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Hello World but in VR

By :

The mission was simple- create some interactive objects and also a futuristic environment. I stood at the crossroads, uncertain where to begin, so the first thing that I did was open YouTube and type-” how to build your first game in VR”. After watching a couple of videos, one thing was definite-” Oculus “. Oculus is the hardware used for most VR applications. So, I went ahead and placed an order for the Oculus which took around 15 days to get delivered. The unboxing felt like I had the key to the future, and now what? I ended up playing some games to understand how VR works and also just playing games.


Imagination part I

Then, I got a call from my manager-” Vignesh, Where is my metaverse?” 

The burgeoning weight of expectations compelled me to set aside gaming and delve into development. So, hopped onto my laptop which at times was a little specced out. Nevertheless, I started to do some research on how to build VR apps on YouTube, Oculus development page, Unity development page, and a few others. The information was quite overwhelming at the beginning and most of it bounced over my head. Took some time to understand the terminologies used in game engines, effective workflows, and finally how to import 3D models from Blender. I made some test Models in Blender with some free source files “sketchfab.com” because that was the fastest way to run a trial in Unity and Blender. Once I got the free resources, I tried to export it to Unity but for some reason, it was not working. So you guessed it right, YouTube became my refuge, and YES I found the solution. The feeling of successfully importing the 3D file to Unity was like I had accomplished 70% of the task but in reality, it was just 10%. There were a lot more things to figure out, like UV unwrapping, texturing, baking, emission materials, and baking animation which I still need to discover. A month’s time had already passed and I had made no major progress just as I grappled with this, a message from my manager appeared:“ Vignesh, when can I see the metaverse??”



Imagination part II

This is when I realized I needed to learn faster and work more efficiently and by chance I ended up on this amazing YouTube channel called Dilmer Valecillos where he teaches and explains VR development fundamentals and also shares the source code for some tutorials. That’s when I came across Oculus Interaction SDK. SDK (Software development kit) is a framework which apps and software are built upon. Thankfully Oculus development site provides their SDK which helps to develop games for Oculus. Having all the necessary knowledge and resources for development, I began to create 3D models in Blender, import them to Unity, and use the interaction SDK to make the models interactable. 

ALL was fine until I had to install the game into Oculus. The game would simply not install on Oculus. So I did some research and found that I had to change some settings in Unity for it to install.

Finally, I donned the Oculus on eagerly waiting for the game to start, when the loading screen disappeared I could see the environment created in VR but I wasn’t able to move or interact with the objects. This was a huge setback after spending nearly 4 months learning different tools and software needed for the development.


OK! Reality

This setback ushered in introspection and I realized my focus was not on learning the software extensively so, made a plan with the guidance of my manager to focus on one tool at a time and to understand it at the fundamental level. The tools were Blender and Unity, I previously had some experience in 3D so Blender was a bit easier to learn compared to Unity which has coding and I don’t know how to code. The fear of coding was hindering my learning curve in Unity but I figured not everything requires coding. Also, my fellow colleague was kind enough to help me out with coding. We decided that I would be focusing on creating 3D environments and some basic interaction on Unity and Rabi would do the coding. So, we set sail and within a few weeks we were ready to finally show the prototype to our manager. We tried our best to get it as expected but it was far from that and it needed more creative inputs, quality renders, and intuitive interactions. These were a few key pieces of feedback we got from presenting the prototype to the manager.

These experiences will undoubtedly shape my growth as a VR developer and provide valuable insights that extend beyond the world of virtual reality. I hope it resonates with many aspiring people who venture into the world of virtual reality.

P.S. The Project Metaverse is still ongoing.

About the Author: Vignesh is a creative visual designer and quirky art director! With a heart full of innovation, he crafts designs that tell vibrant stories and leave lasting impressions. Beyond design, he’s an adrenaline junkie seeking excitement in life.

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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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