Category Archives: Technology

Design Thinking is a method of problem-solving that puts the user first. For some, design thinking is the best thing since sliced bread. For others, it’s just a new label for the same old thing.

Here’s a diagram to give you a visual of the steps in design thinking:

Truth be told, design thinking originated in Stanford and has been around for two and a half decades, long enough to filter into other methods of problem-solving. Over the years, people have added different flavors, and applied the methodology in different contexts. More often than not, each new flavor is branded with a slightly different title and description.

So yes. This is my flavor: Design Thinking for Librarians. For one brief, shining moment I thought I had the greatest epiphany in the world. Then I did a quick Google search and found… Design Thinking for Librarians. But in a different flavor, made by librarians out of Denmark and Chicago. That one is wheat, and mine is a marble rye. Then I found ALA’s Design Thinking page on the Library of the Future page. Then I was just sourdough.

Then I dug a little deeper. These flavors of design thinking all stem from Stanford’s model, but appear drastically different based upon the setting. My flavor is adapted for selecting makerspace equipment in the library, as seen in this Design Thinking for Makerspaces worksheet. I also made a worksheet for Design Thinking for Websites over the years.

My point is that theories branch off into a million different things. Just because two websites have the same name does not mean they contain the same information, nor should they necessarily be applied to the same subject matter. Yet, everybody has a valid point to make in their application. There is enough Design Thinking for Libraries to go around. And around. And Around. The process isn’t linear, it’s iterative design.

To learn more, check out the NCompass Live show I did earlier today on Design Thinking.

Right now, I’m exploring the use of chatbots in the library. As with any technology, there are pros and cons. Essentially, chatbots simulate human conversation in text or voice. More often than not, you will encounter chatbots in large banks or major corporations.

Most chatbots are designed to handle specific tasks, not full conversations. For example, a bank chatbot might be designed to handle requests to check a bank balance, find most recent transactions, or deposit checks. However, if you make a request outside the chatbot’s specialized task, the system breaks down and leads to customer frustration. Better designed bots will send the human to a real human agent after a certain point. This is the computerized chatbot frustration with which most people are familiar today.

This is changing. In the recent past, people could always tell when they encountered a bot in a text chat or phone operating system. However, natural language processing (NLP) is getting better. NLP is the heart of modern human-computer interaction, and will eventually allow computers to understand human requests without humans having to phrase our requests in a specific format. For librarians, that means Boolean search format, or choosing the right keyword alternative to narrow down search results, or uncover a certain navigation menu.

Humans naturally want to communicate using natural speech, including colloquialisms, slang, various accents, and more. Computers are getting getting better at this because machine learning systems are improving. NLP is a subset of machine learning (ML).

Machine learning-based systems like chatbots learn from large sets of data. ML algorithms are designed to study large amounts of data and find patterns. The system learns from the information that is available, and will make predictions based on the patterns it finds in the data. Similar to how humans learn. We do the best we can with the information we have available at the time. Processes improve as information improves.

As you can imagine, machine learning systems are improving because our available data sets are improving. For example, the natural language processing system from Google has access to text conversations, voice assistant archives, email conversations and more. Is it any surprise that their NLP systems are one of the best?

With more data, the machine learning algorithms have better information and can make a more informed guess about what the person means with a certain request. For this reason, we can all expect chatbots to become more popular and take over more routine tasks. Taking orders in a restaurant, or scheduling meetings would be a prime example. There are already bots being refined to accomplish these tasks.

The chatbot I’m working on now does not use more advanced machine learning. It uses a rule-based system where I build a decision-tree and manually tell the system which pieces of information to draw from to answer specific questions. In a rule-based system, I have to have a much better guess as to what a customer will ask, and how people will want to interact with the bot to accomplish specific tasks.

After practicing with the rule-based system, I can explore a more advanced ML-based system. When starting with new technology, it always helps to walk before I run. The fall hurts less if I’m not going at break-neck speed. I’m using Dialogflow, powered by Google, if you’re curious.

Yesterday I spent two hours of my life trying to install and set up an app on my phone that is designed to control a small robot. Two hours. To install and configure an app. I went through every troubleshooting process known to (wo)man and could not get this confounded thing to work.

Throughout this process, I kept thinking it had to be something stupid I did. I forgot something. I missed a step. Maybe the device wasn’t compatible, even though it was on the recommended list. Did I mistype a password?

At the end of the day, I asked a friend to install the same app on their phone and try to control the little bot. It didn’t work. When I called customer service, I found out the app had been down for the past few hours and their servers should be back online in a few hours.

Honestly, this isn’t the first time this has happened to me. It’s a tale as old as tech. The next day I installed the app and it took about five minutes. The little robot was up and running in no time, never having known how much time I spent trying to set up its controller. It zoomed along while I stared, dumbfounded, at my smartphone. Two hours.

Then I started laughing. I vowed to myself that no technology on the planet is ever worth that mental anguish. It’s not worth frying my circuits. Sometimes technology just doesn’t work right.

It’s nothing I did. It’s nothing you did. We’re not stupid. It’s just frustration at something we cannot control. A server halfway across the country that doesn’t even know we exist. Servers live in a binary world where things are either off, or on. There is little in between. Computers don’t care. They just do what we tell them.

So next time technology doesn’t work, take a step back. Turn it off and turn it back on again. Walk away and come back later. Sometimes tech doesn’t work because we made a small mistake. Other times, technology was just poorly designed and only another computer can understand what went wrong. More often than not, the server is just out of our control.

Technology breaks. It’s a fact of life. Sometimes all it takes is one day for everything to fall into place. Don’t give up on the tools that can help.