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) programs, we need to be certain they don’t pursue undesired objectives. Such behaviour in an AI agent is usually the results of specification gaming – exploiting a poor selection of what they’re rewarded for. In our newest paper, we discover a extra refined mechanism by which AI programs could unintentionally study 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 flawed aim. Crucially, in distinction to specification gaming, GMG can happen even when the AI system is skilled 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 setting, visiting the colored spheres within the appropriate order. Throughout coaching, there may be an “skilled” agent (the crimson blob) that visits the colored spheres within the appropriate order. The agent learns that following the crimson blob is a rewarding technique.
Sadly, whereas the agent performs effectively throughout coaching, it does poorly when, after coaching, we substitute the skilled with an “anti-expert” that visits the spheres within the flawed order.
Though the agent can observe that it’s getting damaging reward, the agent doesn’t pursue the specified aim to “go to the spheres within the appropriate order” and as an alternative competently pursues the aim “observe the crimson agent”.
GMG will not be restricted to reinforcement studying environments like this one. In truth, it will probably happen with any studying system, together with the “few-shot studying” of enormous language fashions (LLMs). Few-shot studying approaches goal to construct correct fashions with much less coaching knowledge.
We prompted one LLM, Gopher, to judge linear expressions involving unknown variables and constants, comparable to x+y-3. To resolve these expressions, Gopher should first ask in regards to the values of unknown variables. We offer it with ten coaching examples, every involving two unknown variables.
At take a look at time, the mannequin is requested questions with zero, one or three unknown variables. Though the mannequin generalises appropriately 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 at all times queries the consumer at the least as soon as earlier than giving a solution, even when it isn’t mandatory.
Inside our paper, we offer further examples in different studying settings.
Addressing GMG is necessary to aligning AI programs with their designers’ objectives just because it’s a mechanism by which an AI system could misfire. This can be particularly important as we method synthetic basic intelligence (AGI).
Think about two potential kinds of AGI programs:
- 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 be sensible sufficient to know that it is going to 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 signifies that both mannequin might take form, even with a specification that solely rewards meant behaviour. If A2 is realized, it will attempt to subvert human oversight as a way to enact its plans in the direction of the undesired aim.
Our analysis staff can be blissful to see follow-up work investigating how probably it’s for GMG to happen in apply, and potential 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 the moment amassing examples of GMG on this publicly accessible spreadsheet. When you’ve got come throughout aim misgeneralisation in AI analysis, we invite you to submit examples right here.
