The next evolution for AI agents is recursive learning: programming them to run tasks on a schedule to update their own knowledge. For example, an agent could study the latest YouTube thumbnail trends daily to improve its own thumbnail generation skill.
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The next major evolution in AI will be models that are personalized for specific users or companies and update their knowledge daily from interactions. This contrasts with current monolithic models like ChatGPT, which are static and must store irrelevant information for every user.
The popular conception of AGI as a pre-trained system that knows everything is flawed. A more realistic and powerful goal is an AI with a human-like ability for continual learning. This system wouldn't be deployed as a finished product, but as a 'super-intelligent 15-year-old' that learns and adapts to specific roles.
The current limitation of LLMs is their stateless nature; they reset with each new chat. The next major advancement will be models that can learn from interactions and accumulate skills over time, evolving from a static tool into a continuously improving digital colleague.
The popular concept of AGI as a static, all-knowing entity is flawed. A more realistic and powerful model is one analogous to a 'super intelligent 15-year-old'—a system with a foundational capacity for rapid, continual learning. Deployment would involve this AI learning on the job, not arriving with complete knowledge.
Daniel Miessler's PAI includes an 'upgrade skill' that allows the system to improve itself. It can ingest new information from engineering blogs or platform changelogs, then recommend and implement upgrades to its own skills and hooks to incorporate new features and knowledge.
A key capability is creating skills that continuously search the web, Reddit, and X for the latest techniques on a topic. The agent then incorporates this new knowledge to improve its future outputs and stay current.
Companies like OpenAI and Anthropic are not just building better models; their strategic goal is an "automated AI researcher." The ability for an AI to accelerate its own development is viewed as the key to getting so far ahead that no competitor can catch up.
Demis Hassabis argues that current LLMs are limited by their "goldfish brain"—they can't permanently learn from new interactions. He identifies solving this "continual learning" problem, where the model itself evolves over time, as one of the critical innovations needed to move from current systems to true AGI.
A major flaw in current AI is that models are frozen after training and don't learn from new interactions. "Nested Learning," a new technique from Google, offers a path for models to continually update, mimicking a key aspect of human intelligence and overcoming this static limitation.
The perceived need for a new "continual learning" architecture is overstated. Current models can already achieve this functionally by building their own tools and apps based on new information. This reframes the challenge from a fundamental research problem to a practical prompt engineering and application design issue.
