- WKND AI
- Posts
- How does ChatGPT work?
Remember the first time you chatted with ChatGPT? Maybe you were solving traveling at the speed of light or meal planning your grocery list. Either way it made the impossible feel possible.
But have you ever paused to wonder, "How does ChatGPT work?".
It’s like a black box. Some even liken it to Pandora's box - after all, sci-fi movies have been warning us about artificial intelligence for decades!
Many of us, from newcomers to seasoned enthusiasts, aren't quite sure what's happening under the hood.
So today, we're not just peeking inside the black box. No, we're blowing the lid off!
We'll untangle the complex threads that stitch together ChatGPT. From learning about "tokenization" and "fine-tuning," to the vast infrastructure that powers the supercomputer under the hood, we'll uncover it all.
Before you dive in, here are some definitions of tech jargon you’ll likely encounter:
Tokenization: Breaking up sentences into words, like "I love AI" becomes "I", "love", "AI".
Fine-tuning: Tailoring an AI's general knowledge to a specific task, like a chef specializing in Italian cuisine for a night.
Reinforcement Learning from Human Feedback (RLHF): Like teaching a pet a trick, guiding AI towards correct behavior with rewards.
Proximal Policy Optimization (PPO): A rule for AI to learn gradually, like learning to ride a bike step by step.
Graphics Processing Units (GPUs): Super-powered computer engines for processing lots of information quickly.
Also, if you’re in a rush and want to put ChatGPT into practice right now, check out some of the prompts I use myself to beat procrastination and improve productivity below!
Pro tip: Watch the YouTube videos on 1.5-2.0x playback speed.
🚀💡 Want to boost your productivity with ChatGPT? Looking for the best prompts? 🤖📚
Zain Khan shares the 10 best ChatGPT prompts to increase your productivity. These prompts cover a wide range of productivity-enhancing topics, from prioritizing daily tasks to setting goals for learning new skills. Zain's post is a treasure trove of practical applications for AI in your daily life.🌟🛠️
Use the prompt "Help me to prioritize my tasks for today" to organize your daily tasks. 📚🧠
Try "Create an email template for me to delegate tasks to my team" to delegate tasks effectively. 📝💡
Don't forget to follow Zain Khan on LinkedIn for more insightful content! 🎥👍
🤖💡 Curious about the technical workings of ChatGPT? 🧠🔍
Sahn Lam of ByteByteGo gives a deep dive into the technical architecture of ChatGPT. The video explains how ChatGPT uses Large Language Models (LLMs) like GPT-3.5, trained on massive amounts of text data, to understand and generate human language.
It also discusses the concept of "prompt engineering" and the process of Reinforcement Learning from Human Feedback (RLHF) to fine-tune the model.
The video further explains how the model is used in ChatGPT to answer prompts, including context awareness and primary prompt engineering. 🚀📘
Understand the concept of Large Language Models (LLMs) and how they're used in ChatGPT.
Learn about "prompt engineering" and the process of Reinforcement Training from Human Feedback (RLHF) for fine-tuning AI models.
Don't forget to follow ByteByteGo on YouTube for more technical insights into AI! 🎥👍
🤖💡 Ever wondered what powers ChatGPT? 🧠🔍
Microsoft Mechanics offers an inside look at the AI supercomputer infrastructure that runs ChatGPT and other large language models.
The video explains how Microsoft collaborated with NVIDIA to deliver purpose-built AI infrastructure. It also discusses how Azure's AI supercomputer infrastructure is used for various workloads, including self-driving cars by UK-based company, Wayve.
The video further explains how Confidential Computing works with Azure AI to combine datasets without sharing personally identifiable information for secure multiparty collaborations. 🚀📘
Understand how Azure's AI supercomputer infrastructure can be leveraged for various workloads.
Learn about Confidential Computing and how it works with Azure AI for secure multiparty collaborations.
Don't forget to follow Microsoft Mechanics on YouTube for more insights into AI and technology! 🎥👍
🚀💡 Want to understand the tech behind ChatGPT? 🧠🔍
Till Musshoff breaks down the technical workings of ChatGPT. He explains how ChatGPT uses Large Language Models (LLMs) like GPT-3.5, trained on vast amounts of text data, to understand and generate human language.
He also discusses the concept of "prompt engineering" and the process of Reinforcement Training from Human Feedback (RLHF) to fine-tune the model.
The video further explains how the model is used in ChatGPT to answer prompts, including context awareness and primary prompt engineering. 🚀📘
Understand the concept of Large Language Models (LLMs) and how they're used in ChatGPT.
Learn about "prompt engineering" and the process of Reinforcement Training from Human Feedback (RLHF) for fine-tuning AI models.
Don't forget to follow Till Musshoff on YouTube for more technical insights into AI! 🎥👍
🤖💡 Curious about the magic behind Generative AI models? 🧠🔍
IBM’s Kate Soule provides a comprehensive overview of Generative AI models, specifically large language models (LLMs) like ChatGPT. She explains how these models are trained on vast amounts of text data to understand and generate human language.
The video also covers the concept of "prompt engineering" and the process of Reinforcement Training from Human Feedback (RLHF) to fine-tune the model. The video is a great resource for understanding the technical workings of AI models like ChatGPT. 🚀📘
Generative AI models can drastically outperform traditional models as they are trained on vast amounts of data. 📚🧠
Despite their advantages, these models can be expensive to train and run, and there can be trustworthiness issues due to the vast, unvetted data they are trained on. 📝💡
Don't forget to follow IBM Technology on YouTube for more insights into AI! 🎥👍
🤖💡 Ever wondered how AI can go hilariously wrong? Want to understand the "Black Box" problem? 📦🔍
John Oliver takes a deep dive into the world of AI, highlighting its potential and pitfalls. From AI's 'hallucinating' false information to biases in hiring algorithms, he uncovers the need for transparency and regulation in AI systems.
He also discusses the EU's approach to AI regulation, categorizing AI systems based on risk and subjecting high-risk systems to strict obligations. A must-watch for anyone interested in the ethical implications of AI! 🌐🚦
Be aware of the 'Black Box' problem in AI. It's crucial to understand how an AI system arrives at its results. 📦🔍
Advocate for transparency and regulation in AI systems to prevent biases and ensure fairness. 🏛️⚖️
Don't forget to follow Last Week Tonight on YouTube for more insightful content! 🎥👍
Thank you for subscribing! Your feedback is valuable to us.
Please reply to this email and let us know what you liked or didn't like about the newsletter.
If you enjoyed our content, tell a friend and Subscribe Here.
