Socialgist’s Post

There is a lot of speculation about AI taking jobs and automating many in-demand skills over the next few years. So far, all we've seen is that the AI revolution is only making the skills gap wider and wider as demand for skillsets like NLP model optimization, LLM fine-tuning, and Prompt Engineering, is growing at an exponential rate. Yesterday, Forbes published a thorough and insightful write-up on how we are leveraging AI to close skills gaps with our Develop Enterprise Suite. Take a look here: https://lnkd.in/gKdpgyBq

Enhancing Efficiency in Vision-Language Models: The Impact of ALLaVA’s Synthetic Dataset on Competitive Performance #AI #AItechnology #ALLaVA #artificialintelligence #captioning #Challenges #Efficacy #Efficiency #empiricalevaluations #GPT4V #llm #machinelearning #questiongeneration #syntheticdata #syntheticdatasets #Visionlanguagemodels #VLMefficiency

Check out Harpreet Sahota 🥑’s post where he dives into three interesting benchmarks from this year’s CVPR 2024 show - https://lnkd.in/gcYNamYP * ImageNet-D: Testing the robustness of image classifiers against real-world perturbations. * Polaris: Assessing the ability of vision-language models to follow natural language instructions in interactive environments. * VBench: Comprehensive Benchmark Suite for Video Generative Models Chenshuang Zhang Yuiga Wada #computervision #ai #artificialintelligence #machinevision #machinelearning #datascience #LLM #cvpr #cvpr2024

Large language models have been a game-changer when it comes to natural language processing. With their ability to understand queries and retrieve relevant information from vast amounts of unstructured data, these models have given rise to powerful platforms such as chat bots, search, content creation, and recommenders. However, selecting and optimizing the right LLM model can be a daunting task. Sometimes, you may even need to train the model yourself. Luckily, innovative approaches like semantic retrieval-augmented generation (semantic RAG) can help reduce the time and effort required for LLM setup and maintenance while increasing the precision and reliability of the application. Stay ahead of the curve by leveraging the power of LLM models and exploring new techniques like semantic RAG. https://lnkd.in/dtmZr67Z #symbolicai #knowledgegraphs #rag #llms

🚀 Excited to dive into the world of Large Language Models (LLMs) for your Retrieval Augmented Generation (RAG) projects? 🧠 Check out our latest blog post on how to choose the right LLM for your RAG projects! From OpenAI’s GPT-4 to emerging contenders like Mistral AI and Anthropic’s Claude, we cover the pros and cons of each, helping you make the best decision for your needs. Learn more about: 🔍 Integrating various LLMs with our LLM-agnostic platform Cognition ⚙️ Evaluating models through the LLM Arena 🛠️ Optimizing your RAG pipeline for success Read the full article here: https://lnkd.in/eRUyw_nz #AI #MachineLearning #LLM #RAG

🚨Our Retrieval Augmented Generation (RAG) Introduction course starts TOMORROW – October 24, 2024, from 9:00 AM to 5:00 PM (Eastern Time)! What will you learn? 👀 ✨ How Retrieval Augmented Generation (RAG) combines generative and retrieval-based AI models ✨ The inner workings of Large Language Models (LLMs) ✨ Building a simple RAG using existing LLM and AI tools ✨ Applying RAG techniques to solve real-world business problems The course is online, so grab this chance, mark your calendar, and get ready for a day full of RAG insights and new AI skills! 📅 It's your last call to register 👉🏻 https://lnkd.in/djnPJSzE #AI #MachineLearning #RAG #LLM #TechTraining #LinuxFoundation #ArtificialIntelligence #GenerativeAI

Fuzzy Logic Fuzzy Logic, a form of logic used in AI and decision-making, is unique in its ability to handle imprecise information, unlike traditional binary logic. It was introduced by Lotfi Zadeh in the 1960s as a way to model the uncertainty of natural language. Fuzzy Logic allows AI systems to reason in a way that resembles human thinking by dealing with probabilities and approximations, rather than fixed and exact values. This makes it particularly useful in fields like control systems, expert systems, and certain types of machine learning algorithms.

"closing the loop" - This is a really old concept (a little David Brent-ish). What it meant was that you automated your BI/Analytics directly back into operating processes. It is extremely powerfull and compelling but also feared because of the perception of cutting the human out of the process (but it doesn't mean that). Generative NLP LLM's in the workplace are exactly that. It's just a really powerfull search engine embedded directly into the context of your workflow. There are loads of opportuntities to close the loop without AI; but still not common stratgey in most business... there's always excuses why it can't be done and therefore we should not try. #dataengineering

FUNDAMENTALS ARE THE BUILDING BLOCKS OF GREATNESS IN ANY FIELD. 🧱✨ To grasp the concept of Language Models (LLMs), it's crucial to have a strong understanding of Transformers. Transformers have surpassed their predecessors, and to comprehend why, it's essential to delve into the intricacies of self-attention. However, before diving into self-attention, it's necessary to grasp the basics of the conventional attention mechanism and its origins. I have curated a series of videos that cover these topics, starting from the fundamentals and progressing towards the most advanced concepts. 🎓Attention Mechanism in Deep Learning: A comprehensive Guide youtu.be/mU9hcH9dMx0 🎓Conversational AI : Understanding the Technology behind Chat-GPT youtu.be/JKoJ5YIr2O4 🎓The Good, the Bad, & the Ugly : Exploring the Dual Nature of Conversational LLMs youtu.be/MHfzoHC4kek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

FUNDAMENTALS ARE THE BUILDING BLOCKS OF GREATNESS IN ANY FIELD. 🧱✨ To grasp the concept of Language Models (LLMs), it's crucial to have a strong understanding of Transformers. Transformers have surpassed their predecessors, and to comprehend why, it's essential to delve into the intricacies of self-attention. However, before diving into self-attention, it's necessary to grasp the basics of the conventional attention mechanism and its origins. I have curated a series of videos that cover these topics, starting from the fundamentals and progressing towards the most advanced concepts. 🎓Attention Mechanism in Deep Learning: A comprehensive Guide youtu.be/mU9hcH9dMx0 🎓Conversational AI : Understanding the Technology behind Chat-GPT youtu.be/JKoJ5YIr2O4 🎓The Good, the Bad, & the Ugly : Exploring the Dual Nature of Conversational LLMs youtu.be/MHfzoHC4kek #datatrek #datascience #machinelearning #statistics #deeplearning #ai