3B3 #AI #ML #Automation

Generalized AI models will only go so far. Everyone has access to the same models, so how will you be different? the real value will come from leveraging data that only you have. the next wave of AI will benefit companies that have a robust data infrastructure that can feed data into smaller AI models effectively and effeciently.

GPT-4 is a phenomenal AI tool in 2023. But it costs $20/month to use it. Here are 7 ways to access it for free:

We've just released our latest paper, "Customized LLMs for Secure and Effective Business Intelligence." Discover how our AI-driven platform leverages tailored Large Language Models (LLMs) to provide precise, secure, and adaptable insights, ensuring data confidentiality and superior performance. Dive into the future of AI in business intelligence with us. #BusinessIntelligence #AI #DataSecurity #LLM #Synthexa #Innovation

Large Language Models alone can't solve this. AI is just one piece of the puzzle. The challenge lies in integrating it with existing data systems and achieving widespread adoption within companies. Read more at : www.himcos.com Video Src: https://lnkd.in/gFZQmHtD Himcos #LLM #artificialintelligence