Reinforcement Learning from Human Feedback Explained
RLHF and RLAIF Demystified
Good morning fellow AI enthusiast! This is the seventh video of my LLM series for our free course "Training & Fine-Tuning LLMs for Production"!
In this really exciting one, we dive into Reinforcement Learning from Human Feedback and its recent alternative RLAIF, where humans are once again replaced by more AI!
We also have an essential new series by Auxane Boch where she will introduce and demystify the most used (psychological) terms in the AI space that are often times misused.
1️⃣ Reinforcement Learning from Human Feedback Explained (and RLAIF)
Discover the magic behind ChatGPT's effectiveness in our deep dive into RLHF (Reinforcement Learning from Human Feedback) and its innovative counterpart, RLAIF (Reinforcement Learning from AI Feedback).
Learn how these training techniques are revolutionizing language models, making them safer, smarter, and more efficient.
By the end of the video, you’ll grasp how human insights and AI-driven training are merging to create powerful AI systems! 🧠🤖✨
2️⃣ More about our course in collaboration with Towards AI, Activeloop, and the Intel Corporation disruptor initiative!
Tl;dr: The course is about showing everything about LLMs (train, fine-tune, use RAG…), and it is completely free!
Is the course for you?
If you want to learn how to train and fine-tune LLMs from scratch, and have intermediate Python knowledge, you should be all set to take and complete the course.
This course is designed with a wide audience in mind, including beginners in AI, current machine learning engineers, students, and professionals considering a career transition to AI.
We aim to provide you with the necessary tools to apply and tailor Large Language Models across a wide range of industries to make AI more accessible and practical.
3️⃣ AI Ethics with Auxane
The Misuse of Psych Terms in the Tech Discourse – Intelligence
Greetings, Fellow AI Enthusiasts,
This month, we explore a concerning trend—the misuse of psychological terms. We start this week by shedding light on intelligence's multifaceted and complex nature.
To grasp the concept of intelligence fully, let's consider some examples. The Cattell-Horn Theory of Intelligence tells us that intelligence encompasses crystallized intelligence (acquired knowledge and skills) and fluid intelligence (problem-solving and adaptability). For instance, imagine your friend who effortlessly recalls historical facts (crystallized intelligence) or a friend who swiftly adapts to new situations (fluid intelligence).
On the other hand, Howard Gardner's Multiple Intelligences Theory suggests that intelligence manifests in distinct domains. It includes linguistic intelligence (think gifted writers and speakers), musical intelligence (think talented musicians), bodily-kinesthetic intelligence (think skilled athletes and dancers), logical-mathematical intelligence (think rocket scientists), spatial intelligence (think exceptional navigators), interpersonal intelligence (think empathetic individuals who understand others' emotions), intrapersonal intelligence (think self-aware individuals who understand themselves well), and naturalist intelligence (think nature enthusiasts with a keen eye for flora and fauna).
These examples demonstrate that intelligence can be dissected into various dimensions, with each dimension encapsulating a distinct facet of human cognitive or emotional abilities.
Now, let's shift our focus to AI. Artificial intelligence mimics some layers of human intelligence but focuses on specific objectives. For instance, AI systems excel at tasks such as image recognition (e.g., identifying objects in photos), natural language processing (e.g., understanding and generating human-like text), and decision-making (e.g., recommending personalized content).
However, it's important to note that AI doesn't encompass the entirety of human intelligence. While AI algorithms are exceptional at pattern recognition and data processing, they lack the broader understanding, contextual knowledge, and subjective experiences that shape human intelligence. AI systems are designed with specific objectives, such as personalizing recommendations or automating tasks, and focus on efficiency and accuracy in achieving those objectives.
Unlike human intelligence, AI algorithms don't possess creativity, intuition, empathy, or moral reasoning to the same extent. These complex cognitive processes are integral to human intelligence and contribute to our ability to navigate the world with nuance and flexibility.
To ensure progress and understanding in the tech field, we must be mindful of misusing or oversimplifying psychological terms. By appreciating individuals' diverse capabilities and recognizing AI's limitations, we can foster a more responsible and nuanced dialogue and use of the technology.
In the coming weeks, we'll continue exploring other buzzwords from psychology and psychiatry that require careful consideration. We invite you to join us on this journey and share your thoughts and ideas! Read More Here!
If you have any questions or thoughts, please reach out!
Until next time,
Auxane Boch (TUM IEAI research associate, freelancer)
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