As good as they’ve become, artificial intelligence agents are still largely only as good as the data upon which they were trained. They don’t know what they don’t know. In the real world, people faced with unfamiliar situations and surroundings adapt by watching what others around them are doing and by asking questions. When in …
Read more “Is this a deer I see? Socially aware AI adapts by asking questions of humans”
This paper was accepted at the Workshop on Learning from Time Series for Health at NeurIPS 2022. Decoding information from bio-signals such as EEG, using machine learning has been a challenge due to the small data-sets and difficulty to obtain labels. We propose a reconstruction-based self-supervised learning model, the masked auto-encoder for EEG (MAEEG), for …
Read more “MAEEG: Masked Auto-encoder for EEG Representation Learning”
By Vadim Filanovsky and Harshad Sane In one of our previous blogposts, A Microscope on Microservices we outlined three broad domains of observability (or “levels of magnification,” as we referred to them) — Fleet-wide, Microservice and Instance. We described the tools and techniques we use to gain insight within each domain. There is, however, a class of problems …
Read more “Seeing through hardware counters: a journey to threefold performance increase”
In our collective rush to react to ever-changing marketplace dynamics and shifts in the economy, it’s easy to focus on short-term plans, to the neglect of long-term planning. Today’s leaders need to have several plans – short-term, medium-term, and long-term. Different plans for different needs How do these plans differ? A short-term plan is designed …
Read more “Approaches to long-term planning with IBM Planning Analytics”
Posted by Peter H. Li, Research Scientist, and Sven Dorkenwald, Student Researcher, Connectomics at Google Mapping the wiring and firing activity of the human brain is fundamental to deciphering how we think — how we sense the world, learn, decide, remember, and create — as well as what issues can arise in brain disease or …
Read more “Multi-layered Mapping of Brain Tissue via Segmentation Guided Contrastive Learning”
Medical imaging is an important tool for the diagnosis and localization of disease. Over the past decade, collections of medical images have grown rapidly, and open repositories such as The Cancer Imaging Archive and Imaging Data Commons have democratized access to this vast imaging data. Computational tools such as machine learning (ML) and artificial intelligence …
Read more “Brain tumor segmentation at scale using AWS Inferentia”
Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. It helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML. In 2021, AWS announced the integration of NVIDIA Triton Inference Server in SageMaker. You …
Read more “Serve multiple models with Amazon SageMaker and Triton Inference Server”
Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to quickly build, train, and deploy machine learning (ML) models. With SageMaker, you can deploy your ML models on hosted endpoints and get inference results in real time. You can easily view the performance metrics for your endpoints in Amazon …
Organizations are increasingly using machine learning pipelines to streamline and scale their ML workflows. However, managing these pipelines can be challenging when an organization has multiple ML projects and pipelines at different stages of development. To solve this, we need a way to build upon DevOps concepts and apply them to this ML-specific problem. In …
Read more “Best Practices for managing Vertex Pipelines code”
Two months after their debut sweeping MLPerf inference benchmarks, NVIDIA H100 Tensor Core GPUs set world records across enterprise AI workloads in the industry group’s latest tests of AI training. Together, the results show H100 is the best choice for users who demand utmost performance when creating and deploying advanced AI models. MLPerf is the …
Read more “NVIDIA Hopper, Ampere GPUs Sweep Benchmarks in AI Training”