The Rise of Language Models: A New Era Begins
Reflecting on how large language models are beginning to change our understanding of AI capabilities.
As we enter 2022, it's becoming clear that large language models (LLMs) are not just a research curiosity—they're becoming practical tools that can assist with real work.
What Makes LLMs Different
Unlike previous AI systems that were narrow and task-specific, language models trained on vast amounts of text exhibit something remarkable: they can generalize across many different tasks without being explicitly programmed for each one.
This is a fundamental shift. We're moving from:
- Narrow AI: Systems that do one thing well (chess, image recognition)
- General-purpose models: Systems that can adapt to many tasks through natural language
Practical Implications
I've started experimenting with using these models for:
- Code assistance - They can explain code, suggest improvements, and even write simple functions
- Writing drafts - First drafts of emails, documentation, blog posts
- Research synthesis - Summarizing papers and extracting key insights
The Limitations Are Real
Let's be honest about what these models can't do well:
- They hallucinate confidently incorrect information
- They lack true understanding of the physical world
- They can't learn from a conversation (no persistent memory)
Looking Ahead
I believe we're at the beginning of something significant. The question isn't whether AI will change how we work—it's how we'll adapt our workflows to leverage these new capabilities while mitigating the risks.
More thoughts to come as I continue experimenting.