SAS: 4 real-world artificial intelligence applications
Everyone is talking about AI (artificial intelligence). Unfortunately, a lot of what you hear about AI in movies and on the TV is sensationalized for entertainment.
Indeed, AI is overhyped. But AI is also real and powerful.
Consider this: engineers worked for years on hand-crafted models for object detection, facial recognition and natural language translation. Despite honing those algorithms by the best of our species, their performance does not come close to what data-driven approaches can accomplish today. When we let algorithms discover patterns from data, they outperform human coded logic for many tasks, that involve sensing the natural world.
The powerful message of AI is not that machines are taking over the world. It is that we can guide machines to generate tremendous value by unlocking the information, patterns and behaviors that are captured in data.
Today I want to share four real-world applications of SAS AI and introduce you to five SAS employees who are working to put this technology into the hands of decision makers, from caseworkers and clinicians to police officers and college administrators.
Augmenting health care with medical image analysis
Fijoy Vadakkumpadan, a Senior Staff Scientist on the SAS Computer Vision team, is no stranger to the importance of medical image analysis. He credits ultrasound technology with helping to ensure a safe delivery of his twin daughters four years ago. Today, he is excited that his work at SAS could make a similar impact on someone else’s life.
Recently, Fijoy’s team has extended the SAS Platform to analyze medical images. The technology uses an artificial neural network to recognize objects on medical images and thus improve healthcare.
Designing AI algorithms you can trust
Xin Hunt, a Senior Machine Learning Developer at SAS, hopes to have a big impact on the future of machine learning. She is focused on interpretability and explainability of machine learning models, saying, 'In order for society to accept it, they have to understand it'.
Interpretability uses a mathematical understanding of the outputs of a machine learning model. You can use interpretability methods to show how the model reacts to changes in the inputs, for example.
Explainability goes further than that. It offers full verbal explanations of how a model functions, what parts of the model logic were derived automatically, what parts were modified in post-processing, how the model meets regulations, and so forth.
Making machine learning accessible to everyone
From exploring and transforming data to selecting features and comparing algorithms, there are multiple steps to building a machine learning model. What if you could apply all those steps with the click of a button?
That’s what the development teams of Susan Haller and Dragos Coles have done. Susan is the Director of Advanced Analytics R&D and Dragos is a Senior Machine Learning Developer at SAS. They are showing a powerful tool that offers an API for a dynamic, automated model building. The model is completely transparent, so you examine and modify it after it is built.
Deploying AI models in the field
You can do everything right when building and refining a machine learning model, but if you do not deploy it where decisions are made it will not do any good.
Seb Charrot, a Senior Manager in the Scottish R&D Team, enjoys deploying analytics to solve real problems for real people. He and his team build SAS Mobile Investigator, an application that allows caseworkers, investigators and officers in the field to receive tasks, be notified of risks and concerns regarding their caseload or coverage area, and raise reports on the go.
Moving AI into the real world
When you move past the science project phase of analytics and build solutions for the real world, you will find that you can enable everyone, not just those with data science degrees, to make decisions based on data. As a result, everyone’s jobs become easier and more productive. Plus, increased access to analytics leads to faster and more reliable decisions. Technology is unstoppable, it is who we are, it is what we do. Not just at SAS, but as a species.
Author: Oliver Schabenberger