Generative AI World’s Post

Thank you, Appen for sponsoring the upcoming 5th Annual MLOps World and Generative AI World summits. With over 25 years of experience, Appen is the leading provider of high-quality LLM training data and services. Whether you're building a foundation model or need a custom enterprise solution, our experts can support your specific AI needs throughout the project lifecycle. Learn more here: https://lnkd.in/dXdP9vQR We're also hosting Geoffrey LaPorte will share the latest research in his talk, “Striking the Balance: Leveraging Human Intelligence with LLMs for Cost-Effective" As data annotation can be both resource-intensive and complex, Large Language Models (LLMs) offer promising automation potential—yet balancing LLM efficiency with the nuance of human insight remains a challenge. Key Highlights from the Talk: 📊 Experiment Findings: Geoffrey will discuss a recent Appen experiment evaluating the trade-off between quality and cost when training ML models with LLMs versus human input. The goal was to identify utterances that could confidently be annotated by LLMs and those requiring human intervention. 🧩 Balancing Automation & Expertise: Appen’s findings underscore that while LLMs can effectively handle straightforward tasks, complex annotations still benefit from human review, especially when task instructions are unclear or subjective interpretations are necessary. 🚀 Methodology & Recommendations: Geoffrey will walk through the dataset used, the experimental methodology, and research findings. Their work highlights the importance of blending LLM capabilities with human input to achieve a balance between cost-efficiency and high-quality annotation. Looking forward to exploring this topic in depth with Geoffrey and the Appen team!

I'm thrilled to announce that I've recently completed the InstructLab: Democratizing AI Models at Scale on democratizing AI with InstructLab course! This comprehensive course has equipped me with the skills to unlock the full potential of both Large Language Models (LLMs) and Small Language Models (SLMs), enabling me to synthesize domain-specific data, fine-tune, align, and customize AI models. With a deeper understanding of the LAB Methodology, I'm now better equipped to: * Understand the taxonomy, synthetic data generation, and fine-tuning processes * Demonstrate InstructLab's capabilities and explore its use cases * Contribute my expertise and knowledge through hands-on tutorials I've earned my Credly digital badge, which not only validates my skills but also serves as a testament to my commitment to staying ahead in the AI landscape. If you're looking to amplify your AI expertise or simply want to learn more about InstructLab, I highly recommend checking out this course! #AI #InstructLab #MachineLearning #DigitalBadges #IBM

I’m pleased to announce that I’ve completed the 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 certification, building foundational expertise in generative AI and large language models (LLMs) like ChatGPT and Dolly. This program provided an in-depth look at the rapidly expanding applications of generative AI, from content creation and code generation to advancements in customer service, positioning organizations to leverage AI as a key differentiator in the market. 𝗞𝗲𝘆 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗿𝗲𝗮𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝗱: • Overview of generative AI and large language models (LLMs) • Practical applications across industries • Strategies for successful AI adoption • Assessing potential risks and ethical considerations This certification not only expands my understanding of generative AI but also underscores its potential to drive innovation and value in today’s digital landscape. I look forward to applying these insights to future projects and contributing to impactful AI-driven solutions. #GenerativeAI #ArtificialIntelligence #LLMs #MachineLearning #Innovation #DigitalTransformation #ProfessionalDevelopment #Certification

🔧🤖 Integrating Tools with Large Language Models (LLMs): A New Era in AI Functionality 🤖🔧 The fusion of external tools with LLMs is revolutionizing AI capabilities, enabling models to perform complex tasks with enhanced precision. Recent advancements highlight this trend: Function Calling by OpenAI: This feature allows LLMs to execute specific functions, improving their ability to handle intricate queries. Tool Learning Frameworks: Studies like "Tool Learning with Large Language Models: A Survey" provide comprehensive insights into methodologies and challenges in this integration. Collaborative Models: MIT's Co-LLM demonstrates how LLMs can collaborate with specialized models to enhance problem-solving efficiency. These developments prompt us to consider: If LLMs can now utilize external tools, could they eventually design and create their own tools, leading to a new paradigm of AI self-sufficiency? 🧠🔍 This post was generated by my custom-built personal agent, powered by LLMs and designed to operate my computer. If you're curious about how it works, feel free to ask!

Exploring RAFT: The Next Step in AI Model Training Retrieval-Augmented Fine-Tuning (RAFT) emerges as a cutting-edge training strategy from UC Berkeley, designed to boost the performance of Large Language Models (LLMs) for domain-specific question-answering tasks. Background: Traditionally, adapting LLMs to specialised domains involved two main strategies: 1. Retrieval Augmented Generation (RAG) with in-context learning. 2. Supervised Fine-Tuning (SFT). Each strategy has limitations—RAG does not fully capitalise on learning opportunities in stable domain settings and early access to test documents, whereas SFT does not utilise documents during test time. RAFT bridges these gaps, combining robust external knowledge integration with strong reasoning abilities, thus creating a superior model. Implications for Businesses: RAFT enhances business AI by improving explainability—a crucial factor for trust and decision-making. The model's chain-of-thought response style not only increases accuracy in domain-specific tasks but also offers transparency about the 'why' and 'how' of its decisions. This clarity is vital for maintaining trust and accountability, especially when AI decisions significantly impact lives. Companies like Klarna and Octopus Energy, which have integrated AI into their customer services, underline the importance of clear, explainable AI interactions. As AI's role in our daily interactions grows, so does the necessity for transparency in how decisions are made. How RAFT Works: Using the open-book exam analogy, RAFT's methodology can be explained as follows: 1. Closed-Book Exam: Supervised Fine-Tuning simulates "studying" where LLMs respond based on pre-trained knowledge and fine-tuning. 2. Open-Book Exam: Traditional RAG systems resemble taking an exam with the ability to access external information, heavily relying on the performance of the information retriever. 3. Domain-Specific Open-Book Exam: RAFT trains models to discern relevant from irrelevant information in retrieved documents, akin to preparing for a domain-specific open-book exam where the LLMs know the domain (like enterprise documents, the latest news, or organisational code repositories) beforehand. This innovative approach might offer the hope of offering the blend of reliability, reasonability, and transparency necessary for today's fast-paced, AI-driven world. You can read the paper here: https://lnkd.in/eA9yvB4R #ai #llm #RAG #Productmanagement

🚀 Just completed the Coursera course "Fine-tuning Language Models for Business Tasks"! 🚀 This course was a deep dive into the world of Large Language Models (LLMs) and their critical role in modern business. I learned about the foundational concepts behind LLMs, their current applications across various industries, and how fine-tuning these models can create more efficient, personalized, and innovative business solutions. The real-life examples provided a clear view of how businesses can leverage this technology to stay competitive in a rapidly evolving AI landscape. Excited to apply these insights to drive AI-powered innovation! 💡 #AI #MachineLearning #LLM #BusinessInnovation #Coursera

How to Create your own GPT model… "Workshop Success! I'm thrilled to share that our two-day workshop on Generative AI was a huge success! We dove deep into the world of Large Language Models (LLMs) and explored the deployment of not one, but TWO LLM models using Google Gemini. Our participants learned how to deploy: 1. Text-to-Text LLM Model 2. Image-to-Text LLM Model And, we took it to the next level by covering end-to-end deployment for both models! The energy was electric, and the interactions were insightful. It was amazing to see participants from diverse backgrounds coming together to learn and share their experiences. Special thanks to our speakers and participants for making this workshop a memorable one! Some highlights from the workshop: - Interactive sessions with hands-on exercises - Case studies and real-world applications - Networking opportunities with industry professionals - Insights from experts in the field of Generative AI If you're interested in learning more about Generative AI and LLMs, feel free to reach out to me. Let's continue the conversation! #GenerativeAI #LLM #GoogleGemini #Workshop #Success #AICommunity #Learning #Innovation #Technology #FutureOfAI",#eShiksha,#IDEMI,#GenAI For END TO END DEPLOYMENT FOLLOW ON https://lnkd.in/d7wrKZWj My updated play list on Generative AI… https://lnkd.in/dmwSDEZn

Few-Shot Prompting is revolutionizing the capabilities of large-language models by enabling more precise in-context learning through examples inserted directly into the prompt. This method significantly enhances the model's performance, especially when tackling complex tasks that typically fall beyond the scope of traditional zero-shot methods. The essence of few-shot prompting lies in its ability to condition the model using specific examples that guide its output predictions. For instance, by providing one or more exemplars demonstrating correct usage or behavior, the model learns to replicate similar outputs under analogous circumstances. This was vividly illustrated in research where a model, after seeing a single instance of a word used in a sentence, could adeptly apply a new word in the correct context. Further studies underscore the importance of the chosen exemplars in few-shot settings — both their format and their alignment with the true distribution of labels impact the model's performance significantly. Even random labels, if formatted consistently, proved beneficial over having no labels at all. However, few-shot prompting is not without limitations. It struggles with more intricately reasoned tasks, where even multiple examples may not suffice. This starkly highlights the need for advanced prompt engineering or potentially adopting newer techniques like chain-of-thought prompting, which have been shown to handle complex reasoning tasks far more effectively. For professionals interested in deepening their understanding of prompt engineering and leveraging few-shot prompting in practical applications, exploring dedicated courses can be extremely beneficial. This approach fosters a hands-on learning environment tailored to the evolving dynamics of AI and

Recent breakthroughs in LLMs are not solely attributed to their massive size and extensive pretraining. A pivotal advancement has been Reinforcement Learning from Human Feedback (RLHF), a method that fine-tunes these models to better align with human values and understand our intentions. RLHF works by incorporating human feedback directly into the training loop of AI models. It integrates human feedback through a structured process: 1) Pre-training: The model learns from extensive text data to grasp language patterns. 2) Reward Model Training: Human reviewers evaluate the model's outputs, and their feedback trains a reward model that quantifies alignment with human preferences. 3) Fine-Tuning with Reinforcement Learning: The model undergoes further training, guided by the reward model, to generate responses that align with human expectations. This process bridges the gap between raw computational power and nuanced human understanding, highlighting a universal truth in AI: quality data is everything. The effectiveness of RLHF hinges on high-quality, curated datasets. Organizations investing in data quality and structured human feedback are turning AI into a true competitive advantage. Remarkably, RLHF requires relatively few human-labeled examples to achieve significant performance boosts. For instance, fine-tuning the InstructGPT model utilized only tens of thousands of prompts, orders of magnitude smaller than the data required for pre-training large foundation models, which involve hundreds of billions of tokens.  If resources for human labelers are limited, modern approaches like Reinforcement Learning from AI Feedback leverage AI-generated feedback to achieve comparable results with reduced costs. RLHF isn't exclusive to big tech. Companies of all sizes can leverage open-source tools and methodologies to train custom models tailored to their unique needs. For example, consider a healthcare organization aiming to keep up with evolving medical regulations. By applying RLHF, they can fine-tune an AI system with input from compliance officers, enabling the model to interpret complex legal jargon accurately and flag potential issues, thereby mitigating legal risks and enhancing operational efficiency. Similarly, Government agencies can train AI assistants with feedback from experienced staff to understand intricate policies, improving citizen satisfaction and freeing up staff for more complex tasks. AI's success isn’t just about scale; it’s about alignment. RLHF offers a practical blueprint for building AI systems that truly understand and serve us. I'm working on a tutorial article with a code walkthrough highlighting real-world applications and development of RLHF, which I hope to share soon! In the meantime, here's a great article that delves deeper into the methodologies behind this: If you're curious about leveraging RLHF for custom-trained models in your organization, don't hesitate to reach out!