Artificial intelligence is moving faster than ever, bringing with it some exciting new ways of thinking that could change everything we know about its capabilities. Right now, there’s a lot of buzz around reasoning models that use inference-time computing, which could really shake things up for AI. With Artificial General Intelligence (AGI) often in the spotlight, industry leaders like OpenAI’s CEO Sam Altman are optimistic about how AI could boost economic growth and even help cure diseases. Meanwhile, Anthropic’s CEO Dario Amodei believes AI might soon outperform human abilities. But here’s the catch: today’s AI can’t learn continuously.
Currently, AI models go through two main phases—training and inference. During training, they learn from data, and in the inference phase, they apply that knowledge. But once training’s over, the learning stops. They can’t adapt or learn new things without a full retraining. This is a far cry from how we humans learn, which is a constant, ever-changing process.
The AI community has long been trying to close this gap with ideas like continual learning, lifelong learning, and online learning. These methods aim to help AI adapt to new information, similar to how we do. However, there are still hurdles, like catastrophic forgetting, where new information wipes out what was previously learned. Despite these challenges, progress is happening. Startups and research labs are coming up with creative solutions to make AI learn continually.
Some of the exciting developments include model fine-tuning, retrieval-augmented generation, and in-context learning. These techniques offer some workarounds, but they don’t quite achieve true dynamic, real-time learning. The idea of continual learning is promising, especially for crafting personalized AI experiences. Imagine AI that adjusts to you personally, becoming more intuitive and indispensable. This could give businesses a huge edge. Companies like Writer and Sakana AI are leading the charge in this area. Writer’s self-evolving models and Sakana’s Transformer² approach are pushing the boundaries of what’s possible.
As AI continues to advance, the quest for continual learning is a crucial frontier. Overcoming this challenge could revolutionize AI, allowing it to adapt and improve continuously, much like human intelligence. This shift could unlock new capabilities and change the AI landscape, making it more powerful and adaptable than ever. We’re on this journey now, and the potential impact is huge.
