Quadratic’s Post

Unlock the Power of AI Data Labeling with ZetaForge! In this short tutorial you will learn to create custom block pipelines and share your work with colleagues easily, no dependency hassles!  Watch the full video now on our YouTube channel: https://lnkd.in/efSTXyVF #AI #ZetaForge #AGI

"It’s not man versus machine; it’s man with machine versus man without. Data and intuition are like horse and rider, and you don’t try to outrun a horse; you ride it" - Pedro Domingos AI is only useful if built with purpose. For MaintainX, AI is just another tool to empower maintenance & reliability professionals to do more - specifically: ⏳Predict how long work will take to complete 🚩Surface irregular data and meter readings 🗓️Streamline workload planning and scheduling https://lnkd.in/ehHb8unJ

🚀 RAG vs. CAG: A New Era of Knowledge-Augmented AI! 🤖 Imagine this: Instead of retrieving information in real-time (and facing latency or ranking errors), what if you could preload all relevant knowledge into your AI model's context? Welcome to the Cache-Augmented Generation (CAG) revolution! 🧠✨ 🔍 In our latest blog, we break down: ✅ What is RAG (Retrieval-Augmented Generation) and its limitations ✅ How CAG simplifies architecture and boosts speed & accuracy ✅ Key benefits of CAG: No latency, fewer mistakes, faster inference ✅ Real-world use cases and where this tech fits in 💡 Why it matters? CAG is a game-changer for industries like legal research, document summarization, customer support, and more. While it does have challenges (like dynamic data handling), its potential to revolutionize AI applications is undeniable. 🔥 In this blog, we even dive into an epic face-off: RAG vs. CAG! Who wins? Find out here: 👉 https://zurl.co/tgLqL Highlights: ⚡ Lightning-fast responses 📚 Preloaded knowledge = fewer retrieval errors 🤖 Unified AI context for higher precision Let’s discuss: Do you see CAG overtaking RAG in the near future? Drop your thoughts in the comments! #AI #MachineLearning #LLM #RAG #CAG #FutureOfAI #KnowledgeAugmentation #AIResearch #TechInnovation #ArtificialIntelligence

If you want to use the power of LLMs and AI for Business Intelligence, don't fear compromising your data. Pyramid Analytics' Avi Perez believes your concerns are valid, but they shouldn't discourage you from using AI's full power for business. ➡️ There are ways to secure your data as you integrate LLMs. You can avoid sending your data to LLMs altogether and still use their capabilities! #softwaredevelopment #cto #softwaredelivery

We hear a lot of talk about GenAI chatbots. But what if we could leverage AI to improve the performance of real human CS teams? We spoke with Killian Farrell, Principal Data Scientist at Assurance IQ, about how his team used LLMs to score customer conversations to develop their sales and customer support teams – and how data quality remains fundamental to the performance of their GenAI pipelines. Check out the full story: https://lnkd.in/e2y6uGH2 #GenAI #AI #LLM #dataobservability #dataquality #dataengineering

The replay from our webinar last week "Practical AI Solutions for CFO Teams" is available on Youtube! We present the argument that digital AI agents can analyze data faster and more accurately than any human being. That one of the primary outcomes from the advent of #genai will be a shift from human centric to machine centric data analysis. 🤖 🤖 🤖 Here's the link: https://lnkd.in/dYPUXNeG Thanks again to the Profitability Analytics Center of Excellence for hosting me and Edward Roske!

"So, in today’s blog, we are going to take a deep dive into what the future of LLMs looks like, how are we moving towards agents from RAG pipelines, and what are the challenges in creating a feasible LLM-based AI agent (tool usage, memory, and planning)? And lastly, we look into different types of agents and what the future of AI Agents and RAG looks like." Read this story from Vishal Rajput on Medium: https://lnkd.in/eBk4GrYQ

Ever wondered what cool stuff AI can do for your business? Well, let's get real. While the tech sounds awesome, diving into AI can feel overwhelming. There are heaps of advantages like faster responses, slam dunk accuracy, and slashing costs! Imagine cutting down that tedious workload while getting instant results. At Data Science London, we’ve got your back. We specialize in automating workflows with AI, like chatbots that can handle inquiries instantly and efficiently. It’s all about making your biz run smoother and helping you dream bigger without the stress. Vikky - datasciencelondon.uk Source: https://lnkd.in/dXkX_2a2 #ArtificialIntelligence #AIAdvantage #BusinessAutomation #TechTrends #FutureOfWork Check here your next automation platform: https://lnkd.in/ecwFCrHp

We've been listening to teams like yours. We've heard about the data preparation hurdles, the evaluation complexities, and the constant push for faster, more accurate models 😮💨 That's why we're introducing Snorkel Flow R3. R3 isn't just an upgrade, it's a whole new way of thinking about AI development. It's about making your data work smarter, not harder. It's about turning "that's impossible" into "that was easy." With features like LLM evaluation tools, RAG tuning workflows, NER for PDFs, and sequence tagging tools, we're solving real problems for real AI teams. What's the biggest AI development challenge you're facing right now? 🔗 Read the full release: https://lnkd.in/ebwEsGc4 #SnorkelAI #R3Release #EnterpriseAI #LLM #AIReadyData

My AI Journey: The Validation vs. Test Set Conundrum As I dive deeper into AI, one concept that’s been on my mind is the challenge of ensuring our model doesn’t overfit. The typical advice is to: • Use a training set to teach the model. • Use a validation set to fine-tune the model and assess its performance. • Use a test set to evaluate the model on unseen data, ensuring it’s not overfitting. But here’s where it gets tricky: Every time we validate the model on the validation set and adjust it accordingly, we might indirectly overfit the model to that validation data. To prevent this, we rely on the test set to give an unbiased evaluation, as it remains completely untouched during model development. Now, what happens if we don’t have a separate unseen test set? If we use the test set too many times, don’t we run into the same problem of overfitting, where the model gets too tuned to the data and doesn’t generalize well to truly new data? How do you ensure your model remains unbiased when you’re running low on unseen data? #AIJourney #DeepLearning #MachineLearning #Overfitting #ModelValidation #TestSet #AIInsights