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The Future is Screenless

Screenless technology uses augmented reality to superimpose interactable imageries on users’ surroundings. AR is redefining the future of experiences. This article brings forth applications of augmented reality in designing screenless interfaces. It also discusses the psychological impact of augmenting computer-generated visuals in the real world.

Applications of Augmented Reality in Screenless Technology

According to MarketsandMarkets research, the screenless display market is projected to reach $5.7 billion by 2020. In a near-future, augmented reality would be able to project imagery onto almost any surface and medium. However, there’s another aspect of screenless interfaces accompanied by audio and haptics.

Future is screenless infographic

AR Audio

Imagine you come across a billboard with a picture of diamond jewellery. You’re impressed and want to know more about the ad. Typically, you’ll pick your phone, type some search queries and then get to know the information about the product. What if you can skip the process and get the information instantly?

AR Audio gives audio responses according to the user’s visual cues. It fulfils the user’s need for information on demand immediately. The technology is advancing to an extent that the AR device can measure your gaze direction and locate the objects in your range of vision!

Sturfee’s Visual Positioning Service (VPS) is a remarkable attempt towards AR innovations.





Seamless Projection

The recent development in augmented reality eliminates the need for bulky headsets or special glasses to see an augmented view of the world. In fact, the screenless display market is projected to reach $5.7 Bn by 2020.

This is possible by seamlessly projecting the imagery in a shared physical space. That is, mapping the imagery on a street or a playground, where many people can simultaneously witness the virtual aspects of augmented reality. The ability to project visuals seamlessly on any surface is one of the biggest applications of augmented reality feasible today.

Humane Creatures

The next take on coupling augmented reality with artificial intelligence is the development of humane creatures or avatars. These human-like intelligent beings can act as a learning companion for children suffering from autism. Augmented reality can smartly interact with children, ask questions, encourage, offer suggestions, and can be a companion in their tough time.

In her book – The Art of Screen Time, Anya Kamenetz mentions Alex, a research project directed by Cassell’s PhD student Samantha Finkelstein. Alex is a gender-ambiguous 8-year-old intelligent augmented reality avatar. During an experiment in a classroom at a charter school in Pittsburgh, students along with Alex discuss their know-how about a picture of a dinosaur. Alex couldn’t catch everything that other students were saying and sometimes his responses are inappropriate. But, this illusion of conversation is a step forward towards the new developments in the AR arena.

Screenless Time?

‘Modifying reality’ is putting a question mark on the psychological impact of augmented reality. Augmented reality together with artificial intelligence is creating environments next to real. Are our mental-models ready to adapt? Or a sudden disruption is going to play with our sentiments? Unfortunately, there are no concrete answers to these questions. 

Today, kids (aged between 8 & 18) spend on average more than 7 hours every day looking at screens. However, the new AHA guideline recommends screen time to be at a maximum of two hours per day. In the not so distant future, kids will be growing up with AR accompanying them throughout their day. Whether they are learning about something new or shopping online, AR will have merged and formed a virtual tether with their daily routines. 

While screenless AR does pose several questions around its ethical benefits — with responsible use we can harness the best from this technology.

Augmented Reality Best Practices

  1. While using Augmented Reality in design, keep in mind the users’ real-world context. Do not distract or mislead them for social, political, or economic benefits.
  2. Do not play with emotions or drown user senses into meaningless things.
  3. Augmented Reality is data-rich. Ensure the safety of users’ data.

Concluding Remarks

Haptics, gesture control, Synaptics, and triggered imagery are adding intractability to the screenless technology. Today, video games and retail are harnessing augmented reality the most. The future awaits more applications of augmented reality to build screenless interfaces across different industries.

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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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