In this segment of TLAI, we discuss use cases in life sciences, healthcare, and retail.
Sramana Mitra: Let’s start by introducing our audience to yourself as well as to Exponential Machines.
Florian Quarre: I operate primarily in the field of healthcare technology. I have quite recently joined Exponential AI. Exponential AI is an AI platform focused on accompanying our clients through their digital transformation journey.>>>
Sramana Mitra: I still go back to the gender question though. It’s not entirely invalid. If you’re a man, unless you’re a transgender, you’re most likely not wearing women’s clothes. You may have experience with baby products, but as a man, you would not have experience with women’s clothing.
Ashutosh Garg: Do you want to base it on people’s gender or on experience?>>>
Gary Saarenvirta: We have a theory for retail. We have partial differential equations (PDEs) that govern insurance and risk industries.
We simulate with our PDEs. You simulate what is going to happen in the future if I took these actions. In the same way, our PDEs are the decisions we provide. What products do I promote? What prices do I charge? How much inventory to allocate?Read more
Sramana Mitra: It’s consumer-facing merchandising. You’re not driving the ordering of inventory?
Gary Saarenvirta: We do decide that also. We set the prices and we also do inventory forecasting and allocation. Those are the three core inputs – having the right product, right prices, and having the right inventory.
Sramana Mitra: What kind of quantitative impact do your customers get by bringing you into their workflow?>>>
Sramana Mitra: In 1999, I started one of the first ever online fashion company. A very small subset of merchandise applies to me in a store. I’m a petite, dark-skinned woman with dark hair and dark eyes. I have a certain style.
What I want, which is not possible to do in a physical store, is my personalized store which only focuses on my colors, my style, my body type, my size. This not only draws from data and collaborative filtering; this draws just as well from expert system knowledge, which is what we were doing.>>>
Gary is implementing AI concepts from his Aerospace industry background onto use cases in retail and insurance.
Sramana Mitra: Let’s start by introducing you and Daisy Intelligence.
Gary Saarenvirta: I’m the Founder and CEO of Daisy Intelligence. Daisy Intelligence is an AI platform. We help our clients make smarter operating decisions. Our mission is to empower human beings to do what humans are very good at by letting machines do what machines are good at.>>>
Sramana Mitra: You explained to me the data structure. Can you talk about how the learning algorithm learns?
Ashutosh Garg: That’s our secret sauce. We use recurrent neural network to model some of these things.
Sramana Mitra: How much of this is pattern matching, statistical modeling kind of inferences versus expert system kind of inferences?>>>
Sramana Mitra: You’ve created all these heuristics and you categorized on the basis of that. How many such heuristics do you have?
Ashutosh Garg: That’s the beauty of AI. You can’t do it manually. Today, there’s a million different roles in the industry. We can’t do it manually. We use deep learning to model these things automatically.
Sramana Mitra: Tell me how.>>>