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Digital Media Consumption Behavior and Trends

3 minutes, 25 seconds read

With the relentless treadmill of disruption, the potential of media and entertainment companies to understand their customer’s digital consumption behaviour today is greater than at any time in history. 

Among the digital devices, mobile devices have taken over as the preferred medium of consuming content online. The smartphone market has seen unprecedented growth in the last 5 years. Smartphone devices across the globe grew at a CAGR of 17% as compared to 9.5% growth in all mobile devices. Smartphones crossed 2 billion marks in 2014 and are expected to reach 4.6 billion by 2019. 

This led to an increase in the number of devices capable of supporting digital media in tandem. Billions of screens and increasing internet access speed provided consumers with an option to access the media content of their choice anytime, anywhere.

Consumers are shifting their preferences towards digital media consumption as compared to traditional forms of media such as TV, print press, and radio. People are spending more time on digital forms of media rather than traditional mediums. This increase is mainly coming by cannibalizing traditional advertising mediums.

The increasing popularity of digital media has provided for a paradigm shift in global advertising spends.  Marketers who are seeking to monetize content and capture growth are following the changing trend and increasingly allocating their budget to digital mediums. Spending on digital media as a percentage of total advertising spend has increased from 21% in 2010 to 28% in 2015 and is further expected to reach 36% by 2020.

Gen Z’s digital media consumption trends

Generation Z represents 1.8 billion people or 24% of the world population. Having an invigoratingly different attitude, Gen Z has a tremendous effect on the overall perception and digital media consumption. 

They prove to be more entrepreneurial; growing up with search engines they like to discover content for themselves. They also like to be involved in the process, contribute to the solution and be more absorbed in experiences. 

Though a wide range of digital consumption, the Gen Z capture insights from an array of sources. Translating these resources into viable products, services and business models will go a long way in defining the leaders of today and the leaders of tomorrow

Billion screens into digital consumption powerhouse

With a population of more than 1.3 billion and around 570 million internet subscribers, India has the world’s second-highest number of internet users after China; growing at a rate of 13% annually. India to overtake the US on time spent on digital videos. The global streaming platforms are looking to capitalize on the country’s fast-growing digital content consumption. The impressive scale of the market and a liberal foreign investment environment are strategically appealing to investors.  

Media consumption billion screens

India is among the top five markets in the world based on the number of users for online and mobile gaming; with more than 90% of millennials preferring smartphones over gaming PCs and other devices. Besides, India consumes the highest data per user in the world. In 2019, adults in India, on an average spend 29.9% of their total daily media time on digital. In a recent report, the Telecom Regulatory Authority of India estimated the digital consumption of data to be around 7.69 gigabytes per month.


Leap through these Digital Challenges

India offers global investors enormous opportunities for growth. However, there also are several persistent challenges to consider before making the leap. Increasing use of digital media has accelerated video consumption, but it also has increased the piracy threat. In fact, growing piracy is likely to restrict the full monetization of content. As well as large-scale acceptance of subscription video on demand in India.

Digital advertising, a top-30 focus area of the industry, has lost as much as US$8 billion in revenues. Half of the loss incurs from “nonhuman traffic” — fake advertising impressions; that are neither generated by genuine advertisers nor received by actual consumers. The other half derives from a variety of factors such as ad-blocking and content infringements, like the sharing of passwords.

We provide innovative solutions for growth, customer engagement and streamline business processes. 

Want to make the maximum of your brand? 

Reach out to us at hello@mantralabsglobal.com

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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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