Fondée par Vincent Rigal et Maximum Scholz en 2021, CircularPlace propose une plateforme qui répond aux besoins particuliers de chaque entreprise en matière d’économie circulaire. L’objectif ? Les aider à mieux s’emparer des usages de la seconde vie. Comment ? En misant sur la traçabilité. Explications avec Vincent Rigal.

L’article CircularPlace booste l’économie circulaire des entreprises est apparu en premier sur TheGood.

Newsletters are all the rage now. In recognition of that, I blogged here two years ago about the idea of writing a solo newsletter. Since then I’ve been co-producing this one with Katherine Druckman at Reality 2.o. It’s a Substack one, so I know how that game works on the production as well as the consumption ends.

Recently I also learned that WordPress makes it easy to turn blogs into newsletters, which is why you see “Get New Posts by Email” in the right column here. So, wanting an image to go with the news that this blog can now be a newsletter if you like, I said to ChatGPT, “Draw me Doc Searls blogging on the left side of the image, and producing a newsletter on the right side.” It gave me this, including the caption:

Except for the slouch, I never looked like that. But what the hell. AI is wacky shit, so there ya go.

Feel free to subscribe.

There is a war going on. Humanity and nature are on one side and Big Tech is on the other. The two sides are not opposed. They are orthogonal. The human side is horizontal and the Big Tech side is vertical.*

The human side is personal, social, self-governed, heterarchical, open, and grounded in the physical world. Its model is nature, and the cooperative contexts in which competition, creation, and destruction happen in the natural world.

The Big Tech side is corporate, industrial, hierarchical, competitive, mechanistic, extractive, and closed, even though it produces many positive-sum products and services that are good for people and good for nature. It is also, being competitive and rewarding toward winner-take-most outcomes, dominated by giants.

This war has been fought over many other things in the past, especially in tech. But AI is the big one right now—and perhaps the biggest one of all time.

Over the long run, both sides will win, because we need the best of what both bring to the world’s big round table. In the past, this has happened in countless markets, countries, polities, societies, and other contexts. In tech it happened with the conflict between PCs and mainframes, between the open and public Internet and closed private networks, and between open operating systems and closed ones. None of those conflicts have final outcomes, but they have arrived at stable and cooperative states. They arrived at those states because everyone recognized that people can do more with their own tools than those big entities can do on theirs, or by using people as extracted resources.

This conflict is perhaps best represented by the ironies that have been at work within OpenAI and Sam Altman, its prior and current CEO. We can also see it with sparkling clarity in something amazing that Sam said near the close of his keynote at the first OpenAI Developer Conference, in November of last year:

Lets unpack that.

Individual empowerment means exactly what it says. Agency is the ability to take action or to choose what action to take (Cambridge Dictionary). Scale means growth toward ubiquity.

You have individual empowerment and agency when you ride your bike, drive your car, mow your lawn, or button your shirt. You also have it when you work on your laptop, phone, and other smart rectangles. Scale means your power and agency will grow to the max while the same will happen for nearly everyone else. Scale for you alone won’t cut it. When most of us have it, that’s scale.

Technologies extend us. They enlarge our capacities in the world. Intelligence is one of those capacities. So is memory. Your rectangles help a lot with both. That’s why those have already scaled to ubiquity, or close enough.

AI will do the same, but only if it’s personal. Not if it’s  just “personalized.”

As Jamie Smith made clear here a year ago, “your personal AI isn’t really personal.”

ChatGPT4 doesn’t know that yet. I can tell because I just asked:

Me:
What is “personal AI”?

ChatGPT4:
“Personal AI” refers to artificial intelligence systems that are designed to serve individual users through personalized interactions and services. These AI systems learn from the specific preferences, behaviors, and needs of their users to offer more tailored assistance, recommendations, or information. The goal of personal AI is to enhance user experience by adapting to unique personal profiles.

There are various applications of personal AI, including:

1. Virtual Assistants: Like Siri, Google Assistant, and others, these AIs help with daily tasks such as scheduling, reminders, searching information, and controlling smart home devices, all customized to the user’s habits and preferences.
2. Recommendation Systems: Used in streaming services like Netflix or shopping platforms like Amazon, these AI systems analyze your past behavior to recommend movies, shows, or products you might like.
3. Healthcare: Personal AI can track individual health metrics and provide personalized advice, monitor conditions, or even alert medical professionals if needed.
4. Learning and Development: In education, personal AI can adapt learning materials to the pace and learning style of the student, offering a customized educational experience.
5. Gaming: AI in gaming can adapt to provide a challenge tailored to the player’s skill level or alter the game narrative based on choices made by the player.

The development of personal AI focuses on enhancing user engagement by creating more intuitive and responsive interactions that feel uniquely suited to each individual.

For a second opinion, I asked Google’s Gemini the same question.

Me:
What is “personal AI”?

Gemini:
Personal AI refers to artificial intelligence designed to assist and interact with a specific individual. It’s essentially your own AI companion that learns about you and tailors its services to your needs. Here are some key aspects of personal AI:

1. Personalization: Unlike general-purpose AI assistants, personal AI focuses on you. It learns from your interactions, preferences, and habits to become more helpful over time.
2. Focus on Memory and Knowledge: Some personal AI systems aim to become an extension of your mind. They can store your memories, knowledge, and experiences, making them readily accessible and even usable to help with tasks like writing or communication.
3. Varied Applications: Personal AI can have a wide range of applications. Imagine an AI that helps you:
4. Organize your life: Manage schedules, appointments, and reminders.
5. Boost productivity: Assist with tasks, research, and communication.
6. Enhance creativity: Help you brainstorm ideas or generate creative content.
7. Improve learning: Personalize learning experiences and provide targeted information.

Privacy Concerns: Storing personal data on an AI raises concerns about privacy and security.

Overall, personal AI holds promise for a future where technology seamlessly integrates with our lives, offering personalized assistance and support.

To both companies, personal AI is a personalized service—from them. It’s not something you own and control. It’s not about individual empowerment and agency. It doesn’t extend you. It’s hired prosthesis: an extension of them rather than of you.

Let’s name the category: AIaaS—AI as a Service.

But hats off. OpenAI and Gemini do amazing jobs. So do lots of other AI services by wannabe giants. I use AI every day to improve what I write, to generate and fix images, to answer questions that old-fashioned search engines can’t answer or suck at. I even pay extra for some of it (such as ChatGPT4 and Adobe’s Creative Suite). And it seems they all get better, all the time, at everything. Won’t personalization be good enough, eventually?

No. Because they aren’t you. They also aren’t yours, so they can’t extend you. They can seem to. But they don’t. They also don’t have access to most of your private life. Nor should they.

But your private life could use some AI help. For example—

• Schedules, including on your calendars, past and future
• Health data, including all your medical reports, prescriptions, appointments, insurance information, past and present providers, plus what your watch, phone, and other devices record about you
• Financial records, including bills, receipts, taxes, and anything called an account that involves money
• Travel, including all the movements your phone (and phone company), watch, and car record about where you go and where you’ve been
• Work—past and present, including whatever HR systems know or knew about you
• Contacts—all the people, businesses, and other entities you know
• Business relationships, with brokers, retailers, service providers, whatever
• Subscriptions, including all those “just $1 for the first four weeks” offers you’ve accepted, plus other forms of screwage that are stock-in-trade for companies selling subscription systems to businesses.
• Property, including all the stuff on your shelves, floors, closets, garages, and storage spaces—plus your stocks and real estate.

It’s not easy to visualize what a personal AI might do for those, but let’s try. Here’s how Microsoft’s Copilot (or whatever it’s called this week) did it for me before I got rid of all its misspellings and added my own hunks of text:

All that stuff is data. But most of it is scattered between apps and clouds belonging to Apple, Google, Microsoft, Amazon, Meta, phone companies, cable companies, car makers, health care systems, insurance companies, banks, credit card companies, retailers, and other systems that are not yours. And most of them also think that data is theirs and not yours.

To collect and manage all that stuff, you need tools that don’t yet exist: tools that are yours and not theirs. We could hardly begin to imagine those tools before AI came along. Now we can.

For example, you should be able to take a picture of the books on your shelves and have a complete record of what those books are and where you got them. You’ll know where you got them because you have a complete history of what you bought, where and from whom. You should be able to point your camera in your closets, at the rugs on your floors, at your furniture, at the VIN number of your car that’s visible under your windshield, at your appliances and plumbing fixtures, and have your AI tell you what those are, or at least make far more educated guesses than you can make on your own.

Yes, your AI should be able to tap into external databases and AI systems for help, but without divulging identity information or other private data. Those services should be dependent variables, not independent ones. For full individual empowerment and agency, you need to be independent. So does everyone else with personal AI.

Now imagine having a scanner that you can feed every bill, every receipt, every subscription renewal notice, and have AI software that tells you what’s what with each of them, and sorts records into the places they belong.

Ever notice that the Amazon line items on your credit card bill not only aren’t itemized, but don’t match Amazon’s online record of what you ordered? Your personal AI can sort that out. It can help say which are business and personal expenses, which are suspicious in some way, what doesn’t add up, and much more.

Your personal AI should be able to answer questions like, How many times have I had lunch at this place? Who was I with? When was it we drove to see so-and-so in Wisconsin? What route did we take? What was that one car we rented that we actually liked?

Way back in 1995, when our family first got on the Internet over dial-up, using the first graphical browsers on our PC, and e-commerce began to take off with Amazon, eBay, and other online retailers, my wife asked an essential question: Why can’t I have my own shopping cart that I take from site to site?

Twenty-nine years later, we still don’t have the answer, because every retailer wants you to use its own. And we’re stuck in that system. It’s the same system that has us separately consenting to what sites ironically call “your privacy choices.” And aren’t.

There are countless nice things we can’t have in the digital world today because we aren’t people there. We are accounts. And we are reduced to accounts by every entity that requires a login and password.

This system is a legacy of client-server, a euphemism for slave-master. We might also call it calf-cow, because that’s how we relate to businesses with which we have accounts. And that model is leveraged on the Web like this:

We go to sites for the milk of content and free cookies, whether we want them or not. We are also just “users.”

In the client-server world, servers get scale. Clients have no more scale than what each account—each cow—separately allows. Sure, users get lots of benefits, but scale across many cows is not one of them. And no, “login with Google” and “login with Facebook” are just passes that let calves of ruling cows wander into vassal pastures.

For individual empowerment and scale to happen, we need to be self-sovereign and independent. Personal AI can give that to us. It can do that by solving problems such as the ones listed above, and by working as agents that represent us as human beings—rather than mere users—when we engage with Big Tech’s cows.

This will be a fight at first, because the cows think they run all of nature and not just their own farms. And $trillions are being invested in the same old cattle industry, with AI painted all over the new barns. Comparatively speaking, close to nothing is going toward giving independent and self-sovereign individuals the kind of power and scale Sam Altman says he wants to give us but can’t because he’s on the big cow side of this thing.

So where do we start?

First, with open source code and open standards. We have some already. Llama 3, from Meta AI, is “your own intelligent assistant,” and positions Meta as a more open and user-friendly cow than OpenAI. Meta is still on the top-down Big Tech side of the war we’re in. But hell, we can use what they’ve got. So let’s play with it.

Here on the ground there are all these (with quotage lifted from their sites or reviews such as this one)—

• MindsDB: “an open-source AutoML framework”
• Alt.ai: “It’s an A.I. which aims to digitize users’ intentions and place it on the cloud to let our clones deal with all digital operations.”
• Keras: “a multi-backend deep learning framework, with support for JAX, TensorFlow, and PyTorch”
• PyTorch: “Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration, and Deep neural networks built on a tape-based autograd system
• Tensor Flow: “open-source framework for machine learning”
• CoreNet: a deep neural network toolkit for small and large-scale models, from Apple
• Haystack: an “open source Python framework by deepset for building custom apps with large language models (LLMs).”
• Image Super-Resolution (ISR): “(an) open source tool employs a machine learning model that you can train to guess at the details in a low-resolution image:
• Blender: “A rich interface and numerous plugins make it possible to create complex motion graphics or cinematic vistas”
• DeepFaceLab: “open source deepfake technology that runs on Python”
• tflearn: “an advanced deep learning library”
• PYTensor: “a Python library that allows you to define, optimize/rewrite, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.” (Was Theano)
• LM Studio: “Discover, download, and run local LLMs”
• HuggingFace Transformers: “a popular open-source library for Natural Language Processing (NLP) tasks”
• Fast.ai: “a library for working with deep learning tasks”
• OpenCV: “a popular Computer Vision and Image Processing library developed by Intel”
• Detectron2: “a next-generation library that provides advanced detection and segmentation algorithm” and “a PyTorch-based modular object detection library”
• Ivy.ai: “an open-source deep learning library in Python focusing on research and development”
• OpenAssistant: “a project aimed at giving everyone access to a great chat-based large language model”
• PaddleNLP: “a popular open source NLP library that you can use to glean search sentiment and flag important entities”
• Delphi.AI: “Clone yourself. Build the digital version of you to scale your expertise and availability, infinitely.”
• Fauxpilot: “This is an attempt to build a locally hosted alternative to GitHub Copilot. It uses the SalesForce CodeGen models inside NVIDIA’s Triton Inference Server with the FasterTransformer backend.”
• Ray: “An open source framework to build and scale your ML and Python applications easily”
• Solid: “Solid is a specification that lets individuals and groups store their data securely in decentralized data stores called Pods. Pods are like secure web servers for data. When data is stored in a Pod, its owners control which people and applications can access it.”
• Sagen.ai: “Your very own AI Personal Assistant to manage your digital life.”
• YOLOv7: “is one of the fastest and most accurate open source object detection tools. Just provide the tool with a collection of images full of objects and see what happens next.”

—and lots of others that readers can tell me about. Do that and I will add links later. This is a work in progress.

Below all of those we still need something Linux-like that will become the open base on which lots of other stuff runs. The closest I’ve seen so far is pAI-OS, by Kwaai.ai, a nonprofit I now serve as Chief Intention Officer. I got recruited by Reza Rassool, Kwaai’s founder and chair, because he believes personal AI is required to make The Intention Economy finally happen. So that was a hard offer to refuse. Kwaai also has a large, growing, and active community, which I believe is necessary, cool, and very encouraging.

As with most (maybe all) of the projects listed above, Kwaai is a grass-roots effort by human beings on the natural, human, and horizontal side of a battle with giants who would rather give us personalized AI than have us meet them in a middle to which we will bring personal AI powers of our own. In the long run, we will meet in that middle, because personal AI will be better for everyone than personalized AI alone.

Watch us prove it. Better yet, join the effort.

---

*I am indebted to Lavonne Reimer for introducing and co-thinking the horizontal vs. vertical frame, and look forward eagerly to her own writings and lecturings on the topic.

Cosmetic Valley, organisation professionnelle du secteur de la cosmétique, dresse le bilan des efforts d’innovation du secteur. Son premier baromètre thématique...

L’article L’industrie cosmétique parie sur l’innovation éthique est apparu en premier sur TheGood.

Vous pensiez connaître Castlevania sur le bout des doigts ? Eh bien 25 ans après la sortie de Castlevania: Legacy of Darkness sur notre bonne vieille Nintendo 64, des petits malins ont déniché un tout nouveau code Konami planqué dans les entrailles du jeu.

Ce code Konami est une vraie petite bombe puisqu’il déverrouille d’un coup TOUS les personnages et leurs costumes alternatifs, et ce dès le début de l’aventure ! Fini de devoir se farcir le jeu deux fois pour accéder à Henry et Carrie. Là c’est open bar direct, et ça change complètement la donne !

Moises et LiquidCat, deux fans passionnés du jeu, ont également déniché deux autres codes bien sympathiques. Le premier remplit entièrement votre inventaire, peu importe le héros que vous incarnez. Fini la galère pour trouver des potions et des équipements, vous voilà paré pour latter du vampire en claquant des doigts. Le second code, disponible uniquement dans les versions japonaise et européenne, booste votre arme au max et vous file un stock de joyaux dont même Picsou serait jaloux. De quoi rendre votre quête bien plus funky !

Pour activer ces codes, rien de plus simple :

1. Ouvrez le jeu et sélectionnez le mode histoire.
2. Tapez le code en doublant les inputs du code Konami classique : Up, Up, Down, Down, Left, Right, Left, Right, B, A, Start.
3. Utilisez les boutons directionnels et A/B de votre manette N64.
4. Appuyez sur Start pour confirmer l’activation.
5. Enjoy ! Vous pouvez maintenant jouer avec tous les persos dès le début.

Codes bonus pour les plus curieux :

• Inventaire au max : Up, Up, Down, Down, Left, Right, Left, Right, B, A, Start (x2)
• Arme boostée et argent illimité (versions PAL/JPN uniquement)

Alors certes, dit comme ça, ça peut sembler un poil cheaté mais ça fait un quart de siècle que ce jeu nous nargue avec ses secrets, donc ça va, y’a tolérance. En plus, avouons-le, ces codes tombent à pic pour (re)découvrir cet opus culte car s’il y a bien un reproche qu’on pouvait faire à Legacy of Darkness, c’était ce côté un peu prise de tête avec un seul personnage jouable au début. Un choix curieux qui pouvait rebuter certains joueurs. Mais grâce à ce code Konami providentiel, ce problème est relégué aux oubliettes ! Vous pouvez enfin profiter des cinq perso et de leurs capacités uniques sans vous prendre le chou.

Alors si vous aussi vous avez une Nintendo 64 qui prend la poussière dans un coin (ou un émulateur), c’est le moment ou jamais de ressortir Castlevania: Legacy of Darkness et de tester ces fameux codes.

Source

Les générateurs d’images IA actuels comme Midjourney, Dall-E et j’en passe, font polémique puisqu’ils piochent allégrement dans les œuvres des artistes sans leur consentement ni rémunération. Mais des solutions sont en train de se mettre en place pour les entreprises qui souhaiteraient utiliser l’IA pour illustrer leurs supports sans pour autant piller les artistes.

Une de ces solutions, c’est Tess qui propose une approche que j’ai trouvée intéressante puisqu’elle met en place des collaborations avec des créateurs pour utiliser leur style dans des modèles de diffusion d’images.

Concrètement, chaque modèle d’IA est entraîné sur le style visuel d’un artiste unique, avec son accord puis l’outil utilise un SDXL custom (Stable Diffusion) pour générer les images. Évidemment, à chaque fois qu’une image est générée avec son style, l’artiste touche des royalties ! C’est gagnant-gagnant donc.

L’outil intègre également un système de métadonnées basé sur le protocole C2PA, qui permet d’identifier clairement les images générées par IA et de les distinguer des créations originales.

L’objectif de Tess est donc clair : démocratiser la création d’images artistiques de qualité, en la rendant accessible au plus grand nombre, tout en rémunérant équitablement les artistes et leur permettant de garder la maitrise de leur art. C’est une bonne idée vous ne trouvez pas ?

Et les artistes dans tout ça ? Et bien si j’en crois le site de Tess, ils sont déjà plus de 100 à avoir déjà sauté le pas, séduits par ce nouveau modèle de rémunération. Maintenant si vous voulez devenir clients de la plateforme, ce n’est malheureusement pas encore ouvert à tous, mais une liste d’attente est en place pour vous inscrire et être tenu au courant.

En quelques décennies, une toute petite poignée d’acteurs a pris le contrôle du commerce mondial agricole, des terres à la finance. C’est le constat dressé par la Conférence des Nations unies sur le commerce et le développement. Peut-on laisser à quelques groupes le destin de la sécurité alimentaire mondiale?

Cloudflare nous sort encore un truc marrant : Cloudflare Calls ! Il s’agit d’une plateforme WebRTC serverless qui vous permet de créer des applications temps réel que ce soit de l’audio, de la vidéo ou même de la data. Le tout, sans vous prendre la tête avec l’infrastructure. Ça peut servir d’unité SFU (selective forwarding unit) pour router intelligemment les flux, ou même de système de diffusion pour broadcaster du contenu. Bref, les possibilités sont énormes !

Le gros avantage, c’est que ça tourne sur le réseau mondial de Cloudflare, présent dans des centaines de villes. Donc niveau latence et qualité, vous êtes aux petits oignons et pas besoin de vous soucier de la scalabilité ou des régions, puisque c’est géré.

Pour commencer à bidouiller avec Cloudflare Calls, rien de plus simple. Vous créez une app dans le dashboard, vous récupérez les identifiants, et hop, vous pouvez commencer à coder votre propre app WebRTC. Il y a même un exemple complet sur GitHub, l’app de démo « Orange Meets – room Korben ^^ ». (Non, ça n’a rien à voir avec l’opérateur du même nom, même si c’est de la téléphonie…)

Après, faut quand même mettre les mains dans le cambouis hein. Mais si vous êtes à l’aise avec WebRTC, vous allez vous éclater. Sinon, c’est l’occasion d’apprendre ! Et puis la doc est plutôt bien foutue, avec des tutos pas à pas. Ça ouvre un paquet de possibilités pour créer des apps temps réel fun ou utiles comme un outil de collaboration en ligne, avec un tableau blanc partagé et de la visio. Ou même un petit jeu multijoueur. Avec Cloudflare Calls, vous pouvez prototyper ça rapidement sans vous soucier de l’infrastructure.

Après, attention quand même, c’est encore en beta. Donc à utiliser en prod avec précaution. Mais pour tester et apprendre, c’est parfait. Et puis connaissant Cloudflare, le produit final sera sûrement béton.

Bref, je vous invite à aller jeter un œil à Cloudflare Calls, à tester la démo « Orange Meets », et pourquoi pas, à vous lancer dans le développement de votre propre app WebRTC serverless.

Non, Toolong n’est pas ce qu’a dit votre correspondante américaine la première fois qu’elle vous a vu en maillot de bain. C’est plutôt (le chien) un outil vachement pratique qui s’utilise ne ligne de commande et qui permet d’afficher, suivre en temps réel, fusionner les fichiers de log et d’y rechercher tout ce que vous voulez.

L’outil est capable d’appliquer une petite coloration syntaxique sur les formats de journalisation classique comme ceux d’un serveur web par exemple. Il est également capable d’ouvrir très rapidement de gros fichiers même s’ils font plusieurs gigas.

Si vous travaillez avec des fichiers JSONL, Toolong les affichera également au format pretty print. Il prend également en charge l’ouverture des fichiers .bz et .bz2.

De quoi arriver à vos fins beaucoup plus facilement qu’en jouant avec tail, less, ou encore grep.

Toolong est compatible avec Linux, macOS et Windows et pour l’installer Toolong, la meilleure solution actuelle consiste à utiliser pipx :

pipx install toolong

Vous pouvez également l’installer avec Pip :

pip install toolong

Note : Si vous utilisez Pip, il est recommandé de créer un environnement virtuel pour éviter les conflits de dépendances potentiels.

Une fois Toolong installé, la commande tl sera ajoutée à votre PATH. Pour ouvrir un fichier avec Toolong, ajoutez les noms de fichiers en arguments de la commande :

tl fichierdelog.log

Si vous ajoutez plusieurs noms de fichiers, ils s’ouvriront dans des onglets. Ajoutez l’option --merge pour ouvrir plusieurs fichiers et les combiner en une seule vue :

tl access.log* --merge

Voilà, l’essayer, c’est l’adopter. Si vous manipulez de gros logs, Toolong pourra vous faire gagner un max de temps !

Merci à Lorenper

This post is for the benefit of anyone wondering about, researching, or going into business on the proposition that selling one’s own personal data is a good idea. Here are some of my learnings from having studied this proposition myself for the last twenty years or more.

1. The business does exist. See eleven companies in Markets for personal data listed among many other VRM-ish businesses on the ProjectVRM wiki.
2. The business category harvesting the most personal data is adtech (aka ad tech and “programmatic”) advertising, which is the surveillance-based side of the advertising business. It is at the heart of what Shoshana Zuboff calls surveillance capitalism, and is now most of what advertising has become online. It’s roughly a trillion-dollar business. It is also nothing like advertising of the Mad Men kind. (Credit where due: old-fashioned advertising, aimed at whole populations, gave us nearly all the brand names known to the world). As I put it in Separating Advertising’s Wheat and Chaff, Madison Avenue fell asleep, direct response marketing ate its brain, and it woke up as an alien replica of itself.
3. Adtech pays nothing to people for their data or data about them. Not personally. Google may pay carriers for traffic data harvested from phones, and corporate customers of auctioned personal data may pay publishers for moments in which ads can be placed in front of tracked individuals’ ears or eyeballs. Still, none of that money has ever gone to individuals for any reason, including compensation for the insults and inconveniences the system requires. So there is little if any existing infrastructure on which paying people for personal data can be scaffolded up. Nor are there any policy motivations. In fact,
4. Regulations have done nothing to slow down the juggernaut of growth in the adtech industry. For Google, Facebook, and other adtech giants, paying huge fines for violations (of the GDPR, the CCPA, the DMA, or whatever) is just the cost of doing business. The GDPR compliance services business is also in the multi-$billion range, and growing fast. In fact,
5. Regulations have made the experience of using the Web worse for everyone. Thank the GDPR for all the consent notices subtracting value from every website you visit while adding cognitive overhead and other costs to site visitors and operators. In nearly every case, these notices are ways for site operators to obey the letter of the GDPR while violating its spirit. And, although all these agreements are contracts, you have no record of what you’ve agreed to. So they are worse than worthless.
6. Tracking people without their clear and conscious invitation or a court order is wrong on its face. Period. Full stop. That tracking is The Way Things Are Done online does not make it right, any more than driving drunk or smoking in crowded elevators was just fine in the 1950s. When the Digital Age matures, decades from now, we will look back on our current time as one thick with extreme moral compromises that were finally corrected after the downsides became clear and more ethically sound technologies and economies came along. One of those corrections will be increasing personal agency rather than just corporate capacities. In fact,
7. Increasing personal independence and agency will be good for markets, because free customers are more valuable than captive ones. Having ways to gather, keep, and make use of personal data is an essential first step toward that goal. We have made very little progress in that direction so far. (Yes, there are lots of good projects listed here, but there we still a long way to go.)
8. Businesses being “user-centric” will do nothing to increase customers’ value to themselves and the marketplace. First, as long as we remain mere “users” of others’ systems, we will be in a subordinate and dependent role. While there are lots of things we can do in that role, we will be able to do far more if we are free and independent agents. Because of that,
9. We need technologies that create and increase personal independence and agency. Personal data stores (aka warehouses, vaults, clouds, life management platforms, lockers, and pods) are one step toward doing that. Many have been around for a long time: ProjectVRM currently lists thirty-three under the Personal Data Stores heading. Some have been there a long time. The problem with all of them is that they are still too focused on what people do as social beings in the Web 2.0 world, rather than on what they can do for themselves, both to become more well-adjusted human beings and more valuable customers in the marketplace. For that,
10. It will help to have independent personal AIs. These are AI systems that work for us, exclusively. None exist yet. When they do, they  will help us manage the personal data that fully matters:
    • Contacts—records and relationships
    • Calendars—where we’ve been, what we’ve done, with whom, where, and when
    • Health records and relationships with providers, going back all the way
    • Financial records and relationships, including past and present obligations
    • Property we have and where it is, including all the small stuff
    • Shopping—what we’ve bought, plan to buy, or might be thinking about,
    • Subscriptions—what we’re paying for, when they end or renew, what kind of deal we’re locked into, and what better ones might be out there.
    • Travel—Where we’ve been, what we’ve done, with whom, and when

Personal AIs are today where personal computers were fifty years ago. Nearly all the AI news today is about modern mainframe businesses: giants with massive data centers churning away on ingested data of all kinds. But some of these models are open sourced and can be made available to any of us for our own purposes, such as dealing with the abundance of data in our own lives that is mostly out of control. Some of it has never been digitized. With AI help it could be.

I’m in a time crunch right now. So, if you’re with me this far, read We can do better than selling our data, which I wrote in 2018 and remains as valid as ever. Or dig The Intention Economy: When Customers Take Charge (Harvard Business Review Press, 2012), which Tim Berners Lee says inspired Solid. I’m thinking about following it up. If you’re interested in seeing that happen, let me know.

Pour faire la publicité de ses marques, le géant du luxe enrôle les superstars du sport français qui seront présentes aux Jeux Olympiques de Paris. Loin de la fête populaire que prônent les organisateurs de l’événement, LVMH milite pour l’excellence et les victoires.

Faut-il craindre l’intelligence artificielle ? Une question légitime, mais qui posée comme cela a vite fait de vous poser comme un rétif au changement, un conservateur pusillanime, un luddite inavoué. Bref, un vieux con.

Pourtant, même en étant technophile (et je le suis), la question peut se poser. Les récentes avancées dans les IA génératives, Dall-E, Midjourney pour les images et maintenant ChatGPT (GPT pour Generative Pre-trained Transformer 3), ce logiciel conversationnel d’OpenAI pour répondre à de multiples questions, progressent de manière exponentielle, l’exponentiel est important en l’espèce.

Si tout le monde a joué avec Dall-e pour créer des images improbables ou parfaitement imitées de la réalité, la version 3.5 de ChatGPT a explosé les compteurs. Plus d’un million de personnes ont testé ChatGPT en moins d’une semaine. Que ce soit pour faire du code ou en déboguer, répondre à des questions en tout genre, créer un site web en 10 minutes,
composer de la musique, ou imaginer de nouveaux business plans. D’autres ont même eu l’idée
de créer des prompts (des commandes pour l’IA) pour générer des images sur Dall-E. Une mise en abyme s’il en est.

L’IA tueuse d’emplois ?

Malgré quelques fausses réponses, des approximations liées souvent à la formulation de la question, les résultats sont dans l’ensemble bluffants. Certes, les réponses peuvent être parfois incohérentes. Pour le vérifier, posez plusieurs fois de suite la même question. N’oubliez pas que pour répondre, l’IA générative se fonde sur de la statistique et la probabilité de l’occurrence proxémique des mots dans un corpus sémantique donné. Le résultat peut donner une impression de cohérence, mais une véracité parfois suspecte.

Ce qui est surtout bluffant, c’est la progression de GPT entre la version 3 livrée en 2020 et la 3.5 de 2022. Malgré ces défauts liés à l’IA générative, il est clair que ChatGPT a sidéré tous ceux qui l’ont essayé. Et sans doute créé un petit début d’interrogation, voir d’anxiété sur l’avenir de nombreux emplois pour les travailleurs de la connaissance et industrie du savoir.

 

À juste titre  ou non ? Depuis quelques jours la question divise les réseaux sociaux.

Pour certains, l’IA vient juste comme aide et outil pour accomplir plus rapidement une tâche. En cela ils rejoignent la doxa Schumpétérienne de la destruction créatrice. Les machines se substituent à des travailleurs, mais ne suppriment pas le besoin en main-d’œuvre grâce aux nouveaux métiers et compétences créées. Jusqu’alors le modèle fonctionne et la productivité affiche une hausse linéaire malgré les 4 révolutions industrielles et technologiques. RPA, IA, Machine learning apportent une aide réelle dans de nombreux métiers. Tout ce qui est du ressort du calcul peut bénéficier de l’IA, c’est une évidence.

Autre argument avancé par les optimistes : l’IA n’est pas créative ; elle se fonde sur un corpus fini donc ne peut anticiper ; elle n’est pas originale dans ses réponses ; elle est parfois approximative et se trompe régulièrement dans ses réponses.

 

Pour les optimistes, l’IA sera une aide précieuse pour accomplir des tâches rébarbatives et récurrentes, par exemple écrire des fiches produits en grand nombre, déboguer du code (ou pas), mais aussi pêle-mêle rédiger des dissertations, des billets de blog, et moult autres tâches avec pour seule limite l’imagination.

Craindre l’IA ? Quels arguments ?

Pour les pessimistes aussi les arguments sont nombreux. Et vont dans le sens des optimistes. Oui, l’IA d’Open AI peut écrire du code, mais il est souvent bugué. Il faut donc comprendre le code pour être à même de les identifier. Oui, il peut rédiger des dissertations de bon niveau, Il a beaucoup été écrit sur la fin de l’enseignement tel qu’on le connaît, inutile d’en rajouter.

Il peut bien sûr écrire des posts et articles qui resteront sans grand intérêt, mais le web nous habitue déjà à de nombreux contenus sans intérêts, il y en aura beaucoup plus.

Et ce sera drôle quand l’IA se nourrira de contenus issus de l’IA, là encore une autre mise en abyme.

Autre point : il va remplacer Google et autres moteurs de recherche, voir supprimer les recherches tout court, en répondant directement aux questions, effaçant ainsi toute velléité de recherches croisées pour valider les réponses. Peut-être.

Quoi qu’il en soit, Sam Altman  prévient qu’il faudra faire preuve « d’adaptabilité et de résilience. Nous avons là une piste sérieuse

Si tous les arguments, pour ou contre, sont recevables, le véritable juge arbitre est bien sûr l’usage qui sera fait de l’outil (merci captain obvious).

Si Prométhée amène la connaissance, son frère Epiméthée par étourderie ouvre la boîte de Pandore. ChatGPT est peut-être un moment Épiméthéen.

L’histoire de ces deux décennies démontre si besoin en était l’absence de limite dans l’utilisation des technologies. Les objectifs peuvent être très variés, modifier le cours d’une élection, semer le doute via les fake news envoyées à des millions d’utilisateurs sur un réseau, créer de manière automatisée des sites en scrapant d’autres sites, attaquer les entreprises en exigeant une rançon, la liste est longue.

 

Il y a bien sûr un côté positif dans de nombreux champs.

La technologie est pharmakon et seul l’usage dicte si elle soigne ou rend malade. À ce jour le bilan est mitigé selon où l’on regarde. L’outil est aussi un vecteur d’expression des penchants de l’humanité.

Pour sa part, l’IA exponentialise le champ des possibles, dans un sens ou dans l’autre. Selon les rumeurs, la prochaine version de GPT-4 sera 500 fois plus puissante que GPT 3.5.

 

Comment faudra-t-il considérer rétroactivement la sidération causée par ChatGPT. Quelle régulation prévue pour affronter la vague. Faut-il suivre la Chine qui impose d’intégrer des filigranes pour tout contenu créé avec l’IA ? Les régulateurs n’ont pas l’air pressés.

Avons-nous le temps ?

 

 

PS : si vous voulez jouer avec ChatGPT et Google, Github recense un portefeuille d’app

• Pour avoir ChatGPT en parallèle des résultats Google
• Une extension chrome pour ajouter ChatGPT et l’avoir en permanence
• Github, un répertoire avec de nombreuses applications ChatGPT

The post ChatGPT : faut-il craindre l’intelligence artificielle (spoiler : oui, mais pas pour ce que vous croyez) appeared first on Fabrice Frossard.

Les Tiers-Lieux comme cheville ouvrière du développement local

The post L’Usine Végétale : investir le rural par la transition appeared first on Observatoire des Tiers-Lieux.

L’intelligence artificielle est devenue un catalyseur de la transformation numérique des industries. La technologie fait partie du Peak of Inflated Expectations présentant les technologies émergentes en 2023. Elle devrait impacter les entreprises, offrant une synergie entre main-d’œuvre et machine. Toutefois, à mesure que les technologies d’IA évoluent, les entreprises doivent engager leur responsabilité dans l’élaboration de bonnes pratiques.

Alors que les régulateurs tentent de s’imposer dans ce nouveau paradigme, les entreprises sont en bonne posture pour proposer des outils garantissant sécurité, gouvernance, contrôles et mesures responsables pour l’IA. Voici les raisons pour lesquelles il est crucial pour une organisation de développer des bonnes pratiques pour l’usage de l’IA et que quelques conseils pour renforcer les compétences dans ce domaine.

Evoluer dans le paysage réglementaire

Il faudra des années pour que régulateurs et autorités législatives élaborent un corpus complet de réglementations et de législations concernant l’IA qui concilient innovation, éthique et sécurité. Qui plus est, il s’agit d’un processus continu et donc en constante évolution. Le manque de réglementations strictes peut de prime abord sembler lucratif car il laisse de place à l’innovation. Toutefois, une entreprise avant-gardiste se rendra compte qu’il existe des mises en œuvre de l’IA contraires à l’éthique et potentiellement nuisibles.

Une longueur d’avance en termes de business

Les entreprises qui proposent un guide de bonnes pratiques bénéficient d’un avantage concurrentiel. Lorsqu’il s’agit d’IA, les clients exigent que les solutions soient à la fois dignes de confiance, transparentes, responsables et éthiques. Lorsqu’une entreprise définit des bonnes pratiques, elle crée une émulation auprès des autres entreprises.
Aussi, l’adoption anticipée de ces pratiques peut également protéger les entreprises contre d’éventuelles responsabilités une fois les réglementations et législations mises en place.

Faire preuve d’un leadership éthique

L’IA éthique ne doit pas être considérée comme une tendance. Il s’agit d’un engagement à utiliser cette technologie de sorte à ce qu’elle bénéficie à la société et faire regagner la confiance des utilisateurs. Pour s’inscrire dans un leadership éthique, les entreprises doivent faire preuve de transparence dans leurs pratiques, leurs sources, l’utilisation des données, les algorithmes et les processus de prise de décision ainsi que sur la reproductibilité et l’audibilité. En outre, elles doivent s’appliquer à créer une culture de la responsabilité en élaborant des guidelines et en limitant les biais de leurs systèmes d’IA afin que les décisions prises soient justes et non discriminatoires. Enfin, elles doivent donner la priorité à la confidentialité et à la sécurité des données afin de garantir que les applications d’IA aient autant d’accès que nécessaire aux données des utilisateurs. Cela ne peut se faire que par le biais d’audits réguliers des systèmes d’IA et de la rectification immédiate des problèmes éthiques potentiels. Ces audits doivent être réalisés par des tiers indépendants afin de garantir l’impartialité.

Développer les compétences en IA en entreprise

Les entreprises doivent prioriser le développement des compétences en IA au sein de l’organisation. À l’heure où l’IA remodèle le paysage commercial et social, il est essentiel que les employés acquièrent les connaissances et les capacités nécessaires pour exploiter efficacement son potentiel.

Pour ce faire :

• Les entreprises doivent identifier les référents IA en interne chargés de mener des projets d’IA, de comprendre les subtilités de ces technologies et d’encadrer leurs collègues, mais aussi traduire les connaissances internes et convertir les connaissances en données. Lorsqu’une entreprise favorise l’acquisition d’une expertise en interne, elle réduit sa dépendance vis-à-vis des consultants externes, ce qui permet de réduire les coûts et de mieux intégrer la technologie dans ses opérations. La formation des référents en IA, ainsi que la mise en place de pratiques responsible by design contribue à créer une culture d’apprentissage continue. Ceci est d’autant plus vrai dans le domaine de l’IA, un domaine en constante évolution.
• Se tourner vers les établissements d’enseignement pour former une nouvelle génération d’employés. Aujourd’hui, plusieurs établissements d’enseignement supérieur tiennent compte de la demande croissante d’expertise en IA et proposent une formation dédiée. En collaborant avec des universités à travers le monde et en les aidant à concevoir des programmes de formation à l’IA conformes aux exigences du secteur, les entreprises peuvent recruter des talents dans ce domaine.

L’ère de l’IA a créé une vague d’opportunités et de défis pour les entreprises. Alors que les réglementations régissant l’IA sont encore en cours d’élaboration, les entreprises doivent se montrer proactives dans l’élaboration des bonnes pratiques et des approches responsible by design. L’objectif est de garantir que les technologies de l’IA soient utilisées à la hauteur de leur potentiel, de manière éthique et responsable.

Les neurosciences affectives et sociales permettent d’appréhender les bases cérébrales de l’attachement et de l’intersubjectivité parentale. Cette approche peut déboucher sur des interventions adaptées, guidées par des modèles neurocognitifs.

Lire la suite

L’article Attachement et neurosciences est apparu en premier sur Santé Mentale.

L’article Stress & émotions, une hygiène de vie – Jérémie Mercier est apparu en premier sur Lechou.

In her latest Ars Technica story, Ashley Belanger reports that Patreon, the widely used and much-trusted monetization platform for creative folk, opposes the minimal personal privacy protections provided by a law you probably haven’t heard of until now: the Video Privacy Protection Act, or VPPA. Patreon, she writes, wants a judge to declare that law (which dates from the videotape rental age) unconstitutional because it inconveniences Patreon’s ability to share the personal data of its users with other parties.† Naturally, the EFF, the Center for Democracy & Technology, the ACLU of Northern California, and the ACLU itself all stand opposed to Patreon on this and have filed an amicus brief explaining why.

But I’m not here to talk about that. I’m here to bring up the inconvenient fact that Ars Technica is also in the surveillance business. A PageXray of Ashley’s story finds this—

• 360 adserver requests
• 259 tracking requests
• 131 other requests

—which it visualizes with this:

And that’s just one small part of it.

But will Ashley, or any reporter, grab the third rail of their employer’s participation in the tracking-based advertising business? Or visit that business’s responsibility for what was already the biggest boycott in human history way back in 2015? The odds are against it. I’ve challenged many reporters to grab that third rail, just like I’m challenging Ashley here. In every case, nothing happened.

I never challenged Farhad Manjoo, but he did come through exposing The New York Times (his employer’s) own participation in the privacy-opposed tracking-based adtech business, back in 2019. Here’s a PageXray of tracking via that piece today:

Better, but not ideal.

Five years ago this month, I wrote a column about privacy in Linux Journal with the same title as this post. Here it is again, with just a few tiny edits. Amazing how little things have changed since then—and how much worse they have become. But I do see hope. Read on.

---

If you think regulations are going to protect your privacy, you’re wrong. In fact, they can make things worse, especially if they start with the assumption that your privacy is provided only by other parties, most of whom are incentivized to violate it.

Exhibit A for how much worse things can get is the EU’s GDPR (General Data Protection Regulation). As soon as the GDPR went into full effect in May 2018, damn near every corporate entity on the Web put up a “cookie notice” requiring acceptance of terms and privacy policies that allow them to continue violating your privacy by harvesting, sharing, auctioning off and otherwise using your data, and data about you.

For websites and services in that harvesting business (a population that rounds to the whole commercial web), these notices provide a one-click way to adhere to the letter of the GDPR while violating its spirit.

There’s also big business in the friction that it produces. To see how big, look up GDPR+compliance on Google. You’ll get 232 million results (give or take a few dozen million).

None of those results are for you, even though you are who the GDPR is supposed to protect. See, to the GDPR, you are a mere “data subject” and not an independent and fully functional participant in the technical, social, and economic ecosystem the Internet supports by design. All privacy protections around your data are the burden of other parties.

Or at least that’s the interpretation that nearly every lawmaker, regulatory bureaucrat, lawyer, and service provider goes by. (One exception is Elizabeth Renieris @hackylawyer. Her collection of postings is required reading on the GDPR and much else.) The same goes for those selling GDPR compliance services, comprising most of those 190 million GDPR+compliance search results.

The clients of those services include nearly every website and service on Earth that harvests personal data. These entities have no economic incentive to stop harvesting, sharing, and selling personal data the usual ways, beyond fear that the GDPR might actually be enforced, which so far (with few exceptions), it hasn’t been. (See Without enforcement, the GDPR is a fail.)

Worse, the tools for “managing” your exposure to data harvesters are provided entirely by the websites you visit and the services you engage. The “choices” they provide (if they provide any at all) are between 1) acquiescence to them doing what they please and 2) a maze of menus full of checkboxes and toggle switches “controlling” your exposure to unknown threats from parties you’ve never heard of, with no way to record your choices or monitor effects.

So let’s explore just one site’s presentation, and then get down to what it means and why it matters.

Our example is https://www.mirror.co.uk. If you haven’t clicked on that site already, you’ll see a cookie notice that says,

We use cookies to help our site work, to understand how it is used, and to tailor the adverts presented on our site. By clicking “Accept” below, you agree to us doing so. You can read more in our cookie notice. Or, if you do not agree, you can click Manage below to access other choices.

They don’t mention that “tailor the adverts” really means something like this:

We open your browser to infestation by tracking beacons from countless parties in the online advertising business, plus who-knows-what-else that might be working with those parties (there is no way to tell, and if there was we wouldn’t provide it), so those parties and their “partners” can use those beacons to follow you like a marked animal everywhere you go and report your activities back to a vast marketplace where personal data about you is shared, bought and sold, much of it in real time, supposedly so your eyeballs can be hit with “relevant” or “interest-based” advertising as you travel from site to site and service to service. While we are sure there are bad collateral effects (fraud and malware, for example), we don’t care about those because it’s our business to get paid just for clicks or “impressions,” whether you’re impressed or not—and the odds that you won’t be impressed average to certain.

Okay, so now click on the “Manage” button.

Up will pop a rectangle where it says “Here you can control cookies, including those for advertising, using the buttons below. Even if you turn off the advertising-related cookies, you will still see adverts on our site, because they help us to fund it. However, those adverts will simply be less relevant to you. You can learn more about cookies in our Cookie Notice on the site.”

Under that text, in the left column, are six “Purposes of data collection”, all defaulted with little check marks to ON (though only five of them show, giving the impression that there are only those five). The right column is called “Our partners”, and it shows the first five of what turn out to be 259 companies, nearly all of which are not brands known to the world or to anybody outside the business (and probably not known widely within the business as well). All are marked ON by that little check mark. Here’s that list, just through the letter A:

• 1020, Inc. dba Placecast and Ericsson Emodo
• 1plusX AG
• 2KDirect, Inc. (dba iPromote)
• 33Across
• 7Hops.com Inc. (ZergNet)
• A Million Ads Limited
• A.Mob
• Accorp Sp. z o.o.
• Active Agent AG
• ad6media
• ADARA MEDIA UNLIMITED
• AdClear GmbH
• Adello Group AG
• Adelphic LLC
• Adform A/S
• Adikteev
• ADITION technologies AG
• Adkernel LLC
• Adloox SA
• ADMAN – Phaistos Networks, S.A.
• ADman Interactive SL
• AdMaxim Inc.
• Admedo Ltd
• admetrics GmbH
• Admotion SRL
• Adobe Advertising Cloud
• AdRoll Inc
• adrule mobile GmbH
• AdSpirit GmbH
• adsquare GmbH
• Adssets AB
• AdTheorent, Inc
• AdTiming Technology Company Limited
• ADUX
• advanced store GmbH
• ADventori SAS
• Adverline
• ADYOULIKE SA
• Aerserv LLC
• affilinet
• Amobee, Inc.
• AntVoice
• Apester Ltd
• AppNexus Inc.
• ARMIS SAS
• Audiens S.r.l.
• Avid Media Ltd
• Avocet Systems Limited

If you bother to “manage” any of this, what record do you have of it—or of all the other collections of third parties who you’ve agreed to follow you around? Remember, there are a different collection of these at every website with third parties that track you, and different UIs, each provided by other third parties.

It might be easier to discover and manage parasites in your belly than cookies in your browser.

Think I exaggerate? The long list of cookies in just one of my browsers (which I had to dig deep to find) starts with this list:

• 1rx.io
• 247-inc.net
• 2mdn.net
• 33across.com
• 360yield.com
• 3lift.com
• 4finance.com

After several hundred others, my cookie  list ends with:

• zencdn.net
• zoom.us
• zopim.com

I know what zoom.us is. The rest are a mystery to me.

To look at just that first one, 1rx.io, I have to dig way down in the basement of the preferences directory (in Chrome it’s chrome://settings/cookies/detail?site=1rx.io), where I find that its locally stored data is this:

_rxuuid

Name
_rxuuid
Content
%7B%22rx_uuid%22%3A%22RX-2b58f1b1-96a4-4e1d-9de8-3cb1ca4175b0%22%2C%22nxtrdr%22%3Afalse%7D
Domain
.1rx.io
Path
/
Send for
Any kind of connection
Accessible to script
No (HttpOnly)
Created
Wednesday, December 12, 2018 at 4:48:53 AM
Expires
Thursday, December 12, 2019 at 4:48:53 AM

I’m a somewhat technical guy, and at least half of that stuff means nothing to me.

As for “managing” those,  my only choice on that page is to “Remove All”. Does that mean Remove everything on that page alone or Remove all cookies everywhere? And how can I remember what I’ve had removed?

Obviously, there is no way for anybody to “manage” this, in any meaningful sense of the word.

We also can’t fix it on the sites and services side, no matter how much those sites and services care (which most don’t) about the “customer journey”, the “customer experience” or any of the other bullshit they’re buying from marketers this week.

Even within the CRM (customer relationship management) world, the B2B customers of CRM companies use one cloud and one set of tools to create as many different “experiences” for users and customers as there are companies deploying those tools to manage customer relationships from their side.  There are no corresponding tools on our side. (Though there is work going on. See here.)

So the digital world remains one where we have no common or standard way to scale our privacy and data usage tools, choices, or experiences across all sites and services. And that’s what we’ll need if we want real privacy online.

The simple place where we need to start is this: privacy is personal, meaning something we create for ourselves (which in the natural world we do with clothing and shelter, both of which lack equivalents in the digital world).

And we need to be clear that privacy is not a grace of privacy policies and terms of service that differ with every company and over which none of us have true control—especially when there is an entire industry devoted to making those companies untrustworthy, even if they are in full compliance with privacy laws.

Devon Loffreto (who coined the term self-sovereign identity and whose good work we’ll be visiting in an upcoming issue of Linux Journal) puts the issue in simple geek terms: we need root authority over our lives. Hashtag: #OwnRoot.

It is only by owning root that we can crank up agency on the individual’s side. We have a perfect base for that in the standards and protocols that gave us the Internet, the Web, email, and too little else. And we need it here too. Soon.

We (a few colleagues and I) created Customer Commons as a place for terms that individuals can proffer as first parties, just by pointing at them, much as licenses at Creative Commons can be pointed at. Sites and services can agree to those terms, and both can keep records and follow audit trails.

And there are some good signs that this will happen. For example, the IEEE approached Customer Commons last year with the suggestion that we stand up a working group for machine-readable personal privacy terms. It’s called P7012. If you’d like to join, please do.

Unless we #OwnRoot for our own lives online, privacy will remain an empty promise by a legion of violators.

One more thing. We can put the GDPR to our use if we like. That’s because Article 4 of the GDPR defines a data controller as “the natural or legal person, public authority, agency or other body which, alone or jointly with others, determines the purposes and means of the processing of personal data…” This means each of us can be our own data controller. Most lawyers dealing with the GDPR don’t agree with that. They think the individual data subject will always need a fiduciary or an intermediary of some kind: an agent of the individual, but not an individual with agency. Yet the simple fact is that we should have root authority over our lives online, and that means we should have some degree of control over our data exposures, and how our data, and data about us, is used—much as we do over how we control or moderate our privacy in the physical world. More about all that in upcoming posts.

The original version of this post was published on the Private Internet Access blog. Private Internet Access and Linux Journal at the time were both holdings of London Trust Media.

Also, check out the Privacy Manifesto at the ProjectVRM wiki. I maintain it and welcome bug fixes.

† This is an example of what Cory Doctorow calls “enshittification” and Wikipedia (at that link) more politely calls “platform decay.” It’s a big trade-away of goodwill by Patreon. Says to me they must be making an enshitload of money in the adtech fecosystem.

Eight years ago, I called ad blocking The Biggest Boycott in World History, because hundreds of millions of people were blocking ads online. (The headline came from my wife, by the way.) Then, a few days ago, Cory Doctorow kindly pointed to that post in one of his typically trenchant Pluralistic newsletters.

So I thought I’d check to see how the boycott is doing.

It’s hard to find original sources of hard numbers on ad blocking. Instead, there are lots of what I’ll call claims. But some of those claims do cite or link to sources of some kind. Here are four:

1. Brian Dean‘s Backlinko sources Hootsuite, saying 42.7% of Internet users employ ad blockers. Hootsuite, however, wants me to fill out a form that I am sure will get me spammed. So I’m passing on that. Meanwhile there are other interesting stats cited. Growson Edwards on Cipio.ai surfaces a bunch of Hootsuite graphics with interesting data.
2. Statista last January said “the ad blocking user penetration rate in the United States stood at approximately 26 percent in 2020, indicating that roughly 73 million internet users had installed some form of ad blocking software, plugin, or browser on their web-enabled devices that year. While awareness of these services lies at almost 90 percent, the number of internet users actively leveraging the technology has stagnated in recent years following visible changes in online user behavior. The switch from desktop to mobile has arguably had one of the most significant impacts on ad block usage: As internet users increasingly browse the web via mobile devices, desktop ad block usage rates in the U.S. and many other parts of the world are dropping, albeit at varying speeds. While mobile ad blocking adoption is still at a nascent stage in the U.S., the global number of mobile ad blocking browser users is rapidly increasing.” On another page, Statista says marketers “can conquer ad blocking by offering personalized advertising.” Anybody want that? Give me a show of hands. Thought so.
3. Blockthrough, an advertising company, offers a 2022 adblock report that requires filling out a form. So I passed on that one too, but can report that its “key insights” are these: “With 290M monthly active users globally, adblocking on desktop has climbed back close to its all-time-high from 2018,” and “The average adblock rate across geos and verticals is 21%, as measured across >10B pageviews on 9,453 websites.”
4. Surfshark has some cool maps showing which countries hate ads most and least, based on searches for ad-blocking software. (France was at the top.)

Perhaps more interesting than any of those stats (all of which are unsurprising) is using AI to generate graphics for a post such as this one. At first, I wanted the system (Bing Creator or whatever it’s called this week) to show two separate populations: one living blissfully in a land without advertising, and one with advertising everywhere. That was a fail. I couldn’t get it not to show advertising on both sides. Then I tried to get it to depict the blocking of ads, for example with a wall. That failed too, because advertising always appeared on the wall. Finally, I got the image above with a prompt asking for people who were happy to have advertising inside a giant bottle. Isn’t it crazy how fast the miraculous becomes annoying?