In a small OSPF network, imagine you have 1 datacenter and 20 branch offices. How we can adapt Einstein’s simplicity principle to networking ? If ‘A represents a process, and ‘A***’ represents the simplest possible version of ‘A’, then one should work towards finding ‘A***’, and not towards an intermediate version ‘A**’ or ‘A*.įrom this example, as you can understand, most possible simplicity is recommended by Einstein. It is very long debate what really Einstein was trying to say and unfortunately since He is dead, we can’t ask himself but at least let’s try to understand. ” Everything should be made as simple as possible, but no simpler ” Let’s remember What Einstein said about simplicity. By maintaining our other design principles of Trust, Clarify, Control, and Humanize, our users are able to leverage the intelligent engine for more areas of their work.What is KISS Principle ? Okay it stands for Keep it Simple and Stupid but what does really it mean in networking ?Ĭan we really make things simpler ?. We simplify the user experience whenever we can to allow the user to carry out their tasks with our AI-based products. This is a perfect example of KISS in action: The user has an option to apply or dismiss the recommendation, and a simple UI control that does not require prior knowledge of the intricate details of the algorithm’s technical details is working behind the scenes.īranding these “smart” inputs with a distinct CLAIRE color and appearance in various places further reinforces user’s awareness of these unique items. ![]() The reasoning behind the recommendation is provided in a short tooltip. Recommended stakeholders or tags are shown inside the input field, and visually branded to indicate that they are driven by the CLAIRE engine. Here’s an example of how we handled algorithm-driven recommendations for tags or stakeholder assignment in input fields: We apply these principles consistently throughout our design. We apply this stepped approach-using clear, understandable language-whenever we’re trying to explain tasks that cannot be briefly summarized. Instead, we present explanations in simple language that everyone can quickly understand.Īny complex idea can become a lot simpler when it’s broken down into smaller steps. When explaining AI decisions and rationale, we try to avoid using highly technical or scientific terms related to Artificial Intelligence or Machine Learning those terms would require prior knowledge and education. We use simple, one-sentence explanations to help our users quickly understand and decide on any actions related to AI-based features. We simplify by following a few Simple (J) guidelines:Ī picture is worth a thousand words, and that’s exactly the reason you want to avoid using images to explain a complex task. Applying KISSĪs Informatica products are built with the CLAIRE engine at their heart, it becomes a top priority for us to simplify the design of features that are expanding in back-end complexity. And even the most complex intelligent systems must still feel simple. In order to use them properly, the widest possible audience must be able to understand their output and effect on the user’s task. Highly complex in nature, AI and ML are perceived as a complete and untouchable black box by most users. When you think about it, AI and ML algorithms are an extreme example of the importance of the KISS principle. What’s true for fighter planes and mobile applications is especially true for Artificial Intelligence (AI-) and Machine Learning (ML-) powered features. The simpler the product or execution, the more likely it is that this output will be useful to the user. The end user doesn’t care how clever the creator is, they care about being able to use the output of this creativity, to make it useful to their own application. ![]() ![]() Decades later, this axiom applies, whether it’s conceptual physics, elaborate engineering, or consumer products.
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