The progression of AI language models has been extraordinary, marking significant milestones in the field of artificial intelligence. From GPT-2’s impressive text generation capabilities to the latest o1 model’s advanced reasoning, we’ve witnessed a rapid evolution in AI technology.
GPT-2, released in 2019, set a new standard with its 1.5 billion parameters and ability to generate coherent text. This laid the groundwork for GPT-3, which further improved text generation and demonstrated an impressive ability to perform various language tasks without specific training.
GPT-3.5 and GPT-4 built upon these foundations, introducing more refined instruction-following capabilities and improved contextual understanding. These models excelled at tasks ranging from creative writing to complex problem-solving.
Now, with the introduction of o1 (also known as Strawberry), we’re seeing a shift towards more advanced reasoning capabilities. This model uses Chain-of-Thought (CoT) data to enhance its problem-solving abilities, showcasing particular strength in coding and complex computational tasks.
But what’s on the horizon? The future of AI appears to be moving towards long-horizon tasks and agentic action. This means AI systems that can plan and execute over extended periods, and interact with their environment to achieve goals.
Imagine an AI assistant that doesn’t just answer questions, but can autonomously research a topic, compile findings, and even use web tools to create a comprehensive report. Or an AI that can manage a complex project from start to finish, adapting to changes and making decisions along the way.
While we can currently bootstrap agents and long-horizon systems, the real breakthrough will come when these capabilities are trained directly into the models. Just as we moved from bootstrapping chat and instruction capabilities with GPT-3 to having them built-in with GPT-3.5, we’ll likely see a similar progression with agentic AI.
This isn’t speculative technology. The implications for automation and productivity are substantial and tangible.
Multimodal capabilities are certainly impressive and useful features, but they represent enhancements rather than fundamental shifts in AI capabilities. The real advancement will be AI that can reason, plan, and act over extended timeframes.
As an AI consultant, I’m already exploring ways to leverage these emerging capabilities for businesses. If you want to stay ahead of the curve, start thinking now about how long-horizon AI tasks and agentic actions could transform your operations.
For more insights on the current family of OpenAI models, check out my recent blog post. The rapid progress in this field is astounding, and staying informed is crucial.
What are your thoughts on the future of AI? Are you excited about more autonomous AI agents? Let me know in the comments.
