The specific GPU added to Google Cloud is the NVIDIA A100 Tensor Core which was announced just last month. NVIDIA says the A100 “has come to the cloud faster than any NVIDIA GPU in history.”

NVIDIA claims the A100 boosts training and inference performance by up to 20x over its predecessors. Large AI models like BERT can be trained in just 37 minutes on a cluster of 1,024 A100s.

For those who enjoy their measurements in teraflops (TFLOPS), the A100 delivers around 19.5 TFLOPS in single-precision performance and 156 TFLOPS for Tensor Float 32 workloads.

Manish Sainani, Director of Product Management at Google Cloud, said:

“Google Cloud customers often look to us to provide the latest hardware and software services to help them drive innovation on AI and scientific computing workloads.

With our new A2 VM family, we are proud to be the first major cloud provider to market NVIDIA A100 GPUs, just as we were with NVIDIA T4 GPUs. We are excited to see what our customers will do with these new capabilities.”

The announcement couldn’t have arrived at a better time – with many looking to harness AI for solutions to the COVID-19 pandemic, in addition to other global challenges such as climate change.

Aside from AI training and inference, other things customers will be able to achieve with the new capabilities include data analytics, scientific computing, genomics, edge video analytics, and 5G services.

The new Ampere-based data center GPUs are now available in Alpha on Google Cloud. Users can access instances of up to 16 A100 GPUs, which provides a total of 640GB of GPU memory and 1.3TB of system memory.

You can register your interest for access here.

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During the Google Cloud Next 18 conference, Google Chief AI Scientist Dr. Fei-Fei Li demonstrated a new AI system called Google Contact Center AI which – much like Duplex – sounded incredibly natural in its responses to human queries.

Google seems to have learned its lesson from its Duplex demonstration and wanted to iterate that it’s not designed to replace human operators.

Instead, the system could be used to replace the current dreaded automated messages you often hear when dialling a call center. “Press 1 for… press 2 for…” just writing it makes me shudder.

Contact Center AI could be able to answer some basic questions about the business to provide answers quickly to callers and reduce the waiting times for those needing a human operator.

When a human operator is necessary, the system could automatically transfer the caller to the correct department.

“Contact Center AI is an example for our passion for bringing AI to every industry all the while elevating the role of human talent,” Li told the audience. “We’re creating technology that’s not just powerful but that’s also trustworthy.”

Call centers are something we’ve all had a frustrating experience with, so it’s a good example of the practical uses of AI when explaining to a wider public that may be sceptical about the technology’s benefits.

Google took the right approach when demonstrating Contact Center AI. The company’s Duplex demo spurred a needed debate about whether an AI should always identify itself as such when interacting with a human.

Several companies, including Google, promise their AIs will do so. Some lawmakers are considering legislation that will make it a legal obligation.

What are your thoughts on Google’s contact center AI?

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