Insights, tips, and product updates

Explore the groundbreaking advancements of DeepSeek R1, a reinforcement learning-driven reasoning model. Learn about its distillation into smaller models, ensuring cost-effective, efficient reasoning capabilities across various domains.

Explore LLM reasoning limitations in logical deduction and ongoing efforts to enhance these capabilities through chain-of-thought reasoning, coding, and other novel techniques.

Many companies around the world are currently trying to make the creation & deployment of AI as easy and efficient as possible. Among them, two contestants stand out: Kili Technology and LatentAI. Let's dive into how to use them to your advantage!

Unaware users might unknowingly rely on these AI-generated answers, leading to adverse outcomes, especially in fact-driven fields such as law, healthcare, and finance. In this article, we delve into LLM hallucination, discussing real-world examples, causes, and mitigation strategies.

Discover how to tackle the biggest challenges of satellite imagery and geospatial data annotation. Learn which tools are available to you.

We recently released our red teaming study diving into adversarial prompt injection techniques and the harm categories that LLMs are most vulnerable to. Now, let's have a look at the new and existing studies that serve to combat these adversarial prompting techniques.

Meta's Segment Anything Model 2 (SAM 2) represents a major leap forward in visual segmentation technology. Built on an innovative streaming architecture and trained on the largest video segmentation dataset to date, SAM 2 delivers superior accuracy with 3× fewer user interactions compared to previous approaches. This article explores how SAM 2's memory-equipped design and three-phase data collection methodology translates into practical applications through platforms like Kili Technology.

Today, Kili Technology addresses the fundamental challenges of computer vision data labeling with the integration of Meta's Segment Anything Model 2 (SAM2) into our annotation platform, bringing unprecedented efficiency to both geospatial and video annotation tasks.

Ensuring the safety of large language models (LLMs) across languages is crucial as AI becomes more integrated into our lives. This article presents our red teaming study at Kili Technology, evaluating LLM vulnerabilities against adversarial prompts in both English and French. Our findings reveal critical insights into multilingual weaknesses and highlight the need for improved safety measures in AI systems.

This article explores the latest developments in AI safety, including regulatory frameworks, risk management strategies, and technical approaches to mitigate potential harms. From red teaming and toxicity detection to reinforcement learning from human feedback, we delve into the multifaceted efforts to create AI systems that are not only powerful, but also reliable, ethical, and aligned with human values.

Explore the latest techniques in red teaming large language models (LLMs) and crafting adversarial prompts. Learn how to evaluate and improve AI safety through automated testing methods, benchmark datasets, and robust defense strategies.

Discover how to surpass frontier LLM performance using Reinforcement Learning from Human Feedback (RLHF) with this recap of our latest webinar featuring Adaptive ML.

The successful implementation of SLMs requires careful planning, from dataset preparation to continuous evaluation and monitoring. Organizations must adopt a proactive approach to manage these models, ensuring they continually meet the evolving demands and maintain alignment with business goals.

Explore SmolLM, a compact yet powerful language model challenging the notion that bigger is always better in AI. Learn how its meticulously curated datasets and efficient design deliver high performance with lower resource demands, making it ideal for applications in education, coding, and customer support.

Explore how GPT-4o Mini delivers advanced AI capabilities at a reduced cost. Learn about its key features, performance comparisons, and real-world applications, making it a top choice for businesses seeking efficient and cost-effective AI solutions.

Llama 3.1 is Meta's latest flagship language model, boasting an impressive 405 billion parameters. This article dives into how the model was trained and fine-tuned, and pulls out new insights for domain-specific LLMs.

Imagine a Michelin-starred restaurant. The success of each dish relies on three key elements: premium ingredients (Food), the right tools (Tool), and a skilled team (Team).

Learn more about DBRX's model training, focus on data quality, and evaluation, showcasing how high-performing enterprise-level large language models are made.

This article explores the best practices and insights from FineWeb's documentation, offering a comprehensive guide for anyone seeking to build high quality datasets for their language models.

Generative AI (GenAI) is transforming industries by significantly boosting productivity and efficiency. This article explores GenAI's impact, emphasizing the crucial role of high-quality datasets in its success. We discuss challenges in creating these datasets and propose innovative solutions to enhance data processes, ensuring optimal performance of GenAI models.