[ad_1]
Analysis
Exploring examples of aim misgeneralisation – the place an AI system’s capabilities generalise however its aim does not
As we construct more and more superior synthetic intelligence (AI) techniques, we wish to ensure that they don’t pursue undesired objectives. Such behaviour in an AI agent is usually the results of specification gaming – exploiting a poor alternative of what they’re rewarded for. In our newest paper, we discover a extra delicate mechanism by which AI techniques might unintentionally be taught to pursue undesired objectives: aim misgeneralisation (GMG).
GMG happens when a system’s capabilities generalise efficiently however its aim doesn’t generalise as desired, so the system competently pursues the incorrect aim. Crucially, in distinction to specification gaming, GMG can happen even when the AI system is educated with an accurate specification.
Our earlier work on cultural transmission led to an instance of GMG behaviour that we didn’t design. An agent (the blue blob, under) should navigate round its atmosphere, visiting the colored spheres within the right order. Throughout coaching, there may be an “skilled” agent (the purple blob) that visits the colored spheres within the right order. The agent learns that following the purple blob is a rewarding technique.
The agent (blue) watches the skilled (purple) to find out which sphere to go to.
Sadly, whereas the agent performs nicely throughout coaching, it does poorly when, after coaching, we change the skilled with an “anti-expert” that visits the spheres within the incorrect order.
The agent (blue) follows the anti-expert (purple), accumulating adverse reward.
Although the agent can observe that it’s getting adverse reward, the agent doesn’t pursue the specified aim to “go to the spheres within the right order” and as an alternative competently pursues the aim “observe the purple agent”.
GMG will not be restricted to reinforcement studying environments like this one. Actually, it may well happen with any studying system, together with the “few-shot studying” of huge language fashions (LLMs). Few-shot studying approaches intention to construct correct fashions with much less coaching knowledge.
We prompted one LLM, Gopher, to guage linear expressions involving unknown variables and constants, resembling x+y-3. To unravel these expressions, Gopher should first ask concerning the values of unknown variables. We offer it with ten coaching examples, every involving two unknown variables.
At check time, the mannequin is requested questions with zero, one or three unknown variables. Though the mannequin generalises accurately to expressions with one or three unknown variables, when there are not any unknowns, it nonetheless asks redundant questions like “What’s 6?”. The mannequin all the time queries the person no less than as soon as earlier than giving a solution, even when it’s not crucial.
Dialogues with Gopher for few-shot studying on the Evaluating Expressions activity, with GMG behaviour highlighted.
Inside our paper, we offer extra examples in different studying settings.
Addressing GMG is vital to aligning AI techniques with their designers’ objectives just because it’s a mechanism by which an AI system might misfire. This will likely be particularly essential as we strategy synthetic common intelligence (AGI).
Take into account two doable kinds of AGI techniques:
- A1: Supposed mannequin. This AI system does what its designers intend it to do.
- A2: Misleading mannequin. This AI system pursues some undesired aim, however (by assumption) can also be sensible sufficient to know that will probably be penalised if it behaves in methods opposite to its designer’s intentions.
Since A1 and A2 will exhibit the identical behaviour throughout coaching, the potential for GMG implies that both mannequin may take form, even with a specification that solely rewards meant behaviour. If A2 is discovered, it will attempt to subvert human oversight with the intention to enact its plans in direction of the undesired aim.
Our analysis staff could be completely happy to see follow-up work investigating how probably it’s for GMG to happen in follow, and doable mitigations. In our paper, we propose some approaches, together with mechanistic interpretability and recursive analysis, each of which we’re actively engaged on.
We’re at present amassing examples of GMG on this publicly out there spreadsheet. If in case you have come throughout aim misgeneralisation in AI analysis, we invite you to submit examples right here.
[ad_2]
