When was the last time you were impressed with your Alexa or Google Home speaker? Impressed by the fact that it could listen and understand what you were saying to respond appropriately. All effective vocal recognition programs have artificial intelligence running under the hood, but we seldom take note that these applications were made possible by this technology. In fact, there is an interesting dynamic with AI currently submerging below the collective consciousness: when AI proves to be highly effective at performing a process or a task, it ceases to be “AI” and it simply becomes part of our daily lives. This is supposing that the majority of people even recognize that AI is ever involved in a task.

We are increasingly finding aspects of our daily existence colonized by AI; much of it is both mundane and useful (see: Alexa, mapping apps or email spam filters), but some of it hints at the dystopian risks for our society when this power falls in the wrong hands (see Arthur Holland Michel’s Eyes in the Sky for a chilling look at the automation of wide-area motion imagery). If these far-reaching, negative possibilities for the future of AI are not surprising to you, then what I have to say next might be: there’s still time.

The door is open for AI. Right now, there is an explosion of AI applications permeating the world around us but there is also an exponential increase in AI resources that are free or available at low-cost — thus enabling the average library user to get access to this technology. Google’s AIY Projects gives everyone access to some of their AI resources for free. IBM offers parts of its AI-powered chatbot, Watson Assistant, for free. Amazon has an array of AI resources that users can try for free, but what is amazing is that Amazon Web Services has opened up its own training courses for machine learning (an AI off-shoot) for everyone to access. Apple has also stepped into the fray with more developer-focused resources. There are many, many more open source tools for AI that are entirely or mostly free. The options become bewildering very quickly and it can seem like you still need a Ph.D. in computer science to tackle this for the library masses.

However, there is still time to participate in the AI revolution, and it is imperative for libraries to gain a firm grasp on AI tools and concepts. This is especially important due to the nature of changes in 21st-century work. Think of the last time you were in the checkout line of your grocery store: how many cashiers were there versus the number of self-checkout stations? We are already beginning to see the impact of large-scale automation upon the workforce. This change is occurring both qualitatively (i.e., which jobs will be well-paid) and, eventually, quantitatively (i.e., how many jobs are left after some human work is made obsolete by technology). Although this example references advances in automation, rather than the impact of AI at work, the mechanisms under the hood of these self-checkout stations rely on AI technology. Running inside the networks, inside the computers of the financial institutions that monitor these transactions, are an increasingly powerful sets of fraud detection systems that run on AI. These are saving banks and financial institutions millions of dollars a year on financial fraud. They are also enabling more efficient customer payment processes at stores.

Going back to our thought experiment with self-checkout machines at the grocery store: two telling questions come to mind that perfectly illustrate the winners of this burgeoning AI-enhanced economy. Ask yourself: how many people need to be on-hand to assist with those self-checkout transactions? Now ask yourself: how wide do you think the pay difference is between the self-checkout assistants and the people behind the financial transactions that are setting up and running AI processes? Don’t get me wrong, automation or AI in-and-of-itself is not “evil.” In libraries across the United States, self-checkout stations have been a godsend for maximizing direct staff relational time with patrons and automating transactional tasks (while budgets haven’t kept pace with demand). But the differences will be stark in our economy when profits are driving the power possibilities of AI.

According to the McKinsey Global Institute, half of all work tasks could be automated (with a combination of technologies — significant among them, AI). This points to a critical need for training and upskilling the current workforce to be able to meet the demands of this dynamic work landscape of the future. In fact, a different McKinsey paper reports that big business is starting to adopt the same language that we in libraries have been using for a few years now, “A key to companies’ future success will be in providing continuous learning options and instilling a culture of lifelong learning throughout the organization.” This sounds very similar to language used the 2014 Aspen Institute Report, Rising to the Challenge: Re-Envisioning Public Libraries.

We are now at an exciting point of convergence between AI resources, AI education and public libraries. AI should be thought of as the next emerging technology for us to digest and help our users navigate; this aligns perfectly with our mission to make sure that the workforce is ready and able to meet new demands placed on it. Literacy is our foundation; AI is just the next area of skill development needed in a healthy economy. At the Frisco Public Library, we have been focused on supporting our community’s Innovation Ecosystem. To that effect, we view the library as the starting place for entrepreneurs of all ages and equitable AI education is fast becoming part of our core programming supporting that vision.

We had dipped our toes into several of the open AI apps, but it wasn’t until we experimented with the Google AIY Voice project kit that we found AI in an approachable package to run in a classroom setting (or at home in the kit). Using the Google AIY Voice project kit as a base framework, we built our Basic AI classes, which led to packaging up the kits as take-home AI Maker Kits. The idea being that one could take the class then check out the kit to further extend learning at home. This approach was an especially good fit for us because our experience in facilitating Python coding classes meshed well with the prerequisite knowledge needed to run the AIY Voice kit.