​Posted by Sara Ahmadian and Mehran Kazemi, Research Scientists, Google Research Clustering is a fundamental, ubiquitous problem in data mining and unsupervised machine learning, where the goal is to group together similar items. The standard forms of clustering are metric clustering and graph clustering. In… Read More »Best of both worlds: Achieving scalability and quality in text clustering Google AI Google AI Blog

​ There’s a kind of magic that surrounds a soccer shot so powerful, it leaves spectators, players, and even commentators in a momentary state of awe. Think back to a moment when the sheer force of a strike left an entire Bundesliga stadium buzzing with… Read More »Bundesliga Match Facts Shot Speed – Who fires the hardest shots in the Bundesliga? Tareq Haschemi AWS Machine Learning Blog

​ In recent times, the zero-shot and few-shot capabilities of Large Language Models (LLMs) have increased significantly, with those with over 100B parameters giving state-of-the-art performance on various benchmarks. Such an advancement also presents a critical challenge with respect to LLMs, i.e., transparency. Very limited… Read More »A New AI Research from China Introduces GLM-130B: A Bilingual (English and Chinese) Pre-Trained Language Model with 130B Parameters Arham Islam Artificial Intelligence Category – MarkTechPost

​ In Reinforcement learning (RL), effectively integrating human feedback into learning processes has risen to the forefront as a significant challenge. This challenge becomes particularly pronounced in Reward Learning from Human Feedback (RLHF), especially when dealing with multiple teachers. The complexities surrounding the selection of… Read More »Researchers from UC Berkeley and Stanford Introduce the Hidden Utility Bandit (HUB): An Artificial Intelligence Framework to Model Learning Reward from Multiple Teachers Madhur Garg Artificial Intelligence Category – MarkTechPost

​ Large language model (LLM) research and applications have advanced remarkably in recent years. These generative models have enthralled the artificial intelligence community, and many models trained on various tasks and modalities have been made available lately. A rising agreement has resulted from these developments,… Read More »This AI Research from Google Reveals How Encoding Graph Data Elevates Language Model Performance on Complex Tasks Aneesh Tickoo Artificial Intelligence Category – MarkTechPost

​ MATHVISTA is introduced as a benchmark to assess the mathematical reasoning abilities of Large Language Models (LLMs) and Large Multimodal Models (LMMs) within visual contexts. The standard combines various mathematical and graphical tasks and includes existing and new datasets. Initial evaluations involving 11 prominent… Read More »Deciphering the Math in Images: How the New MathVista Benchmark is Pushing AI Boundaries in Visual and Mathematical Reasoning Adnan Hassan Artificial Intelligence Category – MarkTechPost

​ The field of research in pose-guided person image synthesis has made significant progress in recent years, focusing on generating images of a person with the same appearance but under a different pose. This technology has broad applications in e-commerce content generation and can improve… Read More »Tencent AI Lab Introduces Progressive Conditional Diffusion Models (PCDMs) that Incrementally Bridge the Gap Between Person Images Under the Target and Source Poses Through Three Stages Mahmoud Ghorbel Artificial Intelligence Category – MarkTechPost

​Posted by Xingchen Wan, Student Researcher, and Ruoxi Sun, Research Scientist, Cloud AI Team Recent advances in large language models (LLMs) are very promising as reflected in their capability for general problem-solving in few-shot and zero-shot setups, even without explicit training on these tasks. This… Read More »Zero-shot adaptive prompting of large language models Google AI Google AI Blog

​ Humans can extrapolate and learn to solve variations of a manipulation task if the objects involved have varied visual or physical attributes, given just a few examples of how to complete the task with standard objects. To make the learnt policies universal to different… Read More »Researchers from Stanford Propose ‘EquivAct’: A Breakthrough in Robot Learning for Generalizing Tasks Across Different Scales and Orientations Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost