How can you explain your AI and robotics projects to non-technical people?

Yeah it always easy to connect to non technical audience by the following technique 1. Real world examples 2. Case studies that non technical can understand 3. Drawing parallel from their day to day activities to AI concepts for eg - training AI system is like updating human with more knowledge by reading books, papers etc 4. Story telling 5. Connecting the purpose of AI system with simple terms

Demonstrate simple use cases. For example, generate an image and then use an editor to enhance it followed by using an outpaint canvas to elaborate the scene more. Eventually you get a highly customized professional looking image and the user learns 3 tools along with the importance of each one.

I've recognized that elucidation often outweighs elaboration. I equate explaining complex AI concepts to sharing a favorite novel's plot: distilling the essence without losing the narrative's charm. To elucidate my projects, I often pivot from "how it works" to "what it achieves". For instance, instead of diving into layers of algorithms, I'd say, "Our system mimics how humans identify images, but at a scale beyond our capability." Visuals, too, are powerful translators. Simplified infographics or intuitive animations have often been my allies in demystifying AI's intricacies. In essence, clarity breeds connection, and that's the bridge between the tech-savvy and the tech-curious.

Bridging the technical with the relatable has been pivotal. I've often drawn parallels between AI algorithms and our human intuition. For instance, explaining a recommendation engine, I likened it to a friend suggesting movies based on what they know you've enjoyed before. For robotics, I've painted the picture of a robot as not just a machine, but an apprentice learning tasks we teach, refining over time. Succinct stories resonate best: like how a robot in a warehouse can be seen as a diligent worker who never tires, ensuring orders are always ready on time. At the heart of explaining AI is humanizing the tech, connecting it to experiences everyone understands.

Most people are not into the same field as you. Professionals within security or even a different field of AI might not have much idea regarding the technology and methods that you are using. Unless you are speaking at a highly technical conference, you have to assume that your users know nothing about AI. If you have an entire semester to teach a course or a corporate training program then you can spend the first part introducing the users to every terminology. However, if you are in business and need a pitch or a presentation for general users or investors then you have to simplify things because time is limited. The average user might not need to know how the latest algorithm update was made. They just need to know what it does.

"seeing is believing" has never rung truer. Descriptive jargon might baffle, but a robot gracefully navigating a room or an AI tool effortlessly sorting images captivates even the most non-technical observers. In presentations, I've often integrated interactive demos, where attendees could, for instance, ask questions to a chatbot or watch a robot respond to commands. Such firsthand experiences do more than clarify; they evoke wonder and demystify AI's perceived complexities. Remember, AI and robotics, at their core, are extensions of human ambition. Showcasing them in action makes them palpably relatable, transcending technical barriers.

Your project does not need to be end to end. Unless you are a corporate entity trying to deliver finished product, you can show your skills through a limited project. Usually teams would have hardware engineers, electrical engineers, software engineers, robotics engineers and other roles. But if it is a project that you are passionate about then it's better to just demonstrate what you have done. For example, you don't need a car with multiple cameras integrated with other sensors and path planning algorithms just to demonstrate your project on computer vision. You can do that using videos recorded in real life scenarios, simulations and other means.

Live demonstrations can often give you access to unique perspectives. This feedback might be really helpful to develop additional features or improve existing functionalities for your products. Additionally, being able to interact with a live prototype is a good indicator that this is not just a far fetched idea but something that will soon be available in the market. Unlike concept cars, AI prototypes have a much better chance of hitting the market because scaling up has lesser costs in comparison.

Knowing the challenges and limitations allows you to be on the lookout for solutions and better technologies. It also enables you to explore alternative solutions and continue developing forward. These challenges often make a compelling story because true knowledge is gained from solving problems without relying on someone else's work or research.

Navigating AI and digital arenas, I've consistently found that authenticity in communication builds unparalleled trust. While the marvels of AI often dazzle, it's equally vital to shed light on the hurdles. In projects, while AI models would astound in accuracy, I've candidly discussed the times they've erred, or the intricacies of refining them. Addressing concerns, be it data biases or ethical gray areas, has opened invaluable dialogues, leading to refined solutions. It's also dispelled myths, bridging gaps between the tech and its stakeholders. Transparency, in my perspective, isn't just about acknowledging challenges; it's about embracing the journey of continuous improvement in AI's dynamic realm.

If the audience can find a common ground with you then they will trust more in your ability to improve their lives. They will be more open in terms of feedback and more eager to try out new things. Usually the most successful entrepreneurs are ones who solve a problem for themselves and then do it for the rest of the world. The more you can relate to your audience, the more you will learn about the shared problems which can be solved with the current technology. And solving problems like these will be crucial for success in the booming field of AI and Machine Learning.