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.
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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.
Solving key AI weaknesses like continual learning or robust reasoning isn't just a matter of bigger models or more data. Shane Legg argues it requires fundamental algorithmic and architectural changes, such as building new processes for integrating information over time, akin to an episodic memory.
Hassabis argues AGI isn't just about solving existing problems. True AGI must demonstrate the capacity for breakthrough creativity, like Einstein developing a new theory of physics or Picasso creating a new art genre. This sets a much higher bar than current systems.
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.
The current focus on pre-training AI with specific tool fluencies overlooks the crucial need for on-the-job, context-specific learning. Humans excel because they don't need pre-rehearsal for every task. This gap indicates AGI is further away than some believe, as true intelligence requires self-directed, continuous learning in novel environments.
Google DeepMind CEO Demis Hassabis argues that today's large models are insufficient for AGI. He believes progress requires reintroducing algorithmic techniques from systems like AlphaGo, specifically planning and search, to enable more robust reasoning and problem-solving capabilities beyond simple pattern matching.
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.
A key gap between AI and human intelligence is the lack of experiential learning. Unlike a human who improves on a job over time, an LLM is stateless. It doesn't truly learn from interactions; it's the same static model for every user, which is a major barrier to AGI.
