public:t_720_atai:atai-19:lecture_notes_requirements_for_generality

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public:t_720_atai:atai-19:lecture_notes_requirements_for_generality [2020/03/25 17:14] thorisson [What Do You Mean by Generality?] |
public:t_720_atai:atai-19:lecture_notes_requirements_for_generality [2020/03/25 17:15] (current) thorisson [What Do You Mean by Generality?] |
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| {{public:t-720-atai:afteritleavesthelab.png?750|After it Leaves the Lab}} | | | {{public:t-720-atai:afteritleavesthelab.png?750|After it Leaves the Lab}} | | ||

- | | A: Simple machine learners (<m>L_0</m>) take a small set of inputs (<m>x, y, z</m>) and make a choice between a set of possible outputs (<m>α,β</m>), as specified in detail by the system’s designer. Increasing either the set of inputs or number of possible outputs will either break the algorithm or slow learning to impractical levels. \\ B: Let <m>tsk_i</m> refer to relatively non-trivial tasks such as assembling furniture and moving office items from one room to another, <m>S_i</m> to various situations that a family of tasks can be performed, and <m>e_i</m> to environments where those situations may be encountered. Simple learner <m>L0</m> is limited to only a fraction of the various things that must be learned to achieve such a task. Being able to handle a single such task in a particular type of situation (<m>S_1</m>) with features that were unknown prior to the system’s deployment, <m>L_1</m> is already more capable than most if not all automatic learning systems available today. <m>L_2</m>, <m>L_3</m> and <m>L_4</m> take successive steps up the complexity ladder beyond that, being able to learn numerous complex tasks (<m>L_2</m>), in various situations (<m>L_3</m>), and in a wider range of environments and mission spaces (<m>L_4</m>). Only towards the higher end of this ladder can we hope to approach really general intelligence – systems capable of learning to effectively and efficiently perform multiple a-priori unfamiliar tasks, in multiple a-priori unfamiliar situations, in multiple a-priori unfamiliar environments, on their own. | | + | | A: Simple machine learners (<m>L_0</m>) take a small set of inputs (<m>x, y, z</m>) and make a choice between a set of possible outputs (<m>α,β</m>), as specified in detail by the system’s designer. Increasing either the set of inputs or number of possible outputs will either break the algorithm or slow learning to impractical levels. \\ B: Let <m>tsk_i</m> refer to relatively non-trivial tasks such as assembling furniture and moving office items from one room to another, <m>S_i</m> to various situations that a family of tasks can be performed, and <m>e_i</m> to environments where those situations may be encountered. Simple learner <m>L_0</m> is limited to only a fraction of the various things that must be learned to achieve such a task. Being able to handle a single such task in a particular type of situation (<m>S_1</m>) with features that were unknown prior to the system’s deployment, <m>L_1</m> is already more capable than most if not all automatic learning systems available today. <m>L_2</m>, <m>L_3</m> and <m>L_4</m> take successive steps up the complexity ladder beyond that, being able to learn numerous complex tasks (<m>L_2</m>), in various situations (<m>L_3</m>), and in a wider range of environments and mission spaces (<m>L_4</m>). Only towards the higher end of this ladder can we hope to approach really general intelligence – systems capable of learning to effectively and efficiently perform multiple a-priori unfamiliar tasks, in multiple a-priori unfamiliar situations, in multiple a-priori unfamiliar environments, on their own. | |

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/var/www/ailab/WWW/wiki/data/pages/public/t_720_atai/atai-19/lecture_notes_requirements_for_generality.txt · Last modified: 2020/03/25 17:15 by thorisson