Author: Dr. Tehseen Zia

Generative AI has gained significant traction due to its ability to create content that mimics human creativity. Despite its vast potential, with applications ranging from generating text and images to composing music and writing code, interacting with these rapidly evolving technologies remains daunting. The complexity of generative AI models and the technical expertise required often

In recent years, the race to develop increasingly larger AI models has captivated the tech industry. These models, with their billions of parameters, promise groundbreaking advancements in various fields, from natural language processing to image recognition. However, this relentless pursuit of size comes with significant drawbacks in the form of high costs and significant environmental

In the last few years, the world of AI has seen remarkable strides in foundation AI for text processing, with advancements that have transformed industries from customer service to legal analysis. Yet, when it comes to image processing, we are only scratching the surface. The complexity of visual data and the challenges of training models

The advent of conversational search engines is redefining how we retrieve information online, shifting from traditional keyword searches to more natural, conversational interactions. By combining large language models (LLMs) with real-time web data, these new systems address key issues found in both outdated LLMs and standard search engines. In this article, we’ll examine the challenges

Mathematical reasoning is a vital aspect of human cognitive abilities, driving progress in scientific discoveries and technological developments. As we strive to develop artificial general intelligence that matches human cognition, equipping AI with advanced mathematical reasoning capabilities is essential. While current AI systems can handle basic math problems, they struggle with the complex reasoning needed

In the realm of open-source AI, Meta has been steadily pushing boundaries with its Llama series. Despite these efforts, open-source models often fall short of their closed counterparts in terms of capabilities and performance. Aiming to bridge this gap, Meta has introduced Llama 3.1, the largest and most capable open-source foundation model to date. This

The idea of reading minds has fascinated humanity for centuries, often seeming like something from science fiction. However, recent advancements in artificial intelligence (AI) and neuroscience bring this fantasy closer to reality. Mind-reading AI, which interprets and decodes human thoughts by analyzing brain activity, is now an emerging field with significant implications. This article explores

OpenAI, a leader in scaling Generative Pre-trained Transformer (GPT) models, has now introduced GPT-4o Mini, shifting toward more compact AI solutions. This move addresses the challenges of large-scale AI, including high costs and energy-intensive training, and positions OpenAI to compete with rivals like Google and Claude. GPT-4o Mini offers a more efficient and affordable approach

AI hardware is growing quickly, with processing units like CPUs, GPUs, TPUs, and NPUs, each designed for specific computing needs. This variety fuels innovation but also brings challenges when deploying AI across different systems. Differences in architecture, instruction sets, and capabilities can cause compatibility issues, performance gaps, and optimization headaches in diverse environments. Imagine working

Integrating mobile and wearable devices with generative AI opens a new era in personal health coaching. This combination, referred to as AI health coach, provides continuous, personalized guidance that traditional methods can’t offer. In this article, we explore this exciting development and discover how this dynamic duo is set to transform personal wellness, making it