Buzzword Buzzkill: Excitement & Overstatement in Tech Communications

An illustration of three „pixelated“ cupboards next to each other with open drawers, the right one is black

The use of AI images is not just an issue for editorial purposes. Marketing, advertising and other forms of communication may also want or need to illustrate work with images to attract readers or to present particular points. Martin Bryant is the founder of tech communications agency Big Revolution and has spent time in his career as an editor and tech writer. 

“AI falls into that same category as things like cyber security where there are no really good images because a lot of it happens in code,” he says. “We see it in outcomes but we don’t see the actual process so illustration falls back on lazy stereotypes. It’s a similar case with cyber security, you’ll see the criminal with the swag bag and face mask stooped over a keyboard and with AI there’s the red-eyed Terminator robot or it’s really cheesy robots that look like sixties sci-fi.”

The influence of sci-fi images in AI is strong and one that can make reporters and editors uncomfortable with their visal options. “ “Whenever I have tried to illustrate AI I’ve always felt like I am short changing people because it ends up being stock images or unnecessarily dystopian and that does a disservice to AI. It doesn’t represent AI as it is now. If you’re talking about the future of AI, it might be dystopian, but it might not be and that’s entirely in our hands as a species how we want AI to influence our lives,” Martin says. “If you are writing about killer robots then maybe a Terminator might be OK to use but if you’re talking about the latest innovation from DeepMind then it’s just going to distort the public understand of AI either to inflate their expectations of what is possible today or it makes them fearful for the future.” 

I should be open here about how I know Martin. We worked together for the online tech publication The Next Web where he was my managing editor and I was UK editor some years ago. We are both very familiar with the pressures of getting fast-moving tech news out online, to be competitive with other outlets and of course to break news stories. The speed at which we work in news has an impact on the choices we can make.

“If it’s news you need to get out quickly, then you just need to get it out fast and you are bound to go for something you have used in the past so it’s ready in the CMS (Content management system – the ‘back end’ of a website where text and images are added.),” Martin says. “You might find some robots or in a stock image library there will be cliches and you just have to go with something that makes some sense to readers. It’s not ideal but you hope that people will read the story and not be too influenced by the image – but a piece always needs an image.”

That’s an interesting point that Martin is making. In order to reach a readership, lots of publications rely on social media to distribute news. It was crowded when we worked together and it sometimes feels even more packed today. Think about the news outlets you follow on Twitter or Facebook, then add to this your friends, contacts and interesting people you like to follow and the amount of output they create with links to news they are reading and want to comment upon. It means we are bombarded with all sorts of images whenever we start scrolling and to stand out in this crowd, you’re going to need something really eye-catching to make someone slow down and read. 

“If it’s a more considered feature piece then there’s maybe more scope for a variety of images, like pictures of the people involved, CEOs, researchers and business leaders,” Martin says. “You might be able to get images commissioned or you can think about the content of the piece to get product pictures, this works for topics like driverless cars. But there is still time pressure and even with a feature, unless you are a well-resourced newsroom with a decent budget, you are likely to be cutting corners on images.” 

Marketing exciting AI

It’s not just the news that is hungry for images of AI. Marketing, advertising and other communications are also battling for our attention and finding the right image to pull in readers, clicks or getting people to use a product is important. Important, but is it always accurate? Martin works with and has covered news of countless startup companies, some of which use AI as a core component of their business proposition. 

“They need to think about potential outcomes when they are communicating,” he says “Say there is a breakthrough in deep neural AI or something it’s going to be interesting to academics and engineers, the average person is not going to get that because a lot of it requires an understanding of how this technology works and so you often need to push startups to think about what it could do, what they are happy with saying is a positive outcome.” 

This matches the thinking of many discussions I have had about art and the representation of AI. In order to engage with people, it can be easier to show them different topics of use and influence from agriculture to medical care or dating. These topics are far more familiar to a wider audience than a schematic for an adversarial network. But claiming an outcome can also be a thorny issue for some company leaders.

“A lot of startup founders from an academic background in AI tend to be cautious about being too prescriptive about how their technology could be used because often if they have not fully productised their work in an offering to a specific market,” Martin explains. “They need to really think about optimistic outcomes about how their tech can make the world better but not oversell it. We’re not saying it’s going to bring about world peace, but if they really think of examples of how the AI can help people in their everyday lives this will help people engage with making the leap from a tech breakthrough they don’t understand to really getting why it’s useful.” 

Overstating AI

AI now appears to be everywhere. It’s a term that has broken out from academia, through engineering and into business, advertising and mainstream media. This is great, it can mean more funding, more research, progress and ethical monitoring and attention. But when tech gets buzzy, there’s a risk that it will be overstated and misconstrued. 

“There’s definitely a sense of wanting to play up AI,” Martin says. “There’s a sense that companies have to say ‘look at our AI!’ when actually that might be overselling what is basic technology behind the scenes. Even if it’s more developed than that, they have to be careful. I think focusing on outcomes rather than technologies is always the best approach. So instead of saying ‘our amazing, groundbreaking AI technology does this’ – focusing on what outcomes you can deliver that no one else can because of that technology is far more important. 

As we have both worked in tech for so long, the buzzword buzzkill is a familiar situation and one that can end up with less excitement and more of an eyeroll. Martin shared some past examples we could learn from, “It’s so hilarious now,” he says. “A few years ago everything had to have a location element, it was the hot new thing and now the idea of an app knowing your location and doing something relevant to it is nothing. But for a while it was the hottest thing. 

“Gamification was a buzzword too. Now gamification is a feature in lots and lots of apps, Duolingo is a great example but it’s subtly used in other areas  but for a while startups would pitch themselves saying ‘we are the gamified version of X’.”

But the overuse of language and their accompanying images is far from over and it’s not just AI that suffers. “Blockchain keeps rearing its head,” Martin points out. “It’s Web3 now, slightly further along the line but the problem with Web3 and AI is that there’s a lot of serious and thoughtful work happening but people go ahead with ‘the blockchain version of X or web3 version of Y’ and because it’s not ready yet or it’s far too complicated for the mainstream, it ends up disillusioning people. I think you see this a bit with AI too but Web3 is the prime example at the moment and it’s been there in various forms for a long time now.” 

To avoid bad visuals and buzzword bingo in the reporting of AI, it’s clear through Martin’s experience that outcomes are a key way of connecting with readers. AI can be a tricky one to wrap your head around if you’re not working in tech, but it’s not that hard when it’s clearly explained.”It really helps people understand what AI is doing for them today rather than thinking of it as something mysterious or a black box of tricks,” Martin says. “That box of tricks can make you sound more competitive but you can’t lie to people about it and you need to focus on outcomes that help people understand clearly what you can do. You’ll not only help people’s understanding of your product but also the general public’s knowledge of   what AI can really do for them.”

Avoiding toy robots: Redrawing visual shorthand for technical audiences

Two pencil drawn 1960s style towy robots being scribbled out by a pencil on a pale blue background

Visually describing AI technologies is not just about reaching out to the general public, it also means getting things marketing and technical communication right. Brian Runciman is the Head of Content – British Computer Society (BCS) The Chartered Institute of IT. His audience is not unfamiliar with complex ideas, so what are the expectations for accompanying images? 

Brian’s work covers the membership magazine for BCS as well as a publicly available website full of news, reports and insights from members. The BCS membership is highly skilled, technically minded and well read – so the content on site and in the magazine needs to be appealing and engaging.  

“We view our audience as the educated layperson,” Brian says. “There’s a base level of knowledge you can assume. You probably don’t have to explain what machine learning or adversarial networks are conceptually and we don’t go into tremendous depth because we have academic journals that do this.” 

Of course writing for a technical audience also means Brian and his colleagues will get smart feedback when something doesn’t quite fit expectations. “With a membership of over 60 thousand, there are some that are very engaged with how published material is presented and quite rightly,” Brian says. “Bad imagery affects the perception of what something really is.”

So what are the rules that Brian and his writers follow? As with many publications there is a house style that they try to keep to and this includes the use of photography and natural imagery. This is common among news publications that choose this over illustration, graphics or highly manipulated images. In some cases this is used to encourage a sense of trust in the readership that images are accurate and have not been changed. This also tends to mean the use of stock images. 

“Stock libraries need to do better,” Brian observes. “When you’re working quickly and stuff needs to be published, there’s not a lot of time to make image choices and searching stock libraries for natural imagery can mean you end up with a toy robot to represent things that are more abstract.”

“Terminators still come up as a visual shorthand,” he says. “But AI and automation designers are often just working to make someone’s use of a website a little bit slicker or easier. If you use a phone or a website to interact with an automated process it does what it is supposed to do and you don’t really notice it – it’s invisible and you don’t want to see it. The other issue is that when you present AI as a robot people think it is embodied. Obviously, there is a crossover but in process automation, there is no crossover, it’s just code, like so much else is.”

Tone things down and make them relatable 

Brian’s decades-long career in publishing means he has some go-to methods for working out the best way to represent an article. “I try to find some other aspect of the piece to focus on,” he says. “So in a piece about weather modelling, we could try and show a modelling algorithm but the other word in the headline is weather and an image of this is something we can all relate to.” 

Brian’s work also means that he has observed trends in the use of images. “A decade or so ago it was more important to show tech,” he says. “In a time when that was easily represented by gadgets and products this was easier than trying to describe technologies like AI. Today we publish in times when people are at the heart of tech stories and those people need to look happy.”

Pictures of people are a good way to show the impact of AI and its target users, but it also raises other questions about diversity – especially if the images are predominantly of middle aged white men. “It’s not necessary,” says Runciman. “We have a lot of head shots of our members that are very diverse. We have people from minorities, researchers who are not white or middle aged – of which there are loads. When people say they can’t find diverse people for a panel I find it ridiculous, there are so many people out there to work with. So we tend to focus on the person who is working on a technology and not just the AI itself.”

The use of images is something that Brian sees every day for work, so what would be on his wish list when it comes to better images of AI? “No cartoon characters and minimal colour usage – something subtle,” he muses. “Skeletal representations of things – line representations of networks, rendered in subtle and fewer colours.” This nods at the cliches of blue and strange bright lights that you can find in a simple search for AI images, but as Brian points out, there are subtler ways of depicting a network and images for publishing that can still be attractive without being an eyesore.

AI images – an ecosystem problem with a collaborative solution

A handmade sketch of four figures throwing shadows that look like neural networks

Images of AI have been a problem rattling around my mind for many years. As a degree student studying AI, I naturally read a tonne of articles where the writing was excellent but the images did not match.

For decades as a reporter and editor covering technology stories, AI news would come and go but I was limited and frustrated by the options I had to illustrate stories. Now as an MA Illustration student I find myself returning to the problem and working on creating options so that other editors and picture desks have more to work with. It’s not a blame game, there is an ecosystem that desperately needs fresh input to break a cycle of cliches.

It’s not a blame game, there is an ecosystem that desperately needs fresh input to break a cycle of cliches.

Put in brief terms, many reporters don’t choose images to go with their stories, picture editors don’t always have anything other than stereotypes to put on their stories, photographers are often commissioned to shoot work that reinforces those stereotypes. The bottom line is that readers and content consumers get white robots, terminators and flying maths because it takes time and money, focus and expertise to change this and while good media outlets still need to attract readers by publishing quickly, they often take what they can get and move on.

Being one person working to try and change a visual language sometimes felt like an exercise in hubris. Frankly, it feels lonely! I have interviewed so many people, chatted with AI practitioners and other artists, and searched for other people working to solve the problem or change the ratio of images.

Work that affects society and is pushing for a visual cultural shift needs to be done collaboratively – which is precisely how I love to work.

Better Images of AI means I am not chasing this on my jack jones. The project brings together people of passion and expertise. We all know the problem and we can move beyond griping about it and actually work on solutions. Working with BBC R&D and Better Images of AI means working collaboratively. You can banish the idea of an artist who hides in the attic making paintings for years alone. Work that affects society and is pushing for a visual cultural shift needs to be done collaboratively – which is precisely how I love to work.

I have written more about my frustrations and my journey in a previous blog post which talks about the challenge of embodying AI. If you make work about AI or have ideas that would contribute to the stock photography and rendering work, make sure you get in touch.

I’ve been consulting with BBC R&D to work with artists as this project progresses and bring editorial and artistic views to help steer things. The first artist has been commissioned by BBC R&D, the wonderful Alan Warburton who is excellent in his execution, visionary in his ideas generation and a total pro to work with. You should follow his work.

In the coming year I hope to be able to work with more artists to explore this field and eventually, image by image, I think we can create images that will start to change how people perceive this technology and draw away from those images that for so many years have been one of my points of editorial frustration.

Better Images of AI’s first Artist: Alan Warburton

A photographic rendering of a young black man standing in front of a cloudy blue sky, seen through a refractive glass grid and overlaid with a diagram of a neural network

In working towards providing better images of AI, BBC R&D are commissioning some artists to create stock pictures for open licence use. Working with artists to find more meaningful and helpful yet visually compelling ways to represent AI has been at the core of the project.

The first artist to complete his commission is London-based Alan Warburton. Alan is a multidisciplinary artist exploring the impact of software on contemporary visual culture. His hybrid practice feeds insight from commercial work in post-production studios into experimental arts practice, where he explores themes including digital labour, gender and representation, often using computer-generated images (CGI). 

His artwork has been exhibited internationally at venues including BALTIC, Somerset House, Ars Electronica, the National Gallery of Victoria, the Carnegie Museum of Art, the Austrian Film Museum, HeK Basel, Photographers Gallery, London Underground, Southbank Centre and Channel 4. Alan is currently doing a practice-based PhD at Birkbeck, London looking at how commercial software influences contemporary visual cultures.

Warburton’s first encounters with AI are likely familiar to us all through the medium of disaster and science fiction films that presented assorted ideas of the technology to broad audiences through the late 1990s and early 2000s. 

As an artist, Warburton says it is over the past few years that technological examples have jumped out for him to help create his work. “In terms of my everyday working life, I suppose that rendering – the process of computing photorealistic images – has always been an incredibly slow and complex process but in the last four or five years various pieces of software that are part of the rendering  process have begun to incorporate AI technologies in increasing degrees,” he says. “AI noise reduction or things like rotoscoping are affected as the very mundane labour-intensive activities involved in the work of an animator and visual effects artists or image manipulator have been sped up. 

“AI has also affected me in the way it has affected everyone else through smart phone technology and through the way I interact with services provided by energy companies or banks or insurance people. Those are the areas that are more obscured, obtuse or mysterious because you don’t really see the systems. But with image processing software I have an insight into the reality of how AI is being used.” 

Warburton’s knowledge of software and AI tools has ensured that he is able to critically analyse which tools are beneficial. “I have been quite discriminatory in the way I use AI tools. There’s workflow tools that speed things up as well as image libraries and 3D model libraries. But the latter ones provide politically charged content even though it’s not positioned as such. Presets available in software will give you white skinned caucasian bodies and allow you to photorealistically simulate people but, for example, there’s hair simulation algorithms that default to caucasian hair. There’s this variegated tapestry of AI software tools, libraries, databases that you have to be discriminatory in the use of or be aware of the limitations and bias and voice those criticisms.” 

The artist’s personal use of technology is also careful and thought through. “I don’t have my face online,” he says. “There’s no content of me speaking online, I don’t have photographs online. That’s slightly unusual for someone who works as an artist and has necessary public engagement as part of my job, but I’m very aware that anything I put online can be used as training data –  if it’s public domain (materials available to the public as a whole, especially those not subject to copyright or other legal restrictions) then it’s fair game.

“Whilst my image is unlikely to be used for nefarious ends or contribute directly to a problematic database, there’s a principle that I stick to and I have stuck to for a very long time. There’s some control over my data, my presence and my image that I like to police although I am aware that my data is used in ways that I don’t understand. Keeping control over that data requires labour, you have to go through all of the options in consent forms and carefully select what you are willing to give away and not. Being discriminatory about how your data is used to construct powerful systems of control and AI is a losing game. You have to some extent to accept that your participation with these systems relies on you giving them access to your data.”

When it comes to addressing the issues of AI representation in the wider world, Warburton can see the issues that need to be solved and acknowledges that there is no easy answer. “Over the past five or ten years we have had waves of visual interpretations of our present moment,” he says. “Unfortunately many of those have reached back into retro tropes. So we’ve had vaporwave and post-internet aesthetics and many different Tumblr vibes trying to frame the present visual culture or the technological now but using retro imagery that seemed regressive. 

“We don’t have a visual language for a dematerialised culture.”

“We don’t have a visual language for a dematerialised culture. It’s very difficult to represent the culture that comes through the conduit of the smartphone. I think that’s why people have resorted to these analogue metaphors for culture. We may have reached the end of these attempts to describe data or AI culture, we can’t use those old symbols anymore and yet we still don’t have a popular understanding of how to describe them. I don’t know if it’s even possible to build a language that describes the way data works. Resorting to metaphor seems like a good way of solving that problem but this also brings in the issue of abstraction and that’s another problem.”

Alan’s experience and interest in this field of work have led to some insightful and recognisable visualisations of how AI operates and what is involved, which can act as inspiration for other artists with less knowledge of the technology. Future commissions from BBC R&D for the Better Images of AI project will enable other artists to use their different perspectives to help evolve this new visual language for dematerialised culture.