Jessica’s Twine Task

My team has informed me that I have made the wrong call… too many times!

I was really engaged in Jessica’s cardiac arrest scenario game even though I know nothing about that field. I even made some connections to Miall & Dobson (2001). While going through her game I thought that this must require a large map of screens in Twine. I was correct, it was a lot larger than my 15 screens that was essentially a circle with a few diverging lines. Jessica’s game is a wen-like choose your own adventure with multiple choices on each screen of life or death scenarios. There was the added element of sound, such as a steady pulse to really add some pressure to the experience. Miall & Dobson talk about visual and aural imagery of advertisements, and it applies here too, “to evoke feelings that alter the self-concept of the viewer” (Section 2, para. 6) Jessica mentioned that creating a virtual pathway through the game was quite challenging, though not on the same level, I had to give it some thought too. Jessica’s game is along the lines of Miall & Dobson’s hypertext fiction. “All links in the simulation did not have strong semantic or logical connections to the subsequent material” (Section 4, para. 4). With each slide, the user, a nurse in training, has to understand the data the received from the patient vitals and make the right call to progress.

Miall & Dobson question “to what extent does hypertext change the nature of reading?” (Section 1, para. 2). This made me think of the book House of Leaves by Mark Danielewski. It was an interesting experience for me to read because looking back it is hyper textual in nature. There were footnotes and references in the middle of the prose and I often didn’t know what to do and got lost. Admittedly, I didn’t finish the book because it was a lot of work to remember. Miall & Dobson, quoting Charney, say that reading hypertexts imposes a greater demand on short-term or working memory (Section 3, para. 5). In their study, they concluded that hypertext “as a vehicle for the experience of literary reading itself, [it] appears to promote processes of attention that inhibit the engagement and absorption that are its most characteristic aspects” (Section 4, para. 28).

References

• Miall, D & Dobson, T. (2001) “Reading hypertext and the experience of literature.” Texas Digital Library.

Liana’s Speculative Futures Task

Wow! I absolutely loved Liana’s work on this task the video of both dystopian and utopian futures were very engaging. I also have to mention her vocal performance where she used tones and inflections to contrast the two possible futures.

Using the video format was very captivating because it allowed for the viewer to be briefly in that futuristic world. The visuals aided in seeing the dark bleak dystopian future contrasted by the bright heavenly future. The music also provides contrast of both worlds, dark timbres with most pitches being used in the lower register for dystopian, and bright timbres and middle to higher ranged pitches being used for utopian. Is there a key? Hard to say, I would say the harmonies are ambiguous  on purpose to create an ambience. Overall, video is a very efficient way to convey ideas and feelings for this assignment because a large amount of information is expressed using that medium. Comparing it to my task of attempting to write a narrative which was a lot of work. A writer has to convey those contrasting ideas and feeling using only text. Visuals and aural dimensions aid in world building.

I couldn’t help but think of Apple’s advertisement for the Macintosh back in 1984, see below:

The monitoring devices that Liana speaks of are already here since research in artificial intelligence in education is being more active. Yu & Lu (2021) survey research in education using artificial intelligence. “Smart watches are used to monitor the behaviours of students in the classroom (raising hands, taking notes, etc.) to effectively predict and intervene in students’ learning” (p. 175). They also mention research in the use of “facial recognition technology to investigate the emotional state of learners in the learning process” (p. 175). Furthermore, they speak of the adoption of neuroscience and eventual human-machine integration that are still in their infancy.

This task was fascinating because it allowed us to be creative using the current state of our world and technology to speculate on our future.

Reference

• Yu, S, & Lu, Y. (2021). Introduction to artificial intelligence in education. Springer.

The Development and Use of Machine Translation: Past and Present.

Introduction

I am writing on machine translation (MT) because I started teaching modern languages this year and I found that all of my students were using Google Translate both successfully and unsuccessfully for reading and writing. I am curious to know why that is, and how I can incorporate it into the pedagogy of language acquisition. In this project I will give a brief timeline of machine translation, summarize how the three main types of MT systems work, and where MT fits in modern education. It must be said that that MT is a tool to transfer information, not for language acquisition.

What is machine translation? It is “translation of natural languages by machine” and “the application of computer and language sciences to the development of systems answering practical needs” (Hutchins, 1995, p. 431). Idealistically, the end goal of MT is to create a high level translation between two languages that takes into consideration context, colloquialisms, formalities, and many other linguistical nuances. Realistically, the aim of machine translation “is to give the most accurate translation of everyday texts” (Poibeau, 2017, p. 4).

A Brief Timeline of Machine Translation

Hitchens (1995) and Poibeau (2017) give a thorough timeline of MT. Here are the main points in it’s history:

• 1930s – the French-Armenian, George Artsouni, and Russian, Smirnov-Trojanskij, independently patented designs for translating mechanical machines
• 1949 – Warren Weaver of the Rockefeller foundation outlined the prospects of MT methods through: cryptography, statistical methods, information theory, and the exploration of logic and the universal features of language.
• 1954 – Georgetown University and IBM held the first public demonstration of MT using 49 Russian sentences translated into English using 250 words and 6 grammar rules.
• 1966 – the Automatic Language Processing Advisory Committee (ALPAC) reported that a human translator was more cost-effective and funding for research was in decline since the late 1950s
• 1970s – The University of Montreal developed a syntactic transfer system for English-French translation, TAUM, which achieved the creation of a computational metalanguage, and a set the foundations for a programming language widely used in natural language processing.
• 1980s – Japanese companies developed computer-aided Japanese-English translation software for personal computers and text-processing software.
• 1989 – Up until this year, MT used a rule-based approach, but IBM experimented with a statistical corpus based system which inspired further experimentation and research
• 1990s – Personal computers had access to MT software such as Babel Fish
• 2006 – Google launched Google Translate using statistical machine translation
• 2016 – Google Translate shifted from statistical machine translation to neural machine translation which uses neural networking along with artificial intelligence.

The Three Main Types of MT Systems: Rule-Based, Statistical, and Neural

The workings of machine translation are extremely complicated. In this section, almost everything is a summation from Poibeau’s excellent book Machine Translation (2017), when not, other authors are directly referenced.

Rule-based MT systems are highly sophisticated by using a bilingual dictionary that follow thousands of rules to change word order to the specificities of the target language. Research of rule-based MT came out of World War 2 cryptology, and then the Cold War period which needed English-Russian translation (Hutchins, 1995; Poibeau, 2017). This type of MT factors in morphology, semantics, and syntax to make a translation. Rules needed to be established because a word-for-word translation can be too vague and ambiguous. Poibeau describes Warren Weaver’s four principles to avoid basic errors of word-for-word translation: one, the context of the word needs to be considered according to the topic and genre of the text to be translated, if known; two, it should be possible to determine a set of logical and recursive rules; three, Shannon’s model of communication (a message channel starting with a sender through a transmitter (a medium) to a receiver that reconstructs the senders message (Dubberly, 2011)) which was useful for cryptography would be useful; four, universal elements make up language and these can be used in the translation process to avoid ambiguity. Rule-based MT could not solve semantic ambiguities such as this example: “Little John was looking for his toy box. Finally, he found it. The box was in the pen. John was very happy” (Poibeau, 2017, p. 71).

Statistical MT systems “calculate probabilities of various translations of a phrase being correct, rather than aiming for a word-for-word translation” (Groves & Mundt, 2015, p. 113). A giant body of multilingual text called the bilingual text corpora is used for the MT system to reference. This is the technology that was eventually used for Babel Fish and Google Translate. Statistical MT process uses two steps: one, using the bilingual text corpora to automatically acquire information about the translations of words, and alignment of words at the sentence level, to encode, or learn, the information; two, the information and knowledge extracted from the encoding is then decoded to translate new sentences. In the early 1990s, IBM developed a fundamental equation for MT and Poibeau describes the three step process: “[one], determine the length of the target sentence depending on the length of the source sentence; [two], identify the best possible alignment between the source sentence and the target sentence; [three], find correspondences at the word level” (Poibeau, 2017, pp. 128 – 129). IBM’s model resides in the choice of words for translation to the target language and uses a two step approach: one, to extract as much information as possible from the bilingual text corpora; two, use this information to translate the sentences.

Neural MT, like statistical MT, these systems consists of an encoder and decoder to produce a translation from a given sentence but the encoder analyzes the source language so that the decoder can generate a translation for the target language. Poibeau describes the three characteristics of neural MT systems: one, the system thus tries to identify and group words appearing in similar translational contexts in what it called ‘word embeddings’; two, the models continuously grow as users interact with the system because the neural approach analyzes words, sentences, or groups of words to be compared and identified; three, it is hierarchical in nature since it can discover structure inside of a sentence, and it does this based observations through system training. Even though neural MT systems have come a long way since the rule-based systems of the 1950s this technology still fails to recognize overall sentence structure. Like the other two systems ambiguity remains an issue. In 2016 Google introduced GMNT, Google’s Neural Machine Translation System, and Google Translate began to support 10,000 language pairs (Vu et al., 2016).

Machine Translation in Modern Education

Students often have their smartphones on their desk in the high school classroom. “It seems inevitable that students will also utilize applications that will allow them to not only understand the single meaning of a lexical items but to render entire stretches of text” (Groves & Mundt, 2015, p. 114). While teaching Italian I have seen my students use Google Translate to for single words, sentences, and even entire pages using the Google Lens feature for written work and reading comprehension. The issue is that students do not know how to properly use Google Translate because one has to understand how its neural MT system works. Often times, verb tenses that have not been learned yet appear in the translation, or the translation does not make sense. “The most common error types [are] morphological and in word sense” (Groves & Mundt, 2015, p. 114). In my experience, this is because students use colloquial sayings or they do not clearly articulate what they want to say in English well enough for a good translation. Groves & Mundt (2015) say that Google Translate “is unable to help students align their writing to the norms and expectations of the wider discourse community” (p. 114). How is MT implemented in education? How can it be used honestly? Does it have a pedagogical place in language acquisition?

Implications in Education

Language classes are meant for language acquisition. Groves & Mundt (2015) suggest that “the sanctioned use of translation tools may undermine the actual language acquisition process or even the need to learn another language in the first place, potentially leaving examinations that forbid the use of such devices the main incentive for students to actually learn the language” (p. 119). MT tools are meant to support the student in their learning and is paralleled with the calculator. “The calculator did not remove the need for teaching maths – instead it allowed students to go further, quicker” (p. 120). That being said, if the student’s work has gone through a machine where spelling errors are corrected and the output is often a readable translation, is that output still the student’s voice? They “argue that a translated text is no longer the student’s own work” if it’s to be assessed (p. 119). Furthermore, “Google Translate ” is so good at at conjugating that it often allows lower-level students to produce complicated verb tenses that have not yet been studied” (Ducar & Schocket, 2018, p. 783). A conscientious student would input the translation, receive the output, and then make some adjustments post-translation.

What about entrance in to university as a second language learner? “The output of Google Translate approaches, if not actually exceeds, the minimum language requirement for a large number of English-speaking universities” (Mundt & Groves, 2016, p. 389). Groves & Mundt (2015) are concerned with students learning English for academic purposes, is that community to deny or embrace MT? Those students are already coping with an English language environment, and since academic work is done in a community it is unlikely that those learners would place blind-faith in MT (Groves & Mundt, 2015).

MT Systems as a Pedagogical Tool

If personal devices are important in everyones lives they should be used for educational purposes. Ducar & Schocket (2018) provide five recommendation for teachers to implement MT technologies in the classroom: “[one], they must evaluate their own knowledge of the available and emerging tools; [two], directly teach learners how to use appropriate technology responsibly; [three], review their beliefs about students’ use of supportive technologies; [four] familiarize themselves with their institution’s policies on academic honesty; and [five], decide how they intend to act and react when such policies are violated, all while offering engaging and motivating instruction and assignments (p. 793). With knowledge on how MT technologies work and a conscientious implementation students and teachers will find success.

Successful inclusion of MT in the classroom is possible. A study by Niño (2020) found that students “were aware of [MTs] limitations at the sentence or text level; however they [thought] it works pretty well as a quick reference for words in context and with verb conjugations…the students agreed that [MT] output still needs human input to bring it to an acceptable level of accuracy” (p. 19). In Niño’s study, students did note that MTs use is “questionable for medical or legal interpretation purposes because of the ambiguity and consequent misunderstandings that can arise” (p. 19). Furthermore, Niño found that the use of MT expanded digital literacy, reinforced previous learning, provided discussion on intercultural, subject-related, and linguistic questions, and enhanced metalinguistic reflection (2020).

Conclusion

Neural MT technologies are continually improving as users interact with the system but it is still far from that idealistic definition in the Introduction of this work. I would like to close with this quote by Poibeau (2017): “we should remember that the world chess champion was beaten by a computer in 1997, the world Go champion was beaten by a computer in 2016, but no computer is able to translate accurately between two languages” (p. 195).

References

• Dubberly H. (2011, April 21). Shannon’s Model of Communication. Dubberly Design Office. http://www.dubberly.com/models/shannons_model_conversation.html
• Ducar, C., & Schocket, D. H. (2018). Machine translation and the L2 classroom: Pedagogical solutions for making peace with google translate. Foreign Language Annals, 51(4), 779-795. https://doi.org/10.1111/flan.12366
• Groves, M., & Mundt, K. (2015). Friend or foe? google translate in language for academic purposes. English for Specific Purposes (New York, N.Y.), 37, 112-121. https://doi.org/10.1016/j.esp.2014.09.001
• Hutchins, W. J. (1995). Machine translation: A brief history. Concise history of the language sciences (pp. 431-445). Elsevier Ltd. https://doi.org/10.1016/B978-0-08-042580-1.50066-0
• Mundt, K., & Groves, M. (2016). A double-edged sword: The merits and the policy implications of google translate in higher education. European Journal of Higher Education, 6(4), 387-401. https://doi.org/10.1080/21568235.2016.1172248
• Niño, A. (2020). Exploring the use of online machine translation for independent language learning. Research in Learning Technology, 28, 1-32. https://doi.org/10.25304/rlt.v28.2402
• Poibeau, T. (2017). Machine translation. The MIT Press.
• Vu, Q., & Schuster, M. (2016, September 27). A Neural Network for Machine Translation, at Production Scale. Google AI Blog. https://ai.googleblog.com/2016/09/a-neural-network-for-machine.html

“Literature makes us work so much harder because readers need to construct everything about the fictional world in their imagination” (Dunne & Raby, 2013, p. 75).

I tried very hard to craft two short stories for this assignment, but since this was my first attempt at creative writing, it didn’t go so well. Maybe one day I’ll write my “For sale: baby shoes, never worn.” For this task, I will start with my inspirations for the stories that are tied to the module, I’ll give a brief synopsis of both stories, and then at the bottom I will have my world building notes.

In Yuval Harari’s book Homo Deus, he gives the reader a potential look into the future of personal AI assistants. Using Microsoft’s Cortana he writes how peoples personal assistants could communicate by scheduling meetings and times to socialize. Also, if one wears a device like an Apple Watch or FitBit, those devices can monitor one’s vital statistics and may advise the user to delay a meeting due to stress by reading a high heart rate. We are getting close to this. A personal story here, I once had a job where my commute was one hour and a half in heavy traffic. At the time I was wearing a Fit Bit and I noticed that my heart rate was significantly higher while commuting home. It was one of the main deciding factors in leaving that position for one that was closer to home. Harari’s (2017b) quote, “we will probably have an AI family doctor on our smartphone” (p. 325) is almost here!

Shannon Vallor talks about the task-specific AI that helps us with our everyday lives, and she makes a distinction with Artificial General Intelligence. This is the theoretical, and fictional, intelligence that is at the level of the human mind. We use these task-specific AI’s to help choose a movie to watch, predict our text, they can add events to our calendars.

Harari’s look into the future and Vallor’s discussion of AI is what inspired me with these utopian and dystopian narratives. Either one can be a what-if scenario to the other, and to the future in general. Since Vallor’s definition of an AGI is too far away I attempted write how a task-specific AI would impact a story. We have two main characters – see the character profiles below: Amanda Brenner, and Paris Masterson. For their faces I used an AI face generation website This Person Does Not Exist. 15 years from now, in 2037, both of these characters are are high school seniors, independent, intelligent, and are high achievers. They use their AI assistants to help plan their day, do research, and communicate. The narratives involve the two characters on the same day, that involves a bridge building competition and a scholarship for their tuition to a high level university.

Utopian

The day starts with Amanda waking up on time, journalling with pen and paper, and spending breakfast with her family. It’s the day before their bridge building competition, Amanda’s AI assistant helped her create the bridge, by providing calculations and helping research the design. She needs the scholarship money because her parents can only help her so much to pay the tuition. The AI assistant also helps her with her busy schedule by chunking out time for studies and reminders for commitments such as a lessons and practices. During the school day, Amanda encounters Paris Masterson, her academic and athletic rival. Paris just always seems to win and she makes sure that Amanda knows it. That evening, Amanda fulfils all her commitments, and puts the finishing touches on the bridge. The next day she has a successful competition with her bridge holding the most weight and winning a scholarship to put towards her tuition.

Dystopian

The day starts with Amanda’s alarm going off late, only leaving her enough time to grab her school bag and she barely gets out the door on time to catch the bus. She wonders why her alarm didn’t go off, and asks her AI assistant why it didn’t go off, the AI replies that Amanda set it for 8am, but Amanda was sure it was set for 7am. As the day progresses, her AI assistant, just gives her wrong information, such as a wrong definition of a word in English class, and continues to give her the wrong schedule. Amanda was sure that her AI assistant schedule was correct as of a few days before. She’s also not receiving messages from friends. An encounter with Paris was strange when Paris asked how her day was going, or is she heard from a mutual friend. Which is strange, because Paris never asks Amanda anything. That night when Amanda is giving the finishing touches to her bridge she had second thoughts to what her AI assistant was suggesting. The next day, Amanda’s bridge collapses under a light weight, and Paris wins the competition and scholarship. Amanda goes home disappointed and doesn’t know that Paris’ AI assistant has been sabotaging her.

References

• Dunne, A. & Raby, F. (2013). Speculative Everything: Design, Fiction, and Social Dreaming. Cambridge: The MIT Press. Retrieved August 30, 2019, from Project MUSE database.
• Harari, Y. N. (2017a). Homo deus: A brief history of tomorrow (First U.S. ed.). Harper, an imprint of HarperCollins Publishers.
• Hariri, Y. N. (2017b). Reboot for the AI revolution. Nature International Weekly Journal of Science, 550(7676), 324-327.
• Santa Clara University. (2018, November 6). Lessons from the AI Mirror Shannon Vallor. YouTube.

World Building Content

Story Setting

• Wednesday, May 20th, 2037
• Burnaby, British Columbia
• Soccer season is wrapping up, but qualifying for BC Summer Games is approaching
• Provincial track meets are approaching
• Flute recitals, and band festivals are in full swing
• The annual Bridge Building competition is a day away, and scholarships are on the line

Character Bios

Profile Data

• Name: Paris Masterson
• Age: 18
• Born: January 4, 2020
• Daughter of: Samantha (high Profile Lawyer) and Jeffrey Masterson (Successful Entrepreneur)
• Brothers and Sisters: Only child
• Education: 12th grade, Burnaby North Secondary

Physical Description

• Light brown hair; grey/brown eyes
• Height: 5’9”
• Athletic build

Personality

• Studious, takes school very seriously
• Ambitious, wants to get into the top schools
• Competitive, national level track and field, captain of the volleyball and soccer team

Attributes

• Very independent and self-reliant
• Competitive to a fault
• Can always find time to complete tasks

Habits

• Starts every morning with a 3km run at 7am
• Arrives at school early
• Spends weekday evenings at practicing and studying, in bed at 11pm

Manner

• Often comes across as cold, but warms up with time
• Intense during sport, especially during practice before a big game or event
• Easily frustrated while participating in group projects at school

Paris Masterson. An AI generated face from https://this-person-does-not-exist.com/en

Profile Data

• Name: Amanda Brenner
• Age: 17
• Born: June 12, 2020
• Daughter of: Irene (Stay-at-home mom, former teacher) and Steven (General Contractor, carpenter)
• Brothers and Sisters: Joshua, 15; Samantha, 12
• Education: 12th grade, Burnaby North Secondary

Physical Description

• Dark brown hair; green/brown eyes
• Height: 5’6”
• Athletic build

Personality

• Studious, takes school very seriously
• Ambitious, wants to get into the top schools
• Compassionate, Amanda often considers other

Attributes

• Strong work ethic
• Works well with others
• Very well organized, and a good manager of time

Habits

• Starts every morning with tea and journalling at 7am
• Arrives at school early
• Spends weekday evenings at studying for school, practicing flute, and plays soccer, in bed at 10pm
• Volunteers as a children’s soccer coach

Manner

• Very warm and welcoming
• Works hard during school and during practice for soccer and flute
• Considers her teammates and classmates

Amanda Brenner. An AI generated face from https://this-person-does-not-exist.com/en

First Draft of the Utopian Short Story

At 7am Amanda’s room shined yellow like the sun to wake her up. It was a typical rainy Vancouver day. “I thought May was supposed to be sunny a month” she thought as she looked out her window. Her hot water kettle goes off, pours the water in the tea pot for some earl grey tea, and takes out her Moleskine journal and fountain pen. Not many people her age use pen and paper anymore, but she likes to keep it traditional. After she gets her thoughts in order, she looks to her phone. 

“Hey Siri, what’s on for today?”

“Good morning, Amanda. You have Flex period from 8:40 to 9:10, followed by, Physics, English Literature, Senior Concert Band, and Drama. You’ve submitted your Physics homework on Teams, your English Literature paper is due Friday, so I have scheduled time tonight and tomorrow for you to complete that, nothing for Senior Concert Band, and for Drama, I have sent Shakespeare’s The Tempest to read and listen to on Teams. I have set up Do Not Disturb from 3pm to pm. You have a flute lesson on Zoom from 4pm to 5pm, dinner, then soccer from 7pm to 8pm. I have set up Do Not Disturb from 8:30 to 10:00 to do your homework.”

“Thanks, Siri. Ugh. I’m already tired just thinking about this day.”

Amanda walks down to the kitchen to make her toast with Nutella and sliced strawberries.

“Morning mom, morning dad, morning nerds.”

“Hey! That’s not nice!” said Samantha, her younger sister.

“We’re the nerds?! You’re the one who journals every morning. Dear diary, I am sad…” said Joshua.

“Ok kids, that’s enough, it’s barely 8 o’clock, how about we have a nice start to our day.” Said Amanda’s mom.

“Where’s dad?” Asked Amanda.

“He had to leave for work early today. He’ll be home for dinner. He’s grilling tonight. Quick, get your things ready for school.”

——————

Amanda’s phone flashes at the start of Physics class and the notification said she got 19/20 on her Physics homework. Looking around her class she sees sleepy faces, a group of boys doodling on the white board, and then her phone flashes again.

“Hi class, I’m sick today so my lesson will be sent to your assistants. You all did well on your bridge design homework, though I can tell that some of you have too much help from your assistants. We’re making truss bridges not cable stay bridges people! Have a good day. – Mr. Harvey”

The draft ends here. Thanks for reading!

Elvio’s Network Assignment Using Golden Record Curation Quiz Data Task

I connected with Elvio’s assignment because we were thinking along the same lines: we have this data, we see a large cluster of connections, and we want to know why. We also both sought outside sources to help make sense of the data, Elvio used Google’s Page Rank System and I used Google Trends. Furthermore, we both looked into the reasoning that our peers used to make their 10 choices for the Golden Record Curation. I am not only curious about why people chose that music, but I was curious as why some pieces of composers were more popular than others. In my task (9.2), I looked at the Beethoven and Bach having the most targets. I want to know more!

In order to do this task we needed to be familiar with network theory; to know our edges and nodes. I wanted to explore network theory so I dug a little deeper. I found Borgatti & Halgin’s paper, On Network Theory (2011), and they say:

“The choice of nodes should not generally be regarded as an empirical question. Rather, it should be dictated by the research question and one’s explanatory theory” (p. 1169).

By looking at the sources (people) and targets (songs), I wanted to find empirical evidence as to why. Google Trends for which composers were searched more often. Looking through my classmates tasks for reasoning. I was looking to make inferences, but I couldn’t. What we were doing in this assignment was just looking at our network to see that a “pattern of ties in a network yields a particular structure, and nodes occupy positions within that structure” (p. 1169). I feel that I have been making a mountain out of molehill in this case. I was on the right track though, the “thing to note about network theory is that the core concept of the field – the network – is not only a sociological construct but also a mathematical object” (p. 1174). I wanted to know the qualitative because I already had the quantitative. That being said, there is more here for me to discover because I haven’t had the opportunity to explore the concept of flow between two nodes.

To conclude, this task was an excellent opportunity to see and use data. It allowed me to see what choices my peers made and to explore possibilities as to why by using network theory.

References

• Borgatti, S. P., & Halgin, D. S. (2011). On network theory. Organization Science (Providence, R.I.), 22(5), 1168-1181. https://doi.org/10.1287/orsc.1100.0641
• Stone, T. (2018, October 29). Social Network Theory – a literature review for understanding innovation programs. Medium.

Click on the tweet below to see the full thread.

The following set of tweets is an assignment for ETEC 540 Task 11: Algorithms of Predictive Text. ???? ???? #UBCMET #edtech

— Mark Pepe (@MarkMPepe) July 25, 2022

For this task, I had an expectation of stringing a set of words to form a coherent thought, but after typing the prompt the predictive text led me to nonsensical options. At one point, I was given options in either Spanish or Italian. In this case, predictive text did not express how I would express myself. There likely isn’t enough data to properly predict what I would usually say.

Stoop & van den Bosch (n.d) give a clear explanation on how predictive texts work:

“To be able to make useful predictions, a text predictor needs as much knowledge about language as possible, often done by machine learning […] This works by looking at the last few words you wrote and comparing these to all groups of words seen during the training phase. It outputs the best guess of what followed groups of similar words in the past.”

In their article, they discuss how the algorithm called k Nearest Neighbours predicts texts using Twitter. The algorithm will look at all the past tweets and will create a database and will then use an approach called context-sensitive prediction which depends on similar groups of words being available, list of words frequently used by the author, and limiting the pool of words available based on words already used. The algorithm also models “friends” on Twitter which takes into account conversations, mentions, and a similar accounts. The authors mention how the algorithm will create accurate predictive texts of Lady Gaga and Justin Timberlake because they are likely to tweet about similar things and overlapping topics.

When I finished writing this task in Twitter I couldn’t help but think of Reddit’s r/subredditsimulator. In this subreddit, only bots post and comment to each other, but each bot is a representation of their assigned subreddit. “It’s not a perfect recreation of Reddit, but an adequate caricature of its worst tendencies” (Khalid, 2019). It can also be funny, sarcastic, mean-spirited, helpful, reflective, and it can also uncannily echo the real internet (Khalid, 2019).

r/subredditsimulator uses OpenAI’s GPT-2 language model. “GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text” (OpenAI, 2019). GPT-2 was trained on 40GB of internet text, a data set of 8 million web pages, which allows it to answer questions, summarize, and translate. In a paper by OpenAI, GPT-2 was able to answer questions like “who wrote the book Origin of Species?” or “who is the quarterback of the Green Bay packers?” It answered them correctly with a high degree of probability, over 80%. The answers are Charles Darwin, and Aaron Rogers. Though, the questions it got wrong, had closely associated answers. Largest state in the us by landmass? It answered California, it’s Alaska, but California is the largest state by population. Good guess! Another one was who plays ser Davos in Game of Thrones? GPT-2 answered Peter Dinklage, but it’s Liam Cunningham. Peter Dinklage is a good guess, because he is strongly associated with Game of Throne, in my opinion. Using factoid-style question answering is how OpenAI tests what information is contained in the language model (Radford et al., n.d.). Take a look at Janelle Shane’s twitter thread below for an example of r/subredditsimulator.

There’s now a subreddit that’s populated entirely by neural nets who are themselves simulating other subreddits.

They’re sometimes kind to each other, sometimes awful, and they keep trying to ban each other’s posts.https://t.co/Xv0WVNrw00 pic.twitter.com/NMfoVp9pms

— Janelle Shane (@JanelleCShane) June 5, 2019

Bringing it back to our task, the predictive text option on the iPhone is to help the user type a bit quicker to send that message out faster. This is an example of the virtual assistants that Shannon Vallor speaks of “to aid our daily performance[…]to carry out tasks under our direction” (Santa Clara University, 2018). For that reason, it works well, but to string a set of words to form a coherent thought it doesn’t work so well.

References

• Khalid, A. (2019, June 5). This AI-powered subreddit has been simulating the real thing for years. Engadget.
• OpenAI. (2019, February 14). Better Language Models and Their Implications. OpenAI.
• Radford, A., Wu, J., Child, R., Luan, D., Amodei, A., & Sutskever, I. (n.d.) Language Models are Unsupervised Multitask Learners.
• Santa Clara University. (2018). Lessons from the AI Mirror Shannon Vallor.
• Stoop & van den Bosch. (n.d.) How algorithms will know what you’ll type next. The Pudding.

Zoe’s Golden Record Curation Task

I connected to Zoe’s task because, being a musician, I thought it was clever of Zoe to use software to analyze the music. Using the DJ software Serato, which allows for analyzation of key and beats per minute (BPM), can give a music enthusiast access to information that a high level musician gets after years of experience and study. Key and tempo (BPM) are excellent ways making musical connections. One activity that is taught to conductors is to make a chart of pieces identifying keys, meter and tempo to help study and memorize a piece.

This is the point where I regret getting rid of my old binders from university. They sat around for 10 years, and I never thought that I would need my study charts until now! Thankfully, I still have some of my concert band scores which will do. In the chart, I would have information such as key, tempo, and texture (layers of musical instruments in a particular section of music. Below, you can see section C (at measure 50) with a forward slash at the top and a backward slash at the bottom. The slashes indicates a 4 measure phrase, and there are 5 more phrases after this with a few bars of cadence to conclude the piece. There are also colour coded note, a red FL for flutes, green ALT for alto saxophone, and blue TBN for trombone. The colours are to denote that instruments position in the texture. Red for melody, green for inner voices (harmony; notes that make music major or minor), and blue for the bass line. In chart form, this information makes the whole piece easy to “see” and make connections. This is a very short arrangement at 72 measures, but looking at a whole work, like the opera The Marriage of Figaro, looking at the key will help understand which character or piece of plot is centred. For example, Figaro’s music will always be in G major, so when that is seen in the chart it will help the interpreter make connections with before and after that scene.



Back to the assignment. What I find interesting about Zoe’s analysis is that she found that five pieces from around the world are either in the same key, or a closely related key. Closely related keys have many notes in common. In this case, the keys of D and A, they share all the same notes except A has G# where D has a G natural.

D: D E F# G A B C#
A: A B C# D E F# G#

The pieces in D are:

• India, raga, “Jaat Kahan Ho,” sung by Surshri Kesar Bai Kerkar
• Australia, Aborigine songs, “Morning Star” and “Devil Bird,” recorded by Sandra LeBrun Holmes.

The pieces in A are:

• Java, court gamelan, “Kinds of Flowers,” recorded by Robert Brown
• Senegal, percussion, recorded by Charles Duvelle
• Japan, shakuhachi, “Tsuru No Sugomori” (“Crane’s Nest,”) performed by Goro Yamaguchi.

I find it interesting that the non western pieces have the connection of being in the same key, or a closely related key. I am not sure what to make of it, or if it is just a coincidence.

One more point to look at is Zoe’s analysis regarding tempo and the feeling of the piece. We both spoke about Bach and Stravinsky. My connection with both was a steady pulse, I didn’t talk about tempo. Zoe mentioned that the Bach piece was at 70bpm and was “peaceful and hopeful,” compared to Stravinsky’s 137bpm (almost double that of Bach’s) and that it was “intense” and “represents some chaos.” Bach’s Gavotte is based on a French folk dance so the tempo is implied that it should be fun and lighthearted. Looking at the score for Rite of Spring, Stravinsky only gives a BPM, 126 for an eighth note, and no tempo marking, which would be allegro (fast, quick). That means the recording on the record is fast by 10bpm, making it feel a bit more agitated. Tempo has a big effect on musical feel for the player and for the listener.

When Beethoven was alive, I don’t think he knew that his music would broadcasted across space via radio waves and a golden disc. This networking task demonstrates that Beethoven is the most popular composer on Voyager’s Golden Disc, or is he?

I first took at look at Group B.  Specifically, the targets (pieces of music) with the most sources (people) connected to them were Beethoven’s 5th, Johnny B. Goode, and Melancholy Blues. 7 sources were connected to all 3 targets; 6 sources were connected to 2 of the 3 targets; and 3 sources were connected to 1 of the 3 targets. Only 1 source of all 16 sources connected to the target with the least amount of sources, Pygmy Girls’ Initiation Song.

My hunch for this is that Beethoven, Chuck Berry, and Louis Armstrong are important musical figures in our culture. Beethoven’s da da da dum is in our collective consciousness from children shows, TV, movies, and almost every music class will incorporate that piece into the curriculum. Chuck Berry and Louis Armstrong are amongst a few of the musicians who laid the ground work for pop music. Both of those pieces use the 12 bar blues musical form and the following chords in this pattern:

||: I   | I   | I   | I  | IV | IV | I   | I  | V   | IV| I   | V:||

Any combination of these chords with the addition of the iv chord will give you almost every pop song on the radio. A little divertimento, take a look at this video by Axis of Awesome to hear these chords.

Back to the Task. I thought I would try Google Trends to compare the most popular pieces in Group B with the least popular. Beethoven is clearly the most popular Google search, but it was a challenge figuring out how to search the trend for the Pygmy Girls’ Initiation Song. I would get a 0 for each combination. Bambuti Pygmy Song is what it is called on Spotify.

I then took a look at Group A, my group, to see the connections in communities. To no surprise, the piece that connected all four sources was Beethoven’s 5th. 9 targets connected 3 of the 4 sources, and 6 of those 9 targets were of the Western music tradition. Only one of the community sources gives reasoning as to why they included Beethoven, which corroborates my hunch, popularity in culture. It’s ingrained in western culture. All four sources in my group were also connected by blues based music too: Chuck Berry, Louis Armstrong, and Blind Willy Johnson.

In analyzing these networks I found myself having trouble making inferences. I have the data on my screen and I see the connections, but I feel that I was lacking the qualitative data to make those inferences. That is the reason why I analyzed these networks using data from the quiz, and Google Trends; this showed Beethovens popularity. In Group A, 12 of 17 sources had Beethoven’s 5th as a target, and in Group B, 13 of 18. Well over 50% of sources connected to Beethoven’s 5th in their network. If I include Beethoven’s other piece Quartet No. 13, that brings his total in Group A to 15, and 15 in Group B as well. Totalling 30 sources for Beethoven.

Thank you for reading, but wait…

I’ll be BACH.

Bach had three pieces on the Golden Disc: Brandenburg Concerto, Partita for Violin, and Prelude & Fugue. The totals in Group A was 11 sources, and 20 sources in Group B Bach. Bringing his total to 31! Just edging out Beethoven by 1. Though none of Bach pieces had more sources than Beethoven’s 5th, Bach had more targets, and Group B gave 10 sources to the Brandenburg Concerto compared to 6 sources in Group A. Using Google Trends again, Beethoven wins over Bach.

To conclude, I have posted the networks of Beethoven and Bach below.

Beethoven Group A 15 sources from 2 targets.

Beethoven Group B 15 sources from 2 targets.

Bach Group A 11 sources and 3 targets.

Bach Group B 20 sources and 3 targets.

My strategy to curate was to find pieces that contain an obvious example of the universality of musical elements tempo, dynamics, harmony, melody, and texture. Obviously, all the pieces on voyager contain them, but I tried to pick the ones where those elements were most present and where I could make connections between those pieces composed all around the world. Some common elements that are present are the use of ostinato patterns (repeated rhythms and pitch), chant, call and response musical phrases, and representations of nature. Listen for the steady even pulse and subdivision of rhythm in Western music, and how complex the rhythms can get in Senegal and Zaire maintaining that pulse and adding some groove.

1. Bach, Brandenburg Concerto No. 2 in F. First Movement, Munich Bach Orchestra, Karl Richter, conductor. I chose Bach as the number one piece of music because of Bach’s impact and what it represents. Upon listening, us humans are transported to Baroque period Europe with people wearing powdered wigs. However, this piece is more than that, because it demonstrates logic aurally. Speaking in musical terms, Bach synthesized musical harmony through his compositions. Harmonies that were first observed by Pythagoras (interval ratios, harmony of the spheres). Bach’s synthesized functional harmony gives logic and reason for musical composition. Musical harmony progresses from tonic to subdominant to dominant and finally resolving back to the tonic. This can happen in the span of bar, phrase, movement, to an entire piece of music. Every music student studies Bach’s harmonies the same way that the student in the classroom studies math. Furthermore, Bach’s use of rhythm in this piece demonstrates a steady pulse with it’s subdivisions (think fractions) of quarter notes, eighth notes, and sixteenth notes.
2. Mozart, The Magic Flute, Queen of the Night aria, no. 14. Edda Moser, soprano. Bavarian State Opera, Munich, Wolfgang Sawallisch, conductor. Mozart comes next on the list because it carries on the musical tradition of Bach and it makes the human and emotional more transparent. Our musical logic here becomes more creative by adding contrast: pushing and pulling the tempo, forti and piani, thin and thick instrumental texture. It also demonstrates the extremes of the human voice, and a human’s athletic ability aurally. Also, the universe should hear the beautiful voice of Edda Moser, one of the greats.
3. Beethoven, Fifth Symphony, First Movement, the Philharmonia Orchestra, Otto Klemperer, conductor. Haydn composed 104 symphonies, Mozart composed 41, and Beethoven wrote 9, but he made them count. His greatest was composed while he was going deaf. Beethoven’s Fifth Symphony is likely the most well known symphonic work because it’s impact is heard in all types of media: commercials, television, film. The da da da dum is in our collective conscious. Furthermore, compared to Mozart where emotions begin to surface, Beethoven brings it to a boil. Furthermore, where Mozart started to add contrast with rhythm, dynamics, and texture, Beethoven accentuates those and adds more crunching chromaticism – the use of notes outside of a key.
4. Stravinsky, Rite of Spring, Sacrificial Dance, Columbia Symphony Orchestra, Igor Stravinsky, conductor. Stravinsky, pushes all of the boundaries of musical elements composed by the three previous composers. Stravinsky’s concept of the ballet Rite of Spring is to invoke a prehistoric primal pagan ritual. Stravinsky uses bitonality, two keys at the same time, with constant meters changes, and contrasting duple and triple meters. Stravinsky uses clusters of notes for a percussive effect. Furthermore, even though the rhythms and harmonies are complex there is still a steady pulse as well as pleasing melodies. If Beethoven brought emotions to a boil, Stravinsky boiled them over.This work caused a lot of controversy during the premiere in 1913. Audiences’ whose musical taste includes Tchaikovsky’s Nutcracker were not expecting this! In a way, I wonder if this work was a sign of things to come, World War One, was a year away.
5. Navajo Indians, Night Chant, recorded by Willard Rhodes. This is an example of a chant. One can here the steady pulse of a shaker throughout. Also, there is a call and response to their chanting: the call being the chant in the middle and lower part of the voice, and the response being  in high pitch and sung in falsetto.
6. Senegal, percussion, recorded by Charles Duvelle. This piece from Senegal demonstrates complex rhythms played by multiple drums, but there is still a prominent steady pulse. If one listens closely the rhythm of the high pitched drums start to produce a melody. The addition of the person chanting transforms the music into a trance like experience.
7. Zaire, Pygmy girls’ initiation song, recorded by Colin Turnbull. This Pygmy song, which is completely sung, has four parts. Two groups singing ostinato patterns as a foundation, and a lead chanter giving a call, and another chanter responding. This has a similar trance like experience like the piece from Senegal which is caused by a steady repetition.
8. Japan, shakuhachi, “Tsuru No Sugomori” (“Crane’s Nest,”) performed by Goro Yamaguchi. This piece performed on a traditional Japanese flute demonstrates the ability of a simple instrument being able to play complicated and creative music to represent nature. One instrument presents the life cycle of a crane and uses playing techniques, like fluttering tonguing, to represent flapping wings (International Shakuhachi Society, 2022).
9. Java, court gamelan, “Kinds of Flowers,” recorded by Robert Brown. This piece includes elements from the previous four pieces: call and response solo and group chant (Navajo and Zaire) steady percussive beats played by pitched percussion which creates a melody (Senegal), and a musical representation of nature (Japan), flowers in this case. The gamelan percussion instruments also use ostinato patterns which use steady beats to underlay the piece. Also, notice how there is use of contrasting musical elements of slow and fast tempi, contrast of loud and soft dynamics, thin and thick musical texture. It is important to note that this music has its own tuning system which differs from the 12 equally tempered western tuning system.
10. “Dark Was the Night,” written and performed by Blind Willie Johnson. I close with this piece because the blues is the foundation of North American popular music. Furthermore, the blues take elements from some of the world music previously listed: steady bass line, pedal tone on D, of an open guitar string, melodic playing up the fretboard, and Blind Willie Johnson humming a call and response with his voice and the guitar.

Reference

• International Shakuhachi Society. (2022). Tsuru no Sugomori (Dokyoku). Komuso. https://www.komuso.com/pieces/pieces.pl?piece=2218

Kelcie’s What’s on my kitchen table? Task

I made a connection with Kelcie’s task when I read “many of the objects speak to who we are as people…interspersed with passion work…and connections to important people and places.” I only realized how significant the objects in my bag are once I did this task. Those Italian text books represent me passing on my knowledge of that language and culture because of all the past experiences I had being raised in an Italian household, studying it in high school and university, learning it thoroughly for opera, and using the language while studying music in Italy. Reflecting on this, I made a connection to one of the New London Groups components of pedagogy, situated practice. That Italian books draw from my personal life-worlds of being in the communities of educators, musicians, and of my familial heritage; “their boundaries become more evidently complex and overlapping” (New London Group, 1996).

While working on this Linking Assignment I can’t help but think of the design of Task 1. I feel that it is exemplary of designs of meaning. In this case, we are looking at artefacts that we carry on our persons everyday, and each of those artifacts connect to the “complex systems of people, environments, technology, beliefs, and texts” (p. 73). I look at these artefacts now with a deeper more significant meaning.

References

• Cazden, C., Cope, B., Kalantzis, M., Luke, A., Luke, C., Nakata, M., & New London Group. (1999;1996;). A pedagogy of multiliteracies designing social futures. In B. Cope, & M. Kalantzis (Eds.), Harvard educational review (pp. 19-46). Routledge. https://doi.org/10.4324/9780203979402-6