public:t-720-atai:atai-16:lecture_notes_f-5_26.01.2016

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public:t-720-atai:atai-16:lecture_notes_f-5_26.01.2016 [2018/09/06 17:06] thorisson [Why Generality?] |
public:t-720-atai:atai-16:lecture_notes_f-5_26.01.2016 [2018/09/06 17:07] (current) thorisson [Why 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. \\ Let <m>tsk_i</m> refer to relatively non-trivial tasks such as assembling furniture and moving office items from one room to another, 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. | | + | | 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. \\ Let <m>tsk_i</m> refer to relatively non-trivial tasks such as assembling furniture and moving office items from one room to another, 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 a 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-16/lecture_notes_f-5_26.01.2016.txt · Last modified: 2018/09/06 17:07 by thorisson