Author Archives: Gregory Rocco

The Future Pitfall of an Evolving Digital Landscape

As I was scrolling through my usual feed this morning consisting of ten different platforms with ten similarly guarded accounts, I came across the news that a recent malware exploit, dubbed “gooligan” compromised 1 million Google accounts. The exploit roots your phone (if it is running any flavor of android 4 or 5) and compromises authentication tokens. The process continues by installing unwanted apps to your phone linked to the malware, and rates them to raise the app’s reputation. People (not including myself thankfully because I run the latest OS [Nexus and Pixel users get special treatment]), then flocked to usual safe havens like or to see if their details were compromised. If details were compromised, users have to evaluate their situation with a list of questions:

  • How many accounts was my Google account linked to?
  • Do I use the same information for multiple outlets?
  • Why haven’t I changed my password in over two years?
  • Did I feed my dog this morning?

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Twitter Bots: Useful, Dangerous, or Both

The world of tomorrow is a terrifying place – looks like we have some tidying-up to do.

Recently we were assigned two articles related to twitter bots. Rob Dubbin’s The Rise of Twitter Bots was a more relaxed take on the subject: Twitter bots represent a long spanning gamut between wasting time and a reminder of surveillance. The other, Mark Sample’s piece on protest bots, looked to take a deeper look into how to effectively create protest on the internet by arming bots with more than just simple repetitions, but rather intricate creations of an uncanny environment that captures attention. Twitter has graciously opened their API for development which made all of these creative and powerful ventures possible, but we should rather look towards the future of Twitter bots – specifically how artificial intelligence will affect a platform like this.


The biggest buzz phrase as of recently has to be “deep learning systems.” We’ve officially moved beyond the alpha stages of artificial intelligence and have now developed systems that are finally… well… intelligent. This also means that everyone including your grandmother and the kitchen sink are jumping on-board to develop deep learning systems, and they are definitely increasing infrastructure to do so. Deep learning and A.I. can scale anywhere from YouTube’s recent 8m data project to something more relevant: a Twitter bot with a built in A.I.


TayTweets or @TayandYou was supposed to be an innocent experiment: Microsoft wished to better understand conversation by having a bot learn from discussion with fellow Twitter users. The name “Tay” was based off of “thinking about you,” and released initially in March of this year. Of course the mission progressed from a center of learning to an all out nightmare quite quickly. The bot started picking up on slang, started learning racial slurs, and even at one point sided with Hitler. Tay was even brought back a second time after Microsoft tried cleaning it up, but that was a catastrophic failure as well. But this was just regular hatefulness – the rabbit hole goes deeper than that.

Spearphishing, a recent phenomenon is a more advanced form of phishing that is catered to a specific individual. Recently, a cyber-security firm that specializes in social media, ZeroFOX, developed a project for educational purposes that uses machine learning to target users. The intelligence develops profiles on each target based on what they’ve tweeted, finds the best time to send the tweet and sends it to the unsuspecting user. Recently, a writer from The Atlantic was taking a look at this whole scenario and the tweets that were sent were incredibly realistic. However, in their test, they made the phishing link redirect to The Atlantic rather than a malicious site.


The scary thing to think about is where A.I. and machine learning techniques collide with Twitter bots. Apparently, the success rate of this project (dubbed SNAP_R) averaged around 30% in a test which is quite astounding. The bot was also programmed to take trending topics and unsuspecting users, mesh them, and generate this dangerous content. With the rate at which A.I. is being developed, this is just scratching the surface in regards of what’s to come in the future. Of course we can talk about the positives that Twitter’s open API has afforded us, but the elephant in the room must be addressed at the same time on a deeper level (no pun intended).

Even Twitter has been investing in artificial intelligence. The acquisition of Magic Pony back in june marks their third straight year of acquiring firms. A.I. even has a darker side as well – Elon Musk recently predicted that A.I. will be the future of cyber attacks. Of course they can function on the low level of phishing schemes and hateful statements, but when the technology advances, so does the scope of usage. The future of Twitter bots and learning systems are a lot darker and more powerful than protest or humor: they beget new systems of influence and control, and slowly lead us to question what is real.

For some more resources on A.I. and Deep Learning, check out

Artificial Intelligence: The Future Is Now


DeepMind’s Lip Reading Abilities

What’s the Difference Between A.I., ML and DL?

Deep Learning: The Past, Present and Future of A.I.

Returning to MTurk: It’s Still Impossible to Make Money

In the past I used MTurk because my brother found it to be a decent money maker back when he was in high school in 2008. I was curious after doing the readings for class to revisit Amazon Mechanical Turk and see what’s new on the platform. When I first logged on, I noticed that my hit history was available: 17 hits submitted for a grand total of $12.20. It probably took me at least a couple of hours to make it that far. During our reading, we learned about the harsh realities of MTurk where 52% of users make less than 5 dollars an hour.

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China’s Citizen Scoring

“Imagine a world where an authoritarian government monitors everything you do, amasses huge amounts of data on almost every interaction you make, and awards you a single score that measures how “trustworthy” you are.

In this world, anything from defaulting on a loan to criticising the ruling party, from running a red light to failing to care for your parents properly, could cause you to lose points. And in this world, your score becomes the ultimate truth of who you are – determining whether you can borrow money, get your children into the best schools or travel abroad; whether you get a room in a fancy hotel, a seat in a top restaurant – or even just get a date.

This is not the dystopian superstate of Steven Spielberg’s Minority Report, in which all-knowing police stop crime before it happens. But it could be China by 2020. It is the scenario contained in China’s ambitious plans to develop a far-reaching social credit system, a plan that the Communist Party hopes will build a culture of “sincerity” and a “harmonious socialist society” where “keeping trust is glorious.”

Link to full article here

The Dangers of the Attention Economy



     During our last class we unfortunately didn’t have too much time to spend on the Escaping Attention piece by Sy Taffel. I thought it was quite critical, considering it plays with the implications of focusing too much on content and not enough on the materiality of technologies. Taffel looks to take the reader out of the wonder and splendor that the digital 21st century bestows upon us, and straight into the damaged ecological structures that are a direct effect of the attention economy we participate in. As Taffel frames the question: “To what extent can we justify damages to these ecological systems based on the socio-economic benefits that digital culture brings,” which takes the reader aside for a second and tries to help them see past the nanotainments and tidbits that the attention economy has distracted us with (7). What should receive the lens of focus, the wonder of the information flowing to our digital machines, or the blood that made it possible.

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