Large language models (LLMs) have revolutionized natural language processing by offering sophisticated abilities for a range of applications. However, these models face significant challenges. First, deploying these massive models on end devices, such as smartphones or personal computers, is extremely resource-intensive, making integration impractical for everyday applications. Second, current LLMs are monolithic, storing all domain… →
Agentic AI has emerged as a result of the quick development of Artificial Intelligence (AI). This new wave of AI is changing industries and reinventing how humans and machines work together. It is distinguished by its autonomous decision-making and problem-solving capabilities. In contrast to conventional generative AI, which concentrates on producing content, agentic AI enables… →
Marqo has introduced four groundbreaking datasets and state-of-the-art e-commerce embedding models designed to advance product search, retrieval, and recommendation capabilities in e-commerce. These models, Marqo-Ecommerce-B and Marqo-Ecommerce-L, offer substantial improvements in accuracy and relevance for e-commerce platforms by delivering high-quality embedding representations of product data. Alongside these models, Marqo has released a series of evaluation… →
Despite their advanced reasoning capabilities, the latest LLMs often miss the mark when deciphering relationships. In this article, we explore the Reversal Curse, a pitfall that affects LLMs across tasks such as comprehension and generation. To understand the underlying issue, it is a phenomenon that occurs when dealing with two entities, denoted as a and… →
In real-world settings, agents often face limited visibility of the environment, complicating decision-making. For instance, a car-driving agent must recall road signs from moments earlier to adjust its speed, yet storing all observations is unscalable due to memory limits. Instead, agents must learn compressed representations of observations. This challenge is compounded in ongoing tasks, where… →
Edge AI has long faced the challenge of balancing efficiency and effectiveness. Deploying Vision Language Models (VLMs) on edge devices is difficult due to their large size, high computational demands, and latency issues. Models designed for cloud environments often struggle with the limited resources of edge devices, resulting in excessive battery usage, slower response times,… →
Advancements in large language models (LLMs) have revolutionized natural language processing, with applications spanning text generation, translation, and summarization. These models rely on large amounts of data, large parameter counts, and expansive vocabularies, necessitating sophisticated techniques to manage computational and memory requirements. A critical component of LLM training is the cross-entropy loss computation, which, while… →
Large language models (LLMs), useful for answering questions and generating content, are now being trained to handle tasks requiring advanced reasoning, such as complex problem-solving in mathematics, science, and logical deduction. Improving reasoning capabilities within LLMs is a core focus of AI research, aiming to empower models to conduct sequential thinking processes. This area’s enhancement… →
Say goodbye to frustrating AI outputs—Anthropic AI’s new console features put control back in developers’ hands. Anthropic has made building dependable AI applications with Claude simpler by improving prompts and managing examples directly in the console. The Anthropic Console allows users to build with Anthropic API, meaning it is especially useful for developers. You can… →