Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(3)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(33)

Technology Modernization(9)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(152)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(8)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

From Zero to Hero: How Service Mesh Transforms Microservices Architecture

Maintaining seamless and secure communication between services can feel like a battle in today’s fast-evolving world of microservices. Service Mesh offers the crucial infrastructure layer that powers modern and scalable applications.

What is Service Mesh?

In a microservices architecture, each service is like a character with its strengths and abilities. Managing the interactions between these services becomes increasingly complex as the system grows. This is where a Service Mesh steps in, ensuring that these interactions are handled, observable, and secure. Let’s delve into how Service Mesh can transform your architecture and make the process seamless and efficient.

Robust Security Measures 

A Service Mesh shields your services with robust security measures:

  • Mutual TLS (mTLS): mTLS encrypts communication between services, safeguarding data from prying eyes.
  • Fine-Grained Policies: The Service Mesh defines and enforces specific policies that control how services communicate, ensuring that every interaction is secure and compliant.

Providing deep insights to all the interaction

Service Mesh provides deep insights into the interactions between services:

  • Tracing: Tracing helps you monitor and understand the flow of requests, ensuring everything operates smoothly.
  • Metrics: Metrics offer valuable insights into your services’ health, performance, and behaviour. They provide a clear view of your system’s pulse, helping you detect and address issues proactively.
  • Logging: Centralised logging illuminates what’s happening within your application and aids in debugging and understanding behavior.

Traffic Management: Your Strategic Genius

Effective traffic management is very important for a safe and secure network. Here’s how a Service Mesh orchestrates the flow:

  • Load Balancing: Load balancing ensures that incoming network traffic is evenly distributed across multiple services, preventing overload and maintaining performance.
  • Traffic Shifting: Traffic shifting facilitates the gradual rollout of changes or new service versions, ensuring smooth transitions and minimizing disruption.
  • Fault Injection: Fault injection simulates failures to test the resiliency of services, ensuring that your system can withstand and recover from potential issues.

Choosing Your Service Mesh Superhero

Three prominent contenders in the Service Mesh arena: Istio, Linkerd, and Consul, each bring unique strengths to the table:

  • Istio: Known for its comprehensive feature set and extensive customization options, Istio provides robust security, observability, and traffic management features, making it suitable for complex deployments.
  • Linkerd: If you’re looking for simplicity and performance, Linkerd might be your go-to. It’s designed to be lightweight and easy to use, focusing on delivering essential Service Mesh functionalities without unnecessary overhead.
  • Consul: As a veteran in the service discovery and configuration management space, Consul integrates Service Mesh capabilities with its strong service discovery features. It’s a great choice if you’re already using Consul for other purposes.

Conclusion

Considering a dynamic world of microservices, a Service Mesh ensures that communication between services is secure, observable, and well-managed. It brings together the essential components of your architecture to enhance reliability and scalability.

Whether you’re just starting your microservices journey or looking to optimize an existing setup, implementing a Service Mesh can significantly bolster your system’s capabilities. 

By embracing the power of Service Mesh, you’re not just adding another layer to your architecture, but also you’re equipping your system with the capabilities to handle the most formidable challenges.

Cancel

Knowledge thats worth delivered in your inbox

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

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot