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Tag: Generative AI

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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 20: Now where did it get this cooking tip? A joke post from reddit. Google made a deal with Reddit to train it’s A.I on the site’s data in February 2024. Image: The avatar for reddit yells after the robot who is running off with the image of a glue bottle on it’s monitor. Reddit avatar’s speech bubble: It was a joke!
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

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