u/didiTonic

Depuis le 11 août, les entreprises en France ne peuvent plus appeler des particuliers pour leur vendre un produit ou un service sans avoir obtenu leur accord au préalable.

Quand on sait qu’une bonne partie de nos centres d’appels et BPO travaillent avec le marché français, ça peut quand même avoir un impact important chez nous.

D’après Défi, le secteur emploie entre 10 000 et 15 000 personnes à Maurice. Les gros acteurs disent avoir anticipé le changement en se tournant davantage vers le service client, le back-office et les prospects qui ont déjà accepté d’être contactés.

Euro CRM explique par exemple que 75 % de ses activités sont déjà dans le service client et le back-office, donc beaucoup moins concernées par cette nouvelle règle.

Mais je me demande surtout ce qu’il va se passer pour les petits centres qui dépendent encore beaucoup des appels sortants, de la prise de rendez-vous et de la vente par téléphone.

Est-ce qu’ils vont réussir à s’adapter rapidement ? Ou est-ce qu’on risque de voir moins de campagnes, moins de recrutements, voire des suppressions de postes ?

Avec l’IA, l’automatisation, le chat et le support digital qui prennent déjà de plus en plus de place, cette loi va peut-être simplement accélérer une transformation du secteur qui avait déjà commencé.

Been living in Mauritius for a few years now, and since I dnt drive much, I mostly use taxis to get around. Was heading to grand baie this weekend and, as usual, ended up having a nice chat with the driver and funny enough, I get a lot of local news just from these conversations.

We were talking about the latest fuel hike and he told me things are already quite difficult financially, work isn’t that great, and costs just keep increasing. He hasn’t increased his prices yet, but he said it will probably come eventually because at some point they simply won’t have much choice.

Over the years I’ve seen fuel prices go up, but also come down a few times. What I’m trying to understand is why the ripple effect seems to work mostly in one direction.

When fuel goes up, we hear that transport, deliveries, food and services will eventually cost more. But when fuel comes down, those prices rarely seem to follow.

Maybe I’m wrong, but I can’t remember my taxi fare ever going down after a fuel price decrease.

Saw the news about Shakeel Mohamed’s visit to Pointe d’Esny and the push to remove barriers, ropes and signs blocking public access to the coast.

I genuinely thought some beaches here were private, especially around hotels, villas and campements.

Is the rule basically that the whole coastline is public, or is it more complicated than that?

Also curious if anyone has actually been stopped or told a beach was “private”.

Namecheap is currently experiencing a major outage following a power failure affecting its Phoenix data center.

Web hosting, websites, DNS, email services, and other Namecheap services may currently be unavailable or disrupted.

So if your website or hosting is suddenly offline, the issue is likely on Namecheap’s side rather than with your own server or configuration.

Emergency maintenance is currently underway.

Namecheap est actuellement touché par une importante panne suite à une coupure de courant dans son datacenter de Phoenix.

L’hébergement web, les sites internet, DNS, emails et autres services Namecheap peuvent être indisponibles ou perturbés.

Donc si votre site ou votre hébergement ne fonctionne plus actuellement, le problème vient probablement de chez Namecheap et non de votre serveur ou de votre configuration.

Une maintenance d’urgence est en cours.

I’ve been posting about AI-related stuff for a while now and honestly, the more I see it being pushed everywhere, the more I think we need proper regulation around it.

Not just “hey, we have AI now, let’s put it into everything”.

And now Reddit is going down that road too.

Like... seriously, wtf is going on?

I get that moderation is a pain and AI can probably help with some of it. But this is always how it starts. First it’s there to “assist” people, then little by little it ends up making more and more decisions.

Reddit is also probably one of the worst places to rely too much on AI moderation because so much of this site is sarcasm, jokes, arguments, dark humour, inside jokes, people taking things out of context, etc.

How is an AI supposed to get all of that right?

And what happens when it gets it wrong? You appeal to another AI? 😂

I’m not against AI at all. I use it and I think it can be really useful.

I just don’t understand why the answer to everything suddenly seems to be “add AI”.

Maybe we should figure out the rules and limits first before putting it everywhere.

At this rate we’re going to end up with AI writing posts, AI moderating them, AI reviewing the appeals and humans just scrolling through the mess.

I’ve been posting about AI-related stuff for a while now and honestly, the more I see it being pushed everywhere, the more I think we need proper regulation around it.

Not just “hey, we have AI now, let’s put it into everything”.

And now Reddit is going down that road too.

Like... seriously, wtf is going on?

I get that moderation is a pain and AI can probably help with some of it. But this is always how it starts. First it’s there to “assist” people, then little by little it ends up making more and more decisions.

Reddit is also probably one of the worst places to rely too much on AI moderation because so much of this site is sarcasm, jokes, arguments, dark humour, inside jokes, people taking things out of context, etc.

How is an AI supposed to get all of that right?

And what happens when it gets it wrong? You appeal to another AI? 😂

I’m not against AI at all. I use it and I think it can be really useful.

I just don’t understand why the answer to everything suddenly seems to be “add AI”.

Maybe we should figure out the rules and limits first before putting it everywhere.

At this rate we’re going to end up with AI writing posts, AI moderating them, AI reviewing the appeals and humans just scrolling through the mess.

Just came across this and honestly this is pretty wild.

Researchers used AI to design completely new viruses that don't exist in nature. They then actually made some of them in a lab, and 16 of the designs worked.

Before anyone panics, these are bacteriophages, so they infect bacteria, not humans.

The interesting part is that some of these AI-made viruses were able to kill E. coli, including bacteria that had become resistant to normal phages.

So yeah, there could be a genuinely useful side to this, especially with antibiotic resistance becoming such a big problem.

But at the same time... we now have AI systems capable of coming up with a complete virus genome, then humans can synthesize it and see if it works.

That feels like a pretty big line to cross.

Obviously this doesn't mean someone can just ask ChatGPT to make a deadly virus tomorrow. You still need labs, equipment, biological knowledge etc.

But we've gone from AI generating text and images to designing proteins, genes, and now apparently functioning viruses.

That's moving fast.

I'm not really sure how I feel about it.

On one hand this could lead to new treatments and better ways to fight resistant bacteria.

On the other hand, I really hope the safety side of this is moving as fast as the technology.

https://preview.redd.it/jv2xhyghb6ih1.jpg?width=1120&format=pjpg&auto=webp&s=6e4aac950d7de60fb5fa949cf30d2a17c48f075f

I watched an interview with AI safety researcher Roman Yampolskiy, and it raised a practical question for people who use ChatGPT for advanced workflows.

His broader claim is that increasingly intelligent AI systems may become harder to predict and control. Whether or not you agree with his conclusions about AGI, a smaller version of this problem already exists when we give an AI access to tools.

There is a major difference between asking ChatGPT to draft an email and allowing an agent to send it.

The same distinction applies to:

• Suggesting a database query versus executing it
• Drafting code versus deploying it
• Researching a purchase versus completing the transaction
• Preparing files versus deleting or modifying them
• Recommending calendar changes versus inviting real people

My current view is that the model should generate proposals, while a separate control layer decides whether those proposals are allowed to become actions.

Some possible safeguards include:

1. Giving each agent only the minimum permissions required for its task
2. Requiring approval for irreversible or external actions
3. Validating structured outputs with deterministic code
4. Isolating browsing and code execution from sensitive systems
5. Limiting spending, execution time and the number of actions
6. Keeping complete logs of prompts, tool calls and results
7. Using a second evaluation step before important actions
8. Making every operation reversible wherever possible

The difficult part is deciding where autonomy becomes too risky.

A confirmation step for every action makes the agent frustrating to use. Too few confirmation steps can turn a misunderstood instruction into a real-world problem.

I watched an interview with AI safety researcher Roman Yampolskiy, and it raised a practical question for people who use ChatGPT for advanced workflows.

His broader claim is that increasingly intelligent AI systems may become harder to predict and control. Whether or not you agree with his conclusions about AGI, a smaller version of this problem already exists when we give an AI access to tools.

There is a major difference between asking ChatGPT to draft an email and allowing an agent to send it.

The same distinction applies to:

• Suggesting a database query versus executing it
• Drafting code versus deploying it
• Researching a purchase versus completing the transaction
• Preparing files versus deleting or modifying them
• Recommending calendar changes versus inviting real people

My current view is that the model should generate proposals, while a separate control layer decides whether those proposals are allowed to become actions.

Some possible safeguards include:

1. Giving each agent only the minimum permissions required for its task
2. Requiring approval for irreversible or external actions
3. Validating structured outputs with deterministic code
4. Isolating browsing and code execution from sensitive systems
5. Limiting spending, execution time and the number of actions
6. Keeping complete logs of prompts, tool calls and results
7. Using a second evaluation step before important actions
8. Making every operation reversible wherever possible

The difficult part is deciding where autonomy becomes too risky.

A confirmation step for every action makes the agent frustrating to use. Too few confirmation steps can turn a misunderstood instruction into a real-world problem.

I watched an interview with AI safety researcher Roman Yampolskiy, and it raised a practical question for people who use ChatGPT for advanced workflows.

His broader claim is that increasingly intelligent AI systems may become harder to predict and control. Whether or not you agree with his conclusions about AGI, a smaller version of this problem already exists when we give an AI access to tools.

There is a major difference between asking ChatGPT to draft an email and allowing an agent to send it.

The same distinction applies to:

• Suggesting a database query versus executing it
• Drafting code versus deploying it
• Researching a purchase versus completing the transaction
• Preparing files versus deleting or modifying them
• Recommending calendar changes versus inviting real people

My current view is that the model should generate proposals, while a separate control layer decides whether those proposals are allowed to become actions.

Some possible safeguards include:

1. Giving each agent only the minimum permissions required for its task
2. Requiring approval for irreversible or external actions
3. Validating structured outputs with deterministic code
4. Isolating browsing and code execution from sensitive systems
5. Limiting spending, execution time and the number of actions
6. Keeping complete logs of prompts, tool calls and results
7. Using a second evaluation step before important actions
8. Making every operation reversible wherever possible

The difficult part is deciding where autonomy becomes too risky.

A confirmation step for every action makes the agent frustrating to use. Too few confirmation steps can turn a misunderstood instruction into a real-world problem.

I watched an interview with AI safety researcher Roman Yampolskiy, and it raised a practical question for people who use ChatGPT for advanced workflows.

His broader claim is that increasingly intelligent AI systems may become harder to predict and control. Whether or not you agree with his conclusions about AGI, a smaller version of this problem already exists when we give an AI access to tools.

There is a major difference between asking ChatGPT to draft an email and allowing an agent to send it.

The same distinction applies to:

• Suggesting a database query versus executing it
• Drafting code versus deploying it
• Researching a purchase versus completing the transaction
• Preparing files versus deleting or modifying them
• Recommending calendar changes versus inviting real people

My current view is that the model should generate proposals, while a separate control layer decides whether those proposals are allowed to become actions.

Some possible safeguards include:

1. Giving each agent only the minimum permissions required for its task
2. Requiring approval for irreversible or external actions
3. Validating structured outputs with deterministic code
4. Isolating browsing and code execution from sensitive systems
5. Limiting spending, execution time and the number of actions
6. Keeping complete logs of prompts, tool calls and results
7. Using a second evaluation step before important actions
8. Making every operation reversible wherever possible

The difficult part is deciding where autonomy becomes too risky.

A confirmation step for every action makes the agent frustrating to use. Too few confirmation steps can turn a misunderstood instruction into a real-world problem.

I watched an interview with AI safety researcher Roman Yampolskiy, and it raised a practical question for people who use ChatGPT for advanced workflows.

His broader claim is that increasingly intelligent AI systems may become harder to predict and control. Whether or not you agree with his conclusions about AGI, a smaller version of this problem already exists when we give an AI access to tools.

There is a major difference between asking ChatGPT to draft an email and allowing an agent to send it.

The same distinction applies to:

• Suggesting a database query versus executing it
• Drafting code versus deploying it
• Researching a purchase versus completing the transaction
• Preparing files versus deleting or modifying them
• Recommending calendar changes versus inviting real people

My current view is that the model should generate proposals, while a separate control layer decides whether those proposals are allowed to become actions.

Some possible safeguards include:

1. Giving each agent only the minimum permissions required for its task
2. Requiring approval for irreversible or external actions
3. Validating structured outputs with deterministic code
4. Isolating browsing and code execution from sensitive systems
5. Limiting spending, execution time and the number of actions
6. Keeping complete logs of prompts, tool calls and results
7. Using a second evaluation step before important actions
8. Making every operation reversible wherever possible

The difficult part is deciding where autonomy becomes too risky.

A confirmation step for every action makes the agent frustrating to use. Too few confirmation steps can turn a misunderstood instruction into a real-world problem.

J’ai récemment écouté une interview de Roman Yampolskiy, chercheur spécialisé dans la sécurité de l’intelligence artificielle.

Sa position est radicale : nous savons améliorer rapidement les capacités des modèles, mais nous ne savons pas garantir le contrôle permanent d’un système qui deviendrait plus intelligent que nous.

Sa recommandation n’est pourtant pas d’abandonner l’IA.

Il propose plutôt de concentrer l’innovation sur des intelligences artificielles spécialisées, conçues pour résoudre des problèmes clairement définis : diagnostic médical, optimisation industrielle, logistique, recherche scientifique, analyse documentaire ou automatisation de tâches précises.

Cette distinction me semble particulièrement intéressante pour l’écosystème French Tech.

Aujourd’hui, beaucoup de produits évoluent vers le même objectif : transformer un assistant en agent généraliste capable de naviguer, décider, écrire du code, contacter des personnes et exécuter des actions avec un minimum de supervision.

Mais est-ce nécessairement la meilleure stratégie produit ?

Une IA plus spécialisée pourrait être :

• Plus simple à tester
• Plus facile à intégrer dans les processus existants
• Plus compréhensible pour les clients
• Plus facile à limiter et à auditer
• Mieux adaptée aux contraintes d’un secteur précis
• Moins dépendante d’une promesse d’autonomie totale

À l’inverse, les agents généralistes offrent une proposition beaucoup plus ambitieuse et peuvent automatiser des processus complets. C’est aussi ce qui attire les investissements et l’attention.

Le dilemme pour une startup française est donc réel.

Faut-il essayer de suivre la course internationale vers des systèmes toujours plus autonomes, ou construire une différence autour d’IA sectorielles, contrôlables et profondément intégrées aux métiers ?

La sécurité et la maîtrise d’un système peuvent-elles devenir un véritable avantage concurrentiel, ou les clients choisiront-ils toujours la solution la plus autonome et la plus puissante ?

Je serais intéressé par les retours de fondateurs et de personnes qui intègrent actuellement des agents dans leurs produits.

I watched an interview with AI safety researcher Roman Yampolskiy, and it raised a practical question for people who use ChatGPT for advanced workflows.

His broader claim is that increasingly intelligent AI systems may become harder to predict and control. Whether or not you agree with his conclusions about AGI, a smaller version of this problem already exists when we give an AI access to tools.

There is a major difference between asking ChatGPT to draft an email and allowing an agent to send it.

The same distinction applies to:

• Suggesting a database query versus executing it
• Drafting code versus deploying it
• Researching a purchase versus completing the transaction
• Preparing files versus deleting or modifying them
• Recommending calendar changes versus inviting real people

My current view is that the model should generate proposals, while a separate control layer decides whether those proposals are allowed to become actions.

Some possible safeguards include:

1. Giving each agent only the minimum permissions required for its task
2. Requiring approval for irreversible or external actions
3. Validating structured outputs with deterministic code
4. Isolating browsing and code execution from sensitive systems
5. Limiting spending, execution time and the number of actions
6. Keeping complete logs of prompts, tool calls and results
7. Using a second evaluation step before important actions
8. Making every operation reversible wherever possible

The difficult part is deciding where autonomy becomes too risky.

A confirmation step for every action makes the agent frustrating to use. Too few confirmation steps can turn a misunderstood instruction into a real-world problem.

Je viens d’écouter une longue interview de Roman Yampolskiy, chercheur spécialisé dans la sécurité de l’intelligence artificielle.

Parmi toutes ses prédictions, une idée m’a particulièrement marqué.

Lors des précédentes révolutions technologiques, certaines professions disparaissaient, mais de nouveaux métiers apparaissaient. La réponse était donc relativement simple : se former, se reconvertir et apprendre à utiliser les nouveaux outils.

Mais que se passe-t-il si l’outil est également capable d’apprendre le nouveau métier ?

Selon Yampolskiy, l’IA ne représente pas seulement une nouvelle machine ou un nouveau logiciel. Nous essayons progressivement d’automatiser le travailleur, le développeur, le chercheur et même l’inventeur.

Un nouveau métier apparaît ? Une IA pourrait l’apprendre.

Une nouvelle compétence devient importante ? Une IA pourrait être entraînée dessus.

Une entreprise crée un poste pour superviser les IA ? Cette supervision pourrait elle-même être automatisée.

Il prédit que des capacités proches de l’AGI pourraient apparaître autour de 2027 et que la majorité des activités réalisées sur ordinateur pourraient ensuite devenir techniquement automatisables. Il parle même, à terme, de niveaux de chômage proches de 99 %.

Je trouve ce chiffre extrêmement spéculatif. Entre une capacité technique et son adoption réelle, il existe de nombreux obstacles : le coût, la confiance, la réglementation, les responsabilités et les habitudes des entreprises.

Mais son argument principal mérite quand même d’être discuté :

Si l’IA peut apprendre les nouveaux métiers aussi rapidement que les anciens, vers quoi sommes-nous censés nous reconvertir ?

Pensez-vous que l’IA va surtout transformer les emplois existants, ou qu’elle pourrait réellement rendre une grande partie du travail humain économiquement inutile ?

Et dans ce deuxième scénario, le problème principal serait-il financier, avec la question du revenu, ou psychologique, avec la perte de statut et de sens ?