In-context learning (ICL) in large language models (LLMs) utilizes input-output examples to adapt to new tasks without altering the underlying model architecture. This method has transformed how models handle various tasks by learning from direct examples provided during inference. The problem at hand is the limitation of a few-shot ICL in handling intricate tasks. These tasks often demand a deep comprehension that few-shot learning cannot provide, as it operates under the restriction of minimal input data. This scenario could be better for applications requiring detailed analysis and decisio ..read more

The popularity of AI has skyrocketed in the past few years, with new avenues being opened up with the rise in the use of large language models (LLMs). Having knowledge of AI has now become quite essential as recruiters are actively looking for candidates with a strong foundation in the same. This article lists the top AI courses for beginners to take to help them make a shift in their careers and gain the necessary skills. Google AI for Anyone “Google AI for Anyone” is a beginner-friendly course that teaches about artificial intelligence (AI). The course covers how AI is used in real-world ap ..read more

In Large language models(LLM), developers and researchers face a significant challenge in accurately measuring and comparing the capabilities of different chatbot models. A good benchmark for evaluating these models should accurately reflect real-world usage, distinguish between different models’ abilities, and regularly update to incorporate new data and avoid biases. Traditionally, benchmarks for large language models, such as multiple-choice question-answering systems, have been static. These benchmarks do not frequently update and fail to capture real-world application nuances. They also ..read more

Traditional methods for training vision-language models (VLMs) often require the centralized aggregation of vast datasets, which raises concerns regarding privacy and scalability. Federated learning offers a solution by allowing models to be trained across a distributed network of devices while keeping data locally but adapting VLMs to this framework presents unique challenges. To address these challenges, a team of researchers from Intel Corporation and Iowa State University introduced FLORA (Federated Learning with Low-Rank Adaptation) to address the challenge of training vision-language mo ..read more

Reinforcement learning (RL) is a type of learning approach where an agent interacts with an environment to collect experiences and aims to maximize the reward received from the environment. This usually involves a looping process of experience collecting and enhancement, and due to the requirement of policy rollouts, it is called online RL. Both on-policy and off-policy RL need online interaction, which can be impractical in certain domains due to experimental or environmental constraints. Offline RL algorithms are framed so that they can extract optimal policies from static datasets. Offline ..read more

The 2024 Zhongguancun Forum in Beijing saw the introduction of Vidu, an advanced AI model that can generate 16-second 1080p video clips with a simple prompt. Developed by ShengShu-AI and Tsinghua University, Vidu is set to compete with OpenAI’s Sora, marking a significant milestone for China’s generative AI capabilities and ambition to lead in emerging technologies. Vidu’s primary technology is the Universal Vision Transformer (U-ViT), which combines two AI models – Transformer and Diffusion. This integration enables Vidu to produce dynamic video conten ..read more

Large language models (LLMs) are the backbone of numerous computational platforms, driving innovations that impact a broad spectrum of technological applications. These models are pivotal in processing and interpreting vast amounts of data, yet they are often hindered by high operational costs and inefficiencies related to system tool utilization. Optimizing LLM performance without prohibitive computational expenses is a significant challenge in this field. Traditionally, LLMs operate under systems that engage various tools for any given task, regardless of the specific needs of each operatio ..read more

Scientific Machine Learning (SciML) is an innovative field at the crossroads of ML, data science, and computational modeling. This emerging discipline utilizes powerful algorithms to propel discoveries across various scientific domains, including biology, physics, and environmental sciences. Image Source Expanding the Horizons of Research Accelerated Discovery and Innovation SciML allows for the quick processing and analysis of massive datasets, drastically reducing the time from hypothesis generation to experimental verification. This rapid cycle is pivotal in fields like pharmacology, whe ..read more

Cohere AI has made a major advancement in the field of Artificial Intelligence (AI) development by releasing the Cohere Toolkit, a comprehensive open-source repository designed to accelerate the development of AI applications. Cohere, which is a leading enterprise AI platform, has released the toolkit with future extensions to incorporate new platforms. This toolkit enables developers to make use of Cohere’s advanced models, Command, Embed, and Rerank, across several platforms, including AWS, Azure, and Cohere’s own platform.  By providing a set of production-ready apps that can be easil ..read more

Neural language models (LMs) have become popular due to their extensive theoretical work mostly focusing on representational capacity. An earlier study of representational capacity using Boolean sequential models helps in a proper understanding of its lower and upper bound and the potential of the transformer architecture. LMs have become the backbone of many NLP tasks, and most state-of-the-art LMs are based on transformer architecture. In addition, formal models of computation offer a smooth and accurate formulation to study different aspects of probability distributions that LMs can handle ..read more