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Android Instant Apps: Changing the App development Landscape

Google announces some pretty interesting things at its I/O conference every year. Android Instant Apps is one of the things that really got my attention as it is compatible not only with the upcoming Android N but also with older versions of the popular mobile operating system.

Instant Apps were first introduced at Google’s I/O developer conference 2016. The technology had only been available to select developers until their Google I/O 2017 conference where Company announced that all developers can now build Instant Apps.

But what is Android Instant Apps? This is a feature that will pull bits of Android apps that are published on Google’s Play Store straight to your Android device when you need some functionality that is available in one of those titles.

  • For instance, as Google demoed, if a friend sends you a link to a BuzzFeed video and you tap on it, Android Instant Apps makes it possible for your device to pull just the part that it needs from the corresponding app to display the video but without actually downloading the whole app on your handset.
  • Another example that Google showed at I/O, say that you want to pay for parking but you do not have the time to download an app that lets you do that. Android Instant Apps uses your handset’s NFC chip to get the necessary functionality from a compatible app to let you pay on the spot, and with Android Pay support nonetheless.Screen-Shot-2016-05-18-at-2.45.18-PM-800x447(1)

Instant Apps blurs the line between websites and apps you need to download, potentially shaking up the mobile Web experience. By offering a sliver of an experience of an app, it could also encourage people to download programs they might have skipped

Android Instant Apps makes your device much more useful and powerful. Normally, when you tap on a link, you are looking at a page opening in your favorite browser, which, depending on how optimized it is for use on a mobile device. You do not need to have all the features that an app can offer all the time, but there are times when you want to do more things or do them differently and this is where Android Instant Apps makes a big difference.

Those are just two examples though (there are three more below), and you can see a wider range of benefits to Android Instant Apps as more developers add support for it. As you know it can take a while before such features get traction, but this time round there’s a very big incentive in implementing it, if you do not count Google’s claim that it may take about a day to get this done. Android Instant Apps is compatible with Android versions as old as Jelly Bean.

Google has not specified which Jelly Been iteration is the oldest supported, but even if we are looking at the last one, which came out in 2013, there are still three current major Android distributions that Android Instant Apps works with.

The company is working with Disney on an Instant App version of its Disneyland app for checking wait times on rides. Other partners include blogging platform Medium, apartment rental service Zumper, a Buzzfeed food recipe app and yes, B&H Photo.ig(1)

The technology behind Instant Apps is actually pretty simple, according to Kirkpatrick. As long as an app developer can break their app into modular chunks roughly a few megabytes each, Google can quickly download just the right chunk of the app to a phone and run it as if it were already installed. Apps can prompt the user for permission to share their location, fire up the phone’s camera, or use saved account information to log in.

It doesn’t even require the latest version of Android: Instant Apps will work with versions as old as Android Jelly Bean.

Let’s keep an eye on their developers conference this year, we will surely keep you updated.

Meanwhile, In case, you have any queries on Android Instant Apps, feel free to approach us on hello@mantralabsglobal.com, our developers are here to clear confusions and it might be a good choice based on your business and technical needs.

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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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