Forecasting Sustainable Development Goals (SDG) Scores by 2030: The Sustainable Development Goals (SDGs) set by the United Nations aim to eradicate poverty, protect the environment, combat climate change, and ensure peace and prosperity by 2030. These 17 goals address global health, education, inequality, environmental degradation, and climate change challenges. Despite extensive research tracking progress towards…
Reinforcement learning from human feedback RLHF is essential for ensuring quality and safety in LLMs. State-of-the-art LLMs like Gemini and GPT-4 undergo three training stages: pre-training on large corpora, SFT, and RLHF to refine generation quality. RLHF involves training a reward model (RM) based on human preferences and optimizing the LLM to maximize predicted rewards.…
DVC.ai has announced the release of DataChain, a revolutionary open-source Python library designed to handle and curate unstructured data at an unprecedented scale. By incorporating advanced AI and machine learning capabilities, DataChain aims to streamline the data processing workflow, making it invaluable for data scientists and developers. Key Features of DataChain: AI-Driven Data Curation: DataChain…
The cybersecurity risks, benefits, and capabilities of AI systems are crucial for the security and AI policy. As AI becomes increasingly integrated into various aspects of our lives, the potential for malicious exploitation of these systems becomes a significant threat. Generative AI models and products are particularly susceptible to attacks due to their complex nature…
Large Language Models (LLMs) have become significantly popular in the recent times. However, evaluating LLMs on a wider range of tasks can be extremely difficult. Public standards do not always accurately reflect an LLM’s general skills, especially when it comes to performing highly specialized client tasks that call for domain-specific knowledge. Different evaluation metrics are…
Because LLMs are inherently random, building reliable software (like LLM agents) requires continuous monitoring, a systematic approach to testing modifications, and quick iteration on fundamental logic and prompts. Current solutions are vertical, and developers still have to worry about keeping the “glue” between them, which will slow them down. Laminar is an AI developer platform…
A significant challenge in the field of visual question answering (VQA) is the task of Multi-Image Visual Question Answering (MIQA). This involves generating relevant and grounded responses to natural language queries based on a large set of images. Existing Large Multimodal Models (LMMs) excel in single-image visual question answering but face substantial difficulties when queries…
Multi-target multi-camera tracking (MTMCT) is essential for intelligent transportation systems. Still, it faces challenges in real-world applications due to limited publicly available data and the labor-intensive process of manual annotation. Efficient traffic management has been improved with advancements in computer vision, enabling accurate prediction and analysis of traffic volumes. MTMCT involves tracking vehicles across multiple…
Prior to PILOT, fitting linear model trees was slow and prone to overfitting, especially with large datasets. Traditional regression trees struggled to capture linear relationships effectively. Linear model trees faced interpretability challenges when incorporating linear models in leaf nodes. The research emphasized the need for algorithms combining decision tree interpretability with accurate linear relationship modeling.…
Meta announced the release of Llama 3.1, the most capable model in the LLama Series. This latest iteration of the Llama series, particularly the 405B model, represents a substantial advancement in open-source AI capabilities, positioning Meta at the forefront of AI innovation. Meta has long advocated for open-source AI, a stance underscored by Mark Zuckerberg’s…