Antonym: The AI literacy Edition
Dear Reader
I’m on holiday, but there’s thing about AI literacy that I need to get out of my head into pixels in a public place, so here it is.
I’m in France. It’s lovely. Here’s the view just as I publish.
AI literacy
The best investment you can make right now is in your own AI literacy. Whether you want to embrace the technology, make your fortune, to resist or change its trajectory understanding how it works will help.
But what is AI literacy?
It’s time to get specific. I’m about to write a lot of words about the how of AI literacy, so I need a solid foundation of what it is.
So I’ve been developing a definition of AI literacy that works for my world: business, specifically non-technical professionals and knowledge workers, usually connected with work that includes strategy, decision-making, creativity, capability, innovation and communications.
At the moment for us, “AI” usually means generative AI, like the large language models used via chatbots like ChatGPT and Claude. Other forms of AI are important, but you will develop a deeper understanding of them if you need to, or access experts who will help integrate them.
Here goes:
AI literacy is the ability to understand, evaluate and use artificial intelligence systems and tools in a responsible, ethical and effective way. It is an evolving set of skills, including critical thinking, knowledge of the limitations of AI systems, and the ability to evaluate their output in relation to work in a field and where they can be applied. For example, in decision making and data analysis, knowing when systems are accurate or prone to error, or in creative processes, how systems can complement human cognition.
And here it is broken into three more parseable sentences.
AI literacy is the ability to understand, evaluate and use artificial intelligence systems and tools in a responsible, ethical and effective way.
It is an evolving set of skills, including critical thinking, knowledge of the limitations of AI systems, and the ability to evaluate their output in relation to work in a field and where they can be applied.
For example, in decision making and data analysis, knowing when systems are accurate or prone to error, or in creative processes, how systems can complement human cognition.
Other descriptions of AI literacy are available and I’ve found them useful, but often they are specific to fields like scientific research or learning itself (a.k.a. pedagogy). I need a working definition that I can use and the above is it, for now.
In a recent Nature article, “The Quest For AI Literacy”, the director of the US National Science Foundation, Sethuraman Panchanathan, said that teaching oneself about AI continues a theme he always been passionate about:
“I’ve always looked at this as literacy,” he says. Earlier in his career, at Arizona State University, he remembers saying how every student at the university needs informatics literacy. “And now that matured to data science literacy, now AI literacy,” he says. With AI advances come a need for upskilling and reskilling at all educational levels and throughout one’s career. “The only constant is change,” he says. “And lifelong learning is an important imperative.”
I’ll be talking a lot more about AI literacy in the coming weeks, but for now, let’s take a step back and look at what came before it.
Literacy evolution
Where informatics and data analytics were the Panchanathan’s precursors to AI literacy, digital literacy was ours at Brilliant Noise.
Back in 2011, when I was developing the plan for what would become Brilliant Noise I used the opportunity of a TEDX talk to articulate some nascent ideas about digital literacy and how we could work better with the web and digital tools that were new at that time. We put forward three “super skills” that would
Networks: The importance of understanding how networks worked, both online and offline. Recognising the power of personal and social networks was crucial, as the web allowed people to access larger networks, connect with others, and share information more effectively.
Sharing as a core activity on the web. The ability to share knowledge, resources, and information freely and easily was seen as key to creating value within a network. He argued that instead of deciding what to share, the focus should have been on deciding what not to share, as sharing had minimal cost and greatly strengthened connections.
Focus and Flow: the importance of managing attention and workflow to maximise productivity. This included using techniques like the Pomodoro Technique to focus on tasks in short, intense bursts, and understanding when to engage with the network for information and when to concentrate on deep work without distractions.
In 2013 and 2014 these ideas evolved into a short book for what became Microsoft’s mobile devices division (FKA Nokia) called Design Your Day.
It brought together new insights from neuroscience, with the of practice design thinking and observations about how we use digital tools. Part I introduced foundational concepts like time and energy management, habit formation, and insights from neuroscience, providing a toolkit of ideas for optimising daily routines. Part II guided the reader in applying these concepts through design thinking, encouraging experimentation with daily schedules, prioritisation, and defending against distractions. Together, these parts aimed to help readers create and continuously refine a personalised, effective daily structure for work.
One promotional event for the book included a talk from Caroline Webb, who was then working on her book How To Have a Good Day, which was published in 2016, and has since been translated into 16 different languages and is available in more than 60 countries.
Caroline’s thinking helped us learn a lot about how new insights from neuroscience could be applied to our everyday work and lives and she gave the keynote at our Dots Conference. What was especially useful about her work was the rigour she applied to her research and advice. All the advice was backed by multiple peer reviewed studies, a welcome change from persuasive books built on selective evidence and insights that under closer inspection were built on shaky foundations (see Gladwell’s 10,000 hours rule).
Much of our thinking and work in this area became learning materials used to run programmes for firms like the Financial Times, Barilla and adidas. We also developed a Test–Learn–Lead™ innovation pipeline process to manage ideas as experiments.
The rise of generative AI has made many of the lessons from those experiences
Deep fake grifters target Trump
We started 2024 with many predictions about the danger of deep fakes upsetting the many elections around the world. So far the effects seem to have been marginal and the examples relatively few. So it was interesting to see the most ambitious deep-fake feat so far being crypto-grifters siphoning off some of the audience for a US presidential .
They created an AI-generated live stream that appeared on a fake Donald Trump YouTube channel. The deepfake video featured Trump alongside YouTuber Logan Paul, discussing the promotion of cryptocurrency in the United States if Trump were elected. The scammers used this video to direct viewers to a fake promotional website and QR code for donations in various cryptocurrencies like Bitcoin.
“I mean, just to add to the sense of the postmodern world gone mad, 200,000 people plus had gone on YouTube, believing they were watching the debates. What they were actually watching was a deep fake of the debate, so AI generating a fake example of the debate, designed to sell cryptocurrency. So all of these scams and AI and fake and then tech troubles, because he claimed he was suffering a massive cyber attack.”
For this clip and a discussion by Rory Stewart and Alastair Campbell about the Trump-Musk love-in on X (Twitter) here’s the YouTube video:
Currently reading…
The Road To Hell, by Nick Asbury critiques the popular idea that corporate purpose should drive business decisions, arguing that this leads to hubris and dire unintended consequences. Part of the scene setting is a rejection of the Simon Sinek “Start With Why” thesis, which I’ve never been comfortable with. Of writing the book, Asbury says:
If you’re lucky, you find out your motivation somewhere towards the end of whatever it is you’re doing. It comes from having an open mindset, not setting the goal in advance. Creativity ends with why.
Dawn, by Octavia E Butler. An Afrofuturism novel about a aliens who save the remnants of a post-apocalypse humanity and try to fix the planet so they can start again. Early into this, but it is fantastically gripping already and a neat complement to On The Beach by Neville Shute, which I’ve just finished, which is about the last days fo the last humans alive in southern Australia as radiation from a nuclear war in the Northern Hemisphere bears inexorably down on them.
On my holiday reading list:
Transit, by Rachel Cusk. The second in her trilogy that began with the superb Outline.
The Long Game, by Dorie Clark. About long-term thinking.
The Waiter, by Ajay Chowdhury. A crime novel about a disgraced police detective who leaves Kolkata and starts a new life work in a Brick Lane Indian restaurant.
Antarctica, by Claire Keegan. High recommended short story collection. Can’t wait.
That’s all for this week…
Holiday service continues – so could be this weekend or whenever… Hope you are having a nice summer.
Antony