Max Jakobs’ Post

While established methods for model adaptation like prompt engineering and fine-tuning continue to be useful, a new and increasingly popular technique is emerging. This method known as Control Vector offers a distinct approach to model customization, with its own set of advantages and tradeoffs compared to existing techniques like fine tuning and RAG https://lnkd.in/dksa_nZv

Ever wondered about how LLMs work? This article gives a very clear and simple explanation about how LLMs & Transformers work. How do you see this technology impacting your industry and your work?

AGI engineering: Chapter 3.08 AGI: HOW TO ELIMINATE PERCEPTION-CONCEPT GAP 1. CLUSTERING https://lnkd.in/emrqgC6B

We are proud to introduce an innovative approach for early fault detection in induction motors. Notably, our method requires only the nameplate data of the machine, making it exceptionally suitable for industrial applications. By leveraging analytical modeling, machine learning, and the differential evolution algorithm, we create a powerful multiple coupled circuit model and a neural network for fault classification. Through simulated data training, we establish the network's remarkable 94.81% accuracy in fault classification, excluding bearing faults. Successful validation with real-world data underscores the method's practical applicability and ease of implementation in industrial settings.

Why is Prompt Engineering Vital for LLM-Based Systems? Prompt Engineering is the linchpin in effectively communicating with language models and achieving the outcomes you desire. It's the foundation upon which we build when delving into constructing RAG (Retrieval Augmented Generation) or Fine-Tuning LLMs with our data. Before embarking on the journey of working with these models, it's crucial to understand how to communicate with them effectively. Recently, I came across some invaluable resources in this domain that can empower us: 📚 Explore these insightful Prompt Engineering Techniques on promptingguide.ai: 1️⃣ Zero-Shot Prompting 2️⃣ Few-Shot Prompting 3️⃣ Chain-of-Thought Prompting 4️⃣ Self-Consistency 5️⃣ Generated Knowledge Prompting 6️⃣ Tree of Thoughts (ToT) 7️⃣ Retrieval Augmented Generation (RAG) Additionally, there are some exceptional articles by experts in the field: 📝 "Prompt Engineering" by Lilian Weng 🔗 https://lnkd.in/dxs24reY 📝 "Challenges of Productionizing Prompt Engineering" by Chip Huyen 🔗 https://lnkd.in/dNd9B8sj Let's dive deeper into the world of AI generation and be a part of the change. 🚀 Learn more at ➡️ https://lnkd.in/dYJRjCnh #LLM #MachineLearning #AI #PromptEngineering

I'm really excited to share our publication of an AI/ML technique to generalize from historical physical synthesis samples and provide a recommendation of improving timing, power, and wiring congestion. We are conveying a novel method using an unsupervised learning technique to generalize and utilize the latent encoded features to produce parameters for physical synthesis. https://lnkd.in/eBrmuJp2

Check out my Ultimate Guide on Generative Machine Learning Models #MachineLearning #DataScience #AI #DiffusionModels #Transformers

Representation Engineering: Explained In Depth via #TowardsAI → https://bit.ly/3x10BLx

Prompt Engineering Evolution: Defining the New Program Simulation Prompt Framework ... https://lnkd.in/enrb5Vjw #AI #ML #Automation

Complex system model can sometime be computationally expensive. To speed up the study, GT-SUITE allows you to create metamodels of your systems in these simple steps shown in this episode of the Tech Tip series: https://lnkd.in/gcZDnb5t #WeAreGT #simulation #excellence #GTTechTip  #GTSUITE  #machinelearning  #metamodeling  #engineering  #simulation