In his autobiography Miles Davis tells a story about the 1970 line-up of his touring band - this was the band that featured on the live half of the Live-Evil album - the one that featured the legendary Keith Jarrett on keys and briefly included the equally legendary Gary Bartz on sax.
Bartz had been grumbling a bit in private about Jarrett over-playing 'busy shit' behind his sax solos. Eventually he approached Miles and asked him to have a word with Kieth.
Miles agreed.
Later Keith Jarrett was talking with Miles about some other bits and pieces and as he was leaving Miles calls Keith back to tell him how much Gary Bartz was loving what he was doing behind his sax solos and could he please do even more of that kind of of thing.
Cookin' with Miles.
Nothing cooks without some heat.
The philosopher Daniel Dennett recalls the time computer scientist Joseph Weizenbaum – a good friend of Dennett’s – harboured his own ideas and ambition about becoming a philosopher.
Weizenbaum had recounted how one evening, after ‘holding forth with high purpose and furrowed brow at the dinner table’, his young daughter had exclaimed, ‘Wow! Dad just said a ‘deepity!’
Dennett was suitably impressed – with the coinage, not necessarily his friend’s ambitions in the philosophy department – and subsequently adopted ‘deepity’ as a categorising device and explains correct usage like this.
‘A deepity is a proposition that seems both important and true— and profound— but that achieves this effect by being ambiguous.’
Pictured below is some expensively produced promotional collateral given to attendees of an ‘upfronts’ type showcase from an Australian media organization that we attended recently.
Deepity indeed. ‘Disruptive collaboration' is a favourite but all seem to fit Dennett’s description perfectly.
Strangely out-of-place is the final card promising ‘commercial solutions’. How dull in its pragmatism and downright usefulness.
As they went into their final match of the 1985/86 Scottish football season, away to 6th placed Dundee on May 3, league leaders Hearts had gone a full 27 league games without defeat and needed only to avoid losing to ensure they would be Scottish champions for the first time since 1960.
Two Albert Kidd goals for Dundee in the final 10 minutes shattered Hearts dreams, as Celtic were stuffing St Mirren 5-0 in Paisley and so nicked the title on the last day.
But Hearts still had the Cup to play for.
The final at Hampden against Alex Ferguson's Aberdeen was just a week away.
To try and lift the dejected players for the Cup final the following week, the Hearts management had brought in a top sports psychologist who coached the squad in the week leading up to the final.
Various techniques were employed to attempt to 'erase' the disappointment of blowing the championship and prepare the team to at least lift the cup.
Fergie got wind of the activities at the Hearts training camp.
According to former Aberdeen assistant boss, Willie Garner, as Fergie prepared the Aberdeen players together in the dressing room before the teams walked out at the final, his final instructions were that each Aberdeen player should find an individual Hearts player in the tunnel, shake their hand and offer 'bad luck last week' condolences.
Thus negating any work the psychs might have done to put the bitter disappointment of losing the big prize in the final minutes of the last league game.
Aberdeen went 1-0 up in the first two minutes and added two further goals later on, destroying Hearts 3-0.
Strategy.
Identifying the critical factors in a situation, and designing the means to overcome them.
'The Frankenstein complex' is the term coined by 20th century American author and biochemistry professor Isaac Asimov in his famous robot novels series, to describe the feeling of fear we hold that our creations will turn on us (their creators) — like the monster in Mary Shelley’s 1818 novel.
One hundred years later in 2018 we still seem worried about this idea of subordination. That we might ultimately lose the ability to control our machines.
At least part of the problem are the concerns about AI alignment. Alignment is generally accepted as the ongoing challenge of ensuring that we produce AIs that are aligned with human values. This is our modern Frankenstein complex.
For example, if what has been described as an AGI (Artificial General Intelligence) ever did develop at some point in the future would it do what we (humans) wanted it to do?
Would/could any AGI values ‘align’ with human values? What are human values, in any case?
The argument might be that AI can be said to be aligned with human values when it does what humans want, but…
Will AI do things some humans want but that other humans don’t want?
How will AI know what humans want given that we often do do what we want but not what we ‘need’ to do?
And — given that it is a superintelligence — what will AI do if these human values conflict with its own values?
In the notorious thought experiment AI pioneer Eliezer Yudkowsky wonders if we can specifically prevent the creation of superintelligent AGIs like the paperclip maximizer?
In the paperclip maximizer scenario a bunch of engineers are trying to work out an efficient way to manufacture paperclips, and they accidentally invent an artificial general intelligence.
This AI is built as a super-intelligent utility-maximising agent whose utility is a direct function of the amount of paperclips it makes.
So far so good, the engineers go home for the night, but by the time they’ve returned to the lab the next day, this AI has copied itself onto every computer in the world and begun reprogramming the world to give itself more power to boost its intelligence.
Now, having control of all the computers and machines in the world, it proceeds to annihilate life on earth and disassembles the entire world into its constituent atoms to make as many paperclips as possible.
Presumably this kind of scenario is what is troubling Elon Musk when he dramatically worries that ‘…with artificial intelligence we are summoning the demon.’
Musk — when not supervising the assembly of his AI powered self-driving cars can be found hanging out in his SpaceX data centre’s ‘Cyberdyne Systems’ (named after the fictitious company that created “Skynet” in the Terminator movie series) — might possibly have some covert agenda in play in expressing his AI fears given how deep rival tech giants Google and Facebook are in the space. Who knows?
The demon AI problem is called ‘value alignment’ because we want to ensure that its values align with ‘human values’.
Because building a machine that won’t eventually come back to bite us is a difficult problem. Although any biting by the robots is more likely to be a result of our negligence than the machine’s malevolence.
More difficult is determining a consistent shared set of human values we all agree on — this is obviously an almost impossible problem.
There seems to be some logic to this fear but it is deeply flawed. In Enlightenment Now the psychologist Steven Pinker exposes the ‘logic’ in this way.
Since humans have more intelligence than animals — and AI robots of the future will have more of it than us — and we have used our powers to domesticate or exterminate less well-endowed animals (and more technologically advanced societies have enslaved or annihilated technologically primitive ones), it surely follows that any super-smart AI would do the same to us. And we will be powerless to stop it. Right?
Nope. Firstly, Pinker cautions against confusing intelligence with motivation. Even if we did invent superhuman intelligent robots, why would they want to take over the world? And secondly, knowledge is acquired by formulating explanations and testing them against reality, not by running an algorithm (and in any case big data is still finite data, whereas the universe of knowledge is infinite).
The word robot itself comes from an old Slavonic word rabota which, roughly translated, means the servitude of forced labour. Rabota was the kind of labour that serfs would have had to perform on their masters’ lands in the Middle Ages.
Rabota was adapted to ‘robot’ — and introduced into the lexicon — in the 1920’s by the Czech playwright, sci-fi novelist and journalist Karel Capek, in the title of his hit play, R.U.R. Rossumovi Univerzálnà Roboti (Rossum’s Universal Robots).
In this futuristic drama (it’s set in circa 2000) R.U.R. are a company who initially mass-produced ‘workers’ (essentially slaves) using the latest biology, chemistry and technology.
These robots are not mechanical devices, but rather they are artificial organisms — (think Westworld) — and they are designed to perform tasks that humans would rather not.
It turns out there’s an almost infinite market for this service until, naturellement, the robots eventually take over the world although, in the process, the formula required to create new ‘robots’ has been destroyed and — as the robots have killed everybody who knows how to make new robots — their own extinction looms.
But redemption is always at hand. Even for the robots.
Two robots, a ‘male’ and a ‘female’, somehow evolve the ‘human’ abilities to love and experience emotions, and — like an android Adam and Eve — set off together to make a new world.
What is true is that we are facing a near future where robots will indeed be our direct competitors in many workplaces.
As more and more employers put artificial intelligences to work, any position involving repetition or routine is at risk of extinction. In the short-term humans will almost certainly lose out on jobs like accounting and bank telling. And everything from farm labourers, paralegals, pharmacists and through to media buyers are all in the same boat.
In fact, any occupations that share a predictable pattern of repetitive activities, the likes of which are possible to replicate through Machine Learning algorithms, will almost certainly bite the dust.
Already, factory workers are facing increased automation, warehouse workers are seeing robots move into pick and pack jobs. Even those banking on ‘new economy’ poster-children like Uber are realizing that it’s not a long game — autonomous car technology means that very shortly these drivers will be surplus to requirements.
We have dealt with the impact of technological change on the world of work many times. 200 years ago, about 98 percent of the US population worked in farming and agriculture, now it’s about 2 percent, and then the rise of factory automation during the early part of the 20th century - and the outsourcing of manufacturing to countries like China - has meant that there is much less need for labour in Western countries.
Indeed, much of Donald Trump’s schtick around bringing manufacturing back to America from China is ultimately fallacious, and uses China as a convenient scapegoat.
Even if it were possible to make American manufacturing great again, because of the relentless rise of automation any rejuvenated factories would only require a tiny fraction of human workers.
New jobs certainly emerge as new technologies emerge replacing the old ones, although the jury is out on the value of many of these jobs.
In 1930, John Maynard Keynes predicted that by the century’s end, technology would have advanced sufficiently that people in western economies would work a 15-hour week. In technological terms, this is entirely possible. But it didn’t happen, if anything we are working more.
In his legendary and highly amusing 2013 essay On the Phenomenon of Bullshit Jobs, David Graeber, Professor of Anthropology at the London School of Economics, says that Keynes didn’t factor into his prediction the massive rise of consumerism. ‘Given the choice between less hours and more toys and pleasures, we’ve collectively chosen the latter.’
Graeber argues that to fill up the time, and keep consumerism rolling, many jobs had to be created that are, effectively, pointless. ‘Huge swathes of people, in Europe and North America in particular, spend their entire working lives performing tasks they secretly believe do not really need to be performed.’ He calls these bullshit jobs.
The productive jobs have, been automated away but rather than creating a massive reduction of working hours to free the world’s population to pursue their own meaningful activities (as Keynes imagined) we have seen the creation of new administration industries without any obvious social value that are often experienced as being purposeless and empty by their workers.
Graeber points out that those doing these bullshit jobs still ‘work 40 or 50 hour weeks on paper’ in reality their job often only requires working the 15 hours Keynes predicted — the rest of their time is spent in pointless ‘training’, attending motivational seminars, and dicking around on Facebook.
To be fair, robots are unrivaled at solving problems of logic, and humans struggle at this.
But robot ability to understand human behavior and make inferences about how the world works are still pretty limited.
Robots, AIs and algorithms can be said to ‘know’ things because their byte-addressable memories contain information. However, there is no evidence to suggest that they know they know these things, or that they can reflect on their states of ‘mind’.
Intentionality is the term used by philosophers to refer to the state of having a state of mind — the ability to experience things like knowing, believing, thinking, wanting and understanding.
Think about it this way, third order intentionality is required to for even the simplest of human exchanges (where someone communicates to someone else that someone else did something), and then four levels are required to elevate this to the level of narrative (‘the writer wants the reader to believe that character A thinks that character B intends to do something’).
Most mammals (almost certainly all primates) are capable of reflecting on their state of mind, at least in a basic way — they know that they know. This is first-order intentional.
Humans rarely engage in more than fourth-order intentionality in daily life and only the smartest can operate at sixth-order without getting into a tangle. (‘Person 1 knows that Person 2 believes that Person 3 thinks that Person 4 wants Person 5 to suppose that Person 6 intends to do something’’).
For some perspective, and in contrast, robots, algorithms and black boxes are zero-order intentional machines. It’s still just numbers and math.
The next big leap for AIs would be with the acquisition first or second-order intentionality — only then the robots might just about start to understand that they are not human. The good news is that for the rest of this century we’re probably safe enough from suffering any robot apocalypse.
The kind of roles requiring intellectual capital, creativity, human understanding and applied third/fourth level intentionality are always going to be crucial. And hairdressers.
And so, the viability of ‘creative industries’ like entertainment, media, and advertising, holds strong. Intellectual capital, decision-making, moral understanding and intentionality.
For those of us in the advertising and marketing business it should be stating the obvious that we should compete largely on the strengths of our capability in those areas, or the people in our organisations who are supposed to think for a living.
By that I mean all of us.
For those who can still think any robot apocalypses are probably the least of our worries. But take a look inside the operations of many advertising agencies and despair at how few of their people are spending time on critical thinking tasks and creativity.
Even more disappointing is when we’d rather debate whether creativity can be ‘learned’ by a robot rather than focusing on speeding up the automation of the multitude of mundane activities in order to get all of our minds directed at fourth, fifth and (maybe) sixth order intentionality. The things that robots’ capabilities are decades away from, and that we can do today, if we could be bothered.
By avoiding critical thinking, people are able to simply get shit done and are rewarded for doing so.
Whilst there are often many smart people around, terms like disruption, innovation and creativity are liberally spread throughout agency creds power point decks, as are ‘bullshit’ job titles like Chief Client Solutions Officers, Customer Paradigm Orchestrators or Full-stack Engineers, these grandiose labels and titles probably serve more as elaborate self-deception devices to convince their owners that they have some sort of purpose.
The point being that far from being at the forefront of creativity most agencies direct most of their people to do pointless work giving disproportionate attention to mundane zero-order intentionality tasks that could and should be automated.
Will robots take our jobs away? Here’s hoping.
Perhaps the AI revolution is really the big opportunity to start over. To hand over these bullshit jobs — the purposeless and empty labour we’ve created to fill up dead space — and give us another bite at the Keynes cherry, now liberated to be more creative and really put to use our miraculous innate abilities for empathy, intentionality and high level abstract reasoning.
To be more human.
Because, and as evolutionary theory has taught us, we humans are fairly unique among species. We haven’t evolved adaptations like huge fangs, inch-thick armour plating or the ability to move at super speed under our own steam.
All of the big adaptations have happened inside our heads, in these huge brains we carry around, built for creativity and sussing out how the world works and how other humans work.
That’s the real work. Not the bullshit jobs.
In The Inevitable, Kevin Kelly agrees that the human jobs of the future will be far less about technical skills but a lot about these human skills.
He says that the ‘bots are the ones that are going to be doing the smart stuff but ‘our job will be making more jobs for the robots’.
This article is an adapted excerpt from his second book ‘What’s The Point of Anything? More Tales from the Dunning-Kruger Peak’ due at the end of 2018.
"I’m 71, and I’ve been doing this for a little over 50 years, doing it at a fairly visible level for 40.
By this time it’s all second nature. It’s all a machine that works a certain way: the observations, the immediate evaluation of the observation, and then the mental filing of it, or writing it down on a piece of paper.
I’ve often described the way a 20-year-old versus, say, a 60- or a 70-year-old, the way it works.
A 20-year-old has a limited amount of data they’ve experienced, either seeing or listening to the world. At 70 it’s a much richer storage area, the matrix inside is more textured, and has more contours to it.
So, observations made by a 20-year-old are compared against a data set that is incomplete. Observations made by a 60-year-old are compared against a much richer data set. And the observations have more resonance, they’re richer."
Adding to Bob Hoffman's observation last week that 'People over 50 aren't creative enough to write a f***ing banner ad, but they are creative enough to dominate in Nobels, Pulitzers, Oscars, and Emmys.'
The nefarious activities of bad actors in the Facebook/Cambridge Analytica debacle may spark an unwarranted moral panic around the use of psychometric profiling in consumer research, argues Eaon Pritchard.
Science is what it is.
As the saying goes, the universe is under no obligation to make sense to you. No moral sense, at least.
It’s been widely reported Cambridge Analytica and others actors in the Facebook data debacle have appeared/claimed to use personality profiling and psychometric techniques as ‘weapons of psychological warfare’ (sic).
This is concerning because we do not need any moral panic around established science simply because of the application by bad actors.
As my good friend Richard Chataway commented on Twitter this week:
This (the CA/Facebook situation) does not invalidate the science. Psychometrics (i.e. Big 5 personality traits) have a much greater predictive power for behaviour than demographics or other segmentation types typically used in comms.
What CA and the other actors in the Facebook data debacle have done with data in combination with the other elements of skullduggery and dirty-tricks reported should be rightly condemned.
But this does not invalidate the science. And it would be very dangerous for this idea to spread.
For those unfamiliar with the big 5, I’ve summarised below. This summary is based on the chapter in ‘Spent’, an evolutionary perspective on consumer behaviour by the psychologist Geoffrey Miller. It’s the best description - and most accessible to the lay person - that I have found.
Most people will understand the distribution of human intelligence. It forms a bell curve, with most people clustered around the middle, close to IQ 100 – the average. Distribution tapers off fairly quickly as scores deviate, so that blockheads and geniuses are rarer.
All the Big Five personality traits follow a similar bell-curve distribution.
Most people sit near the middle of the curve on the other traits, openness, conscientiousness, agreeableness, emotional stability and introversion/extraversion, either slightly lower or higher.
The Big 5 (plus IQ) is established science whereas the typical demographic/personality types used in market segmentation studies, for example, are mostly complete fiction.
When sex/gender, birthplace, language, cultural background, economic status, and education appear to predict consumer behavior the real reason is because these factors correlate with the big 5 + IQ traits, not because they directly cause the behavior.
Similarly the common organisational ‘personality’ frameworks like Myers-Briggs and HBDI are also nonsensical – because traits are normally distributed.
These universal traits are fairly independent and don’t correlate particularly, people display all six traits in different ways and combinations.
Although intelligent people tend to be more open than average to new experiences, there are plenty of smart people, who stick to their football, reality tv and the pub.
Likewise there are plenty of open-minded people who love strange ideas and experiences, but who are not very smart. This explains the market for dubious new technology products and things like homeopathy. Open minded but not so smart = gullible.
(For ad industry observers, much of the research suggests that short-term creative intelligence is basically general intelligence plus openness, while long-term creative achievement is also predicted by higher than average conscientiousness and extraversion traits. Planners would need to score fairly high on intelligence and conscientiousness but are more likely to be disagreeable. Account people could get by on middling for most traits but above average emotional stability is a must-have.)
Importantly, for the situation under discussion, these traits can predict social, political, and religious attitudes fairly well and can therefore be used to nudge people to act in line with their make-up (and corresponding moral foundations).
Left leaning people tend to show higher openness (more interest in diversity), lower conscientiousness (less bothered with convention), and higher agreeableness (concern for care and fairness)
Conservatives show lower openness (more traditionalism), higher conscientiousness (family-values, sense of duty), and lower agreeableness (self-interests and nationalism etc).
That’s one data point.
In my book ‘Where Did It all Go Wrong’ I speculate that the real opportunity for applications of Machine learning and AIs offer us much more than just the better mousetraps of targeting and delivery. 'The big opportunity is for understanding what people value, why they behave the way they do, and how people are thinking (rather than just what).
Everyone will be familiar with the words of the data-scientist W. Edwards Deming who asserts ‘Without data you are just another person with an opinion’.
In our business there are no shortage of opinions.
Deming, quite rightly, demands the objective facts. And we have more facts and data at our disposal than at any time in human history.
However to complete the picture, and to take the opportunity that data and technology give for creativity, I propose an addendum to Deming’s thesis.
Without data you are just another person with an opinion? Correct.
But, without a coherent model of human behaviour, you are just another AI with data.
This could bring new, previously hidden, perspectives to inform both the construction of creative interventions and deeper understanding exactly where, when and how these interventions will have the most power.'
It’s important in light of recent events to note that these methods can can be used by bad actors for nefarious means or the slightly less bad.
The problem of AI alignment is generally accepted as the challenge of ensuring that we produce AI that is aligned with human values.
For example, if an AGI (Artificial General Intelligence) ever did develop at some point in the future would it do what we (humans) wanted it to do?
Would/could any AGI values ‘align’ with human values?
What are human values, in any case?
The argument might be that AI can be said to be aligned with human values when it does what humans want, but...
Will AI do things some humans want but that other humans don’t want?
How will AI know what humans want given that we often do do what we want but not what we ‘need’ to do?
And – given that it is a superintelligence - what will AI do if these human values conflict with its own values?
In the notorious thought experiment AI pioneer Eliezer Yudkowsky wonders if we can specifically prevent the creation of superintelligent AGIs like the paperclip maximizer?
In the paperclip maximizer scenario a bunch of engineers are trying to work out an efficient way to manufacture paperclips, and they accidentally invent an artificial general intelligence.
This AI is built as a super-intelligent utility-maximising agent whose utility is a direct function of the amount of paperclips it makes.
So far so good, the engineers go home for the night, but by the time they’ve returned to the lab the next day, this AI has copied itself onto every computer in the world and begun reprogramming the world to give itself more power to boost its intelligence.
Now, having control of all the computers and machines in the world, it proceeds to annihilate life on earth and disassembles the entire world into its constituent atoms to make as many paperclips as possible.
The problem is called ‘value alignment’ because we want to ensure that its values align with ‘human values’.
Because building a machine that won’t eventually come back to bite us is a difficult problem.
Determining a consistent shared set of human values we all agree on is obviously an almost impossible problem.
The Facebook/Cambridge Analytics kerfuffle ‘exposed’ this weekend by the Guardian and New York Times is an example.
The Guardian are outraged because ‘It’s now clear that data has been taken from Facebook users without their consent, and was then processed by a third-party and used to support their campaigns’
Ya think?
In fact CA just cleverly used the platform for what it was ‘designed’ for.
This is exactly what Don Marti nicely captured as ‘the new reality… where you win based not on how much the audience trusts you, but on how well you can out-hack the competition.
Extremists and state-sponsored misinformation campaigns aren’t “abusing” targeted advertising. They’re just taking advantage of a system optimized for deception and using it normally.’
And are the Guardian and NYT outraged because parties who’s values don’t align with theirs out-hacked them?
After all, back in 2012 The Guardian reported with some excitement how Barack Obama's re-election team built ‘a vast digital data operation that for the first time combined a unified database on millions of Americans with the power of Facebook to target individual voters to a degree never achieved before.’
Whoever can build the best system to take personal information from the user wins, until it annihilates life on the internet and disassembles the entire publishing world into its constituent atoms.
Is data-driven advertising going to be the ad industry’s own paperclip maximizer?
Any AGI is a long way off but in a more mundane sense we already have an alignment problem.
The KFC apology ad from last week was interesting from a few standpoints. Most obviously it was a cute creative execution. Deftly reworking KFC into FCK and the almost Gossage-esque copy.
Secondly, there's the pratfall effect. Brands are fallible, so if a brand is open about its failings and can admit to the odd weakness it's a tangible demonstration of a degree of honesty and, therefore, makes other claims a bit more more believable.
But on a more basic level the choice of media in which to deliver the apology is worthy of comment.
KFC took out full page press ads in the Metro and Sun newspapers.
Why is that significant?
The Handicap Principle is a hypothesis originally proposed in 1975 by Israeli biologist Amotz Zahavi to explain how evolution may lead to 'honest' or reliable signaling between animals which have an obvious motivation to bluff or deceive each other.
Zahavi describes how - in order to be effective - signals must be:
1. Reliable
2. And in order to be reliable, signals have to be costly.
It’s an elegant idea: waste makes sense - ‘Conspicuous’ waste in particular.
In my recent book I make several references to The Handicap Principal, here's one excerpt:
‘By wasting [conspicuously], one proves conclusively that one has enough assets to waste and more. The investment - the waste itself - is just what makes the advertisement reliable.’
Psychologists will tell you that humans are pretty good intuitive biologists.
We have innate abilities to be able to identify the kinds of plants that are safe to eat, or animals that are likely to be predators or venomous.
We are also pretty good intuitive psychologists. We can identify what others are thinking and feeling, or what kind of mood they are in with very few cues.
I’d also argue that people are pretty good intuitive media strategists.
We don’t know how much a full-page ad in the broadsheet newspaper costs, exactly. But we do know that it was pretty damn expensive.
We don’t know exactly how much that retargeting banner ad costs but we know that it’s pretty cheap.
Likewise, we can easily and intuitively detect high or low production values that reflect the level of economic investment in any piece of communications. All these indicators are signals.
The kinds of signals that carry an implicit sense of ‘cost’ on behalf of the signaler can be trusted, to a degree.
The signaler has put their money where their mouth is'.
For this reason The KFC apology can be 'trusted' to a degree. It's the extravagance of the gesture that contributes to advertising effectiveness by increasing credibility.
That's the Colonel's secret recipe.
It's not data-driven, there's no surveillance-fed algorithms, no targeting or tracking or data-leakage, it needs not know anything at all of it's audience.
It's just a big, juicy, costly, zinger of a signal.
'The Renaissance (1350–1600) produced favorable conditions for charlatans. Old ways of thinking were cast aside, and it seemed that anything was possible.
A semiliterate village dweller might have been aware of a new discovery, but he or she was probably not sufficiently educated to distinguish fact from fiction. Charlatans could not have flourished without the support of a willing, naïve audience.
The extraordinary power of impostors is therefore only to be understood after a consideration of the minds and circumstances of their gullible victims, the crowds who sought them out, half convinced before a word was spoken.
If charlatans had not existed, villagers would have invented them.'
‘A proto-meme is beginning to ‘go critical’. This book is a part of that meme.
The meme is not fully formed but at its core is one thought. Somewhere the advertising business has kinda lost the plot, we’re not sure exactly sure where.
So many incompetents, who can’t know we are incompetent because the skills we need to produce the right answers are exactly the skills we lack in order to know what a right answer is.
What happens now? Who knows?
But this book tackles it head-on with punk rock, cheap philosophy and evolutionary psychology as we take a hair-raising ride to the Dunning-Kruger peak of advertising…’
With a foreword written by Mark Earls ( author of Herd, I'll Have What She's Having and CopyCopyCopy etc) the book is available on Amazon worldwide and in more discerning bookstores.
There is also a Kindle version, however the 200 page paperback fits nicely in the back pocket of your selvage for optimum disagreeableness trait signaling.
To properly understand advertising, it needs to be viewed as part of popular culture.
When it works it is often because this is the environment it inhabits.
Not any particular media vehicle.
If anything, this has only become more important as the number of potential media choices and environment grows.
I'm fond of Paul Feldwick's 'showbusiness' argument, that goes something along these lines.
Advertising and entertainment have forever been inextricably linked.
The best advertising has always borrowed most of its creative themes from 'show business'.
The popular music, comedy, celebrities, sport, drama, sexiness and fashions of the day.
Advertising and popular culture are two parts of the same whole.
Paul suggests that not much has really changed since PT Barnum and The American Medicine Show.
A song-and-dance to put a smile on their faces, and put them in the mood to buy.
Sure, they can help with engagement by attracting an audience appropriate for the message and maybe keeping a bit of attention.
Media engagement, however, does not equate to advertising engagement. Nor is that media's job.
Paradoxically, in spite of the infinite number of media channels now available, when great contemporary advertising works it is often because it truly inhabits the broader culture - and it stands up on its own.
Human biological evolution solves only ‘adaptive’ problems, the kind that concern surviving long enough to successfully pass on our genes into the next generation.
Among these problems are; what to eat, avoiding getting eaten, finding the best quality mating partners, and competing with each other for status and resources.
These are the kinds of problems that were the most common in the ‘environment of evolutionary adaptedness’ - the stone age hunter-gatherer environment our ancestors navigated - not our modern world of technology, media, celebrities and consumerism.
It was during this time - that’s approximately 99% of human existence, the stone age lasted for a couple of million years - that our minds did almost all of their evolving. A time when we lived in small groups of maybe only a few dozen people gathering plants and hunting animals.
Our modern world is a tiny, tiny blip in comparison.
We only developed agriculture about 10,000 years old, the industrial revolution was just over 200 years ago and the internet has only been around for about 20 years. Not nearly enough time has elapsed for our minds to adapt to these new conditions. Our modern minds are designed for solving ancient stone age problems, not for dealing with the supernormal stimulus of the 21st century.
The theory of supernormal stimulus was developed in the 1950s by biologist and ornithologist Nikolaas ‘Niko’ Tinbergen. He found that biologically salient objects, like beaks and eggs, generated far more interest from his bird subjects when they were painted, pimped and blown up in size.
In one experiment herring gull chicks pecked more at big red knitting needles than adult herring gull beaks, because they were bigger and redder and longer than real beaks.
A young student of Tinbergen called Richard Dawkins experimented with male stickleback fish and supernormal dummy females. The real female sticklebacks naturally swell up when they are fertile and full of eggs.
By making his dummy female fish much bigger and rounder than normal the males became more attracted to the dummies. Dawkins is credited with introducing ‘sex bomb’ into the lexicon in describing this example.
Evolution has designed male Australian jewel beetles go after for cues of shiny amber-brown surfaces with the presence of dimples, as these were almost certain to be female beetles. This normal stimulus triggered a normal adaptive behaviour. But Australian beer bottles – stubbies - give off these exact same cues, only much bigger and shinier.
They are everywhere in the male beetles' environment and the boys are getting distracted. Beer bottles are a super-normal stimulus for male beetles, triggering a maladaptive behaviour.
Of course, many animals exaggerate features to attract mates, mimic other species or protect themselves against predators. But these changes happen slowly over evolutionary time.
Supernormal is a term that can be used to describe any stimulus that elicits a response stronger than the stimulus for which it evolved.
Junk food is a super stimulus version of real food to humans. Things like sugar and fat – that were biologically salient, but scarce in the stone-age environment – are all around us, in abundance, every day.
But it’s not just the external cues that are super-normal, but the internal rewards too. A Big Mac gives you a bumper hit of sugar, fat, and flavour far more intensely than a bowl of rolled oats or boiled cabbage.
Oscar Wilde famously stated ‘I can resist everything but temptation’.
None of us can. Stuffing our faces with calories, drinking and taking drugs, gambling, obsessing over the lives of celebrities whom we are never likely to meet instead of going out in to the real world and forming real relationships, competing for status at work and generally wasting time with people who wouldn’t care if we lived or died rather than spending time with our families. These are just a few examples of common, and maladaptive, behaviours.
Of course, all of these new temptations mentioned are hard to resist, because in the world our minds evolved to inhabit they didn’t exist. They are supernormal stimuli that elicit a response stronger than the stimuli for which their response mechanisms evolved.
Humans, however, now have the cultural tools that allow us to consciously manipulate these signals in real time, and the makers of these tools know this very well.
If you were the planner in an ad agency anytime between 1965 and about 10 years ago, your work was fairly straightforward. You would do your research, find some insights and – if you were any good – develop an interesting platform that creatives could jump from to make the ads.
But the sexier modern advertising environment has raised our reward thresholds. The old rewards just don’t synergise 24-7 mindshare, do they?
Our new blockchain content glasses are super-normal stimulus causing maladaptive behaviours.
The super successful products of the digital economy like Facebook, Twitter, Tinder, Instagram are all supernormal stimuli. They work so well because they are perfectly adapted to create supernormal stimuli for our stone-age minds. We are wired to compete for status among our peers in the small groups on the savannahs we used to inhabit. But now we can super-compete with millions of strangers on the internet.
So, the next time you hear about how the internet is rewiring our brains, it’s really the internet adapting to and exploiting how our brains work.
Because, rather than being an all-purpose information processor, the mind consists of a number of specialised ‘modules’, or apps, designed by evolution to cope with certain recurring adaptive problems.
The mind’s ‘apps’ are specific processes that evolved in response to our ancestral environment. Our minds have apps for mating behaviour, gossip, looking out for family members, making deals with strangers, signalling personality traits and so on. The successful products of the digital economy are the ones that mirror and exaggerate these response mechanisms.
What’s modern is in our environment, not in our minds.
And an OS update takes thousands of generations to load, unfortunately.
So for your next disruptive innovation idea, just find a super-stimulating version of a natural reward. But make it sexier, cuter, sweeter, bigger, louder or with more teeth.
There’s a free strategy for you. Off you go.
Psychological junk food.
Although, AI robot sex dolls is already becoming a crowded category.
The above is an excerpt, adapted from Eaon's forthcoming book 'Where Did It All Go Wrong? Adventures at the Dunning-Kruger Peak Of Advertising' which comes out in January 2018 and will be available for pre-order soon on Amazon worldwide.
Onora O'Neill's 2002 Reith Lectures series 'A Question of Trust' are as apt today as they were then.
In the 5th of her lectures, 'Licence to Deceive', the Cambridge Emeritus Professor of Philosophy was principally referring to the state of journalism but, in 2017, we can apply her insight to what has happened to advertising in general and by advertising technology in particular.
'Do we really gain from heavy-handed forms of accountability? Do we really benefit from...demands for transparency? I am unconvinced.
I think we may undermine professional performance and standards...by excessive regulation, and that we may condone and even encourage deception in our zeal for transparency.'
The final sentence is perhaps the most disturbing.
How can we discern the trustworthy from untrustworthy? O'Neill argues that we should perhaps focus less on grandiose ideals of transparency and rather more on limiting deception.
This means media agencies stepping up, taking back our lunch money. Reclaiming the control of strategy that - in a decade of Dunning-Kruger peak stupidity - we've ceded to our Silicone Valley overlords. The smiling assassins.
(As a fun police aside, I would put a stop to agency staff walking around wearing the swag they have received from vendors. Facebook and Google t-shirts etc. Enclothed cognition!)
And O'Neill was some 15 years ahead of my Google/Facebook 'crunchy-on-the-outside-fluffy-on-the-inside' metaphor.
'The new information technologies may be anti-authoritarian , but curiously they are often used in ways that are also anti-democratic. They undermine our capacities to judge others' claims and to place our trust.'
The IAB and others say, 'We need to make measurement sexy. It's a topic we need to embrace and give a lot more love to'.
Good luck with that.
Because it's when trust moves out, that measurement moves in.
And not everything that can counts can be counted.
When love breaks down, The lies we tell, They only serve to fool ourselves.
We are where we are, and it's going to be a long road back.
It's common to hear 'digital' conflated with 'the internet', when the two are obviously interconnected but not the same thing.
'Digital' is not a thing, it’s an adjective. The internet is not strictly a thing either but is certainly more thing-like. Or at least a 'place', of sorts.
If the internet is a place, digital is it's underlying structure.
We came across this splendid analogy from the film-maker Adam Curtis which seems to help with the distinction. “[The internet] will become a bit like a John Carpenter movie. You go there, amidst the ruins, and it’s weird, and you can be nasty — just have fun and be bad, like a child. From about ’96 to about 2005 people built these lovely websites, they put up masses and masses of fantastic information. They’ve left them sitting there, but it’s like a city that everyone’s gone from. And what’s come in instead is a weird world where you don’t know what’s real — just people shouting at each other. It’s good fun, but it’s not real.”
Then creative people mess with it and create something new and unexpected.
Artists never invented oil paint, or the movie camera but they saw the opportunity the technology gave for creativity.
Bill Drummond once made this point (I sometimes see it attributed to Lee Clow, either way it’s a useful insight).
Historically, the advertising business has erred on the side of caution in its adoption of new technology. The first ever TV ad, a whopping $4 dollar production for Bulova Watches, ran in 1941 but it was almost 20 further years before the industry embraced television as a platform.
But things have speeded up in recent years.
In fact it’s been a head-first dive into digital and social media, then virtual and augmented reality, black boxes of every flavour and now artificial intelligences and machine learning.
As a bonus, with each of these new developments in technology comes the processing of huge amounts of new consumer data – we have more than any other generation of communicators could have even imagined - so it should naturally follow, fully stacked, we can now connect with consumers better than any other generation of marketers.
Yet it can often feel like more data actually means less. We are even less connected.
Because, in spite of the bluster and gusto, advertising hasn’t had a good time figuring out how to make tech, data and creativity work together, and therefore doesn’t appear to have a clear articulation of its own future.
Indeed, in most of the industry the conversation is still stuck with a false dilemma.
As if the data-driven and creative are incompatible.
It need not be this way, and we need to resolve this dichotomy fairly urgently.
Data is everywhere, and every day there is more and more data.
For many, simply being exposed to the idea of data at this scale is enough to just switch off and become misty-eyed for simpler times, whereas for others the accumulation of data has become something of an end in itself, as if simply possession of the data constitutes a silver bullet.
But the daily reality, for the most part, is more mundane. Agencies may tend to limit their view of data as either, oft times inconvenient, input to inform or rationalise strategic choices, or as, equally inconvenient, output in the form of metrics and measurement.
What’s even worse is that during this process they tend to obsess over the wrong data, giving disproportionate focus to small and insignificant differences, get distracted by noise rather than finding the signal, get dazzled by vanity metrics and miss the big important things that really matter in guiding strategy.
From that standpoint, any lofty ambitions to assimilate data as a part of the creative process seem a long way off.
Direct marketers and digital marketers will, of course, disagree. They will crow of how they can already effortlessly track and retarget elusive consumers, whilst micro-segmenting audiences and optimising each campaign to within an inch its life.
But is that all there is? Efficiency?
All of the time each of us spends on the internet, and on our smartphones, all the websites we visit, the apps and services we use, everything we buy or think about buying and the people we talk to generates an incredible amount of data on our behaviour and our preferences that could be used by brands to better connect. However, just this observation is banal.
Yes, the domination of programmatic delivery, automation and further advertising technology is inevitable. Very soon all media will be bought and distributed in this way. It’s a wonderful thing, but the tech, on its own, is not good enough.
We desperately need our best creative minds to grab the opportunity that data and technology provide for creativity. But we need a bridge to connect the two.
To that end, I propose that the role for strategic planning in agencies will have to change in this new data-rich environment.
While no planners should be strangers to data analysis - some may even have a basic grasp of statistics and recognize a NBD curve when they see it - but the key imperative for strategic thinking in agencies will be to provide the human understanding that connects the data and technology to the creative product.
As a starting point it’s worth remembering that any data is really only as useful as the questions asked of it. Data has no intrinsic value.
Understanding what consumers actually do rather than what they say they do is critical. We’ve learned from the recent advances in behavioural economics and consumer psychology that consumers have, pretty much, no access to the unconscious mental processes that drive most of their decision-making.
However, this doesn’t prevent people providing plausible-sounding rationalisations for their behavior, when asked. Even the process of asking people what they think exerts its own unconscious influence. To the extent that much of the survey data that has traditionally fueled marketing decision making is, at worst, a total fiction or at best only an artefact of the research process, itself.
The consumer psychologist Philip Graves famously channeled Edgar Allen Poe by remarking ‘Trust nothing consumers say, about half of what we see them do, and nearly everything the sales data tells us they have done’.
Graves is adamant that real sales data and covert behavioural observation should always be the start point of any research.
The use of the words ‘covert observation’ can quickly divide a room. However when the focus of any research is overt – i.e. the participants are aware of what’s being investigated – then, while it feels like it’s more transparent or ‘ethical’ it is mostly useless. Knowing one’s behavior is being observed is intrinsically biasing. When people are aware that they are being observed they become more self-conscious and their behaviour changes.
This is where the new developments in data technology might become interesting.
Artificial Intelligence and Machine Learning are two buzz phrases being used right now - often interchangeably - but they are not quite the same thing. For our purposes as advertisers, it’s enough to know that one is effectively an application of the other.
Machine Learning, then, is a particular application of one AI based around the idea that - given access to enough data - machines can learn for themselves. Put simply, a machine learning AI is essentially a system fueled by algorithms, and as these algorithms are exposed to new data they teach themselves and grow.
Basic Machine Learning applications can read and interpret text (making inferences about the tone of the text it is reading), all programmatic ad trading is applied AI, chuck in other applications like self-driving cars, Siri and rudimentary speech recognition and a lot of this kind of applied AI is all around us, now. But these examples are what the boffins would label ‘narrow’ AI.
Narrow or not, these developments are reasonably impressive from the technology standpoint and present a platform for creative people to do something new and unexpected.
In simple terms, the ability to identify an individual consumer, rather than trying to make sense of multiple cookies and multiple devices that may be associated with an individual, is not just about micro targeting and extreme personalization. This ‘narrow’ view (to borrow the technical jargon of our AI engineer friends) is just more of the Peppers and Rogers circular logic.
AIs and PII (Personally Identifiable Information) are going to be far more useful in accurately sizing markets, uncovering the real sales and behavioural data and the necessary covert behavioural observation that allows us to group together bigger sets of consumers through shared insights.
Advertisers should be interested in observing these network effects. As anyone with even a basic understanding of simple network theory will tell you, the value of a network increases as it grows bigger. A simple applied description of machine learning with personal information is described nicely for the lay person (or advertising practitioner) Kevin Kelly’s 2017 book ‘The Inevitable’ and in the chapter on ‘Cognifying’ (one of the 12 tech forces that he predicts will be the most important in the next couple of decades).
‘The more people who use an AI, the smarter it gets. The smarter it gets, the more people who use it, the more people who use it, the smarter it gets. And so on’
Kelly tells of a moment in 2002 when this became clear to him. While making conversation with assorted engineers at a private party within Google HQ he came to the realisation that we had been looking at our Silicon Valley overlords ultimate goals the wrong way round. Google were not interested in the application of AIs to make their core products like search better, it was OUR usage of search that was feeding Google’s AIs. Google was fundamentally an AI company.
Our usage feeds the AI. The more we use it the smarter it gets, and so on.
Today, smartphone data is obviously they key - about 90% of all these devices are uniquely identifiable with an individual – we can know almost the exact composition of a total audience, as well as where and when media is used. It’s also worth noting that the full-tilt expansion of personal media means that the next decade promises to bring new technologies with capabilities far beyond the abilities of our smartphones.
The mainstreaming of machine learning capabilities, will provide agencies with better building blocks for smarter campaigns, and constitutes something of a leap in marketing intelligence, but as we’ve noted before, simply turbo-boosting targeting and delivery of ads is not where the real potential for AI applications in communications lies. Even adding the benefit of population level behavioural data and insights we are still working with ‘narrow’ AIs.
Things start to get much more interesting when we can map human psychology onto the data.
We live in a modern world of complex social networks. We interact with hundreds of people each day, in both physical and virtual environments. Success in this environment means being best adapted to interacting with, and working with other people.
And getting what you want from others.
Each of us has things that annoy us and things that make us happy. We have become very skilled good at remembering other people’s preferences and they, ours.
But we are limited by our cognitive capacity. It takes a huge amount of cognitive effort to remember other people’s preferences. But the pay-offs are there when we get it right.
This skill evolved long ago in our ancestral past, one of many adaptations that shaped our minds into the way they are because these adaptations enabled our stone-age ancestors to succeed with their (and our) principal concerns, namely survival, reproducing, forming mutually beneficial alliances and looking after families.
When the anthropologist Robin Dunbar was trying to solve the problem of why primates (including humans) and other social species devote so much time and effort to this kind of ‘grooming’ behavior, he happened upon his eponymous number.
Dunbar’s number (around 150) described a theoretical limit to the number of people with whom any individual is able to sustain a stable or meaningful social relationship.
150 is a best case number and even in the age of digital social networks, the number of friends with whom you keep in touch, and groom, is likely to be significantly less than Dunbar’s number.
But for brands, companies and institutions – for whom the Holy Grail is to sustain stable relationships, keep in touch with and groom literally millions of consumers - the really big opportunities that the harnessing the tsunami of personally identifiable data and the power machine learning and other AI applications offer lie in these areas.
The ability to manage relationships with and remember the (often implicit and unarticulated) preferences, of millions of individuals with the same intimacy as these tight-knit groups of humans manage their own relationships, is the bridge that finally connects the technology, the data and the creativity.
To a degree, I’m carried by Kelly’s optimism when he proposes, ‘There is almost nothing we can think of that cannot be made new, different, or interesting by infusing it with some extra IQ. In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI.’
Take market research and add AI.
Take consumer psychology and add AI.
Take creativity and add AI.
So, in theory, machine learning and AIs do offer us much more than just the better mousetraps of targeting and delivery. The big opportunity lies in how these technologies will aid understanding what people value, why they behave the way they do, and how people are thinking (rather than just what). This could bring new, previously hidden, perspectives to inform both the construction of creative interventions and understanding exactly where, when and how these interventions will have the most power.
The more sensible proponents for the digital economy have always hoped for this, but if it were that simple then perhaps a lot more would have already been achieved by earlier iterations of the internet and this indicates that there are significant hurdles still to be overcome.
For a start, the impersonality of digital communication almost certainly affects our interactions with others in comparison to face-to-face communications. Spend five minutes on Twitter or in the comments section of any of the ad industry trade websites and this should be self-evident.
These challenges also have their roots deep in human nature and our evolutionary past.
In ‘The Evolution of Language’ Dunbar also notes that ‘Whenever person-to-person interaction is a necessary feature of the process (as in the striking of deals), the old and trusted cognitive mindsets will come into play. Suspicion of the unknown and the fear of being duped by untrustworthy strangers will continue to dictate our decisions…the lack of personalized contacts means that individuals lack that sense of personal commitment that makes the world of small groups go round’
Anyone who uses, the slightly more transparent and therefore marginally more civilized, LinkedIn will be familiar with the words of the data-scientist W. Edwards Deming, which seem to pop up in my own feed at least twice a week.
‘Without data you are just another person with an opinion’.
In our business there are no shortage of opinions. Unfortunately, many are spectacularly uninformed opinions.
Deming, quite rightly, demands the objective facts. And we have more facts and data at our disposal than at any time in human history.
However to complete the picture, and to take the opportunity that data and technology give for creativity, I propose an addendum to Deming’s thesis.
Without data you are just another person with an opinion? Correct.
But, without a coherent model of human behavior, you are just another person (or AI) with data.
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This is the original and longer version of an op-ed that appeared in AdNews in August.
There’s a Bill Bernbach quote that appears from time to time.
It’s the one where Bill takes aim at a particular flavour of advertising that was popular in the early 60’s.
“There are a lot great technicians in advertising. And unfortunately they talk the best game. They can give you fact after fact after fact. They are the scientists of advertising. But there’s one little rub. Advertising is fundamentally persuasion and persuasion happens to be not a science, but an art.”
When Bernbach goes after ‘science’, I’d propose that he is really just offering the ‘creativity’ counter position to the harder selling advertising as championed by the likes of his rival, Rosser Reeves.
Reeves was influenced by the writings of Claude Hopkin who had published a ‘manual’ for this kind of functional approach entitled ‘Scientific Advertising’ and was dismissive of overly creative executions.
Over time Bill’s statement has become contentious, and fuels the continuous Art v Science false dichotomy. As with most dichotomies the truth is more about the entwinement of the two propositions.
I’d argue that when Bill says ‘science’ he really means ‘formulaic’. I’d also argue that Bill himself might have been more scientific in his approach than the ‘scientists’ that he found irritating.
The Scientific Method is an organised way that helps scientists, strategists or creatives answer a question or begin to solve a problem.
Start with an observation.
If you're not naturally curious about the world then you are unlikely to be able to solve problems creatively. Half the battle is just noticing things, saving them for further thought and investigation and connecting them with other things you’ve noticed.
Have an interesting question.
After making an interesting observation, this should next form an interesting question. These kind of questions usually begin with ‘why?’
Now form a hypothesis.
A hypothesis is an informed guess as to the possible answer to the question. The hypothesis may arrive as soon as the question is posed, or it may require a lot of fiddling about. There’s often a few different hypotheses. Another word for this is ‘ideas’.
Conduct experiments.
Ideas must be tested. Bernbach wasn’t a fan of pre-testing. Rightly so, if pre-testing worked then everyone would love all the advertising. The best experiment is putting it out into the world.
Analyse the data and draw a conclusion.
Here’s where we could all do better. We obsess over the wrong data, give disproportionate focus to the insignificant and are distracted by noise. But when we look in the right place then perhaps we have an observation that starts us on the cycle again.
To conclude, Bill Bernbach was as much scientific as creative. The two fields are not incompatible, they are one and the same.
Indeed, Bill was also something of an intuitive evolutionary psychologist.
‘It is fashionable to talk about changing man. A communicator must be concerned with unchanging man, with his obsessive drive to survive, to be admired, to succeed, to love, to take care of his own.’
