Author: Dr. Tehseen Zia

Conspiracy theories have always been a part of human history, drawing people in with stories of secret plots and hidden truths. But in today’s connected world, these theories are not just harmless gossip; they’ve become a global problem. With social media, false ideas like “9/11 was an inside job” or “vaccines have microchips” can spread

AI is reshaping the world, from transforming healthcare to reforming education. It’s tackling long-standing challenges and opening possibilities we never thought possible. Data is at the centre of this revolution—the fuel that powers every AI model. It’s what enables these systems to make predictions, find patterns, and deliver solutions that impact our everyday lives. But

Generative AI has redefined what we believe AI can do. What started as a tool for simple, repetitive tasks is now solving some of the most challenging problems we face.  OpenAI has played a big part in this shift, leading the way with its ChatGPT system. Early versions of ChatGPT showed how AI could have

As the demand for generative AI grows, so does the hunger for high-quality data to train these systems. Scholarly publishers have started to monetize their research content to provide training data for large language models (LLMs). While this development is creating a new revenue stream for publishers and empowering generative AI for scientific discoveries, it

AI is becoming a more significant part of our lives every day. But as powerful as it is, many AI systems still work like “black boxes.” They make decisions and predictions, but it’s hard to understand how they reach those conclusions. This can make people hesitant to trust them, especially regarding essential decisions like loan

Generative AI has made impressive strides in recent years. It can write essays, create art, and even compose music. But when it comes to getting facts right, it often falls short. It might confidently tell you that zebras live underwater or that the Eiffel Tower is in Rome. While these mistakes might seem harmless, they

Artificial intelligence has transformed the way we live, powering tools and services we rely on daily. From chatbots to smart devices, most of this progress comes from digital AI. It is incredibly powerful, processing vast amounts of data to deliver impressive results. But this power comes with a significant cost: energy use. Digital AI demands

Artificial intelligence has taken remarkable strides in recent years. Models that once struggled with basic tasks now excel at solving math problems, generating code, and answering complex questions. Central to this progress is the concept of scaling laws —rules that explain how AI models improve as they grow, are trained on more data, or are

Quantum computing has the potential to change many industries, from cryptography to drug discovery. But scaling these systems is a challenging task. As quantum computers grow, they face more errors and noise that can disrupt the calculations. To address this, DeepMind and Quantum AI introduced AlphaQubit, a neural network that predicts and fixes errors before

Artificial intelligence (AI) is everywhere these days. It is helping us shop online, diagnose diseases, and even drive cars. But as AI systems get more advanced, they also get more complicated. And when things are complicated, they are harder to change, scale, or fix. That is a big problem in a world that is constantly