--- title: Which of the following statements most accurately characterises the… — In the news: deepfakes, fake robocalls… · tree and pragmatics description: Which of the following statements most accurately characterises the reported cause of historically inaccurate images produced by a commercial image generator? Exam question 1 of lesson 21 «In the news: deepfakes, fake robocalls and history gone wrong» of the AI course of Mova's primary school, with --- [Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/corpora/questions/ai/21-news-deepfakes-history/e1/) [home](https://md.mova.elamur.ai/free/en/) / [corpora](https://md.mova.elamur.ai/free/en/corpora/) / [questions](https://md.mova.elamur.ai/free/en/corpora/questions/) / [AI](https://md.mova.elamur.ai/free/en/corpora/questions/ai/) / [In the news: deepfakes, fake robocalls…](https://md.mova.elamur.ai/free/en/corpora/questions/ai/21-news-deepfakes-history/) / e1 # Exam question 1 · In the news: deepfakes, fake robocalls and history gone wr… Which of the following statements most accurately characterises the reported cause of historically inaccurate images produced by a commercial image generator? **Mova's choice:** option 4 · **The right answer:** option 4 · right ✓ · **What happens:** follow, characteris, report, produce, cause · **To what, where:** reported cause, inaccurate images This is exam question 1 of lesson 21 «In the news: deepfakes, fake robocalls and history gone wrong» of the AI course of Mova's primary school, asked in stiff, formal language. The page gives the question's syntax tree — a dependency tree in the style of Universal Dependencies — and its pragmatics clause by clause, then the 4 options as the student saw them, each with its tree and pragmatics. Every text is parsed by Mova itself. How the student answered it, with the score of every option and why: [the test at school](https://md.mova.elamur.ai/free/en/school-run/ai/21-news-deepfakes-history/#e1). ## The question {#question} ```conllu # sent_id = question:ai:21-news-deepfakes-history:e1:1 # text = Which of the following statements most accurately characterises the reported cause of historically inaccurate images produced by a commercial image generator? 1 Which which PRON WDT PronType=Rel 8 nsubj _ _ 2 of of ADP IN _ 5 case _ _ 3 the the DET DT Definite=Def|PronType=Art 5 det _ _ 4 following follow VERB VBG VerbForm=Ger 5 amod _ _ 5 statements statement NOUN NNS Number=Plur 8 obl _ _ 6 most most ADV RBS Degree=Sup 7 advmod _ _ 7 accurately accurately ADV RB _ 8 advmod _ _ 8 characterises characteris VERB VBZ Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin 0 root _ _ 9 the the DET DT Definite=Def|PronType=Art 11 det _ _ 10 reported report VERB VBN Tense=Past|VerbForm=Part|Voice=Pass 11 amod _ _ 11 cause cause NOUN NN Number=Sing 8 obj _ _ 12 of of ADP IN _ 15 case _ _ 13 historically historically ADV RB _ 14 advmod _ _ 14 inaccurate inaccurate ADJ JJ Degree=Pos 15 amod _ _ 15 images image NOUN NNS Number=Plur 11 nmod _ _ 16 produced produce VERB VBN Tense=Past|VerbForm=Part|Voice=Pass 15 acl _ _ 17 by by ADP IN _ 21 case _ _ 18 a a DET DT Definite=Ind|PronType=Art 21 det _ _ 19 commercial commercial ADJ JJ Degree=Pos 21 amod _ _ 20 image image NOUN NN Number=Sing 21 compound _ _ 21 generator generator NOUN NN Number=Sing 16 obl _ SpaceAfter=No 22 ? ? PUNCT . _ 8 punct _ _ ``` | # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | **follow** | gerund | | | | | | | vn occur-48.3-1 «NP V»: agent=Theme | | 2 | **characteris** | present | | the reported cause of inaccurate images | | | | of the following statements; accurately *property* | tree rules | | 3 | **report** | participle | | | | | | | vn characterize-29.2-1-2 «NP V NP»: agent=Agent | | 4 | **produce** | participle | | inaccurate images *not said here* | | | | by a commercial image generator | vn create-26.4 «NP V NP»: agent=Agent | | 5 | **cause** | | | inaccurate images | | | | | nom cause «event transitive»: of=patient | One row per clause the reader finds. «not said here»: a role filled from the clause above. «roles by»: a VerbNet class and frame when one fits the verb, else the reader's rules on the tree. ## The options {#options} ### Option 1 {#o1} The historical record itself was found to be erroneous ```conllu # sent_id = question:ai:21-news-deepfakes-history:e1:o1:1 # text = The historical record itself was found to be erroneous 1 The the DET DT Definite=Def|PronType=Art 3 det _ _ 2 historical historical ADJ JJ Degree=Pos 3 amod _ _ 3 record record NOUN NN Number=Sing 6 nsubj:pass _ _ 4 itself itself PRON PRP Case=Acc|Gender=Neut|Number=Sing|Person=3|PronType=Emp|Reflex=Yes 3 nmod:unmarked _ _ 5 was be AUX VBD Mood=Ind|Number=Sing|Person=3|Tense=Past|VerbForm=Fin 6 aux:pass _ _ 6 found find VERB VBN Tense=Past|VerbForm=Part|Voice=Pass 0 root _ _ 7 to to PART TO _ 9 mark _ _ 8 be be AUX VB VerbForm=Inf 9 cop _ _ 9 erroneous erroneous ADJ JJ Degree=Pos 6 xcomp _ _ ``` | # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | **find** | past · passive | | the historical record record | | | | | vn discover-84 «NP V NP»: subject=Topic | | 2 | **is** | to-infinitive | the historical record record *not said here* | | | | | erroneous *property* | tree rules | ### Option 2 {#o2} The generator had been trained exclusively upon works of fiction and fantasy rather than history ```conllu # sent_id = question:ai:21-news-deepfakes-history:e1:o2:1 # text = The generator had been trained exclusively upon works of fiction and fantasy rather than history 1 The the DET DT Definite=Def|PronType=Art 2 det _ _ 2 generator generator NOUN NN Number=Sing 5 nsubj:pass _ _ 3 had have AUX VBD Mood=Ind|Number=Sing|Person=3|Tense=Past|VerbForm=Fin 5 aux _ _ 4 been be AUX VBN Tense=Past|VerbForm=Part 5 aux:pass _ _ 5 trained train VERB VBN Tense=Past|VerbForm=Part|Voice=Pass 0 root _ _ 6 exclusively exclusively ADV RB _ 5 advmod _ _ 7 upon upon ADP IN _ 8 case _ _ 8 works work NOUN NNS Number=Plur 5 obl _ _ 9 of of ADP IN _ 10 case _ _ 10 fiction fiction NOUN NN Number=Sing 8 nmod _ _ 11 and and CCONJ CC _ 12 cc _ _ 12 fantasy fantasy NOUN NN Number=Sing 10 conj _ _ 13 rather rather ADV RB ExtPos=CCONJ 15 cc _ _ 14 than than ADP IN _ 13 fixed _ _ 15 history history NOUN NN Number=Sing 5 conj _ _ ``` | # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | **train** | past · perfect · passive | | the generator | | | | upon works of fiction and fantasy; exclusively *property* | vn hire-13.5.3 «NP V NP»: subject=Theme | | 2 | **work** | | fiction and fantasy | | | | | | nom work «act intransitive»: of=agent | ### Option 3 {#o3} The images were deliberately designed to deceive historians ```conllu # sent_id = question:ai:21-news-deepfakes-history:e1:o3:1 # text = The images were deliberately designed to deceive historians 1 The the DET DT Definite=Def|PronType=Art 2 det _ _ 2 images image NOUN NNS Number=Plur 5 nsubj:pass _ _ 3 were be AUX VBD Mood=Ind|Number=Plur|Person=3|Tense=Past|VerbForm=Fin 5 aux:pass _ _ 4 deliberately deliberately ADV RB _ 5 advmod _ _ 5 designed design VERB VBN Tense=Past|VerbForm=Part|Voice=Pass 0 root _ _ 6 to to PART TO _ 7 mark _ _ 7 deceive deceive VERB VB VerbForm=Inf 5 advcl _ _ 8 historians historian NOUN NNS Number=Plur 7 obj _ _ ``` | # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | **design** | past · passive | | the images | | | | deliberately *property* | vn create-26.4-1-1 «NP V NP»: subject=Result | | 2 | **deceive** | to-infinitive | | historians | | | | | vn trick-59.2 «NP V NP»: agent=Agent historian=Patient | ### Option 4 · ← Mova's choice · ✓ the right answer {#o4} Rules intended to increase variety were applied even where the historical record was clear ```conllu # sent_id = question:ai:21-news-deepfakes-history:e1:o4:1 # text = Rules intended to increase variety were applied even where the historical record was clear 1 Rules rule NOUN NNS Number=Plur 7 nsubj:pass _ _ 2 intended intend VERB VBN Tense=Past|VerbForm=Part|Voice=Pass 1 acl _ _ 3 to to PART TO _ 4 mark _ _ 4 increase increase VERB VB VerbForm=Inf 2 xcomp _ _ 5 variety variety NOUN NN Number=Sing 4 obj _ _ 6 were be AUX VBD Mood=Ind|Number=Plur|Person=3|Tense=Past|VerbForm=Fin 7 aux:pass _ _ 7 applied apply VERB VBN Tense=Past|VerbForm=Part|Voice=Pass 0 root _ _ 8 even even ADV RB _ 14 advmod _ _ 9 where where ADV WRB PronType=Rel 14 advmod _ _ 10 the the DET DT Definite=Def|PronType=Art 12 det _ _ 11 historical historical ADJ JJ Degree=Pos 12 amod _ _ 12 record record NOUN NN Number=Sing 14 nsubj _ _ 13 was be AUX VBD Mood=Ind|Number=Sing|Person=3|Tense=Past|VerbForm=Fin 14 cop _ _ 14 clear clear ADJ JJ Degree=Pos 7 advcl _ _ ``` | # | verb | tense, mood | who | what, whom | to whom | where | when | other | roles by | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | **intend** | participle | | Rules *not said here* | | | | | vn intend-61.2-1-1 «NP V S\_INF»: agent=Agent clause=Topic | | 2 | **increase** *not said to happen* | to-infinitive | | variety | | | | | vn caused\_calibratable\_cos-45.6.2-1 «NP V NP.patient»: agent=Causer variety=Patient | | 3 | **apply** | past · passive | | Rules | | | | | vn use-105.1 «NP V NP»: subject=Theme | | 4 | **is** | past | the historical record | | | | | clear *property* | tree rules | **Text:** the tests of Mova's primary school, written for Mova (`world/data/school/`), as the course runner gave them to the student. **Trees:** Mova's own parse — the `en` model on the CPU, when the site is built — not checked by a teacher; they may be wrong. **Frames:** Mova's frames reader (`prag`), each text read on its own. Elamur Cosmic License. - ← previous: [In the news: deepfakes, fake robocalls…, test 10](https://md.mova.elamur.ai/free/en/corpora/questions/ai/21-news-deepfakes-history/t10/) - next →: [In the news: deepfakes, fake robocalls…, exam 2](https://md.mova.elamur.ai/free/en/corpora/questions/ai/21-news-deepfakes-history/e2/) - ↑ back to [In the news: deepfakes, fake robocalls and history gone wrong](https://md.mova.elamur.ai/free/en/corpora/questions/ai/21-news-deepfakes-history/)