AI Personal Assistants – The Death of Shopping as we Know it

Predictions are, in the near future, we will each have a personal assistant with artificial intelligence (AI)1 that runs our life. It’ll order household items before we run out, book social engagements, reminds us of upcoming events and related purchases (like birthday gifts, a bottle of wine for the hostess, or a new outfit to wear to the party).

More elaborate predictions have the AI constantly searching for better deals on services like vehicle sharing, archery lessons or landscaping services. It’ll sample the news wire for updates on unhealthy foods or ethically produced music, keep up to date with product reviews (posted by other people’s AI personal assistants) and use this collected wisdom to amend our purchase decisions (which the AI made in the first place, so we won’t even know).

This got me to imagining the end of marketing as we know it. No more emotional buying decisions. Every single purchase would be made with the maximum amount of data and, hopefully, solid facts.

Why would an AI be interested in brand loyalty? An AI would access all available information to determine if the latest version of a brand name item delivered on the quality expected, and if not, find another brand that did. Far fewer buying decisions would be based on the logic ‘I’m buying Apple because Apple makes good technology’. Your AI would buy Apple if there was proof it was the best available technology. And the proof would come from objective tests and the unbiased reports of AI’s everywhere (because why would an AI lie?).

Trickier is image, prestige, lifestyle or that thing where you buy a certain brand because it reflects who you want to be. Would your AI get that, have the same image of you as you do? That you wear a certain type of sneaker because people who share your values do.

Then there’s the ability to forget things you prefer to forget. Like booking a dentist appointment because you don’t like going to the dentist, so putting it off another month would be fine. Would your handy personal assistant let you do that? The dentist would be happy if you came back more often, so the dentist’s AI would encourage yours to book, maybe offer a discount. The same rationale could apply for the vet, furnace cleaning, arranging a visit to those relatives you find tedious, getting the oil changed in the car you jointly own, and a few dozen other things that fall into the category of adulting ( willingly doing things you know are good for you but are unpleasant, no fun, boring etc).

Then there’s retail therapy. Could your AI pick out the perfect new sweater for you, when you don’t need a new sweater and can’t afford it, but accidentally yelled at your boss, spilled milk on your toddler, and got a ticket for not going through a green light all in one day?

Is having an excuse to get out of the house a thing any more? Shopping used to be a good neutral destination that always worked if you needed something to do or to get away from the humans you lived with. You can’t get your AI to do that for you. Unless it pretends to be your friend who has to meet you at the mall.2

There will always be new ways of doing things. But humans are humans. We learned to live much of our life online, but we shop for more reasons than to get stuff. We also forget things on purpose. We act on our emotions because that’s what makes us human.

I think I’ll sneak out of the house, tell my AI personal assistant I’m on my way to the dentist, then cancel the appointment so I can go shop for stuff I don’t need, but want.

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1Purchased from a large tech company and embodied as a hockey puck-size matt silver thing that sits on the kitchen counter.

2If this sentence doesn’t make sense to you, please review a TV show or movie from the 1970’s for context.

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How Smart is Artificial Intelligence?

Cyberspace isn’t much like space at all. It’s crammed full of bytes of information, churning and frothing with intelligence agents who gnash and dissect the data in search of new knowledge, or at least something else to sell us. This is big data and at least one embodiment of artificial intelligence.

Recently, I heard an elegant explanation¹ of machine learning, or the ability of machines to create programs and algorithms that deduce things that they haven’t been programmed to – how machines learn. Consider what would be involved if you had to write program to tell a computer how to distinguish between a cat and a dog. I’d put together a logic chart and add up the cat vs dog points:

Cat

Dog

Meow noise

1

0

‘ark-ark’ noise

0

1

retractable claws

1

0

floppy ears

0

1

stripes

1

0

lolling tongue

0

1

it’s ignoring you

10

0

it thinks you are the smartest, most desirable person in the world

0

10

thrax ignoringand I’m sure you can come up with many other criteria, some less than absolute, such as curly fur (much more common in dogs but not impossible in cats).

In machine learning, you’d give the computer a million videos labelled cat and a million videos labelled dog to watch and let it figure out its own algorithm to tell the difference. Who wouldn’t want the job of watching a million cat and dog videos? Most of us already have. I am curious about the computer’s algorithm: does it use tail wagging frequency, that silly whining noise dogs make, or hissing, as selection criteria?

What if after all that the AI comes to the wrong conclusion. It might decide the true difference between cats and dogs is that cats are the overlords of the planet and dogs are service animals. It’s easy enough for a mere human to decide if the computer has done a good job of differentiating between the two animals. But what happens when they start predicting things we have no prior knowledge of, like how long a pair of socks will last?

And this is a trivial application of artificial intelligence. There is so much data out there, silly names for bigger numbers have emerged. According to this BBC article, 2.5 exabytes (billion gigabytes) of data were generated in one day in 2012 and the US National Security Agency has the capacity to store a yottabyte (one thousand trillion gigabytes) of data. That’s a lot of Facebook likes, tweets, diagnostics at the auto-mechanic, GPS locations, term marks and everything else. If we set AIs to learning from all this data, it seems like a tremendous wealth of knowledge will emerge. This might fall in a few categories:

1. Important and life saving intelligence such as diagnosing serious health events like heart failure and intercepting terrorist plans, so interventions can be made earlier.

2. Efficient systems, such as automated traffic flow to relieve congestion or business processes like finding items (books, events to attend, cheese) people might be interested in based on their preferences.

3. Predictions – varying from novelty (suggestion of what the name of you next pet should be) to kinda useful (prediction of what your partner might like for dinner tonight) to downright world changing (motivational media reports – this is one of my personal dreams).

The biggest question in my mind right now is how do we know if the machines are right?

Sure, we can test each conclusion the machine reaches after it’s made but that will take some time, especially if it’s a long range projection. And who owns the predictions? Is information about me that I don’t know, like what diseases I will develop in my old age, my personal information?

Yikes, I don’t want to go a bad place with such a potentially good thing. Like most new technologies, there is the possibility of misuse and misinformation with machine learning and artificial intelligence. Maybe we can use machine learning to figure out how to avoid the misappropriation of information for improper purposes. That would be cool. A truly self-regulating system.

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¹ I believe it was from Steve Brown,Chief Futurist and Evangelist, Intel at the Plenary Session ‘Innovation: Steering Disruption’ of the International Economic Forum of the Americas in Toronto July 8, 2015

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