The Setup

Generative AI, and specifically Large Language Models (LLMs) like ChatGPT, have been all over media lately. When OpenAI unleashed GPT3.5 on the masses November 30, 2022, it was like opening Pandora's box. The Generative AI genie is out of the box and I'm pretty sure no one is putting it back in again.

But beyond the current reality star of the Generative AI world, Generative AI models have quietly been having a significant impact on the way many businesses operate day to day for several years now.

My belief is that these types of models and systems are here to stay, that the way we operate will adapt to include them, and that the world won't end as a result.

The Challenge

For senior leaders and managers the rapid incorporation we are seeing poses a few challenges:

1. How do we enable our teams to use these great new resources effectively, securely, intelligently, and safely to add value to the products and systems we deliver for our clients? Bias in AI is real and has been documented already with many of the AI systems that have been deployed into the wild. Whether you look at Amazon's now discontinued AI tool used for recruiting, discussed in this link, or other examples of bias, it doesn't take long to realize that bias can influence the decisions made by AI systems even with the best of intentions in their creation. It's important to consider the impact AI can have on the way we operate, especially from the perspective of preventing improper bias. And there are also security concerns related to these resources, such as those talked about in this article about the ChatGPT data breach.

2. How can we encourage our employees to build the new skills they need quickly? Individuals vary widely in their acceptance and adoption of change; it is up to senior leaders to provide their teams with the tools they need to understand the change and its value, develop the skills they need to use the new tools, and reinforce usage of the new tools until doing so becomes part of the organization's culture.

3. How can we keep our employees engaged and enthusiastic in the transactional employment environment so many operate within? Loyalty to employers, and to employees, has been in an overall decline since 2020. Between the impacts of the pandemic, the move to a more remote workforce, and recent widespread layoffs across the tech sector, employees don't feel as engaged as they used to with their company. In fact, over 50% of employees in a recent poll indicated they're looking for new opportunities. This is documented in this San Diego Union Tribune article. Hiring employees is costly; much more so than keeping existing employees. According to this article "the average turnover cost per employee is equivalent to 6-9 months of an employee's salary, while others state it could cost up to two times the employee's annual salary."

A Solution

So how do we address these challenges cost effectively? There are many ways to go about targeting pieces of the problem, and no single right answer. But here's one I've used successfully to improve employee engagement, increase mutual loyalty, skill up our staff, and give employees a great, memorable experience in the process.

Gamification to Encourage Upskilling

The concept of gamification is not new. Using it to upskill employees isn't new either. The term itself was coined by Nick Pelling in 2002. But the idea behind it was used way, WAY back in 1896 by S&H, who issued stamps to customers in a rewards program designed to encourage customer loyalty. This article has a great history of gamification across industry and is a quick read.

Using this strategy to upskill can build community, give employees a sense of excitement, and just inject flat out fun into the workplace. But...planning gamification events can be tricky. Especially when you are looking to upskill quickly in a topic as broad as LLMs and their proper usage.

Thankfully, AWS has provided tools to help companies who wish to upskill their teams. Last year, I organized a company-wide DeepRacer event to encourage our employees to build knowledge in Reinforcement learning. We had so much success with that initiative that this year I was able to plan a new gamification event - this time one centered around Generative AI - which is currently underway.

The Event

What is this year's event, you ask? We are calling it "Parsons' Battle of the AI Bands" and it is exactly that. Unlike the DeepRacer event last year, this event is a team, or band, event, where each band uses AWS' DeepComposer service to craft an original song. The band provides the melody; DeepComposer's Generative AI model adds the harmony. Each band also picked their band name, created a band logo, and came up with album art to represent their song. Songs and album art were loaded to SoundCloud, and now the entire company is listening to the playlist and voting on their favorite entries across 4 distinct categories.

We will be holding an awards show after all the voting concludes to announce the winning bands, and winning teams will receive prize packs that include a cash bonus along with band-related paraphernelia including band t-shirts, custom cut vinyls with their album art and all the submitted tracks, and other fun awards.

Bands are really getting into the voting, reaching out to coworkers and peers across the company and encouraging them to vote for their favorite submissions. And participants are learning about Generative AI in a fun and memorable way.

Benefits of Using AWS DeepComposer

One of my favorite things about running an event like this using AWS DeepComposer to enable the process is that doing so allows us to benefit from AWS' prioritization of enabling learning through and about their tools. When you log in to the DeepComposer console, you are greeted with a few links set up specifically to help you learn Generative AI.

These links lead to a lovely 7-chapter learning module that takes its viewers through the basics of Machine Learning and on into Generative AI specifically. Then it dives into the specific algorithms available in AWS DeepComposer, U-Net and MuseGAN, and what they were designed to do, followed by a strong look at Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Autoregressive (AR) Models. The next chapter talks about how computers understand music, which is different than how humans do, and the whole module wraps up with an intro to AWS DeepComposer itself. Just working through the module provides a good foundational understanding of Generative AI. And for those who are curious after going through the module, AWS provides a deeper dive directly from the console into 5 additional topics.

Sounds Fun, But So What?

Humans are curious creatures. Giving them a memorable experience with friendly competition draws them in. Giving them an easy opportunity to learn from that experience with hints and easy routes to deeper dive learning encourages exploration. And bringing the whole company together through an organized event gets people talking, builds engagement, and demonstrates the company's commitment to its people.

Does the exercise of creating music using AWS DeepComposer make our people experts at Generative AI? No, of course not. But it plants the seeds, gets people talking and thinking about the realm of the possible, and encourages them to stay curious.

Admittedly, AWS DeepComposer doesn't train our folks in Prompt Engineering, which will become a more and more essential skill as AI continues to become ever more ubiquitous. So we will still need to do that. I bet we can come up with a fun gamification event to build that skillset in our teams!

What have you done to encourage your employees to stay curious, to learn new skills, and to adapt to the game changing (see what I did there?) capabilities at our fingertips?