Industries Making the Best Competitive Use of AI in 2019

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Today we continue the insideBIGDATA Executive Round Up, our annual feature showcasing the insights of thought leaders on the state of the big data industry, and where it is headed. In today’s discussion, our panel of experienced big data executives – Ayush Parashar, Co-founder and Vice President of Engineering for Unifi Software, Robert Lee, Vice President & Chief Architect, Pure Storage, Inc., and Oliver Schabenberger, COO & CTO at SAS – take a look at the industries making the best competitive use of AI in 2019.

The conversation is moderated by Daniel D. Gutierrez, Managing Editor & Resident Data Scientist of insideBIGDATA.

insideBIGDATA: What industries do you feel will make the best competitive use of AI, machine learning, and deep learning in the next year? Pick one industry and describe how it will benefit from embracing or extending its embrace of these technologies?

Robert Lee, Vice President & Chief Architect for Pure Storage

Robert Lee: Financials, industrial/manufacturing, transportation and retail are among the industries most poised to effectively compete with AI/ML/DL in the next year. As an example, fraud or anomaly detection is one commonly found in the finserv industry, but is also applicable in many security and network use-cases.

Fraud/anomaly detection is a great application for both supervised learning (where you can both provide pre-labelled training data of fraudulent and non-fraudulent activity) as well as unsupervised learning (where you may not be able to predict or supply examples ahead of time of all types of fraud to be detected). Because most activities are (by definition) not anomalous, this makes unsupervised learning (essentially self-organizing or self-categorizing approaches) an effective approach. And once identified, an anomaly can be added to a continuous retraining process to further refine the model. This combination of supervised and unsupervised learning and continuous retraining will help applications in the fraud/anomaly detection space stay one step ahead – and give a fighting chance to identify new types of fraud or attacks before they’ve been fully understood by humans.

Oliver Schabenberger, COO & CTO at SAS

Oliver Schabenberger: AI has and will continue to benefit all industries, but strong examples in healthcare tend to highlight the capabilities in an accessible way. In fact, I’ve seen it save one of my employee’s lives.

One day in early 2017, I received an email informing me that Jared Peterson, the young and talented software manager who runs our cognitive computing team, was in the hospital with what appeared to be a stroke. Medical imagining, and the use of predictive analytics, optimization and machine learning to process and analyze Jared’s MRI images saved his life. It was serendipitous that his team at SAS had been working on adding capabilities to analyze medical imaging to our own products.

Through embracing the big data that hospitals gather and applying AI, we can continue to glean new insights for the diagnosis and treatment of diseases and save lives like Jared’s.

It is of course particularly important within the healthcare space that we strike a balance between innovation and transparency in AI.

And it is also worth noting that AI and machine learning will complement people, especially knowledge workers, augmenting rather than replacing many jobs. For example, AI algorithms can read diagnostic scans with great accuracy, freeing doctors from repetitive tasks so they can help patients by applying their most valuable training and skills.

Ayush Parashar, Co-founder and Vice President of Engineering for Unifi Software

Ayush Parashar: In the last couple of years, AI made it big in the lifestyle industry with the likes of Alexa and Siri making life easy for consumers. And, there have been huge advancements and refinements in transportation with the growing use of AI to improve autonomous driving. However, I believe healthcare is where we’ll see the greatest competitive, and certainly beneficial, use for the ability to aid in decision making for patient diagnosis and care.

Doctors performing robotic surgeries, for example, will have an enhanced experience because of AI recommendations prompting them during procedures. Then there are areas in drug discovery where AI combined with big-data can help inform critical findings. Finally, there are areas where AI, ML and deep learning together may help to provide a quicker and better public health response to cancer by guiding the best biomedical informatics, information and communication technology available.


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