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Ethical Machines episode 3: Alex J. Champandard and Gene Kogan

For any artists that are work­ing in this field now, if I was good at paint­ing I’d prob­a­bly be look­ing at how to find styles that work well with these kind of rep­re­sen­ta­tions and make them eas­i­ly automat­able or trans­fer­able so that if I had fans as an artist they could say, Hey, I would like to have a pic­ture of my cat paint­ed.”

Are We Living Inside an Ethical (and Kind) Machine?

This is a moment to ask as we make the plan­et dig­i­tal, as we total­ly envel­op our­selves in the com­put­ing envi­ron­ment that we’ve been build­ing for the last hun­dred years, what kind of dig­i­tal plan­et do we want? Because we are at a point where there is no turn­ing back, and get­ting to eth­i­cal deci­sions, val­ues deci­sions, deci­sions about democ­ra­cy, is not some­thing we have talked about enough nor in a way that has had impact.

Sleepwalking into Surveillant Capitalism, Sliding into Authoritarianism

We have increas­ing­ly smart, sur­veil­lant per­sua­sion archi­tec­tures. Architectures aimed at per­suad­ing us to do some­thing. At the moment it’s click­ing on an ad. And that seems like a waste. We’re just click­ing on an ad. You know. It’s kind of a waste of our ener­gy. But increas­ing­ly it is going to be per­suad­ing us to sup­port some­thing, to think of some­thing, to imag­ine some­thing.

The Algorithmic Spiral of Silence

A cou­ple of major plat­forms like Facebook and Twitter, YouTube, have become in many places around the world a de fac­to pub­lic sphere. Especially in coun­tries that have less than free Internet, less than free mass media. And these coun­tries have tran­si­tioned from a very con­trolled pub­lic sphere to a commercially-run one like Facebook.

Surveillance and Race Online

[The] ques­tion of what hap­pens when black­ness enters the frame can kind of neat­ly encap­su­late the ways I’ve been think­ing and try­ing to talk about sur­veil­lance for the last few years.

Forbidden Research: Why We Can’t Do That

Quite often when we’re ask­ing these dif­fi­cult ques­tions we’re ask­ing about ques­tions where we might not even know how to ask where the line is. But in oth­er cas­es, when researchers work to advance pub­lic knowl­edge, even on uncon­tro­ver­sial top­ics, we can still find our­selves for­bid­den from doing the research or dis­sem­i­nat­ing the research.

Applying Algorithms to Minimize Risk

The United States plants more than 170 mil­lion acres of corn and soy­beans a year, more than any coun­try in the world. And the pri­ma­ry mech­a­nism in the US that we use to sub­si­dize agri­cul­ture is actu­al­ly called the Federal Crop Insurance Program. So, the crop insur­ance pro­gram in the US is also the largest such pro­gram glob­al­ly, with over $100 bil­lion in lia­bil­i­ties annu­al­ly. So it’s a very big pro­gram.

Who and What Will Get to Think the Future?

There’s already a kind of cog­ni­tive invest­ment that we make, you know. At a cer­tain point, you have years of your per­son­al his­to­ry liv­ing in somebody’s cloud. And that goes beyond mere­ly being a mem­o­ry bank, it’s also a cog­ni­tive bank in some way.

What Our Algorithms Will Know in 2100

A lot of the sci­ence fic­tion I love the most is not about these big ques­tions. You read a book like The Diamond Age and the most inter­est­ing thing in The Diamond Age is the medi­a­tron­ic chop­sticks, the small detail that Stephenson says okay, well if you have nan­otech­nol­o­gy, peo­ple are going to use this tech­nol­o­gy in the most pedes­tri­an, kind of ordi­nary ways.

What Should We Know About Algorithms?

When I go talk about this, the thing that I tell peo­ple is that I’m not wor­ried about algo­rithms tak­ing over human­i­ty, because they kind of suck at a lot of things, right. And we’re real­ly not that good at a lot of things we do. But there are things that we’re good at. And so the exam­ple that I like to give is Amazon rec­om­mender sys­tems. You all run into this on Netflix or Amazon, where they rec­om­mend stuff to you. And those algo­rithms are actu­al­ly very sim­i­lar to a lot of the sophis­ti­cat­ed arti­fi­cial intel­li­gence we see now. It’s the same under­neath.

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