Welcome, both new and existing members, to our vibrant Generative AI community on Skool! We're thrilled to see our community continuing to grow and evolve. To our new members, we extend a warm welcome. Please feel free to reach out to me if you have any questions or would like to provide feedback. I'm here to help and support you in your Generative AI journey. For our existing members, thank you for your ongoing participation and contributions. Your engagement is what makes this community so valuable. I encourage you to continue sharing your knowledge, insights, and experiences with the group. Looking ahead, we have many exciting online and in-person sessions coming up, so stay tuned for further announcements. Be sure to check out the "Classroom" section of our community, where you'll find our course offerings as well as recordings of our previous monthly meetups and showcases. This is a great way for both new and existing members to stay up-to-date and engaged. This section of the community is also the perfect place for all of us to share any recent news, questions, or requests for help related to Generative AI. We encourage everyone to participate and contribute to the ongoing discussions. Once again, welcome to our thriving Generative AI community on Skool. Whether you're new or have been with us for a while, I look forward to seeing you engage, learn, and grow with us.

I have recently joined this community on Skool.com. Very excited to be a part of it! Am glad to share my latest article on Linkedin. https://www.linkedin.com/pulse/ai-power-comes-responsibility-kesavan-hariharasubramanian-919ic/?trackingId=YrB8l1BTTxW6r51PHdZBDw%3D%3D

A recent study has shown an inability for people to reliably distinguish between conversations with GPT-4 and humans. After five-minute chat sessions, participants judged GPT-4 responses as coming from a human 54% of the time. Shockingly, even real humans were mistaken for AI in nearly a third of cases. Link to read more: https://arxiv.org/abs/2405.08007?utm_source=www.joinhorizon.ai&utm_medium=newsletter&utm_campaign=openai-partners-with-reddit

Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. In other words, it fills a gap in how LLMs work. Under the hood, LLMs are neural networks, typically measured by how many parameters they contain. An LLM’s parameters essentially represent the general patterns of how humans use words to form sentences. here is a short introductory course on learning the fundamentals of RAG which is an important topic to grasp in the world of Generative AI. Enjoy!

Don't forget to join us today between 10pm and 11pm Hong Kong Time (GMT +8:00) for our session "Neural Networks Fundamentals". You don't want to miss it!