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00:00:02
a professor should grow we a slab and with my quarters work on to accept
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um so today if it kind of safe if you go to
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an airport and pass although security checks done by humans
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should you feel safe windows security chit checks we did on
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the machine learning algorithms artificial intelligence well yes and no
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i know if i tell you that today uh terrorists country d. printable changed
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if you pick sets a few meters in the colour of the bob
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and make a classifier in the airport say this is a cute find this actually happened with a classifier
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and yes a a a because more and more people and researchers are working on
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the field i sit in in particular here at e. p. f. l.
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we've uh come up with a couple of solutions to the vulnerabilities of machinery machine
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and it has a lot of unabridged i'll talk about one in particular average
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machine learning relies heavily on average and if you have done some basics astrology you'd know that
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averaging is the worst way to compare two populations for example if you take the g. d. p. of denmark
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averaged over the population of that market gets a lower value if you do the same averaging over the
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us population and no one is full to the to say that the typical us citizen is richer
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or comparable to the done and then use it is the same for this room is the is the billionaire in the room
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the average income of this room is a hundred million that's this is a very bad way to classify rules were academics
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so uh of course machinery resources no an alternative to average of the media
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the meeting is very easy in single dimension of uh of variables you just take the
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bible's rank them and take the one that separates the population into two parts
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and this would be robust classifier that's determined that machine learning gives in very
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high dimensional space is a models are vectors of a hundred billion parameters
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and you can just rank them and take the how the this value that separates the need to to hop operations
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so we michael cocoa cultures would come up with a with alternatives
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that are practical and that we proved mathematically are safe
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uh both alternatives i'm somehow inspired by the median but others
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are lip shoots filters or uh other utterances we've
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presented our results uh to the machine and you'd you so uh they are kind of
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accepted and now uh we are implementing that as a system on top of concerts
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i would also tackled some other topics in i. c. t. like safe interrupt ability and three
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years this can happen inside a neural network at an individual neural more on uh
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a level and if you're interested in details and the details of this works so

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Conference program

Welcome address
Andreas Mortensen, Vice President for Research, EPFL
7 June 2018 · 9:49 a.m.
Introduction
Jim Larus, Dean of IC School, EPFL
7 June 2018 · 10 a.m.
The Young Software Engineer’s Guide to Using Formal Methods
K. Rustan M. Leino, Amazon
7 June 2018 · 10:16 a.m.
Safely Disrupting Computer Networks with Software
Katerina Argyraki, EPFL
7 June 2018 · 11:25 a.m.
Short IC Research Presentation 2: Gamified Rehabilitation with Tangible Robots
Arzu Guneysu Ozgur, EPFL (CHILI)
7 June 2018 · 12:15 p.m.
Short IC Research Presentation 3: kickoff.ai
Lucas Maystre, Victor Kristof, EPFL (LCA)
7 June 2018 · 12:19 p.m.
Short IC Research Presentation 5: CleanM
Stella Giannakopoulo, EPFL (DIAS)
7 June 2018 · 12:25 p.m.
Short IC Research Presentation 6: Understanding Cities through Data
Eleni Tzirita Zacharatou, EPFL (DIAS)
7 June 2018 · 12:27 p.m.
Short IC Research Presentation 7: Datagrowth and application trends
Matthias Olma, EPFL (DIAS)
7 June 2018 · 12:31 p.m.
Short IC Research Presentation 8: Point Cloud, a new source of knowledge
Mirjana Pavlovic, EPFL (DIAS)
7 June 2018 · 12:34 p.m.
Short IC Research Presentation 9: To Click or not to Click?
Eleni Tzirita Zacharatou, EPFL (DIAS)
7 June 2018 · 12:37 p.m.
Short IC Research Presentation 10: RaaSS Reliability as a Software Service
Maaz Mohiuddlin, LCA2, IC-EPFL
7 June 2018 · 12:40 p.m.
Short IC Research Presentation 11: Adversarial Machine Learning in Byzantium
El Mahdi El Mhamdi, EPFL (LPD)
7 June 2018 · 12:43 p.m.
20s pitch 1: Cost and Energy Efficient Data Management
Utku Sirin, (DIAS)
7 June 2018 · 2:20 p.m.
20s pitch 2: Gamification of Rehabilitation
Arzu Guneysu Ozgur, EPFL (CHILI)
7 June 2018 · 2:21 p.m.
20s pitch 4: Neural Network Guided Expression Transformation
Romain Edelmann, EPFL (LARA)
7 June 2018 · 2:21 p.m.
20s pitch 5: Unified, High Performance Data Cleaning
Stella Giannakopoulo, EPFL (DIAS)
7 June 2018 · 2:21 p.m.
20s pitch 6: Interactive Exploration of Urban Data with GPUs
Eleni Tzirita Zacharatou, EPFL (DIAS)
7 June 2018 · 2:22 p.m.
20s pitch 7: Interactive Data Exploration
Matthias Olma, EPFL (DIAS)
7 June 2018 · 2:22 p.m.
20s pitch 8: Efficient Point Cloud Processing
Mirjana Pavlovic, EPFL (DIAS)
7 June 2018 · 2:23 p.m.
20s pitch 9: To Click or not to Click?
Eleni Tzirita Zacharatou, EPFL (DIAS)
7 June 2018 · 2:24 p.m.
20s pitch 10: RaaSS Reliability as a Software Service
Maaz Mohiuddlin, LCA2, IC-EPFL
7 June 2018 · 2:24 p.m.
20s pitch 11: Adversarial Machine Learning in Byzantium
El Mahdi El Mhamdi, EPFL (LPD)
7 June 2018 · 2:24 p.m.
Machine Learning: Alchemy for the Modern Computer Scientist
Erik Meijer, Facebook
7 June 2018 · 2:29 p.m.