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Is AI replacing Architects?

Architecture is perhaps the most complex discipline operating in more dimensions than any other coordinated human activity. However with the advancement of artificial intelligence, like every other profession, architects to are worried about the level of automation that has already taken away specific tasks from their roles.

While the ‘Humans are hooked and Machines are learning’, AI and ML are disrupting all manner of industries. Although AI has taken decades to go from crazy lab demos to a finished consumer product — today, there are immense possibilities for the industry to be augmented and enhanced by artificial intelligence. 

The earliest sense of advancement in the construction field came with Building Information Modelling (BIM) — a term that has existed since the 1970s, but came to its penultimate fore in the early 2000s, when Autodesk began popularizing the tag. 

The resulting by-product was the BIM software which is a type of intelligent 3D-modelling process used by architecture, engineering, and construction (AEC) practitioners to design and construct any kind of infrastructure. BIM software includes computer-aided design (CAD software) tools and libraries specifically targeted toward architectural design and construction and goes beyond traditional drawings to generate a fully digital model. 

Over several years the BIM (Building Information Modelling) software has had a huge influence on the day-to-day operations undertaken in an architectural firm

The Parametric design or the programming architecture can scrape through several design styles in no time and can come up with a perfect Zaha style building plan — that would otherwise take years to be designed. 

Over the last few decades, BIM has transformed the roles of engineers, contractors, architects, developers, and consultants by allowing them to communicate the same language and collaborate better. It has quite literally revolutionized both the design process itself and the designs themselves. 

BIM software produces an immense volume of big data, so much so that most architecture firms and their consulting partners don’t know what to do with them. Once AI permeated the technological landscape and bled over into every imaginable business use case — the industry learned to create value by collecting, organizing and storing building-related data (collected from models, simulations, etc.) It is now widely believed, that the scope for innovating the most optimal designs for each construction project becomes completely conceivable.

AI BIM = Optimized [Affinity]

When ‘parametric design’ technology is combined with AI that can actually use 6D BIM-models, and can record the whole life cycle of the building — it can come up with better decisions and insights into project execution by learning from the mistakes of the past.

Today, there are machines that can run through an infinite number of datasets, simulate for each model, pick the best option, verify its efficiency and continue to learn and communicate when introduced with the new autonomous building technology.

AI is the next frontier for architecture
Changes in the demographics, technology and business models have opened up a plethora of far-reaching opportunities for architects to explore areas like urban housing in more ecosystems than ever before.

Let’s have a look at some architectural products augmented and enhanced by AI.

Road Printers
The six meters wide machine that can pave entire streets at once. Naturally, the stones fall on the road directly into the appropriate pattern. The device is simple to handle and can finish the work in no time.

Concrete 3D Printers
3D printing as a core method to fabricate buildings or construction components. At a construction scale, it will have a wide variety of applications within the private, commercial, industrial and public sectors. The concrete 3D printers enable faster construction, lower labor costs, increased accuracy, greater integration of function and less waste produced.

Brick Laying Machine
The bot can lay between 300 to 400 bricks an hour, compared to a human which can only lay around 60 to 75 bricks an hour. It works 5 times faster than a human and can alleviate the labor shortage.

Brick Laying & 3D Printing Concrete Drone
Though in its infancy, researchers from Imperial College London have taken the first step towards making this a reality with their work on a drone that is able to ‘3D print’ while it is in flight.

However efficient bots may be, it will always lag in understanding the personality and the character of the customer — and this is where humans intervene.

Architects with the help of AI can create something different from the one-size-fits-all range of products already in the marketplace, to create more personalized solutions that perfectly align with user needs — but it is the imperfections in our creative decisions that truly makes something personal and truly unique.

What is your opinion about AI in architecture? Do you think AI will either augment or eliminate every profession in the near future?

Let us know by commenting.

To know us in person, reach us on hello@mantralabsglobal.com  

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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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