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Cloud Computing Is Reshaping Digital Businesses during Pandemics

6 minutes read

In an ever-changing business climate, especially amid the COVID-19 pandemic waves, it’s imperative for small and medium business owners to be able to access data as and when they need it, regardless of the device they’re on or their physical location. 

Accenture reports that “2020 has been a pivotal year for the cloud as it played a lead role in facilitating remote work solutions. It allowed organizations to fuse existing organizational processes with novel cloud technologies to allow for greater flexibility during these uncertain times. COVID-19 has facilitated a focus on cloud capabilities as companies compete to thrive in this new remote work environment. The cloud has become an essential part of continuing business and is the key to unlocking organizational growth. Worldwide spending on public cloud services is even forecast to grow 18.4 percent in 2021.” 

According to a NASSCOM report, the Indian cloud computing market is currently valued at $2.2 billion with projected growth at 30 percent YOY, expected to reach $7.1 billion by 2022. 

Predictions for cloud computing revenues to 2021 from 451 Research.

A Forrester report titled, Predictions 2021: Cloud Computing Powers Pandemic Recovery, on the other hand, says that “In 2021, cloud will power how companies adapt to the “new, unstable normal.” No one knows how far into 2021 we’ll continue to work from home, shop primarily online, or avoid air travel — but it’s clear that every enterprise must become more agile, responsive, and adaptive than ever before.” 

Source: Forrester.com

With this pandemic and its subsequent lockdown-led change in landscape, businesses are trying to venture out and combine services and technology namely IoT services, Big Data, and cloud computing. According to Financial Express, “cloud computing will play the role of a common workplace for IoT, the source of data and big data as a technology is the analytic platform of the data.”  

Cloud computing has been in use for approximately two decades now, with few early adopters of this technology, however, a large number of businesses continue to operate without it even today. According to a study conducted by the International Data Group, “69% of businesses are already using cloud technology in one capacity or another, and 18% say they plan to implement cloud computing solutions at some point.” 

A Verizon study also showed that 77% of businesses feel cloud technology gives them a competitive advantage, and 16% believe this is a significant advantage. 

Why should small businesses consider cloud computing? 

Network downtime costs more than $10,000 an hour, according to CloudRadar. For most small businesses, investing in robust data recovery would be an ideal yet imperative choice to implement in their regular processes. Due to the scale and expertise of cloud-based services, quick data recovery is also possible for all kinds of data disasters, including being able to remotely wipe data from a lost device. 

CIOinsight.com reported that “Cloud computing, the offloading of company data functions to offsite cloud providers, has been hailed as the tool that enabled the decentralization of business during the COVID economy. It’s also become utterly mainstream in business, with Cisco reporting that 92 percent of data workloads were handled in 2020 by cloud computing. The same report also showed that the United States led the globe in cloud computing workloads.”

As cloud systems have increasingly matured over time, it’s also given way to a consensus on a mixed approach – both public and private – to cloud service-based environments to meet the needs of enterprises. To overcome the challenges posed by either public and private cloud computing services, namely, data security, flexibility, and performance, 82% of enterprises have now taken a hybrid approach to their cloud infrastructure, as per Flexara’s 2021 State of the Cloud report.

Research firm MarketsandMarkets has estimated that the hybrid cloud market will be worth $97 billion by 2023 banking on characteristics such as scalability, cost-efficiency, security, and agility. 

Amazon Web Services (AWS) said that amid the COVID-19 pandemic, there was an evident acceleration in cloud computing adoption and consumer behavior wrt cloud in the country. Mantra Labs, while working with Manipal Hospitals, offered solutions around Server Setup & Deployment; Cloud Monitoring; Database Setup; Load Balancing; and Network Security & Monitoring. These helped with 66% improvement in application performance; 57% reduction in code deployment time; 2x more ROI from continuous delivery. 

Cloud computing is also promoting sustainable practices across organizations given the current state of the environment. Hosting on the cloud is environmentally friendly and results in a lesser carbon footprint.

Cloud-based infrastructures support environmental proactivity; virtual services instead of physical products and hardware; lesser paper waste; optimized energy efficiency; easy work-from-home access and collaboration. 

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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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