• Data from a phase 0 trial
• Informing patient-centered trial design
• Effectively implementing digital wearable technologies

As customer success stories from AI accelerator start ups starting to proliferate, and traction starting to ramp up, it is starting to become clear which ML workloads are most amenable to domain specific architectures, and which market sectors are most likely to adopt novel AI acceleration technologies. 

With one company still retaining the majority of market share in the datacenter, and the edge currently a complete wilderness, it might still be a difficult time to launch a new accelerator company. But opportunities for capturing market share across the cloud-edge continuum definitely exist! In the world of HPC, certain ML and non-ML scientific workloads have seen extraordinary, demonstrable speed ups on novel ML systems architectures, and the scientific community only sees demand for acceleration of these types of workloads growing. At the edge some AI chip companies are already shipping in volume, while new applications emerge continuously.

This panel will look at what it takes to make it in the AIHW game, what might shift the balance of power in the datacenter, and how companies can find a niche at the edge. 

As AI makes its way into healthcare and medical applications, the role of hardware accelerators in the successful deployment of such large AI models becomes more and more important. Nowadays large language models, such as GPT-3 and T5, offer unprecedented opportunities to solve challenging healthcare business problems like drug discovery, medical term mapping and insight generation from electronic health records. However, efficient and cost effective training, as well as deployment and maintenance of such models in production remains a challenge for healthcare industry. This presentation will review a few open challenges and opportunities in the healthcare industry and the benefits that AI hardware innovation may bring to the ML utilization.

One of the biggest challenges in the US is managing the cost of healthcare.  Although we have high healthcare costs in the US, our life expectancy is still average.  In this talk we will look at some of the core causes of healthcare costs and what modern AI hardware can do to lower these costs.  We will see that faster and bigger GPUs alone will not save us.  We need detailed models to across a wide swath of our communities and perform early interventions.  We need accurate models of our world and the ability to simulate the impact of policy changes to overall healthcare costs.  We need new MIMD hardware with cores and memory architecture that keep cores fed with the right data.