The combination of 5G and edge computing promises to fast-track the use of artificial intelligence (AI) in the Internet of Things (IoT).

With 10 to 20 times faster speeds and dramatically lower latency, 5G makes it much more feasible to process AI workloads locally at the edge.

Hardware is about to get interesting again.

This speed and reduced latency is essential to an array of IoT applications such as smart cities, transportation, intelligent manufacturing, e-health, smart farming, and so on. This is why Gartner predicts that 75 percent of enterprise-generated data will be processed outside a traditional data center or cloud by 2022, compared with 10 percent today.

Google introduced Tensor Processing Units or TPUs in four years ago.

TPUs, unlike GPUs, were custom-designed to deal with operations such as matrix multiplications in neural network training.

Here’s a great beginner’s guide to the technology.

Google TPUs can be accessed in two forms — cloud TPU and edge TPU. Cloud TPUs can be accessed from Google Colab notebook, which provides users with TPU pods that sit on Google’s data centres. Whereas, edge TPU is a custom-built development kit that can be used to build specific applications. In the next section, we will see the working of TPUs and its key components.

Databricks live streamed this interview with Matei Zaharia, an assistant professor at Stanford CS and co-founder and Chief Technologist of Databricks, the data and AI platform startup.

During his Ph.D., Matei started the Apache Spark project, which is now one of the most widely used frameworks for distributed data processing. He also co-started other widely used data and AI software such as MLflow, Apache Mesos and Spark Streaming.

Are you curious how data scientists and researchers train agents that make decisions? 

Learn how to use reinforcement learning to optimize decision making using Azure Machine Learning.  We show you how to get started.

Time Index:

  • [00:36] – What is reinforcement learning?
  • [01:37] – How do reinforcement learning algorithms work?
  • [04:10] – Reinforcement Learning on Azure – Notebook sample
  • [05:17] – Reinforcement Learning Estimator
  • [07:21] – Sample training Python script
  • [09:06] – Training Result
  • [10:15] – What kind of problems can you solve with reinforcement learning?

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