Shape AI, Before It Shapes You: A Glimpse Into 2024

Data Basics Stephanie Griffiths

AI is driving a disruptive revolution poised to transform our work and life significantly. But, while machines are self-learning, what are we doing as humans? How can we remain in control, autonomous, and always a step ahead? Instead of merely analyzing data, the focus should be on transforming data into creativity and making us creators. 

Whatever our role and interests, we should all become “AI shapers.” Here are my three tips to help you get ahead this year. Spoiler: You don’t have to be a tech guru to put them to practice!

  1. Embrace durable skills.
  2. Master your relationship with technology.
  3. Welcome unpredictability.

Illustration Eric Giriat - Article from Marion Dupuis “Praising Nuance” - Madame Figaro.

Illustration Eric Giriat - Article from Marion Dupuis “Praising Nuance” - Madame Figaro. 

1. Embrace Durable Skills

Durable skills are skills that can be transferred across domains and contexts and that endure regardless of tech and sectoral evolution,” as explained by Minerva University. The priority for businesses will be to turn data into business creativity. Focusing on creative problem-solving, communication, and collaboration will make data and business experts stronger. 

To nurture critical thinking, aim to become something a bit different this year, maybe … a data artist?! Here are several hands-on ways you can put this tip into practice today:

  • Explore & keep on asking “why” when you are confronted with data.
  • Collaborate with various departments to check if you have the correct data (keep in mind only 10% of your company data is structured) and refine scenario planning.
  • Define the value you want to create. Go beyond pure economic value and think about social and environmental return
    • Sakichi Toyoda wanted to ease his mother’s work and developed “Jidoka” — automation with a human touch. Improving productivity was not the primary purpose, but it led to the famous Toyota Production System while aiming first for safety & well-being. Predictive maintenance is another crucial area through which security can be improved. 
    • WWF is doing great work setting up the Science-Based Targets Initiatives (SBTi) in the transition to a net-zero economy.
  • Use tools like hackathons. With time pressure and mixed teams of business & data experts, you will explore and solve challenges differently.
  • Finally, anticipate how your job might change and make yourself obsolete before anyone else does. 

2. Master Your Relationship With Technology:

By 2030, Gartner suggests that a significant portion, namely 80% of people, will engage with intelligent robots daily. If your kids only go to bed if Alexa says it is “sleep time,” the theme of safeguards and co-existence with machines will become even more relevant to you in 2024.

Humans are “political animals” (Aristotle) who create social bonds through every tiny interaction; even if machines can self-learn and have a convincing social presence, they are not sentient. Without realizing it, we bond with machines, whereas machines merely speculate. Consequently, designers should rethink the aesthetics (language & form) of devices (how far should we take anthropomorphism?). 

Here are several hands-on ways you can put this tip into practice today:

  • Do a fast LLM course: Understand it is all about probability and not intelligence. 
  • Check what GPT can and can’t do by playing with Nicolas Carlini. 
  • Learn by doing: Use ChatGPT, Midjourney, and other tools as assistants. Inspire yourself with great prompts shared via Linkedin. Don’t expect the moon and see how they can be efficient tools depending on your needs.
  • If you are a designer, focus on voice and embedded systems to help us “raise our eyes” from screens (borrowed from Victor Fersing’s work in French schools) and be more human. 
  • Be transparent: We don’t need to pass the Turing test every time we interact with a machine. You can train your LLMs to reply empathetically but ensure users know they are interacting with a machine. The idea of the “empathetic machine” needs to remain an oxymoron.

3. Welcome Unpredictability:

As predictability becomes the norm, there may be a backlash for those in society who thrive on serendipity. As everything becomes planned, understood, and measured, we will miss the charm of exploration.

Some voices are already warning us about too much predictability: “AI thrives when our need for originality is low and our demand for mediocrity is high” (Ray Nayler, The Times).

To avoid being trapped, we have an obligation of curiosity. Here are several hands-on ways you can put this tip into practice today:

  • Reinvent your digital path daily to avoid being overexposed to similar stories. Encourage algorithms to do gymnastics. Stop watching the first line of content suggested by Netflix, Spotify, and others. Don’t always select articles with for/against type of content. Be brave. 
  • Work with neuro-diverse profiles. AI can’t replace the soft skills that every organization needs, like innovation, lateral thinking, complex problem solving, and interpersonal skills — which are dyslexic thinking skills. (If your kids are dyslexic, watch this video together:
  • To sharpen your curiosity and fact-check what you read, inspire yourself with Belllingcat’s principles: Think critically. Check the source. Reflect on location. Be savvy to manipulation & AI generation.

Putting It All Together

Whoever you are (tech nerd, banker, data lover, philosopher, linguist, designer, journalist, the list goes on), thanks to no-code tools and via Generative AI, you can already talk to data. You are democratizing AI. But now that AI is on every lip, I see 2024 as a pivotal moment where we need “nuance.” The teenage years are over. 

The debate is more complex than “Will you shape AI? Or get shaped?”

As brilliantly illustrated by Eric Giriat & written by Marion Dupuis, “And if the refusal of binary thinking and judgments sliced ​​was the last boldness in a society of immediacy? Voices are raised to defend the sense of measurement.” (Le Figaro Madame) Fighting data bias, encouraging transparency of your algorithms, making sure it is easy for your teams to centralize and scale AI projects safely, and avoiding data and concept drift are great starts. But how could the nuance concept be transferred to algorithms? Can we dream of subtle and moderate AI, creating more space for debate? It’s an opportunity to shape each other!

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