Author: Dr. Tehseen Zia

For years, search engines and databases relied on essential keyword matching, often leading to fragmented and context-lacking results. The introduction of generative AI and the emergence of Retrieval-Augmented Generation (RAG) have transformed traditional information retrieval, enabling AI to extract relevant data from vast sources and generate structured, coherent responses. This development has improved accuracy, reduced

In the race to advance artificial intelligence, DeepSeek has made a groundbreaking development with its powerful new model, R1. Renowned for its ability to efficiently tackle complex reasoning tasks, R1 has attracted significant attention from the AI research community, Silicon Valley, Wall Street, and the media. Yet, beneath its impressive capabilities lies a concerning trend

Artificial intelligence has made remarkable strides in recent years, with large language models (LLMs) leading in natural language understanding, reasoning, and creative expression. Yet, despite their capabilities, these models still depend entirely on external feedback to improve. Unlike humans, who learn by reflecting on their experiences, recognizing mistakes, and adjusting their approach, LLMs lack an

Game development has traditionally been a labor-intensive process requiring artistic creativity, technical expertise, and large-scale production efforts. Developers spend months, sometimes years, crafting environments, animations, and dialogue, working across teams that require significant financial investments. The rise of generative AI is beginning to change that. AI-driven tools can now assist in creating game environments, characters

Rapid advancements in AI have brought about the emergence of AI research agents—tools designed to assist researchers by handling vast amounts of data, automating repetitive tasks, and even generating novel ideas. Among the leading agents include Google’s AI Co-Scientist, OpenAI’s Deep Research, and Perplexity’s Deep Research, each offering distinct approaches to facilitating researchers. This article

Large Language Models (LLMs) have significantly advanced natural language processing (NLP), excelling at text generation, translation, and summarization tasks. However, their ability to engage in logical reasoning remains a challenge. Traditional LLMs, designed to predict the next word, rely on statistical pattern recognition rather than structured reasoning. This limits their ability to solve complex problems

Large language models (LLMs) like OpenAI’s o3, Google’s Gemini 2.0, and DeepSeek’s R1 have shown remarkable progress in tackling complex problems, generating human-like text, and even writing code with precision. These advanced LLMs are often referred as “reasoning models” for their remarkable abilities to analyze and solve complex problems. But do these models actually reason

Artificial intelligence has long been trying to mimic human-like logical reasoning. While it has made massive progress in pattern recognition, abstract reasoning and symbolic deduction have remained tough challenges for AI. This limitation becomes especially evident when AI is being used for mathematical problem-solving, a discipline that has long been a testament to human cognitive

In recent years, Large Language Models (LLMs) have significantly redefined the field of artificial intelligence (AI), enabling machines to understand and generate human-like text with remarkable proficiency. This success is largely attributed to advancements in machine learning methodologies, including deep learning and reinforcement learning (RL). While supervised learning has played a crucial role in training

The way we seek and process information has experienced a significant transformation over the past few years. Advances in artificial intelligence are fundamentally redefining knowledge discovery. The advent of AI, followed by the rise of generative AI, and now agentic AI, has allowed machines to retrieve information, synthesize and analyze it. This shift has not