Tag: bigdata

BBC Newsnight on Cambridge Analytica

https://www.youtube.com/watch?v=k6Vn45Xt3y8

Was Britain’s EU referendum hijacked by the American alt-right using a technique known as psychographics? Gabriel Gatehouse from BBC Newsnight reports on the data analytics firm Cambridge Analytica.

I’ve said so much stuff about this already but frankly “Overzealous PR?” is total laughable crap! I actually laughed quite a lot when I heard this. Its very clear they were involved (to one degree or not) and like a kid with their finger in the cookie jar, they got caught.

​Cambridge analytica: The Rise of the Weaponized AI Propaganda

cambridgeanalytica

I’ve been studying this area for a long while; when I talk about perceptive media people always ask how this would work for news?  I mean manipulate of feelings and what you see, can be used for good and obviously for very bad! Dare I say those words… Fake news?

Its always given me a slightly unsure feeling to be fair but there is a lot I see which gives me that feeling. In my heart of hearts, I kinda wish it wasn’t possible but wishing it so, won’t make it so.

It was Si lumb who first connected me with the facts behind the theory of what a system like perceptive media could be ultimately capable of. Its funny because many people laughed when I first talked about working with perceptiv whose mobile app under pinned the data source for visual perceptive media; I mean how can it build a profile about who I was in minutes from my music collection?

I was skeptical of course but the question always lingered. With enough data in a short time frame, could you know enough about someone to gage their general personality? And of course change the media they are consuming to reflect, reject or even nudge?

According to what I’ve read and seen in the following pieces about Cambridge analytics, the answer is yes! I included some key quotes I found interesting

The Rise of the Weaponized AI Propaganda Machine

Remarkably reliable deductions could be drawn from simple online actions. For example, men who “liked” the cosmetics brand MAC were slightly more likely to be gay; one of the best indicators for heterosexuality was “liking” Wu-Tang Clan. Followers of Lady Gaga were most probably extroverts, while those who “liked” philosophy tended to be introverts. While each piece of such information is too weak to produce a reliable prediction, when tens, hundreds, or thousands of individual data points are combined, the resulting predictions become really accurate.
Kosinski and his team tirelessly refined their models. In 2012, Kosinski proved that on the basis of an average of 68 Facebook “likes” by a user, it was possible to predict their skin color (with 95 percent accuracy), their sexual orientation (88 percent accuracy), and their affiliation to the Democratic or Republican party (85 percent). But it didn’t stop there. Intelligence, religious affiliation, as well as alcohol, cigarette and drug use, could all be determined. From the data it was even possible to deduce whether deduce whether someone’s parents were divorced.

Some insight into the connection between Dr. Michal Kosinski and Cambridge Analytica

Any company can aggregate and purchase big data, but Cambridge Analytica has developed a model to translate that data into a personality profile used to predict, then ultimately change your behavior. That model itself was developed by paying a Cambridge psychology professor to copy the groundbreaking original research of his colleague through questionable methods that violated Amazon’s Terms of Service. Based on its origins, Cambridge Analytica appears ready to capture and buy whatever data it needs to accomplish its ends.

In 2013, Dr. Michal Kosinski, then a PhD. candidate at the University of Cambridge’s Psychometrics Center, released a groundbreaking study announcing a new model he and his colleagues had spent years developing. By correlating subjects’ Facebook Likes with their OCEAN scores

What they did with that rich data. Dark postings!

Dark posts were also used to depress voter turnout among key groups of democratic voters. “In this election, dark posts were used to try to suppress the African-American vote,” wrote journalist and Open Society fellow McKenzie Funk in a New York Times editorial. “According to Bloomberg, the Trump campaign sent ads reminding certain selected black voters of Hillary Clinton’s infamous ‘super predator’ line. It targeted Miami’s Little Haiti neighborhood with messages about the Clinton Foundation’s troubles in Haiti after the 2010 earthquake.’”

Because dark posts are only visible to the targeted users, there’s no way for anyone outside of Analytica or the Trump campaign to track the content of these ads. In this case, there was no SEC oversight, no public scrutiny of Trump’s attack ads. Just the rapid-eye-movement of millions of individual users scanning their Facebook feeds.

In the weeks leading up to a final vote, a campaign could launch a $10–100 million dark post campaign targeting just a few million voters in swing districts and no one would know. This may be where future ‘black-swan’ election upsets are born.

“These companies,” Moore says, “have found a way of transgressing 150 years of legislation that we’ve developed to make elections fair and open.”

The Data That Turned the World Upside Down

When it was announced in June 2016 that Trump had hired Cambridge Analytica, the establishment in Washington just turned up their noses. Foreign dudes in tailor-made suits who don’t understand the country and its people? Seriously?

“It is my privilege to speak to you today about the power of Big Data and psychographics in the electoral process.” The logo of Cambridge Analytica— a brain composed of network nodes, like a map, appears behind Alexander Nix. “Only 18 months ago, Senator Cruz was one of the less popular candidates,” explains the blonde man in a cut-glass British accent, which puts Americans on edge the same way that a standard German accent can unsettle Swiss people. “Less than 40 percent of the population had heard of him,” another slide says. Cambridge Analytica had become involved in the US election campaign almost two years earlier, initially as a consultant for Republicans Ben Carson and Ted Cruz. Cruz—and later Trump—was funded primarily by the secretive US software billionaire Robert Mercer who, along with his daughter Rebekah, is reported to be the largest investor in Cambridge Analytica.

Revealed: how US billionaire helped to back Brexit

The US billionaire who helped bankroll Donald Trump’s campaign for the presidency played a key role in the campaign for Britain to leave the EU, the Observer has learned.

It has emerged that Robert Mercer, a hedge-fund billionaire, who helped to finance the Trump campaign and who was revealed this weekend as one of the owners of the rightwing Breitbart News Network, is a long-time friend of Nigel Farage. He directed his data analytics firm to provide expert advice to the Leave campaign on how to target swing voters via Facebook – a donation of services that was not declared to the electoral commission.

Cambridge Analytica, an offshoot of a British company, SCL Group, which has 25 years’ experience in military disinformation campaigns and “election management”, claims to use cutting-edge technology to build intimate psychometric profiles of voters to find and target their emotional triggers. Trump’s team paid the firm more than $6m (£4.8m) to target swing voters, and it has now emerged that Mercer also introduced the firm – in which he has a major stake – to Farage.

Some more detail as we know from the other posts previously

Until now, however, it was not known that Mercer had explicitly tried to influence the outcome of the referendum. Drawing on Cambridge Analytica’s advice, Leave.eu built up a huge database of supporters creating detailed profiles of their lives through open-source data it harvested via Facebook. The campaign then sent thousands of different versions of advertisements to people depending on what it had learned of their personalities.

A leading expert on the impact of technology on elections called the relevation “extremely disturbing and quite sinister”. Martin Moore, of King’s College London, said that “undisclosed support-in-kind is extremely troubling. It undermines the whole basis of our electoral system, that we should have a level playing field”.

But details of how people were being targeted with this technology raised more serious questions, he said. “We have no idea what people were being shown or not, which makes it frankly sinister. Maybe it wasn’t, but we have no way of knowing. There is no possibility of public scrutiny. I find this extremely worrying and disturbing.”

There is so much to say about all this and frankly its easy to be angry. But like Perceptive Media, it started off out of the academic sector. Someone took the idea and twisted it for no good. Is that a reason why we shouldn’t proceed forward with such research? I don’t think so…

Welcome to the world of the implicit

Advertising?

A while back I wrote a blog about how implicit data is the dark matter to the explicit. I also write about it in my wired/tired/expired post.

Well I thought its about time I started writing why the implicit is so rich and may become the dominating model in the future. Of course if you know anything about me and the BBC R&D project Perceptive Media, I have an interest in this area, I actually talked about context before but didn’t really make it clear that context is a part of the implicit dataset.

It started with personalised ads, currently sits with Google Now and ends with the end of internet advertising as a thing.

Yes I said it… The end of advertising… (which isn’t the same as the end of marketing btw)

Most people now prefer targeted advertising than wholesale advertising but hate the idea of minority report’s advertising nightmare. What was missing was the context.

Duhhh… In the future, surely a smart advert wouldn’t show tom cruise cars and expensive gifts while he was running down the shopping centre trying to escape. Yes it sounds pretty dumb when you add the context to the scene. Maybe showing Tom visions of holidays and trainers would make much more sense.

The end of advertising might seem a little premature but look at Google now. Then imagine Google now serving up adverts instead…

Double Duhhh… of course Google will be using the same algorithm to serve adverts if it can be proven to be even a slight bit more effective.

Although its far from perfect, the fact is with enough data and a insight into your context and implicit motivations. Google really can start to serve up adverts for things I want before I realise I actually want it (sounds freakish but its already happening). And if that fills you with fear, you better get ready as its not easily stopped. This might rely on the likes of our government not being greedy and short sighted, actually reflect the good of the people who put them there.

I’m thinking about a future where its too expensive and too inefficient to do the mass/wholesale advertising…A future where adblockers and VRM – Vendor Relationship Management is the norm, people pay to never see the adverts not targeted at you. Yes a bit of a dream but you got to dream a little bigger darling.

I have been using the term, Micro Data which is a specific part of the Big Data puzzle. Micro data is the implicit data, the data which is personal and we generate all the time. Its that Microdata which will power the next generation of services, apps and products. You can clearly see why I’m at the Quantified Self conference…

Implicit data is the anti-matter of big data

Dylan [Two thumbs up for Photographers]

Almost everything we’ve focused on recently has been the explicit actions and feedback of people. But as pointed out in Perceptive Media, the rich stuff is the implicit actions and feedback. This is also the stuff which advertisers would cream in their pants for… And it sometimes feels too intimate for us to ever let it be collected… However that has never stopped anyone.

This obviously scares a lot of people including myself but I think the future is about the implicit.

I wrote a blog following a audio piece about how 2012 was the year of big data. But the fundamentally all that data is explicit data not implicit. Something I also made clear during a panel in London at last years Trans-media festival.

In a recently interview Valve’s Gabe Newell talked about the Steam Box’s future. Steam is a very interesting gaming ecosystem and recently Valve’s been moving to Linux after Microsoft said Windows 8 must work the way they said it does. Anyhow the important thing is Gabe’s discussion regarding implicit forms of data

Speaking of controllers, what kind of creative inputs are you working on?
Valve has already confessed its dissatisfaction with existing controllers and the kinds of inputs available. Kinect? Motion?

We’ve struggled for a long time to try to think of ways to use motion input and we really haven’t [found any]. Wii Sports is still kind of the pinnacle of that. We look at that, and for us at least, as a games developer, we can’t see how it makes games fundamentally better. On the controller side, the stuff we’re thinking of is kind of super boring stuff all around latency and precision. There’s no magic there, everybody understands when you say “I want something that’s more precise and is less laggy.” We think that, unlike motion input where we kind of struggled to come up with ideas, [there’s potential in] biometrics. We have lots of ideas.

I think you’ll see controllers coming from us that use a lot of biometric data. Maybe the motion stuff is just failure of imagination on our part, but we’re a lot more excited about biometrics as an input method. Motion just seems to be a way of [thinking] of your body as a set of communication channels. Your hands, and your wrist muscles, and your fingers are actually your highest bandwidth — so to trying to talk to a game with your arms is essentially saying “oh we’re going to stop using ethernet and go back to 300 baud dial-up.” Maybe there are other ways to think of that. There’s more engagement when you’re using larger skeletal muscles, but whenever we go down [that path] we sort of come away unconvinced. Biometrics on the other hand is essentially adding more communication bandwidth between the game and the person playing it, especially in ways the player isn’t necessarily conscious of. Biometrics gives us more visibility. Also, gaze tracking. We think gaze tracking is going to turn out to be super important.

I’ve recently upgraded my phone to run Google now and its so weird…

When talking about it, people say show me and I have nothing to show them except the weather and maybe a couple of calendar things like someone birthday or a appointment I have upcoming. But when waking up this morning, the phone had tons of information about getting to work. Every time I would look at the screen another option was available to me (as time passed). The lack of ability to dig up stuff and look back at stuff is really interesting, as google now is simply that… Now!

Interestingly like google now, I discovered when showing people the first perceptive media prototype, futurebroadcasts.com. I would need to use my own machine because it relies on your implicit data for parts of the play. Meaning I couldn’t just load it up on another persons machine (or at least reliably), and expect it to work the same way.

I already said its the difference which in the future will be more interesting than the similarities, and I stick to that.

I know how people love quotes… So here’s one… Implicit data is the anti-matter of big data…

The trends, forecasts, etc will all be displaced (change) once we know implicit data’s place in the over all sum. We’ll throw our hands in the air and shout, well of course! How silly of us to make judgements with an incomplete sum… The early adopters are already homing in on this fact.

Big Data should be the word of the year

bigdata_network

I heard Geoff Nunberg’s piece on NPR’s podcast and I got to say, although I’m pretty much big dated out from BBC Backstage (in a nice way) I’m in total agreement. Here’s a few key points… Well worth listening to in audio form…

Whether it’s explicitly mentioned or not, the Big Data phenomenon has been all over the news. It’s responsible for a lot of our anxieties about intrusions on our privacy, whether from the government’s anti-terrorist data sweeps or the ads that track us as we wander around the Web. It has even turned statistics into a sexy major. So if you haven’t heard the phrase yet, there’s still time — it will be around a lot longer than “gangnam style.”

What’s new is the way data is generated and processed. It’s like dust in that regard, too. We kick up clouds of it wherever we go. Cellphones and cable boxes; Google and Amazon, Facebook and Twitter; cable boxes and the cameras at stoplights; the bar codes on milk cartons; and the RFID chip that whips you through the toll plaza — each of them captures a sliver of what we’re doing, and nowadays they’re all calling home.

It’s only when all those little chunks are aggregated that they turn into Big Data; then the software called analytics can scour it for patterns. Epidemiologists watch for blips in Google queries to localize flu outbreaks; economists use them to spot shifts in consumer confidence. Police analytics comb over crime data looking for hot zones; security agencies comb over travel and credit card records looking for possible terrorists.

It’s the amalgamation of all that personal data that makes it possible for businesses to target their customers online and tailor their sales pitches to individual consumers. You idly click on an ad for a pair of red sneakers one morning, and they’ll stalk you to the end of your days. It makes me nostalgic for the age when cyberspace promised a liberating anonymity. I think of that famous 1993 New Yorker cartoon by Peter Steiner: “On the Internet, nobody knows you’re a dog.” Now it’s more like, “On the Internet, everybody knows what brand of dog food you buy.”