Deep Learning: An Interview with Gary Saarenvirta, CEO, Daisy Intelligence Corporation

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Businesses are “drowning in data but starving for insight”, as the saying goes, and that problem is about to get worse. As society becomes increasingly connected, marketers are facing a rising tide of digital interactions. Somewhere inside that massive pool of data are the answers to questions marketers haven’t even thought to ask – yet are essential to creating a better customer experience.

Relief is on the way in the form of artificial intelligence. The capability to skip right to the answers without even forming the questions will be the salvation of marketers, who until now have had to rely on their own made-up rules or overworked data scientists. With AI, the analytical load shifts to machine learning algorithms that will help marketers reach the promised land of hyper-personalization. The first wave of commercial AI solutions has already made landfall. AI is being used today to improve audience targeting for programmatic media buying; make dynamic content and product recommendations; and drive demand-level pricing. Most of the major marketing automation and CRM vendors have already integrated AI capabilities into their platforms (like Salesforce’s Einstein). Companies also have the option of outsourcing the analytical work to software-as-a-service platform providers who will help them benefit from the technology immediately.

One of those SaaS providers is Toronto-based Daisy Intelligence founded by CEO Gary Saarenvirta. The company, which specializes in retail merchandising solutions and insurance fraud detection, was recently awarded the first-place prize at the 2018 Elevate AI Pitch competition in a tough field of 16 start-ups. The “deep learning” platform developed by Daisy Intelligence can ingest a massive amount of SKU-level transactional data and through its self-learning algorithms determine the best product price points; adjust the promotional mix to minimize cannibalization; identify optimal store locations and layout, and much more, saving merchandisers from needing to figure it out themselves. All the retailer needs to do is turn over as much sales history as possible, and let the platform perform its magic.

“Deep learning” is very much like magic because no one can ever say how it arrives at the answers it comes up with. Known as a “convolutionary neural network”, the idea was first conceived by Toronto computer math wizard Geoffrey Hinton in 1986. Since that time advancements in GPU computing has allowed “deep learning” to grab the pole position in the AI race, much to the excitement – and sometimes dread – of futurists who imagine a world where machines are smarter than humans. Remember Elon Musk’s dire warning that AI would “summon the demon”?

For the time being, AI evangelists like Gary Saarenvirta are proving that “deep learning” offers clear advantages over traditional approaches to data mining and analysis, both in speed and precision. And he should know: Gary ran the analytics practice at Loyalty Consulting Group for years, and once led IBM’s analytics and data warehousing practice areas. He’s also a trained rocket scientist, having entered the workforce with an aerospace engineering degree. But today Gary’s focus is much more grounded in making AI an indispensable tool for data-driven businesses. He explains why in this “deep learning” interview which started with the genesis of the company’s name.