A significant challenge in the field of artificial intelligence is to facilitate large language models (LLMs) to generate 3D meshes from text descriptions directly. Conventional techniques restrict LLMs from operating as text-based components and remove multimodal workflows that combine textual and 3D content creation. Most of the existing frameworks require additional architectures or massive computational…
Instruction-tuned large language models (LLMs) have redefined natural language processing (NLP), offering significant improvements in generating coherent, context-aware responses. However, a pressing challenge persists—access to high-quality, diverse, and task-specific instruction-response datasets. Traditional instruction-tuning approaches often depend on curated datasets that are costly and time-intensive to develop. Moreover, such datasets may lack the breadth and depth…
Machine learning (ML) engineers face many challenges while working on end-to-end ML projects. The typical workflow involves repetitive and time-consuming tasks like data cleaning, feature engineering, model tuning, and eventually deploying models into production. Although these steps are critical to building accurate and robust models, they often turn into a bottleneck for innovation. The workload…
Mixture of Experts (MoE) models represents a significant breakthrough in machine learning, offering an efficient approach to handling large-scale models. Unlike dense models, where all parameters are active during inference, MoE models activate only a fraction of their parameters. This approach balances computational efficiency with scalability, making MoE models highly attractive for various use cases.…
Kili Technology recently released a detailed report highlighting significant vulnerabilities in AI language models, focusing on their susceptibility to pattern-based misinformation attacks. As AI systems become integral to both consumer products and enterprise tools, understanding and mitigating such vulnerabilities is crucial for ensuring their safe and ethical use. This article explores the insights from Kili…
Retrieval-augmented generation (RAG) systems are essential in enhancing language model performance by integrating external knowledge sources into their workflows. These systems utilize methods that divide documents into smaller, manageable sections called chunks. RAG systems aim to improve both the accuracy and contextual relevance of their outputs by retrieving contextually appropriate chunks and feeding them into…
While today’s LLMs can skillfully use various tools, they still operate synchronously, only processing one action at a time. This strict turn-based setup limits their ability to handle multiple tasks simultaneously, reducing interactivity and responsiveness. For example, in a hypothetical scenario with an AI travel assistant, the model can’t respond to a quick weather query…
The exponential growth of multi-dimensional data across various fields, such as machine learning, geospatial analysis, and clustering, has posed significant challenges to traditional data structures. One such structure, the kd-tree, has long been a fundamental tool for managing high-dimensional datasets, supporting queries like nearest neighbors, range searches, and clustering analysis. However, the rapidly increasing size…
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…