smartocto’s Post

https://lnkd.in/gfKtfVwE Fine-tuning large language models (LLMs) for classification tasks is an exciting frontier in AI! By customizing these powerful models with your specific dataset, you can unlock incredible accuracy and relevance for your unique needs. Imagine turning a generalist model into a specialist that perfectly understands and categorizes your data. Whether it's sorting through customer feedback or identifying trends in social media, fine-tuning allows your LLM to become an expert in your domain. It's like giving a brilliant student tailored lessons to ace the exam! Check out my latest project on how I fine-tuned a model to achieve top-notch classification results. 🚀🔍 #MachineLearning #AI #DataScience #FineTuning

Kevin has some amazing insights in this post!

“An AI must fundamentally understand the world around us, and we argue that this can only be achieved if it can learn to identify and disentangle the underlying explanatory factors hidden in the observed milieu of low-level sensory data.” Over the past two decades, representation learning has matured from a pioneering idea to the mainstream technique which shapes today's AI landscape. In this second part of the RAG blog, we explain what is text embedding and review the major research milestones which lead to the success of LLMs. https://lnkd.in/d6AmMvJq

Building a strong intuition for a complex system is really important. I wrote this blog for Stack Overflow to share a spatial and historical intuition for text embeddings, a concept at the core of AI technology. https://lnkd.in/gtfDBA4Z

Are your systems AI ready? What about GenAI ready?!? At InterSystems we have expanded our #dataplatform with Vector Search to support next-generation #AI applications. Find out more below and reach out to discover how you can avoid being left behind by the competition. https://lnkd.in/eUcNmJ6s #finserv #captialmarkets

Stand back and take a look at the last two years of AI progress as a whole... AI is catching up with humans so quickly, in so many areas, that we need new tests. AI has already beaten us in a large number of significant benchmarks. In 2015, it surpassed us in image classification, then basic reading comprehension (2017), visual reasoning (2020), and natural language inference (2021). Many of the benchmarks used to this point are now obsolete. Indeed, researchers are scrambling to develop new, more challenging benchmarks. AIs are getting so good at passing tests that now we need new tests – not to measure competence, but to highlight areas where humans and AIs are still different, and find where we still have an advantage. Two areas stand out currently in my mind where we do have an advantage - intuition and imagination. But how long will this last? #AI #innovation #future

🚀 Exciting News in AI! 🚀 I've just published an article diving into the capabilities of the new GPT-4o model. 🌟 This advanced version takes AI to the next level with enhanced language understanding, greater contextual accuracy, and improved efficiency. Whether you're a tech enthusiast, a professional in AI, or just curious about the latest advancements, this is a must-read! 🔗 For more information, read the full article here: https://lnkd.in/g5Q4qR73 Let's explore the future of AI together! 💡 #AI #GPT4o #MachineLearning #ArtificialIntelligence #TechInnovation #SkillsFoster

Are you interested in learning more about the power of AI language models? Check out this insightful article that discusses the differences between prompt design and prompt engineering, and how they can unleash the full potential of AI. Some key takeaways from the article include: • Prompt design focuses on creating prompts that elicit desired responses from the model • Prompt engineering involves fine-tuning the model to improve its performance on specific tasks • Both approaches are important for unlocking the full potential of AI language models Read the full article to learn more! #AILanguageModels #PromptDesign #PromptEngineering https://lnkd.in/gWjCSYfQ

In the rapidly evolving world of AI, multimodal foundation models are leading the way. A recent study from Stanford University evaluates these models as they scale from few-shot to many-shot in-context learning, revealing fascinating insights into their performance and efficiency. 📈 Key Highlights: Enhanced performance with increased demonstrating examples. Higher ICL data efficiency with Gemini 1.5 Pro. Improved zero-shot performance with batched querying. For an in-depth analysis and detailed findings, check out the full blog post here - https://lnkd.in/gMMnDKS4 😍 Follow AI Toolhouse for more such amazing content. 🌟 Explore 𝟑𝟔𝟎𝟎+ latest AI Tools here for FREE ➡️ https://lnkd.in/dpQB7xZU #AI #MultimodalModels #InContextLearning #MachineLearning #AIResearch #StanfordAI

If you have doubts what these mean: HITL, RAG, Prompt Injection, Semantic Retrieval, then quickly head over to our Generative AI Glossary of Terms page https://lnkd.in/dNScHDqj