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Numerous challenges underlying human-robot interaction exist. One such challenge is enabling robots to display human-like expressive behaviors. Traditional rule-based methods need more scalability in new social contexts, while the need for extensive, specific datasets limits data-driven approaches. This limitation becomes pronounced as the variety of social interactions a robot might encounter increases, creating a demand…

Recent advancements in machine learning and artificial intelligence (ML) techniques are used in all fields. These advanced AI systems have been made possible due to advances in computing power, access to vast amounts of data, and improvements in machine learning techniques. LLMs, which require huge amounts of data, generate human-like language for many applications. A…

In the world of advanced AI, a common challenge developers face is the security and privacy of data, especially when using external services. Many businesses and individuals have strict rules about where their sensitive information can be stored and processed. The existing solutions often involve sending data to external servers, raising concerns about compliance with…

Large language models (LLMs) like GPT-4 require substantial computational power and memory, posing challenges for their efficient deployment. While sparsification methods have been developed to mitigate these resource demands, they often introduce new complexities. For example, these techniques may require extra data structures to support the sparse representations, complicating the system architecture. The potential speedups…

A groundbreaking development is emerging in artificial intelligence and machine learning: intelligent agents that can seamlessly adapt and evolve by integrating past experiences into new and diverse tasks. These agents, central to advancing AI technology, are being engineered to perform tasks efficiently and learn and improve continuously, thereby enhancing their adaptability across various scenarios. One…

With its structured format, Tabular data dominates the data analysis landscape across various sectors such as industry, healthcare, and academia. Despite the surge in the use of images and texts for machine learning, tabular data’s inherent simplicity and interpretability have kept it at the forefront of analytical methods. However, while effective, the traditional and deep…

With the rise of Large Language Models (LLMs) in recent years, generative AI has made significant strides in the field of language processing, showcasing impressive abilities in a wide array of tasks. Given their potential in solving complex tasks, researchers have made quite a number of attempts to apply these models in the field of…

For LLMs, auto-regressive decoding is now considered the gold standard. Because LLMs generate output tokens individually, the procedure is time-consuming and expensive. Methods based on speculative sampling provide an answer to this problem. In the first, called the “draft” phase, LLMs are hypothesized at little cost; in the second, called the “verification” phase, all of…

In computational linguistics and artificial intelligence, researchers continually strive to optimize the performance of large language models (LLMs). These models, renowned for their capacity to process a vast array of language-related tasks, face significant challenges due to their expansive size. For instance, models like GPT-3, with 175 billion parameters, require substantial GPU memory, highlighting a…

In the realm of artificial intelligence, Large Multimodal Models (LMMs) have exhibited remarkable problem-solving capabilities across diverse tasks, such as zero-shot image/video classification, zero-shot image/video-text retrieval, and multimodal question answering (QA). However, recent studies highlight a substantial gap between powerful LMMs and expert-level artificial intelligence, particularly in tasks involving complex perception and reasoning with domain-specific…