Tag Archives: Buma/Stemra

Why would we care?

New York is all up in sixes and sevens, even as they aren’t really confused, some are not seeing the steps that are following and at this point giving $65 billion for 21st Century Fox is not seen in the proper light. You see, Comcast has figured something out, it did so a little late (an assumption), but there is no replacement for experience I reckon. Yet, they are still on time to make the changes and it seems that this is the path they will be walking on. So when we see ‘Comcast launches $65bn bid to steal Murdoch’s Fox away from Disney‘, there are actually two parties to consider. The first one is Disney. Do they realise what they are walking away from? Do they realise the value they are letting go? Perhaps they do and they have decided not to walk that path, which is perfectly valid. The second is the path that Comcast is implied to be walking on. Is it the path that they are planning to hike on, or are they merely setting the path for facilitation and selling it in 6-7 years for no less than 300% of what it is now? Both perfectly valid steps and I wonder which trajectory is planned, because the shift is going to be massive.

To get to this, I will have to admit my own weakness here, because we all have filters and ignoring them is not only folly, it tends to be an anchor that never allows us to go forward. You see, in my view the bulk of the media is a collection of prostitutes. They cater in the first to their shareholders, then there stakeholders and lastly their advertisers. After that, if there are no clashes, the audience is given consideration. That has been the cornerstone of the media for at least 15 years. Media revolves around circulation, revenue and visibility, whatever is left is ‘pro’ reader, this is why you see the public ‘appeal’ to be so emotionally smitten, because when it is about emotion, we look away, we ignore or we agree. That is the setting we all face. So when a step like this is taken, it will be about the shareholders, which grows when the proper stakeholders are found, which now leads to advertising and visibility. Yet, how is this a given and why does it matters? The bottom dollar will forever be profit. Now from a business sense that is not something to argue with, this world can only work on the foundation of profit, we get that, yet newspapers and journalism should be about proper informing the people, and when did that stop? Nearly every paper has investigative journalism, the how many part is more interesting. I personally belief that Andrew Jennings might be one of the last great investigative journalists. It is the other side of the coin that we see ignored, it is the one that matters. The BBC (at https://www.bbc.co.uk/programmes/b06tkl9d) gives us: “Reporter Andrew Jennings has been investigating corruption in world football for the past 15 years“, the question we should ask is how long and how many parties have tried to stop this from becoming public, and how long did it take Andrew Jennings to finally win and this is just ONE issue. How many do not see the light of day? We look at the Microsoft licensing corruption scandal and we think it is a small thing. It is not, it was a lot larger. Here I have a memory that I cannot prove, it was in the newspapers in the Netherlands. On one day there was a small piece regarding the Buma/Stemra and the setting of accountancy reports on the overuse of Microsoft licenses in governments and municipality buildings and something on large penalty fees (it would have been astronomical). Two days later another piece was given that the matter had been resolved. The question becomes was it really? I believe that someone at Microsoft figured out that this was the one moment where on a national level a shift to Linux would have been a logical step, something Microsoft feared very very much. Yet the papers were utterly silent on many levels and true investigation never took place and after the second part, some large emotional piece would have followed.

That is the issue that I have seen and we all have seen these events, we merely wiped it from our minds as other issues mattered more (which is valid). So I have no grate faith (pun intended) into the events of ‘exposure‘ from the media. Here it is not about that part, but the parts that are to come. Comcast has figured out a few things and 21st Century Fox is essential to that. To see that picture, we need to look at another one, so it is a little more transparent. It also shows where IBM, Google, Apple and some telecom companies are tinkering now.

To see this we need to look at this first image and see what there is, it is all tag based, all data and all via mobile and wireless communication. Consider these elements; over 90% of car owners will have them: ‘Smart Mobility, Smart Parking and Traffic priority‘. Now consider the people who are not homeless: ‘Smart grids, Utility management, hose management like smart fridges, smart TV and data based entertainment (Netflix)‘ and all those having smart house devices running on what is currently labelled as Domotics, it adds up to Megabytes of data per household per day. There will be a run on that data from large supermarket to Netflix providers. Now consider the mix between Comcast and 21 Century Fox. Breaking news, new products and new solutions to issues you do not even realise in matters of eHealth, road (traffic) management and the EU set 5G Joint-Declarations in 2015, with Japan, China, Korea and Brazil. The entire Neom setup in Saudi Arabia gives way that they will soon want to join all this, or whoever facilitates for the Middle East and Saudi Arabia will. In all this with all this technology, America is not mentioned, is that not a little too strange? Consider that the given 5G vision is to give ‘Full commercial 5G infrastructure deployment after 2020‘ (expected 2020-2023).

With a 740 million people deployed, and all that data, do you really think the US is not wanting a slice of data that is three times the American population? This is no longer about billions, this will be about trillions, data will become the new corporate and governmental currency and all the larger players want to be on board. So is Disney on the moral high path, or are the requirements just too far from their own business scope? It is perhaps a much older setting that we see when it is about consumer versus supplier. We all want to consume milk, yet most of us are not in a setting where we can be the supplier of milk, having a cow on the 14th floor of an apartment tends to be not too realistic in the end. We might think that it is early days, yet systems like that require large funds and years to get properly set towards the right approach for deployment and implementation. In this an American multinational mass media corporation would fit nicely in getting a chunk of that infrastructure resolved. consider a news media tagging all the watchers on data that passes them by and more importantly the data that they shy away from, it is a founding setting in growing a much larger AI, as every AI is founded on the data it has and more important the evolving data as interaction changes and in this 5G will have close to 20 times the options that 4G has now and in all this we will (for the most) merely blindly accept data used, given and ignored. We saw this earlier this year when we learned that “Facebook’s daily active user base in the U.S. and Canada fell for the first time ever in the fourth quarter, dropping to 184 million from 185 million in the previous quarter“, yet the quarter that followed the usage was back to 185 million users a day. So the people ended up being ‘very’ forgiving, it could be stated that they basically did not care. Knowing this setting where the bump on the largest social media data owner was a mere 0.5405%; how is this path anything but a winning path with an optional foundation of trillions in revenue? There is no way that the US, India, Russia and the commonwealth nations are not part of this. Perhaps not in some 5G Joint-Declarations, but they are there and the one thing Facebook clearly taught them was to be first, and that is what they are all fighting for. The question is who will set the stage by being ahead of schedule with the infrastructure in place and as I see it, Comcast is making an initial open move to get into this field right and quick. Did you think that Google was merely opening 6 data centres, each one large enough to service the European population for close to 10 years? And from the Wall Street journal we got: “Google’s parent company Alphabet is eyeing up a partnership with one of the world’s largest oil companies, Aramco, to aid in the erection of several data centres across the Middle Eastern kingdom“, if one should be large enough to service 2300% of the Saudi Arabian population for a decade, the word ‘several‘ should have been a clear indication that this is about something a lot larger. Did no one catch up on that small little detail?

In that case, I have a lovely bridge for sale, going cheap at $25 million with a great view of Balmain, first come, first serve, and all responsibilities will be transferred to you the new predilector at the moment of payment. #ASuckerIsBornEachMinute

Oh, and this is not me making some ‘this evil Google‘ statement, because they are not. Microsoft, IBM, and several others are all in that race; the AI is merely the front of something a lot larger. Especially when you realise that data in evolution (read: in real-time motion) is the foundation of its optional cognitive abilities. The data that is updated in real-time, that is the missing gem and 5G is the first setting where that is the set reality where it all becomes feasible.

So why would we care? We might not, but we should care because we are the foundation of all that IP and it will no longer be us. It gives value to the users and consumes, whilst those who are not are no longer deemed of any value, that is not the future, it is the near future and the founding steps for this becoming an actual reality is less than 60 months away.

In the end we might have merely cared too late, how is that for the obituary of any individual?

 

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Data illusions

Yesterday was an interesting day for a few reasons; one of the primary reasons was an opinion piece in the Guardian by Jay Watts (@Shrink_at_Large). Like many article I considered to be in opposition, yet when I reread it, this piece has all kinds of hidden gems and I had to ponder a few items for an hour or so. I love that! Any piece, article or opinion that makes me rethink my position is a piece well worth reading. So this piece called ‘Supermarkets spy on them now‘ (at https://www.theguardian.com/commentisfree/2018/may/31/benefits-claimants-fear-supermarkets-spy-poor-disabled) has several sides that require us to think and rethink issues. As we see a quote like “some are happy to brush this off as no big deal” we identify with too many parts; to me and to many it is just that, no big deal, but behind the issues are secondary issues that are ignored by the masses (en mass as we might giggle), yet the truth is far from nice.

So what do we see in the first as primary and what is behind it as secondary? In the first we see the premise “if a patient with a diagnosis of paranoid schizophrenia told you that they were being watched by the Department for Work and Pensions (DWP), most mental health practitioners would presume this to be a sign of illness. This is not the case today.” It is not whether this is true or not, it is not a case of watching, being a watcher or even watching the watcher. It is what happens behind it all. So, when we recollect that dead dropped donkey called Cambridge Analytics, which was all based on interacting and engaging on fear. Consider what IBM and Google are able to do now through machine learning. This we see in an addition to a book from O’Reilly called ‘The Evolution of Analytics‘ by Patrick Hall, Wen Phan, and Katie Whitson. Here we see the direct impact of programs like SAS (Statistical Analysis System) in the application of machine learning, we see this on page 3 of Machine Learning in the Analytic Landscape (not a page 3 of the Sun by the way). Here we see for the government “Pattern recognition in images and videos enhance security and threat detection while the examination of transactions can spot healthcare fraud“, you might think it is no big deal. Yet you are forgetting that it is more than the so called implied ‘healthcare fraud‘. It is the abused setting of fraud in general and the eagerly awaited setting for ‘miscommunication’ whilst the people en mass are now set in a wrongly categorised world, a world where assumption takes control and scores of people are now pushed into the defence of their actions, an optional change towards ‘guilty until proven innocent’ whilst those making assumptions are clueless on many occasions, now are in an additional setting where they believe that they know exactly what they are doing. We have seen these kinds of bungles that impacted thousands of people in the UK and Australia. It seems that Canada has a better system where every letter with the content: ‘I am sorry to inform you, but it seems that your system made an error‘ tends to overthrow such assumptions (Yay for Canada today). So when we are confronted with: “The level of scrutiny all benefits claimants feel under is so brutal that it is no surprise that supermarket giant Sainsbury’s has a policy to share CCTV “where we are asked to do so by a public or regulatory authority such as the police or the Department for Work and Pensions”“, it is not merely the policy of Sainsbury, it is what places like the Department for Work and Pensions are going to do with machine learning and their version of classifications, whilst the foundation of true fraud is often not clear to them, so you want to set a system without clarity and hope that the machine will constitute learning through machine learning? It can never work, that evidence is seen as the initial classification of any person in a fluidic setting is altering on the best of conditions. Such systems are not able to deal with the chaotic life of any person not in a clear lifestyle cycle and people on pensions (trying to merely get by) as well as those who are physically or mentally unhealthy. These are merely three categories where all kind of cycles of chaos tend to intervene with their daily life. Those are now shown to be optionally targeted with not just a flawed system, but with a system where the transient workforce using those methods are unclear on what needs to be done as the need changes with every political administration. A system under such levels of basic change is too dangerous to get linked to any kind of machine learning. I believe that Jay Watts is not misinforming us; I feel that even the writer here has not yet touched on many unspoken dangers. There is no fault here by the one who gave us the opinion piece, I personally believe that the quote “they become imprisoned in their homes or in a mental state wherein they feel they are constantly being accused of being fraudulent or worthless” is incomplete, yet the setting I refer to is mentioned at the very end. You see, I believe that such systems will push suicide rates to an all-time high. I do not agree with “be too kind a phrase to describe what the Tories have done and are doing to claimants. It is worse than that: it is the post-apocalyptic bleakness of poverty combined with the persecution and terror of constantly feeling watched and accused“. I believe it to be wrong because this is a flaw on both sides of the political aisle. Their state of inaction for decades forced the issue out and as the NHS is out of money and is not getting any money the current administration is trying to find cash in any way that they can, because the coffers are empty, which now gets us to a BBC article from last year.

At http://www.bbc.com/news/election-2017-39980793, we saw “A survey in 2013 by Ipsos Mori suggested people believed that £24 out of every £100 spent on benefits was fraudulently claimed. What do you think – too high, too low?
Want to know the real answer? It is £1.10 for every £100
“. That is the dangerous political setting as we should see it; the assumption and believe that 24% is set to fraud when it is more realistic that 1% might be the actual figure. Let’s not be coy about it, because out of £172.3bn a 1% amount still remains a serious amount of cash, yet when you set it against the percentage of the UK population the amount becomes a mere £25 per person, it merely takes one prescription to get to that amount, one missed on the government side and one wrongly entered on the patients side and we are there. Yet in all that, how many prescriptions did you the reader require in the last year alone? When we get to that nitty gritty level we are confronted with the task where machine learning will not offer anything but additional resources to double check every claimant and offense. Now, we should all agree that machine learning and analyses will help in many ways, yet when it comes to ‘Claimants often feel unable to go out, attempt voluntary work or enjoy time with family for fear this will be used against them‘ we are confronted with a new level of data and when we merely look at the fear of voluntary work or being with family we need to consider what we have become. So in all this we see a rightful investment into a system that in the long run will help automate all kinds of things and help us to see where governments failed their social systems, we see a system that costs hundreds of millions, to look into an optional 1% loss, which at 10% of the losses might make perfect sense. Yet these systems are flawed from the very moment they are implemented because the setting is not rational, not realistic and in the end will bring more costs than any have considered from day one. So in the setting of finding ways to justify a 2015 ‘The Tories’ £12bn of welfare cuts could come back to haunt them‘, will not merely fail, it will add a £1 billion in costs of hardware, software and resources, whilst not getting the £12 billion in workable cutbacks, where exactly was the logic in that?

So when we are looking at the George Orwell edition of edition of ‘Twenty Eighteen‘, we all laugh and think it is no great deal, but the danger is actually two fold. The first I used and taught to students which gets us the loss of choice.

The setting is that a supermarket needs to satisfy the need of the customers and the survey they have they will keep items in a category (lollies for example) that are rated ‘fantastic value for money‘ and ‘great value for money‘, or the top 25th percentile of the products, whatever is the largest. So in the setting with 5,000 responses, the issue was that the 25th percentile now also included ‘decent value for money‘. So we get a setting where an additional 35 articles were kept in stock for the lollies category. This was the setting where I showed the value of what is known as User Missing Values. There were 423 people who had no opinion on lollies, who for whatever reason never bought those articles, This led to removing them from consideration, a choice merely based on actual responses; now the same situation gave us the 4,577 people gave us that the top 25th percentile only had ‘fantastic value for money‘ and ‘great value for money‘ and within that setting 35 articles were removed from that supermarket. Here we see the danger! What about those people who really loved one of those 35 articles, yet were not interviewed? The average supermarket does not have 5,000 visitors, it has depending on the location up to a thousand a day, more important, when we add a few elements and it is no longer about supermarkets, but government institutions and in addition it is not about lollies but Fraud classification? When we are set in a category of ‘Most likely to commit Fraud‘ and ‘Very likely to commit Fraud‘, whilst those people with a job and bankers are not included into the equation? So we get a diminished setting of Fraud from the very beginning.

Hold Stop!

What did I just say? Well, there is method to my madness. Two sources, the first called Slashdot.org (no idea who they were), gave us a reference to a 2009 book called ‘Insidious: How Trusted Employees Steal Millions and Why It’s So Hard for Banks to Stop Them‘ by B. C. Krishna and Shirley Inscoe (ISBN-13: 978-0982527207). Here we see “The financial crisis appears to be exacerbating fraud by bank employees: a new survey found that 72 percent of financial institutions say that in the last 12 months they have experienced a case of data theft by one of their workers“. Now, it is important to realise that I have no idea how reliable these numbers are, yet the book was published, so there will be a political player using this at some stage. This already tumbles to academic reliability of Fraud in general, now for an actual reliable source we see KPMG, who gave us last year “KPMG survey reveals surge in fraud in Australia“, with “For the period April 2016 to September 2016, the total value of frauds rose by 16 percent to a total of $442m, from $381m in the previous six month period” we see number, yet it is based on a survey and how reliable were those giving their view? How much was assumption, unrecognised numbers and based on ‘forecasted increases‘ that were not met? That issue was clearly brought to light by the Sydney Morning Herald in 2011 (at https://www.smh.com.au/technology/piracy-are-we-being-conned-20110322-1c4cs.html), where we see: “the Australian Content Industry Group (ACIG), released new statistics to The Age, which claimed piracy was costing Australian content industries $900 million a year and 8000 jobs“, yet the issue is not merely the numbers given, the larger issue is “the report, which is just 12 pages long, is fundamentally flawed. It takes a model provided by an earlier European piracy study (which itself has been thoroughly debunked) and attempts to shoe-horn in extrapolated Australian figures that are at best highly questionable and at worst just made up“, so the claim “4.7 million Australian internet users engaged in illegal downloading and this was set to increase to 8 million by 2016. By that time, the claimed losses to piracy would jump to $5.2 billion a year and 40,000 jobs” was a joke to say the least. There we see the issue of Fraud in another light, based on a different setting, the same model was used, and that is whilst I am more and more convinced that the European model was likely to be flawed as well (a small reference to the Dutch Buma/Stemra setting of 2007-2010). So not only are the models wrong, the entire exercise gives us something that was never going to be reliable in any way shape or form (personal speculation), so in this we now have the entire Machine learning, the political setting of Fraud as well as the speculated numbers involved, and what is ‘disregarded’ as Fraud. We will end up with a scenario where we get 70% false positives (a pure rough assumption on my side) in a collective where checking those numbers will never be realistic, and the moment the parameters are ‘leaked’ the actual fraudulent people will change their settings making detection of Fraud less and less likely.

How will this fix anything other than the revenue need of those selling machine learning? So when we look back at the chapter of Modern Applications of Machine Learning we see “Deploying machine learning models in real-time opens up opportunities to tackle safety issues, security threats, and financial risk immediately. Making these decisions usually involves embedding trained machine learning models into a streaming engine“, that is actually true, yet when we also consider “review some of the key organizational, data, infrastructure, modelling, and operational and production challenges that organizations must address to successfully incorporate machine learning into their analytic strategy“, the element of data and data quality is overlooked on several levels, making the entire setting, especially in light of the piece by Jay Watts a very dangerous one. So the full title, which is intentionally did not use in the beginning ‘No wonder people on benefits live in fear. Supermarkets spy on them now‘, is set wholly on the known and almost guaranteed premise that data quality and knowing that the players in this field are slightly too happy to generalise and trivialise the issue of data quality. The moment that comes to light and the implementers are held accountable for data quality is when all those now hyping machine learning, will change their tune instantly and give us all kinds of ‘party line‘ issues that they are not responsible for. Issues that I personally expect they did not really highlight when they were all about selling that system.

Until data cleaning and data vetting gets a much higher position in the analyses ladder, we are confronted with aggregated, weighted and ‘expected likelihood‘ generalisations and those who are ‘flagged’ via such systems will live in constant fear that their shallow way of life stops because a too high paid analyst stuffed up a weighting factor, condemning a few thousand people set to be tagged for all kind of reasons, not merely because they could be optionally part of a 1% that the government is trying to clamp down on, or was that 24%? We can believe the BBC, but can we believe their sources?

And if there is even a partial doubt on the BBC data, how unreliable are the aggregated government numbers?

Did I oversimplify the issue a little?

 

 

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