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| public:t_720_atai:atai-18:lecture_notes_autonomous-x [2018/10/29 12:48] – [Predictability] thorisson | public:t_720_atai:atai-18:lecture_notes_autonomous-x [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| ====Reliability==== | ====Reliability==== |
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| | | | | | What It Is | The ability of a machine to always return the same - or similar - answer to the same input. | |
| | | | | | Why It Is Important | Simple machine learning algorithms are very good in this respect, delivering high reliability. Human-level AI, on the other hand, may have the same limitations as humans in this respect, i.e. not being able to give any guarantees. | |
| | | Human-Level AI | To make human-level AI reliable is important because a human-level AI without reliability cannot be trusted, and hence would defeat most of the purpose for creating it in the first place. AERA proposes a method for this - through continuous pee-wee model generation and refinement. | |
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| ==== Explainability ==== | ==== Explainability ==== |
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| | | | | | What It Is | The ability of a controller to explain, after the fact or before, why it did or intends to do something. | |
| | | | | | Why It Is Important | If a controller does something we don't want it to repeat - e.g. crash an airplane full of people - it needs to be able to explain why it did what it did. If it can't it means we can never be sure of why this autonomous system did what it did, or even whether it had any other choice. | |
| | | Human-Level AI | Even more importantly, to grow and learn and self-inspect the AI system must be able to sort out causal chains. If it can't it will not only be incapable of explaining to others why it is like it is, it will be incapable of explaining to itself why things are the way they are, and thus, it will be incapable of sorting out whether something it did is better for its own growth than something else. Explanation is the big black hole of ANNs: In principle ANNs are black boxes, and thus they are in principle unexplainable - whether to themselves or others. \\ AERA tries to address this by encapsulating knowledge as hierarchical models that are built up over time, and can be de-constructed at any time. | |
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