ibis.ai’s Post

Recently, I've been diving deep into the Visual language model ( VLM) the fascinating fusion of vision and language understanding. These models don't just process text or images separately, they combine both, allowing machines to interpret visuals, reason about them, and respond in natural language✨ From image capturing to visual reasoning and even multi model chatbots, VLMs are transforming how we interact with AI. This document breaks down the concept in a simpler way, end-to-end. #AI #VLM #Multimodel #Deeplearning #MachineLearning #Computervision #GENAI #NLP

Imagine a world where interacting with AI is as simple as talking to a colleague. The key is natural language processing, enabling individuals to prompt AI using everyday language to solve problems, no coding required. Organizations can experiment by inputting data into a model that employees can query. Starting with something simple like an employee handbook can open up possibilities for engaging with this technology. #AI #NLP #NaturalLanguageProcessing #Innovation #Technology #DigitalTransformation

💡 LLMs That Think Twice: Deliberation Networks Traditional language models often respond in a single pass-like writing without editing. Deliberation networks change the game by reasoning, reflecting, and revising over multiple passes. 🔹 How it works: 1️⃣ First pass: Drafts a rough answer 2️⃣ Second pass: Refines, corrects, and polishes with broader context 🔹 Why it matters: Reduces reasoning errors Handles complex, multi-step tasks Unlocks emergent problem-solving skills 🔹 Applications: Machine translation Math & logic problem solving Recommendation systems Dialogue & summarization ⚠️ Challenges: Speed, cost, and scalability-but the payoff is AI that rethinks, backtracks, and truly reasons. #AI #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks #LargeLanguageModels #LLM #NaturalLanguageProcessing #NLP #GenerativeAI #TechInnovation #FutureOfAI #AITechnology #SmartAI #AIResearch #Innovation #AIThinking #AIInsights #AITrends #EmergingTech #Aristotlei #AristotleIntelligence

Discover how "Make your LLM fully utilize the context" tackles a crucial challenge in large language models: the inability to make full use of lengthy input data. This paper introduces innovative strategies to overcome the notorious lost-in-the-middle problem, enhancing how LLMs process and retain information spread across long contexts. These advances directly impact fields such as legal analysis, scientific research, and any domain requiring comprehensive document understanding. This is essential reading for those interested in pushing LLM capabilities beyond current memory limitations. https://lnkd.in/dFWZ3yxK #LLM #AI #DeepLearning #NaturalLanguageProcessing #NLP #Research #MachineLearning #ContextualAI #NeurIPS2024 #ArtificialIntelligence

💡 Machines don’t understand language — they process it. NLP changes that. From syntax and semantics to sentiment analysis, topic modeling, and machine translation, Natural Language Processing bridges the gap between human thought and machine logic. It’s how your chatbot understands intent, how your assistant predicts context, and how AI learns to communicate. 🔍 Here’s a breakdown of NLP types, algorithms, and real-world implementations — from healthcare and finance to e-commerce and law. #AI #NLP #MachineLearning #ArtificialIntelligence #DataScience #Automation

I analyzed 3,100 employee comments using NLP and natural language processing. When I processed these comments systematically: ✓ Identified 5 major themes ✓ Quantified sentiment over time ✓ Built a "psychological safety index" Result: 87% alignment between quantitative and qualitative data. Deep dive here 👇 Link to Blog: https://lnkd.in/evU2ADPy #NLP #EmployeeExperience #DataScience

How LLMs Actually Work Before 2017, models like RNNs & LSTMs read text word by word, remembering only recent context. In 2017, Google introduced the Transformer architecture (“Attention is All You Need”) a game changer! Now models process multiple words in parallel using self-attention, understanding how every word relates to every other. That’s how modern LLMs (Large Language Models) like GPT understand full meaning, context, and nuance just like humans do. #AI #MachineLearning #LLM #Transformers #NLP #ArtificialIntelligence

A major challenge in large language models (LLMs) is hallucination — when the model confidently gives incorrect answers. Most benchmarks only test this in English and at a broad level. PsiloQA — a new multilingual benchmark for detecting hallucinations at the span level (the exact part that’s wrong) across 14 languages! 🧩 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀? 1️⃣ Generates Q&A pairs from Wikipedia using GPT-4o. 2️⃣ Collects potentially hallucinated answers from multiple LLMs. 3️⃣ Uses GPT-4o to automatically mark incorrect spans. 🧠 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: 🔹 Encoder-based models perform best. 🔹 Works well across languages (great cross-lingual generalization). 🔹 Much more cost-efficient than human annotation. #AI #LLMs #MachineLearning #HallucinationDetection #ResponsibleAI #NLP

When evaluating binary classification, relying only on accuracy or Gini index often hides deeper insights. Complementary checks like: Vocabulary similarity → reveals how overlapping language can confuse models Context similarity → highlights whether meaning shifts between classes are captured These metrics don’t just measure performance — they guide us to focus on the right areas for data quality and model improvement. In ML, the story is never told by one metric alone. #MachineLearning #NLP #AI #ModelEvaluation