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| public:t_720_atai:atai-18:lecture_notes_w5 [2018/09/11 09:04] – [Requirements For AGI Systems] thorisson | public:t_720_atai:atai-18:lecture_notes_w5 [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| | Goal Diversity: \\ Breadth of goals | If a system X can meet a wider range of goals than system Y, system X is more //powerful// than system Y. | | | Goal Diversity: \\ Breadth of goals | If a system X can meet a wider range of goals than system Y, system X is more //powerful// than system Y. | |
| | Generality | Any system X that exceeds system Y on one or more of the above we say it's more //general// than system Y, but typically pushing for increased generality means pushing on all of the above dimensions. | | | Generality | Any system X that exceeds system Y on one or more of the above we say it's more //general// than system Y, but typically pushing for increased generality means pushing on all of the above dimensions. | |
| | General intelligence... | ...means less is needed to be known up front when the system is created, the system knows how to handle itself. | | | General intelligence... | ...means less is needed to be known up front when the system is created, the system can learn to figure things out and how to handle itself, in light of **LTE**. | |
| | And yet: \\ The hallmark of an AGI | A system that can handle novel or **brand-new** //open problems//. The level of difficulty of the problems it solves would indicate its generality. | | | And yet: \\ The hallmark of an AGI | A system that can handle novel or **brand-new** //open problems//. The level of difficulty of the problems it solves would indicate its generality. | |
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| ^Key^What it Means^Why it's Important^ | ^Key^What it Means^Why it's Important^ |
| | Mission | R1. The system must fulfill its mission – the goals and constraints it has been given by its designers – with possibly several different priorities. | This is the very reason we built the system. We should have pretty good ideas as to why. Shared by all AI systems. | | | Mission | R1. The system must fulfill its mission – the goals and constraints it has been given by its designers – with possibly several different priorities. | This is the very reason we built the system. We should have pretty good ideas as to why. Shared by all AI systems. | |
| | AILL | "After it Leaves the Lab" R2. The system must be designed to be operational in the long-term, without intervention of its designers after it leaves the lab, as dictated by the temporal scope of its mission. | All machine learning methods today are "before it leaves the lab", meaning that the task-environment must be known and clearly delineated beforehand, and the system cannot handle changes to these assumptions. To be more autonomous we must look at the life of these systems "beyond the lab". | | | AILL | "After it Leaves the Lab" R2. The system must be designed to be operational in the long-term, without intervention of its designers after it leaves the lab, as dictated by the temporal scope of its mission. | All machine learning methods today are "before it leaves the lab", meaning that the task-environment must be known and clearly delineated beforehand, and the system cannot handle changes to these assumptions. To be more autonomous we must look at the life of these systems "beyond the lab". | |
| | Domain-independence | R3. The system must be domain- and task-independent – but without a strict requirement for determinism: We limit our architecture to handle only missions for which rigorous determinism is not a requirement. | It is easy to implement domain dependence in software systems: Virtually //all// software today is made this way. Domain independence is necessary if we want to build more autonomous systems. | | | Domain-independence | R3. The system must be domain- and task-independent – but without a strict requirement for determinism: We limit our architecture to handle only missions for which rigorous determinism is not a requirement. | It is easy to implement domain dependence in software systems: Virtually //all// software today is made this way. Domain independence is necessary if we want to build more autonomous systems. | |
| | Modeling | R4. The system must be able to model its environment to adapt to changes thereof. | A good controller not only reacts to changes in its environment, it anticipates them. Anticipation, or prediction, is only possible with a decent model the system whose behavior we are predicting. A good model allows detailed and long-term prediction. | | | Modeling | R4. The system must be able to model its environment to adapt to changes thereof. | A good controller not only reacts to changes in its environment, it anticipates them. Anticipation, or prediction, is only possible with a decent model the system whose behavior we are predicting. A good model allows detailed and long-term prediction. | |
| | Anytime | R5. As with learning, planning must be performed continuously, incrementally and in real-time. Pursuing goals and predicting must be done concurrently. | A good system learns //all the time// and is planning and revising its plans //all the time//. Anything less makes the system less fit ("dumber"). | | | Anytime | R5. As with learning, planning must be performed continuously, incrementally and in real-time. Pursuing goals and predicting must be done concurrently. | A good system learns //all the time// and is planning and revising its plans //all the time//. Anything less makes the system less fit ("dumber"). | |
| | Attention | R6. The system must be able to control the focus of its attention. | Any system in a world that is vastly more complex and large than its resources allow to explore at any one time, must select what to apply its thinking, memory, and behavior to. Such "resource management" when applied to thinking is called "attention". | | | Attention | R6. The system must be able to control the focus of its attention. | Any system in a world that is vastly more complex and large than its resources allow to explore at any one time, must select what to apply its thinking, memory, and behavior to. Such "resource management" when applied to thinking is called "attention". | |
| | Self-Modeling | R7. The system must be able to model itself. | Any cognitive growth (development) requires comparing or evaluating a new state or architecture of the system to an old one. Unless the system has a model of self such self-modification cannot be evaluated a priori, and all changes are random explorations, which is the most inefficient method to apply to goal-directed behavior, and certainly not "intelligent" in any way. | | | Self-Modeling | R7. The system must be able to model itself. | Any cognitive growth (development) requires comparing or evaluating a new state or architecture of the system to an old one. Unless the system has a model of self such self-modification cannot be evaluated a priori, and all changes are random explorations, which is the most inefficient method to apply to goal-directed behavior, and certainly not "intelligent" in any way. | |
| | No Certainty | R8. The system must be able to handle incompleteness, uncertainty, and inconsistency, both in state space and in time. | In any large world there will be unintended and unforeseen consequences to all changes, as well as potential errors in measurements (perception). Certainty can never be 1. \\ In other words, "Nothing is 100% (not even this axiom!)." | | | No Certainty | R8. The system must be able to handle incompleteness, uncertainty, and inconsistency, both in state space and in time. | In any large world there will be unintended and unforeseen consequences to all changes, as well as potential errors in measurements (perception). Certainty can never be 1. \\ In other words, "Nothing is 100% (not even this axiom!)." | |
| | Abstractions | R9. The system must be able to generate abstractions from learned knowledge. | Abstractions are a kind of compression that allows more efficient management of small details, causal chains, etc. Abstraction is fundamental to induction (generalization) and analogies, two cognitive skills of critical importance in human intelligence. | | | Abstractions | R9. The system must be able to generate abstractions from learned knowledge. | Abstractions are a kind of compression that allows more efficient management of small details, causal chains, etc. Abstraction is fundamental to induction (generalization) and analogies, two cognitive skills of critical importance in human intelligence. | |
| | | Reasoning | R10. The system must be able to use applied logic - reasoning - to generate, manipulate, and use its knowledge. | Reasoning in humans is not the same as reasoning in formal logics; it is non-axiomatic and is always performed under uncertainty (per R8). | |
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| ====Laird et al.: Requirements for AGI==== | ====Laird et al.: Requirements for AGI-Aspiring Cognitive Architectures==== |
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