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Unveiling the Art of Emotional Design: Crafting Experiences Beyond Functionality

Imagine a digital landscape where pixels aren’t just pixels; they’re emotions waiting to be felt. It’s a realm where design isn’t merely a visual feast but an emotional journey that captivates and transforms.

In this vast expanse of design, it’s not just about shapes and colors but the emotions they evoke. It’s where an app isn’t just an app; it’s a companion offering solace, a symphony of empathy woven into its very DNA of design.

Welcome to the immersive realm of emotional design, where functionality meets feelings, and every interaction crafts an unforgettable story.

Emotions are the compass guiding our responses, decisions, and connections in life. They serve as signals, helping us navigate complex situations, make choices, and form relationships.

Emotions facilitate social connections, fostering bonds that form the foundation of our relationships, communities, and societies.

Moreover, emotions are vital for our decision-making processes. They act as a filter, influencing our perceptions and judgments. Even seemingly rational decisions are often influenced by our emotional responses.

Emotions aren’t just fleeting moments—they’re threads that weave the fabric of our lives, guiding our behaviors, relationships, and the very essence of what makes us human.

Consider the impact of positive emotions like joy and excitement on digital products—they act as magnets, drawing users in and fostering a sense of delight. When an app or website evokes joy through playful interactions or surprises, users are more likely to engage longer, share their experiences, and return.

Conversely, negative emotions, such as frustration or confusion, can quickly repel users. An interface that causes frustration due to complexity or lack of clarity may drive users away, impacting engagement and loyalty.

To leverage these emotions effectively, digital products can be designed to evoke specific emotional responses strategically. By incorporating elements that resonate with users’ emotions—such as relatable visuals, intuitive interfaces, or personalized interactions—designers can create an emotional resonance that builds connections. Aesthetics, tone of communication, and user journeys can all be fine-tuned to evoke desired emotional responses. When done thoughtfully, this emotional design not only enhances user experience but also cultivates loyalty, advocacy, and stronger brand loyalty.

To uncover the transformative power of emotional design, let’s understand this with Don Norman’s framework of design.

Visceral Impact: The Initial Encounter

Imagine entering a beautifully designed hotel lobby. The lighting is warm and inviting, the furniture is sleek and stylish. Instantly, you feel a sense of comfort and luxury. This immediate emotional reaction—your gut feeling of comfort and luxury—is visceral design at work. The colors, textures, and ambiance evoke an immediate emotional response without any conscious thought.

The visceral level involves the initial emotional reaction to an object. It’s that immediate, gut-level response we have when we encounter something—a website, an app, a physical space, or a product.

In the digital realm, consider the iPhone’s release. Its sleek and minimalist design, coupled with its smooth and seamless functionality, evoked an emotional response in users—a visceral reaction to its elegance and simplicity before they even engaged with its functionalities.

Integrating visceral design involves understanding user emotions and preferences through research, aiming to create an immediate emotional impact. This entails focusing on the first impressions of the product, utilizing visual, tactile, and auditory elements that evoke specific emotions. Embracing simplicity, consistency in branding, and utilizing design elements to narrate a story contribute to the emotional resonance of the product. Continuous iteration and user testing validate design choices, ensuring that the product not only functions seamlessly but also elicits the intended emotional response, fostering a stronger connection between users and the design.

Behavioral Symphony: Functionality and Emotion in Harmony

Behavioral design focuses on usability and functionality, ensuring that the product is intuitive and easy to use. Google’s search engine interface is a classic example of behavioral design. The simplicity of its search bar and the ease with which users can find information without any unnecessary clutter or complexity exemplify behavioral design principles.

Usability and functionality play pivotal roles in emotional design.

Humanizing a banking app isn’t just about transactional interfaces; it’s about infusing reassuring tones and empowering language, creating an emotional connection. It’s the fusion of utility and emotion that crafts a compelling experience.

Reflective Legacy: Crafting Memories

Consider a beloved family heirloom—a piece of furniture passed down through generations. Its value goes beyond its functionality; it’s laden with memories, stories, and emotions. Its design has created a deep emotional and intellectual connection over time, becoming more than just an object.

Reflective design involves the emotional and intellectual connection that develops over time. It’s the layer where experiences linger, where products become intertwined with our memories and perceptions.

Beyond the immediate, emotional design seeks to leave an indelible mark. Think of Instagram—it’s not just a platform; it’s an emotional canvas. Norman’s concept of reflective design resonates here, as Instagram becomes a repository of memories and emotions, intertwining the past and present in a visual tapestry.

The Evolving Canvas: Paving the Path Ahead

As we navigate the ever-shifting landscape of design, emotional intelligence emerges as the guiding star. Drawing from Norman’s vision, designers metamorphose into architects of emotion, shaping experiences that resonate deeply with users, transcending the conventional boundaries of functionality.

In Conclusion

Our exploration of emotional design opens doors to a realm where pixels become storytellers, where functionality intertwines with emotions, leaving an imprint that lingers in the heart and mind.

About the Author:

Pranava Sundar is leading the charge in UX/UI design innovation. With a passion for crafting seamless digital experiences, he orchestrates designs that marry aesthetics with functionality.

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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