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Trending PHP Frameworks in 2020

PHP, one of the most popular server-side scripting languages, is widely used for creating basically anything on the Web. In fact, the original abbreviation stood for Personal Home Page.

Among its various examples such as Joomla, Drupal, Magento, MediaWiki and more; WordPress is the most regularly used server running on PHP. It alone powers over 30% of the web. 

Having highly dynamic web-development trends; as of 2019, 78.9% of the websites and app development companies use PHP frameworks and Cloud. This proactive approach helps them obtain the best of both the technologies and avail cost-effective, scalable and flexible web solutions at the same time. 

78.9% of websites use PHP for server side programming

Apart from frameworks like CodeIgnitor, Cake PHP, Phalcon and Aura lets have a brief of top PHP trends in 2020.

Top PHP Frameworks in 2020

Laravel

Laravel Framework

This all-inclusive framework helps in speeding up app development, integrating it with the model-view-controller (MVC) architecture. Apart from MVC, features like ORM, RESTful controllers, the lightweight Blade template engine, unit testing, and its comprehensive packaging systems, make Laravel one of the most used PHP framework.

Benefits of Laravel infographic

In Laravel, an in-built robust composer is available to help add the packages with ease, making it easy to work with NoSQL structures, such as MongoDB and Redis.

Yii

Yii Framework

Yii is one of the fastest and secluded PHP frameworks trending. The Yii framework has a component bundle that makes it lightweight and more proficient. Yii is known for its easy installation process and can be easily extended. In this framework, PHP integrates with CodeCeption which is a popular testing framework that allows faster web application development and testing.

Kohana

Kohana PHP Framework

Kohana was built with PHP OOP in mind.

Advantages of Kohana:

  • Visibility protection 
  • Abstracts
  • Automatic class loading
  • Interfaces
  • Overloading
  • Singletons

Slim

Slim - a micro PHP Framework

Being a popular PHP micro-framework, Slim is minimalist in design. It is excellent for small web applications where a full-stack PHP framework is not a requirement.

Packed with rich features like URL routing, client-side HTTP caching, session and cookie encryption, etc; it’s used by many PHP developers to develop RESTful APIs and web services.

Zend

Zend Framework

Zend is one of the most prominent go-to professional PHP frameworks. It is commonly used It is built with an extensive set of features such as security, extensibility keeping in mind its performance. Zend has an enterprise driven nature with the support of numerous components such as feeds, forms, services and more.

Business benefits of Zend Framework infographic

For more information on other PHP, frameworks read 

Latest PHP trends in 2019
Latest PHP trends in 2018
Latest PHP trends in 2017

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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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