Abstract::

Computer science has grown out of the seed of imitation. From von Neumann's machine to the famous Turing test, which sparked the field of AI, algorithms have always tried to imitate humans and nature. Examples of such ``imitation algorithms'' are simulated annealing which imitates thermodynamics, genetic algorithms which imitate biology, or deep learning which imitates human learning.
In this talk, I describe an algorithm which imitates human psychology. Specifically, I discuss $M$ algorithms, which serve as a simple example of psychology-based imitation algorithms. The $M$ algorithm is one of the simplest natural language processing (NLP) algorithms.
Respecting the long tradition of imitation algorithms, the $M$ algorithm is simple yet powerful. Like other imitation algorithms, the $M$ algorithm is able to efficiently solve difficult problems. The $M$ algorithm pinpoints critical events in films, theater productions, and other scripts, revealing the rhythm of the texts.
At first glance, when trying to design an algorithm which pinpoints critical events of a text, it seems necessary for the algorithm to understand the complete text. Additionally, it would be expected that all layers of the narrative, background information, etc., would also be necessary. In short, it would be expected that the algorithm would imitate the human process of comprehending a text.
Surprisingly, the $M$ algorithm utilizes the structure of the complete text itself without understanding even a \emph{single} word, sentence, or character in order to discover critical events. The content of the narrative is not necessary for the algorithm to work. Other than an awareness of the illusion of time, borrowed from psychology, the $M$ algorithm circumvents the human process of reading.
In the links below, we can see the computerized summary of the Oscar winner movies "American Beauty" and ``The Godfather,''. The $M$ algorithm extracted the critical points in the movie's script (a  total of 7 such points). I then located those sentences in the movie itself, and edited out 60 seconds around those points (30 seconds before, 30 seconds after). This synopsis provides an "executive" summary of the movie see: https://youtu.be/-wX-ko93c3k   summary of the movie ``The Godfather,'' https://youtu.be/y66pndi56Es
This talk is based on a book (in the process).

Speaker: Zvi Lotker (https://dblp.org/pers/hd/l/Lotker:Zvi, Ben Gurion University)
Short Bio:  Zvi Lotker is an associate professor in the Communication Systems Engineering department at Ben Gurion University in Beer Sheva in the Communication Systems Engineering department, and associate professor in Faculty of Engineering at Bar-Ilan University, Israel. He received a BSc in Mathematics and Computer Science, and a BSc in Industrial engineering, from Ben Gurion University in 1991. In 1997 he received his MSc in Mathematics and in 2003, he received his PhD in Algorithms both from Tel Aviv University. He was a Postdoctoral Researcher at CWI, in Amsterdam, MPI in Saarbrücken Germany, and Mascotte in Nice France from 2003 to 2006. His main research areas are communication networks, online algorithms, sensor networks and recently, social networks.
In 2018 He revived the  SIROCCO Prize for Innovation in Distributed Computing
See: the home page of SIROCCO-2018

Where: room 26-00/101 LIP6

When: 26th September 2018 , 10h30