Sprint Review: More Than Just A Demo

Advancements in Online Agents Recent progress in Large Language Model (LLM) online agents has led to new designs that enhance autonomous web navigation and interaction. These agents can now perform complex online tasks more accurately and…

Scientists have created a soft fluidic switch using an ionic polymer artificial muscle, capable of lifting objects 34 times its weight with ultra-low power. Its small size and light weight allow for use in industrial areas…

Large Language Models (LLMs) such as GPT, PaLM, and LLaMa have enhanced AI and NLP by enabling machines to comprehend and produce human-like content. Finetuning is crucial to adapt these generalist models to specialized activities. Approaches…

The research explores efficient ways to update large language models (LLMs) without the need for time-consuming re-training. The approach, continual pre-training, integrates new data while retaining previous knowledge, effectively reducing computational load. Researchers demonstrate its effectiveness…

Deep reinforcement learning aims to teach agents to achieve goals using a balance of exploration and known strategies. The challenge lies in effectively scaling model parameters, which often underutilize the capacity of neural networks. Researchers have…

BarbNet is a deep-learning model tailored for automated detection and phenotyping of barbs in grain crops’ microscopic images. It utilizes advanced techniques to analyze awn and barb properties, aiding genetic and phenotypic investigations. Though achieving a…

Researchers from Codec Avatars Lab, Meta, and Nanyang Technological University have developed URHand, a Universal Relightable Hand model. It achieves photorealistic representation and generalization across viewpoints, poses, illuminations, and identities by combining physically based rendering and…

This article discusses the growing popularity of data science as a career choice, particularly among young professionals. It highlights that while the term “Data Science” has been around since the 1970s, it only gained widespread attention…

Hydragen is a transformative solution in optimizing large language models (LLMs). Developed by research teams from Stanford University, the University of Oxford, and the University of Waterloo, Hydragen’s innovative attention decomposition method significantly enhances computational efficiency…

Understanding Language Model Pre-Training The pre-training of language models (LMs) is essential for their ability to understand and generate text. However, a major challenge is effectively using diverse training data from sources like Wikipedia, blogs, and…

Introduction to AI Models AI is evolving with the emergence of powerful large language models (LLMs) and multimodal models. This includes both open-source models and proprietary ones. One notable example is DeepSeek-R1, an open-source AI model…

Neural text embeddings are crucial for NLP applications. While traditional embeddings from autoregressive language models have limitations, researchers devised “echo embeddings” to address the issue. By repeating input sentences, echo embeddings ensure comprehensive understanding. Demonstrated experiments…

Bilingual Chat Assistant Implementation In this tutorial, we will implement a Bilingual Chat Assistant using the Meraj-Mini model from Arcee AI. The assistant will be seamlessly deployed on Google Colab using T4 GPU, demonstrating the capabilities…

Researchers from Microsoft and Georgia Tech developed TongueTap, a wearable tech interface that uses tongue gestures to control devices without hands or eyes. It combines data from IMUs and PPG sensors in headsets for gesture recognition…

Understanding Recommendation Systems Recommendation systems help users find relevant content, products, or services. Traditional methods, known as dense retrieval, use complex models to represent users and items. However, these methods require a lot of computing power…

TinyGPT-V is a novel multimodal large language model aiming to balance high performance with reduced computational needs. It integrates a 24G GPU for training and an 8G GPU/CPU for inference, leveraging Phi-2 language backbone and pre-trained…