What are some techniques for explaining complex ML concepts to non-technical interviewers?

Explaining complex machine learning (ML) concepts to non-technical interviewers involves using relatable analogies, visual aids, and real-world examples to simplify the concepts. Focus on high-level understanding, emphasizing the purpose and impact of ML in solving problems and driving outcomes. Use plain language, avoid jargon, and encourage active participation and questions from the interviewers. Provide additional resources for further learning if they have a genuine interest. By employing these strategies, you can effectively convey ML concepts to non-technical interviewers and foster a shared understanding.

In my experience, the best way to land complex ML concepts is to make them relatable to the business problem straight away and maybe focusing on the underlying ML approach, only to perhaps deepen interest in the conversation (read the room before doing this, however :)). For example, I would only talk about the underlying principles of an isolation forest algorithm (I.e. it works by isolating data by studying randomly selected attributes in turn) if we are in a conversation about identifying anomalies from a large dataset, with a view to building confidence about what ML can potentially achieve. I probably wouldn’t even use the term “Isolation Forest” in the conversation!

People want to understand for themselves, so you've got to make it relevant in their experiences. Avoid the big words. With ML, you don't even need to use analogies! I've always started my talks with explaining how Linear Regressions work. Everyone has created a 'line of best fit' on a scatter chart at school. 1. You take some [training] data and use it to plot points, 2. You work out the line of best fit, calculate the equation [train the model], then 3. Use that equation to estimate future values! That is machine learning. You use data to train a model that the computer uses to predict future values. Sure, GPT et al involves far far more complex maths than a simple linear regression, but the principle remains the same.

Using simple terms and simplifying complex terms is one way to reduce the difficulty. Like instead of saying batch normalization batches need to be trained in smaller sets etc.

When we create a model we do things like Data Gathering, Data Cleaning, Feature Engineering & Feature Selection. Here it is better to explain the things we did, how we interacted and what kind of problems we faced at the time of understanding data and at the time of cleaning the data than what code we wrote or what techniques we used. What problem finally we solved and how we got to the solution matters a lot more than what kind of code we wrote.

Use simple graphs, 3d visuals and very simple pictures. Many ML concepts have complex diagrams and very detailed graphs - using simple ones will be more understandable.

Real-world examples are a great way to help non-technical interviewers understand the practical applications of ML. For example, you could talk about how ML is used to recommend products on e-commerce websites, detect fraud, or diagnose diseases.