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Tracking (known) delivery of the material #82
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@leighphan, @kjallen, @jmjamison, and I are teaching it on May 8-9 at the UCLA Library https://ucla-data-science-center.github.io/2023-05-08-UCLA/. We are really looking forward to it and hope to gather some good feedback. |
Awesome! thanks for letting us know. Very happy to get feedback after this :) |
Suggestions for Chapter 01 – Welcome – Intro to AI for GLAM A visual demo of AI would be helpful early as a way to introduce what the curriculum will cover. Recommendation:
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Suggestions for Chapter 02 – Artificial Intelligence (AI) and Machine Learning (ML) in a nutshell
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Suggestions for Chapter 03 – Machine Learning Modelling Concepts Overall this lesson seemed very advanced for novice learners, the concept of a model/features/labels was difficult to convey in a short time in a clear, concise manner and would benefit from more visual graphics to help explain some of the core concepts. Recommendations:
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Suggestions for Chapter 04 – What is Machine Learning good at? – Intro to AI for GLAM This portion was of great interest to learners, specifically how this can be applied and where these concepts are being used in the learner's workplace. Recommendations:
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Suggestions for Chapter 05 - Understanding and managing bias – Intro to AI for GLAM Overall this is a very dense lesson and could benefit from breaking up and adding more interactive exercises or demos to reduce cognitive overload and increase understanding.
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Suggestion to add a jargon-busting or jam session type discussion to kick off the workshop as part of the lesson rather than informal add on. Our target audience for this workshop was library staff and IS graduate students, with the assumption of no background knowledge of Artificial Intelligence and no technical background requirements. Specific areas we addressed in our jargon busting session and to consider for more structured discussion addition to the lessons:
There was a lot of interest around topics not explicitly covered by the workshop including:
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Lesson Structure ObservationsThe lessons contain a lot of information for someone new to AI/ML to take in. This includes a significant number of acronyms, basic vocabulary and some statistical concepts to absorb. Some suggestions to flatten the learning curve:
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I gave a 2-hour version of this workshop to an internal group of almost 50 Smithsonian employees last Tuesday (Jan 23). Slides are available via FigShare here: https://doi.org/10.25573/data.25105826.v1. New sections I tested out are the addition of LLMs, added slides about Risk to the episode on Ethics. Ran very low on time -- which totally makes sense since I added material to a supposedly 3-hour workshop -- but survey respondents especially enjoyed the activities. |
We are creating specific issues to address the comments on this issue. Please reference this issue when creating an issue for a specific comment so we can track unfinished change requests. |
It would be good to try (as far as practical) to track delivery of the materials. This will be useful to:
Suggest posting replies to this thread as a low-key way to track this. cc @MikeTrizna @mark-bell-tna @noramcgregor
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