Author Archives: Amanda Sweet

I was typing an email on my phone last night and Google kept trying to put words in my mouth. Some of them were things no human being would ever say, others were eerily close to what I was planning to say. I have a Google Pixel smartphone and I recently found out this feature is called Smart Compose. Instead of only recommending the end of a word I’m typing, the system might recommend a full sentence.

I decided to try an experiment. Every time those light grey recommended words popped in, I tapped on them to accept the recommendation. I got some interesting results. Some were way off, others were eerily accurate. I tried it with responses to emails, and fresh email drafts with no recipient. Here’s what I started to notice:

• Responses to emails resulted in longer recommended phrases
• Initial email drafts with no recipient resulted in small, one-word recommendations to the end of sentences.
• Business formatted emails resulted in long recommendations of formal sentences
• Casual emails resulted in off-the-wall recommendations of things I would never say.

Good times were had by all. Anyway, the lesson plan idea is pretty simple here. Have learners observe their auto-response texts and emails for a while. If the person has auto-response deactivated on their device, temporarily reactive the service. It can be turned off later.

This observation can be done over the course of a brief lesson, or observed over the span of a week. Just ask people to write an email or text, then choose the autocomplete option. Highlight or mark out the autocomplete section so it can be identified later. This activity can only be done with Gmail or another Google service that has Smart Compose enabled.

Ask people to consider these questions:

1. Would you ever say any of the things Google recommends?
2. Did Google’s recommendation change what you were originally going to type?
3. Was the recommendation better or worse than what you had planned (consider phrasing, politeness, grammar, etc.)?
4. Do you like the Auto Complete option?
5. What do you think Google is looking at to make these recommendations?
6. How will this change the way we communicate?

Additional Resources

Gmail Autocomplete Feature Unveiled (YouTube): See Smart Compose in action and hear from the team that made the feature.

Here’s How to Use Gmail’s Smart Compose: The Verge shows you how to enable Smart Compose if you haven’t upgraded to the new Gmail yet.

Google’s AI-powered Smart Compose feature is coming to Google Docs: The Verge describes in more detail how the system works and how to use the beta in Google Docs.

It seems a bit impossible to truly be Future Ready. We do not know what the future holds. Now we are trying to prepare schools and our workforce for the unknown. Currently we are two decades into the 21st Century.

My search for a solution to the problems of the future left me with a series of big questions:

1. What do you want the world to be?
2. What do your friends want the world to be?
3. What are the problems of today that are preventing your ideal future from happening?
4. How do different systems in the world work together to impact the world? What does this look like closer to home?
5. What are your current skills and interests?
6. How can these skills be used to solve current problems?
7. Which skills can you develop to solve these problems?
8. How can we all work together to find solutions?

Mostly, how can the library help to bring the community together and provide the resources to learn? The real problem seems to be agreeing on a problem, the root cause(s), and a solution. One person can agree with themselves, but one person cannot change the world in a bubble.

Here’s a fun fact: the United Nations built a framework for their ideal world. This idealism appears in a neat infographic:

This is a nice starting point. But what does this mean for each individual? What about for the community as a whole? Which of these issues do people care about enough to take action? Here in Lincoln, NE, the Urban Development team is focused on reducing poverty, building sustainable communities, and creating decent work and economic growth for lower-income individuals, as seen in the Urban Development Team’s 5 Year Strategic Plan. That planning committee cares.

Are other people in the community aware of these goals? Do they have skill sets that can help? We know the problem, and there is a need. What makes people take action? How can the library raise awareness of this problem, find out how people can help, then provide tools and a platform to act?

Honestly, there is no universal solution to the problems of the world. These problems are systemic. One-shot programming makes people think. It does not change life-long habits. We are preparing for the 21st Century, not the 22nd. What causes long-term, positive change? What requires change?

I don’t post this because I have all the answers. I post this because I don’t. I post this because it is the only voice I have. I decided that if I’m going to use my voice for anything, I will try to say something that will have a positive impact on the world. That is a skill that will stand the test of time.

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.