Practical uses of AI in retail
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Healey Cypher, CEO, ZIVELO Brian Gill, SVP, Technology, Full Price Selling, Nordstrom
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Practical uses of AI in retail
- 1. Practical uses of AI in retail Brian Gill SVP, Technology Nordstrom Healey Cypher CEO ZIVELO
- 2. Practical Uses of AI in Retail! “practical,” huh?
- 3. Brian Gill SVP, Technology Nordstrom Healey Cypher CEO ZIVELO @htcypher@zivelo
- 5. The future of physical is interactive digital.
- 6. PROPRIETARY AND CONFIDENTIAL 6 CUSTOMER TECH ASSOCIATE TECH PROPRIETARY AND CONFIDENTIAL T H E S E L F - A W A R E S T O R E 6
- 7. PROPRIETARY AND CONFIDENTPROPRIETARY AND CONFIDENT 10 HERALDED PRODUCTS Industry-recognized leader in an range of attractive products major brands love. POWERFUL SOFTWARE The first purpose-built O.S. with a modern SDK for kiosks + interactive experiences EASY INSTALL/MAINTAIN Integrated install/maintain/warranty services with direct connections to support services. S O L U T I O N UNITS DEPLOYED: 10,000 EXAMPLE
- 8. PROPRIETARY AND CONFIDENTIALPROPRIETARY AND CONFIDENTIAL 14
- 9. PROPRIETARY AND CONFIDENTIALPROPRIETARY AND CONFIDENTIAL 15
- 10. PROPRIETARY AND CONFIDENTIALPROPRIETARY AND CONFIDENTIAL 16
- 11. Leave with a playbook! what are the goals today? Talk about A.I. in the Real Retail World #RRW Quick 101: What exactly is Artificial Intelligence?
- 15. Goal: clarify ideas about ‘thinking machines’ #NBD (rough latin translation: “vote for clams in the desert”) Dartmouth Summer Research Project on Artificial Intelligence
- 19. Simple Heuristics - what is it?
- 20. Simple Heuristics - what’s an example? If object has two sleeves, no collar, and is cotton… it’s probably a t-shirt.
- 21. Simple Heuristics. Problem: it’s directional rules that are often simple and non -precise.
- 22. Algorithm - what is it?
- 23. Algorithm - what is it? Problem: small changes in inputs can lead to HUGE changes in outputs…
- 24. Machine Learning - what is it? The ability for a computer to create the best output without being explicitly programmed.
- 25. It can be really hard to get the exact right algorithm tuned in to perfection… and in reality it doesn’t matter how we get to the best answer as long as it really is the best answer. So… let the computer figure it out. Machine Learning - why does it matter?
- 26. Machine Learning - what’s an example? Dynamic Recommendation Engine INPUTS: engine built on item similarity matrix, purchase similarity matrix, external factor matrix (weather) OUTPUTS: the best recommendation for THE UPSELL COOKIE
- 27. Neural Network - what is it? The ability for a computer to summarize data/decisions by layers (each layer summarizes complex concepts) to reach accelerated, nonlinear conclusions. Designed to mimic how humans learn.
- 28. Well… because we often want binary outputs when the inputs are completely relative. Example: What are these? Neural Network - why does it matter?
- 29. Computer vision! How the heck else would a computer figure out if there are shirts in each one of these photos? Neural Network - what’s an example?
- 31. yo fam, we still talking retail?
- 32. AI + Chat Bots AI + Voice Agents AI + Big Data Robots + Humans Where does AI Make sense? What are Limitations?
- 34. Brian Gill SVP, Technology Nordstrom Healey Cypher CEO ZIVELO @htcypher@zivelo
- 35. thanks!
Editor's Notes
- Change ‘vision” Numbers no populating
- NOTES: https://www.retailcustomerexperience.com/news/consumers-like-self-service-more-than-associate-interaction-reveals-survey/
- There is no single company that does all three things. Until now. Introducing ZIVELO - the first single company to provide beautiful hardware, powerful software tools, and integrated services all in one shop.
- I think this works, the next slide include 2019 feature goals, but it makes the slide cluttered. -Nord
- I think this works, the next slide include 2019 feature goals, but it makes the slide cluttered. -Nord
- I think this works, the next slide include 2019 feature goals, but it makes the slide cluttered. -Nord
- I think this works, the next slide include 2019 feature goals, but it makes the slide cluttered. -Nord
- Professor in Computer Science who, at the time was at Dartmouth, and who is widely considered the GodFather of Artificial intelligence.
- He decided to organize a group for a six-week to clarify and develop ideas about "thinking machines”
- Also believed computing should be a utility - and his enablement of mainframe time-sharing is largely credited for being a huge reason that the internet accelerated at the rate that it did.
- decided to organize a group to clarify and develop ideas about "thinking machines”
- decided to organize a group to clarify and develop ideas about "thinking machines”
- Basically “directional rules” that will probably be right, but it’s not overly precise.
- Algorithms are basically a bunch of heuristics that create precise tasks when a computer needs to perform a function. Think of it like algebra with weighted coefficients The benefit is they are much more precise than any simple rules or heuristics because they follow logic sequences.
- The challenge is that when algorithms become really complex, any small change to an input or rule can lead to a HUGE change in the output - like a mini butterfly effect.
- So then we get to machine learning.
- Machine learning is simple - it’s essentially telling the computer “hey, this is really complicated and it would be unreasonable to try to tweak the perfect algorithm or set of algorithms… so you just figure it out.”
- decided to organize a group to clarify and develop ideas about "thinking machines”
- decided to organize a group to clarify and develop ideas about "thinking machines”
- decided to organize a group to clarify and develop ideas about "thinking machines”
- decided to organize a group to clarify and develop ideas about "thinking machines”
- decided to organize a group to clarify and develop ideas about "thinking machines”
- decided to organize a group to clarify and develop ideas about "thinking machines”
- 1. Discoverability 2. Personalization 3. Connectedness 4. Operational Excellence
- 1. Discoverability 2. Personalization 3. Connectedness 4. Operational Excellence