MIRI
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MIRI’s mission is to ensure that the creation of smarter-than-human intelligence has a positive impact. We aim to make advanced intelligent systems behave as we intend even in the absence of immediate human supervision.
MIRI focuses on AI approaches that can be made transparent (e.g., precisely specified decision algorithms, not genetic algorithms), so that humans can understand why AI systems behave as they do. For safety purposes, a mathematical equation defining general intelligence is more desirable than an impressive but poorly-understood code kludge.
Much of our research is therefore aimed at putting theoretical foundations under AI robustness work. We consider settings where traditional decision and probability theory frequently break down: settings where computation is expensive, there is no sharp agent/environment boundary, multiple agents exist, or self-referential reasoning is admitted.
Using training data to teach advanced AI systems what we value looks more promising than trying to code in everything we care about by hand. However, we know very little about how to discern when training data is unrepresentative of the agent’s future environment, or how to ensure that the agent not only learns about our values but accepts them as its own.
Additionally, rational agents pursuing some goal have an incentive to protect their goal-content. No matter what their current goal is, it will very likely be better served if the agent continues to promote it than if the agent changes goals. This suggests that it may be difficult to improve an agent’s alignment with human interests over time, particularly when the agent is smart enough to model and adapt to its programmers’ goals. Making value learning systems error-tolerant is likely to be necessary for safe online learning.
In addition to our mathematical research, MIRI investigates important strategic questions. What can (and can’t) we predict about the future of AI? How can we improve our forecasting ability? Which interventions available today appear to be the most beneficial, given what little we do know?
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