Venture capitalists are increasingly being pitched by AI startups claiming to have solved "continual learning." However, many of these are simply using clever workarounds, like giving a model a 'scratch pad' to reference new data, rather than building models that can fundamentally learn and update themselves in real-time.

Many AI projects fail to reach production because of reliability issues. The vision for continual learning is to deploy agents that are 'good enough,' then use RL to correct behavior based on real-world errors, much like training a human. This solves the final-mile reliability problem and could unlock a vast market.

A major security flaw in AI agents is 'prompt injection.' If an AI accesses external data (e.g., a blog post), a malicious actor can embed hidden commands in that data, tricking the AI into executing them. There is currently no robust defense against this.

A novel safety technique, 'machine unlearning,' goes beyond simple refusal prompts by training a model to actively 'forget' or suppress knowledge on illicit topics. When encountering these topics, the model's internal representations are fuzzed, effectively making it 'stupid' on command for specific domains.

A core pillar of modern cybersecurity, anomaly detection, fails when applied to AI agents. These systems lack a stable behavioral baseline, making it nearly impossible to distinguish between a harmless emergent behavior and a genuine threat. This requires entirely new detection paradigms.

Unlike traditional software where a bug can be patched with high certainty, fixing a vulnerability in an AI system is unreliable. The underlying problem often persists because the AI's neural network—its 'brain'—remains susceptible to being tricked in novel ways.

Demis Hassabis identifies a key obstacle for AGI. Unlike in math or games where answers can be verified, the messy real world lacks clear success metrics. This makes it difficult for AI systems to use self-improvement loops, limiting their ability to learn and adapt outside of highly structured domains.

Training Large Language Models to ignore malicious 'prompt injections' is an unreliable security strategy. Because AI is inherently stochastic, a command ignored 1,000 times might be executed on the 1,001st attempt due to a random 'dice roll.' This is a sufficient success rate for persistent hackers.

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.