Well, At Least We'll Learn A Lot From These Lunatics

"You only learn when things to wrong," my first legal boss used to remind me, and he wasn't wrong. Four years at the bar, sifting through the debris of old deals, taught me a lot about negotiating new ones. Observing the slow decline of Reuters in the mid-90s was another tutorial, as was enduring the tech boom and bust, working through a bunch of old loans that GE had bought a decade later and then launching Zopa into the teeth of the credit boom and ensuing financial crisis. Advising on the odd regulatory hiccup since then has reinforced the concept, which also helps with morale, of course. And with so much 'going wrong' on such a grand scale right now, maintaining morale is more important than ever. So what do we stand to learn? Well, I reckon the top 3 lessons of 2020 will be: the importance of facts, that the worst is yet to come and we need to figure out how to preserve government know how for when it can be used again...

You Can't Fight The Facts: The Truth Will Out...

The current crop of populist leaders have all seized power by targeting nationalistic lies at the gullible. The marriage of patriotism and intelligence has ended in divorce. Whether it's #ScottyFromMarketing downunder, Trump, Modi, Maduro, Bolsinaro, Erdogan, Orban or our very own #BrexitBoris, they've all avoided letting the truth get in the way of an emotive story. 

It's not unusual for politicians to lie, misinform and gaslight their voters. What is unusual is the sheer scale of the latest political mendacity. 

Yet, the bigger the lie, the harder it is to control or suppress the truth, and one by one these fascistic fantasists are finding themselves facing hot blasts of unadulterated fact. Eventually their lies will be exposed for all to see, and any majority support will melt away.

But don't hold your breath...

Nationalist Economies Will Get Much Worse Before They Get Better...

The 'quid pro quo' (to borrow a well-worn phrase from 2019) for this love affair with lies is that nationalist governments are not focused on their societies' genuine problems, let alone solving the root causes of those problems. Nationalism involves denying the real problems and blaming others for imaginary ones. This creates new problems while the country's infrastructure and governing processes decay. This has been a constant feature of the Trump regime, in particular (as Michael Lewis has observed), but is perhaps best encapsulated by Brexit. 

Reversing the decay will require an electorate to fall out of love with the lies and support reform. That would give politicians permission to identify and define the actual problems, prioritise the most pressing ones, burrow into the data to identify the root causes and the improvements that would provide the most bang for the buck, and put in place the warning systems to alert us to future failings. The changes would need to be communicated carefully, in the face of inevitable resistance by the rump of nationalist disciples. But that process would take a looooong time, since anyone who understands the issues today will have lost interest, retired or found a new role by the time things get bad enough for anyone to want to fix them, let alone muster widespread support for doing so.  

Compare that process with Dum Cummings latest blog post (ironically entitled 'Two hands are a lot' since he couldn't find his arse with both of them). His undoubted success as a right wing, nationalist, populist political strategist will be dwarfed by his failure as a government strategist. But the Johnson government will have to be seen to fail before anyone else will get a mandate to undertake the huge job of reversing the decline.

This raises the problem of how to avoid losing government know how in the meantime... 

Preserving Government Knowledge

How to manage the transition from one manager to another (succession-planning) is a major issue for everyone, especially large organisations and government departments. Michael Lewis has revealed that it was not something that ever concerned Donald Trump, and there is plenty of evidence from British civil servants that it was not a high priority for Cameron or May, and it is certainly lost on Boris Johnson. Many senior civil servants have left government, often simply to retire. Their knowledge and experience will have been lost without adequate transition arrangements. Meanwhile, the ministerial leadership, policies and/or performance of departments like the Home Office, Health, Work and Pensions, Prisons and Transport seem on the slide from bad to worse.

Similarly, areas of policy and funding that the UK agreed to centralise within the EU, and the framework on which Britain trades with the EU and other countries under EU free trade deals will be lost. Britain doesn't have any civil servants duplicating tasks that were performed at EU level (like funding the EU's least economically developed regions, 6 of which are in the UK); and the EU trade deals cannot be replicated outside the EU (and certainly not within the 11 months May and Johnson negotiated).

In the microcosm of a large government department poorly overseen by ignorant ministers and deserted by seasoned officials, or a region dependent on development knowledge and funding, this represents a massive dislocation. To put this in context, Venezuela's institutions collapsed in under 20 years, and the Soviet Union fell apart in 6 years. Hell, it only took 40 years for Britain's entire economy to collapse after the Romans left. 

The history of the British civil service is littered with experiments on how best to equip the nation's institutions with the right knowledge, expertise and experience. It does not make encouraging reading, but if Britain's economic history is anything to go by, it seems likely to take at least 10 years to turn things around, if there's the will to begin the process and work at it...

Conclusion

If we only learn when things go wrong, we're going to learn an awful lot!  

How To Enjoy Boris Johnson's Amazing* Brexit ShitShow™ - Season 5

Boris Biosuit - flameproof for winters in Oz or Brazil

Peering through the smoke billowing out of Scotty Morrison's Amazing* Coalfired CookOut™ I can dimly make out the shape and outline of someone or something that should make 2020 a truly fabulous year for the little Englander. But what about the rest of you? Here are my top tips, in no particular order...

Lock the doors and only deal with private couriers

The downside to travelling from all over Britain to participate in million-people marches is that Duminic Cummings knows what you look like and where you live. Moving around outside your house is ill-advised. Dim Martin controls the high streets through his network of pubs, and soon your postman will be replaced by a robot distributing IEDs manufactured by Britain First.

Robot delivering an IED made by Britain First

So make the most of house arrest by adopting industrial security measures and, say, redecorating the bedroom like your favourite hotel suite. Get sand and a plastic coconut palm for the living room. Have family members choose a different name, accent and style of beachwear each week to simulate your desired foreign resort experience.

And remember, use only private couriers to receive deliveries that have been specifically ordered by you personally, dig a deep trench just outside the front door and never accept a delivery for the nice neighbour(s). Those times are over.

Hedge Rising Food Costs by Speculating On All Black Tickets

Kiwi sheep are agents of the NZRU

The ride is over for farmers, particularly those who breed sheep. Once the British sheep have all been burnt or buried, agricultural subsidies will only go to landlords developing caravan parks for Tory voters. This will also be great news for Kiwi shepherds, who will dominate the British lamb market. All New Zealand sheep are secret agents of the New Zealand Rugby Union, so you can easily hedge your exposure to rising food costs by pre-purchasing tickets to All Black matches and flogging them on secondary ticketing sites or agreeing profit-share deals with local touts.

Commit to, Say, Building a Zip-line From Dover To France

Last mile of Dover to Paris Zip Line

As I've explained before, the key to success, wealth and happiness in this neo-post-truth world lies not in a hard day's work for a fair day's pay. No. In 2020, you will only be able to finance your house-bound fantasies by leveraging the 'bandwagon' and 'snowball' effects. To do this you must concoct a hugely ambitious, unique, deceptively simple, vaguely plausible scheme that is not actually achievable or demonstrable but is nevertheless the kind of thing in which your victims investors can have faith.  Boris Johnson himself has succeeded with commitments to an airport in the middle of the Thames, a 'garden bridge' in London, Brexit (of course) and, most recently, a bridge from Scotland to Northern Ireland - or vice versa, depending on your point of view - whereas the best part about a zip-line from the UK to France is that it's one-way.

* causing great surprise or wonder; astonishing.

The Age Of Conspicuous Thrift Returns With A Vengeance

A decade ago, amidst the echoes of the global financial crisis, we were talking about a new Age of Conspicuous Thrift and the 'counter-Veblen effect' when "preferences for goods increase as their price falls, over and above the traditional supply and demand effect, due to a conspicuous thrift amongst some consumers." A week ago, it was pointed out there are more food banks than McDonalds outlets in the UK. And a survey by Mazuma Mobile, the mobile phone recycling firm, has found that 85% of Brits regularly purchase pre-owned products instead of new, from books and handbags, to computers and mobile phones. In fact, 45% plan on giving pre-owned items as gifts this Christmas and 52% would happily receive them.

Over the past decade we've had very low interest rates, plenty of cheap credit, £36bn in PPI compensation (though some is yet to be paid), strong employment and some feeble growth, yet UK household debt remains at record levels and the UK economy is shrinking. That means 2020 is set to be a very hard year, which explains why so many Brits are battening down the hatches - particularly those in the early stages of their careers. Previously, those aged 25 – 34 were most likely to purchased pre-owned items as gifts, but they've been overtaken by 18 – 24 year olds. 

Londoners (60%) and the Northern Irish (66%) are happiest to receive pre-owned items as presents, yet even the lowest figure is still 43% (in the North West), which means conspicuous thrift, rather than conspicuous consumption has really taken hold in the UK. 

Craig Smith from Mazuma Mobile says: 

“Attitudes are changing towards pre-owned shopping as people become savvier with their cash...  many of us are simply happy to receive something we like, especially when it’s better for the environment and our wallets. In fact, over a third of those we surveyed (37%) said they would consider having an entirely pre-owned Christmas this year, including gifts and decorations!"

The top 10 pre-owned items purchased among the 2,000 people surveyed by Mazuma Mobile via OnePoll in October 2019 were:

1. Books (68%) 

2. Cars (62%) 

3. Furniture (49%) 

4. Clothes (43%) 

5. Jewellery (37%) 

6. Musical instruments (35%) 

7. Mobile phones (34%) 

8. Televisions (32%) 

9. Handbags (29%) 

10. Computers (29%) 

It's Time To Focus On Johnson's AltRight And Russian Links

With Boris Johnson desperate for a quick General Election, it's important to be aware that the Trump Presidency is simultaneously unravelling over links with Dmitry Firtash, securities fraudster Lev Parnas and the mob and the British authorities are reported to have their own concerns about some of Johnson's relationships. The legality of these need to be clarified if we are to have any chance of avoiding another election featuring fake news and dodgy funding, though Facebook isn't proving helpful either...

Johnson's links with Trump's AltRight pals include Steve Bannon and the Breitbart Boys - like Trump's speechwriter and rally fluffer, Stephen Miller. Bannon has advised Boris Johnson, among other European right wing political party leaders. And Miller attended the Innotech Summit hosted by Johnson's pole-dancing "friend" Jennifer Arcuri, whose links with Johnson are also the subject of investigation. Matthew Elliott chaired Johnson's Vote Leave campaign, and brings an array of AltRight contacts and funding links.

Johnson has also been linked to Russian tycoons Lebedev and Temerko, while Tory Brexidiots and Vote Leave's campaign director and Johnson adviser, Dom Dum Cummings, have long-standing ties to Dmitry Firtash - the same Ukrainian 'oligarch' at the heart of Trump's attempt to lift sanctions imposed on Russia for its invasion of Ukraine and for the Ukrainians to publicly investigate his 2020 campaign opponent - for which Trump is now being impeached.

That's also important because Vanity Fair recently reminded us that:

"...Trump called Johnson on July 26, two days after the new U.K. prime minister took office, apparently to ask BoJo for help compiling evidence to undermine the investigation into his campaign‘s ties to Russia. For those of you keeping up at home, that’s just one day after Trump spoke to Ukraine President Volodymyr Zelensky and asked for “a favor...”

Note that US Republicans consider that donations arranged by Trump henchman Lev Parnas are so dodgy that they were simply returned.

All of this also makes you wonder again where Arron Banks' £8m donation to the Leave cause might have ultimately came from... even if it was not unlawful.

And with Fakebook determined not to police political advertising and yet to admit any wrongdoing to the UK's Information Commissioner over Cambridge Analytica, we could well be in for another dodgy British election.

Do You Know You Are Using And/Or Exposed To Artificial Intelligence?

Source: datamation

Amid the news that the Department of Work and Pensions is using artificial intelligence to decide benefits claims, a third of UK financial firms recently told the Bank of England and Financial Conduct Authority they were not using 'machine learning' (a subset of AI) in their businesses. That's pretty concerning, given the kind of AI we all use without realising and the fact that anyone wrongly denied benefits will need short term finance. That response also begs the question whether those firms know how their many stakeholders are using AI (whether unwittingly or not). If their suppliers, intermediaries, customers, investors, competitors and the government are using AI, then how does that affect their own strengths, weaknesses, opportunities and threats? And how does that in turn affect their stakeholders? No city on Earth is ready for the disruptive effects of artificial intelligence. Also worrying is the finding that smaller, fintech firms seem to believe that machine learning is no use to them. And given the problems with AI explained below, it's important for everyone to consider whether and how they rely on or are otherwise exposed to the two thirds of financial firms who are actually using AI... hyping the benefits without understanding the shortcomings will harm our trust AI where it could be helpful.

What is AI?

The term "AI" embraces a collection of technologies and applications, with machine learning usually featuring at some point:

• narrow artificial intelligence
• machine learning
• artificial neural networks 
• deep learning networks 
• automation
• robotics
• autonomous vehicles, aircraft and vessels
• image and facial recognition
• speech and acoustic recognition
• personalisation
• Big Data analytics

At the recent SCL event I chaired in Dublin, Professor Barry O'Sullivan explained that AI technologies themselves may be complex, but the concepts are simple. In essence, machine learning differs from traditional computer programming in that:

• traditionally, we load a software application and data into a computer, and run the data through the application to produce a result (e.g. how much I spent on coffee last year);

• while machine learning involves feeding the data and desired outputs into one or more computers or computing networks that are designed to write the programme (e.g. you feed in data on crimes/criminals and the output of whether those people re-offended, with the object of producing a programme that will predict whether a given person will re-offend). In this sense, data is used to ‘train’ the computer to write and adapt the programme, which constitutes the "artificial intelligence".

What is AI used for?

AI is used for:

1. Clustering: putting items of data into new groups (discovering patterns);
2. Classifying: putting a new observation into a pre-defined category based on a set of 'training data'; or
3. Predicting: assessing relationships among many factors to assess risk or potential relating to particular conditions (e.g. creditworthiness).

The Challenges with AI

The primary concerns about AI relate to:

1. cost/benefit - $50m in electricity to teach an AI to beat a human being at Go, hundreds of attempts to get a robot to do a backflip, but it can't open a door;
2. dependence on data quantity, quality, timeliness and availability;
3. lack of  understanding - AI is better at some tasks than humans (narrow AI) but general AI (same as humans) and superintelligence (better than humans at everything) are the stuff of science fiction. The AI that can predict 79% of European Court judgments doesn't know any law, it just counts how often words appear alone, in pairs or fours;
4. inaccuracy - no AI is 100% accurate;
5. lack of explainability - machine learning involves the computer adapting the programme in response to data, and it might react differently to the same data added later, based on what it has 'learned' in the meantime; 
6. the inability to remove both selection bias and prediction bias - adding a calibration layer to adjust the mean prediction only fixes the symptoms, not the cause, and makes the system dependent on both the prediction bias and calibration layer remaining up to date/aligned over time; 
7. the challenges associated with the reliability of evidence and how to resolve disputes arising from its use; and
8. there's a long list of legal issues, but lawyers aren't typically engaged in development and deployment.

This means the use of AI cannot be ignored. We have to be careful to understand the shortcomings and avoid hyping the benefits if we are to ensure trust in AI. That means challenging its use where the consequences of selection bias or false positives/negatives are fatal or otherwise unacceptable, such as denying fundamental rights or compensation for loss.

Instead, artificial neural networks and deep learning are better used to automate decision-making where "the level of accuracy only needs to be "tolerable" for commercial parties [who are] interested only in the financial consequences... than for individuals concerned with issues touching on fundamental rights."

Being realistic about AI and its shortcomings also has implications for how it is regulated. Rather than risk an effective ban on AI by regulating it according to the hype, regulation should instead focus on certifying AI's development and transparency in ways that enable us to understand its shortcomings to aid in our decision about where it can be developed and deployed appropriately.