Luxury Brands Maximize Experiences in Sports Events
Tuesday, April 22, 2025 • Duration 07:24
How do luxury brands maximize experiences in sports events? I attended the 2025 Monte-Carlo Masters, which showed a strong presence of elite brands fighting for high-end customer engagement. Brands such as Rolex, Sergio Tacchini, and Replay can be found advertised almost everywhere at the famous tennis tournament. These brands use the values of this tennis tournament’s identity, which are class, prestige, excellence, and exclusivity, to reinforce their brand image. In this article we will be looking into the strategy behind premium companies and their connection to the Monte-Carlo Masters Tennis Tournament.
Luxury Brands Maximize Experiences in Sports Events
This photo was made using Midjourney and Adobe Photoshop.
Sports Sponsorship in Luxury Branding
Luxury brands have had a history of gravitating towards sports such as tennis, golf and equestrian sports because these sports emphasized precision, elegance, and tradition. Brands saw that it seemed like a good fit for their deluxe identity due to the traditional affluent audiences that these sports offered.
Only the best of the best athletes competing at these events align with the values of the most luxurious brands that they are the best of what they do. These brands are able to prolong their exclusivity while opening up visibility to sports viewership.
As brands become bigger and sports viewership grows, stylish brands are opening up to collaborations with bigger sports that may not have as much class or prestige, such as football and basketball.
Strategic Brand Positioning at the Monte-Carlo Masters
What once was known as the Monte-Carlo Masters is now known as the Rolex Monte-Carlo Masters. Rolex has positioned themselves front and center at a prestige tournament. Not only are they in the title of the tournament, they are on the logo and can be found everywhere at the tournament itself.
Another brand that has strategically positioned itself is Sergio Tacchini. Being at the tournament itself, it is impossible to miss; every ball kid and many employees working for the tournament wear a piece of clothing from Sergio Tacchini. Just being at the tournament, you are constantly being advertised to, whether you realize it or not; everywhere you look, you are reading another brand name.
Other brands, such as Maserati and Emirates, help back the elitist and prestigious image of the tournament.
Monaco, home of the tournament, is known for its wealth as well as its opulent residents, one more reason to advertise an elegant brand, as the target market is mainly wealthy individuals. “According to the World Population Review, Monaco is the richest country in the world in terms of GDP per capita and is regarded as the “billionaires’ playground.”
Celebrities and top-level athletes being at the tournament make being at the event feel like it’s only for those of wealth, class, and elegance.
This photo was made using Midjourney and Adobe Photoshop.
Brand and Customer Experiences
Some of the most exclusive experiences at the Monte-Carlo Masters are sponsored by posh brands. VIP lounges and luxury suites are curated for high-end customers and guests.
Additionally, behind-the-scenes access, meet-and-greets with athletes, and fancy gifting moments allow brands to showcase their exclusivity even more to only those that can afford them. Hospitality packages include gifts, discounts, special access, and events made to feel extraordinarily classy.
The Société des Bains de Mer (SBM), which is responsible for venues at the event, creates gourmet dining opportunities as well as private lounges mimicking luxury brands.
Customers at the Monte-Carlo Masters are rewarded just by being at the event itself. These rewards include access to limited edition merchandise, entries to giveaways or raffles, and the opportunity to use the VR Tennis simulator.
To further show status of class and order, boutiques at the event are limited to a certain amount of people at a time to prevent cluttering.
When a customer buys a ticket for the tennis match, they are not just coming to watch one game, they are spending practically the whole day there. Even in between matches there are activities to be done around the venue.
These activities include, finding something to eat (there are a lot of choices), VR tennis simulator, walking around and exploring the area, shopping in the countless boutiques and tennis stores.
Scarcity and “Limited Edition”
The value of going to a sports event comes from the electric anticipation of not knowing what will happen. Fans come to witness firsthand the action and to purchase limited edition products showing they were there. Rolex and other brands offer merchandise exclusive to the event itself, signifying someone was at the event.
A piece of limited edition merchandise to show you were at the Rolex Monte-Carlo Masters is also an advertisement every time you wear it in public.
Short-term promotions and exclusive collaborations build a sense of urgency used to encourage customers to buy their product. Brands use this to drive up their exclusivity, giving only people that were at the event a chance to purchase something that will remind them of how special that moment was.
This photo was made using Midjourney.
Content and Social/Digital Media
Social media has completely changed this world as we know it, and that is not only limited to the social aspect, but it has changed the business world drastically. Content creation is a great way to gain publicity, and what better place than a sports event to promote your upscale brand? Brands such as Replay and Malongo took advantage of this event sponsorship and made social media promotions showing their collaboration with the renowned event.
Rolex and Tennis
Rolex has been partnering with tennis for 46 years, since 1978.
Made with Napkin AI.
Key Statistics about Luxury Brands in Sports Events
These statistics show that the tournament continues to improve their technology and assets. Furthermore increasing their brand identity relating to top class.
Maximizing luxury brands customer engagement in sports events: facts and figures about the 2025 Monte-Carlo Masters – infographic done with Canva
Conclusion
Classy, lavish brands strive to take advantage of events such as the Monte-Carlo Masters tournament to build brand identity and to increase publicity. The Monte-Carlo Masters serves as a sort of “playground” for luxurious brands to strategize, promote, and attach themselves to the values that the event portrays.
Brands that strive to align with excellence, class, prestige, and elitism promote themselves using the Monte-Carlo Masters to represent these traits. These companies take advantage of the tournament knowing that the audience is generally wealthier.
If you are dying to understand the various GenAI prompting methods, how AI interacts with your prompt, and why this is key to optimising your results, this free prompting guide was made for you. This manual describes GenAI chatbots and the different methods of prompting. It was put forward by Frederic Cavazza, a digital transformation expert, consultant and speaker with over 25 years of experience.
A Free Prompting Guide for Aspiring GenAI Experts
This free GenAI prompting guide written by Frederic Cavazza was translated and adapted by yours truly.
I’ve known Frederic Cavazza for years and I’ve even had the pleasure of working on a few engagements with him. As we were on our way to a GenAI client workshop a few months ago, he showed me this guide and I thought to myself: “This is exactly what I’d like to share with my readers and students”.
Hence this translation and adaption of Frederic’s prompting guide, with his kind permission. I tried his tricks myself and I can guarantee that the mega prompt he describes at the end of the guide is really something you should test, copy, paste, adapt and keep in your own prompt library.
A GenAI Guide about the Art of Prompting
Artificial intelligence is booming, and chatbots like ChatGPT are radically transforming the way we interact with digital tools, changing the way we work. This guide aims to introduce you to the art of ‘prompting’, a key skill for engaging effectively with these artificial intelligence platforms and making the most of their potential.
If you’re researching a topic on the Web and you type in a simple key phrase, the result may not be very compelling. On the other hand, if you structure your search well, you’ll get more relevant answers. And so it goes with artificial intelligence tools like chatbots or digital assistants.
Frederic Cavazza is the author of this great prompting guide entitled the ABC of prompting – photo portrait by antimuseum.com
GenAI Prompting?
Three Recommended GenAI Prompting Methods
1. RTF Method
2. CRAFT Method
3. COAT-SITES Method
Test Drive the Prompting Methods
Tips and Tricks
Download for Free the ABC of Prompting for Aspiring GenAI Experts by Frédéric Cavazza
Influencer Marketing: Average European Spend at €3.5m Annually
Wednesday, October 30, 2024 • Duration 08:16
The state of influencer marketing in Europe 2024 is a survey conducted by Kolsquare, a leading European influencer marketing agency. It provides a particularly interesting perspective on influencer marketing budgets, how influencer marketing is handled and its future trends. Besides, its comparison of Europe’s main markets for IM is clearly enlightening. It’s one of the first if not the first of its kind and it sheds light on the way that businesses are conducting marketing with Key Opinion Leaders, at least for business to consumers. One of the most striking takeaways from this study is the sheer size of the average European influencer marketing budget which is evaluated at a whacking €3.375 million annually.
European Businesses Spend nearly €3.5m Annually on Influencer Marketing
The Kolsquare/NewtonX 2024 European survey shows that Influencer Marketing has clearly become pivotal in the B2C marketing mix.
Methodology of the 2024 Influencer Marketing Survey
This 2024 European Influencer Marketing (IM) survey was conducted by Kolsquare and NewtonX. It involved 385 decision makers representing medium to large organisations across various sectors (Beauty and fashion, IT, SaaS and Telecommunications, Retail food and beverages, entertainment …). All respondents had more than two years of experience in influencer marketing. The sample is relatively large for that kind of B2B survey with five countries surveyed (France, Germany, Spain, Italy, and the United Kingdom) and approximately 80 respondents in each of these countries.
Influencer marketing 2024 survey methodology
The European influencer marketing landscape
Thanks to this survey, we now have evidence that the influencer market is really significant with €3.375 million spent on influencer marketing by European businesses annually, and Germany topping the list at €5.74 million per annum. Micro influencers (10,000 to 100,000 followers) being the most popular partners for the surveyed European businesses. Respondents’ expectations on growth are very optimistic with 54% of them expecting to increase their influencer marketing budget next year. Unsurprisingly, the influencer marketing landscape has shifted towards three main platforms: Instagram, TikTok, and YouTube.
Influencer marketing landscape
Social network usage by marketers and Key Opinion Leaders
In conclusion
About KolSquare
Cyber threat Landscape Europe, 2024
Wednesday, October 23, 2024 • Duration 04:07
The Cyber threat landscape in Europe is quite worrying. A recent survey by Cloudflare was conducted amongst 4,261 IT executives responsible for cybersecurity in Europe. 24% of the sample is made from small enterprises (150–999 employees), 24% from medium-sized businesses (1,000–2,500 employees) and 52% from large organisations (above 2,500 employees). All major European countries were surveyed by Cloudflare in their study entitled Shielding the future: Europe’s cyber threat landscape. The report paints a rather bleak picture but stresses that solutions exist… as long as leadership teams understand what new Cyber threat countermeasures like Zero Trust are about. All in all, this will require all management teams, not just IT, to better understand the ins and outs of such dangers.
European Cyber threat Landscape: a bleak picture but there is still hope
European Cyber threat Landscape: Cloudflare paints a very bleak picture but there is still hope
The sample of this survey is quite comprehensive given its high profile and B2B orientation.
The Sample of the 2024 Cyber threat survey by Cloudflare
Some of the takeaways from this report on the European Cyber threat landscape include:
– All kinds of businesses are impacted by cyber threats with 72% of respondents reporting at least one incident in the last 24 months,
– 84% of respondents reported more incidents compared to past years. With a staggering 43% of those organisations experiencing 10 or more attacks in the past 12 months,
– Attackers are resorting to a variety of methods, with phishing and Web attacks on top of the list,
– A remarkable low number of respondents (29%) state that they are well prepared for future incidents, therefore leaving 71% out of that picture,
– Over half of respondents anticipate that their organisation will dedicate to ,
– There is a that “adding “. With nearly half of respondents ranking “simplifying and consolidating their cybersecurity stack” as one of their top three priorities,
– Moving to but reported that their this model.
Let’s face the music, How many IT execs are feeling comfortable with the understanding of how our complex online systems should be protected? Well, not that many. However much I hate the idea, it seems that too much openness of such systems isn’t making our lives easier.
NotebookLM by Google: Artificial Voices, Real Concerns
Monday, October 21, 2024 • Duration 09:20
Content creation with artificial intelligence is already old hat as it’s been going on for a few years and, unfortunately, slop is now populating the Internet at an increasing pace. Yet, when I received this message from a good friend of mine last week regarding Google’s new app entitled NotebookLM, I was shellshocked. I tried it and tested it and felt immediately overwhelmed. Having slept over it for a few days, I’m just recovering, so here are my impressions.
NotebookLM by Google: Artificial Voices, Real Concerns
NotebookLM by Google: Artificial Voices, Real Concerns — an image produced with Midjourney and our own special personalised mode
The other day, a friend of mine sent me a message about Google’s new NotebookLM AI application. As I always do that kind of thing, I tried and tested it immediately. The catchphrase for NotebookLM is “Think Smarter, Not Harder” and Google presents it as “The ultimate tool for understanding the information that matters most to you, built with Gemini 1.5”.
NotebookLM: made to ‘understand’ information?
I wonder about that. Is it really a tool made to “understand information”? This sounds a bit dubious.
The aim of NotebookLM is to turn a piece of text, a video, a web link into a conversational podcast. It is semi customisable and not quite finished. But it gives you an idea of what the future has in store for us, content creators.
On the one hand, the technology is great and works fine, barring a few glitches, on the other hand, it’s a window on a very weird and dark future (once again, it’s not the tool that is the problem but the people using the tool, as Bradbury remarked).
For my initial test, I selected one of my English pieces about artificial intelligence (AGI). I copied and pasted my text into the window and hey presto! a few seconds later, a proper conversational podcast between an American man and woman was available. Here it is
One more test
NotebookLM by Google lets you customise the result, well… almost
A few worrying signs
In conclusion
The KilledbyGoogle.com website. Please note that Google didn’t manage to kill Squarespace (yet). This is an add. Will Notebooklm be added to this list? Time will tell.
Transcripts of both NotebookLM podcasts
AGI (General Artificial Intelligence), Myth or Reality?
Tuesday, October 8, 2024 • Duration 18:17
Whereas Ed Zitron is castigating the major Tech players responsible for the peak of inflated expectations surrounding AI, many tech pundits are still touting that AGI (Artificial General Intelligence) is within reach. To find out if AGI is a myth or a reality, I interviewed J.G. Ganascia, a long-time AI researcher and philosopher. In the course of our discussion, I gathered that the singularity and AGI weren’t the same thing. This interview set a lot of the record straight, particularly regarding the notions of intelligence and sentience or consciousness. But its striking conclusion is undoubtedly that, like Ray Bradbury, we should certainly be less wary of pseudo-intelligent AIs, let alone AGI, than of the wily intelligent humans behind these technologies.
General Artificial Intelligence (AGI), Myth or Reality?
In J.G. Ganascia’s opinion, it is absolutely essential to retain control over the machine. And be wary not of artificial intelligence, but of the people behind it. An advice previously delivered by Ray Bradbury in his time – Image produced with Midjourney, whom I trained not to show robots. But in this instance, it was hard to avoid. At least this one is under human control, which is reassuring…
The Singularity, AGI and Superintelligence
J.GGanascia. Transhumanism led to many projections about artificial intelligence, of which the technological singularity was one of the avatars. There are others today like Nick Bostrom’s Superintelligence.
The singularity, AGI and Superintelligence are very different notions. Above, the cover of the book on Superintelligence by Swedish author Nick Bostrom
The singularity, technological dream or nightmare
J.G. Ganascia at an AI conference in Paris in March 202. AGI isn’t on the agenda according to him. But beware of those pulling the strings.
AGI and superintelligence
As of 2024, is the singularity still a myth?
And yet these AIs are amazing!
Yann Le Cun is dead against GenAI, yet he believes in AGI. Are you prepared to change you mind about the subject?
Multiple intelligences according to Howard Gardner – source:
The machine could, however, reprogram itself or correct some of its errors
Other philosophers like Daniel Andler aren’t sure that machines are not sentient, though
The first poems, incidentally, date from 1957
The pseudo-intelligence of AIs, less dangerous than the harmful intelligences of humans?
About Jean-Gabriel Ganascia
Harnessing AI to Combat Fraud in Retail and E-Commerce
Monday, September 23, 2024 • Duration 11:50
Our reporters attended the Paris Retail Week 2024 event, a trade show of which we are media partners, to take stock of fraud and the role of AI. We collected a lot of valuable feedback on threats (both in-store and e-commerce) and the countermeasures proposed by artificial intelligence. To do so, we interviewed Gilles Bijaoui, head of CX at Fujitsu (Customer Experience being the name of the retail division chosen by the Japanese company). During this discussion, which took place at the opening of the show, he gave us a wealth of information on fraud and described the AI solutions designed to combat it. Such solutions have now been deployed for almost two years in retail chains of all sizes.
When AI fights fraud in-store and on websites
AI and fraud detection in retail: Fujitsu’s Gilles Bijaoui, Head of Customer Experience at Paris Retail Week on 17 September 2024 in Paris
Isn’t AI-driven fraud reduction deja vu?
Gilles Bijaoui. Indeed, this has been a recurring topic over the last five years, whether at NRF or Paris Retail Week. But it wasn’t really implemented in the field. But over the last two years or so, that has completely changed. Post Covid, we find these solutions in production in many retailers of all sizes.
There are several types of AI involved, including generative AI. This has been rolled out by several large retail chains and the feedback is fantastic. Be it regarding performance, reducing employee theft and shoplifting, whether for physical retail or e-commerce.
What kind of fraud are we talking about?
GB. There are two types of fraud in retail and e-commerce:
Firstly, in-store fraud, just over half of which is linked to theft in the shop and around 40% is due to shop assistants themselves.
When it comes to e-commerce, it’s more likely to be linked to electronic flows of information, either payment fraud or identity theft (or phishing). It’s more complex and also more damaging.
We caught up with Gilles Bijaoui at Paris Retail Week to talk about AI and fraud detection.
Are there any regional variations regarding fraud in retail?
Let’s now talk about combatting fraud with artificial intelligence
Personalisation With AI
How can one avoid hallucinations on such recommendations?
Is there a good business case you implemented with a retail chain?
How does it work in practice?
What about e-commerce?
Could fraud ever disappear with AI?
About Fujitsu
GenAI impact on jobs: doom or boon?
Monday, July 1, 2024 • Duration 13:05
What is the likely impact of AI and GenAI in particular on jobs, especially in Europe? Two recent reports on the topic, one in the UK and another one in France shed light on this question. According to the French report, such impact could amount to 5%. Yet another case for precision vs accuracy. That figure seems counter-intuitive when so many self-proclaimed AI gurus, especially on LinkedIn, are hailing the GenAI “revolution“. Besides, the authors of the UK report don’t agree at all with that. As ChatGPT would have it, let’s “delve” into those reports and find out more.
GenAI impact on jobs: boon or doom?
What impact will GenAI have on jobs in Europe? The answer to that question unmistakeably depends on which job and which associated tasks you are talking about. The French Commission on AI reassures us on this point – photo: a tailor’s workshop in the 10th arrondissement of Paris – photo Yann Gourvennec antimuseum.com.
Our own empirical analysis suggests a positive effect of AI on employment in companies that adopt AI, because AI replaces tasks, not jobs. In 19 out of 20 jobs, there are tasks that AI cannot perform. Jobs that can be directly replaced by AI would therefore represent only 5% of jobs in a country like France. What’s more, the generalisation of AI will spur job creations, in new occupations as in old ones. To sum it all up, some industries or geographies could experience net job losses, therefore requiring Government support, but this does not mean that AI will have an overarching negative effect on national employment in France.
French Commission on Artificial Intelligence report, March 2024 – p. 41
Anxiety in the eyes of some of my younger students
I often talk to young students from all areas about the impact of GenAI on jobs and careers. I often sense a bit of reticence and even anxiety in them at a time when young adults are still asking themselves many questions about the future and aren’t necessarily clearly determined about what they want to do in the future. Beyond that, the current state of hype around GenAI further blurs these students’ vision by making them feel the weight of an uncertainty that is already difficult for some to stomach.
The impact of generative AI on employment is not easy to assess. And we’ve had to struggle with our image-generating AI tools to get them to avoid a doomsday view of the future of work with robots everywhere… What if, in the end, the future of work was a mere evolution of today’s work practices? Image generated with MidJourney
Recent reports have added fuel to the fire, such as this one from the IMF.
A more nuanced report on the impact of AI on jobs
A More Thorough and Subtle Report
Impact of AI on jobs: Antonin Bergeaud’s projections are extremely smart and way above my mathematical abilities. In the top left-hand corner one can see the jobs of accountants and telemarketers, professions of which I’ve been reading about the disappearance since the 1980s (accountants) and 1990s (telemarketers). It’s bound to happen one day, but is ChatGPT to blame? It’s doubtful, and you don’t need a PhD in Quantum physics for this – diagram taken and adapted from Antonin Bergeaud’s .
The report is in disagreement with previous approaches, pitting them against each other and pointing out that, in the end, there may be no need to panic:
Five percent impact of GenAI on jobs… why not 5.2%?
Automation Is neither Easy Nor Happens Overnight
GenAI and jobs: looking at the big picture
Predictions lie but figures don’t
Music and AI: Back to the Future
Friday, June 7, 2024 • Duration 17:38
Whether it’s music and AI, or innovation in the broad sense of the term, at Visionary Marketing we like to look back in time. A few days ago, while doing the housekeeping of some of our 3,000 articles, we rediscovered this post by Mia Tawile written in July 2016. Eight years is the equivalent of 8 dog years on the Internet, to use that hackneyed motto from the early days of the Web. That is to say, 64 years and 3 months. And at a time when Suno is sending shivers down the spines of every musician on the planet wondering what will become of them, there are two lessons to be learned from this post that demonstrate that we don’t understand the history of innovation as Scott Berkun would have it.
Music and AI: Back to the Future
What better than an image, reminiscent of the 1970s for this evocation of the first attempts to compose music with computers – music and AI nonetheless take us down a much more tortuous path. A real philosophical, artistic and economic challenge for creators – image produced with Midjourney and retouched and enhanced with Photoshop and Firefly Beta.
“Hello dear human friends” is the introduction to this striking video by Laurent Couson, a French composer who analysed the capabilities of Suno, a popular application that lets you compose music of almost any style in 30 seconds. “Before, you had to learn music theory, orchestration and instrumentation – at the very least, ten years of practice – to become an accomplished composer,” he continues. He could have added, “Provided you’re gifted.”
900,000 Pieces of Music per Day
This piece of software,” he went on, “generates 900,000 pieces of music a day, while no well-known composer, not even the most prolific, has 1,000 in his catalogue”. And it’s true that the results are amazing.
900 000 pieces of music being produced every day. Imagine that! Midjourney dit it for you (with our help)
A very basic prompt
The First Computer-Generated song
Democratisation or the end of creation?
IA and music: a long-standing innovation
Interesting Examples
The 2016 original post on AI and Music
Google’s Magenta and its music band
AI music is on Google’s agenda
Two goals
No plans yet for Facebook on the AI music front but they are using AI so that blind
AI Music: Beyond Limits
Learning AI with the help of robots
Wednesday, June 5, 2024 • Duration
Thomas Deneux is the founder of Learning Robots whose aim is to help pupils, students and businesses to learn AI, with the help of home-made self-driving gizmos. These little machines on two wheels are more serious than you’d think. They are all about the teaching of advanced computing. Thomas described his philosophy to me during this interview conducted at the heart of the Neuroscience Institute of the CNRS (French National Centre for Scientific Research). In essence, a no-nonsense approach to teaching and learning AI.
When AI and robots join forces to teach artificial intelligence
Behind Learning robots’ self-driving gizmos – seen on their training track here in Saclay – there is a teaching philosophy and a full-fledged training corpus.
Who are these friendly colourful robots?
We’re overwhelmed with social media posts and news about AI. Often, pundits will tell you that you need to know how to use ChatGPT and make prompts. That’s all very well, but we must free ourselves from the tech giants who build these models.
At Learning robots, we want to spark vocations among people who are interested in finding out how it works and want to use AI better.
What is artificial intelligence anyway? AI gave birth to these fantastic tools and programs. Yet, at the same time many people are scared. Our aim, with these user-trained robots, is to make AI accessible and friendly.
What’s behind these robots?
In our introductory activities, the user drives a robot as if it were a remote-controlled car. But behind this robot is an AI that will record all the necessary data. Next, the robot takes over from the user in autopilot mode and drives around the circuit.
Then we organise a race between the robots that have become autonomous in this way, and users may therefore observe that not all of them will perform equally well. It’s natural because performance depends on the quality of the training.
We’ve been training our Midjourney AI to produce an infographic based on Thomas’s interview and here’s how it came up with these AI self-driving car races…. This one isn’t as nicely organised as those by Learning Robots.
The stochastic parrot as seen by Midjourney, who is definitely very creative.
What prospects can we expect from this kind of robot?
Small but powerful. The AI robots by Learning robots – source Leaning Robots
What is your philosophy behind all this?
AI can be fun
Can we imagine a world, where chores are all carried out by machines?
Finally, there is hope for human beings
The more structured the prompt, the more relevant the outcome
The way you ask AI chatbots questions or give them instructions is conducive to more or less convincing results. This is what is called ‘prompting’, i.e., the art of formulating clear and precise instructions to guide the work of artificial intelligence models.
In essence, a well-structured prompt is like a well-formulated search query. When done properly, it shall provide relevant results.
There is no one-size-fits-all prompt methodology, as use cases differ from one user to another. However, we recommend you use one of these three methods based on your needs.
These three methods are entitled RTF (Role, task, format), CRAFT (Context, role, action, format, tone of voice) and COAT-SITES (context objective, acumen, task, specimen, impediments, tone of voice, encoding, scrutiny). Each technique works best depending on expected results. RTF was made for quick results, CRAFT, for simple questions with more accurate results and COAT-SITES, for clear cut questions and extensive results.
So, what are these methods about? Here they are in more detail.
With RTF, the prompts specify the role, task and format that AI should adhere to. It consists in a simple, “you are…, you must…, your answer must…” Role indicates who the AI bot should impersonate, providing a contextual framework. Task, gives AI the precise action or problem to be solved, guiding AI towards the expected objective. And Format specifies the type and structure of the outcome.
Should you be looking for more accurate results, it might then be a good idea to expand your prompt to incorporate more context. The CRAFT method is therefore what you would have to resort to. CRAFT implies providing specifics to the AI chatbot in your prompt such as, “I’m in charge of…, you are… you have to…, your answer should include…, choose the following tone of voice.”
With the CRAFT method, Context describes the overall situation or requirement. Role, tells AI the character it should enact. Action, specifies what AI must do, directing the LLM. Format, provides examples or details clarifying final expectations. Tone of voice, defines the expected style or category AI must follow, aligning your response with your objective and audience. Expanding your prompt to formulate a better structured and accurate result.
In the case of COAT-SITES technique, your prompt is not only expanding the context of the situation but giving AI strict guidelines to narrow the margin of misunderstanding. Allowing for your results to be more accurate and extensive. This includes Context, Task, Tone of voice, as in the previous methods but also includes Objective, Acumen, Specimen, Impediments, Encoding and Scrutiny.
Objective and Acumen, give AI the tools to reach your expected result with the correct level of expertise. Specimen and Impediments, provide clear examples and guidelines of what is wanted and what should be avoided. By providing models or illustrations, you clarify the expectations, just like defining what cannot be done narrows down your result. Lastly, COAT-SITES encompasses Encoding and Scrutiny. Encoding defines the output format syncing the results to your objective and scrutiny offers a final sanity check to ensure the results comply with your stated guidelines.
Once you have familiarized yourself with these three prompting methodologies, give them a go. Here are some tips and tricks to keep in mind when navigating GenAI chatbots. Frederic details them all in the guide for you.
After you have signed in to a chatbot, enter a few questions into the prompt window to get a feel for how it replies. After you’ve done that, try testing out the suggested prompting methods, saving a specific topic or tricky task for COAT-SITES.
When interacting with your favourite chatbot, remember to select relevant keywords, stick to one question at a time, test and tweak your prompts and follow up. Don’t settle for half-baked answers and consider asking a different chatbot to critique your results.
Lastly, remember at all times that the better formulated your prompt, the better AI can provide accurate and relevant results. So give these methods a test and see how your interaction with GenAI chatbots evolves.
More than ever, influencer marketing is here to stay with 27% of respondents saying that it will become more important in the marketing mix. And even 6% stating that it will become the most important part of the overall marketing spend.
UK marketers seem less prone to spend huge chunks of their budgets on influencer marketing with a yearly average of £848,000 (still a whopping €1.02 million!) Brands are also declaring that they will become more selective in the influencers with whom they work (56%). Ethics is topping the list of preoccupations in Italy and France, but less in the UK and not that much at all in Germany.
Indeed, Germany is described by Kolsquare as “the big spender”, but not very keen on ethical considerations. Unlike the French and Italians, who said to be prioritising corporate ethics when selecting influencers.
This emphasises a significant shift in the market, whereas four or five years ago we were stressing the fact that ethics weren’t really on French influencer marketing managers’ priority list.
When it comes to social network usage by influencer marketers, the shift towards Instagram, TikTok and YouTube is significant. However, Facebook has not disappeared from the IM landscape completely, as it is still the platform of choice in the UK.
X has slipped down the ladder and further down one can find niche platforms such as Twitch, Pinterest, Snapchat and a flurry of Chinese platforms that are clearly less attractive to European marketers. What is surprising, though is that LinkedIn is definitely not part of this list, meaning that the survey is mostly geared towards Business to Consumer marketing. For all intents and purposes, one should emphasise once more that Business to Business amounts to approximately 80% of the production of wealth worldwide.
When it comes to size, One can spot a good balance of macro, mega, micro and nano-influencers in marketers’ choices of partnerships. Whereas patterns are relatively similar across countries, micro influencers definitely top the list in almost all of them.
One-Shot Versus Long-term Influencer Marketing Collaborations
It seems that in Germany and France businesses prefer to work in the long-term with Key Opinion Leaders. However, the structure is rather similar in all countries with a three-tier pattern: approximately 30% of collaborations with long-term partners, 30% for a mix of new and existing influencers, and another 30% of new kids on the Instagram block. This pattern varies slightly according to countries.
Content Forms: How the Brands Collaborate With Opinion Leaders
The variety of content types that is offered by influencer marketing is noteworthy, with sponsored posts and influencer events as well as product reviews topping the list. However, there are differences according to countries with sponsored posts not being very popular in France (only 23% of respondents vs. 58% on average across all countries).
Approximately one third of businesses are keen on performing co-creation with influencers and even nearly 25% of respondents are conducting product creation with them.
This study is instrumental in showing how pivotal influencer marketing has become in B2C marketing. Barring a few variations, one can say that IM patterns are relatively similar across the five main European countries surveyed.
Spending levels are stellar, with German businesses being on a buying spree. One can only hope that IM will help them fight the current economic slump in Europe’s biggest economy. France and Italy are keen believers in IM too. The United Kingdom and Spain are lagging a bit behind, or can be considered more reasonable, it depends on the point of view.
The most important indicator (KPIs) for influencer marketers is not the number of followers but the quality of the engagement. And as it suits B2C, it’s even shifting towards sales and conversions.
Last but not least, there are a number of challenges to influencer marketing such as striking the right balance between influencer freedom and brand control. A major issue we have consistently highlighted for the past 20 years we have spent in that domain.
It’s a significant pain point in most European countries and especially in France, where brand control is tightening on influencers.
Measuring ROI and ROAS (Return on Ad Spend) is on top of Italy’s list of issues related to influencer marketing.
Apart from that, authenticity and the quality and tone of voice of influencer content is definitely what entices European brands to work with Key Opinion Leaders.
What is also most striking is the significance of ethics according to countries. The results seemed very counter-intuitive to me but reassuring, with Southern countries showing a lot of concern for ethics compared to Northern ones.
Kolsquare is Europe’s leading Influencer Marketing platform, a data-driven solution that allows brands to scale their KOL Marketing strategies and implement authentic partnerships with KOLs (Key Opinion Leaders). Kolsquare’s technology enables marketing professionals to easily identify the best Content Creators’ profiles by filtering their content and audience, and to build and manage their campaigns from A to Z, including measuring results and benchmarking performance against competitors. Kolsquare was founded by Quentin Bordage in 2018.
It seems that an increasingly dangerous cybersecurity landscape is causing more and more aggravation within organisations. The growing complexity of open networks with access to increasing amounts of money is too big a temptation for most cybercrooks to resist. Besides, the staggering complexity of IT, networking and especially cybersecurity solutions such as zero trust explain why there are so few companies that are ready to implement such solutions. However much sense they may make.
However much I hate the idea, it seems that too much openness of such systems isn’t making our lives easier.
I must admit I couldn’t believe my ears when I heard this podcast generated from a mere piece of text. It was both brilliant and daunting. I immediately thought that anybody could produce an audio conversation out of anybody’s blog piece and I suppose that some of the laziest of content creators will do just that.
When I looked into the podcast in greater detail, I spotted that there were a few glitches here and there and especially the quote by Ray Bradbury which is definitely not taken from Fahrenheit 451. It’s clearly mentioned in my text.
Man | 01:24.308
It’s like that line from Fahrenheit 451. I’m not afraid of robots. I’m afraid of people, people, people.
Woman | 01:28.691
Yeah.
Well… nope, sorry (and by the way, I hate your “yeahing” at me). This was taken from Bradbury’s 1974 letter to Brian Sibley. It’s clearly stated and the link to the source file is explicit.
This very morning, I went back to the application and tried it once more. I inserted a YouTube video and it failed a couple of times. So I gave up and copied a web URL and it worked wonders. This time I used my fraud and AI piece with Fujitsu.
I tried to customise the podcast but I couldn’t change the American voices nor the tone of voice which isn’t consistent with mine. I tried to turn it into a more professional, less casual, tone of voice. This didn’t work as planned. But my instructions aimed at making the podcast more factual and to focus on the numbers were executed correctly by the AI.
That said, when you listen to the entire podcast, especially towards the end you will realise that the AI is adding quite a lot of content to it and making its own commentary.
This experiment raises quite a few questions.
To start with, a lazy content writer could start publishing its own podcast channel from scratch using other content creators’ content without even mentioning their names;
Second, hallucinations are still part of this equation and they are quite wicked and hidden and hard to track. Once again, if you are a lazy content writer, then it doesn’t matter at all. On the contrary, if you are a conscientious content creator then it all makes the difference.
The fact that this AI app is adding content is another kind of hallucination even though the text is perfectly sensible compared to the original piece. But I didn’t write that, I didn’t think that and I’m not even sure I want to add it. This is not only annoying, but downright worrying.
Last but not least, the casual tone of voice used by the application and the voices and accents that aren’t customisable yet are a showstopper as far as I’m concerned. But I suspect that this can be easily corrected.
As I mentioned already, I find this kind of application a little worrying. On the one hand, it is great to be able to produce a conversational podcast which is very engaging, very quickly. In hindsight, this is probably conducive to producing even more slop on the Internet, which will probably end up collapsing in on itself. It’s a matter of years if no one stops this nonsense.
It could be quite tempting to use NoteboookLM to produce conversational podcasts without any efforts. But I will never use because we have this 100% human content commitment on visionary marketing. I may end up being the last of the Mohicans in that concern but I intend to keep the upper hand in this content creation process. I’m the one doing the thinking here, not the tool I’m using to write it.
JGG. The technological singularity is an idea from the 1950s. It claimed that at some point machines would become as powerful as humans, causing a shift in human history.
This meant that at some point, machines would have taken over. Either they would overtake us completely, at which point humanity as we know it would disappear. Or humanity would submit to the power of machines, and humans would become their slaves.
Another possibility was that we grafted ourselves onto machines and downloaded our consciousness onto computers, and that this consciousness could then be reincarnated onto robots. According to this theory, we could then continue to exist beyond our biological bodies. This is what I described in a novel written under the name Gabriel Naëj, this morning, Mum was uploaded (in French only).
This is the story of a young man whose mother decides, once deceased, one should download her consciousness and reincarnate her as a robot. What is very disconcerting for this young man is that she has chosen the most beautiful body possible, that of a sex robot!
JGG. What we call AGI, Artificial General Intelligence is a different kettle of fish. It’s the idea that, with current artificial intelligence techniques, there are specific human cognitive functions that can be mimicked by machines, and that one day we’ll be able to emulate them all.
It means there is a way of deciphering intelligence, and that once we find it, it opens up infinite possibilities. In essence it’s a gateway to superintelligence. The very principle of the technological singularity assumed that there was a general intelligence and that all cognitive capacities could be emulated by machines.
General intelligence isn’t quite on par with the technological singularity and at the same time suggests it’s the ultimate goal. AGI has nothing to do with downloading human consciousness, though. this is just the ability to build machines with very high intellectual power.
This ties in with Nick Bostrom’s plans for superintelligence, which focuses on the day when the intelligence of machines is greater than that of humans.
There are links between these concepts, but they’re not quite the same thing.
JGG. The early science fiction writers who mentioned the technological singularity, including Vernor Vinge, predicted that it would happen in 2023. Now, clearly, it’s not here yet. Unless we’ve all already been downloaded onto machines without knowing…
JGG. Artificial intelligence has made considerable headway. Machines are capable of mastering language to the point where, when asked a question, they generate texts that are well formulated, even though not always relevant.
We can also produce images of people that bear an uncanny resemblance to real humans. Videos too. It’s all very intriguing.
Until now, one thought that language was first and foremost a matter of grammar, then syntax and vocabulary. Now we are realising that these linguistic abilities can be reproduced with just a few probabilities.
It’s really exciting from an intellectual point of view.
But that doesn’t mean that the machine will suddenly take over, or that it will have a will of its own. It doesn’t even mean that it will tell the truth.
These AIs almost write like humans. Most of the time their content is based on common knowledge. But sometimes this “common knowledge” is a little absurd. And as soon as you shift the situation a little, they produce results that are completely wrong. I’m often playing tricks on them with logic puzzles and I’m having great fun as they fail.
It’s understandable , in fact, because that’s not what they were made for. They are just made of modules capable of selecting words based on probabilities.
JGG. Absolutely not! I think there’s a misunderstanding regarding the meaning of the term ‘intelligence’. Besides, artificial intelligence is a scientific discipline.
What AI does is stimulate different cognitive functions. What are they? Perception, reasoning, memory (in the sense of processing information, not storing it) and communication. We have made considerable progress in these areas.
Take perception, for example. AI is capable of recognising an individual out of hundreds of thousands, whereas we ourselves can’t always remember the people we met a day before. These performances are extraordinary.
But where there is a misunderstanding when one states that the machine will be more intelligent than man. Intelligence is a set of cognitive abilities. It may well be that each cognitive capacity is better emulated by machines than by humans. Yet, that doesn’t mean that machines will be more intelligent than us, since they have no consciousness.
Machines do not “see” things nor have a will of their own. In any case, consciousness is the crux of the problem.
There’s another meaning for the word ‘intelligence’, which is related to ingenuity or inventiveness.
An ingenious or clever pupil is said to be ‘intelligent’ because he or she can solve everyday life or mathematical problems. Are machines more clever than we are, though? It depends. There are some cases, of course, where they outdo us. We’ve known for a very long time, 25 years now, that machines play better chess than we do. More recently so for the game of Go. Thus, from that point of view, of course, they are more intelligent, but that doesn’t mean they’re better than we are. In any case, they have no willpower per se.
Blaise Pascal, just over 400 years ago, explained that his calculating machine came closer to thinking than anything animals could do, but that there was a limit to it.
340. The arithmetical machine produces effects which approach nearer to thought than all the actions of animals. But it does nothing which would enable us to attribute will to it, as to the animals.
Blaise Pascal, Pensées (Musings)- page 69
As it happens, computers are like Blaise Pascal’s arithmetical machine. Their effects are closer to thought than anything done by any animal, including humans. But there’s nothing to say that they can have willpower like animals.
I think that’s where the misunderstanding really lies.
After that, of course, you can list all the performances of the machines, and you’d be right to label them as extraordinary. But it can’t be compared to man’s thinking.
When it comes to consciousness, we can dig a little further. One of the AI pioneers, Yoshua Bengio co-authored last August a long 88-page article in which he explained that machines today are showing signs of consciousness. He has taken up the work of neuroscientists on consciousness and declares that this is a possibility. Above all, he suggests that machines will soon have such sentience.
Once again, this is the result of a misunderstanding.
The term sentience, or consciousness, like the term intelligence, is one of many meanings.
First of all, we can say that a machine is sentient in the sense that we project an animal onto it. This is what happens with your mobile phone when you say “Siri is completely mistaken today” as if Siri were a real person. Or with a robot vacuum cleaner when you say “Well, he went there because he knows there’s dust out there”. One tends to assume these inanimate objects are like humans, but they aren’t.
This is called, in technical terms, a cognitive agent. An American philosopher, Daniel Dennett, calls it intentional systems. And there’s nothing wrong with that.
The second meaning of sentience or consciousness is that of ‘musing’ or ‘reflecting’. It’s sentience as self-knowledge as in “Know thyself!“. In other words, we are in the process of becoming aware of ourselves and wondering, “I’m doing this, now is it the right thing to do?” That’s why we talk about moral awareness, where we can say to ourselves, “I’ve done this or that in the past, and I can do a lot better now”.
We can have machines, for example, that learn by looking at what they have done in the past, and then try to ensure that their future behaviour will be more effective moving forward.
If they have hesitated between different possible paths before, in a similar situation, they will no longer hesitate, but will only take the right path. The same applies to moral consciousness.
My team is working on computational ethics, which means that before acting, the machine tries to look at the consequences of its actions, and from that moment on, it will take the decisions that are most in line with the prescriptions given.
There is also a third meaning of sentience or consciousness, which is very likely to be the most important: that of emotion. Can a machine experience emotions? And what does that mean?
If a machine were to feel this way, it might think: “I want those good vibes!”, and if you ask it to do something at that moment, it won’t give in. So you ask an autonomous car, “I want to go to the beach” and it says, “No, because there’s too much sand over there. I’m going to take you to the pictures, to a place where there are very clean car parks.”
Such a machine would be a disaster. Fortunately, it doesn’t exist. It’s absolutely essential that machines don’t make decisions on their own; they must always be submitted to our will and control.
When major AI players like Sam Altman tell us that these machines are going to take over, we have to be wary. It’s a bit like them telling us
We’re the ones with the knowledge, because we’re the pundits of artificial intelligence, and you don’t know anything. So leave it all to us and we will help you!
Like many of the engineers working for major digital companies, Altman is fascinated by these machines. So he thinks there are no limits to what they will do in the future. He simply means that they will do all sorts of tasks better than we can.
An open letter was signed by some major Internet players over a year ago. Sam Altman was not a signatory. But this initiative did include Yoshua Bengio, Geoffrey Hinton, Elon Musk… They told us we had to stop Generative Artificial Intelligence because it’s a potential threat to us.
Should we develop non-human minds that could one day be more numerous, more intelligent, more obsolete and replace us? Should we risk losing control of our civilisation? Pause Giant AI Experiments: An Open Letter
I’m sorry, but I disagree strongly with this vision. I’ve been working on artificial intelligence for years on end. I have never seen a “non-human mind”. These machines are competing with us on high-level tasks. And more generally, cognitive science has been telling us for a long time, and Howard Gardner in particular, that there are multiple intelligences. There are as many kinds of intelligence as there are people.
Functional neuroimaging allows us to visualise the active areas of our brain according to the tasks we perform, and these areas vary according to each individual. Similarly, when we map them out, we realise that the areas of the brain are not developed in the same way for all individuals, depending on their upbringing, genetics and so on.
All this suggests that intelligence cannot be general, since it varies for each individual.
JGG. That’s exactly the definition of machine learning. It’s a machine that is capable of rewriting its own programme based on a certain number of observations, experiments. From that point of view, it’s nothing new.
The question is rather whether this machine has a will. That’s why Pascal poses the problem admirably.
JGG. I think we also need to go back to the definition of the term sentience. Scientists have been musing about creative machines for a very long time. Alan Turing, in his 1950 article Computing Machinery and Intelligence, contradicted a number of objections to the idea that a machine could be intelligent. And among these objections was one that said “A machine cannot create”.
And his point was that a machine can very well create. But what is creation? It’s about producing something that will take us by surprise. But he added that he could easily devise a very short programme of just a few lines whose behaviour could not be anticipated. From that point of view, one can make machines that create.
The view that machines cannot give rise to surprises is due, I believe, to a fallacy to which philosophers and mathematicians are particularly subject. This is the assumption that as soon as a fact is presented to a mind all consequences of that fact spring into the mind simultaneously with it. It is a very useful assumption under many circumstances, but one too easily forgets that it is false.
Alan Turing, The Computing Machinery and Intelligence, 1950
There is a whole history of creativity in machines that predates generative AI.
JGG. In the musical composition programme Illiac Suite by Lejaren Hiller and Leonard Isaacson (1957), the final movement included elements of random programming and creativity. Indeed, the use of randomness in this context was seen as a means of producing something ‘new’ or unpredictable, emulating a form of creativity.
Some artists have also used computers. This is the case with Pierre Barbaud (1911-1990), who was a great pioneer in that field. Painters too, including Vera Molnar (1924-2023), who created some magnificent paintings with her machines.
One could debate about the quality of what is generated by AI. Just because I made a fake Van Gogh with AI doesn’t mean it has anything to do with Van Gogh or that it’s interesting.
But that’s beside the point.
Does this machine have a will of its own that would contradict ours? In other words, at a given moment, that it could decide to stop for no reason or to take you to a place that you hadn’t imagined and that doesn’t correspond to a given objective.
I don’t think we need to worry about that.
Machines are not going to become autonomous. But society is changing. And the major issues are political, and that’s what we need to be very aware of.
In particular, we should be wary of those who own these technologies. So it’s Mr Sam Altman we need to be wary of. He has a tendency to mesmerise us, to cast a kind of smokescreen behind his intentions.
Sam Altman, in fact, is the danger!
Similarly, when Elon Musk wants to protect us against artificial intelligence by enhancing our cognitive abilities and putting chips in our heads. If we go his way, it will be Mr Elon Musk who decides what will be in our heads.
And it will be the worst dictatorship we’ve ever imagined. That’s the danger for the future!
You have to be vigilant, but you have to know where to look and what to be wary of.
JGG. Absolutely! Ray Bradbury, the author of Fahrenheit 451 wrote this famous line:
A professor at the Paris-based Université Pierre et Marie Curie (UPMC) and a member of the Institut Universitaire de France, Jean-Gabriel Ganascia was appointed chairman of the CNRS Ethics Committee in September 2016. An IT expert, holder of a PhD and doctoral thesis from the Université d’Orsay (Paris), he specializes in artificial intelligence. His current research work focuses on machine learning, text mining, the literary aspect of digital humanities and computational ethics. An IT professor at the UPMC since 1988, he heads the Cognitive Agents and Symbolic Machine Learning (ACASA) team at the LIP6 computer science research laboratory. He also set up and led the Sciences de la cognition (“cognitive science”) scientific interest group at the CNRS. Jean-Gabriel Ganascia is a member of the CERNA (ethics in digital science research commission) at the Digital Science and Technologies Alliance, Allistene.
Fraud on e-commerce platforms accounts for almost 20% of e-commerce sales worldwide. That’s a huge amount! If you compare this figure with in-store theft or fraud, it’s down to only 2-3%.
We’re definitely on a different scale with e-commerce. It’s also linked to the volume of transactions, which soared during and after the Covid crisis.
But the good news is that, thanks to AI technologies, we’ve seen a reduction in the growth of these fraudulent activities over the last two years or so. Previously, electronic fraud was growing at a rate of around 20% a year, but thanks to AI we’re now down to less than 15% per annum. And it’s getting even better with the education of individuals and businesses.
GB. Not in Europe, all countries are pretty much the same. Only one country is an exception, and that’s the UK, which has even more fraud, as in the US, whether it’s physical or e-commerce.
That’s a difference of around 10 points over France. What’s more, the figures are reversed. Physical retail theft in the United States and Great Britain is higher than in the rest of Europe. And conversely, e-commerce is better controlled there than on the continent. Most likely because they decided to deploy these solutions before other countries.
On the other hand, given the massive in-store fraud in the United States, many everyday consumer products are under lock and key.
GB. For physical stores, there have been two eras, and two different systems.
Previously, artificial intelligence was based on data. The more data we had, the more artificial intelligence systems learned, and were able to identify recurring patterns. This has changed to the extent that now, we’re relying more on the definition and identification of behaviours and movements.
Some of these modern solutions have recorded hundreds of human behaviours that can help determine a person’s intentions. Based on what we call “patterns”, we are able to tell, with the in-store camera system and artificial intelligence, whether a person intends to buy, steal, or just potter around. This is a starting point for AI to trigger personalised in-store promotions.
GB. For example, if you’re standing in front of the aisle dedicated to formula milk in a supermarket and you’re hesitating, artificial intelligence will advise you based on the information available. It can also provide you with recipes based on the food you are buying or suggest that you virtually try the garments you are about to purchase.
We can also offer promotions tailored to the person based on their loyalty cards, and instantly offer a coupon on an item a consumer is looking at.
These marketing and merchandising efforts are no longer restricted to online commerce. Now, they are also available at the point of sale. And all this, thanks to artificial intelligence.
GB. We have to fight this idea that humans will be disappearing because of AI. These systems are not autonomous. They need to be controlled. Certain words must be banned, for example. The way we address people has to be calibrated so as not to offend or be too intrusive. All this has to be strictly supervised.
Marketing messages must be constantly calibrated, both by us and our customers.
Let’s take an example. We’re currently working on fitting rooms. A lot of fraud is happening there, both by consumers and shop assistants. And RFID doesn’t really help fight this issue.
We have therefore combined several solutions so that we can identify that if a person comes in wearing red and goes out wearing black, something is clearly wrong. Similarly, if that person goes in wearing a size 12 and comes out with size 24, something isn’t quite right either. Behaviours are also analysed, as I pointed out earlier. At the end of the day, though it’s always the customer who decides where to draw the line. There are also laws governing the use of these technologies. For example, one isn’t allowed to recognise faces in continental Europe.
GB. We’ve worked with a well-known German hard discount chain for which we have deployed anti-theft solutions at the checkout. The solution makes it possible to identify fruit and vegetables in a relevant way. If you take a bottle of wine and you change the label or the barcode is not the right one, the system is able to detect it.
With this solution, we have succeeded in reducing thefts by 60%. Into the bargain, improving in-store communication, both towards shop assistants and customers, also helps to reduce thefts by around 20%.
GB. For automatic checkouts, a camera is placed on top, coupled with sensors that can measure not only the weight, but the typology of the product, its firmness, size, colour and even its density. The progress made with cameras is considerable. We’re able to spot the right product with around 90% success rate.
And it’s the same at the physical checkout, in other words, it also prevents certain employees – let me remind you that this accounts for 40% of fraud – from passing the wrong products, or forgetting to pass a product. If this happens, an alarm rings.
And once a certain number of alarms have been triggered, human intervention is required. We also know that most checkout fraud occurs in the last two hours before closing time. The pace picks up enormously at that point, because there are longer queues and dishonest employees figure it’s going to be easier to get away with theft.
Yet it is precisely where controls will be tightened, alarms will be more frequent, there will be greater vigilance over data reconciliation and a speeding up of the process. What is very important to bear in mind at all times is that all these systems are controlled and validated by human beings.
In the event of a mistake or problem, human intervention will either trigger apologies or reverse an incorrect identification. Artificial intelligence is an ally of commerce, but it is not inhuman if it is properly implemented.
GB. On the e-commerce systems that we deploy, we observe that fraud often comes from the same IP addresses and the same geographical areas. Fraud is often linked to an initial failure to enter a credit card number or make a payment. This triggers alerts accordingly.
In response, we will either block the payment or notify the end customer’s bank. The computing power is such that fraudsters can hardly get away with it. And banks have also made efforts in this direction, with 3D secure and dual authentication of purchases via mobile applications, email or text messages.
All online businesses, even the smallest merchant sites, are equipped with these solutions, which help to prevent attacks, the theft of customer files and phishing.
GB. We’re not that far off. Especially as we develop new technologies, including fingerprinting, retinal scanning and even palm scanning. Probably a lot of e-commerce sites or personal computers will soon be equipped with them.
In partnership with Ingenico, we have developed a system that recognises only the palm of the hand. The venous system is unique to each individual. And you can’t reproduce it with a photograph. Add to this heat measurement and you have an ultra-secure system that will further reduce fraud.
Fujitsu is a company that is both well-known and little known. It is a large Japanese technology company that started out over 100 years ago with telecommunications and switched to services in the 2000s. It employs just over 130,000 people worldwide, and has a turnover of 33 billion dollars. Its retail division (aka Customer Experience) accounts for just over 20% of the Japanese company’s worldwide sales. Its activities are now focused on services and platforms for points of sale and e-commerce, not forgetting integration services, consulting and infrastructure for retailers. Fujitsu is even the 8th largest technology services company in the world.
Almost 40 percent of global employment is exposed to AI, with advanced economies at greater risk but also better poised to exploit AI benefits than emerging market and developing economies. In advanced economies, about 60 percent of jobs are exposed to AI, due to prevalence of cognitive-task-oriented jobs. A new measure of potential AI complementarity suggests that, of these, about half may be negatively affected by AI, while the rest could benefit from enhanced productivity through AI integration.
IMF 2024 report on AI and the impact on employment and the future of work
The British government has also published a report on this subject. Its more task-oriented approach is a little more nuanced, but still fairly unappealing.
Advances in Artificial Intelligence (AI) are likely to have a profound and widespread effect on the UK economy and society, though the precise nature and speed of this effect is uncertain. It has been estimated that 10-30% of jobs are automatable with AI having the potential to increase productivity and create new high-value jobs in the UK.
Gov.uk report on the impact of AI on jobs, Nov 2023
It’s worthy of note, however, that the authors are resorting a great deal to the conditional tense. This undoubtedly urges us to interpret these results with caution.
The approach of the French report makes a clear distinction between GenAI and AI, and even automation in the broad sense (i.e. aimed at the manufacturing industry). It’s a distinction that seems crucial to me, given the many misconceptions linked to the measuring of the impact of AI. Which AI? Generative AI? Machine Learning, deep learning, neural networks? Or even just plain good old IT, unless we are mentioning robotisation, automated supply chains…
In short, AI is everything and everything is AI. That seems to me a silver bullet for generating panic among the general public and especially young students who are trying to find their way in the future.
The French report is therefore more precise than the others I’ve read, in that it makes a clear distinction between GenAI and the others. It is also focusing on tasks rather than jobs. This approach has also been that of the British government.
This approach using the exposure of tasks, vs jobs, to GenAI makes it possible to estimate aggregate effects at the level of the economy as a whole, and to allow comparisons between countries. However, it has several limitations. Here are the two main ones. On the one hand, it is a static approach: the studies are based on existing tasks and therefore do not take account those tasks that could be created as a result of the development of AI […] On the other hand, it is based on an estimate of the probability of different tasks being replaced by AI (see above diagram).
In short, even if you think it’s a better approach, thinking of the impact of AI in terms of tasks isn’t really possible. It’s like painting a picture of a landscape from the window of your intercity train at 100 miles per hour. On top of that the painter has left his glasses at home and is therefore making assumptions about whether he should add cows, or sheep, in the meadow in his painting.
[…] overall, the deployment of AI in the economy should have a positive effect on the number of jobs. Catastrophic predictions about the end of work are no more credible than similar predictions made in the past. Especially as even the task-based approach represents the upper limit for the impact of AI. Indeed, it makes the assumption that it is profitable to automate all the tasks that can be automated. But this assumption is far from being true today. The diminishing cost of AI systems and the possibility of distributing the same AI system to a very large number of users will be key factors in determining the impact of GenAI on tasks and jobs.
Antonin Bergeaud
In conclusion, if the result is not negative, it must be positive, even if it is undoubtedly just as difficult to prove as the opposite.
As for the 5% figure announced in the French report (see the quote above), I suppose it should be taken as an order of magnitude. There is a nuance added to the report in that respect. The authors mention that these 5% may vary from one occupation to another. What I take from this is that for the vast majority of occupations, this figure of 5%, is probably not to be taken at face value. Some occupations will not be affected by artificial intelligence at all, especially generative artificial intelligence. This doesn’t come as a shock to us. It takes us back to our work on jobs in 2030, where we already showed the prevalence of non-automatable occupations (surface technicians and others) in the most sought-after professions.
Occupations that are apparently easy to automate, such as bookkeeping, for example (if we fail to take its more consultancy-like aspects into account) have been on the chopping block for years. But despite the doomsday predictions, including our own, it has to be said today that the jobs of chartered accountants remain among the most in demand.
Yet all the technology is available to automate both bean counters’ tasks and data transmission. Nowadays, almost all invoices are dematerialised even though they are only unstructured PDF files. And yet most of the work of accountants remains manual whether we like it or not. Whether it’s ticking boxes between reconciliation systems or copying figures into a general ledger. The change lies mainly in the declining technical nature of the job.
Ditto for banking. Experts have been naming banks dinosaurs for years. Here again, we have to make amends. And yet there have been many restructurings, and they didn’t wait for OpenAI’s ChatGPT and its clones. But here’s the thing: changes don’t happen overnight. Besides innovation in organisations isn’t governed by wizardry but resistance to change.
Finally, let’s return to an occupation that was in the top left-hand corner of Antonin Bergeaud’s schematic. I mean that of secretaries. An occupation that has already been largely transformed since the 1990s. It has also been steadily declining to the point of disappearance at least in the United States (they only amount to a fraction of European employees now, i.e. a small proportion of 19% of all jobs). And yet, the impact of artificial intelligence between the 1980s and the year 2000 was bound to be close to zero. I should know, I was in charge of an AI project in those days. In that same period, though, I witnessed and even played an active role in the boom of the deployment of IT in businesses.
We therefore need to get back to these forecasts with a critical look. Starting with those of the IMF. And this report by the French Committee on Artificial Intelligence deserves credit for playing down the most hairy-fairy statistics on this subject.
In conclusion, after reading all these reports, the future isn’t any more predictable than it was before that. We might even venture to say that we are even more confused. Admittedly, as the authors of the French report point out, we are already seeing, and will continue to see, employees that are made redundant in professions where business models are already being jeopardised by ICTs, such as journalism.
But is this sufficient for us to reckon that what we are going through today is a “revolution” in terms of employment? There are no indications on this. All we could surmise is that a minority of jobs will be hit — be it 5%, less or more.
Time will tell whether this figure or that of the International Monetary Fund was the right one, but I’m inclined to believe that the ballpark figure quoted by the French Artificial Intelligence Commission is closer to reality.
Finally, to end on an intellectual note, let’s quote Vaclav Smil in his book Numbers don’t lie.
Being realistic about innovation
Modern societies are obsessed with innovation.
We are to believe that innovation will open every conceivable door: to life expectancies far beyond 100 years, to the merging of human and machine consciousness, to essentially free solar energy.
This uncritical genuflection before the altar of innovation is wrong on two counts: It ignores those big, fundamental quests that have failed after spending huge sums on research.
And it has little to say about why we so often stick to an inferior practice even when we know there’s a superior course of action.
Vaclav Smil, Numbers don’t lie
It’s this last sentence that I think is important. All forecasting exercises start from an assumption: that which state that when a technology improves our lives, it’s bound to be implemented.
It may seem like a no-brainer at first glance. What I have learned in the field throughout my career, however, is that when a solution is better, especially when it is better, resistance to change is all the greater. And it’s rarely the most obvious and cost-effective solutions that win. Especially because human decisions are seldom rational.
Thus, assuming that generative AI is without contest a boon to productivity gains, a theory I’m not at all sure I buy into, it would be wrong to believe that the mere fact that it exists guarantees its rapid and universal implementation.
[Verse]
In a world of bytes and tangled wires
The cyberspace that once glowed with fire (with fire)
Now fades away, its brilliance lost
As darkness falls, at such a cost
[Verse 2]
Once a realm of endless possibility
Now echoes silence and fragility
The Internet, a dying art
Fading now, tearing us apart
[Chorus]
Oh, the dying cyberspace (cyberspace)
Once so full of life and grace (life and grace)
Now it withers, slowly dies (slowly dies)
Leaving us with empty skies (empty skies)
Admittedly, the lyrics are a bit cheesy, but considering the time spent (less than a minute), the result is more than satisfactory. All the more so as the prompt used was really minimal.
A song on the death of the cyberspace, neoclassical
The possibilities are endless with this tool, you can even invent Russian songs in Post-Punk mode. And if you ask Deepl to translate the lyrics, you’ll realise that they’re pretty creative. Maybe not on par with Pushkin, but certainly well above the average of what you hear on Spotify (well I can only surmise because I subscribed to Qobuz).
An enamelled vessel
A window, a bedside table, a bed
It is difficult and uncomfortable to live
But it’s more comfortable to die
Эмалированное судно
Окошко, тумбочка, кровать, –
Жить тяжело и неуютно
Зато уютно умирать
(I can’t guarantee the translation from Russian into English, so I’ll take deepl’s word for it).
According to popular belief, music is linked to mathematics, even if this interconnection is not completely proven. As a result, it’s not totally astounding that a computer manages to do this. In fact, computers have been making music since PopCorn (1969). Note that the dancers are slightly out of sync, no doubt baffled by the technological prowess of the end of that decade.
I remember well, when I was 7, the announcement of this song on the radio: the first song produced by a computer. It was extraordinary, and way ahead of its time.
But AI in music also raises a whole range of questions:
First of all, the machine has virtually every style at its disposal. You can ask it to imitate one without having to master it and especially not by working for 10 years. This raises the question of the value of creation. How much could we pay Mr Couson to produce a song like this? We could even go further, and get Suno – or its clones – to compose a symphony or an opera. It might have to go through a few steps, but it’s a lot less tiring than inventing Einstein on the Beach or Die Zauberflöte from scratch.
And the associated question: if there is no longer any value in creating music, how many musicians will still have a go at it?
There also arises, and this is the third point, the question of creation itself. If it’s so easy to create music that isn’t all that bad, aren’t we in danger of going round in circles? Also, can we innovate, in content and form, if the basis is a collection of existing music data? Won’t the novelty wear off? Some would say that this is already the case to some extent, I suppose, but this will just finish the job.
The training data for these programmes is based on the work of hundreds of thousands of musicians over hundreds of years. It is – as with Midjourney – the plunder of our cultural heritage that raises the question of the protection of intellectual property. Or rather, it might make such IP redundant, unless legal proceedings are successful (but justice is slow, and AIs are fast).
It also raises the bar for tomorrow’s content creators who will want to show their creativity and beat the machines. This will really demand a lot of imagination.
When everyone becomes a creator, does that mean that creation no longer exists or, on the contrary, that everyone has become a true creator, even without talent? And is pressing a button and waiting for a program to produce a result a creative act? Is “prompting” sufficient? Tomorrow, will humans become the blue collars of artistic creation whereas machines produce all the thinking?
There are many questions raised. And the undeniable fun that one can have when dealing with this type of programme should not allow us to forget about them. What’s more, it’s a guilty pleasure. If we are endowed with a conscience, it’s hard not to feel, as with tools like Midjourney, one feels as if one were faking artistry.
From Gershon Kingsley to Wally Badarou (who was composing on the Mac in the early 90s) to Klaus Schultze (and his fabulous Ludwig Zwei von Bayern with his fully synthesised string orchestra in 1978) or Zoe Keating, who records and plays her sound loops thanks to a pedal connected to a MacBook Pro, artists’ experiments with computer music have been numerous.
But producing music with AI goes a step further. However, here too, these attempts are not recent. Digging around on this site, we found an old article by Mia Tawile written in 2016 about a Google project named Magenta and of which there are still a few scattered traces on the Internet.
There’s also some pretty interesting music here, provided by artificial intelligence, the fruit of Google’s early work in this area. The results are promising but without a future, like so many aborted attempts by this Internet giant, which seems so focused on its business model. So much so that it may be suffering from the innovator’s dilemma.
Example of chamber music produced by Magenta. Not quite Haydn, but it sounds a bit like it (Midi style).
My optimism leads me to believe that we will still need Mr Couson and his colleagues. If only to host concerts. Of course, in these artistic performances, it would not be surprising to find a few computers and loops invented by AI. In this respect, these artists will no doubt be the worthy heirs of the pioneers I mentioned earlier. After all, didn’t musicians like Wim Mertens and Philip Glass imitate repetitive computer music with real instruments? And more recently, haven’t Nils Frahm, Nicklas Paschburg or Grandbrothers included these technologies in their music to the point we end up forgetting about them?
Creators always find a way of circumventing issues like these.
Below is Mia’s post from 2016. My two cents about this with nearly 10 years of hindsight.
Innovation tales time. And no, it won’t happen overnight (lesson number one);
Google missed the boat (again I daresay), even though the transformer researchers were working for Alphabet at the time.
Enjoy the AI Time Machine.
We have all heard of Mozart, Chopin, and Beethoven, but not all of us know Google’s artificial intelligence and its ability to generate music with AI. Yes, a robot has joined the club. And yes, it plays music. (If the song We are the robots by Kraftwerk is playing in your head right now, it is completely normal, don’t worry.) This new robot/artist that creates a lot of debate is called Magenta. You might have seen in my previous article about Facebook’s artificial intelligence how machine learning works on images and videos. This article will describe a concept that is similar yet different. The main question here is: Can you use machine learning to create a music piece? That’s exactly what I will touch on in this article.
Magenta is Google’s Brain Team project that answers the question mentioned above: Can we use artificial intelligence and machine learning to play music?
AI music according to Mia TawileFor Artificial Intelligence researchers, the sky is the limit. They always look for new features to develop, and new ways of developing machines.
Google is inviting people who are interested in this project to join the community. Actually, a part of the project is accessible to the general public, and is waiting for people’s input.
A lot of people are scared of such technological growth. Well, they might be right. When machines start recognising pictures, and videos, and describe them to us, it means that technology is taking these robots beyond their limits.
The good news is that there is a use to this technology. It is not only developed to win a challenge, or defy the limits of research and technology. As I mentioned earlier, Facebook uses artificial intelligence to expand its community, without excluding anyone.
When it comes to artificial music, some applications identified this new trend and worked around it. There’s a mobile app called @life that plays music according to your state of mind and your mood. Y
ou might ask yourself, how does a machine know what one is feeling in real time? The machine gathers information about the person’s behaviour or their location and analyses their mood. Some data analysts use Instagram filters for example, to identify the user’s mood: dark colours reflect sadness, whereas bright colours represent happiness. This music mobile application is said to help people in pain by distracting them and using the popular benefits and virtues of music.
Maybe machines will help us create new music genres, by combining different algorithms. Or maybe this new invention will help people manage their stress or heal their pain, music being the cure to everything! We can see the bud of that technology today with Spotify that can detect your running speed and adapt the music type and tempo.
The aim is to make people understand that AI machines do not become “intelligent” out of the blue.
Behind AI, there are humans who have gathered data. And AI will only be as good as its data.
Today’s AI is still at the stage where it reproduces patterns. It’s a mere “stochastic parrot“.
In the early days of AI, there were expert systems, which worked with ever more sophisticated knowledge bases. Then we realised that rather than predicting all the potential situations, we could simply feed the AI with samples based on existing data sets and implement self-learning algorithms.
With large language models (LLMs), humongous quantities of text have become available. So much so that AI has become capable of generating text by itself. But the principle is the same: the basis is those samples provided by humans.
With AlphaAI, everything is very simple. A sensor will tell the machine what to do, for example turn left when there is light on the left. Or turn right when there is light on the right-hand side. This helps users understand the basics. After that, it’s just a matter of scaling up to more advanced AI.
When you interact with a Large Language Model (LLM), you are essentially producing text, even though you could also generate images, music, videos, etc.
Robotics is the future of AI
But what I see emerging is that the future of AI is about robotics. The Figure start-up has just raised $675 million and has signed agreements with OpenAI, Microsoft and Nvidia to develop humanoid robots. It’s flavour of the month. Our role is not to enter this competition, however.
Our vocation is educational. We want everyone to be able to get to grips with these technologies.
Our aim is to enable people to train their own AI, so that they can easily develop their own ideas, such as home automation projects for instance. And also make AI accessible to SMEs. Our development plans could evolve in the future to move away from teaching and training, towards a plug and play solution for introducing AI and automation into the business world.
I’m a technophile, yet I’m not at all a techno enthusiast. I think there are some really pertinent questions being asked. And that’s why I think we need to focus on training and education.
We need to keep as many people as possible informed, to debunk all the myths about AI.
On the one hand, AIs have their limits;
Secondly, users feel immediately more comfortable with a tool after getting to grips with it.
Let me tell you about an anecdote.
We work with a well-known luxury goods company in Paris, France, for whom we run autonomous robot races. Their employees train their robots for the race. The first feedback from learners on these training courses is: “I’ve realised how much AI is fun!”
It’s true that digital tools also have their downsides, such as creating addictions. But if you get to grips with them, you can achieve great results.
We need to evangelise about AI adoption, there are so many exciting potential applications for it.
I’m involved in a number of AI think tanks and I’ve realised that what the general public expects from researchers is to be told what the future will be. In fact, it’s very hard to predict the future. Innovation is about trial and error. Sometimes its adoption is faster than we think, at other times it’s not.
Always the unexpected happens.
I think so. We’re already seeing it in the home construction business. Tomorrow, it’s very likely that AIs will be performing a certain number of tasks. However, I hope there will still be room for humans’ creative skills.
For instance, manmade products are highly valued by consumers these days. Mass-producing widgets is easy. But creating something unique is more rewarding.
There will always be room for human creativity.
I think so. Some people are depressed because they think they are going to be dominated by AI. But look at self-driving cars: they were supposed to be ubiquitous by 2010, and it didn’t happen.
But we shouldn’t be wearing our rose-coloured spectacles either.
Both citizens and politicians need to get to grips with the issues related to AI. As far as I am concerned, I remain optimistic about what can be achieved with these tools.
Discover shows related to English language Visionary Marketing Podcasts, based on actual content similarities. Explore podcasts with similar topics, themes, and formats, backed by real data.
