GPT-4 and other Large Language Models (LLMs) have proven to be highly proficient in text analysis, interpretation, and generation. Their exceptional effectiveness extends to a wide range of financial sector tasks, including sophisticated disclosure summarization, sentiment analysis, information extraction, report production, and compliance verification. However, studies have been still going on about their function in…
Advancements in AI have led to proficient systems that make unclear decisions, raising concerns about deploying untrustworthy AI in daily life and the economy. Understanding neural networks is vital for trust, ethical concerns like algorithmic bias, and scientific applications requiring model validation. Multilayer perceptrons (MLPs) are widely used but lack interpretability compared to attention layers.…
Foundation models are central to AI’s influence on the economy and society. Transparency is crucial for accountability, competition, and understanding, particularly regarding the data used in these models. Governments are enacting regulations like the EU AI Act and the US AI Foundation Model Transparency Act to enhance transparency. The Foundation Model Transparency Index (FMTI) introduced…
People who work with large language models often need a quick and efficient way to interact with these powerful tools. However, many existing methods require switching between applications or dealing with slow, cumbersome interfaces. Some solutions are available, but they come with their own set of limitations. Web-based interfaces are common but can be slow…
The memory footprint of the key-value (KV) cache can be a bottleneck when serving large language models (LLMs), as it scales proportionally with both sequence length and batch size. This overhead limits batch sizes for long sequences and necessitates costly techniques like offloading when on-device memory is scarce. Furthermore, the ability to persistently store and…
Cryptocurrency systems have evolved significantly since Bitcoin was introduced in 2009. As the landscape becomes increasingly complex, integrating artificial intelligence (AI) offers promising avenues for enhancing security and efficiency. AI’s capabilities in data analysis, pattern recognition, and predictive modeling make it a critical tool for addressing the unique challenges advanced cryptocurrency systems face. Image Source…
Transformers have greatly transformed natural language processing, delivering remarkable progress across various applications. Nonetheless, despite their widespread use and accomplishments, ongoing research continues to delve into the intricate workings of these models, with a particular focus on the linear nature of intermediate embedding transformations. This less explored aspect poses significant implications for further advancements in…
Foundation models enable complex tasks such as natural language processing, image recognition, etc. These models leverage large datasets and intricate neural networks to deliver previously unattainable results with traditional algorithms. The use of these models has revolutionized the field of AI, allowing for more accurate and sophisticated analysis and interpretation of data. Researchers face the…
Regarding robotic learning, the standard practice is to use datasets tailored to the particular robot and job at hand to train policies. Starting from scratch in this manner necessitates a substantial amount of data collection for every activity, and the policies that are produced typically display little generalizability. Theoretically, data gathered from previous robots and…
Reinforcement learning (RL) is predicated on agents learning to make decisions by interacting with an environment. RL has achieved remarkable feats in various applications, including games, robotics, and autonomous systems. The goal is to develop algorithms that enable agents to perform tasks efficiently by maximizing cumulative rewards through trial-and-error interactions. By continuously adapting to new…