20 Takeaways From The CogX Event, London

IMG_5043First, a clarification. I visit events such as the CogX events to be stimulated, to have new thoughts and to have the neurons in my brain fired in new ways. I go to learn, not to network. So I agonise over sessions to attend, and importantly the sessions I miss. Of which there is an overwhelming majority, as this event was running 5–7 conference tracks at any time, as well as a lot of other small stage events. By and large though it’s a weirdly monastic experience, surrounded by people, but very much alone in my head, to the point where I’m actually a little bit annoyed when somebody wants to talk to me! This then is the list of things that made me think.

  1. If there was one session that made attending the event worthwhile for me, it was Zavain Dar’s session on the New Radical Empiricism (NRE). His argument is that the traditional scientific method is based on certain rational assumptions — which are now challenged. In the classic method, you would hypothesise that the earth was round, find the right experiments to run, collect data and prove/ disprove your hypothesis. This runs into trouble when the computational models are too complex and / or changing too often — such as gene sequencing or macroeconomic data. Also this is not efficient when the range of options is vast and we don’t know what data might be relevant — e.g. curing cancer. The traditional methods may yield results, but it might take a lifetime of research and work to get there. What Dar calls the NRE is the opposite — a data driven view which allows machine learning to build hypotheses based on patterns it finds in the data. So in the NRE world, rather than starting with whether the earth is round, you would share a lot of relevant astronomical data and ask the machine to discover the shape of the world. This approach works best in areas where we have a data explosion such as genomics and computational biology. Or where there is plenty of data but is shackled by traditional hypotheses based methods, such as macroeconomics. An additional problem that NRE solves is where the problem space is simply to complex for human minds to compute — both the examples above are instances of this complexity. You may know that Radical Empiricism is by itself a construct from the late 19th century by William James — which eschews intuition and insists on physical experiences and concrete evidence to support cause and effect. Its worth noting that there are plenty of examples of environments where quantifiable data is not yet abundant, where experts still follow the traditional method driven by hypotheses. VC investing, ironically, is such an area!
  2. This also led to a discussion on Deeptech led by Azeem Azhar of Exponential View and panelists from Lux, Kindred Capital and Episode1 Ventures. Deeptech is defined from an investment perspective as companies and start ups who are building products which involve technical risk. Not using existing tech to solve new problems. Usually involving products and ideas which a few years ago would have to subsist on research grants and be housed by academic institutions.
  3. Jurgen Schmidhuber’s session on LSTM was another highlight. Schmidhuber’s PhD thesis on LSTM (Long Short Term Memory), in 1997 was a foundation of the AI advancement which was used by a number of technology products and subsequent development. Schmidhuber presented an excellent timeline of the evolution of AI in the past 20 years and ended with a long view where he explored the role of AI and ML in helping us reach resources that were not on earth but scattered across the solar system, the galaxy and beyond. And how we might perceive today’s technology and advancement in a few thousand years.
  4. One of Schmidhuber’s other points was around curiosity driven learning. Mimicking the way an infant learns, by exploring his or her universe. This is the idea that a machine can learn through observation and curiosity, about it’s environments.
  5. Joshua Gans, the author of Prediction Machines, and professor of Economics and Tech Innovation, talked about AI doing to prediction what computers did to arithmetic. Essentially they dramatically reduced the cost of complex arithmetical operations. AI does the same for prediction or inference. Which is essentially making deductions about the unknown based on the known. And bringing down the cost of prediction has a massive impact on decision making because that’s what we’re doing 80% of the time, at work, as managers.
  6. Moya Green, the CEO of Royal Mail talked about the transformation that Royal Mail went through — including an increase in technology team size from 60 to over 550 people. She also made the comment that most managers still under-appreciate the value of tech, and overestimate their organisations capability to change, and absorb new tech.
  7. Deep Nishar of Softbank used an excellent illustrative example of how AI is being used to provide personalised cover art for albums by digital streaming and media providers, based on users choices and preferences.
  8. Jim Mellon, long time investor and current proselytiser of life-extending tech suggested that Genomics would be a bigger breakthrough than semiconductors. He was joined by the chief data officer for Zymergen, which works on bio-manufactured products, based on platforms which work with microbial and genetic information.
  9. A very good data ethics panel pondered the appropriate metaphors for data. We’ve all heard the phrase data is the new oil. Yet that may be an inadequate descriptor. Experts on the panel posited metaphors such as ‘hazardous material’, ‘environment’, ’social good’ etc. because each of these definitions are useful in understanding how we should treat data. Traditional property based definitions are limited and it was mentioned that US history has plenty of examples of trying to correct social injustice via the property route (reservations for native Americans), which have not worked out. Hence we need these alternative metaphors. For example, the after-effects of data use is often misunderstood, and sometimes it needs to be quarantined or even destroyed, like hazardous material, according to Ravi Naik of ITN Solicitors.
  10. Michael Veale of the UCL suggested that ancient Greeks used to make engineers sleep under the bridges they built. This principle of responsibility for data products needs to be adopted for some of the complex products being built today by data engineers. Data use is very hard to control today, so rather than try and control it’s capture and exploitation, the focus perhaps should be on accountability and responsibility.
  11. Stephanie Hare made the excellent point that biometric data can’t be reset. You can reset your password or change your email, phone number, or even get a completely new ID. But you can’t get new biometrics (yet). This apparent permanence of of biometrics should give us pause to think even harder about how we collect and use it for identification, for example in the Aadhaar cards in India.
  12. Because of the inherently global flows of data and the internet, the environmental model is a good metaphor as well. Data is a shared resource. The lines of ownership are not always clear. Who owns the data generated by you driving a hired car on a work trip? You? Your employer? The car company? The transport system? Clearly a more collective approach is needed and much like social goods, such as the environment, these models need to validate the shared ownership of data and it’s joint stewardship by all players in the ecosystem.
  13. Stephanie Hare, who is French Historian by education provided the chilling example of how the original use vs ultimate use of data can have disastrous consequences. France had a very sophisticated census system and for reasons to do with it’s muslim immigrants from North Africa captured the religion of census correspondents. Yet, this information was used to round up all the jewish population and hand them over to the Nazis because that’s what the regime at the time felt justified in doing.
  14. On a much more current and hopeful note, I saw some great presentations by companies like Mapillary and SenSat, and Teralytics which focus on mapping cities with new cognitive tools. Especially for cities which are of less interest to tech giants, and using crowdsourced information and data, which may include mobile phone and wifi usage, or street level photographs all used with permission, for example.
  15. At a broader level, the smart cities discussions, strongly represented by London (Theo Blackwell) and TFL (Lauren Sager Weinstein) shows the transition from connected to smart is an important one. Very good examples by TFL on using permission based wifi tracking at platforms to give Line Managers for each of the tube lines much more sophisticated data on the movement of people, to make decisions about trains, schedules and crowd management, over and above the traditional ways which include CCTV footage or human observation on platforms.
  16. At a policy level, a point made by Rajiv Misra, CEO of Softbank Investment Advisors (aka the Vision Fund) is that while Europe leads in a lot of the academic and scientific work being done in AI, it lags in the commercial value derived by AI, notably to China and the US. A point echoed by the House of Lords report on AI which talks about the investments and commitment needed to sustain the lead the UK enjoys in AI, currently. Schmidhuber’s very specific solution was to mimic the Chinese model — i.e. identify a city and create an investment fund of $2bn to put into AI.
  17. I also sat through a few sessions on Chatbots and my takeaway is that chatbots are largely very much in the world of hype machines. There is very little ‘intelligence’ that it currently delivers. Most platforms rely on capturing all possible utterances and coding them into the responses. Even NLP is still at a very basic stage. This makes chatbots basically a design innovation — where instead of finding information yourself, you have a ‘single window’ through which to request all sorts of information. Perhaps its a good thing that the design challenges are getting fixed early, so that when intelligence does arrive, we won’t stumble around trying to design it.
  18. Within the current bot landscape, one useful model that I heard is ‘Treat a bot like a new intern that doesn’t know much’ and let it have a similar personality so that it provides responses that are appropriate and also sets expectations accordingly. It might just start with a ‘hello, I’m new so bear with me if I don’t have all the answers’, for example.
  19. Dr Julia Short, who has built Spot — a chatbot to handle workplace harassment provided a very interesting insight about the style of questions such a bot might ask. A police person’s questions on the one hand are all about capturing in detail exactly what happened and making sure that the respondent is clear and lucid about events, incidents, and the detail. A therapists questions and line of discussion on the other hand is all about helping a victim get over some the details and get on with their lives. This suggests that you need to be clear whether your bot is an extension of the law enforcement or a counselling body. It also suggests that you might want to do the former before the latter.
  20. A really important question that will not leave us is: what do we do if the data is biased? If we are conscious of certain biases which are to do with gender, race or age, then we can guard against them either at the data level or at the algorithmic level, but we also need to be able to detect biases. For example, the example which I’ve now read in a few places of how the leniency of sentences handed out by judges in juvenile courts in the US vary inversely with the time since the last meal of the judge.

Clearly all of this really represents under 20% of the great discussions over the 2 days. Please do add your own comments, takeaways and thoughts.

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Reading List: 7 for 7 – April 23: Palantir > Facebook, Generative Design, Alexa With Eyes, and More…

The 7 most interesting things I’ve read over the past 7 days.
 future doors
(image credit: Pixabay)

(1) The One Thing You Should Read about AI this week: 
In March, we ran a TCS DEX event where we posed the question to our partners and clients, around whether every company should have an AI strategy. While there was general agreement about the need for an AI strategy, there was no clear starting point. This may be the challenge for most companies. And perhaps the first steps towards a strategy are gathering information and running experiments.
If you read one thing this week, read this AI paper by the McKinsey Global institute – they publish results from a comprehensive survey and analysis of AI across industries, functions, and use cases and by the relevance of the different techniques, such as Transfer Learning, Reinforcement Learning, and Deep Learning Neural Networks. If some of that sounds obscure to you, I suggest reading up a little bit as these will become common business parlance in the not-too-distant future, and clients will be asking about them. In any case, succinct explanations are provided in the paper. It will probably take you a couple of hours to read (not skim) the 40 odd pages. Here are some of the very high-level takeaways:
  1. Industries where the number of use cases are the highest, include (1) Insurance, (2) Banking, (3) Retail, and (4) Automotive & Assembly.
  2. Functions with the highest number of use cases include (1) Supply Chain management and manufacturing, and (2) Marketing and Sales
  3. Specific domains where the impact might be the highest include (1) customer service & management (2) risk modelling (3) predictive service / intervention (4) workforce productivity and efficiency (5) analytics-driven hiring and retention, and (6) yield optimisation.
Some other takeaways:
  • The highest absolute impact of AI is to be found in Retail, but Travel and transport & logistics can extract the highest incremental value over other analytics techniques.
  • Image data is the highest value, after structured and time series data, and ahead of text.
  • Challenges and limitations: (1) labelling training data (2) obtaining large enough data sets (3) explaining the outcomes and decisions in clear enough terms – e.g. for product classification or regulatory (4) transferring of findings to adjacent use cases, and (5) risk of bias in data/ algorithms

(2) Data: Facebook is a misguided amateur compared to Palantir 
Palantir is much more dangerous than FB. Why? (1) Because Peter Thiel, the founder is a man of metamorphosis – he has quixotic views of the world – such as ‘freedom is not compatible with democracy’; (2) because Palantir is a much more shadowy and secretive organisation but built specifically for next-generation analytics for powerful clients. (3) Because this kind of analytics power can be destructive if individuals go rogue – the article talks about Peter Caviccia who ended up running his own spying operation within JPMorgan in what is described in the article as Wall Street meets Apocalypse Now, and (4) because tools like this are being used by police forces such as LAPD to predict crime – but also to do that to build deep and intricate views of a lot of individuals and their lives. The article also provides a very good visual model of Peter Thiel’s incredible original Paypal team and network which includes Elon Musk, Reid Hoffman (LinkedIn), Steve Chen (Youtube) and many others.

(3) Design: Welcome to Generative 3D Design 
What do you do when you need to design and build a spinal implant that needs to be appropriately strong, light and pliant? You use an algorithm-driven design process called generative design with 3D printing. Algorithmic design takes in your specifications or requirements and generates a number of options, which are developed faster than humans and enables a lot more personalisation of complex materials. In future, these will probably be custom built to specs in a way that humans simply can’t. It also uses the least amount of material possible (it’s one of the constraints/ objectives). This story in the Wired magazine talks about how Nuvasive does this using AI and 3D.

(4) eCommerce and Retail – change of guard, and disruption for the economy
This week we had a direct comparison between M&S vs ASOS: M&S is a struggling brand – losing share in apparel, and under pressure on foods. Other brick and mortar retailers like New Look are also in trouble. ASOS sales, on the other hand, hit £1.9 bn 2017 which amounted to a 33% increase. It’s also instructive to note that eCommerce contributes some 25% of British clothes retail numbers. In fact, the UK has the highest amount of online commerce (as a % of overall retail numbers – almost 18%), but the retail industry also accounts for 10% people and 10% of the economy – so significant disruptions lie ahead.

(5) Asset-light business models 
We’ve seen them in telecoms (MVNOs), in retail, and also in utilities. Lightweight, direct to consumer competitors who don’t carry the baggage of their larger competitors. They have no legacy IT and are built ground up on digital platforms, for a start, and also have a much more nimble operating model. Companies like Asos and Ovo energy are successful because they attract a particular consumer niche, operate in an agile way and are not weighed down by the legacy business and IT challenges of their larger peers (zero inventory, for example). This trend goes all the way down to micro brands in the consumer goods space. Many of these businesses will die or stay micro, but once in a generation, they will lead to the next FB or Amazon.

(6) Alexa Fashion – a glimpse of the future 
What’s Alexa’s next trick? How about a camera that can give you fashion feedback? Amazon’s Echo Look (not yet launched to the public, but on invitation only basis) has a camera and lets you take selfies and gives you feedback on what you’re wearing. For those worried about whether Amazon was listening to all your conversations, this will definitely be a step too far! This piece is a good take on the social and psychological implication of a tool like this. Of course, if you want algorithmic advice but don’t want something that invasive, you can always turn to Miquela

(7) Battery Wars 
We all know that a move to electric cars is a ‘when’ and not an ‘if’ question by now. What that means, however, is a near insatiable demand for batteries and a huge spotlight on battery technology. Currently, the minerals that go into batteries such as Lithium and Magnesium are seeing a huge spurt in demand. It turns out that DR Congo is the worlds dominant source of Magnesium. In all of this, the UK is seeking to play a leadership role in battery technology. But is it either feasible or desirable? On the other hand, Williams has been working on safer batteries which are tough-tested in the Formula E competition – where electric-only cars race, collide and crash.

Simplicity – A Very Complex Problem

The holy grail of almost every product and service is simplicity. After all, nobody sets out to create a complicated product. But with digital success so squarely predicated on  engagement and user experience, simplicity has evolved from being an unstated philosophy to a raucous war-cry, uttered often and fervently in board-rooms and product meetings, and KISS (Keep It Simple, Stupid) signs adorning complex project plans. 

Yet, simplicity remains a fiendishly complex challenge. 

Simplicity is a loaded term, it typically refers to things which are basic, or easy to use, or intuitive. It can be interpreted as doing things that bring calmness. In the Indian epic Mahabharata, Judhishtir was so named because he could be calm, in battle. We are usually able to point to examples of simplicity. Whether it’s a home-cooked meal, or the joy of a sunset, our favourite beverage consumed in our favourite chair. The consensus for simplicity in products, tends to be towards intuitiveness. Something that does not require education, training or a manual for example, the Nokia phones and an the iPhone, both in their way wonderfully intuitive. The power of simplicity is also obvious. Simplicity drives acceptance and adoption. It is the reason why soccer is the worlds favourite game, or why the world wide web is indeed world-wide. Simplicity for many people is a deeply held philosophy. For some it is typified by a child like state. 

One might argue that simplicity is born, not created. Some people have a great ability to make things appear incredibly simple. Listen to Alex Fergusson speak about football strategy and tactics, and it will seem like anybody could have done it. And yet, he stands ruthlessly alone in his decades-long and consistent success as a football manager. Closer to home, one of my closest friends from childhood, Kabir, is a well respected and successful leader in the retail industry in India. There are two things I will always remember about Kabir. Almost 20 years ago, when he was a middle manager, handling a menswear category, I asked him why he didn’t stock shoes in his store and he said there was too little return per square-foot of space, where space is the most expensive part of retail, in India. I used to tease him about his fascination about returns per square foot but he had already drilled down to the nub of the problem. The other vivid memory from about the same time, was walking into his office and seeing his desk, an expanse of table surface with nothing but a desk-phone. For somebody like me, who lives and works in perma-clutter, this has always been a utopean and other-worldly fantasy. We see examples of simplicity all around us – Steve Jobs famously had a home environment that embodied simplicity. Mark Zuckerberg’s wardrobe rationale is now well known. Living simple clearly has a contribution to make to your output. But those who can simplify effectively often are seemingly able to reach a higher plane of thinking about a problem. 

Some people naturally simplify, some don’t analyse it, but can go with the flow, for some it’s not desirable – simplicity isn’t a universal desire. It’s not a panacea. But clearly it only makes sense in context. Hence, like beauty, it depends on the eye of the beholder. It may be contextual – if you give a hunter-gatherer a plough, she may not know what to make of it. To a farmer, it may be intuitive. It stands to reason therefore that to design something simple you must be able to see it from the users frame of reference, and appreciate his or her skill and capability. It also means that a way of solving complex problems is to change the frame of reference. Not surprisingly, some of the quoted methods involve asking ‘what would a child do?’ or ‘what would granny do?’. Seen this way, simplicity implies a kind of innocence. A happy naïveté even. Einstein’s warning is probably relevant here. “You must make things as simple as possible, but no simpler.” Over-simplification is a trap to avoid – lest you bore the user. And of course, when your toddler son or daughter asks you how babies are made, you  are caught in the very vortex of the problem of simplification. 

There are probably tomes of scientific papers dealing with simplicity and complexity. Daniel Kahneman’s System 1 thinking is an obvious surrogate for simplicity. The purpose of this post is not to delve into the scientific understanding of how we deal with simplicity, although it would probably be a useful perspective to add. 

The problem is, while many of know how we would define it, and almost all of us would recognize it if we saw it, very few are able to find the path to it. And yet, simplicity is as much a journey as a destination. 

The next time, when you switch on your TV set at the end of a long week and settle in to watch a primetime show on Saturday night, maybe the next series of Breaking Bad, all you’ll need to do is switch on the TV, the set top box, and find the right channel. But for the broadcaster, the process may have started almost a year before. In fact considering the complexity of a broadcast operation – including all the scheduling, planning, program acquisition, ad-sales operations, the movement of physical and digital media, transcoding, automation and transmission, compliance and legal and other areas, it is an everyday miracle that you switch the TV on and there’s something there to watch. (And this is without even considering the effort and challenge of producing the show, manufacturing the TV set and getting the signal into your living room). 

Think of some of your other simple examples – withdrawing money from an ATM, receiving the newspaper at your door in the morning, turning the ignition key in your car; each of these and a hundred more simple tasks often mask an ocean of complexity that goes on unnoticed behind the scenes. This is the first lesson of simplicity. Often, to make something simple, especially for an end user of a product or process, you have to take on and resolve an enormous amount of complexity. Personally speaking, nothing annoys me more than managers who cut short a discussion around a problem by claiming the faux high-ground of simplicity. Complexity doesn’t vanish, it gets resolved, in great detail, by somebody else, and kept under the hood, so you can just turn a key or press a button to start a car. 

In the lives we live today, simplicity is often reductive – it involves removing the noise and complexity. Very rarely is our starting point for anything we’re trying to design, simple. We need to untangle, and even unlearn. And more often than not, it’s a journey. A sentiment echoed by a lot people who responded to my request for interpreting the term. Achieving simplicity is therefore, anything but simple. Especially so, for an organisation or a group, rather than an individual. It therefore involves the mastery of concepts such as minimalism, essentialism or lean. In each of these, we are trained to take Occam’s razor to a problem space. But it can take years of experience to arrive at a state where the eye can spot the waste and the extraneous. The second great lesson of simplicity therefore is the wisdom of spotting the signal in a sea of noise. Or conversely, identifying the waste from the core. This is as true of everyday life, as it is of product design. 

Identifying the waste is just a part of the problem. Sometimes, the far greater challenge is the choice making. To deliver the shiny elegance that is the iPhone, Steve Jobs & co had to make some pretty big choices. An obvious one amongst them being the complete inability of the user to add memory, or change the battery of the phone. Features as core to a phone, you might think, as a leg, to a table. And yet, this was the stark choice exercised by Apple. When was the last time you let go of something significant, to achieve simplicity? 

There are some very successful digital platforms and products out there who owe their success at least to a significant part to their simplicity. Off the top of my head, I can think of Dropbox, Evernote, Trello, and Spotify as some of the highly popular and simple products. The bar for simplicity can often be reset. Salesforce.com is a platform which has succeeded because it greatly simplified the sales management process, but today, if you look at tools like Pipedrive, they make salesforce look like the older, complex model. Remember Einstein’s wisdom about ’no simpler’ – and yet, this can be redefined and barriers can be broken. The journey is the point. 

You only have to look at some of the most successful products, apps and websites to understand how visually simplicity works. From fonts and colours, to choices and tasks, there is now as much science as art to creating the intuitive usability in products that we all crave. But of course, simplicity does not start and end on the screen, it needs to be carried through a range of interactions and experiences. Is it simple to speak with somebody on the phone? Is it simple to return goods? Pay a bill? Find information? Upgrade? Downgrade? And remember for each of these, the simpler you make it, the more complex it often is, behind the scenes. 

A part of the problem is that there is no formula. Most people tend to view simplification as decluttering, removal, or reduction to the basics or essentials. Yet, while this idea is by itself appealing, there are times when simplicity may come through synthesis. Remember the story about the blind men and the elephant. Or imagine trying to describe a car by it’s components. Sometimes simplicity comes from the whole, rather than the parts. Focusing on the whole provides clarity of thought. 

And then there’s the problem of time. What starts of as simple invariably grows complex. Just look at the number of sects of any major religion. Or consider Twitter – from the idea of creating a simple 140 character update, an entirely new language, syntax and ontology of acronyms has risen. Reading a tweet is anything but simple today. It is human nature to complicate, it seems, so the yin and yang of simplicity and complexity must coexist, over time. 

The bottom line is, whether you treat simplicity as a philosophical foundation of your life, or a professional preference, and whether it manifests in your relationships or your products, its clear that you may need to treat it as a series of simplifications and pay as much attention to the process as the outcome. And the journey may be far from easy, and paved with false results. After all, as HL Mencken said, “For every complex problem there is an answer that is clear, simple, and wrong”. 

Ranting At The C-Word!

So…

I was at a Creative Industries event organised by the Technology Strategy Board yesterday. An event designed to bring together the creative and technology disciplines. And one of the many issues touched upon was the need to break out of the ‘creative industries’ straightjacket and to explore creative roles and jobs in other industries. Which led me to thinking about one of my favourite subjects: 

The rampant misuse of the C-word. 

In fact, it’s a very close call as to which word is more bastardised – ‘creative’? or ‘innovation’? But today I’m talking about the former. What does the word mean to you? Is it a skill? Is it a profession? Is it a role? Is it perhaps just a trait? 

I have experienced at close quarters for many years, the cliched and lazy labelling within the technology industry of the concept of creative work. This usually assumes that (a) all ‘creatives’ are the same, and makes no distinction between architectural or conceptual skills versus execution and tool-level skills, for example; (b) phrases like ‘UX’, ‘UI’, ‘creative’, ‘design’ and anything else which smells of visual design related activity are all synonyms and can be used interchangeably. 

This isn’t one sided. This kind of apathetic adjectivisation is common even among creative communities, when it comes to others. You’re a techie, or a suit. Or you’re a quant… you need classifying, not for your good but more for people to be able to pigeonhole you in their mental cubicles. 

I like to think of creativity as a trait. A problem solving capability which looks at the same problem differently. By this definition Newton, Gauss and Einstein would be among the most creative people the world has known. Serial inventors, entrepreneurs and technologists are creative. The best enterprise or solution architects in technology companies demonstrate this kind of creativity on an everyday basis. 

What we commonly call ‘creative’ is actually either referencing design or art. And again, these are two very distinct worlds. Design is a science, a discipline with a clear deliverable against a defined need. You design a house, or a doorknob, or a website or a logo with a view to delivering some fairly clearly defined objectives. Consequently good design is often dependent on the effective articulation of the objectives. 

Art is also a discipline and involves technique and often, structure, but by definition the objective of art is defined by the creator and as such it becomes a form of expression. The creator might want to make a point, raise an issue, support a cause, but equally, she might pander to a whim, be overcome by a subliminal urge, be provocatively abstract or seek no meaning at all. 

You can, therefore, have creative people who have nothing to do with design or art. You can have designers who are good but not particularly creative. Unusual but true. You can have great artists who would make bad designers and vice versa. Usually on the basis of their willingness or ability to work within the structure of a brief and the tyranny of an objective.

If you wrote a book with the purpose of making money, creating a bestseller, or selling a movie script, you would be designing. If you wrote a book that wasn’t governed by the outcome, you would be an artist. You might be a great wordsmith but poor at plots. Those are examples of the techniques you need for any discipline.

Advertising therefore is more design than art. Except for those wonderful ads which are great except that you can’t remember the product or brand. That’s good art, not good design. Or those ads which are made for awards. Or perhaps, that too, should be called design. 

In the context of film-making, who is the creative brain? Definitely the director, and to an lesser but important extent, the editor, the cinematographer and choreographer, to name a few. But usually not the actors. They are usually following the directors brief. They are in effect, designing a performance.

Of course, I’m stretching a point. In every one of these examples, there is a need for creativity and artistic expression, which may well make the difference between good and mediocre, and between making history and being history. I’m simply driving these giant imaginary wedges between art, design, and creativity, to make the point bluntly.

And to state the blindingly obvious, design is greatly enhanced by creativity. You only have to look at some of the best design to see the magic touch of a creative insight or treatment. This chair, this ambulance redesign, this wheelchair, this plug, and this folding wheel, all have a creative spine which makes them stand taller than their peers. It’s just important to distinguish between the terms for better results, especially when you’re in the results (read: design) business. 

And so, every time at work I see people lumping terms together, I bite my tongue and control my fingers from typing that shouty email. But the irony is of course, that when you’re designing a mobile app, which is a highly constrained experience in so many ways, the creativity often needs to come from the technologist, and the discipline, from the designer. 

As to art, you can always find it in the gallery.