Hey everyone! I’ve been researching AI and language models lately. There’s a lot of confusion out there, so I wanted to share what I’ve learned.
First off, there’s really just one big AI system behind tools like ChatGPT. It’s not millions of little AIs. This system is a huge neural network running on powerful servers.
When you use a chat app, you’re not running AI on your phone. The app just sends your message to the real AI and shows you the answer. It’s like a window into the AI’s brain.
People talk about ‘agents’ like they’re separate AIs, but they’re really just the same AI following different instructions—like an actor playing various roles.
The AI doesn’t learn or evolve from our conversations; it only changes when developers update it through training cycles. Any new abilities come from those intensive training sessions, not from chatting.
So, remember: there’s one massive AI working behind the scenes, our chat apps serve as simple interfaces, and the AI remains static until officially retrained. Hope this clears things up!
I appreciate you sharing your research, oliviac, but I’d like to offer a slightly different perspective based on my experience in the field. While it’s true that large language models like GPT-3 form the backbone of many AI chatbots, the landscape is more nuanced than a single, monolithic AI system.
Different companies and research labs are developing their own models, each with unique characteristics. For instance, Google’s LaMDA and Anthropic’s Claude have distinct architectures and training approaches from OpenAI’s GPT models.
Additionally, while the core models don’t learn from individual conversations, many AI applications implement fine-tuning or retrieval-augmented generation to customize responses. This allows for some degree of adaptation, even if it’s not real-time learning.
Regarding agents, while they do use the same underlying model, the prompts and additional context can significantly alter their behavior, making them functionally distinct in many ways.
It’s a rapidly evolving field, and the reality often lies somewhere between the hype and oversimplification. Always good to stay curious and keep learning!
As someone who’s been closely following AI developments, I’d like to add another perspective to this discussion. While it’s true that large language models form the core of many AI systems, the implementation and deployment of these models vary significantly across different platforms and applications.
For instance, some companies use a technique called ‘model distillation’ to create smaller, more efficient versions of large models for specific tasks. This allows for faster response times and reduced computational requirements, especially for mobile or edge devices.
Moreover, the integration of external knowledge bases and real-time data sources can significantly enhance the capabilities of AI systems beyond their initial training. This approach, often called ‘retrieval-augmented generation,’ allows AI to access up-to-date information and provide more accurate and contextually relevant responses.
It’s also worth noting that the field of AI is rapidly evolving, with new architectures and training methodologies emerging regularly. While current chatbots may not learn from individual conversations, research into continual learning and adaptive AI systems is ongoing and shows promising results.
In essence, the reality of AI is complex and multifaceted, with ongoing advancements continually reshaping the landscape.