Salesforce AI research team present FOFPred, a language driven future optical flow prediction framework that connects large vision language models with diffusion transformers for dense motion forecasting in control and video generation settings. FOFPred takes one or more images and a natural language instruction such as ‘moving the bottle from right to left’ and predicts…
Introducing D4RT, a unified AI model for 4D scene reconstruction and tracking across space and time. Anytime we look at the world, we perform an extraordinary feat of memory and prediction. We see and understand things as they are at a given moment in time, as they were a moment ago, and how they are…
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# Introduction
Docker has simplified how we build and deploy applications. But when you are getting started learning Docker, the terminology can often be confusing. You will likely hear terms like "images," "containers," and "volumes" without really understanding how they fit together. This article will help you understand the core…
Black Forest Labs releases FLUX.2 [klein], a compact image model family that targets interactive visual intelligence on consumer hardware. FLUX.2 [klein] extends the FLUX.2 line with sub second generation and editing, a unified architecture for text to image and image to image, and deployment options that range from local GPUs to cloud APIs, while keeping…
Today, Veo is getting more expressive, with improvements that help you create more fun, creative, high-quality videos based on ingredient images, built directly for the mobile format. We’re excited to bring new creative possibilities for everyone from casual storytellers to professional filmmakers. We’re releasing: Improvements to Veo 3.1 Ingredients to Video, our capability that lets…
Evaluating OCR systems that convert PDFs or document images into Markdown is far more complex than it appears. Unlike plain text OCR, OCR-to-Markdown requires models to recover content, layout, reading order, and representation choices simultaneously. Today’s benchmarks attempt to score this with a mix of string matching, heuristic alignment, and format-specific rules—but in practice, these…
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# Introduction
As a machine learning practitioner, you know that feature engineering is painstaking, manual work. You need to create interaction terms between features, encode categorical variables properly, extract temporal patterns from dates, generate aggregations, and transform distributions. For each potential feature, you test whether it improves model performance, iterate…
Large language models (LLMs) are increasingly becoming a primary source for information delivery across diverse use cases, so it’s important that their responses are factually accurate. In order to continue improving their performance on this industry-wide challenge, we have to better understand the types of use cases where models struggle to provide an accurate response…
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# Introduction
Learning AI today is not just about understanding machine learning models. It is about knowing how things fit together in practice, from math and fundamentals to building real applications, agents, and production systems. With so much content online, it is easy to feel lost or jump between…
AI presents an opportunity to build a more prosperous and secure world. The UK has already laid a strong foundation to seize this moment and is uniquely positioned to translate AI innovation into public benefit. That’s why we are excited to deepen our collaboration with the UK government to accelerate this work and offer a…