Shopping assistants, understanding human emotions and more…

A Scanner Darkly, 2006

Yesterday I was reading an article on the “7 trends for AI in 2016” when something on the list caught my attention:

Thats where I learned that Northface.com had enabled an IBM Watson shopping assistant on their site for the holiday season. I’ll take the shopping assistant for a spin later on in this article.

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I became interested in this concept last week when I observed a coworker shopping for a gym bag on Lululemon.com and wanted to know if the bag was in stock at a particular location. She used to company’s “Live Chat” feature to ask if the bag was in stock.

What started off as a seemingly normal conversation with Lyndall took a semi-strange turn after she inquired about the bag’s intended use and got even more personal asking: “What activites are you planning on using this bag for?”

Rosiee answered “to go to the gym and back home”. The interaction with Lyndall lacked sincerity. Why would a chat agent want to know that information? Lyndall returned and told Rosiee “This is a good bag for that” -but didn’t answer Rosiee’s original question so she asked one more time — “Do you have this bag at this location?” Lyndall answered that they “have four left in stock”. The whole thing seemed a little nosy and invasive since there was no indication if the agent was a human or computer.

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Artificial Intelligence isn’t a new concept, it’s been around for decades in the form on science fiction. One of the biggest sci fi writers of the century Phillip K. Dick immortalized the concept of “man vs. machine” in his many books and Hollywood film adaptations (Minority Report, Bladerunner, Total Recall, A Scanner Darkly) and further memorialized the notion that computers would someday rule our world.

According to Andy Morro (who writes about common uses of AI in modern and eventual customer service) :

I wanted to check out how powerful IBM’s Watson could be in assisting me with my shopping needs. The first screen loads and is filled in with a query on “jackets”. I decided to go with the suggestion — and I was off to find a jacket!

I usually ski in Utah, so answering the first question was easy. At this point I continue on with my quest to find a jacket to ski in Utah.

Now it want’s to know my sex? This seems appropriate since jackets can differ by gender. I also noticed a section of the search bar is underlined in red making me aware that I am answering a series of questions.

I continue on searching for a women’s jacket to ski in Utah with.

It’s quick to return a selection of women’s jackets that are appropriate to ski in Utah with. The UI is beautiful — highlighting the “best match” — (aka highest price) option. Now Watson shopping assistant wants to know: “When are you going”? — With a hint underneath it: “I’ll be using this during the winter”.

The predictive search is rich at this point and I decide to take it further.

To which I answer “Next week”. I wasn’t sure the relevance of this question until I realized that jackets differ by season. I told my shopping assistant “next week” and continued.

The results haven’t changed yet when it asked me “what color jacket do you want”? I decided to try a non generic color and type in “Rainbow”

The results came back to me as “Watson has not been trained on Rainbow. Please try again”. That’s where a I caught a “siri like” glimpse of the beta limitations.

I decided to return to the beginning and search again for “Shoes”.

Game over!

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Although Northface’s shopping assistant isn’t fully ready to help me find everything I’m looking for (hint: anything other then jackets), the potential is shown for a fully functional shopping assistant. The difference between Northface’s experience and Lululemon’s is disclosure. I respect the fact that Northface let me know I was interacting with a robot vs. a human agent. Although both companies asked similar questions, the context was different. Whenever possible — disclose as much information as you can about your underlying technology.

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