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Author: brylevkirill

Artificial Intelligence.md

@@ -525,6 +525,14 @@
 >	"This paper presents the first formal measure of intelligence for agents fully embedded within their environment. Whereas previous measures such as Legg’s universal intelligence measure and Russell’s bounded optimality provide theoretical insights into agents that interact with an external world, ours describes an intelligence that is computed by, can be modified by, and is subject to the time and space constraints of the environment with which it interacts. Our measure merges and goes beyond Legg’s and Russell’s, leading to a new, more realistic definition of artificial intelligence that we call Space-Time Embedded Intelligence."
 
 
+#### ["Evaluation in Artificial Intelligence: From Task-oriented to Ability-oriented Measurement"](https://riunet.upv.es/handle/10251/83598) Hernandez-Orallo
+>	"The evaluation of artificial intelligence systems and components is crucial for the progress of the discipline. In this paper we describe and critically assess the different ways AI systems are evaluated, and the role of components and techniques in these systems. We first focus on the traditional task-oriented evaluation approach. We identify three kinds of evaluation: human discrimination, problem benchmarks and peer confrontation. We describe some of the limitations of the many evaluation schemes and competitions in these three categories, and follow the progression of some of these tests. We then focus on a less customary (and challenging) ability-oriented evaluation approach, where a system is characterised by its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several possibilities: the adaptation of cognitive tests used for humans and animals, the development of tests derived from algorithmic information theory or more integrated approaches under the perspective of universal psychometrics. We analyse some evaluation tests from AI that are better positioned for an ability-oriented evaluation and discuss how their problems and limitations can possibly be addressed with some of the tools and ideas that appear within the paper. Finally, we enumerate a series of lessons learnt and generic guidelines to be used when an AI evaluation scheme is under consideration."
+
+>	"AI is the science and engineering of making machines do tasks they have never seen and have not been prepared for beforehand."
+
+  - `book` ["The Measure of All Minds: Evaluating Natural and Artificial Intelligence"](https://cambridge.org/core/books/measure-of-all-minds/DC3DFD0C1D5B3A3AD6F56CD6A397ABCA) by Hernandez-Orallo
+
+
 #### ["Measuring Intelligence through Games"](http://arxiv.org/abs/1109.1314) Schaul, Togelius, Schmidhuber
 >	"Artificial general intelligence refers to research aimed at tackling the full problem of artificial intelligence, that is, create truly intelligent agents. This sets it apart from most AI research which aims at solving relatively narrow domains, such as character recognition, motion planning, or increasing player satisfaction in games. But how do we know when an agent is truly intelligent? A common point of reference in the AGI community is Legg and Hutter’s formal definition of universal intelligence, which has the appeal of simplicity and generality but is unfortunately incomputable. Games of various kinds are commonly used as benchmarks for “narrow” AI research, as they are considered to have many important properties. We argue that many of these properties carry over to the testing of general intelligence as well. We then sketch how such testing could practically be carried out. The central part of this sketch is an extension of universal intelligence to deal with finite time, and the use of sampling of the space of games expressed in a suitably biased game description language."
 

Machine Learning.md

@@ -213,10 +213,9 @@
   ["Crash Course on Learning Theory"](https://blogs.princeton.edu/imabandit/2015/10/13/crash-course-on-learning-theory-part-1/) by Sebastien Bubeck  
   ["Statistical Learning Theory"](https://web.stanford.edu/class/cs229t/Lectures/percy-notes.pdf) by Percy Liang  
 
-  [course](http://www.mit.edu/~9.520/fall17/) by Tomaso Poggio, Lorenzo Rosasco, Georgios Evangelopoulos `video`  
+  [course](https://www.mit.edu/~9.520/fall19/) by Tomaso Poggio and others ([videos](http://youtube.com/playlist?list=PLyGKBDfnk-iB4Xz_EAJNEgGF5I-6OzRNI), [videos](http://youtube.com/playlist?list=PLyGKBDfnk-iAtLO6oLW4swMiQGz4f2OPY), [videos](https://www.youtube.com/playlist?list=PLyGKBDfnk-iCXhuP9W-BQ9q2RkEIA5I5f))  
   [course](http://work.caltech.edu/telecourse.html) by Yaser Abu-Mostafa `video`  
   [course](http://youtube.com/watch?v=jX7Ky76eI7E) by Sebastien Bubeck `video`  
-  [course](http://youtube.com/playlist?list=PLyGKBDfnk-iAtLO6oLW4swMiQGz4f2OPY) by Tomaso Poggio and Lorenzo Rosasco `video`  
 
 
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@@ -236,7 +235,7 @@
 
   [**deep learning**](https://github.com/brylevkirill/notes/blob/master/Deep%20Learning.md#theory)
 
-  [**reinforcement learning**](https://github.com/brylevkirill/notes/blob/master/Reinforcement%20Learning.md#problems)
+  [**reinforcement learning**](https://github.com/brylevkirill/notes/blob/master/Reinforcement%20Learning.md#theory)