Author: Dr. Tehseen Zia

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

In recent years, artificial intelligence (AI) has emerged as a key tool in scientific discovery, opening up new avenues for research and accelerating the pace of innovation. Among the various AI technologies, Graph AI and Generative AI are particularly useful for their potential to transform how scientists approach complex problems. Individually, each of these technologies

Artificial intelligence (AI) has come a long way, with large language models (LLMs) demonstrating impressive capabilities in natural language processing. These models have changed the way we think about AI’s ability to understand and generate human language. While they are excellent at recognizing patterns and synthesizing written knowledge, they struggle to mimic the way humans

Artificial Intelligence (AI) is making its way into critical industries like healthcare, law, and employment, where its decisions have significant impacts. However, the complexity of advanced AI models, particularly large language models (LLMs), makes it difficult to understand how they arrive at those decisions. This “black box” nature of AI raises concerns about fairness, reliability

For years, artificial intelligence (AI) has been a tool crafted and refined by human hands, from data preparation to fine-tuning models. While powerful at specific tasks, today’s AIs rely heavily on human guidance and cannot adapt beyond its initial programming. This dependence limits AI’s ability to be flexible and adaptable, the qualities that are central

AI has come a long way in visual perception and language processing. However, these abilities are not enough for building systems that can interact with the physical world. Humans handle objects or make controlled movements using the sense of touch. We feel texture, sense temperature, and gauge weight to guide each action with accuracy. This

The evolution of artificial intelligence (AI) is rapidly changing how we work, learn, and connect, transforming industries around the globe. This shift is primarily driven by AI’s advanced ability to learn from larger datasets. While bigger models boost AI’s data processing power, they also require more processing power and energy efficiency. As AI models become

In today’s politically polarized world, finding common ground on complex social and political issues is becoming increasingly challenging. As societies grow more diverse, disagreements on crucial matters like climate change, immigration, and economic policy have only extended. Bringing people together to find consensus on complex issues often requires time, resources, and a level playing field

After the rise of generative AI, artificial intelligence is on the brink of another significant transformation with the advent of agentic AI. This change is driven by the evolution of Large Language Models (LLMs) into active, decision-making entities. These models are no longer limited to generating human-like text; they are gaining the ability to reason

Anthropic has just released Claude 3.5, a powerful new version of its LLM series. While this model brings improved reasoning and coding skills, the real excitement centers around a new feature called “Computer Use.” This capability lets developers guide Claude to interact with the computer like a person—navigating screens, moving cursors, clicking, and typing. Unlike