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Angular-2 – Developers Preview

Angular 2 is a big upgrade from Angular 1. It is a framework for mobile apps and can be used for desktop as well. Like Angular 1, Angular 2 (currently in alpha) is built on a set of concepts that are used throughout the framework and they would be used directly or, indirectly while writing applications.

Angular 2 separates updating the application model and reflecting the state of the model in the view into two distinct phases. The developer is responsible for updating the application model. Angular, by means of change detection, is responsible for reflecting the state of the model in the view. The framework does it automatically on every VM turn.

Angular 2 Features:

Component-based UI
Angular is adopting a component-based UI, a concept that might be familiar to React developers. In a sense, the Angular 1.x controllers and directives blur into the new Angular 2 Component. This means that in Angular 2 there are no controllers and no directives. Instead, a component has a selector which corresponds to the html tag that the component will represent and View to specify an HTML template for the component to populate.

User Input with the Event Syntax
Angular 2 applications now respond to user input by using the event syntax. The event syntax is denoted by an action surrounded by parenthesis (event). You can also make element references available to other parts of the template as a local variable using the #var syntax.

Goodbye $scope
Even though ‘$scope’ has been replaced by “controller as” as a best practice since Angular 1.2, it still lingers in many tutorials. Angular 2 finally kills it off, as properties are bound to components.

Better Performance
With an ultra fast change detection and  immutable data structures, Angular 2 promises to be both faster and more memory efficient. Also, the introduction of uni-directional data flow, popularized by Flux, helps to ease some of the concern in debugging performance issues with an Angular app. This also means no more two-way data binding which was a popular feature in Angular 1.x. Not to worry, even though ng-model is no more, the same concept can be solved in a similar way with Angular 2.CWcQuqmWsAE8UKK

In any front-end web, frameworks is the technique used for change detection. Angular 2 adds a powerful and much flexible technique to detect changes on the objects used in the application. In Angular 1, the only way the framework detects changes, is through dirty checking. Whenever digest cycle runs in Angular 1, the framework checks for changes on all objects bound to the view and it applies the changes wherever they are needed. The same technique is used for any kind of objects. In AngularJS 2, we don’t have a chance to leverage the powers available in objects – like observable and immutable. Angular 2 opens this channel by providing a change detection system that understands the type of the object being used.

In addition, the change detectors in Angular 2 follow a tree structure to detect changes. This makes the system predictable and it reduces the time taken to detect changes.

If plain JavaScript objects are used to bind data on the views, Angular 2 has to go through each node and check for changes on the nodes, with each browser event. Though it sounds similar to the technique in Angular 1 but the checks happen very fast as the system has to parse a tree in a known order. If we use Observables or, Immutable objects instead of the plain mutable objects, the framework understands them and provides better change detection.

Angular 2 is written from the ground-up using the latest features available in the web ecosystem and it brings several significant improvements over the framework’s older version. While it retires a number of Angular 1 features, it also adopts a number of core concepts and principles from an older version of the framework.angular-2-better-or-worse-26-638-1

Short Summary:

  • Angular 2 separates updating the application model and updating the view.
  • Event bindings are used to update the application model.
  • Change detection uses property bindings to update the view. Updating the view is unidirectional and top-down. This makes the system a lot more predictable and performant.
  • Angular 2 embraces unidirectional data-flow.
  • You can use the same mindset when building Angular 1.x applications.

The team has collaborated with the TypeScript team at Microsoft, both the teams are working really hard to create a great framework and they are also working with TC39 team to make JavaScript a better language. The best is yet to come and hence the future is going to be exciting for all developers.

In case, you have any queries on Angular 2 framework, 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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