I attended Internet Librarian 2019 in Monterey, CA, from October 21 – 23. Monterey is a beautiful place, where as soon as I walked out of the hotel I could hear the sea otters and seagulls, and smell the sea salty air. One of the pre-conference activities was a walking tour of Cannery Row, where we learned about the town’s ties to writer John Steinbeck and marine biologist Ed Ricketts. We also walked by some of the quirkier tourist shops, like the Sea Otter Shirts shop that flaunted “Hairy Otter” pun shirts in the window. While it’s difficult to distill everything I learned, here are some takeaways that stuck with me.
Read Your Life
Conferences are very good at reigniting something, whether it’s an idea, or a connection, or a goal. This conference reignited my desire to start reading again. Book recommendations were plentifully tossed out by presenters and fellow attendees, and I was reminded that taking a deep dive into a book and revisiting reading as a hobby (and a kind of old friend, I suppose) is a must for me. Not just “skimming the internet” reading, but voracious reading, where I want to do it more than anything else. I’ve always been a reader – when I was a kid I always had a stack of books going on my end table by the bed --but as an adult, I hardly measure up to that jungle cat-level appetite.
Why is that? I’m sure I’m not alone in this moment in human history in saying I find myself easily distracted. My fingers reach out impulsively for the smart phone, or the Nintendo Switch, and time invested in reading is diverted to impulsive scrolling or farming on Stardew Valley. I’ll admit, reading more has been a “New Year’s Resolution” type of thought I’ve had many times in this current decade that is now hurtling towards its end, but since getting home from the conference, I’ve had a few good evenings after work of tuning my brain into reading.
There is so much to read, and there really is not enough time. It seems like every time I turn around, a new pile of books shows up on my bookshelf at home that are just waiting for me to notice them. I love bookstores, and I love buying books. So really, this voracious reading goal is a challenge to keep up with my voracious book buying habit. My action plan for 2020, that I am allowing myself to get started on now (because the engine needs some time to get warmed up), is to read 12 books that further my understanding of some aspect of being an information worker – one for every month (plus a few bonus weeks), and one article a week, every week. This is in addition to reading fiction and other nonfiction, because that list is full of graphic novels, science fiction, fantasy, and more. There is perhaps nothing more sobering than finding out, realistically, how many books you still have time to read. Life is short, and my reading list is very, very long.
Searching Smarter
And now on to a topic perhaps close to an information worker’s heart: search. I went to a couple talks given by Mary Ellen Bates, a search expert, who gave some excellent advice on doing research for clients. She emphasized that the results we give back to our clients should be something more than what the client could get doing a google search on their own. We should take the approach that we are different than google, even if we are just “better google searchers.” When doing a search for a client, know when to say “when.” Know how much time you want to devote to it. Decide “this project gets X amount of time” before you start. Then when you’re 75% done, step away from the keyboard for a bit. Come back, and devote the remaining 25% time to post-processing.
What is post-processing? Giving the client something better than “IAOTWFF’ (it’s all on the web for free). Synthesize the results, make sense of them, look at how you can make results visibly tangible to users. Organize the results, put an introduction, make the answer the client needs clear. Don’t just dump data. Phrase results so they are really useful. Before you turn in the results to the client, you can ask another librarian “Am I missing anything?” that way you can confirm to the client “I contacted a colleague who confirmed that this is a complete search” or something to that effect.
AI – This Generation’s Jaws
There was a lot of discussion about AI, machine learning, and robots at the conference, and a lot of generalized anxieties about the coming of the bots to take our jobs, so to speak. Much like past generations had Jaws and the “Get out of the water!” fears that followed everyone when they went to the beach, it seems like now we worry about AI coming out of the water to eat us all. Or something like that. In any case, there was some excellent discussion of what information workers (i.e. humans) can do that AI can’t, and also, what we can do to “robot proof” ourselves.
AI and machine learning are potentially an information worker’s friend, if we would accept them. They are helpful tools that can keep information workers from wasting time doing tasks that automation can do. They are not “magic,” as Meredith Broussard, author of Artificial Unintelligence, and also our keynote speaker for day two, said. Code and machine learning are aspects of math. There are people writing these subroutines, and we can interpret them. It’s also worth the investment to understand it. More than one speaker mentioned that it’s important to read up and have at the very least, a popular science-level of understanding of AI and machine learning – read an article a week, at minimum. As information workers, we should be as proficient at AI technologies as the average Wall Street Journal reader.
There are also things that AI, machine learning, and robots can’t do as well as humans. Some examples are: writing documentation, giving webinars, becoming a subject expert, acting on emotional intelligence, being curious, evaluating fake news, having empathy, being nurturing, strategizing to understand where an organization wants to go, and helping them to get there with critical thinking.
Where information workers can “robot proof:” in taking the lead on demonstrating to our organizations that AI is dependent on relevant, good, and accurate information. AI is dependent on information from the outside world to do anything. That’s where we come in to play. We can own the AI discussion by being information interpreters and guides. We can push and advocate for more transparent AI processes, making sure that what is going in and coming out isn’t hidden bias and inaccuracy. Going forward, our approach should be that while we may not have answers, we have a lot of questions. We need to make management aware of the role of good information in AI implementation.
The way to getting management to see us as stakeholders in AI implementation is to embed into the discussions, and to continue to learn. Be willing to listen mindfully and ask meaningful questions. Walk up and down the proverbial hallways of your organization and ask people what they’re working on. Once you ask questions, they’ll be more willing to break down silos and invite you in.
Still worried about the coming of the bots? You can take the “Will A Robot Take Your Job?” test at https://gigaom.com/quiz/
Data Curation
Data, as we know it, is messy, folks. The digital junk drawer haunts nearly anyone with a computer, I’m willing to bet.
Matt Benzing, Engineering and Computing Librarian at Miami University, gave an excellent talk on “Data Curation and Opportunities.” He stated that there’s a reproducibility crisis, and many classic experiments can’t be reproduced. Vaccines, climate change, and the moon landing are “debatable” in part because of this issue.
So how about data curation? Matt believes that there is a part for humans in curation – in data moving from researcher to repository. There’s a space in between that still requires human beings asking questions and making decisions. If information workers don’t fill the gap, others will, and we can take the lead in good data documentation. Data needs cleaning, de-dooping, validating, visualizing, and more. Information workers can also educate those in their organizations on the importance of a data management plan (that isn’t just a copy/paste situation), open data, and privacy.
Matt’s slides have a lot of great resources and are available here. I would also recommend watching the video he shared during his presentation, a lovely animation that illustrates the tribulations of data sharing in science communities.
Extras
• Teach information hygiene to clients. Teach them to distinguish between “containers and information.” This idea of “information hygiene” is an interesting one to me – perhaps a sibling of information literacy.
• We are our own advocate. Tell everyone how wonderful we are and all that we are working on. Keep a log throughout the year, keep note of feedback, and projects, and follow up with individuals to ask how it went. Failure is also success. When we do encounter failure, sit in office with those involved, ask how it went wrong, and what we could do better. It will de-escalate things. Take that feedback and listen to what they have to say. Sometimes their feedback isn’t valid, but they want to be heard.
• Makerspaces: in the closing remarks for the conference, one speaker, Brian Pichman, who has been heavily involved with makerspaces, said that the makerspace idea may be “slowly dying.” Companies at the forefront of the makerspace movement (ie Make magazine) were doing well, but they’re starting to “fizzle out.” I interpret this to mean that the idea of a “makerspace” as seen by commercial interests is probably not viable long term, but that doesn’t mean the spirit behind makerspace is irrelevant. Brian mentioned that his organization is partnering up with the UN to help solve global issues such as poverty, hunger, and clean water shortages, and that they are using the makerspace momentum to better themselves. With climate change knocking on our door (and with my home state currently being on fire in more places that I can keep track), I can see an opportunity for makerspaces to get very active In their communities on this front.
• Data curation and information hygiene seem like in some essence climate change issues. Preserving climate data is one part of that, as is making sure we are curating our information so as not to create waste and excess and more “digital junk drawers.” I don’t know much about the carbon footprint of cloud services or servers, but I’d like to read more and find out about this.
That’s all, folks, for now! Thanks for reading!