Wednesday, April 19. 2017
Obviously a very serious entry and in no way, an excuse for the headline.....
This is Kasper, our Portuguese Water Dog, who is now on the Internet, thanks to his GPS/GSM tracker. Kasper has a fascination with the local muntjacs. Fortunately, they run much faster than him, but he does sometimes end up lost after a chase. So we have fitted him out with a tracker and it works quite well, allowing us to track him in real time via a mobile app. The battery lasts over 24 hours, so if he was seriously lost we would have a good chance to locate him. He has his own location systems and usually finds us first!
The point of this (really!) is the growing ubiquity of the IoT (Internet of Things) and in this case the IoD (Internet of Dogs).
Tuesday, April 18. 2017
Above - our system busy predicting the German election - still far too early to tell very much, but this slide is showing examples of the Centre Right CDU moving rightwards.
We had already set our systems onto watching the French and German elections, and will have something to say about the French Round One very soon. And now, courtesy of the UK Government, we have a UK election to track again too!
As you may recall, we have set our analytic engines onto election watching for Brexit last year which we got right, and it managed to predict the Trump election result publically (see here). .
Our current Euromodel election hypothesis, which was built after an analysis of the Dutch election, predicts that (in broad terms):
1, The main Centre-Right party moves considerably more Right, adopting quite a few of the Far-Right policies and narratives. We're seeing this in France, and starting to in Germany - see above chart. Arguably Theresa May's post-Brexit Tories have already done this. This ploy may lose some of its more centrist supporters (to who though?) but it prevents the Far Right from taking far more right wing supporters.
We assume the Scots will stay SNP unless a major shift in Labour towards Remain occurs, which is risky for them (see "Deplorables" above).
Anyway, using this as the initial hypothesis for building a system dynamic model (although these days its called "machine learning", and if the hype carries on it will be "AI" by next year) it is then theoretically* possible to calibrate the social media monitoring we can do to look at cause and effect and the deltas, and refine the model and thus predict the outcome of an election.
We shall see....
*We've done it already for US and Brexit, but there is quite a lot of judgement required for interpretation, and some luck - so "training" the system (and training ourselves to read it) is very important.
Monday, March 20. 2017
Was asked how we do this at Broadstuff Towers, in the light of our extraordinarily good prediction record ....so in true Listicle BS style, here are 10 "Red Flags" (to use the emotive form of "pointers"). The more of these it scores, the more the chance of hype, bullshit and eventual shocking, painful collapse:
Some of the red flags are more traditional analysis based - "Type 1" - about the core technology, economics or regulatory frameworks:
(i) It uses the hype tech du jour (aka AI or IoT today) - hype tech is typically nowhere near the promised capability of the Hype, so is a good sign of impending failure
However you can also discern quite a lot by "Type 2" red flags - analysing the activities of the commentariat, whose job it is to raise hype. (as noted above, hype is a good sign of an impending failure) - these are typical signs of this process:
(vi) It's a prediction from Planet Mobile (over 11 years of writing Broadstuff, we've found Mobile predictions are always the most, er, optimistic)
As an aside, most Tech media, especially free to reader, is optimistic in nature. Also most Tech journalists are not STEM trained so don't always know what's happening "under the hood" - so caveat reador. Right now nothing beats the Type 1 BS around AI stuff, except maybe the Type 2 BS around the sexier Unicorns.
Friday, March 17. 2017
Dutch election outcome, swing by ward (blue = right, orange = left) Image hat tip to @JossedeVoogd, dank je wel
As you may recall, we have set our analytic engines onto election watching for Brexit last year (see here), which it got, and it managed to predict the Trump election result (see here). We have now set it onto watching the French and German elections, and will have something to say about the French one soon.
Tracking the data flow is one thing, but to make systems predictive it is necessary to build systemic mathematical models that can dynamically adjust as new data comes in (this is the basis of machine learning as well) and for that it's worth doing a quick analysis of the Dutch election to see if some form of model is emerging. Our analysis is that the following occurred in Holland:
1, The main Centre-Right party moved considerably more Right (see diagram above), adopting quite a few of the Far-Right policies and narratives. It lost some of its more centrist supporters but prevented the Far Right from robbing its more right wing supporters.
2. The Centre-Left was decimated, We suspect that what happened in the UK and US has played out here too, in that the "white blue collars" went left and right - ie the pattern of the white blue collar class deserting their traditional party affiliation holds true here as it did in UK and US. It's where they went that differs from the US and UK, in that there was quite a shift to more Left parties like the Greens (arguably if Bernie Sanders had stayed in the US race US as a 3rd, it could have looked more like this).
3. We have a hypothesis (awaiting more detail) that the white blue collars are not in such a bad position economically in the EU as in the UK/US (better training, better working conditions and welfare), so are not as desperate/willing to embrace the populist option as a last hope - yet (see * below).
At any rate the above is a good start for modelling how Germany and France will play out, in that we assume the main Right Wing parties will adopt more Far Right clothing, and move considerably to the right to ensure they don't leak support there. (To an extent this is arguably what the Tories are doing, betting that their more centre-ist voters won't go to LibDems or Labour)
What this will mean is that a shift to the Right will be of similar size to the US/UK across the EU, just the "traditional" parties have learned from UK/US to embrace not reject the Far Right policies, to keep themselves in power. But the policies will shift to the Right so the effect is similar.
Anyway, if one uses that as an initial dynamic flow system model, it is then possible to calibrate the social media monitoring to look at cause and effect and the deltas, and refine the model.
An aside - one of the main reasons polls got it "wrong" in the US and the UK was an unwillingness of mainstream groups to believe the incumbent side could lose (see here). In Holland it was the opposite before the election, a common assumption was the Far Right would do way better than they did - but within hours of the outcome they were saying it was "back to normal/Far Right was defeated". This is very wrong too, as noted above. It will be interesting to see if the mainstream media/polls behave in the same way in France and Germany.
*A note - The summer had not yet come at the time of the Dutch election, and won't really have started by the French one - but it will have ended by the German election and if there has been a repeat of the migrant flows of recent years, one can hypothesize it will be much harder stemming the voter flow to the the Far Right than in Holland.
Wednesday, February 8. 2017
Facebook is closing hundreds of its Oculus VR pop-ups in Best Buys after some stores went days without a single demo
Well, that was quick - Broadstuff predictions for Tech 2017, No. 11, Dec 31 2016:
Hate to say "We told ya", but.....
Friday, February 3. 2017
After our systems predicted Trump's win, we were asked a number of times about the impact of Fake News (and Bots, Russian Hacking etc - we will cover those in separate posts) and here is a summary of some of the useful research we looked at:
Stanford/ NYU Research
Firstly, research by Hunt Allcott of NYU and Matthew Gentzkow of Stanford, published by Stanford University looked at the sources and takeup of Fake News. They defined “fake news” as "news stories that have no factual basis but are presented as facts". By news stories they meant stories that originated in social media or the news media, i.e. excluded false statements originated by political candidates or major political figures. They also excluded websites well-known to be satirical, such as the Onion.
Firstly, they found that in the US elections, people mainly got their news by from sources other than websites and social media (see pie chart below, left). But online media (websites and social media) was where most Fake News was disseminated. They also looked at how Fake News was disseminated on the online media (below, right) and the majority was transmited via social media with a significant minority going direct (to websites or their feeds) or finding it in search results, This contrasts hugely with how top news was disseminated, mainly via older channels but online the major source was via direct access and then search.
They also looked at how people reacted to Fake News, ve Mainstream media news, and also inserted Placebo news (stories they made up) to test reactions. The chart below shows how people reacted:
The Figure presents the share of headlines that survey respondents that recall seeing (blue bar) vs. recall seeing and also believing (red bar). They averaged responses across all the headlines within four categories of headlines they presented - "Big" true stories; Smaller true stories; Fake stories and Placebo stories that they had made up headlines for. In short they found that 15 percent of people reported seeing the Fake stories, and 8 percent reported seeing and believing them (about 55%). But the chart also shows a number of other interesting tendencies:
The last test they did was to model what impact Fake News would have had to make to shift opinion in the most closely fought wards to ensure the Democrats won. For Clinton to have won the election, Trump’s margin of victory would have to decrease by ~ 0.51% of the voting age population, which would have shifted Michigan, Pennsylvania, and Wisconsin into Clinton wins and deliver the Electoral College. Thus, the core question was whether fake news could have increased Trump’s margin of error by more than 0.51 percent of the voting age population. The table below summarise the outcome of their model. In summary, the column on the far right looks at how many times more effective the Fake News would have had to be compared to TV advertising to have had to have shifted the vote. For example, on line 1 a Fake News story as it performed in reality was would have had to be 37 time more effective to shift opinion. If recall was 7% of all stories, it would have had to be 27 times more effective. Line 8 sows that if Fake News shares were 20x greater it would still have to have been 13 times more effective
Their overall conclusion was that Fake News was very unlikely to have had a major effect:
Another study was done by IPSOS for Buzzfeed on the impact of Fake News on Facebook, as Facebook had by far the largest reach of any social network for Fake News (see study here) and conclusions were in line with the above work:
IPSOS Online survey of 1,007 American adults
Percentage of consumed news in the past the month by channel showed
Print, TV and Twitter was relatively more trusted than Facebook
Far lower trust of news on Facebook all or most of the time
However, other research by IPSOS suggests that trust is not the same as belief — Another poll by Ipsos/BuzzFeed News foundon average about 75% of American adults believed fake news headlines about the election when they recalled seeing them. This contradicts the Stanford finding of c 55%, but as their model showed, even that belief level would not have changed the election outcome
In short, both studies show a minority of news was received from the online world, and it was by and large not widely believed, so the impact was relatively small. However, 2 caveats to the Stanford work:
In other words this may underplay the total impact of Fake News, but even so the model is still showing it has to be a LOT more effective to actually swing the votes. Our view is its a marginal contributor, but in a 50/50 split election (which in effect this was) even small margins can be effective, especially if used in conjunction with a number of other small nudges.
Also, the Stanford model's definition of "Fake News" is very strict - we believe there is far more "False" news - news that bends the truth, or is economical with it - in circulation, and that acts in a similar manner. A lot of this sort of news is meat and drink to more "respected" media as well (and it is they that are leading the complaints against "Fake" news).
At any rate, expect more use of Fake News in future campaigning, and in attempts to persuade in general.
We have looked at how our systems can counter this, and believe we have some solutions
Tuesday, January 10. 2017
Tronc won the "silly renaming" rights for 2016, and we aren't long in 2017 for the first contender - Bits of Yahoo! not sold to Verizon will be named Altaba (without an ! even!) - TechCrunch:
Despite hiccups*, Yahoo’s planned sale to Verizon appears to be moving forward — but some portions of the company will be left behind and renamed Altaba Inc.
There is no truth in the rumour that Verizon will be re-named Verizon!
*That'll be the many millions of accounts found hacked in 2013/14, and the Peanut Butter problem
Friday, January 6. 2017
Deja views...the bitcoin valuation from c 2013. Graph courtesy coindesk.com
Yes, we've been tracking the ups and downs of Bitcoin value since as long as they have been traded, and after a rapid boom in the last few weeks the expected bust has set in - so far only about 12% down on it's high, which was about where it got before the big 2013 bustup
Monday, January 2. 2017
Amazon drone patent drawing
Amazon has had a patent granted to fly blimp warehouses above cities so drones can deliver your goodies from the blimps. Leaving aside the "how the hell do you get a patent for something so damn obvious and that has already been done", the question is why blimps?
The answer is the appalling logistic costs of transporting products by drone. As we showed in the previous note on this area (over here), drone delivery has some major problems:
(i) Small payload - many trips are required to satisfy even a moderately large order, a weekly shop would take 20+ drones to deliver. Heavy lifting drones are unlikely to be a feature of urban environments anytime soon, they are very dangerous if anything goes wrong.
So there are essentially two solutions to the problem - either put physical warehouses in very high cost urban land (ie buy the Royal Mail or Big Yellow Storage) which hugely increases costs of warehouse square footage, or have mobile warehouses that move in closer so reducing the back and forth distance each drone flies.
Why blimps? Simply put, our analysis shows that the sheer number of drones needed to replace vans would be tantamount to huge swarms of these devices, the noise, flying traffic and risk of having them at street level would intolerable. Thus the only other available option is to fly them up and down to hovering blimps. Downside is the blimps are aircraft and thus have to fly at a safe height (tens of thousand feet), so drones (which, remember, for this application are basically helicopters so cannot benefit from any lift generated by wings) will have to expend a lot of energy climbing
We say only available option - the other option is of course vans, travelling on roads. These are high payload carrying, can optimise multiple drops and are non intrusive as they stick to existing transport networks. Best of all, they don't fall out of the sky.
Also, as we showed in the previous note, vans have a useful device - called a driver - that can negotiate that most tricky of problems, the last yards delivery to the customer....
Saturday, December 31. 2016
It's time for Broadstuff's annual Tech predictions for 2017, or rather where we Puncture The Hype. Given our whole operating mantra is to give realistic advice to clients on new technology opportunities, getting behind the hype is essential and if we can do it a bit tongue in cheek, well that adds to the entertainment (You can see our stellar 2016 record over here). Of course it is in Listicle format as countless analytics show more people read listicles than ordinary articles, so consider yourself "nudged".
Anyway, here goes:
1. The Reality of Legacy - the weightless rise of cloud, social, analytics etc platform companies will increasingly find the gravitational pull of legacy platforms restrains them. That is where much of the data and a lot of the core processes flow, and for these new systems to move from the periphery to the centre of the enterprise they will need to interoperate with the old gradgrinds - which will force reality into all the overblown economic "savings" projected from all the new shiny products hitting your screens.
2. Cloud comes to Earth - Not only the above, but the limiting economics of the rental cloud model, the lower service responsiveness for sophisticated users, and its security risks will weigh more heavily on heavy users of computing power. Expect far more to be made of hybrid cloud + user managed services. Last year we believed Cloud had already hit these buffers and (as has been the trend) would rename itself again - - we were wrong, but will predict that 2017 is the year of transition and we'll see more Cloud - X and Y-Cloud services
3. Social, meet Regulation - the lesson of 2016 is that Social as a vector of Fake News, heavy trolling and abuse, use to communicate "ist" dogmas, a vessel of Millenial mental illness et al has sparked increasing ire (whether you believe its valid or not) in a lot of groups and organisations which do have political influence, so expect increasing pressure for social platforms to be regulated or constrained in a raft of ways. In other news, all Social platforms will busily copy each others' features to differentiate themselves until they all look the same.
4. 2017 will be hyped as The Year of Mobile, will disappoint, and 2018 will later become the New Year of Mobile - In short, we doubt the planet will be eaten by Mobile in 2017. Its days of exponential hypergrowth are ending, and it's settling down into a mature system with comfortable growth and new services largely cannibalising old ones, like the PC industry once it came to the top of its S curve. Worry about Ad-blocking will continue to grow faster than Ad-blocking, but levels of Ad-blocking will start to impact service models, driving increasing concern in the Ad industry but expect no reform yet.
5. AI / Machine Learning - hype will continue to grow to stratospheric levels while the underlying services massively undershoot the rah-rah. Actual deployment will continue to be in tight, narrowly defined verticals with manageable solution spaces, while theoretical deployment will rocket to the stars, to infinity and beyond. The major problem with AI right now though is much of what is being called "AI" in the hypewave are in fact just closed loop system dynamic algorithms, which is nothing more than a variant of...
6. Big Data and Analytics - people are starting to realise that "big + data" is not the same as "useful information".and that pure analytics is useless without knowing what to do with it. This will be the real focus in 2017 and for the next new years. AI or not, suspicion and distrust of these systems will grow and there will be a few high profile system failures in 2017 that will increase pressure to regulate or force transparency on what these algorithms are doing. Also, on the security/privacy front there will be no letup in data heists - much big data is concentrated by entities whose main concern is its exploitation, not safekeeping - so it's only going to get worse.
7. Internet of Things - the current consumer IoT wave will increasingly look like a busted flush as ongoing concerns about security, privacy and long term service viability grow owing to more data heists, failed services and pulled products. It will sink into the Gartner hype cycle's "slough of despond" in 2017. Lack of universal standards and producer preference for walled gardens means that interworking and open data transportation will remain a pipe dream. Thus Industrial IoT and tightly specific consumer verticals with simpler offerings will be where the real growth is. Wearables, a consumer vertical of IoT, underperformed hugely vs forecasts in 2016 and will continue this slow trend in 2017 owing mainly to the standards and service risk issues.
8. Robots/Mechatronics/Drones - are essentially "vertical" applications of AI/Analytics maths that create real and tangible value (the real cost of physical production essentially drives out all the spurious "Unicorn" business models that drives headline hypergrowth however). But drone/robotic delivery is being totally oversold, once the sheer volumes of these things required for package delivery and thus moving along sidewalks/flying in suburban areas becomes clear, the only hockey stick curve will be urban resident resistance. Those most hyped of robots, automatic cars, are a pipe dream for several years still. There will be high-hype trials of course, but outside of extremely well manicured environments these devices will still require human control* - so the first major deployments will be industrial (after all, that's where all the previous generations went first) and very structured and dedicated usage paths ie "road-trains/trams".
9. Remote Production (3D Printing et al) - will continue to grow, but also will not "eat the world" in 2017. Expect the real growth to be in remote production by more conventional devices like textile weaving & printing, CNC machining etc rather than deposition (aka "printing") techniques. It is still largely high cost and low quality compared to older technologies, so will remain in niches where its unique properties create extraordinary value. There will continue to be "Much Wow" headlines but big picture will be "so Small". Makers are not going to replace Manufacturers any time soon.
10. Unicorns / "New Ways of Working" / Gig Economy - 2017 will be Shit or Bust year for many Unicorns that rely on "New Ways of Working" using worker exploitation or (and, in some cases) regulatory arbitrage based business models. Many Unicorns are shitting themselves, some big names will go bust in 2017 - especially at risk are those that are growing by using investor money to buy market share that then run into regulatory headwinds. There is much flummery going on as Unicorns caught on the horn of this dilemma attempt to pivot. You can't make book on this yet, unfortunately...
And, as its now the rage in "curating" listicles, here's a Bonus Prediction:
11. AR/VR - Useful bits of AR will become integrated into Mobile and Wearable devices over time, VR will be a niche pursuit until (if) price points come down hugely and even then its not likely to expand much farther than the gaming aficionado market. Resist all blandishments that this is the future, it won't be.
*As an aside, there have been a number of deaths from hybrid auto-cars beinh used outside their "safe zones", so far the makers haven't been touched. So far....
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