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Course Level Date Clip# Line Interlocutor Content
Oral Assessments Graduate 2019-08-14 3 96 S01 the (.) benign images and make- uh fool the (.)
Oral Assessments Graduate 2019-08-14 3 97 S01 um (.) adversar- fool the machine learning ↑models→
Oral Assessments Graduate 2019-08-14 3 98 S01 and uh they- this- (.) this thing is very important
Oral Assessments Graduate 2019-08-14 3 99 S01 because uh (1.3) it is a ⁎uh⁎ very powerful attack
Oral Assessments Graduate 2019-08-14 3 100 S01 methods especially for the hackers↘ uh if hackers use
Oral Assessments Graduate 2019-08-14 3 101 S01 this (.) algorithm to attack (.) some machine learning
Oral Assessments Graduate 2019-08-14 3 102 S01 system in (.) uh society→ it may cause great harm↘
Oral Assessments Graduate 2019-08-14 3 103 S01 for example if we go to the (.) ↑hospital and many
Oral Assessments Graduate 2019-08-14 3 104 S01 hospital use machine learning models to diagnose↘
Oral Assessments Graduate 2019-08-14 3 105 S01 so uh if if the machine learning system gives a
Oral Assessments Graduate 2019-08-14 3 106 S01 wrong answer about your (.) ⁎uh⁎ (1.0) disease↘
Oral Assessments Graduate 2019-08-14 3 107 S01 that's wi- that will be very dangerous↘
Oral Assessments Graduate 2019-08-14 3 108 S01 and so: (.) mm so uh (.) so it is for the: au-
Oral Assessments Graduate 2019-08-14 3 109 S01 autonomous driving↘
Oral Assessments Graduate 2019-08-14 3 110 S01 if you driv- if you are sitting in the car
Oral Assessments Graduate 2019-08-14 3 111 S01 and the: (.) machine learning models driving-
Oral Assessments Graduate 2019-08-14 3 112 S01 drives the car and it gives you a wrong answer↘
Oral Assessments Graduate 2019-08-14 3 113 UNK (0.4)
Oral Assessments Graduate 2019-08-14 3 114 S01 uh for example the un the traffic light's already
Oral Assessments Graduate 2019-08-14 3 115 S01 red but it still passes↘ so that's very dangerous↘
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