How can you explain reinforcement learning to stakeholders?

Reinforcement learning, a form of machine learning, empowers an agent to interact with its environment to maximize rewards, adjusting actions based on feedback to achieve optimal cumulative dividends over time. It’s akin to learning to ride a bike or a gamer mastering a level, where feedback refines actions to reach a goal. Unlike other learning types, it balances exploration and exploitation without needing labeled data or explicit corrections. To comprehend it fully, explore various resources, implement practical projects, and engage with the learning community, enveloping the theoretical understanding in real-world applications and communal insights.

🤔 What is reinforcement learning within Artificial Intelligence (AI) and how can it help your business? Think about a call center. Let’s say there are 1000 call center agents collectively answering 1 million calls per year. Each agent has certain skills like English, French or both. Callers are placed in skill queues based on the number they press on their phone. Also consider that lots of performance data exists on these agents. Deploying a reinforcement learning AI algorithm could optimize your call center performance by determining who to route each call to in order to maximize performance in your center for average speed of answer, average handle time etc. 👍 Like to support this

One of the easiest ways to explain RL to a novice is to put a linear equation into a spreadsheet and show them how you can keep guessing what x is via trial and error until you get it right (versus solving for x algebraically). This makes it easy for people to see how ML can be used solve a simple equation with data versus mathematically.

While training leaders at a global bank, I was demystifying AI concepts, including RL. Banking professionals, though savvy in finance, often find AI terminologies bewildering. When they hear a jargon, they might tune out of the session. Instead of bombarding them with terms like 'Q-learning' or 'policy gradients', I used relatable examples. I likened RL to a credit system: rewarding good financial behaviors (like timely loan repayments) and penalizing bad ones, and iterating until an optimal credit strategy is formulated. This resonated well.

When explaining RL to non-programmers I like to use A/B testing as an example (oops, there’s jargon!). I tell them to imagine two versions of an advertisement for a product (A and B) pushed out to similar audiences. Then the AI can measure the performance of each advert by demographics live and start to customize who sees which based on the success (e.g. purchases) of each ad. No jargon and a simple example of RL.

RL has several challenges from data reliability to algorithmic biases. Think of it as the fine print in an investment prospectus — necessary, instructive, and pivotal to making informed decisions. Helping stakeholders understand them not only sets the right expectations but also opens up avenues for innovation. Importantly, it builds trust with your audience.

Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment, the agent receives feedback in the form of rewards or penalties.