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Category Archives: ETEC 511
ETEC 511 IP 2: Artificial Intelligence
Table 1. A side-by-side comparison of my responses to a series of questions vs the responses of Chat GPT (OpenAI, 2023).
| My Response |
Chat GPT’s Response |
| 1. Who were these people, and how did/does each contribute to the development of artificial intelligence? How did/does each think “intelligence” could be identified? (~ 50 words each) | |
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Alan Turing |
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| Alan Turing was a British mathematician who paved the way for computer sciences and the development of artificial intelligence (Biography.com, 2020). He proposed a machine that could compute anything and created the design that led to the first personal computers. He described an Imitation Game that would test machine intelligence by observing it in conversation with a human (Turing, 1950).
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Alan Turing was an English mathematician and computer scientist who helped crack Nazi codes during WWII and is considered the father of computer science. He proposed the concept of a machine, now called a Turing machine, that could perform any mathematical calculation, laying the foundation for modern computers. Turing believed that if a machine could successfully mimic human conversation, it could be considered intelligent. This became known as the Turing test. |
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John McCarthy |
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| John McCarthy, a professor of computer science, was a researcher of artificial intelligence and the creator of Lisp programming language. He defines intelligence as “the computational part of the ability to achieve goals in the world” but believes it is not yet possible to define machine intelligence without relating it to human intelligence (McCarthy, 2007).
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John McCarthy was an American computer scientist and mathematician known as one of the founders of artificial intelligence. He helped develop the first AI programming language, LISP, and organized the Dartmouth Conference in 1956, considered the birth of AI as a field. McCarthy believed intelligence could be defined as the ability to solve problems and make decisions, and that machines could be made to demonstrate this through programming and algorithms. |
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Herb Simon |
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| Herb Simon was a researcher of management science who focused on decision making. His work on psychology, economics, computer science, and math made him one of the founding fathers of artificial intelligence (Donovan, n.d.). His programming was informed by a computational theory of human intelligence where large amounts of data are collected to understand the background of a problem (Lee, 2013).
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Herbert Simon was an American economist and psychologist who was awarded the Nobel Prize in Economics in 1978. He made significant contributions to the study of decision-making and artificial intelligence. He believed intelligence could be identified by a system’s ability to process information efficiently and make decisions that achieve its goals. He also introduced the concept of “bounded rationality”, suggesting that decision-making is limited by the information available and the cognitive abilities of the decision-maker. |
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Marvin Minsky |
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| Marvin Minsky, cofounder of the Massachusetts Institute of Technology’s Artificial Intelligence Lab, built one of the first neural network machines and strove to design machines that were capable of human common-sense (BBC, 2016). His theory of intelligence was based on the idea that many small non-intelligent agents can work together in a society to produce something intelligent (Minsky, 1986).
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Marvin Minsky was an American cognitive scientist and computer scientist who is considered a pioneer in the field of artificial intelligence. He co-founded the MIT Artificial Intelligence Laboratory and contributed to the development of early AI systems. Minsky believed intelligence could be identified by a machine’s ability to use symbols and rules to represent and manipulate knowledge, leading to “common-sense reasoning”. He also developed the theory of “frames”, which is used to represent knowledge in AI systems. |
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Timnit Gebru |
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| Timnit Gebru, a computer scientist and artificial intelligence ethics researcher, has analyzed representation of women, people of colour, and less developed countries in AI-produced output (Hao, 2020). The work that caused her ousting from Google outlined the risks of large language models. She believes that intelligent models of AI would be built with community contexts in mind (Walsh, 2022).
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Timnit Gebru is an Ethiopian-American AI researcher and computer scientist who has made significant contributions to the field of artificial intelligence ethics and bias. She co-led Google’s ethical AI team before leaving the company in 2021. She advocates for responsible AI practices and has published research on the unintended consequences of biased data and algorithmic decisions. Gebru believes intelligence can be identified by a machine’s ability to learn and understand complex patterns in data and make decisions that align with human values and ethics. |
| 2. How do “machine (programming) languages” differ from human (natural) ones? (~100 words) | |
| While both machine and human languages are forms of communication with semantics and syntax, they are different. Harris (2018) outlines three major differences: 1) Human languages can change depending on context, while machine languages have defined rules and do not change; 2) Natural languages include nonverbal communication that can impart emotion and nuances that are not possible to convey in machine language; and 3) in human language, errors, accents, slang and other forms of improvisation do not necessarily affect the meaning, while in machine language, these things may cause errors and render the communication ineffective. | Machine languages are designed for computers to understand and execute, whereas human languages are for communication between people. Machine languages are based on strict syntax and grammar rules, whereas human languages can be more flexible and context-dependent. Machine languages consist of a limited set of commands and symbols, whereas human languages have a large vocabulary and the ability to form new words. Machine languages are designed for efficiency and speed of processing, whereas human languages have evolved for nuanced communication and expression of emotions. Machine languages can only understand specific tasks, whereas human languages can understand and convey abstract concepts. |
| 3. How does “machine (artificial) intelligence” differ from the human version? (~100 words) | |
| Machine intelligence is often measured based solely on the efficiency and speed with which the AI can perform a skill, whereas human intelligence is informed by prior experience (Chollet, 2019). Traditionally, intelligence in AI was determined by success in a specific task, but human intelligence can be applied generally, over multiple different tasks. According to Chollet (2019), “information processing systems form a spectrum between two extremes: on one end, static systems that consist entirely of hard-coded priors… and on the opposite end, systems that incorporate very few priors and are almost entirely programmed via exposure to data,” while humans “combine ample amounts of both priors and experience” (p. 19). | Artificial intelligence (AI) refers to the ability of machines to perform tasks that typically require human-level intelligence, such as perception, reasoning, learning, and decision making. Human intelligence, on the other hand, is the ability of a person to think, understand, and learn from experiences and environment. AI operates based on pre-programmed algorithms and rules, whereas human intelligence is based on complex neural networks, emotions, biases, and past experiences. AI can process large amounts of data much faster than humans, but lacks creativity, empathy, and common sense that humans possess. AI can be programmed to make objective decisions, while human intelligence is subjective and influenced by emotions and personal experiences. |
| 4. How does “machine learning” differ from human learning? (~100 words) | |
| Machine learning is different from human learning because AI makes decisions based on patterns seen a limited data set without the ability to apply context or seek out new sources of information. Like humans, machines develop biases, but theirs are the result of data sets and variables from which they do not deviate (Heilweil, 2020). Janardhanan (2020) summarizes the major differences by stating that “an intelligent human being will be able to solve problems related to unforeseen domains, whereas a machine will not.” | Machine learning is a type of artificial intelligence that allows computer systems to learn from data without being explicitly programmed. Unlike human learning, machine learning algorithms don’t require a teacher or human intervention to change its behavior. Human learning is based on experience, perception, and feedback while machine learning relies on algorithms and large amounts of data to make predictions. Human learning can be influenced by emotions, biases, and past experiences, while machine learning is based solely on the data it was trained on. Both processes involve changing behavior over time, but machine learning is much faster and can process much larger amounts of data than human learning. |
| 5. How do your answers differ from what a machine could generate? Make sure you reference the specific differences between your answers and Chat GPT’s. (~200 words) | |
| Chat GPT did not cite any sources, whereas I was careful to only make claims I could back up with sources. For example, I know that the Imitation Game designed by Turing is commonly called the Turing Test, but since the paper I consulted did not mention that I made a conscious decision not to include the information. When I entered the questions into the Chat GPT textbox, I prefaced them with the number of words I expected in the answer (e.g., In 100 words, who was…). Chat GPT went over the word limit each time. I initially went over my word limit for each question but went back and deleted every word that I could without losing meaning or important information. In some instances, Chat GPT made claims that I was unable to confirm through internet searches, for example the statement about what Gebru believes to be the definition of intelligence. Unless she was quoted somewhere as saying those words, I think that the sentence is speculation and should have been worded as such. In comparison, my answer about her beliefs was less specific but can be traced back to a specific piece of writing. | |
References
BBC. (2016, January 26). Ai pioneer Marvin Minsky dies aged 88. BBC News. Retrieved January 31, 2023, from https://www.bbc.com/news/technology-35409119
Biography.com (Ed.). (2020, July 22). Alan Turing. Biography.com. Retrieved January 31, 2023, from https://www.biography.com/scientist/alan-turing
Chollet, F. (2019, November 5). On the measure of intelligence. Google, Inc. Retrieved January 28, 2023, from https://arxiv.org/pdf/1911.01547.pdf
Donovan, P. (n.d). Herbert A. Simon: Do we understand human behavior? The economics of altruism. Retrieved January 30, 2023, from https://www.ubs.com/microsites/nobel-perspectives/en/laureates/herbert-simon.html
Hao, K. (2020, December 4). We read the paper that forced Timnit Gebru out of google. here’s what it says. MIT Technology Review. Retrieved January 31, 2023, from https://www.technologyreview.com/2020/12/04/1013294/google-ai-ethics-research-paper-forced-out-timnit-gebru
Harris, A. (2018, November 1). Human languages vs. programming languages. Medium. Retrieved January 31, 2023, from https://medium.com/@anaharris/human-languages-vs-programming-languages-c89410f13252
Heilweil, R. (2020, February 18). Why algorithms can be racist and sexist. Vox. Retrieved January 31, 2023, from https://www.vox.com/recode/2020/2/18/21121286/algorithms-bias-discrimination-facial-recognition-transparency
Janardhanan, P. S. (2020, April 2). Human learning and machine learning – how they differ? Data Science Central. Retrieved January 31, 2023, from https://www.datasciencecentral.com/human-learning-and-machine-learning-how-they-differ/#:~:text=Let%20us%20examine%20the%20difference,the%20form%20of%20past%20data
Lee, J. A. N. (2013). Herbert A. Simon. Computer Pioneers – Herbert A. Simon. Retrieved January 30, 2023, from https://history.computer.org/pioneers/simon.html
McCarthy, J. (2007, November 12). What is artificial intelligence? Basic questions. Retrieved January 30, 2023, from http://www-formal.stanford.edu/jmc/whatisai/node1.html
Minsky, M. L. (1986). The society of mind. Simon and Schuster. Retrieved January 30, 2023, from https://archive.org/details/societyofmind00marv/page/17/mode/2up
OpenAI. (2023, January 25). CHATGPT: Optimizing language models for dialogue. OpenAI. Retrieved January 31, 2023, from https://openai.com/blog/chatgpt/
Turing, A. M. (1950). Computing, machinery and intelligence. Mind, 49(236), 433-460. Retrieved January 30, 2023, from https://www.cs.mcgill.ca/~dprecup/courses/AI/Materials/turing1950.pdf
Walsh, D. (2022, May 26). Timnit Gebru: Ethical AI requires institutional and structural change. Stanford University. Retrieved January 30, 2023, from https://hai.stanford.edu/news/timnit-gebru-ethical-ai-requires-institutional-and-structural-change
IP 1: Usability
Assignment instructions
- Formulate your own conception of usability based on the first reading.
- Think about what is missing from your conception from an educational perspective, then create your conception of educational usability.
- Identify and discuss 2 of Woolgar’s examples of how usability studies ended up configuring users.
- Discuss the differences between these two quotes:
- “…the usability evaluation stage is an effective method by which a software development team can establish the positive and negative aspects of its prototype releases, and make the required changes before the system is delivered to the target users” (Issa & Isaias, 2015, p. 29).
- “…the design and production of a new entity…amounts to a process of configuring its user, where ‘configuring’ includes defining the identity of putative users, and setting constraints upon their likely future actions” (Woolgar, 1990).
- Figure out an effective way to reduce your writing to a maximum of 750 words without losing meaning.
Usability
Usability can be defined as the degree to which a system can be used by people to achieve certain goals. Issa & Isaias write that the goal of usability is “making systems easy to learn, easy to use, and with limiting error frequency and severity” (2015, p. 24). Ease of communication between humans and machines by means of an intuitive user interface, comfortable physical interaction, and use of familiar language in programs all increase the usability of a digital tool. Usability increases productivity and the speed of task completion without creating additional frustration.
Educational usability
In an educational setting, there are additional considerations for usability of digital learning tools. They should be affordable to avoid cost as a barrier and easy to operate for students who experience neurodivergence, learning disabilities, or physical constraints that could interfere with operation of the tool. The design must take into consideration the ages, relative knowledge levels, and experience of the students who are expected to use them for learning, and provide relevant and engaging content that promotes interest. Care should be taken to represent diversity and inclusivity so that the tool can easily be used by all students regardless of race or gender.
Woolgar’s accounts of user configuration
In contrast with the aforementioned concept of educational usability, Woolgar (1990) recounts instances where usability testing focused more on how a company could determine who would use their tools and how they would be used. For example, Woolgar makes a connection between the case of the computer and boundaries between the users and the company (1990, p. 79). The myriad of warnings about electrocution, void warranty, and possible damage to computer components if the case was opened or tampered with, coupled with a redirection of the user to a manual or hotline, ensured that the users behaved in such a way that their interaction with the computer was distinct from that of the company. In doing so, the company was able to retain control over the prescribed use of the machine, but forfeited possible valuable input about usability from the users.
Another example Woolgar provided of usability trials gone wrong was the testing of th user manual. User manuals that accompany the hardware and software configure users in the sense that they outline the correct sequence of actions that a user should take (Woolgar, 1990 p. 81). By testing how easy the manuals were to follow during usability trials, the company was testing the effectiveness of their manual in instructing the users how to behave instead of assessing how intuitive the system was to use, and ensuring they could maintain control of the users’ future actions.
Differing perspectives of usability testing
In the following quotations, the authors show a stark contrast between their views of usability testing:
“…the usability evaluation stage is an effective method by which a software development team can establish the positive and negative aspects of its prototype releases, and make the required changes before the system is delivered to the target users” (Issa & Isaias, 2015, p. 29).
“…the design and production of a new entity…amounts to a process of configuring its user, where ‘configuring’ includes defining the identity of putative users, and setting constraints upon their likely future actions” (Woolgar, 1990).
On one hand, Issa and Isaias (2015) describe usability testing as a process where the user and developers are in communication to improve the usability of prototypes prior to the release of the final iteration of the machine. By taking into account the opinions and desires of the people who will be using the final product, the designers admit that they cannot possibly perfectly predict what users will need.
On the other hand, Woolgar (1990) describes the process of usability testing as deciding who the users should be and essentially testing them to determine the amount of control the company will have over how the machine is used. This gives the impression that the company believes their initial design is flawless and the importance is placed on ways to ensure the user can be trained to use the product effectively.
In conclusion, it is clear that if designers share the perspective of Issa and Isaias (2015), they will be far more likely to produce a tool with a high level of usability, whereas designers who operate in the way that is described by Woolgar (1990) face the possibility of producing a frustrating experience for the user.
References
Issa, T., & Isaias, P. (2015). Usability and human computer interaction (HCI). In Sustainable Design (pp. 19-35). Springer.
Woolgar, S. (1990). Configuring the user: The case of usability trials. The Sociological Review, 38(1, Suppl.), S58-S99.
Word count: 737
Truth and Reconciliation Assignment
The Assignment
This assignment challenged us to select searchable educational history-related works, search the texts for representations of Indigeneity and Indigenous people, and explain the potential impact of the text on educational history and/or teacher professional development.
For my document, I chose to download the Canadian Plains Research Centre’s (now called University of Regina Press) The Encyclopedia of Saskatchewan entry titled “Education,” written by Ken Horsman. I was interested in this text because it attempts to provide an overview of public education in the province from before confederation to the present day. Before reading the entry, I started thinking about education “before confederation” and wondered how the author would describe it. Unfortunately, there was less than a paragraph discussing the traditional education of Indigenous youth before colonization. The rest of the document was dedicated to describing the evolution of schools. In fact, that single paragraph about traditional education ended with a sentence that jumped forward in time to 1840 with the creation of the first school in Cumberland House.
The Question
Realizing that the document was written from a Western perspective, I endeavored to see if the language used throughout would give clues as to the relationships between Indigenous people and western education in Saskatchewan between 1840 and 2006 (the date the Encyclopedia of Saskatchewan was published). I searched the document for occurrences of the following words and phrases: First Nations, Indian, Native, Indigenous, Aboriginal, Métis, and then Cree and Dene as two of the major Indigenous language groups in Saskatchewan.
The Results
| First Nations | Indian | Native | Indigenous | Aboriginal | Métis | Cree | Dene |
| 20 | 16 | 4 | 0 | 6 | 11 | 1 | 1 |
Initially, I was surprised to find the word Indigenous was not used at all. However, I recall that as recently as 2018 the University of Saskatchewan officially used the term Aboriginal for any programming aimed at First Nations, Métis, and Inuit students. I believe it has been only the past few years that people in Saskatchewan have adopted the widespread use of the term Indigenous, and considering the text was written prior to 2006, I understand why the word did not appear.
The words Cree and Dene each appeared only once, both in the same sentence that mentioned language courses offered as a part of the Northern Teacher Education Program (NORTEP). The term Native produced only 4 search results, all in the context of either courses in Native Studies or as part of the name of educational programs or schools (e.g. Saskatchewan Native Teacher Education Program and Saskatoon Native Survival School). The author used the word Aboriginal six times, sometimes in the context of non-Aboriginal. It seems to be used as a general term throughout to differentiate students of Indigenous ancestry from those of settler ancestry.
The most-used search terms were First Nations (20 times), Indian (16 times), and Métis (11 times). First Nations was used throughout to refer to specific groups of Indigenous people and individuals from those groups. Indian was used once in reference to the title of an 1879 report about “Industrial Schools for Indians and Half-breeds,” once in reference to Indian residential schools, once when the town Indian Head was mentioned, and the rest of the time to indicate legal entities (e.g. Federation of Saskatchewan Indian Nations). Finally, the word Métis was used throughout, often in the context of “First Nations and Métis students,” seemingly to include all students of Indigenous heritage.
Limitations
In hindsight, the major limitation to answering my question with the word search was the medium that I chose. My initial reasoning of choosing an encyclopedia entry was to have a searchable document that spanned educational history over more than a century in my home province. However, I failed to take into consideration the fact that an encyclopedia entry is authored by a single person at a certain time in history. This means that wherever possible, the document was written with the most appropriate terminology at the time and does not reflect how language changes over time.
Analysis
Although my methodology was flawed, I still got an overall impression from this document of the changing nature of the relationship between Indigenous groups and western education. The overall trend was a long history of settlers “othering” Indigenous people and attempting to assimilate their children through the teaching of western ideals, most often in church-run boarding schools. Over time, Indigenous youth began to be recognized as students with individual needs and values, but unfortunately this is a recent development in the overall and the church still had a residential school in operation as recent as the 1990s. Finally, Indigenous people can work towards reclaiming control over what and how their children learn, and western educational institutions are starting to work towards a pedagogy that is inclusive and welcoming for Indigenous children. However, there is much more work that needs to be done to unlearn too many years of systemic racism in education.
Conclusion
If this encyclopedia entry is any indication of what kind of educational history is being taught to teachers in training, I think that it may be doing a disservice to our future students and educators. While the historical documents written by settlers from the 1800s onwards are important to study (after all, we need to know about residential schools so that we can work towards reconciliation), it is also important to acknowledge the hundreds of years of educational traditions that were happening in what is now called Saskatchewan before colonization. I think that to produce culturally sensitive learning environments, we as teachers need to be willing to consult with both Indigenous knowledge keepers and engage with western educational history.