Prediction is always difficult, said Yogi Berra, especially about the future. However, it's something we do quite a bit of (have you seen our work on election prediction - Brexit, Trump, UK 2017, Germany etc - we got them all right) so I went along to ...

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broadstuff"broadstuff" - 5 new articles

  1. NESTA on predicting the Future
  2. Twitter is for bullet points, not essays
  3. Dulce et decorum est, pro Patria mori....but not such a good plan if you don't
  4. Adversarial Perturbation - fooling AI Image Processing, one Pixel at a time
  5. Fake News about Fake News, or How do I hire those Russians?
  6. More Recent Articles

NESTA on predicting the Future

Prediction is always difficult, said Yogi Berra, especially about the future. However, it's something we do quite a bit of (have you seen our work on election prediction - Brexit, Trump, UK 2017, Germany etc - we got them all right) so I went along to the NESTA Futurescoping 101 event on this last night see what they had that I could steal to offer.

There were 4 talks on various ways of predicting the future, or more accurately 4 ways of making scenarios (reading entrails of dead animals is still outre, but with the return to the Age of Unreason we are seeing I wouln't be surprised it it makes a comeback, albeit maybe with dead plants). Anyway, in summary they covered these 4 approaches:

Speculative Design - Cat Drew

Speculative Design is primarily the use of physical things to provoke debate, rather than using documents/words/numbers etc. For example Cat did one session on blockchain visualisation using a blood transfusion drone made of felt and other blue petery stuff to stimulate thoughts of what (electronic vampire?) blood donation services may be like.

Whether you agree with the approach or not (In my experience with Tech you do often do have to build a Future Thing for people to be able to think concretely about, and this looks useful for that - but it's risky to abstract the results too far without doing the hard sums....) she also noted some things that are important to scenario building no matter what your approach, viz:
- multidisciplinary teams
- can't be fantasy, don't make it too remote
- base it on evidence of research
- Suspend belief but no trickery

Games and Simulation - Florence Engasser

Simulation and game design are more my comfort zone area, so to an extent I knew more abot this approach than the others - anyway, it was all sensible stuff and Florence noted the main problems one hits with respect to modelling complex and unpredictable things (aka most future problems), and the benefits. As she notes, simulation helps in a number of ways:
- Model the trade-offs and outcomes (and some aspects often become clear in the dynamics of the model)
- Visualisation makes it easy to see things
- Collaboration - seeing things the same way (modelling often is a good way to bring people up to speed with a situation)
- Experimentation - safe place to experiment with changing factors and assumptions

Her summary of how to make it work was good, I liked the checklist of what a simulation or game needs, not so much the first 3 which are almost a given, but the last 3 which are often neglected by techie types, and are part of the "soft factors" that are so essential:
- objectives (getting these right is non trivial in my experience)
- mechanics (wish she'd said more on what they do here)
- balance between complex and simple (again critical for model acceptance)
- narrative for the scenarios
- choices people can make
- a journey, putting it all together

What I didn't know is there are a bunch of Nesta games in existence or production:
- Innovate
- Superbug
- Consortium

Inclusive Place based futures - Harry Armstrong

This was about how to have inclusive conversations with people about changes to their cities, buildings etc. Typically top doesn solution go awry, it's better to get the locals involved for a variety of reasons and avert failure:
- Appeal to democratic/ethical notions to get buy in
- To drive collective action needs a shared vision
- Driving nclusion of a large number can show wider benefits to make the project more attractive, and show flaws and avert failure that a small group won't see

Examples used were the Newcastle 2065 future city strategy and the Island of Aruba's 2000+ strategy for itself. Looking at the latter:
- Aruba in the early noughties took at collective approach for its future strategy and engaged 60% of the island's c 100 000 population,
- They used the Appreciative Inquiry approach (Discover, Dream,Design, Deliver )
- Then went on with scenario planning approach, across 10 workstreams

What interested me about this example is that Aruba had a change of Govt during the process and teh new people wanted to change or cancel some of the streams, but concerted citizen action forced their continuation - now that is buy in

Lessons about this approach are salutary:
- works if it's authentic,
- don't do it if you won't act, expectation raising can be risky
- needs buy in from major players
- need neutral spaces for trust
- need to manage "the usual suspects" being involved

DiY Scenarios - David Finnigan

The approach here is to acting out a scenario as a short play or event. His view is that a scenario is an Imagined vision - doesn't have to be correct, but you need a range including a best and worst. So far same old same old, but his approach is to use the play as a vehicle to slip in the assumptions and outcomes as just part of teh narrative.

This evening he acted out making a nature documentary in the future, but members of the audience had to answer assumptions on the role of nature reserves in the future, impact of tourism, and the various other assumptions about the future, using the play-acting as a vehicle to sideways slide in the assumptions of the real scenario, ie the setup conditions as part of the building the story, and the edenouemaent is anarrative end of teh reults of the various 2x2 essumption matrices.

Overall impression - the evening seemed a bit heavy on the qualitative and light on the quantitative, my experience is at some point someone with the power of decision making says "show me the numbers behind the grand vision" (which is where simulation type modelling has an advantage), so while I could see how some of the more narrative approaches could really help the envisioning.

I think - as Cat noted in the first talk - always base your scenarios on evidence of research.


Twitter is for bullet points, not essays

So, Twitter has expanded the 140 character limit to 280 for all users. I've used it for a week or so like this, and overall what I've found is:

- It's useful as you don't have to artificially concatenate twts, and you can be clearer in what you mean
- If you stick to around 140 characters and stick to the point, it flows as well as the original.

- Some users (too many) put no new, useful information in the extra 50%, the signal-to-noise ratio is effectively halved
- A pageful of 280 character twts is half as much information as one of 140 character twts, and given too many are full of woffle it can be even worse.
- A 280 character tweet needs some formatting functions to be easy to read.

Now no doubt Twitter could put in basic editing functions, but this seems to be adding complexity to a system where the major attraction is speed and simplicity.

IMO Twitter is for fast-to-read bullet point information, not micro-essays, and especially not micro-essays by the verbose.

Dulce et decorum est, pro Patria mori....but not such a good plan if you don't

Lest we never forget, not all unknown soldiers are dead

(Rogers cartoon, Pittsburgh Post-Gazette)

Adversarial Perturbation - fooling AI Image Processing, one Pixel at a time

Cat registering as Guacamole on Google Inception V3

Covered in boingboing and LabSix- what happens when people start to spoof image processing algorithms..Can you make a cat look like guacamole?

Three researchers from Kyushu University have published a paper describing a means of reliably fooling AI-based image classifiers with a single well-placed pixel.

It's part of a wider field of "adversarial perturbation" to disrupt machine-learning models; it's a field that started with some modest achievements, but has been gaining ground ever since.

But the Kyushu paper goes further than any of the research I've seen so far. The researchers use 1, 3 or 5 well-placed pixels to fool a majority of machine-classification of images, without having any access to the training data used to produce the model (a "black box" attack).

Must admit I'm intrigued and a bit heartened, there may be a way out of the continual CCTV snooping world that is emerging after all. Research paper is over here .

Fake News about Fake News, or How do I hire those Russians?

Latest news on the Russians on Facebook brouhaha from Recode

At Facebook, roughly 126 million [or about half the voting population] users in the United States may have seen posts, stories or other content created by Russian government-backed trolls around Election Day, according to a source familiar with the company’s forthcoming testimony to Congress....

Google, which previously had not commented on its internal investigation, will break its silence: In a forthcoming blog post, the search giant confirmed that it discovered about $4,700 worth of search-and-display ads with dubious Russian ties. It also reported 18 YouTube channels associated with the Kremlin’s disinformation efforts, as well as a number of Gmail addresses that “were used to open accounts on other platforms.”

And Twitter will tell Congress that it found more than 27,000 accounts tied to a known Russian-sponsored organization called the Internet Research Agency:

There is a bit more from NBC:

Facebook says in the testimony that while some 29 million Americans directly received material from 80,000 posts by 120 fake Russian-backed pages in their own news feeds, those posts were “shared, liked and followed by people on Facebook, and, as a result, three times more people may have been exposed to a story that originated from the Russian operation
Beforehand, Facebook had said they had found about $100,000 of Russian spend on the platform over the election.

To put these numbers into context, the Clinton campaign spent about $1.4 billion on media during this period, and that is without the free publicity from nearly every US media organ being mainly on the Democrat side. The Russian media pieces would have been swamped by this spend and volume, never mind all the rest of the pieces of media on the average social media timeline. The Trump campaign spent c $1bn itself, not on Russians.

A BBC article says Facebook estimates that:

"just one in 23,000 or so messages shared on the network were from the Russians".

That's a ratio of 23,000/1 (about 0.004%), and if you compare the ratio between the Russian spend of c $100k vs the c $2.5 bn spent by Trump & Clinton (25,000/1) then they are no more effective in getting their message transmitted than any other piece of media on average. If the Russian spend is underestimated then they are in fact quite poor at writing attractive content, unless of course the volume is also underestimated in which case it approaches this average again. .

The election was won on c 55,000 voters voting for Trump in marginal wards, of a total of c 130m voters. about 0.04% of all voters. So for the Russians to have "stolen the election" with 0.004% of all Ads, their Ads must be 10x as persuasive as all others just to be have an equal chance. Or they have to be extremely targeted. But from the above, it seems the Russians reached c 50% of the potential voting population or about 130m people , so doing the paper-napkin maths at the most optimistic (The 130m messages seen went only to the "right 50% of the 130m who actually voted) so 23,000/1 is now 11,500/1, about 0.02%, so they are 2x more targeted and thus have to be only 5x as persuasive.

So in order to believe the Russians "stole the election" you either have to believe that either:

(i) They are extraordinarily persuasive copywriters and great targeters to boot, and can do wonders on a shoestring, beating the best US Ad agencies that Democrat money could buy (they bought the best) or

(ii) Facebook (and the others) are massively underestimating both their spend and % of all messaging, but it has to be at least 10x more just to get parity on reaching those 0.04% of marginal voters who turned it for Trump, never mind persuading them

Or alternatively, you have to believe they were a pretty marginal force in the election (albeit with intent).

Incidentally, we used our data analytics technology to predict Trump's win (and a few other recent elections too) correctly, and we did it by analysing social media support for Trump vs Clinton. It was highly predictive, forecasting a neck and neck race, and we edged it to Trump based on small signals. We're pretty confident our system is fairly good at negating the obvious bots (which are the ones that operate in volume) but there's no doubt we saw some Russian Fake News in the results. However our main impression is by and large people knew which candidate they wanted (or quite frequently, which one they most certainly did not want) and shared media that agreed with them in their own filter bubbles.


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