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.
What forms of pAI—personal AI—are Apple, Mozilla, Google, Meta, Microsoft and the rest not doing?
Let’s look at those first two because they’re at the top of the news LIFO buffer.
Apple Intelligence (“coming in beta this fall*“), announced yesterday, will help you with writing and creating images while giving you less lame answers from Siri. (Which they should re-name. Siri is Apple’s Clippy.) It “can draw on larger server-based models, running on Apple silicon, to handle more complex requests for you while protecting your privacy.” The “larger models” will be white-labeled ChatGPT, plus Apple’s own small language models (SLMs).
This program is designed to empower independent AI and machine learning engineers with the resources and support they need to thrive. It aims to cultivate a more innovative AI ecosystem, and it’s one of Mozilla’s key initiatives to make AI meaningfully impactful — alongside efforts like Mozilla.ai, the Responsible AI Challenge and the Rise25 Awards.
The Mozilla Builders Accelerator’s inaugural theme is local AI, which involves running AI models and applications directly on personal devices like laptops, smartphones, or edge devices rather than depending on cloud-based services…
We chose Local AI as the theme for the Accelerator’s first cohort because it aligns with our core values of privacy, user empowerment, and open source innovation. This method offers several benefits including:
Privacy: Data stays on the local device, minimizing exposure to potential breaches and misuse.
Agency: Users have greater control over their AI tools and data.
Cost-effectiveness: Reduces reliance on expensive cloud infrastructure, lowering costs for developers and users.
Reliability: Local processing ensures continuous operation even without internet connectivity.
Looks to me like both of these are Big AI writ small. It’s “local,” not personal. It’s made to serve your needs with what BigAI offers through APIs. It is still essentially AIaaS (AI as a Service), rather than truly personal AI (pAI): personalized more than personal.
That’s also what I see when I read between the lines at Mozilla’s AI job openings. Take platform engineer. This person will (among other things), “assist in managing and orchestrating workloads across multiple cloud providers.” That’s fine. I’m sure true pAIs will do that too. But most of pAI will be more personal than that. It will deal with the mundanities of your everyday life. Not with coughing up answers that can only come from AIaaSes.
The problem with personalizing AI giant offerings is that they are large language models (LLM) trained on everything that can be crawled on the Internet, plus who knows what else. Not on your truly personal stuff. This is why “prompt engineering” worthy of the noun is ” not for anybody:
Prompt engineering is crucial for deploying LLMs but is poorly understood mathematically. We formalize LLM systems as a class of discrete stochastic dynamical systems to explore prompt engineering through the lens of control theory. We investigate the reachable set of output token sequences $R_y(\mathbf x_0)$ for which there exists a control input sequence $\mathbf u$ for each $\mathbf y \in R_y(\mathbf x_0)$ that steers the LLM to output $\mathbf y$ from initial state sequence $\mathbf x_0$. We offer analytic analysis on the limitations on the controllability of self-attention in terms of reachable set, where we prove an upper bound on the reachable set of outputs $R_y(\mathbf x_0)$ as a function of the singular values of the parameter matrices. We present complementary empirical analysis on the controllability of a panel of LLMs, including Falcon-7b, Llama-7b, and Falcon-40b. Our results demonstrate a lower bound on the reachable set of outputs $R_y(\mathbf x_0)$ w.r.t. initial state sequences $\mathbf x_0$ sampled from the Wikitext dataset. We find that the correct next Wikitext token following sequence $\mathbf x_0$ is reachable over 97% of the time with prompts of $k\leq 10$ tokens. We also establish that the top 75 most likely next tokens, as estimated by the LLM itself, are reachable at least 85% of the time with prompts of $k\leq 10$ tokens. Intriguingly, short prompt sequences can dramatically alter the likelihood of specific outputs, even making the least likely tokens become the most likely ones. This control-centric analysis of LLMs demonstrates the significant and poorly understood role of input sequences in steering output probabilities, offering a foundational perspective for enhancing language model system capabilities.
But all that stuff applies mostly when we’re prompting a big LLM system.
What about using AI in our own lives, where the data that matters most are in our calendars, contacts, financial and health records, our travels, our correspondence (email, chat, whatever)? And how about all the location data we might get from our cars, phone apps, and phone companies? These should be much easier for a pAI to gather, examine, and help us do useful things. Caring about much less data also means a pAI will be less likely to give wrong (hallucinated) answers.
Today the mental frame almost everybody uses for AI is the Big kind, ingesting everything they can get their crawlers on, and munching all of it in giant compute farms. Those systems are great for lots of stuff, but they still don’t deal with personal data listed in the last paragraph.
Not yet, anyway.
Look at it this way. For each of us, there are three data pools:
The entire Net, which is what gets crawled by all the giant LLM operators, plus whatever else they can get their claws on.
One’s personal life, some of which is digitized in useful form (contacts, calendar, mail, stuff in folders inside PCs and attached drives).
Tell us who is working on what there, preferably with open source, and not sitting on walled garden silicon.
[Later… ] Since readers told me I had small language models (SLMs) wrong in one of the paragraphs above, and I’m not sure I had them right, I rewrote them out of the piece. I invite readers to post comments to further correct and expand on the subject of pAIs and what they can do.
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:
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.
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.
Healthcare: Personal AI can track individual health metrics and provide personalized advice, monitor conditions, or even alert medical professionals if needed.
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.
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:
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.
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.
Varied Applications: Personal AI can have a wide range of applications. Imagine an AI that helps you:
Organize your life: Manage schedules, appointments, and reminders.
Boost productivity: Assist with tasks, research, and communication.
Enhance creativity: Help you brainstorm ideas or generate creative content.
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)—
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”
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.”
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 Economyfinally 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...
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?
Prompt: “a field of many different kinds of people being harvested by machines and turned into bales of fertilizer.” Via Microsoft CoPilot | Designer.
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.
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.
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,
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,
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.
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,
Increasing personal independence and agency will be good for markets, becausefree 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.)
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,
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,
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.
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.
Prompt: “A panopticon in which thousands of companies are spying on one woman alone in the center with nothing around her.” Via Microsoft Bing Image Creator
In her latestArs 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—
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 exposingThe 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:
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 fewexceptions), 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:
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:
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.
† 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:
Brian Dean‘s BacklinkosourcesHootsuite, 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.
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.
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.”
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?
Bing Create paints “Adam Smith and Karl Marx being rained out in a brainstorm.”
Just sharing some stuff I said on social media recently.:
It’s easy to make an ad hominem argument against anything humans do.
If we had to avoid every enterprise with owners we don’t like, we might as well graze on berries or something.
Capitalism is way too broad a brush with which to paint all of business. As Peter Drucker put it, most people don’t start a business to make money. They do it to make shoes.
The tech world we’ve had for the last few decades is deeply weird in many ways, such as its mix of thrown-spaghetti venture investments and psychotic incentives, e.g. wanting to break things, to run the world, to replace humans with cyborgs, and to work toward exits that will doom what’s already built while breaking faith with customers, workers, and other dependents. Economic thinkers of the industrial age, from Adam Smith and Karl Marx all the way forward, could hardly have imagined any of this shit. I still haven’t encountered any economic theory that can make full sense of it. (Though I’m not saying there isn’t one.)
The prompt for the AI art is a riff on #4. Note that the AI doesn’t have a clear idea of how Adam Smith looks.
Were it left to me to decide whether we should have a government without newspapers, or newspapers without a government, I should not hesitate to prefer the latter.
— Thomas Jefferson
“Breaking the News” is the title of an exhibit at the Monroe County History Center here in Bloomington, Indiana.* It traces the history of local news from the mid-18oos, when several competing newspapers served a population of a thousand people or less, to our current time, when the golden age of newspapers is long past, and its survivors and successors struggle to fill the empty shoes of local papers while finding new ways to get around and get along.
Most of the exhibits are provided by what’s left of the city’s final major newspaper, the Herald-Times, which thankfully still persists. Archives of the paper are also online, going back to 1988. I am told that there are microfilm archives going back farther, available at the Monroe County Public Library. Meanwhile, bound volumes of the paper, from the 1950s through 2013, are up for auction. (More here, including word that older bound volumes are apparently lost.)
Meanwhile, in our other hometown, the Santa Barbara News-Press is gone after serving the city for more than 150 years. The Wikipedia article for the paper now speaks of it in the past tense: was. Its owner, Ampersand Publishing (for which I can find nothing online), filed for bankruptcy late last month. You can read reports about it in KSBY, the LA Times, the Independent, Noozhawk, Edhat, and a raft of other local and regional news organizations.
From what I’ve read so far (and I’d love to be wrong) none of those news reports touch on the subject of the News-Press‘ archives, which conceivably reach back across the century and a half it was published. There can’t be a better first draft of history for Santa Barbara than that one. If it’s gone, the loss is incalculable.
Back here in Bloomington, Dave Askins of the B Square Bulletin, which reports on what public offices and officials are up to, has issued a public RFQ for a digital file repository that will be a first step in the direction of what I suggest we call deep news. Namely, the kind that depends on archives. It begins,
Introduction:
The B Square is seeking proposals from qualified web developers to create a digital file repository. The purpose of this repository is to provide a platform where residents of the Bloomington area can contribute and access digital files of civic or historical interest. This repository will allow users to upload files, add metadata, perform searches, and receive notifications about new additions. We invite interested parties to submit their proposals, outlining their approach, capabilities, and cost estimates for the development and implementation of this project. For an example of a similar project, see: https://a2docs.org/ For the source code of that project, see: https://github.com/a2civictech/docstore.
The links go to a project in Ann Arbor (where Dave used to live and work) that was clearly ahead of its time, which is now.
We also need wide news, which is what you get from lots of organizations and people doing more than filling the void left by shrunken or departed newspapers. (Also local radio, most of which is now just music and talk programs piped in from elsewhere.)
News reporting is a process more than a product, and the Internet opens that process to countless new participants and approaches. Many of us have been writing, talking, and working toward Internet-enabled journalism since the last millennium. Jim Fallows (see below), Dan Gillmor, Dave Winer, JD Lasica, Jay Rosen, Jeff Jarvis, Emily Bell & crew at the Tow Center, and Joshua Benton and the crew at NiemanLab, are among those who come to mind. (I’ll be adding more.) Me too (for example, here).
Wide news, when it happens, is a commons: an informal cooperative. (The Ostrom Workshop, where my wife and I are visiting scholars, studies them.) I think we are getting there in Santa Barbara. But, as the LA Times story on the News-Press suggests in its closing paragraphs, there are gaps:
Santa Barbarans have turned to other sources as the newspaper’s staff withered to just a handful of journalists. Along with the Independent and Noozhawk, some locals said they turn to KEYT television and to Edhat, a website that relies heavily on “citizen journalists” to report on local events.
Melinda Burns, one of many reporters who left the paper after feuding with management, now provides freelance stories to many of the alternative news organizations. Burns, who has spent decades in the news business, including a stint at the Los Angeles Times, said she has seen gaps in coverage in recent years, particularly in the areas of water policy and the changes wrought by legalized cannabis. She continues to report on those topics and said she gives away her in-depth stories free to reach as many people as possible.
“It keeps me engaged with the community and, God, do we need the coverage,” she said. “The local news outlets are valiant but overworked. It’s just a constant scramble for them to try to keep up.”
Maybe it helps to know that a landmark local news institution is gone, and the community needs to create a journalistic commons, together: one without a single canonical source, or a scoop-driven culture.
I think the combination of deep and wide news is a new thing we don’t have yet. I’ll call it whole news. We’ll know it’s whole by what’s not missing. Is hard news covered? City hall? Sports? Music? Fashion? Culture? Events? Is there a collected calendar where anyone can see everything that’s going on? With whole news, there is a checkmark beside each of those and more.
Toward one of those checkmarks (in addition to the one for city hall), Dave Askins has put together a collective calendar for Bloomington. Wherever you are, you can make one of your own, filled by RSS feeds and .ics files.
At the close of all his news reports, Scoop Nisker (who just died, dammit) said, “If you don’t like the news, go out and make some of your own.”
I wrote this today for a list that’s mostly populated by folks in overlapping music, broadcasting, legal, tech, and other businesses who share a common interest in what’s happening to the arts and artists they care about in a world now turning almost completely digital.—Doc
Here is a question I hope can get us out of our heads, our histories in the businesses (music, broadcasting, entertainment, publishing, law, pick-your-art), and up past the 30,000-foot level, out into space, so its possible, at least conceptually, to see the digital world that now coexists with the physical one, but with completely new blessings and curses that may have little to do with the physical world models that operate with and under it.
With that in mind, let’s try putting our minds outside the supply side of the marketplace, with all its incumbent mechanisms and rules, and where all of us have operated for the duration. We’re in space now, looking down on the digital and physical worlds, free to see what might be possible in these co-worlds.
Now try visiting this question: As a consumer or customer (not all the same) of artistic goods, what would you be willing to pay for them if payment was easy and on your terms and not just those of incumbent industries and their regulatory frameworks?
For example, Would you pay the recording artists, performers, producers, and composers the tiny amounts most of them get from a play on Spotify, Amazon, YouTube, Apple Music, Pandora, SiriusXM, a radio station or indirectly through the movies or TV shows that feature those goods?
Try not to be mindful of standing copyright regimes, deals made between all the parties in distribution chains, and subscription systems as they stand. In fact, try to put subscription out of your minds and think instead of what you would want to pay, value-for-value, in a completely open marketplace where you can pay what you like for whatever you like, on an á la carte basis. Don’t think how. Think how much. Imagine no coercion on the providers’ side. You’re the customer. You value what you use and enjoy, and are willing to pay for it on a value-for-value basis.
To help with this, imagine you have your own personal AI: one that logs all the music you hear, all the programs you watch, all the podcasts you listen to, all the radio you play in your car, and can tell you exactly how much time you spent with each. Perhaps it can tell you what composers, writers, producers, labels, and performers were involved, and help you know which you valued more and which you valued less. (Again, this is your AI, not Microsoft’s, Google’s, Facebook’s, or Apple’s. It works only for you, in your own private life.)
Then look at whatever you’re spending now, for all the subscription services you employ, for all the one-offs (concerts, movies in theaters, bands night clubs) you also pay for. Would it be more? Less? How much?
The idea here is to zero-base the ways we understand and build new and more open markets in the digital world, which is decades old at most and will be with us for many decades, centuries, or millennia to come. It should help to look at possibilities in this new non-place without the burden of leveraging models built in a world that is physical alone.
I submit that in this new world, free customers will be more valuable—to themselves and to the marketplace—than captive ones. And that sellers working toward customer capture through coercive subscription systems and favorable regulations will find less advantage than by following (respecting Adam Smith) the hand-signals of independent customers.
We don’t know yet if that will be the case. But we can at least imagine it, and see where that goes.
When I was a kid in the 1950s and early 1960s, AM was the ruling form of radio, and its transmitters were beyond obvious, taking the form of towers hundreds of feet high, sometimes in clusters formed to produce directional signals favoring some directions over others. These were typically landmarks out on the edge of town, or standing oddly on salt bogs or farmland.
From my bedroom in New Jersey, not far across the Hudson from New York City, I could see the red lights on the tops of towers standing in the “Meadowlands” (we called them swamps then) with Manhattan’s skyline beyond.
The towers in the photo above are three of those, tasked with beaming WMCA/570 and WNYC/820 toward New York’s boroughs from a pond of impounded water beside the Hackensack River and the east spur of the New Jersey Turnpike. Built in 1940, these three towers have by now become the most-seen AM radio signal source on Earth. For a while, they were also the most heard. That’s because, in its prime, which ran from 1958 to 1966, WMCA was also the leading top 40 music station in the world’s leading radio market. (WABC, with a signal ten times as strong, ruled the suburbs, with a night signal heard across half the country.)
While these days WNYC is the AM side of New York’s public radio empire (which brings in more money, largely from listeners, than any of the commercial stations in town), it is most famous for Mayor Fiorello LaGuardia’s “Talk to the People” show, which ran in the first half of the 1940s. (Back then WNYC had its own towers standing on what’s now WNYC Transmitter Park, alongside the East River in Brooklyn.)
Prior to the Internet, major media comprised a fewness of sources, in both print and broadcast. That fewness is over now, and the writing of over-the-air broadcasting’s end is being written on the Internet’s walls, perhaps most purposefully by yours truly. Because I’ve shot and shared thousands of photos of transmitters and antennas, knowing that the land under the most vulnerable ones—those on the AM band—tends to be worth more than the signals themselves. Many of these sites have already been sold off, with signals moving to shared towers on other stations’ sites, or just going dark.
Radio itself is also slowly being eaten alive: on the talk side by podcasts and on the music side by streaming services and webcasters. So I publish those photos as historical evidence of what in a few years (decades at most) will be no more. (Sorry, but no amount of lawmaking or regulation will save AM radio. Much as many of us—me included—still love it, neither the tech nor the economics can compete with the Internet, smartphones, the cellular system, and computers.)
So I recently ran a test of a theory: that it is good to have a conversation about all these developments, at least among professionals both active and retired in the broadcast engineering world. What follows is a post I put up for a private group that includes more than a dozen thousand of those.
Some hopefully fun detective work.
First, an ad in the November 14, 1949 issue of Broadcasting, the Youngstown-based company that built (or supplied steel) for countless AM stations in that band’s golden age. The image is of the array of six 400-foot tall self-supporting towers putting out the directional night signal for WFMJ, now WNIO/1390. HT for scanning and publishing that page goes to David Gleason, who gives us the amazing and valuable [https://worldradiohistory.com/](https://worldradiohistory.com/)
Second is a Google StreetView of what I think is the current view of the same site, with the transmitter shack and the six towers replaced. One of those is also a tower in WKBN’s own directional nighttime array. (Also, in the distance is another tower that appears not to participate in either station’s system.)
Third is a Bing Birds Eye (a fixed-wing aircraft) view of the whole site:
And a fourth is the Google view from space of the same.
Of possible relevance is that WNIO and WKBN are non-directional by day, the former from a tower at another site in town. Also that WNIO was a 5kw DA-N from the site for most of its life and is now 9.5kw from the day site and 4.8kw from the night site we see in these images—and that its six towers have six different electrical lengths, ranging from 105.8° to 215.1°, apparently in slightly different positions on the ground. Also that WKBN has been 5kw day and night since the late 1940s.
We can also see from the Truscon ad that the original address of WFMJ was on Poland-Broadmans Road, which I think is now just Broad. (The current shack for WNIO is on East Western Reserve Road, while WKBN’s is at the end of a long driveway off that same road.) One can also see from above something of the entrance off broad and possibly something of the original footprint of the original tower layout.
So, some questions are:
1) Is the first photo from the entrance to the site in the Truscon ad?
2) When did WKBN show up, or was it already at this site?
3) Are the different lengths of towers in the current WNIO array the result of more efficient towers in it, and also why the 4.8kw signal roughly matches the old 5kw footprint on the ground?
4) In 1949, were six towers about the limit of what one could do with a directional array using long math, trig tables, and graph paper, and perhaps a record number for its time?
5) Was Truscon the outfit that pioneered narrow rather than fat towers, and ones with three sides rather than four?
There are other variables, of course. But I just enjoy this kind of detective work, and I’m kinda chumming the waters to bait others who like to do the same. Thanks in advance.
We’ll see who rises to the bait and with what.
[Later…] Old pal Scott Fybush pointed to one of his transmitter visit reports and added this: “Summary: the current WNIO night site is not the original 1949 six-tower site. That was on what’s now Boardman-Poland Road (US 224) at what’s now the Shops at Boardman Park strip mall. It succumbed to development in the early 1990s, at which point 1390 moved to what’s now its current day tower. The current six-tower night array on Western Reserve Rd. was built in 2003, next to the 1977-vintage WKBN array. WKBN’s original DA was at the WKBN studios at 3930 Sunset, which is still the WKBN-TV facility.”
Given that radio’s content (as we now call it) is gone at the speed of short-term memory (unless it’s recorded, which mostly it isn’t), this kind of reporting may be the only history it has. So, if history matters, this kind of inquiry also matters.
The top photo is one of many I’ve shot on en route to EWR (Newark Liberty International Airport). By the way, these towers were built when one could walk on the land there. One needed boots and a scythe, but it was possible. The water was impounded in the 1990s, I think. Here are some shots from a visit to the site nine years ago.
The full theme of the salon series is Beyond the Web: Making a platform-free online marketplace for goods, ideas and everything else, about which you can read more here.
Robin’s work with transportation and peer production has been VRooMy from the start, and especially consistent with our work with the Ostrom Workshop on the Intention Byway in Bloomington, Indiana.
Upcoming speakers in the Salon Series (mark your calendars) are Ethan Zuckerman and Shoshana Zuboff. Both are BKC veterans and, like Robin, devoted to moving beyond status quos that vex us all. Ethan will be with us on March 7 and Shoshana on April 11. Days and times for both are Mondays at 2:00 PM Eastern. Details at those links.<
These events are all participatory, informative, challenging and fun. Please join us.
