Comment pouvez-vous communiquer la vision par ordinateur aux parties prenantes non techniques ?

Problem: Non-technical stakeholders may struggle to grasp computer vision concepts. Solution: Use analogies and visuals to simplify explanations. Compare it to a human visual system, where the computer "sees" and interprets images. Highlight real-world applications and benefits, emphasizing user experience and efficiency gains. Avoid jargon and focus on tangible outcomes to bridge the technical gap.

Highlighting real-world applications and your project's value is crucial for gaining support. Showcasing how computer vision can improve tasks like manufacturing inspection or medical diagnosis shifts the focus from technical details to practical benefits. Live demonstrations further solidify understanding, and quantifying progress with measurable metrics ensures stakeholders have a clear view of the project's impact. However, it's essential to be transparent about the limitations of the technology to maintain realistic expectations and trust among stakeholders. Simplicity is critical in explaining a computer vision project. Start by describing your project's problem and your proposed solution in clear, straightforward language.

I am a big fan of using analogies and Metaphors! We can liken computer vision to the way a person learns to recognize objects.

Communicating computer vision to non-technical stakeholders requires using simple language, relatable examples, and visual aids. Focus on explaining real-world applications and benefits, demonstrating outcomes with before-and-after visuals, and offering hands-on demonstrations to convey the technology's potential. Engage stakeholders in dialogue, addressing concerns, and highlighting opportunities for their domains. How do you effectively communicate complex concepts like computer vision to non-technical stakeholders? #ComputerVision #Communication #ArtificialIntelligence

To explain computer vision to non-technical stakeholders, liken it to the game of "memory" where computers use labeled data (like a grid of numbers) to recognize patterns. Highlight real-world uses, such as inventory tracking in retail and disease detection in healthcare, to showcase its practical impact. Visual aids can further demystify the technology, making its benefits clear without technical jargon. This approach helps stakeholders understand computer vision's efficiency and broad applicability, fostering informed collaboration.

● Retail: Show shelf images analyzed to identify missing/misplaced items, optimize layouts. ● Manufacturing: Display production line images with computer vision identifying defects, irregularities instantly. ● Healthcare: Present medical scans with abnormalities flagged by computer vision for enhanced diagnosis. ● Entertainment: Provide computer vision showcasing scene understanding in videos, auto-tagging moments. ● Transportation: Share city camera feeds with traffic flow, incidents called out by computer vision. ● Satellite: Highlight computer vision analyzing aerial views to map landscapes, track assets. ● Security: Illustrate surveillance feeds with computer vision detecting risks in real-time.

Real world examples are the best way to bring this technology to life: 📱”Ever wonder how your iPhone recognizes you?” Face ID is a prime example of computer vision in action. When setting up Face ID, your iPhone uses a special sensor to project 30,000+ invisible dots onto your face to create a detailed 3D map. Every time you look at your phone, computer vision springs to life; the sensor quickly scans your face and matches it against the stored 3D map. This process seamlessly confirms its you based on your facial features. 👍Super practical and everyday use of computer vision.

At Lexlink AI, we understand the power of showing rather than telling. We use visuals to demonstrate our computer vision system's capabilities, like comparing before-and-after scenarios of document processing. Through interactive demos, stakeholders can see our technology in action: extracting key clauses from contracts or spotting inconsistencies in legal documents. These examples not only illustrate our system's efficiency but also its practical impact, making complex technology relatable and understandable.

Visual aids like diagrams and videos can illustrate how computer vision works. For instance, show how facial recognition in smartphones identifies faces or how self-driving cars detect obstacles. These examples help non-technical stakeholders grasp the concept and potential applications of computer vision technology.

Again, for non-technical stakeholders, focus on simplicity and clarity. Customize your visualizations to match the interests and expertise level of your audience. Select chart types that best represent your data and message. For example, use bar charts for comparing defect rates before and after implementing AI, line charts for showing trends over time, or heatmaps for visualizing defect hotspots. Additionally, you can highlight your insights to convey a key message. Use colors, labels, and annotations to draw attention to key insights or trends in the data. Make sure the most critical information stands out.

The summary here is totally what is wrong with the gap between business and tech. Why on earth would I say we use an algorithm which mimics how the human brain processes information? That will just increase expectations ten-fold. CNNs are nowhere near how the human brain functions. Humans don't need 1000s of cat images to learn to predict a cat like a CNN It's better to be upfront about what technology or model you use but simplify it with visual aids and examples. CNNs and Vision Transformers work in different ways, so it's necessary to highlight the details of the process but only after talking about the objective and results obtained which are more important. Simplify the language but don't paint a wrong oversimplified picture.

Totally agree. To recap, another example could be: Instead of saying "We're leveraging natural language processing (NLP) techniques to analyze customer feedback," you could say "We use technology that helps our system understand and interpret customer comments and suggestions, just like a human would."

For example, instead of "AI employs NLP for IE," say "Artificial intelligence understands and extracts information from text." Keeps your message clear and accessible.

There is a saying: "If you cannot explain it to a 5-year old, then you don't know what it is." This is true more often than not, and I have experienced it firsthand. When I am not confident in a term, a theory, or anything, I catch myself using complex words and jargon to avoid getting questions. This is because most people do not want to be caught not knowing something they think they should know, so they prefer not to ask questions. So when you are explaining a technical term to a non-technical person, think about this: "How do I explain it to a 5-year old?"

It's crucial that you're extremely relatable to none-technical stakeholders. The moment you use jargon or acronyms that are unknown to them is the moment you loose their attention. Remember, BE RELATABLE.

Break down complex ideas into basic parts and use familiar examples. Like explaining the internet as a global library where every book talks to each other. Keeps concepts digestible and relatable.

Speak their language by bringing use cases during dialog and connect the dots around their current processes vs expected outcome.

To convey technical information to non-technical audiences, it’s critical to leverage empathy, engagement, impact, evidence, and alignment. These are the five drivers of Innovation Storytelling. To ensure that these conditions are met within a technical explanation, look to Generative AI platforms like Narratize that are designed specifically to support innovators and conveying complex ideas to non-technical audiences.

Imagine you have a magic book that can look at pictures and tell you what's in them, like if there's a cat or a flower. That's kind of what computer vision does! It's like giving a computer eyes and a brain to see and understand pictures. We don't need to know all the secrets behind how it works, just like you don't need to know how a magician does their tricks. The important thing is that this magic book can help us find things in pictures really fast and tell us if something is not quite right, like if there's a tiny scratch on your toy that's hard to see.

Simplify the complexity of computer vision for non-technical stakeholders. Focus on key points. For instance, "We train our system with a diverse image dataset to improve accuracy and robustness.

- Simplify technical concepts - Use analogies and real-world examples - Highlight practical applications and benefits - Emphasize user impact and outcomes - Address limitations and challenges transparently

This is a critical step that cannot be overstated. Failure to communicate the limitations of the technology will cause an expectation gap. Try to remain positive but make clear what is possible and what is not.

Acknowledge the limitations and challenges of computer vision, such as accuracy issues, data biases, and privacy concerns. Explain how these factors can impact the technology's performance and reliability. Offer insights into ongoing efforts to overcome these challenges and emphasize the importance of continuous improvement and ethical considerations in computer vision development and deployment.

Acknowledge computer vision limitations and challenges like data quality, privacy, bias, and ethics. Be transparent about how you address or mitigate these issues. For example, "We prioritize user privacy and only use images for our project's purpose. We also test for bias and correct it if detected.

You can establish trust by recognizing and addressing the limitations of the system. Share proactive steps being taken to address these limitations/challenges such as establishing ethical guidelines, and seeking third-party evaluations on the system's fairness. Such steps, if taken continually also demonstrate the long-term commitment towards responsible development of the system.

Computer vision is like playing “memory”. You start with a data set that has human made labeled data. It’s written with numbers. Let me show you an example: 00080008000 00080008000 00088888000 00080008000 00080008000 Can you see the letter H?Computers read numbers. When humans make data sets and label them from numbers like this, it’s called a “library”. Computers can only see and identify what’s in their library (data). Hope that helps!

It is crusial to get feedback about our platform and engagement with the non technical stakeholder. People who cannot fully understand why something is operating like that, help us understand our value to human gaps and become even better. Ask questions, communicate and come back with improvements

Feedback is an integral part of an iterative process of development of any system. It will help to hold regular update meetings or workshops where stakeholders can see progress, ask questions, and offer insights. This ongoing dialogue ensures the project remains aligned with user needs and expectations, and helps to establish a sense of ownership and partnership among all involved. Feedback can be a valuable resource because it helps to shape a technology that truly meets the needs of the audience it is meant to serve.

Absolutely. You can better understand what the stakeholders curious about or what they might be confused about, when you open to listen to their questions and concerns. By being open and respectful, you show you value their opinions and that you're there to help. This can make them feel more comfortable and trusting in the project. Feedback helps you learn how to communicate better. When everyone feels like they can share their thoughts and ask questions, it makes working together easier and more fun. It's like being on a team where everyone gets to play a part.

Encourage non-technical stakeholders to provide feedback and ask questions. Create an open and inclusive environment that allows for discussions and clarifications. This will help address any concerns or misconceptions and ensure that stakeholders feel engaged and involved in the decision-making process.

Having visuals and diagrams is good. However, what I've found most useful is to have them just try the AI model. And as they're using the computer vision model, you can explain what's happening and why/how it solves the problem you're trying to solve.

Communicating computer vision concepts to non-technical stakeholders requires clarity, simplicity, and relevance to their interests and objectives. One effective approach is to use analogies and real-world examples to illustrate how computer vision technology works and its potential applications. For instance, comparing computer vision algorithms to human vision processes, such as recognizing objects or detecting patterns, can help convey the underlying concepts in a relatable manner.

Additionally, consider discussing other factors such as cost, time, and resources required to implement and maintain computer vision systems. Highlight any potential risks or ethical considerations that may arise. Provide case studies or success stories from similar projects to further demonstrate the benefits and potential return on investment.

Finally, consider the broader implications of implementing computer vision, including ethical considerations, privacy concerns, and the potential for job transformation. Discussing these aspects shows a comprehensive understanding of the technology and its place within the larger societal and business ecosystems. Highlighting ongoing developments and future possibilities can also spark enthusiasm and support for exploring computer vision solutions.

Use an LLM to help. The prompt is simply or “explain taking into account I have no technical background” or even “explain like I’m 12” followed by whatever technical information you want to convey. If you know more about your audience (I.e. profession), you can say “Explain like I’m a {profession} with limited technical background.