Himmelfarb Comic – The Rotation: A Himmelfarb Library Blog

Comic: Librarians 101

r.kyserSeptember 25, 2024October 23, 2024

What do librarians do anyway? If you want to find out, check out the comic below!

Panel 1:
Narration: When people think of librarians, they often picture the following: an older woman with glasses and a cardigan.
Image: A librarian, who looks as the narration described, shushes the audience.

Panel 2:
Narration: Who mainly spends her time shushing people, reading and sitting at a desk.
Image:The same librarian from the first panel sits at a desk with a sign that says “silence is mandatory”

Panel 3:
Narration: Like all stereotypes, some folks do fit the mold demographically, the realities of the job are much different than what folks expect.
Image: Rebecca, a librarian with brown curly hair and glasses, shrugs and says “honestly, I can’t remember the last time I shushed anyone.”

Panel 4: The tools librarians use might look different in the digital age, but the basic goal of ensuring information is accessible and discoverable remains the same.
Image: A stack of books is placed next to a laptop.

Panel 5: To achieve this goal, along with other library functions, there’s actually a wide variety of types of librarians.
Image: A librarian building is held up by the silhouettes of five people of various body types and skin colors.

Panel 6: Let’s take a closer look at some of the variety out there…
Image: There are eight boxes, each featuring a different person. The box labeled Circulation contains a heavy set pale blonde man with glasses. The Acquisition box contains a lean person of older age with gray hair, brown skin and wrinkles. The box labeled Serials contains a man with dark skin and dreads, wearing a red suit. The box labeled Scholarly Communication, has a medium sized woman with tan skin and black hair. The box labeled childrens has a heavy set black woman with her hair in two buns, smiling. The box labeled Specialists, features a lean tan man with a goatee and wrinkles with graying hair. The box labeled archivists features a pal skinned red headed woman with glasses. The last box contains Rebecca, and is labeled reference.

The circulation librarian sits at a desk, facing the audience. He says “Circulation doesn’t just check out books, but things like chargers, electronics or even dry erase markers.”

Panel 8: Like circulation acquisitions can also be a team effort. Acquisitions involves ordering and acquiring new material for the library.
Image: The acquisitions librarian sits behind a stack of books, saying “an acquisitions librarian works with vendors and publishers to acquire material”

Image: A laptop is open on a desk and on the screen is the serials librarian. He says “these days many serial publications can be found digitally.”

Panel 10: In academic libraries, scholarly communication librarians help students and faculty alike publish their work. This can be by helping journals to publish in or managing an institutional repository.
Image: The scholarly communications librarian is in a sailor’s outfit on a tiny boat, where a shark swims. She says “when it comes to avoiding predatory publishers, we can help.”

Panel 11: Children’s librarians work with children and young adults. They help select material for different age groups and organize events and outreach for patrons. Storytime for young kids is a good example of an event they run.
Image: The children’s librarian sits in front of a group of children with a picture book she is reading out loud. Behind her is a teddy bear and books.

Panel 12: Specialists librarians work in special libraries where the often specialize in a topic or field. Law librarians and medical librarians fall into this category.
Image: The specialist librarian stands in front of a cabinet with a scale on it. He says “around one-third of law librarians have a law degree”

Panel 13: Archivists aren’t the same as librarians but they often have the same masters degree. They are specialists in preserving material and helping people access it.
Image: The archivist reads a book in front of a desk and several file cabinets

Panel 14: Reference librarians, like myself, help people do research and find materials. They may also specialize in a topic or subject area.
Image: Rebecca stands in a hedge maze, with a torch. She says “There’s so much material to look through it can feel like a maze. Our job in reference is to help people navigate that maze.”

Panel 17: There are initiatives to recruit librarians from underrepresented groups and organizations dedicated to supporting these groups.
Image: Rebecca is shown, saying “there are efforts to also recruit librarians from underrepresented groups.”

Panel 17: Libraries seek to protect intellectual freedom and preserve privacy. The American library association opposed the Patriot Act and in 2006. Four connecticut librarians went to court regarding gag orders.
Image: Uncle Sam stands in front of an American flag, saying “I wasn’t trying to spy on patron records…I just wanted…uh. Book recommendations.”

Panel 18: Libraries also promise literacy and not just for books.
Image: Different types of images are shown next to types of literacy: a computer with computer literacy, a stethoscope next to health literacy, a dollar next to financial literacy, a ballot box next to civic literacy and a phone next to media literacy.

Panel 19: Teaching, instruction, budget management, programming, research: all of these skills are needed to keep libraries going.
Image: two shelves are shown with different kind of hats on them. The hats are labeled with different kinds of library skills.

Panel 20: And only one of them, on rare occasions, is to shush people. END
Image: Rebecca stands in front of two shelves and a reminder to keep quiet on the second floor. She winks as she says “shush”

Acquisitions | ALA. American Library Association. May 5, 2009. Accessed September 16, 2024. https://www.ala.org/tools/topics/atoz/profresourcesacquisitions/acquisitions
Serials | ALA. American Library Association. April 29, 2010. Accessed September 16, 2024. https://www.ala.org/tools/atoz/Serials/serials
Education. American Association of Law Librarians. Accessed September 24, 2024. https://www.aallnet.org/careers/about-the-profession/education
What’s an Archivist? National Archives. June 7, 2022. Accessed September 19, 2024. https://www.archives.gov/about/info/whats-an-archivist.html
ALA Standards & Guidelines | ALA. American Library Association. June 13, 2008. Accessed September 25, 2024. https://www.ala.org/tools/guidelines
Vinopal J. The Quest for Diversity in Library Staffing: From Awareness to Action – In the Library with the Lead Pipe. In the Library With The Lead Pipe. January 13, 2016. Accessed September 25, 2024. https://www.inthelibrarywiththeleadpipe.org/2016/quest-for-diversity/
Elliott J. Remember When the Patriot Act Debate Was All About Library Records? ProPublica. June 17, 2013. Accessed September 20, 2024. https://www.propublica.org/article/remember-when-the-patriot-act-debate-was-about-library-records

Comic: Why Can’t We Just Google It?

r.kyserSeptember 4, 2024September 4, 2024

Today on the blog, we answer a common query: why can't we use Google? Hopefully this comic provides an answer.

Panel 1:

Narration: It’s wild to remember how information seeking used to work before Google.
Image: A picture of the google search string is shown

Panel 2:
Narration: I don’t have the best memory of it myself: I was a 90’s kid who was still in 6th grade when the digital revolution started to really hit its stride.
Image: A picture of the cartoonist, Rebecca, as a child is shown. She has glasses, a green day American idiot shirt on, and a bob haircut. Next to her labels read as follows: My early 2000s look: thick pair of glasses, bowl cut unsuited for curly hair, i-pod mini blasting MCR, t-shirt from hot topic

Panel 3:
Narration: I was taught to use a card catalog in the library and until age 12, I viewed the computer mostly as a machine to play games on.
Image: A screenshot from oregon trail is shown. It is the outline of a bull and wagon in bright green. Under the image, text reads: you have died of dysentery.

Panel 4:
Narration: Google was around along with Ask Jeeves and Yahoo, but it wasn’t quite the default search engine it is today.
Image: a 6th grade Rebecca now wearing a different shirt, looks intent as she uses an old apple computer from the 2000s. A thought bubble next to her reads “how to catch regirock pokemon ruby”

Panel 5:
Narration: In high school, however, google had won its spot as king of the search engines.
Image: The cartoonist, now age 15 in a batman shirt is shown, with straight hair and no glasses. Text next to her is labeled as follows: My mid-late 2000’s look: burnt hair from hair straightener, contacts, first pair of earrings, t-shirt still from hot topic.

Panel 6: It was so well known, it’s now a very: the term “google” was added to multiple dictionaries in 2006 (1).

Image: Two women, one with her hair up in a bun and a blue sweater, and one with long straight hair with a scarf stand in a baking grocery aisle. The one in the blue sweater says “Hey can you google what we need to make red velvet?” and the woman with the scarf, checking her phone says “Yeah sure.”

Panel 7: I use Google frequently to this day. Just this morning I googled… a question about Pokemon.

Image: The cartoonist, now, wearing a purple shirt, a light purple blouse and glasses, holds up her phone. She says “some things never change.” In the bottom right hand corner, text reads “I wish this was a joke, but sure enough my history is “encounters for Tornadus”

Panel 8:
Narration: “Now with all of this said, let’s dive into a question I hear a lot from medical students
Image: Four different students, a man with a beard and a beanie, a women with a pink shirt and red hair, a man with glasses and a tie and a man with curly hair and a green jacket all ask “why do you not want us to use google.”

Panel 12:
Narration: It may appear like asking folks to take the long way around.
Image: A map is shown with a blue and a red route. The red route appears shorter and a speech bubble from off screen says “Red is faster!”

Panel 13:
Narration: The key word here is “appears.” Because the fact of the matter is, trying to find academic papers with Google may take longer than just using a database.
Image: The same map is shown, but this time we can see that the red route goes through a body of water and mountains. A speech bubble from off screen says “wait a second…look at this version.”

Panel 14: This is because Google indexes webpages. Scholarly articles are often not indexed as Webpages. So they won’t appear with a Google search (2).
Image: A magnifying glass with green glass is shown on a purple whiteboard. On the whiteboard, text reading “webpage” is written in black, while text in green reads “article.” Due to the green glass in the magnifying glass, the text reading “articles” can’t be seen. A speech bubble from off screen reads “I don’t see anything there.”

Panel 12:
Image: A bald man with a goatee and a blue long sleeved shirt asks: “what about google scholar?”
Across from him, Rebecca, replies “Good question.”

Panel 13:
Narration: Google scholar has its uses, but it doesn’t index quite as comprehensively as a library database. Also, since it’s highly automated, it can contain articles from predatory publishers or other untrustworthy sources (3)..
Image: A man with blonde hair and a tie standing at a podium looks skeptically at a shark with a top hat and a red bowtie. He asks “You’re a publisher” which the shark replies “You betcha.” Text in the upper right hand corner reads “see the comic predatory publishing for more on ponzi the shark (4).”

Panel 14:
Narration: Another advantage to databases is their filters. Google has some filters like publication date but there’s not much to limit results unless you like long search strings.

Image: Rebecca stands in front of a computer monitor. The monitor reads: Search: Headache Results: 2,300,000. Rebecca says “that’s a lot of results!”

Panel 15:
Narration: Given the size of the web, without ways to filter results, you might find yourself looking for a metaphorical needle in a haystack.
Image: A person with short brown hair, a red jacket and an orange shirt looks at a stack of hay. They say, looking resigned “Time to find a pitchfork, I guess:

Panel 16: Databases have a variety of filters that allowTYPO us to be very specific regarding our searches. To use our haystack metaphorTYPO, an academic database is like searching with a metal detector.

Image: A women with a green shirt and long black hair holds out a metal detector towards the haystack. She says, excited “found it!” The person from the last panel, has their head poking out of the haystack, says “I’m never gonna get this hay out of my hair.”

Panel 17:
Narration: Finding Good medical information with google might not only take longer than a database but it might take you to a biased or out of date answer.
Image: Dorthy, from the wizard of Oz, holding Toto, explores what looks to be a dungeon with cobwebs, a bookshelf full of outdated books on “The 4 Humors and Miasma.” She says “I don’t think we’re in accurate medical literature anymore, Toto.”

Panel 20:
Narration: Google can also provide results that are flat out wrong.
Image: Dorthy still with Toto, stands in Mordor from lord of the rings. They eye of Saroun, Mnt Doom and fire can be seen in the distance. Near Dorthy, a sign reads “Sick? Try the one ring!” with a picture of the ring. Dorthy says “We’re definitely not in accurate medical literature anymore.”

Panel 21:
Narration: Back when I was in grad school, studying covid misinformation, the second result google gave me for “covid care” was an anti vax org.
Image: Rebecca, sitting at a desk with her computer, a cup of coffee and a lamp, leans back as a duck wearing a white lab coat pokes out of her computer screen. Rebecca, disgusted says “Ah! A Quack!” The Duck says “Have you tried my magic tonic water?!”

Panel 22:
Narration: (Also, Google’s AI summaries are making the false information problem worse. See the comic on AI for more info) (6).
Image: A robot holds a tray with a slice of hot pizza on it. On the monitor that us the robot’s head, text reads as “Menu: Pizza with Glue”

Panel 23:
Narration: This isn’t to say google has no uses: if you need to look up something straightforward, like the scientific name for viruses that can cause the common cold, it can probably do that (7).
Image: Three virus molecules are shown labeled as “rhinoviruses” ”Coronaviruses” and “adenoviruses.” The rhinoviruses has a rhino horn on it’s head. The coronaviruses says “please don’t confuse me with my cousin!” The Adenoviruses says “Time to pray at the porcelain throne.”

Panel 24:
Narration: But I wouldn’t necessarily go to google to get treatment advice for the common cold.
Panel: Three types of advice are shown. Next to an image of a glass of water and tea text reads: drink lots of fluids? Good. Next to the image of a variety of fruits such as orange, strawberries and lemons text reads :Eat stuff with vitamin C? Uh, well it won’t hurt you, I guess (and tasty)? Next to the image of three pills, text reads as “antibiotics? Noooo.” One of the pills says “Wait, did you say viruses?”

Panel 25:
Narration: Think of Google as one tool in your toolbelt. It’s good for some tasks, but that doesn’t mean you should use it for everything.
Image: A hammer labeled as google, tries to hammer a screw into a piece of wood.

Panel 26:
Narration: So make sure to use the right tool for the task. Dynamed or Epocrates for practice guidelines, pubmed or scopus for peer reviewed articles, access medicine or clinical key for textbooks.
Image: A toolbelt containing a scraper, a screwdriver and pliers hangs down the side of the page.

Panel 27:
Narration: And Google for where to find a snack after a job well done.
Image: Rebecca sits at a table with a giant bowl full of pasta. Her eyes are closed and she looks content, with little hearts next to her. In the bottom right hand corner, text reads as “fin.”

Comic: Mimic in the Machine: Chat-GPT and A.I.

r.kyserAugust 5, 2024October 16, 2024

One of the most frequent questions we get at the library in recent months is in regards to A.I. What is A.I? Is A.I the future? Are we all about to be replaced by robots? In this month's comic strip, we simplify A.I. in order to make sense of what's realistic, what's plausible and what's still science fiction.

Speech Bubble 1:Ever since AI burst onto the scene, I’ve seen a lot of folks misunderstand how it works.
Image: Rebecca, a librarian with light skin and dark curly brown hair in a ponytail speaks in front of a bunch of tech items.

Panel 4:
Narration: In reality, while AI can write or talk, it’s not “thinking” like humans do
Image: The robot displaying a blank expression is next to a thought bubble showing binary code.

Narration: To understand how AI “thinks” we need to understand what this kind of AI is and how it works.
Image: There is a monitor and on it, a pixilated version of Rebecca is shown next to the text “Understand A.I.” Then under that is the text A: Y B: N

Panel 6:
Narration: First, the kind of AI seen in movies is not the same kind in chat-gpt. That is, self-aware AI currently doesn’t exist outside of fiction.
Image: Two books are shown. One of the books has a picture of a robot on it stating “foolish: it is statistically unlikely to be lupus” The title of the book is “Watt.Son M.D”

Panel 7:
Speech Bubble: The AI we see discussed today is known as generative AI. It can produce things like text, images and audio by being trained on large amounts of data (1).
Image: A flow chart is shown. A bunch of file cabinets is first, then an audio icon next to the text or then a picture of a monitor next to the text or and then a smiley face drawing.

Panel 7:
Narrator: I’m going to vastly simplify. Say we want an AI to make images of sheep. First we’d grab a bunch of images of sheep as our training data.
Image: A table is covered with a variety of photos of sheep. The sheep are all different sizes and colors.

Panel 8:
Narration: Over time, as we feed the model more pictures of the sheep, the model starts to identify common shared characteristics between the images.
There is a little white sheep with a black face. Next to it, text states: Aspect: fluffy Feature 2(ear) Feature 2(eye) feature: tail= sheep

Panel 9:
Narration: Now, when this works as intended, after tons of images, our AI can start to produce images of sheep itself based off the training data. This is why it’s called “generative” AI; it is generating new content.
Image: The robot from early has an excited expression on it’s monitor. It points to a fridge where a picture of a sheep is displayed.

Panel 10:
The AI is able to produce these images not because it now “knows” what a sheep is, but by essentially large scale probability. I’m still vastly simplifying, but the AI makes the sheep fluffy not because it knows what wool is, but because 100% of its training data includes wool.
Image: Rebecca stands in front of a television screen. On the screen, the robot looks confused at a black sheep in a field.
Panel 11:
Narration: So if we apply this to words, AI is not so much writing as it is calculating the probability of what word is most likely to follow the word it just typed. Sort of like autocorrect.
Image: The background is a thunderstorm. There is text that reads: it was a dark and stormy _____? A. Night 90% B. Evening 7% C Afternoon 2% D. Day 1%

Panel 12:
Narration: Okay so why bother making this distinction. Why does it matter?
Image: The robot is shown with it’s monitor displaying a buffering message. Above it, a chibi Rebecca says “let me explain.”

Panel 13:
Narration: AI relies on its training data. Let’s consider the sheep example from earlier. In the photos I drew, none of them show a sheep’s legs.
Image: Rebecca sits in front of her tablet with a drawing pen. She gestures to the viewer, exasperated.
Rebecca ‘s Speech Bubble: “Look, I only have so much time to draw these things.”

Panel 14:
Narration: If all the images I feed our hypothetical AI are of sheep from the middle up we might get something like this.
Image: Three pictures of sheep are displayed. None of the sheep have legs and instead are puffballs of wool. One sheep is square shaped.
Narration Con: Our AI can only generate based on its data. So if we feed it no pictures of sheep with legs, we get no pictures of sheep with legs (frankly is also shouldn’t make images of a sheep where the entire body is in the frame either). The backgrounds will be a mashup too, as the AI will consider it as part of the image. This leads to interesting results with a wide range of background types.

Panel 15:
Narration: This is one of the reasons AI images struggle with details like fingers: how many fingers you can see in an image of a person varies widely depending on their pose and the angle of the photograph (2).
Image: Four hands with different skin tones are shown, each with a different gesture. In a little bubble to the left, Rebecca is shown looking tired.
Rebecca Speech Bubble: Drawing hands is hard…

Panel 16:
Narration: The same thing goes for writing: when AI writes out “it was a dark and stormy night” it has no comprehension of any of those words. It’s all based on probability. And this is the misconception that leads to so many problems.
Image: The robot is seated at a chair, typing at a computer. From the computer, text reads “it was a dark and stormy night” and from the robot speech bubble we get more binary.

Panel 17: Narration: For example let’s take AI hallucinations. AI Hallucinations refer to when AI makes things up, essentially lying to the user. Now that we understand how AI works, we can understand how this happens.
Image: The robot is shown its monitor full of a kaleidoscope of colors and two big white eyes. The background behind it is also a mix of colors.
Panel 18: Narration: AI has no comprehension of lies or the truth. It is regurgitating its training data. Which means that if it doesn't have the answer in the training data, or is fed the wrong answer, what you’re going to get is, the wrong answer.

Panel 19: For example, Google AI made headlines when it recommended people use glue to make the cheese stick on their pizza. (3).
Image: A man with dark skin, glasses and a beard stands in front of a pizza and a bottle of glue. He is wearing an apron.
Man’s speech bubble: “A least it said to use non-toxic glue.

Panel 21: That example was pretty harmless, but it can be much worse. AI has told people to eat poisonous mushrooms (4), provided dieting advice on a hotline for eating disorders (5) or displayed racial bias (6).
Image: The grim reaper is shown, wearing a little shef scarf with his sythe. Next to him is a display of mushrooms. Underneath text reads: guest chef death showcases favorite deadly mushrooms.

Panel 22: Generative AI systems also comes up with fake citations to books and papers that don’t exist. Often is mashes up real authors and journals with fake doi numbers
Image: Three journals are shown composed of fragments of other journals on their covers, each stitched together

Panel 23: Narration: And don’t get me started on the ways images can go wrong (8).
Image: Rebecca stands next to a table with school supplies and a rat. The rat is looking up with her with a question mark over its head.
Rebecca’s speech bubble: Just look up AI rat scandal and you’ll understand why I didn’t draw an example.

Panel 24: Image: The rat from the last panel is shown.
Rat speech bubble: So AI is worthless?
Narration: Absolutely not!
Panel 25:
Narration: AI absolutely has uses. While it’s still in early stages, AI has shown promise in helping doctors identify potentially cancerous moles
Image: The robot and a doctor look at a monitor
Doctor: Should I make a biopsy of both?
Robot: 71%
Doctor: Both it is!

Panel 25:
Narration: But it’s not a magical solution to every problem. And when we forget that, our “artificial intelligence” is more artificial than anything intelligent.
Image: The robot’s monitor is shown with the citations for this comic displayed.

Comic written and drawn by: Rebecca Kyser

Citations:

1.Christian B. The Alignment Problem : Machine Learning and Human Values. W.W. Norton & Company; 2021.

2. Lanz D/ JA. AI Kryptonite: Why Artificial Intelligence Can’t Handle Hands. Decrypt. Published April 10, 2023. Accessed August 5, 2024. https://decrypt.co/125865/generative-ai-art-images-hands-fingers-teeth

3. Robison K. Google promised a better search experience — now it’s telling us to put glue on our pizza. The Verge. Published May 23, 2024. Accessed August 5, 2024. https://www.theverge.com/2024/5/23/24162896/google-ai-overview-hallucinations-glue-in-pizza

4. AI-powered mushroom ID apps are frequently wrong - The Washington Post. Accessed August 5, 2024. https://www.washingtonpost.com/technology/2024/03/18/ai-mushroom-id-accuracy/

5. Wells K. An eating disorders chatbot offered dieting advice, raising fears about AI in health. NPR. https://www.npr.org/sections/health-shots/2023/06/08/1180838096/an-eating-disorders-chatbot-offered-dieting-advice-raising-fears-about-ai-in-hea. Published June 9, 2023. Accessed August 5, 2024.

6. Noble SU. Algorithms of Oppression : How Search Engines Reinforce Racism. New York University Press; 2018. doi:10.18574/9781479833641

7. Welborn A. ChatGPT and Fake Citations. Duke University Libraries Blogs. Published March 9, 2023. Accessed August 5, 2024. https://blogs.library.duke.edu/blog/2023/03/09/chatgpt-and-fake-citations/

8. Pearson J. Study Featuring AI-Generated Giant Rat Penis Retracted, Journal Apologizes. Vice. Published February 16, 2024. Accessed August 5, 2024. https://www.vice.com/en/article/4a389b/ai-midjourney-rat-penis-study-retracted-frontiers

9. Lewis. An artificial intelligence tool that can help detect melanoma. MIT News | Massachusetts Institute of Technology. Published April 2, 2021. Accessed August 5, 2024. https://news.mit.edu/2021/artificial-intelligence-tool-can-help-detect-melanoma-0402

Tag: Himmelfarb Comic

Comic: Librarians 101

Comic: Why Can’t We Just Google It?

Comic: Mimic in the Machine: Chat-GPT and A.I.

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