In the realm of artificial intelligence, enabling Large Language Models (LLMs) to navigate and interact with graphical user interfaces (GUIs) has been a notable challenge. While LLMs are adept at processing textual data, they often encounter difficulties when interpreting visual elements like icons, buttons, and menus. This limitation restricts their effectiveness in tasks that require…
Efficiently handling long contexts has been a longstanding challenge in natural language processing. As large language models expand their capacity to read, comprehend, and generate text, the attention mechanism—central to how they process input—can become a bottleneck. In a typical Transformer architecture, this mechanism compares every token to every other token, resulting in computational costs…
Whole Slide Image (WSI) classification in digital pathology presents several critical challenges due to the immense size and hierarchical nature of WSIs. WSIs contain billions of pixels and hence direct observation is computationally infeasible. Current strategies based on multiple instance learning (MIL) are effective in performance but considerably dependent on large amounts of bag-level annotated…
As artificial intelligence (AI) continues to gain traction across industries, one persistent challenge remains: creating language models that truly understand the diversity of human languages, including regional dialects and local cultural contexts. While advancements in AI have primarily focused on English, many languages, particularly those spoken in the Middle East and South Asia, remain underserved.…
In recent years, language models have been pushed to handle increasingly long contexts. This need has exposed some inherent problems in the standard attention mechanisms. The quadratic complexity of full attention quickly becomes a bottleneck when processing long sequences. Memory usage and computational demands increase rapidly, making it challenging for practical applications such as multi-turn…
In this tutorial, we will do an in-depth, interactive exploration of NVIDIA’s StyleGAN2‑ADA PyTorch model, showcasing its powerful capabilities for generating photorealistic images. Leveraging a pretrained FFHQ model, users can generate high-quality synthetic face images from a single latent seed or visualize smooth transitions through latent space interpolation between different seeds. With an intuitive interface…
Vision Language Models have been a revolutionizing milestone in the development of language models, which overcomes the shortcomings of predecessor pre-trained LLMs like LLama, GPT, etc. Vision Language Models explore a new territory beyond single modularity to combine inputs from text and image videos. VLMs thus bestow a better understanding of visual-spatial relationships by expanding…
Understanding financial information means analyzing numbers, financial terms, and organized data like tables for useful insights. It requires math calculations and knowledge of economic concepts, rules, and relationships between financial terms. Although sophisticated AI models have shown excellent general reasoning ability, their suitability for financial tasks is questionable. Such tasks require more than simple mathematical…
Addressing the evolving challenges in software engineering starts with recognizing that traditional benchmarks often fall short. Real-world freelance software engineering is complex, involving much more than isolated coding tasks. Freelance engineers work on entire codebases, integrate diverse systems, and manage intricate client requirements. Conventional evaluation methods, which typically emphasize unit tests, miss critical aspects such…
Large language models have demonstrated remarkable problem-solving capabilities and mathematical and logical reasoning. These models have been applied to complex reasoning tasks, including International Mathematical Olympiad (IMO) combinatorics problems, Abstraction and Reasoning Corpus (ARC) puzzles, and Humanity’s Last Exam (HLE) questions. Despite improvements, existing AI models often struggle with high-level problem-solving that requires abstract reasoning,…