Gboard, Google’s mobile keyboard app, operates on the principle of statistical decoding. This approach is necessary due to the inherent inaccuracy of touch input, often referred to as the ‘fat finger’ problem, on small screens. Studies have shown that without decoding, the error rate for each letter can be as high as 8 to 9…
In recent years, the integration of ML and AI into biomedicine has become increasingly pivotal, particularly in digital health. The explosion of high-throughput technologies, such as genome-wide sequencing, extensive libraries of medical images, and large-scale drug perturbation screens, has resulted in vast and complex biomedical data. This multi-omics data offers a wealth of information that…
Document understanding is a critical field that focuses on converting documents into meaningful information. This involves reading and interpreting text and understanding the layout, non-textual elements, and text style. The ability to comprehend spatial arrangement, visual clues, and textual semantics is essential for accurately extracting and interpreting information from documents. This field has gained significant…
Predicting the scaling behavior of frontier AI systems like GPT-4, Claude, and Gemini is essential for understanding their potential and making decisions about their development and use. However, it is difficult to predict how these systems will perform on specific tasks as they scale up, despite the well-established relation between parameters, data, compute, and pretraining…
Analogical reasoning, fundamental to human abstraction and creative thinking, enables understanding relationships between objects. This capability is distinct from semantic and procedural knowledge acquisition, which contemporary connectionist approaches like deep neural networks (DNNs) typically handle. However, these techniques often need help to extract relational abstract rules from limited samples. Recent advancements in machine learning have…
Large Language Models (LLMs) have become increasingly prominent in natural language processing because they can perform a wide range of tasks with high accuracy. These models require fine-tuning to adapt to specific tasks, which typically involves adjusting many parameters, thereby consuming substantial computational resources and memory. The fine-tuning process of LLMs presents a significant challenge…
In large language models, understanding how they work and what they pay attention to is crucial for improving their performance. However, analyzing the attention patterns of these models, especially in large-scale scenarios, can be daunting. Researchers and developers often need to gain insights into how tokens interact with each other during processing. Existing solutions for…
Researchers from C4DM, Queen Mary University of London, Sony AI, and Music X Lab, MBZUAI, have introduced Instruct-MusicGen to address the challenge of text-to-music editing, where textual queries are used to modify music, such as changing its style or adjusting instrumental components. Current methods are required to train specific models from scratch, are resource-intensive, and…
In a groundbreaking development, Timescale, the PostgreSQL cloud database company, has introduced two revolutionary open-source extensions, pgvectorscale, and pgai. These innovations have made PostgreSQL faster than Pinecone for AI workloads and 75% cheaper. Let’s explore how these extensions work and their implications for AI application development. Introduction to pgvectorscale and pgai Timescale unveiled the pgvectorscale…
Most LMMs integrate vision and language by converting images into visual tokens fed as sequences into LLMs. While effective for multimodal understanding, this method significantly increases memory and computation demands, especially with high-resolution photos or videos. Various techniques, like spatial grouping and token compression, aim to reduce the number of visual tokens but often compromise…