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… →
Optimization theory has emerged as an essential field within machine learning, providing precise frameworks for adjusting model parameters efficiently to achieve accurate learning outcomes. This discipline focuses on maximizing the effectiveness of techniques like stochastic gradient descent (SGD), which forms the backbone of numerous models in deep learning. Optimization impacts various applications, from image recognition… →
Large Language Models (LLMs) have revolutionized various domains, with a particularly transformative impact on software development through code-related tasks. The emergence of tools like ChatGPT, Copilot, and Cursor has fundamentally changed how developers work, showcasing the potential of code-specific LLMs. However, a significant challenge persists in developing open-source code LLMs, as their performance consistently lags… →
In recent years, developing realistic and robust simulations of human-like agents has been a complex and recurring problem in the field of artificial intelligence (AI) and computer science. A fundamental challenge has always been modeling human behavior with convincing accuracy. Traditional approaches often involved using pre-defined rule-based systems or simple state machines, but these fell… →
In recent years, large language models (LLMs) have become a cornerstone of AI, powering chatbots, virtual assistants, and a variety of complex applications. Despite their success, a significant problem has emerged: the plateauing of the scaling laws that have historically driven model advancements. Simply put, building larger models is no longer providing the significant leaps… →
As the language models are improving, their adoption is growing in more complex tasks such as free-form question answering or summarization. On the other hand, the more demanding the task – the higher the risk of LLM hallucinations. In this article, you’ll find: what the problem with hallucination is, which techniques we use to reduce… →
In today’s world, CLIP is one of the most important multimodal foundational models. It combines visual and textual signals into a shared feature space using a simple contrastive learning loss on large-scale image-text pairs. As a retriever, CLIP supports many tasks, including zero-shot classification, detection, segmentation, and image-text retrieval. Also, as a feature extractor, it… →
Embodied artificial intelligence (AI) involves creating agents that function within physical or simulated environments, executing tasks autonomously based on pre-defined objectives. Often used in robotics and complex simulations, these agents leverage extensive datasets and sophisticated models to optimize behavior and decision-making. In contrast to more straightforward applications, embodied AI requires models capable of managing vast… →