A new paper from Google DeepMind Technologies Ltd., the artificial intelligence research laboratory that is part of Alphabet Inc., this week laid out a comprehensive framework for navigating the risks and responsibilities of developing artificial general intelligence, marking one of the clearest commitments yet from the company on AGI safety.

AGIs are theoretical AI systems that would be capable of performing any intellectual task that a human can, with the ability to generalize knowledge across domains. Differing from existing narrow AI models, which are designed for specific tasks, AGI aims for broad cognitive flexibility, learning and adapting in ways that mirror human reasoning.

Put more simply, AGI is AI that would surpass human capabilities — a system smarter than humans. But although AI development isn’t at that stage yet, many predict that AGI may only be a few years away. When the point is hit, it will offer amazing discoveries but also present serious risks.

Google DeepMind discusses many of those risks in its “An Approach to Technical AGI Safety & Security” paper, which outlines its strategy for the responsible development of AGI.

The paper categorizes the risks of AGI into four primary areas: misuse, misalignment, accidents and structural risks. Misuse of AGI is the concern that such systems could be weaponized or exploited for harmful purposes, while misalignment refers to the difficulty of ensuring these systems consistently act in line with human values and intentions.

Accidents with AGI could involve unintended behaviors or failures that could emerge as systems operate in complex environments, while structural risks in AGI include the potential of the technology to have societal impacts, such as economic disruption or power imbalances.

According to the paper, addressing misalignment will involve ensuring that AGI systems are trained to pursue appropriate goals and accurately follow human instructions. Training of AGI systems should include developing methods for amplified oversight and uncertainty estimation to prepare AGI systems for a wide range of real-world scenarios.

To mitigate these threats, DeepMind is focusing on enhanced oversight, training techniques and tools for estimating uncertainty in AGI outputs. The company is also researching scalable supervision methods, which aim to keep increasingly capable models grounded in human intent even as they grow more autonomous.

The paper stresses the importance of transparency and interpretability in AGI systems. DeepMind says it’s investing heavily in interpretability research to make these systems more understandable and auditable — key steps for aligning them with human norms and ensuring responsible use.

Though the paper does discuss AGI through what Google DeepMind is doing, it notes that no single organization should tackle AGI development alone. The paper argues that collaboration with the broader research community, policymakers and civil society will be essential in shaping a safe AGI future.

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