Apple's researchers have risen to the challenge of developing AI language models that prioritize efficiency, accuracy, ethical considerations, and user privacy. Two such models have been developed: one with three billion parameters that is optimized for on-device use, and a larger server-based model made for Apple's Private Cloud Compute. These models take us closer to…
This article presents research by scientists from the University of Helsinki, who have developed advanced algorithms for detecting dense subgraphs in temporal networks. Their work addresses two key challenges in temporal network analysis: identifying Jaccard Constrained Dense Subgraphs (JCDS) and discovering Jaccard Weighted Dense Subgraphs (JWDS).
The goal of their research was to maximize total…
Direct Preference Optimization (DPO) is a sophisticated training technique used for refining large language models (LLMs). It does not depend on a single gold reference like traditional supervised fine-tuning, instead, it trains models to identify quality differences among multiple outputs. Adding reinforcement learning approaches, DPO can learn from feedback, making it a useful technique for…
The rapid development of Large Language Models (LLMs) has transformed multiple areas including generative AI, Natural Language Understanding, and Natural Language Processing. However, hardware constraints have often limited the ability to run these models on devices such as laptops, desktops, or mobiles. In response to this, the PyTorch team has developed Torchchat, a versatile framework…
Time series data, which involves sequential observations recorded over time, is essential in various aspects of life including business and environmental studies. There are numerous models and tools available for time series analysis, but their diverse APIs and complexities pose challenges to users. To address these difficulties, a company called Unit8 developed Darts, an open-source…
Time series data is prevalent in various sectors, including weather forecasting, business strategizing, and complex systems monitoring. Effective processing of this data can aid in areas like strategic business planning and anomaly detection. Despite the availability of numerous tools for time series analysis, their complexities often pose challenges to the user. Addressing this issue, a…
Google's AI research team, DeepMind, has unveiled Gemma 2 2B, its new, sophisticated language model. This version, supporting 2.6 billion parameters, is optimized for on-device use and is a top choice for applications demanding high performance and efficiency. It holds enhancements for handling massive text generation tasks with more precision and higher levels of efficiency…
Carnegie Mellon University researchers are exploring the complexities of multi-agent imitation learning (MAIL), a mediation strategy in which a group of agents (like drivers on a road network) are coordinated through action recommendations, despite the mediator lacking knowledge of their utility functions. The challenge of this approach lies in specifying the quality of those recommendations,…
Researchers from Carnegie Mellon University are examining the challenge of a mediator coordinating a group of strategic agents without knowledge of their underlying utility functions, referred to as multi-agent imitation learning (MAIL). This is a complex issue as it involves providing personalised, strategic guidance to each agent without a comprehensive understanding of their circumstances or…
Researchers from Baidu Inc., China, have unveiled a self-reasoning framework that greatly improves the reliability and traceability of Retrieval-Augmented Language Models (RALMs). RALMs augment language models with external knowledge, decreasing factual inaccuracies. However, they face reliability and traceability issues, as noisy retrieval may lead to incorrect responses, and a lack of citations makes verifying these…