algorithms

Deep Neural Networks are Easily Fooled

Interesting paper from Anh Nguyen, Jason Yosinski and Jeff Clune about evolved images that are unrecognizable to humans, but that state-of-the-art algorithms trained on ImageNet believe with certainty to be a familiar object. Abstract:

Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects. Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision.

[read more] [via stop the cyborgs]

Google researchers working on artificial intelligence developing “thought vectors” for reasoning and logic

By Hannah Devlin -

Computers will have developed “common sense” within a decade and we could be counting them among our friends not long afterwards, one of the world’s leading AI scientists has predicted.

Professor Geoff Hinton, who was hired by Google two years ago to help develop intelligent operating systems, said that the company is on the brink of developing algorithms with the capacity for logic, natural conversation and even flirtation.

READ MORE ON THE GUARDIAN | SCIENCE

On Playboy’s new feminism.

By Lisa Wade, PhD

I’m going to start this post even though I don’t have an ending.

About a year ago I was asked to start writing for Playboy. The editor said that he was helping to transform the magazine’s website into one that “was a destination for smart writing on sex.” I said that I’d keep the offer in mind but, between you and me, the answer was no.

Around the same time, I heard of some other high-profile feminist writers being invited as well. “Huh,” I thought, “they may actually be serious about this.”

Since then, I’ve ended up on the Playboy website a couple of times, following links by like-minded people who found material they thought was valuable. I’ve been surprised and tentatively impressed. Then, this week there was a flurry of links to a piece by Noah Berlatsky, deftly and smartly analyzing feminist responses to trans woman Laverne Cox’s decision to pose nude for Allure.

The article began with a cropped screenshot of Cox’s photograph featuring her face and de-emphasizing her body and a quote from Cox about the widespread belief that black women and trans women, and especially black trans women, can’t be beautiful.

Berlatsky then goes on to discuss the challenges intersectionality poses to feminism, conflicts within feminism about whether trans women count as women, debates over cosmetic surgery and the problem with trying to live up to patriarchal standards of beauty, and whether Cox’s decision to pose naked is degrading. You don’t have to agree with all Berlatsky says to notice that he is no stranger to feminist theory.

And he seems to look upon Cox’s photograph with a delicate and sensitive gaze, describing what he sees like this:

Cox is not fashion-model-thin. She’s not fashion-model-petite or willowy, either. She has very large hands, which are not hidden, boldly displayed. In the photo, Cox lies on a blanket; her body taut rather than relaxed, her head in one big, strong hand, eyes closed, a slight smile on her face — like she’s a little embarrassed and amused at being embarrassed. She’s voluptuous and awkward and sweet all at once. In her simultaneous enjoyment of and discomfort before the camera, she seems, in the frankly staged pose, startlingly natural — and beautiful.

And, as I reach the end of the article, I was considering sharing a post from Playboy for the very first time when, this happened:

That’s a screenshot of a pop-up that arrived on my screen when I reached the end of Berlatsky’s thoughtful, feminist essay. It says: “Enter your email to see a 45-year-old with an amazing booty.” In other words, “Click right now to see a woman still fuckable after 40!”

This is where I’m at a loss.

Is this what change looks like? Is this what change looks like, specifically, when it comes from inside of an organization? A slow, stuttering shift from misogyny to feminism, replete with missteps and contradictions?

Who’s in charge over there? What is their strategic plan? Are they trying to appropriate feminism? It’s not like they haven’t done it before. What role do they see this feminist discourse playing in a space that’s still so misogynist?

Or is the right hand just not paying attention to what the left hand is doing? Maybe Berlatsky was as surprised by the pop-up as I was, thinking “Come on, guys!” Or do they not think that their pop up was sexist at all?

And, from a feminist perspective, does this do anyone any good? I don’t mean this rhetorically. I honestly don’t know how to answer that question. And, on the flipside, could this hurt feminist activism?

What say you?

4

Predictive Policing by Jeffrey Brantingham

The Guardian reveals that one of the main researchers behind predictive policing, now offered under the company PredPol is UCLA Anthropologist Jeffrey Brantingham. The PredPol website and Guardian article only touch the surface on how the algorithms work, safely making claims that ‘this is not minority report’ and that the predictions are only for when and where crime may occur, not who the criminal will be. To dig deeper I’ve read some of Brantingham's research papers. I took an interest in one particular paper that devises Reaction-Diffusion models of crime. This grabbed my attention in particular because Generative Artists usually employ these computational methods to generate biological-looking forms. What Brantingham has done is argue that “reaction-diffusion models provide a mechanistic explanation for crime pattern formation given simple assumptions about the diffusion of crime risk and localized search by offenders”.  The diagram labelled A to E is captioned with an outline of the algorithm’s process: 

The conditions for crime hotspot formation. Local diffusion of elevated risk from stochastic fluctuations in crime nucleate into crime hotspots.

(A) Urban space may be thought of as being partitioned into areas uniquely associated with each individual crime (black dots), here shown as Voronoi polygons (gray lines). Individual crimes also produce elevated risk that diffuses out over an area (dashed circles) centered on the crime location.

© Only when risk diffuses over relatively short distances, binding local crimes together but not more distant ones, do crime hotspots emerge.

The paper goes on to demonstrate how the hotspots can be suppressed by police intervention, which I assume is part of what later informs a 'policing prediction’ once the model is fed with live and historical data of crime in an area. 

youtube

Google Translate Bot Discussion

We fed our phones one random sentence, using the impromptu text-to-speech translation feature within the Google Translate app. Then left them to discuss…

The kept talking for 15 minutes and more if left alone. The messages were from completely senseless to utterly terrifying. (e.g. “I am aware of who I am’)

[via interweb3000]

We suck at dealing with abuse and trolls on the platform and we’ve sucked at it for years. It’s no secret and the rest of the world talks about it every day…

…I’m frankly ashamed of how poorly we’ve dealt with this issue during my tenure as CEO. It’s absurd. There’s no excuse for it. I take full responsibility for not being more aggressive on this front. It’s nobody else’s fault but mine, and it’s embarrassing.

We’re going to start kicking these people off right and left and making sure that when they issue their ridiculous attacks, nobody hears them.

— 

Twitter CEO Dick Costolo, via a February 2015 internal Twitter message board post. The Verge, Twitter CEO: ‘We suck at dealing with abuse’.

The News: Twitter’s unveiled new algorithms, policies and personnel to deal with ongoing harrassment on its platform. As Slate’s David Auerbacher explains:

Twitter will attempt to detect abusive tweets proactively, using a combination of signals including the age of the account and a tweet’s similarity to other tweets deemed abusive by the social network’s internal team. This automated flagging will not block the tweet from being sent or cause any restrictions on the sender’s account. Instead, if my nemesis creates a new account and declares his desire to punch me in the face, I won’t see it in my notifications, even if he tags my Twitter account in his tweet.

Depending on how aggressive Twitter is, this change could actually make Twitter a nicer place. Twitter is attempting to thread the needle here by taking a view of abuse as something that one person directs at another person, rather than a statement simply made in public. By preventing a harasser from making contact with his target, Twitter hopes to leave the harasser off in his corner to rant and threaten away while no one is paying attention.

The balance, of course, is fostering free speech while ensuring basic decency on a platform that runs 140 characters long.

That said, Auerbach thinks Twitter is fundamentally broken and argues that policies and algorithms aside, Twitter will only survive if users collectively accept abusive behavior as an acceptible cost of having a mostly “free” communications platform.

So I couldn't figure out how to actually get rid of my Seeking Arrangements account that I sent up once.

This hasn’t been an issue because I hadn’t received any messages in months and months. I have now had eight in the last eighteen hours. I asked one of these potential suitors if there had been a feature on the site in an article or on tv recently or if getting messages just made the algorithm just pushed me back into rotation. He said he was surprised that I knew what an algorithm was. Seems like I’ll be spending my snow day being mean to men. #misandry

-K

How Cultures Move Across Continents

They may look like flight paths around North America and Europe. Or perhaps nighttime satellite photos, with cities lit up like starry constellations.

But look again.

These animations chart the movement of Western culture over the past 2,000 years, researchers report Friday in the journal Science.

To make these movies, art historian Maximilian Schich and his colleagues mapped the births and deaths of more than 150,000 notable artists and cultural leaders, such as famous painters, actors, architects, politicians, priests and even antiquarians (people who collect antiques).

A shimmering blue dot lights up each new birth, while red dots represent each death.

We can watch as artists flock from rural areas to urban centers like London, Paris, Rome and Berlin after the Renaissance. Then in the late 17th century, people start to catapult from Europe into the eastern U.S. and then eventually leapfrog over to the West Coast.

“We’re interested in the shape of the coral reef of culture,” says Schich, of the University of Texas at Dallas. “We are taking a systems biology approach to art history.”

After mapping the births and deaths, Schich and his team analyzed demographic data to build a model for how people and their cultural achievements ebb and flow across continents.

Right now the team has only maps for the U.S. and Europe. But Schich hopes to extend these visualizations beyond the Western world.

Continue reading.

Graphic: Where were the artists, politicians and religious leaders? A blue light denotes a birth while a red light signals a death. The lines connect the two. (Maximillian Schich and Mauro Martino)

Data archaeology helps builders avoid buried treasure

IN 2010, when builders were excavating the site of the former World Trade Center in New York, they stumbled across something rather unusual: a large wooden boat, later dated to the 1700s.

Hitting archaeological remains is a familiar problem for builders, because the land they are excavating has often been in use for hundreds, if not thousands, of years.

Democrata, a UK data analytics start-up, wants to help companies guess what’s in the ground before they start digging. Using predictive algorithms, their new program maps where artefacts might still be found in England and Wales, in order to help companies avoid the time and cost of excavation. “It’s an expensive problem to have once you’ve started digging,” says Geoff Roberts, CEO of Democrata. Read more.

What Happens When You Like Everything?

Journalists can be a masochistic lot.

Take Mat Honan over at Wired who decided to like everything in his Facebook News Feed:

Or at least I did, for 48 hours. Literally everything Facebook sent my way, I liked — even if I hated it. I decided to embark on a campaign of conscious liking, to see how it would affect what Facebook showed me…

…Relateds quickly became a problem, because as soon as you like one, Facebook replaces it with another. So as soon as I liked the four relateds below a story, it immediately gave me four more. And then four more. And then four more. And then four more. I quickly realized I’d be stuck in a related loop for eternity if I kept this up. So I settled on a new rule: I would like the first four relateds Facebook shows me, but no more.

So how did Facebook’s algorithm respond?

My News Feed took on an entirely new character in a surprisingly short amount of time. After checking in and liking a bunch of stuff over the course of an hour, there were no human beings in my feed anymore. It became about brands and messaging, rather than humans with messages…

…While I expected that what I saw might change, what I never expected was the impact my behavior would have on my friends’ feeds. I kept thinking Facebook would rate-limit me, but instead it grew increasingly ravenous. My feed become a cavalcade of brands and politics and as I interacted with them, Facebook dutifully reported this to all my friends and followers.

After 48 hours he gives up “because it was just too awful.”

Over at The Atlantic, Caleb Garling plays with Facebook’s algorithm as well. Instead of liking though, he tries to hack the system to see what he needs to do so that friends and followers see what he posts:

Part of the impetus was that Facebook had frustrated me. That morning I’d posted a story I’d written about the hunt for electric bacteria that might someday power remote sensors. After a few hours, the story had garnered just one like. I surmised that Facebook had decided that, for whatever reason, what I’d submitted to the blue ether wasn’t what people wanted, and kept it hidden.

A little grumpy at the idea, I wanted to see if I could trick Facebook into believing I’d had one of those big life updates that always hang out at the top of the feed. People tend to word those things roughly the same way and Facebook does smart things with pattern matching and sentiment analysis. Let’s see if I can fabricate some social love.

I posted: “Hey everyone, big news!! I’ve accepted a position trying to make Facebook believe this is an important post about my life! I’m so excited to begin this small experiment into how the Facebook algorithms processes language and really appreciate all of your support!”

And the likes poured in: “After 90 minutes, the post had 57 likes and 25 commenters.”

So can you game the Facebook algorithm? Not really, thinks Garling. Not while the code remains invisible.

At best, he writes, we might be able to intuit a “feeble correlation.”

Which might be something to like.

How can the public learn the role of algorithms in their daily lives, evaluating the law and ethicality of systems like the Facebook NewsFeed, search engines, or airline booking systems?

How can research on algorithms proceed without access to the algorithm?

What is the algorithm doing for a particular person?

How should we usefully visualize it?

How do people make sense of the algorithm?

What do users really need to know about algorithms?

—  Some very relevant questions raised in a conversation hosted by MIT Center for Civic Media titled Uncovering Algorithms

On the afternoon of Friday, March 27, as several news outlets reported at the time, somebody apparently made $2.4 million from a tweet.

That tweet was a bit of breaking news from Wall Street Journal writer Dana Mattioli: Quicker than any human seemingly could have done it, someone—or rather something—bought $110,530 worth of cheap options on Altera, a company that makes digital circuits.* Over the next several minutes and until the end of the day, as humans digested Mattioli’s takeover rumor at human speed, Altera’s stock price rose. When all was said and done, those cheap options had resulted in a $2.4 million profit.

Speculation immediately centered on the idea that an automated program (a “bot”) had scanned the tweet, interpreted its meaning, and instantly bought those options based on an algorithm. The robot had read the tweet and made a killing on it before anyone knew what was going on.