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This research aims to comprehensively explore building a multimodal foundation model for egocentric video understanding. To achieve this goal, we work on three fronts. First, as there is a lack of QA data for egocentric video understanding, we automatically generate 7M high-quality QA samples for egocentric videos ranging from 30 seconds to one hour long …
Read more “MM-Ego: Towards Building Egocentric Multimodal LLMs”
Training a frontier model is highly compute-intensive, requiring a distributed system of hundreds, or thousands, of accelerated instances running for several weeks or months to complete a single job. For example, pre-training the Llama 3 70B model with 15 trillion training tokens took 6.5 million H100 GPU hours. On 256 Amazon EC2 P5 instances (p5.48xlarge, …
Read more “Reduce ML training costs with Amazon SageMaker HyperPod”
When it comes to AI, inference is where today’s generative AI models can solve real-world business problems. Google Kubernetes Engine (GKE) is seeing increasing adoption of gen AI inference. For example, customers like HubX run inference of image-based models to serve over 250k images/day to power gen AI experiences, and Snap runs AI inference on …
Read more “New GKE inference capabilities reduce costs, tail latency and increase throughput”
Building a generalist model for user interface (UI) understanding is challenging due to various foundational issues, such as platform diversity, resolution variation, and data limitation. In this paper, we introduce Ferret-UI 2, a multimodal large language model (MLLM) designed for universal UI understanding across a wide range of platforms, including iPhone, Android, iPad, Webpage, and …
Read more “Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms”
Agents are revolutionizing how businesses automate complex workflows and decision-making processes. Amazon Bedrock Agents helps you accelerate generative AI application development by orchestrating multi-step tasks. Agents use the reasoning capability of foundation models (FMs) to break down user-requested tasks into multiple steps. In addition, they use the developer-provided instruction to create an orchestration plan and …
Read more “Implement human-in-the-loop confirmation with Amazon Bedrock Agents”
Today we’re unveiling new AI capabilities to help cloud developers and operators at every step of the application lifecycle. We are doing this by: Putting applications at the center of your cloud experience, abstracting away the infrastructure complexities of the traditional cloud model. Now you can design, observe, secure, and optimize at the application level, …
Read more “Delivering an application-centric, AI-powered cloud for developers and operators”
Large language models (LLMs) could be valuable personal AI agents across various domains, provided they can precisely follow user instructions. However, recent studies have shown significant limitations in LLMs’ instruction-following capabilities, raising concerns about their reliability in high-stakes applications. Accurately estimating LLMs’ uncertainty in adhering to instructions is critical to mitigating deployment risks. We present, …
Read more “Do LLMs Estimate Uncertainty Well in Instruction-Following?”
By Cheng Xie, Bryan Shultz, and Christine Xu In a previous blog post, we described how Netflix uses eBPF to capture TCP flow logs at scale for enhanced network insights. In this post, we delve deeper into how Netflix solved a core problem: accurately attributing flow IP addresses to workload identities. A Brief Recap FlowExporter is …
Read more “How Netflix Accurately Attributes eBPF Flow Logs”
Headquartered in São Paulo, Brazil, iFood is a national private company and the leader in food-tech in Latin America, processing millions of orders monthly. iFood has stood out for its strategy of incorporating cutting-edge technology into its operations. With the support of AWS, iFood has developed a robust machine learning (ML) inference infrastructure, using services …
Progress in natural language processing enables more intuitive ways of interacting with technology. For example, many of Apple’s products and services, including Siri and search, use natural language understanding and generation to enable a fluent and seamless interface experience for users. Natural language is a rapidly moving area of machine learning research, and includes work …
Read more “Apple Workshop on Natural Language Understanding 2024”