I’ve found Mike Caulfield’s recent online workshops about how to use AI to fact-check (or get context about claims) very useful!
If you’re not familiar with him, he’s the creator of the SIFT method for information literacy: Stop, Investigate the source, Find better coverage, Trace claims to the original context.
Lately he’s been working on methods for using AI to get context around claims — ideas we as librarians can use when teaching information literacy. You can read his latest post here: SIFT for AI: Introduction and Pedagogy.
Nano Banana Pro for creating infographics and comics
Now this post is not about how to use his method… instead it’s for showing my fun experiments with using Nano Banana Pro to illustrate his ideas.
Since Google just released Nano Banana Pro recently, I’ve been experimenting with using it to create infographics and comic panels. It works well! I use it in the paid version of Gemini.
So I thought I’d use the “three moves, seven tips” idea to experiment with. Here are some of my results.
Feel free to copy and use these for any purpose.
Prompt: Make an infographic that shows these tips. Do it in the style of a colorful modern mind map. Click on the image for full size.
Prompt: Now do it in the style of a 1950s comic. Click on the image for full size.
Prompt: Now do it in the style of the Simpsons. Click on the image for full size.
Prompt: Now do it in the style of a modern comic with three students using mobile phones. Click on the image for full size.
I liked the modern comic best, so it’s the featured image at the top of this post.
Learn more about Nano Banana Pro
Here are a couple of videos that inspired me (from two of my favorite channels). They show what Nano Banana Pro can do.
(The days of recognizing AI-generated images by mangled text are over. I’ll need to update this tutorial soon).
Suno is a generative AI tool for creating music. I’ve been experimenting with it for generating music clips. It’s fun!
At first I thought pretty much all the Suno clips sounded the same (similar pop music styles). But I’ve been experimenting and I’ve come to like it! You really can generate many different styles of music.
My musical background
My first career was as a classical musician (I played pipe organ and harpsichord). I have a Masters in Music from Boston Conservatory and I spend my twenties working as a performing musician in Boston. That meant playing for weddings, funerals, freelancing in different orchestras, and teaching lessons.
I became a music librarian in order to support myself as a performing musician. My first job out of library school was as the music librarian at the Longy School of Music, in Cambridge, MA.
By the time I turned thirty, I decide that performing was too stressful, and I enjoyed my library work more. So I transitioned to working as a full time librarian. I’ve worked as a general reference librarian (Emmanuel College, Boston), a systems librarian (Bose Corporation), a library web manager & head of UX (MIT Libraries), and now I’m an elearning developer (University of Arizona Libraries).
Since I’m very interested in testing new AI tools, and also have a music background, Suno piqued my curiosity!
How does Suno work?
Enter a text prompt and it will create a short clip of music based on that prompt. You can then extend your clip to make a longer version.
You can generate music with or without vocals. It uses ChatGPT to create lyrics for you, or you can write your own lyrics. Free and paid accounts are available. You can create about 10 songs per day with the free version.
If you are using a free version of Suno, they retain ownership of the songs you generate, but you’re allowed to use those songs for non-commercial purposes.
If you are a paying subscriber to Suno, then you own the songs you generate. (I’m a paying subscriber).
I think it’s great fun! I actually like some of the clips I’ve generated.
I’ve generated many different genres, not just the various popular styles you usually hear from them.
Like all generative AI tools you need to keep experimenting with different prompts and you won’t like every output. And be sure to use “custom mode” if you don’t want it to rewrite your prompt. A good place to get ideas for musical styles and terms is in this Wikipedia article: List of music genres and styles.
Here are some clips I’ve generated — just to see various styles I could get it to output.
Jazz piano
Bebop
Light orchestral
Klezmer music
Afro-pop female vocal (ChatGPT made the lyrics, and then I used ChatGPT to translate it to French)
Hypnotic, electronic, synth-pop, krautrock
Bluegrass fiddle band
Circus clown
How well does it do with classical music?
Even thought the technology is pretty good for many types of music, it does get a bit weird when asked for specific styles of classical music.
For example, when asked for impressionist piano music (in the style of Debussy or Ravel), it often creates something that sounds more like Mozart. And for other genres and periods it often generates the basic sounds and structure pretty well, but the harmonies are weird. I would guess that it either lacks the training data, or I just haven’t found the best words to prompt it with.
Here are a couple of clips that came out ok, though. They aren’t masterpieces, but they could work for background music when you want certain kinds of atmosphere.
Renaissance boy choir (I gave it the lyrics to Gloria in Excelsis Deo)
Baroque pipe organ
What does this mean for professional musicians?
I really don’t think people will ever stop learning to play instruments, attending live concerts, or making music for fun. What technology like this might be used for is creating clips to use for YouTube videos, advertisements, and other types of background music. It could compete with various royalty free music collections that are used for background music everywhere.
Also, AI music generators make it easier for people to create music even if they lack formal musical training or access to expensive equipment and lessons. This opens up musical expression to a wider range of people.
By the way, here’s someone who has made an AI version of her own voice. She’s got some interesting ideas about all of this. See AI is changing music forever, Holly Herndon and Mat Dryhurst.
I’m working on a new short book, to published by Libraries Unlimited later this year.
Siri, Alexa, and Other Digital Assistants: The Librarian’s Quick Guide
Here’s the draft table of contents.
1. What is Voice-First Computing?
Introduction
Why It’s Important for Librarians to Be Familiar with This Topic
Definitions
Platforms Overview (Alexa, Google Assistant, Siri, Cortana)
Typical Tasks
Statistics
History of Voice Computing
Advantages of Voice Computing
Speech Recognition and Natural Language Processing
2. Hardware and Skills
Hardware
Smart Speakers (Amazon Echo devices, Google Home Devices, Apple’s HomePod, Microsoft’s Cortana on the Harmon Kardon Invoke)
Smart Home Devices
TV Devices
Smart Toys & Robots
Voice Computing in Cars
Comparing Platforms
Third-Party Skills
Creating Skills
Using Automation to Connect Tasks with Other Apps
3. Real-World Uses
Hands-Free Situations
Workplace Uses
Benefits for the Elderly
Benefits for People with Disabilities
Ideas for Uses in Libraries
Design Issues for Voice Interfaces
4. Privacy and Ethical Concerns
Uses of Voice Data in Ways You Didn’t Intend
How to Turn Off Microphones and Delete Recordings
Privacy Recommendations for Design of Voice Assistants
Sexism in Voice Computing
Children and Voice Assistants
What Librarians Need to Know
5. The Future of Voice Computing
A Primary Way of Using Computers
Voice Everywhere
Hearables & Smart Glasses
Moving from a Mobile-First to an AI-First World
Possible Future Uses in Libraries
Appendix
Bibliography
Index
This is a fun and interesting topic to write about! It’s a rapidly changing technology, so I’m publishing this on a quick timeline. If you have ideas for what else you would like to see discussed in the book, let me know!
Postloudness is a good place to start, but there are many more diverse podcasts available. In order to assist librarians with recommending podcasts for diverse audiences, I’ve complied lists in the following categories:
◆ produced or hosted by women
◆ racial and ethnic diversity (African-Americans, Latinos, Asian-Americans, Native Americans)
◆ LGBTQ (lesbian, gay, bisexual, transgender, queer or questioning)
◆ aging and ageism (elders’ & children’s rights)
◆ homelessness, poverty and economic class
◆ people who are (or were) incarcerated
◆ adult literacy
◆ neurodiversity and mental health issues
◆ physical disabilities
To get this annotated list of podcasts for diverse audiences, watch for my report from ALA Tech Source, available in 2017.
Do you ever get questions from people who want to see what your new device can do? Here are a few apps for showing off the potential of mobile devices for educational use. (more…)