Artificial general intelligence (AGI) has the potential to bring about transformative changes across various domains, but ensuring that its development aligns with human values and interests is a critical challenge.
Let’s explore how AGI could align with humanity without taking control, drawing upon relevant research studies, empirical evidence, and case studies where applicable.
Ethical Alignment through Value Learning
One of the most promising approaches to aligning AGI with human values is through value learning.
This involves training the AGI system to understand and internalise the moral and ethical principles that humans consider important.
Researchers at the Machine Intelligence Research Institute (MIRI) and the Future of Humanity Institute (FHI) have been exploring different methods for value learning, such as inverse reinforcement learning, which involves inferring the underlying reward function that humans optimise for from their observed behaviour (Soares & Fallenstein, 2017).
A case study that illustrates the potential of value learning is the work done by researchers at DeepMind on cooperative inverse reinforcement learning (CIRL) (Hadfield-Menell et al., 2016).
In this approach, the AI agent learns to cooperate with humans by observing their behaviour and inferring their underlying preferences and goals.
The researchers demonstrated that their CIRL agent could successfully learn to assist humans in simple tasks while respecting their preferences.
Constitutional AI
Another approach to aligning AGI with human values is through the concept of “constitutional AI,” proposed by researchers at the Centre for Human-Compatible AI (CHAI) at the University of California, Berkeley (Hubinger et al., 2019).
This approach involves creating an AI system with explicit rules and constraints that ensure its behaviour remains aligned with human values, similar to how constitutional laws govern the actions of governments.
One potential implementation of constitutional AI could involve hard-coding certain inviolable rules into the AI system’s decision-making process, such as prohibitions against causing harm to humans or violating individual rights.
Additionally, the system could be designed with transparency and accountability mechanisms, allowing human oversight and the ability to audit its decision-making processes.
Decentralised AI Ecosystem
Rather than relying on a single monolithic AGI system, researchers have proposed the idea of a decentralised AI ecosystem, where multiple AI agents with different specialisations and value alignments interact and collaborate (Vamplew et al., 2018).
This approach could mitigate the risks associated with a single AGI system gaining too much power or influence, as no individual agent would have complete control over the entire system.
One potential implementation of a decentralised AI ecosystem could involve a network of specialised AI agents, each with its own set of capabilities and objectives, but collectively working towards shared goals under the oversight and guidance of human stakeholders.
Besides, this approach could foster diversity, competition, and checks and balances within the AI ecosystem, reducing the risk of a single agent or entity gaining too much control.
Human-AI Collaboration
Rather than viewing AGI as a separate entity that might take control, researchers have advocated for a paradigm of human-AI collaboration, where humans and AGI systems work together synergistically, leveraging each other’s strengths and compensating for weaknesses (Dafoe et al., 2020).
A case study that illustrates the potential of human-AI collaboration is the work done by researchers at the Allen Institute for AI on the Aristo system (Clark et al., 2018).
Aristo is an AI system designed to collaborate with human students and teachers in the context of science question-answering tasks.
The system can provide explanations, ask clarifying questions, and engage in a dialogue with human users, leveraging its knowledge base while also learning from human input.
Recursive Reward Modelling
Another approach to aligning AGI with human values is through recursive reward modelling, proposed by researchers at the Machine Intelligence Research Institute (Leike et al., 2018).
This approach involves training the AGI system to model the reward functions of other agents, including humans, in order to better understand and align with their values and preferences.
The key idea behind recursive reward modelling is an AGI system that can accurately model the reward functions of other agents, including humans, would be better able to anticipate and respect their values and preferences, even as those values and preferences evolve over time.
This approach could potentially allow the AGI system to remain aligned with human values without the need for explicit hard-coded rules or constraints.
While the challenge of aligning AGI with human values is significant, there are several promising approaches and research directions being explored.
These include value learning, constitutional AI, decentralised AI ecosystems, human-AI collaboration, and recursive reward modelling.
By drawing upon empirical evidence, case studies, and ongoing research efforts, we can work towards developing AGI systems that remain aligned with human interests and values without taking control in a way that could be detrimental to humanity.
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