Four Surprising Ways Inference Is Putting AI into Action

by Sid Sharma

From voice assistants like Alexa and Google Maps navigation to Bing’s conversational search, AI has become a part of daily life for many.

These tasks are performing deep learning inference, which might be thought of as AI put into action.

The deep learning neural networks that power AI are trained on massive amounts of data. Putting this training to work in the digital world — to recognize spoken words, images or street signs, or to suggest the shirt you might want to buy or the next movie to watch — is inferencing.

And the breadth of inference applications on GPUs may surprise you. It’s pervasive in everything from the lumber industry to research that delves into reading ancient Japanese texts.

Below are four diverse ways inference running on GPUs is already making a difference.

Fighting Fraud

PayPal is using deep learning inference on GPUs to pinpoint fraudulent transactions — and help ensure they don’t happen again.

The company processes millions of transactions every day. Advances in AI — specifically logistic regression-powered neural network models — have allowed it to filter out deceptive merchants and crack down on sales of illegal products.

The deep learning models also help PayPal optimize its operations by identifying why some transactions fail and spotting opportunities to work more efficiently.

And since the models are always learning, they can personalize user experiences by serving up relevant advertisements based on people’s interests.

Weather Insight

Boston-based ClimaCell is working to bring unprecedented speed, precision and accuracy to weather forecasting by listening closely to a powerful voice: Mother Nature’s.

The company uses inference on GPUs to offer so-called “nowcasting” — hyper-local, high-resolution forecasts that can help businesses and people make better decisions about everything from paving projects to wind generation to planning a daily commute to avoid bad weather. The company also offers forecasting and historic data.

ClimaCell’s nowcasting GPU model in action.

To achieve this, the company writes software that turns the signals in existing communication networks into sensors that can analyze the surrounding environment and extract real-time weather data.

ClimaCell’s network quickly analyzes the signals, integrates them with data from the National Oceanic and Atmospheric Administration and then weaves it all together using predictive models run on NVIDIA GPU accelerators.

Detecting Cancer

Mammogram machines are effective at detecting breast cancer, but expensive. In many developing countries, this makes them rare outside of large cities.

Mayo Clinic researcher Viksit Kumar is leading an effort to use GPU-powered inferencing to more accurately classify breast cancer images using ultrasound machines, which are much cheaper and more accessible around the world.

Kumar and his team have been able to detect and segment breast cancer masses with very good accuracy and few false positives, according to their research paper.

Mayo Clinic ultrasound deep learning research
The red outline shows the manually segmented boundary of a carcinoma, while the deep learning-predicted boundaries are shown in blue, green and cyan.

The team does its local processing using the TensorFlow deep learning framework container from the NGC registry on NVIDIA GPUs. It also uses NVIDIA V100 Tensor Core GPUs on AWS using the same container.

Eventually, Kumar hopes to use ultrasound images for the early detection of other forms of the disease, such as thyroid and ovarian cancer.

Making Music

MuseNet is a deep learning algorithm demo from AI research organization OpenAI that automatically generates music using 10 kinds of instruments and a host of different styles — everything from pop to classical.

People can create entirely new tracks by applying different instruments and sounds to music the algorithm generates. The demo uses NVIDIA V100 Tensor Core GPUs for this inferencing task.

Using the demo, you can take spin up twists on your favorite songs. Add guitars, leave out the piano, go big on drums. Or change its style to sound like jazz or classic rock.

The algorithm wasn’t programmed to mimic the human understanding of music. Instead, it was trained on hundreds of thousands of songs so it could learn the patterns of harmony, rhythm and style prevalent within music.

Its 72-layer network was trained using NVIDIA V100 Tensor Core GPUs with the cuDNN-accelerated TensorFlow deep learning framework.

Learn more about the world’s most powerful and efficient inference platform.