This AI Paper Explores If Human Visual Perception can Help Computer Vision Models Outperform in Generalized Tasks

The Value of Stochastic Blockmodels in Social Network Analysis Practical Solutions and Value The use of relational data in social science has surged over the past two decades, driven by interest in network structures and their…

Understanding In-Context Reinforcement Learning (ICRL) Large Language Models (LLMs) are showing great promise in a new area called In-Context Reinforcement Learning (ICRL). This method allows AI to learn from interactions without changing its core parameters, similar…

Researchers have developed a framework called Language Models for Motion Control (LaMo) that incorporates Large Language Models (LLMs) for offline reinforcement learning. LaMo combines pre-trained LLMs with Decision Transformers (DT) and introduces innovations like LoRA fine-tuning…

Researchers at UCSD and Adobe have introduced the DITTO framework, enhancing control of pre-trained text-to-music diffusion models. It optimizes noise latents at inference time, allowing specific and stylized outputs. Leveraging extensive music datasets, the framework outperforms…

Lincoln Laboratory is working to reduce the energy requirements of AI models by promoting energy usage transparency and improving training efficiency.

Challenges in Building AI Agents Creating AI agents that work with various services can be tough, especially when managing authentication. Developers often find it hard to set up OAuth for Gmail or manage API keys for…

Practical Solutions for Speech Recognition Meeting the Demand for Precise Transcription Accurately transcribing spoken language is essential for accessibility services and clinical assessments. Capturing the details of human speech, including pauses and filler words, presents challenges…

Machine learning’s push for personalization is transforming fields such as recommender systems, healthcare, and finance. Yet, regulatory processes limit its application in critical sectors. Technion researchers propose a framework, r-MDPs, and algorithms to streamline approval processes…

Enhancing Efficiency of Large Language Models (LLMs) with Q-Sparse Practical Solutions and Value Recent research aims to enhance Large Language Model (LLM) efficiency through quantization, pruning, distillation, and improved decoding. Q-Sparse enables full activation sparsity, significantly…

CLOVA, a groundbreaking closed-loop AI framework, revolutionizes visual assistants by addressing their adaptability limitations. Its dynamic three-phase approach, incorporating correct and incorrect examples, advanced reflection schemes, and real-time learning, sets it apart in the field. This…

Researchers from the University of Georgia and Mayo Clinic tested the proficiency of Large Language Models (LLMs), particularly OpenAI’s GPT-4, in understanding biology-related questions. GPT-4 outperformed other AI models in reasoning about biology, scoring an average…

Introduction to Phi-4 Large language models have improved significantly in understanding language and solving complex problems. However, they often require a lot of computing power and large datasets, which can be problematic. Many datasets lack the…

Universities and Global Competition Universities are facing tough competition worldwide. Their rankings are increasingly linked to the United Nations’ Sustainable Development Goals (SDGs), which assess their social impact. These rankings affect funding, reputation, and student recruitment.…

This work confirms that multigroup fairness concepts yield strong omniprediction—loss minimization across diverse loss functions. The study establishes a reciprocal link, showing that multicalibration and omniprediction are equivalent. New definitions are proposed. (47 words)

Understanding the Challenge in Evaluating Vision-Language Models Evaluating vision-language models (VLMs) is complex because they need to be tested across many real-world tasks. Current benchmarks often focus on a limited range of tasks, which doesn’t fully…