Microsoft Research | Scalable and Efficient AI: From Supercomputers to Smartphones @MicrosoftResearch | Uploaded August 2023 | Updated October 2024, 1 week ago.
Speakers: Torsten Hoefler, Professor in the Scalable Parallel Computing Lab at ETH Zürich
Host: Saeed Maleki, Principal Research SDE at Microsoft Research
Billion-parameter artificial intelligence models have proven to show exceptional performance in a large variety of tasks ranging from natural language processing, computer vision, and image generation to mathematical reasoning and algorithm generation. Those models usually require large parallel computing systems, often called "AI Supercomputers", to be trained initially. We will outline several techniques ranging from data ingestion, parallelization, to accelerator optimization that improve the efficiency of such training systems. Yet, training large models is only a small fraction of practical artificial intelligence computations. Efficient inference is even more challenging - models with hundreds-of-billions of parameters are expensive to use. We continue by discussing model compression and optimization techniques such as fine-grained sparsity as well as quantization to reduce model size and significantly improve efficiency during inference. These techniques may eventually enable inference with powerful models on hand-held devices.
Speakers: Torsten Hoefler, Professor in the Scalable Parallel Computing Lab at ETH Zürich
Host: Saeed Maleki, Principal Research SDE at Microsoft Research
Billion-parameter artificial intelligence models have proven to show exceptional performance in a large variety of tasks ranging from natural language processing, computer vision, and image generation to mathematical reasoning and algorithm generation. Those models usually require large parallel computing systems, often called "AI Supercomputers", to be trained initially. We will outline several techniques ranging from data ingestion, parallelization, to accelerator optimization that improve the efficiency of such training systems. Yet, training large models is only a small fraction of practical artificial intelligence computations. Efficient inference is even more challenging - models with hundreds-of-billions of parameters are expensive to use. We continue by discussing model compression and optimization techniques such as fine-grained sparsity as well as quantization to reduce model size and significantly improve efficiency during inference. These techniques may eventually enable inference with powerful models on hand-held devices.
![Podcast | Collaborators: Teachable AI with Cecily Morrison & Karolina Pakėnaitė [ASL interpretation]](https://i.ytimg.com/vi/e2Kd0O8Wr-8/hqdefault.jpg "Podcast | Collaborators: Teachable AI with Cecily Morrison & Karolina Pakėnaitė [ASL interpretation]
The Microsoft Research Podcast series Collaborators explores the relationships behind the projects, products, and services being pursued and delivered by researchers at Microsoft and the diverse range of people they’re teaming up with.
In this episode, host Gretchen Huizinga speaks with Cecily Morrison, MBE, a Senior Principal Research Manager at Microsoft Research, and Karolina Pakėnaitė, a PhD student and member of the citizen design team working with Morrison on the research project Find My Things. An AI phone application designed to help people who are blind or have low vision locate their personal items, Find My Things is an example of a broader research approach known as Teachable AI. Morrison and Pakėnaitė explore the Teachable AI goal of empowering people to make AI experiences work for them and discuss how “designing for one” leads to innovative solutions and what they learned about optimizing these systems for real-world use (spoiler: it’s not necessarily more or higher-quality data).
For audio of the episode, a full transcript, and other resources, visit the episode landing page: https://www.microsoft.com/en-us/research/podcast/collaborators-teachable-ai-with-cecily-morrison-and-karolina-pakenaite/")
