Transformers are at the forefront of modern artificial intelligence, powering systems that understand and generate human language. They form the backbone of several influential AI models, such as Gemini, Claude, Llama, GPT-4, and Codex, which have been instrumental in various technological advances. However, as these models grow in size & complexity, they often exhibit unexpected…
In traffic management and urban planning, the ability to learn optimal routes from demonstrations conditioned on contextual features holds significant promise. As underscored by previous research endeavors, this methodology rests on the assumption that agents seek to optimize a latent cost when navigating from one point to another. Factors such as trip duration, comfort, toll…
The evolving field of aerial robotics has seen considerable advancements, particularly in the autonomous operation of Micro Aerial Vehicles (MAVs) during nighttime. Despite significant progress, night operations remain a complex challenge due to the inherent limitations of low-light environments. Let’s explore the integration of advanced sensing technologies and vision-based algorithms to enable robust autonomous navigation…
In the vast world of data science, countless tools are available to help analysts and researchers make sense of data and build powerful machine-learning models. While some tools are widely known and used, others might not be as familiar to many. Here are the ten great Python packages that can significantly enhance your workflow. 1.…
Recently, the UK government-backed AI Safety Institute has introduced Inspect, an Artificial Intelligence (AI) safety review tool, as a major step towards improving the safety and accountability of AI technologies. This unique instrument has the potential to strengthen AI safety assessments worldwide and promote cooperation amongst various parties involved in AI R&D. With Inspect, a…
Knowledge Distillation has gained popularity for transferring the expertise of a “teacher” model to a smaller “student” model. Initially, an iterative learning process involving a high-capacity model is employed. The student, with equal or greater capacity, is trained with extensive augmentation. Subsequently, the trained student expands the dataset through pseudo-labeling new data. Notably, the student…
The need to convert PDF documents into more manageable and editable formats like markdowns is increasingly vital, especially for those dealing with academic and scientific materials. These PDFs often contain complex elements such as multi-language text, tables, code blocks, and mathematical equations. The primary challenge in converting these documents lies in accurately maintaining the original…
Have you ever wondered how current AI systems, like those powering chatbots and language models, can comprehend and generate natural language so effectively? The answer lies in their ability to memorize and combine knowledge fragments, a process that has long eluded traditional machine learning techniques. This paper explores a novel approach called “Memory Mosaics,” which…
In the expanding natural language processing domain, text embedding models have become fundamental. These models convert textual information into a numerical format, enabling machines to understand, interpret, and manipulate human language. This technological advancement supports various applications, from search engines to chatbots, enhancing efficiency and effectiveness. The challenge in this field involves enhancing the retrieval…
In the quest for Artificial General Intelligence, LLMs and LMMs stand as remarkable tools, akin to brilliant minds, capable of diverse human-like tasks. While benchmarks are crucial for assessing their capabilities, the landscape is fragmented, with datasets scattered across platforms like Google Drive and Dropbox. lm-evaluation-harness sets a precedent for LLM evaluation, yet multimodal model…