Opex AI Roundup — November 2018. by Gabriella Runnels and Macon McLean
By: Opex Analytics
Tags: Artificial Intelligence, Machine Learning, Data Science

by Gabriella Runnels and Macon McLean

The Opex AI Roundup provides you with our take on the coolest and most interesting Artificial Intelligence (AI) news and developments each month. Stay tuned and feel free to comment on any you think we missed!

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A Portrait of the A.I. as a Young Man

Photo by Daian Gan from Pexels

The three members of the French art collective “Obvious” expected to receive up to $10,000 for Portrait of Edmond Belamy, a smudged portrait of a seemingly old-timey gentleman, according to the auctioneers’ best guess. It’s difficult to say what went into this estimation, as it’s virgin territory for Christie’s, the auction house to which Obvious consigned the work. What makes this particular piece of art challenging to appraise is the artist (or lack thereof): a kind of A.I. called a “generative adversarial network,” or GAN. That’s right — a machine produced a painting sold for $432,500 at auction. However, the question of whom should receive credit is not — pardon the pun — obvious at all. It appears that much of the code that Obvious used to generate the portrait of the dashing Belamy belongs to a 19-year-old from West Virginia, who has shared the source code for his own GANs online. It may never be clear who the painting’s true creator is, but one thing’s for sure: somebody overpaid for this… thing.

Far From StrA.I.ghtforward

Photo by Brett Sayles from Pexels

A self-driving car is hurtling towards five data scientists passed out on the street. It can swerve to avoid them and hit a lone data scientist coding on a nearby bench, or it can stay on its current trajectory and trample all five of our suddenly coma-struck colleagues. What should it do?

In one of the largest cross-cultural studies ever conducted on ethical intuitions, researchers probed participants’ priorities when faced with variations of the above scenario, what experimental philosophers term “the trolley problem.” The results showed that answers to this question differ significantly from place to place, with some cultures consistently choosing to save more lives, some saving the young over the old, some prioritizing passengers over pedestrians, and some even choosing to save fewer people of higher socioeconomic status rather than more regular folks, all else equal (yikes).

The survey results don’t totally clarify the moral murkiness involved in designing the safety protocols of self-driving cars, and that’s just fine. They’re really meant to help engineers consider the ethical implications of new technologies, and the particularity of what’s considered right. (Personally, I recommend altogether avoiding cities where trolleys are prevalent.)

A.I.n’t You Glad I Didn’t Say Banana?

Photo by rawpixel.com from Pexels

From website to warehouse to drop-off, Amazon uses A.I. to improve efficiency, reduce delivery times, and better serve the customer. Using its massive stores of customer-generated data, Amazon is able to identify patterns and generate forecasts that help it predict what, where, and when you’ll buy. Some of the patterns it picks up are pretty obvious — “You don’t sell very much Chicago Cubs gear in Hawaii,” says Jenny Freshwater, a software director in Amazon’s Supply Chain Optimization Technologies group. But AI has uncovered other patterns that aren’t as intuitive. For example, Amazon’s AI figured out that shoppers frequently dump their whole virtual cart if bananas are unavailable in their area. (Amazon’s AI doesn’t appear to recall the immortal words of Julia Child: “It’s not a meal if it doesn’t prominently feature bananas.”) From shopping to shipping, AI transforms the Amazon customer experience.

A Turkey’s Story: BlockchA.I.n Edition, brought to you by Cargill

Extremely elegant turkey photo by Peter Lloyd on Unsplash

Have you ever wondered where your Thanksgiving turkey comes from? No? You’re missing out, because agricultural conglomerate Cargill is rolling out a program that allows turkey enthusiasts to know their feathered friend’s life story. Purchasers of a Cargill turkey in one of thirty major metropolitan areas will be able to punch in a code via text message or browser and get back “the location of the [turkey’s] farm, the name of the farmer or family, images and any other information the producer wanted to share.”

This technology is enabled by blockchain (stay with me), which is a method of decentralized, secure transaction tracking (maybe?) that makes internet commerce more secure than ever before. It’s the stuff that powers bitcoin (pretty sure that’s true) (Editor’s note: is it?), and the food industry thinks that it could be used to make their products’ chains of custody more transparent. The availability of such information could help identify contaminants more quickly (e.g. finding where not to buy your Romaine lettuce), as well as allow customers to know their dinner’s journey.

The New FA.I.rness Doctrine

Photo by rawpixel on Unsplash

Algorithmic bias is a challenging but important problem to solve. Models learn by using data from the past, and they’re often constructed by individuals of a particular kind (i.e. wealthy, educated, etc.), which can unduly influence training and prediction. The problem isn’t really about any particular quality leaking into the model, but that homogeneity of inputs can reduce a model’s generalizability. Luckily, Google has taken corrective measures by recently releasing a new course “to teach people about fairness in machine learning.” Participants will better understand how human bias can burrow its way deep into the foundation of a machine learning model, as well as learn how to quantify the extent to which a model’s bias colors its judgments.

That’s it for this month’s roundup! Check back in December for more of the most interesting news and developments in the AI community (from our point of view, of course).

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