Il faut souffrir. Supporting any football club involves an emotional rollercoaster ride. With some clubs the highs are higher. With others the lows are lower. Sometimes, as in the case of my team tangibly so. Sometimes the lows are just dumb frustrations. Like the fact that, this season, my team is beating all the top clubs and losing to all the bottom ones. To the point where, because we were scheduled to play a relegation-zone team today, I was 100% confident that we would lose.

Part of that confidence, these days comes from running our PanSensic software. Every week, our manager gives a press conference, and I’ve started to put the narrative into the software. This is what it told me this week:

The important thing to look out for is the high ‘Innocence’ score in the Archetypes dial. This score is only high during the press conferences prior to playing opponents at the bottom of the league. When we’re about to play the top clubs ‘Innocence’ is almost zero and the ‘Pilgrim’ and ‘Warrior’ scores go off the end of the scale.

Against the big clubs, the manager seems to know its going to be a tough day. When we’re about to play one of the bottom clubs the message usually involves phrases like ‘no complacency’ and – this week’s humdinger, ‘we are not naïve enough to think it will be that simple’.

I don’t think anyone from the Club is ever likely to read my insignificant nonsense, but I kind of wish they would. Maybe then the manager would realise that the human brain doesn’t process words like ‘no’ and ‘not’. When we tell someone not to think of pink elephants, so says the cliché, its almost impossible to avoid thinking about pink elephants. We’re not wired to process negatives. So, ‘no complacency’ is heard (by the team as well as everyone else) as ‘complacency’. Likewise, ‘not naïve’ is heard as ‘naïve’.

Then there’s the killer word ‘that’. It’s the word that reveals the manager also isn’t processing the negative. He’s trying to tell everyone not to be complacent and to not think the game will be simple, but he’s already convinced himself that its okay to be complacent and that the game will be simple. Put simply, we lost today because of what he said on Thursday.

During the eighties and nineties it was rare to visit a client where there weren’t Dilbert cartoons all over the walls. The joke was funny then. Today I see less of them on the walls. I don’t know whether that’s because management have banned them, or because Baby-Boomer and Generation X workers don’t like being reminded that they’re still living in the same Kafka-esque nightmare today that they were thirty years ago. Either way, you sometimes still have to love a Dilbert cartoon.

This one is a particular favourite. I’m in the contradiction business, so I guess I have to love it. Even better, this strip describes a great trilemma. A lot like the ‘cost, quality, schedule’ problem and the ‘which two would you like?’ question, Alice’s response to the Communication, Integrity and Teamwork triad defined by management is a classic ‘two out of three’ solution.

If Alice had used TRIZ, she might have re-framed the problem to look something like this:

The Contradiction ‘Bubble Map’ is in effect a way of drawing trilemmas. The physical contradiction – ‘I should be honest and I should not be honest’ – being the part of the trilemma Alice was unable to solve.

If we map the problem onto the Contradiction Matrix we ought to be able to tap into the solutions used by others to solve the whole problem. When we do this, the ‘three-out-of-three’ solution to the Communication, Integrity and Teamwork trilemma is likely to make use of these Inventive Principles:

   7 – Nested Doll

 13 – The Other Way Around

   2 – Taking Out

 17 – Another Dimension

All of which, suggests that Dilbert and Wally in this case – rarely – are the ones that hold the key to the ultimate solution. Damn, Scott Adams is good.

According to our research last year’s US election was pretty much won on the basis of nine syllables. Whatever your politics and whatever you think about the morality of spending money to influence how people vote, I think its fair to say that a certain level of genius had been at play.

The nine syllables came in the form of three three-syllable phrases that Donald Trump managed to use in nearly all of his campaign speeches. In many they were turned into crowd-chants.

Drain The Swamp. Build The Wall. Lock Her Up.

They say a picture speaks a thousand words. Here were nine words that each spoke a thousand pictures. Ah, sweet Resonance.

Whoever was responsible for the genius, clearly doesn’t seem to be in place any more.

The person that came up with Rocket Man got the three syllables part right but, right from the get-go it sounded more like a pretty cool compliment than the belittling jibe it was intended to be. Recognising the mistake, some bureaucrat quickly tried to modify the phrase to ‘Little Rocket Man’. This didn’t help. Ten more minutes of thinking and they might just have come up with ‘Rocket Boy’. That would have done the intended job far more effectively. Albeit at the possible risk of nuclear Armageddon.

Things didn’t go much better this week, when Drain The Swamp got down-rated to ‘Spread the Swamp’. When the bureaucrats realised those words didn’t work, they switched to ‘Move The Swamp’. That was vaguely less clumsy, but still utterly missed the killer instinct of the original. And the ultimate election winning aphorism was now turned into an embarrassing climb-down.

Amazing how smart people can work out the DNA of election success and then ten minutes later completely screw things up.

Single syllable words. Three word phrases. Emotive verb. Thousand-pictures object. Simple.

I can feel a new SI mission statement coming on…

One of Facebook’s earliest executives has said the social network is “destroying how society works” and that he feels “tremendous guilt” about his work. Chamath Palihapitiya, who joined Facebook in 2007, accused it of “programming” its users and said he no longer uses the website or allows his children to access it. “It literally is at a point now where I think we have created tools that are ripping apart the social fabric of how society works,” he told an audience at Stanford University. “We are in a really bad state of affairs right now in my opinion, it is eroding the core foundations of how people behave by and between each other.”

Daily Telegraph, front page, 12 December 2017

Society makes progress when contradictions get identified and solved. The bigger the contradiction, the greater the progress potential. In this context, when two goliaths of the modern business world find themselves embroiled in a very public argument we perhaps get to see a really big contradiction.

Zuckerberg says that Artificial Intelligence is good; Musk says it is bad. Who’s right and who’s wrong? Or are they both right? Or both wrong?

What we do know for certain is their argument centres around a contradiction: we want AI and we don’t want AI. AI will eventually help society to solve an awful lot of its current problems; AI today is probably doing more harm than good. And therein lies a clue. Two clues. Eventually and today.

Once we understand what we’re doing, then we open up the possibility for AI to be our best friend rather than our worst enemy. We solve the contradiction, in other words, in time (eventually…) and on condition (…once we know what we’re doing).

What Elon Musk seems to understand that Mark Zuckerberg clearly doesn’t is that the vast majority of today’s AI solutions – and especially those being deployed by Facebook – have not been coded with an understanding of complex systems. Once we accept that a system is complex, the only sensible hope of affecting it in a positive manner is to understand behaviours within the system at a First Principles level. Elon Musk represents the epitome of First Principles thinking. Mark Zuckerberg is, as far as I can tell, the precise opposite. He naively believes that by connecting everyone you create one big happy family. The reality is that when people are motivated by Autonomy, Belonging, Competence and Meaning (the First Principles of human behaviour), any attempt to connect everyone ends up connecting no-one. For every tribe we connect to become a member of, a dozen other tribes that oppose what we think appear.

Zuckerberg, in other words, might be right about AI in the long term, but right now, Musk knows that Zuckerberg is the very person preventing AI from going beyond its current manifest dysfunctions.

There aren’t too many advantages to being an aerodynamicist, but one of them is not having to sweep up fallen leaves in the Autumn. Even when we had a garden the size of a postage stamp, it was a pain. Now we have two-acres and look after a dozen or so hundred-and-fifty-year-old trees, the task has, in previous years, been, how shall I put it, onerous.

Last Autumn, I figured it was time to apply a little TRIZ to the problem: how could I get the leaves to collect themselves?

It didn’t seem too likely, but on the other hand a quick search for free resources highlighted a couple of big ones. Firstly, the prevailing wind direction served to blow all the leaves off the trees and towards the house. Secondly, the shape of the house lends itself to creating an aerodynamic vortex that I could use to coax the leaves into a ‘trap’:

I’d noticed some kind of naturally propensity for leaves to find themselves entering the vortex in previous Autumns, the problem was it didn’t happen as fast as I’d like and when it did happen, the leaves tended to accumulate in front of the door. Which in turn meant that when the door was opened, the leaves tended to migrate into the house.

So what to do? How to increase the vortex flow rate? And how to subtly re-direct the leaves so they entered the vortex but stayed away from the door?

Another search for resources…

Realisation number one: changing the places the cars are parked changes the vortex entry. Park the cars differently and the vortex trap works better.

Realisation number two: outdoor plants. Judicious placing of plant pots lowest pressure point of the vortex and the leaves accumulate around the pots rather than the door.

Add a couple of days of experimental adjustment of car and pot placement and hey presto, we have self-collecting leaves.

Now all I need to do is wait a couple of days for the new mountain to appear, rake them into the wheelbarrow and take them down to the compost bin, then wait another couple of days.

A multiple hour chore now becomes a 20-minute game. Autumn? My new favourite season.

It feels like a long time ago when I wrote the Systematic (Software) Innovation book. One of the main, albeit inadvertent, themes of the book was to highlight a dichotomy that, now we all live in the first stirrings of a Big Data tsunami, seems to be getting worse rather than better: software engineers and architects, the book described, are simultaneously the people most likely to determine how society evolves in the coming decades, and also the people least well qualified to take on such responsibilities.

Last week I came across an online article bearing the ominous title, ‘Machine Learning Is Racist Because The Internet is Racist’. If I ever needed a way to exemplify the dichotomy, I think this might just make it into my Top Five.

The article represents the sort of complete abdication of responsibility now becoming quite typical of the ICT industry. An industry that still – two months after it hit the media – hasn’t done anything to tackle the problem that recommendation algorithms now teach people how to make bombs. ‘People who bought this product also bought…’ and hey presto, everyone knows how to make their very own explosive device. This the same industry that – they tell the media – can’t be blamed for providing a conduit for terrorist communications.

At the crux of the issue here is Artificial Intelligence. Plenty of the former, not so much of the latter it seems. There’s perhaps a telling irony in the fact that the software geeks effectively try to tell us all that their AI algorithms can’t be blamed for mimicking the content of what they find on the internet. The irony being that if they are able to declare a significant enough amount of internet content is ‘racist’, how come they weren’t able to create a racist-comment-detecting algorithm and thus exclude such trash from the data they use to train their algorithms?

Sure, the Internet might be ‘racist’ right now. But in no way does that give AI professionals a ‘get out of jail free’ card excuse for creating racist AI. It’s not rocket science.

Or maybe it is. Not in the offending blame-dodging article under examination here, but I’ve heard from other ICT ‘thought leaders’ that any racism (or any other kind of ‘ism) detection algorithm cannot be their responsibility, because who are they to decide what is and what is not racist? Only the politicians can tell us what the rules are, they claim. My confidence that our politicians can help solve the problem is frankly quite low. Not so much because the problem requires any rocket-science per se, but rather that it requires someone to come at the problem with a contradiction-solving mindset.

I suspect every person on the planet is guilty of at least half a dozen ‘ism-crimes’ during any given day. I can be confident of this because spending a couple of hours watching any trending ‘controversial’ topic on Twitter quickly reveals the rapid appearance of a quite staggeringly broad spectrum of responses. Every single respondent sits somewhere along an -ism spectrum. From one day to the next, their position might shift, as might those of every other person wanting to join the conversation. The fact this spectrum exists and that it might be dynamic, however, does not mean the job of the AI algorithms (or their programmers) is to define ‘the’ point along the spectrum at which ‘on average’ the boundary between racist and not-racist sits. Making decisions based on any kind of average is a pretty dumb thing to do in any kind of complex environment.

The only meaningful design response in this kind of dynamic spectrum situation is to solve the contradiction between the two ends of the spectrum: make people at each extreme ‘happy’ and everyone in the middle is also happy. If every person on the planet draws the racist/not-racist boundary somewhere different, the AI needs to take into account that personal boundary when delivering content to that individual.

Solve one contradiction, of course, and the next one inherently appears in its wake. Every person on the planet is entitled to hold their own opinion about where the racist/non-racist (or sexist/non-sexist, etc) boundary exists for them personally, but by the same token they are absolutely not entitled to hold their own truths. Truth-wise, then, the new problem becomes whether it is ever possible to objectively determine what racism is and is not? This too would appear to require a dynamic way of thinking. I’ve heard several comedians making jokes around the phrases ‘different times’. What was apparently ‘acceptable’ in the 1970s, clearly appears not to be today. Whether that makes it appropriate to judge historical ‘misdemeanors’ according to today’s norms is yet another contradiction to be solved.

But again, it is ‘merely’ a contradiction. Systematic (Software) Innovation was intended to help software engineers – the future rulers of society, right? – to identify and resolve such conflicts. If any of them was in any way smart, they’d be writing AI algorithms that automatically identified society’s conflicts and conundrums. If they were smarter still, they’d be writing self-evolving code that also helped solved these conflicts and then automatically identified the next contradictions. More fool them if they haven’t started the journey yet. The slower they are to the game, the further ahead PanSensic gets. AI gets intelligent by asking intelligent questions, and intelligent questions start by measuring what’s important rather than what’s easy to measure. It’s very easy to measure racism. Or sexism. Or any other kind of ism. (We know because we do it every day.) Real intelligence is knowing why we’re measuring it. And what contradictions we’re intending to solve when we do find it. Perhaps our next PanSensic lens should be one for detecting software engineers abdicating their moral and ethical responsibility to think before they code. I don’t think that’s rocket science either.

Everyone is somewhere on an Autistic Spectrum. Some people will stay fixed where they are, others will move. This is neither a good nor a bad thing, merely a pair of statements of fact. Vive la difference and all that.

Where a person sits on that Spectrum only starts to become relevant when we think about specific contexts. If I’m looking at a person taking on the task of devising global strategy on social media and the role of artificial intelligence within that strategy, for example, I think I’d prefer to have someone closer to the non-Autistic end of the spectrum than someone at the other end. Key word in that sentence being ‘social’. If I’m going into the social business I think having some social empathy skills would be useful.

The fact that Mark Zuckerberg in all likelihood doesn’t nor can’t sit on this end of the spectrum means that the moment he opens his mouth, I’m already doubting the validity of what he’s about to say. Especially now he’s been successful and is surrounded by people agreeing with every word he says irrespective of its validity. This inferno of agreement-without-understanding ought to ring a number of alarm bells, irrespective of how right the words that emerge from his mouth might eventually turn out to be.

Before that, however, because what I see in Zuckerberg is a severe lack of understanding of the majority of (first principle lead) things in life, I can be pretty certain, when he tells me that Facebook is in the business of ‘building community and bringing the world closer together’, that the sentiment is both naïve and has no way whatsoever of translating into reality. Mission without theory is far worse than theory without mission. Just ask any ego-driven despot throughout the annals of history.

I try and avoid Twitter these days. And I especially try and avoid the trending hit parade on the left-hand side of my screen. The other morning, when I saw that comedian, James Corden was trending with 30 plus thousand Tweets, my first thought was that he must be dead. Rather, it turns out, he happened to make a couple of perhaps ill-timed jokes about Harvey Weinstein. The world – even, it seems, a massive proportion who clearly hadn’t heard the jokes – within a matter of minutes had divided itself into two. One camp who thought that Corden was worse than Hitler and should be deported, and the other that thought Corden is quite a nice guy and leave him alone… oh, and, by the way, didn’t we used to have a thing called free-speech in this country?

I don’t know if Zuckerberg was watching, but I imagine him glowing with pride at the two brand new ‘communities’ his Social Media world has just – in a veritable instant – created. We now have a tribe of Corden-ites and a 180degree opposite tribe of Corden-lynchers demanding he be – word of the year – ‘deplatformed’.

Social Media in its current clueless evolutionary stage is indeed good at creating communities. The problem is there is no such thing as the plural of community. Today’s Social Media is inadvertently creating a million and one ‘us’s. But at the same time, it is also creating double that number of ‘them’s. Everyone shouting louder and louder and listening less and less. We’re only allowed to listen to the people in our tribe because that’s who the algorithms tell us to listen to, and, if we step out of line, we risk being ex-communicated from our tribe and thrown to the deplatform-lions.

Extrapolate that forward and pretty soon we’ll all need to be wearing a thousand lapel badges defining which tribes we belong to. And then being shot when we accidentally step into a store wearing the wrong colour socks.

Every trend direction comes to an end eventually, of course. Let’s hope there’s still enough of us alive to see that day. The day we see Mark Zuckerberg sent to a distant corner of the remedial psychology classroom and told to wear a dunce cap for the next decade. And the day, also, where the contradiction-solvers get the community-building due they deserve.

It’s political conference season here in the UK. Which is usually a good reason for me to be out of the country. This year I got it wrong, so, sadly, much as I have tried to stay away from the media commentary, some of it has still leaked to my book-writing, patent-drafting cocoon.

This week it has been the turn of the Conservative Party, and today the turn of Theresa May to give her big speech.

Full disclosure. I’m not a fan of the party or the person. Then again, if you’ve read any of my previous posts, you’ll know that I’m not a fan of any of the other political parties either. The one I want to vote for doesn’t exist yet.

Not being a fan of Theresa May might in theory have meant that I’d be rubbing my hands with glee at the ‘biggest disaster ever’ diagnosis delivered by each and every single media commentator.

On the other hand, when I look at the three reasons for the ‘disaster’ conclusion, I see the prank of a fourth-rate comedian, some dodgy scenery and a tickly cough. Two of which had nothing to do with the Prime Minister, and none of which, as far as I can tell had any relevance whatsoever to the content of the speech.

This seemed a tad, how shall I put it, ‘harsh’ to me. But worse than that, when I watched the two most reliable television media organisations devote 95% of their reporting to the three reasons and 5% to the content, it felt like I was witnessing something far more sinister. It felt distinctly like I was witnessing the death of meaning.

My take-away thought is that the words that come out of politicians’ mouths have now become so utterly meaningless, it really doesn’t matter what they say any more. Much more important is the orange-ness of their skin, the madness of their hair, or how well they suck a throat sweet. Somehow the media appear to have interpreted that the audience isn’t listening, doesn’t understand, and moreover doesn’t want to understand what is being said, and they have as a consequence placed journalism into a tailspin-like race to see who can lower the lowest common denominator fastest.

Is today the death of meaning? Or, in the world of politics at least, did it already die some time ago? If I had to guess, I’d say it received a mortal wound during the Brexit referendum, and breathed its last breathe about halfway through the US presidential election last year. All I really saw from the British media today was a final sprinkling of the ashes.

I made Joshua Greene’s book ‘Moral Tribes’ book of the month in our ezine this month (http://systematic-innovation.com/current-e-zine.html), but I’m still more than a bit freaked out by the book’s assertion that ‘happiness’ is the ultimate measure of individual and societal well-being. Greene is by no means alone in the assertion: enormous chunks of the self-help literature seem to point towards the same definition of the meaning of life. Other thinkers have different opinions, the most coherent of which seems to be the Kierkegaard/Frankl concept of ‘will to meaning’. Beyond that, there seems to be a lot of confusion – meaning is a necessary (but insufficient) condition for happiness; happiness is an outcome following attainment of meaning; happiness and meaning are opposite ends of a spectrum; happiness is meaning. The philosophers of the world have a lot to answer for. Of all the domains of human endeavour right now, they seem to be the ones that are the most lost.

Being lost is, in theory at least, an important – some might say ‘necessary’ – precursor to breakthrough. But only if you’re asking the right questions. And that’s where it seems to why the world of academic philosophy is so adrift. No-one seems to know what the right questions are. Or, maybe, it’s that there are too many questions and no-one can make sense of them?

From a Hegelian or TRIZ perspective, the ‘right’ questions almost always involve the definition of contradictions.

Taken from this perspective as soon as we hear questions like ‘is it happiness or meaning that defines a successful life?’ we know the answer is both. There is no such thing as either/or when it comes to defining good questions.

Meaning and happiness are two essential components of a ‘good life’. They are also largely independent of one another. Which in turn means that it’s very possible to achieve one without affecting the other. Which then means I can draw happiness and meaning as two orthogonal axes of a matrix. Something like this:

As with any good 2×2 matrix, the top right-hand quadrant define the ideal state in which we achieve the best of both of the parameters defining the two axes. It’s also the corner where the conflicts and contradictions between the two parameters are successfully resolved.

It’s also, too, useful with 2×2 matrices to be able to find words to characterise each of the quadrants. I’m not sure I’ve got the labels totally right in all four case, but I’m pretty certain the ‘eudaimonism’ word in the top right-hand is the right word. The fact that I’d never heard of it before tells me two things. The first of which is that not many people have thought about the need for a word to define a ‘happy and meaningful’ situation. As it happens the word comes from Aristotle and the Ancient Greeks. Which is perhaps ironical given their Socrates-driven insistence on the kind of either-or thinking that got us all into this mess in the first place. But this then leads on to the second thought: has anyone ever thought about explicitly formulating (and, better yet, resolving) the contradiction between happiness and meaning?

As far as I can see, the answer to that second question is ‘barely’. Which to my mind seems to offer up a big reason for the mass of philosophical confusion. Enter TRIZ.

Happiness and Meaningful are largely independent and fundamentally in conflict with one another. Key word ‘fundamentally’. The reason they’re in conflict is because of contradictions between the now and the future, taking and giving, or staying in our comfort-zones and escaping from those comfort-zones. Something like this:

They say a problem well defined is a problem half-solved. Solutions may still take a while, but I feel a lot more confident than I was ploughing through the philosophers’ confused ramblings, that this picture offers up a much better problem to work on.

Scene: Virgin Atlantic Check-in desk, Gatwick

Sunday morning

Darrell waits in the queue, then when it’s his turn, walks up to the counter, smiling

Darrell (handing over passport): Hi, I’m checked-in already, just need to print out my boarding pass.

Counter: Thanks. Flying to Orlando?

Darrell: Mmm, yes. Thanks.

Counter: Any bags to check-in?

Darrell (turning head to the left and right to indicate the bags hanging off the respective shoulder): No, thanks. I just have my laptop and my overnight bag.

Counter (not smiling any more): You’ll need to check one of them in.

Darrell (looking puzzled): Really? Is there a problem?

Counter: Let me weigh them. Put the bag on the scale for me.

Darrell (places overnight bag on the scale): Okay?

Counter: And the laptop bag.

Darrell (places the laptop bag on top of the overnight): Okay?

Counter: Sorry. You’re only allowed 10kg.

Darrell (looks over at the scale reading, adopts puzzled expression): It says 10.2kg.

Counter (switching on laser eye-beam stare): You’re only allowed 10kg.

Allow me to stop the scene at that point. In my head I am about to explode. In practice, I know that venting frustration in these situations is rarely a good idea. Respond immediately and my limbic brain is doing all the talking; pause for a second and my more rational pre-frontal cortex has a chance to survey the situation.

Fortunately, I managed to get past the limbic moment. Spooling the clock forward a couple of hours to the point I was boarding the plane and realised there were over 200 empty seats, I think that if I’d known this while I was at the counter, it would have been much more difficult to stop my emotions from doing the talking. With 200 empty seats, every passenger on board could be carrying an anvil and the plane still wouldn’t be over-weight.

Getting past the ‘limbic moment’ is necessary in order to create a win-win solution. I’m pretty certain I didn’t want to check my bag in and thus ensure a 20 minute wait at baggage claim in Orlando in order to retrieve it, and I’m also pretty certain it was better for all the Virgin baggage handlers if I handled my own bag. A limbic reaction would have almost guaranteed a lose-lose outcome.

Once my PFC was in control of the situation, I knew I was in a classic ABC-M emotion game. And that meant if I was to be allowed to keep my bags it was necessary for me to get the Counter Agent feeling like her Autonomy, Belonging and Competence were heading in the right direction.

The ‘Belonging’ aspect seemed like it was the biggest challenge: she was in officious Virgin Atlantic Check-In Staff ‘us’ mode and I was ‘them’ – another troublesome passenger. If there was going to be a solution to this problem, my first challenge was going to be getting us both into the same tribe.

I looked around at the other passengers in the queue. Desperately trying to find a tribal link between me and the Agent. Then I said to her, ‘it looks like I’m the only non-tourist on the plane. Not sure how much work I’m going to get done on the plane.’

I wasn’t sure this was going to work, but when she smiled, I had an inkling we were now both in the same tribe. The tribe of ‘people working when everyone else around them is on vacation’.

Next up Autonomy and Competence. I looked at her and shrugged my shoulders.

There was a pause.

‘I expect you have a bottle of water in your bag,’ she said.

I looked at her. My turn to smile. ‘I expect I do,’ I replied.

‘Make sure you drink it before you board.’

‘Definitely,’ I responded.

‘Here’s your boarding pass,’ she looked into my eyes, ‘I moved you away from the worst of the small children’.

‘Thanks,’ I said, ‘my hero’.

We both knew there was no bottle of water in my bag.