Thinking Machines and the Future of Humanity

 

The Federal government doesn’t have its own cloud, the government runs on Amazon.

Amy Webb Professor, NYU Stern School of Business; Founder, Future Today Institu...
Session

Thinking Machines and the Future of Humanity

Setup

Within our lifetimes, AI will, by design, begin to behave unpredictably, thinking and acting in ways which defy human logic. Big tech companies may be inadvertently building and enabling vast arrays of intelligent systems that don't share our motivations, desires, or hopes for the future of humanity. Is it too late to change course and realize a human-centered future for artificial intelligence?

The US and China: different mindsets, different futures
The US and China: different mindsets, different futures
Who owns your face?
Should everyone get a “digital dividend?”
Wal-Mart shopping carts and the nuance of AI integration
The brains behind AI are homogenous, and that’s a problem
1.

The US and China: different mindsets, different futures

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04:29

Chinese and US companies dominate AI technologies in the present and are set to control their future, says Future Today Institute CEO Amy Webb. But the idiosyncrasies of these two innovation powerhouses are resulting in dramatically different visions for the future of humanity’s relationship with AI. Webb explains these differences, and why they matter:

Amy Webb Thinking Machines 2019
The US and China: different mindsets, different futures

2.

Who owns your face?

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12:52

Artificial intelligence technologies are ravenous for data — the more data they can gather, the more effective they are. Amy Webb warns that we haven’t grappled with fundamental questions about our relationship to AI and data, and that we’re facing existential uncertainty as AI data collection works its way deeper into our lives.

Amazon is working to make its smart speakers emotionally intelligent by collecting data from users. After collecting and analyzing a user’s speech, the smart speaker will (theoretically) be able to tell what mood the user is in based on what they say and how they say it. The smart speaker can then interact with users in new ways, perhaps playing music or suggesting different products.

The answer to “Who owns your face?” should be obvious, says Webb, but the rise of facial recognition software has complicated that question. What rights do individuals have to their own data when they walk through a store that utilizes facial recognition, for example? Webb says these are the sorts of questions about data collection that we, as a society, are ignoring.

3.

Should everyone get a “digital dividend?”

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21:02

Amy Webb has a radical proposal to create what she calls a “digital dividend.” Her idea is to use blockchain and a collective pot of earnings to give everyone a cut of the money that tech companies are making through data collection:

Amy Webb Thinking Machines 2019
Should everyone get a “digital dividend?”

4.

Wal-Mart shopping carts and the nuance of AI integration

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26:23

Would you use a Wal-Mart shopping cart outfitted with a biometric reader? The reader, in the handle of the cart, transmits the shopper’s vitals to in-store employees. Wal-Mart officials think this technology will make for a better shopping experience. It will also make the big box store more money (which is why they’re developing it). Amy Webb says there are inherent problems in this model.

Amy Webb Thinking Machines 2019
Wal-Mart shopping carts and the nuance of AI integration

There may be a place for biometric shopping carts in our society, but Webb thinks the financial incentives behind AI technologies are rushing the profound conversations needed while developing such a technology.  


5.

The brains behind AI are homogenous, and that’s a problem

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29:47

Although modest gains towards diversity have been made in recent years, the workforce developing AI technologies is overwhelmingly white and male. And most graduated from the same cadre of universities. Amy Webb suggests a compelling case for diversity in the AI technology workforce:

This excerpt has been lightly edited for clarity

  • Amy Webb: The people who built the [TSA screening technology] weren’t people who were familiar with enormous hair, or weaves, or bras with underwire...The system wasn’t trained to recognize me. Why? Because somebody like me wasn’t in the room when the data set was built, when the algorithms to use that data were built, when the testing was done, and when the learning was done. That entire chain of decision-making excluded me, which now means that when I go through this stupid machine at the airport I can expect someone to get very familiar with me. That’s a small inconvenience. We could probably spend three hours going through serious infractions in the criminal justice system, and [discover] serious ways in which this is being used to really hurt people.

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Additional Information

Resources

Here’s the clearest picture of Silicon Valley’s diversity yet — Reveal

"The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity" by Amy Webb

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