Pour les images et les vidéos, je ne suis pas vraiment d'accord avec ce point de vue. Dans l'environnement actuel d'Internet, la vitesse de diffusion des images et des vidéos est supérieure à celle des textes. Dans ce cas, une grande quantité d'images et de vidéos générées (de plus en plus réalistes) est produite rapidement à faible coût, ce qui non seulement augmente le coût de discernement de l'information et rend la vérification des faits plus difficile, mais complique également la confiance des gens envers certaines choses non conventionnelles (même si elles sont réelles). D'ailleurs, les algorithmes des plateformes ont tendance à privilégier la mise en avant des contenus récemment publiés. Par conséquent, même un contenu produit en grande quantité (mais peut-être de moindre qualité) peut bénéficier d'une plus grande opportunité de diffusion en raison des algorithmes.

for - AI - inside industry predictions to 2034 - Leopold Aschenbrenner - inside information on disruptive Generative AI to 2034

document description - Situational Awareness - The Decade Ahead - author - Leopold Aschenbrenner

summary - Leopold Aschenbrenner is an ex-employee of OpenAI and reveals the insider information of the disruptive plans for AI in the next decade, that pose an existential threat to create a truly dystopian world if we continue going down our BAU trajectory. - The A.I. arms race can end in disaster. The mason threat of A.I. is that humans are fallible and even one bad actor with access to support intelligent A.I. can post an existential threat to everyone - A.I. threat is amplifier by allowing itt to control important processes - and when it is exploited by the military industrial complex, the threat escalates significantly

• to - YouTube - 4 hour in-depth interview with Leopold Aschenbrenner on the disruptive and existential impacts of A.I. super-intelligence
  • https://hyp.is/zk8hdjEoEe-EIHtdo3U81w/www.youtube.com/watch?v=zdbVtZIn9IM

for - Generative AI music - producers and artists using for inspiration

comment I would agree with this. Especially since the AI music currently sounds lo-fi

for - progress trap - Generative AI - copyright infringement vs Unfair business practice argument

For - progress trap - AI - Generative AI - IP - Yale Law Journal

Alexander 2011, p. 59; Alexander 1968, p. 605

Also see:

• Systems generating systems — architectural design theory by Christopher Alexander (1968) – Coevolving Innovations

I ran across an AI tool that cites its sources if anyone's interested (and heard of it yet): https://www.perplexity.ai/

That's one of the things that I dislike the most about ChatGPT is that it just synthesizes/paraphrases the information, but doesn't let me quickly and easily check the original sources so that I can verify (and learn more about the topic by doing further reading) the information for myself. Without access to primary sources, it often feels no better than a rumor — a retelling of what someone somewhere allegedly, purportedly, ostensibly found to be true — can I really trust what ChatGPT claims? (No...)

This is depressing because no matter how well-intentioned my application would be if we don't change teachers' mindsets first. That's the actual problem it seems in most schools. Why do these teachers feel this way? I mean, there must be a reason for this attitude.

The title of the document

https://www.meetup.com/edtechsocal/events/296723328/

Generative AI: Super Learning Skills with Data Discovery and more!

This section helped me understand a key feature

(Chen, NeurIPS, 2021) Che1, Lu, Rajeswaran, Lee, Grover, Laskin, Abbeel, Srinivas, and Mordatch. "Decision Transformer: Reinforcement Learning via Sequence Modeling". Arxiv preprint rXiv:2106.01345v2, June, 2021.

Quickly a very influential paper with a new idea of how to learn generative models of action prediction using SARSA training from demonstration trajectories. No optimization of actions or rewards, but target reward is an input.

• for: interspecies communication

• paraphrase

• question
  • can a generative model interact meaningfully with an other species?
  • can other species respond in meaningful ways?
  • playing back AI trained generative vocal signals back to specific species and monitoring behavior

https://www.theguardian.com/technology/2023/sep/01/mushroom-pickers-urged-to-avoid-foraging-books-on-amazon-that-appear-to-be-written-by-ai

https://claude.ai/

https://chat.openai.com/auth/login

Mills, Anna, Maha Bali, and Lance Eaton. “How Do We Respond to Generative AI in Education? Open Educational Practices Give Us a Framework for an Ongoing Process.” Journal of Applied Learning and Teaching 6, no. 1 (June 11, 2023): 16–30. https://doi.org/10.37074/jalt.2023.6.1.34.

Annotation url: urn:x-pdf:bb16e6f65a326e4089ed46b15987c1e7

Search: https://jonudell.info/h/facet/?user=chrisaldrich&max=100&exactTagSearch=true&expanded=true&addQuoteContext=true&url=urn%3Ax-pdf%3Abb16e6f65a326e4089ed46b15987c1e7

Might generative AI worries follow the track of the MOOC scare? (Many felt that creating courseware was going to put educators out of business...)

How exactly? Give us an example? Otherwise not very clear.

T9 (text prediction):generative AI::handgun:machine gun

Link to: https://hypothes.is/a/n6wXvkeNEe6DOFexaCD-Qg

Some may not realize it yet, but the shift in technology represented by ChatGPT is just another small evolution in the chain of predictive text with the realms of information theory and corpus linguistics.

Claude Shannon's work along with Warren Weaver's introduction in The Mathematical Theory of Communication (1948), shows some of the predictive structure of written communication. This is potentially better underlined for the non-mathematician in John R. Pierce's book An Introduction to Information Theory: Symbols, Signals and Noise (1961) in which discusses how one can do a basic analysis of written English to discover that "e" is the most prolific letter or to predict which letters are more likely to come after other letters. The mathematical structures have interesting consequences like the fact that crossword puzzles are only possible because of the repetitive nature of the English language or that one can use the editor's notation "TK" (usually meaning facts or date To Come) in writing their papers to make it easy to find missing information prior to publication because the statistical existence of the letter combination T followed by K is exceptionally rare and the only appearances of it in long documents are almost assuredly areas which need to be double checked for data or accuracy.

Cell phone manufacturers took advantage of the lower levels of this mathematical predictability to create T9 predictive text in early mobile phone technology. This functionality is still used in current cell phones to help speed up our texting abilities. The difference between then and now is that almost everyone takes the predictive magic for granted.

As anyone with "fat fingers" can attest, your phone doesn't always type out exactly what you mean which can result in autocorrect mistakes (see: DYAC (Damn You AutoCorrect)) of varying levels of frustration or hilarity. This means that when texting, one needs to carefully double check their work before sending their text or social media posts or risk sending their messages to Grand Master Flash instead of Grandma.

The evolution in technology effected by larger amounts of storage, faster processing speeds, and more text to study means that we've gone beyond the level of predicting a single word or two ahead of what you intend to text, but now we're predicting whole sentences and even paragraphs which make sense within a context. ChatGPT means that one can generate whole sections of text which will likely make some sense.

Sadly, as we know from our T9 experience, this massive jump in predictability doesn't mean that ChatGPT or other predictive artificial intelligence tools are "magically" correct! In fact, quite often they're wrong or will predict nonsense, a phenomenon known as AI hallucination. Just as with T9, we need to take even more time and effort to not only spell check the outputs from the machine, but now we may need to check for the appropriateness of style as well as factual substance!

The bigger near-term problem is one of human understanding and human communication. While the machine may appear to magically communicate (often on our behalf if we're publishing it's words under our names), is it relaying actual meaning? Is the other person reading these words understanding what was meant to have been communicated? Do the words create knowledge? Insight?

We need to recall that Claude Shannon specifically carved semantics and meaning out of the picture in the second paragraph of his seminal paper:

Frequently the messages have meaning; that is they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem.

So far ChatGPT seems to be accomplishing magic by solving a small part of an engineering problem by being able to explore the adjacent possible. It is far from solving the human semantic problem much less the un-adjacent possibilities (potentially representing wisdom or insight), and we need to take care to be aware of that portion of the unsolved problem. Generative AIs are also just choosing weighted probabilities and spitting out something which is prone to seem possible, but they're not optimizing for which of many potential probabilities is the "best" or the "correct" one. For that, we still need our humanity and faculties for decision making.

Shannon, Claude E. A Mathematical Theory of Communication. Bell System Technical Journal, 1948.

Shannon, Claude E., and Warren Weaver. The Mathematical Theory of Communication. University of Illinois Press, 1949.

Pierce, John Robinson. An Introduction to Information Theory: Symbols, Signals and Noise. Second, Revised. Dover Books on Mathematics. 1961. Reprint, Mineola, N.Y: Dover Publications, Inc., 1980. https://www.amazon.com/Introduction-Information-Theory-Symbols-Mathematics/dp/0486240614.

Shannon, Claude Elwood. “The Bandwagon.” IEEE Transactions on Information Theory 2, no. 1 (March 1956): 3. https://doi.org/10.1109/TIT.1956.1056774.

We may also need to explore The Bandwagon, an early effect which Shannon noticed and commented upon. Everyone seems to be piling on the AI bandwagon right now...

A Generative AI Primer on 2023-08-15 by Brian Basgen

ᔥGeoff Corb in LinkedIn update (accessed:: 2023-08-26 01:34:45)

Epstein, Ziv, Hertzmann, Aaron, Herman, Laura, Mahari, Robert, Frank, Morgan R., Groh, Matthew, Schroeder, Hope et al. "Art and the science of generative AI: A deeper dive." ArXiv, (2023). Accessed July 21, 2023. https://doi.org/10.1126/science.adh4451.

Abstract

A new class of tools, colloquially called generative AI, can produce high-quality artistic media for visual arts, concept art, music, fiction, literature, video, and animation. The generative capabilities of these tools are likely to fundamentally alter the creative processes by which creators formulate ideas and put them into production. As creativity is reimagined, so too may be many sectors of society. Understanding the impact of generative AI - and making policy decisions around it - requires new interdisciplinary scientific inquiry into culture, economics, law, algorithms, and the interaction of technology and creativity. We argue that generative AI is not the harbinger of art's demise, but rather is a new medium with its own distinct affordances. In this vein, we consider the impacts of this new medium on creators across four themes: aesthetics and culture, legal questions of ownership and credit, the future of creative work, and impacts on the contemporary media ecosystem. Across these themes, we highlight key research questions and directions to inform policy and beneficial uses of the technology.

Abstract

Recent innovations in artificial intelligence (AI) are raising new questions about how copyright law principles such as authorship, infringement, and fair use will apply to content created or used by AI. So-called “generative AI” computer programs—such as Open AI’s DALL-E 2 and ChatGPT programs, Stability AI’s Stable Diffusion program, and Midjourney’s self-titled program—are able to generate new images, texts, and other content (or “outputs”) in response to a user’s textual prompts (or “inputs”). These generative AI programs are “trained” to generate such works partly by exposing them to large quantities of existing works such as writings, photos, paintings, and other artworks. This Legal Sidebar explores questions that courts and the U.S. Copyright Office have begun to confront regarding whether the outputs of generative AI programs are entitled to copyright protection as well as how training and using these programs might infringe copyrights in other works.

Generative AI abstracts away the open web that is the substrate it was trained on. Abstracting away the open web means there may be much less incentive to share on the open web, if the LLMs etc never point back to it. Vgl the way FB et al increasingly treated open web URLs as problematic.

https://web.archive.org/web/20230328130227/https://librarian.aedileworks.com/2023/03/27/the-push-to-ai-is-meant-to-devalue-the-open-web-so-we-will-move-to-web3-for-compensation/

blogged https://www.zylstra.org/blog/2023/03/22930/

Author's note by Robin Sloan<br /> November 2022

I don't think "all" software needs or all human endeavors benefit from generative AI. Especially when you consider the associated prerequisitve internet access or huge processing requirements.

Look at market share for tensorflow and pytorch which both offer first-class nvidia support and likely spells out the story. If you are getting in to AI you go learn one of those frameworks and they tell you to install CUDA

All current generation models have more-or-less the same architecture and training regimes. Differentiation is in the training data and the number of hyper-parameters that the company can afford to scale to.

OpenAI have already dropped prices on their GPT-3/3.5 models and relatively few apps have emerged. This could be because companies are reluctant to build their core offering around a third party API

There's definitely a middle ground of taking an open source model that is suitably mature and fine-tuning it for a specific use case. You could start without a moat and build one over time through collecting use data (similar to network effect)

Companies that rely on underlying AI models without adding value via model improvements are going to find that they have no moat.

Infrastructure vendors are laughing all the way to the bank because companies are dumping millions on GPUs. Meanwhile, the people building apps on top of these models are struggling. We've seen this sort of gold-rush before and infrastructure providers are selling the shovels.

Generative AI requires prompt engineering, likely a new profession

What domain experience does a prompt engineer need? How might this relate to relate to specialty in librarianship?

How often does one meet a conversational partner that is interested in generative thought? This practice takes some work, but how could one particularly encourage it in classroom setting?

Example of Brian Eno using combinatorial creativity using cards to generate music.

This sounds similar to a process used by Austin Kleon which I've noted before.

🤔

https://meso.tzyl.nl/2022/12/04/a-two-month-experiment/

These models are "children of Tay", the story of the Microsoft's bot repeating itself, again

Brian Eno's experiments in generative music mirror some of the ideas of generative and experimental fiction which had been in the zeitgeist and developing for a while.

Certainly the fictional ideas were influential to the zeitgeist here, but the technology for doing these sorts of things in the musical realm lagged the ability to do them in the word realm.

We're just starting to see some of these sorts of experimental things in the film space and with artificial intelligence they're becoming much easier to do in all of these media spaces.

In some of the film spaces, they exist, but may tend to be short in nature, in part given the technology and processing power required.

see also: Deepfake TikTok of Keanu Reeves which I've recently run across (algorithmically) on Instagram: https://www.dailydot.com/debug/unreal-keanu-reeves-ai-deepfake/

Had anyone been working on generative art? Marcel Duchamp, et al? Some children's toys can mechanically create generative art which can be subtly modified by the children using axes of color, form, etc. Etch-a-sketch, kaleidoscopes, doodling robots (eg: https://www.amazon.com/4M-Doodling-Robot-Packaging-Vary/dp/B002EWWW9O).

Interesting metaphor for why humans are happy to trust outputs from generative models

Generative ambiguity is the process of giving names, potentially tentative, to a nebulous collection of nascent and unclear ideas in an effort to help make sense of them both to themselves as well as others.

https://dougbelshaw.com/blog/2015/01/22/volcanoes-and-ambiguity/

<small><cite class='h-cite via'>ᔥ <span class='p-author h-card'>Aaron Davis </span> in 📑 The Two Definitions of Zettelkasten | Read Write Collect (<time class='dt-published'>11/18/2022 19:54:00</time>)</cite></small>

Noam Chomsky felt that there was a tendency for people to confuse the ideas of deep structure with the ideas of either generative grammar or universal grammar. He also thought that professional linguists confused what he called "the creative aspect of language use" with the recursive property of generative grammars.

Example of writing advice that builds the links in after-the-fact instead of cross-linking ideas into initial networks as they're finding them. Compare/contrast this to the creation of these networks in the zettelkasten tradition as well as Sönke Ahrens description.

There's less upfront work in creating these links at the start than there is in reloading the context in one's brain to create these links after the fact. Collecting ideas without filing, linking, or organizing them upfront also means that one is more likely to only use these ideas in the context of specific projects which one already has in mind rather than keeping them for a lifetime's work which might also create generative projects one hadn't expected.

Writing is a circular generative process and not a finite, linear one.

Even if the paper or book you're reading doesn't answer the particular question you're researching, you're bound to come across other novel ideas and potential questions. Don't let these go to waste, but instead note them down and save them into your note taking system. They may be useful in the future, particularly if you found them interesting or intriguing.

It turns out "waste not, want not" is applicable to ideas as well.

I can't help but also thinking "waste note, want note" as an interesting turn of expression.

At what point could digital tools be said to be thinking? Do they need to be generative? It certainly needs to be on the other side of serendipitously juxtaposing two interesting ideas. One can juxtapose millions of ideas, it's the selection of a tiny subset of these as "better" or more interesting than the others and then building off of that that constitutes this sort of generative thought.

when Rangle first started to rally around redux - it did feel like a generative learning event

Altmann, E. G. (2020). Spatial interactions in urban scaling laws. ArXiv:2006.14140 [Physics]. http://arxiv.org/abs/2006.14140

This is a helpful guide to deep inquiry.

I like questions like “Why do I like chicken nuggets?”

When a girl in the back of the room blurts out this question, half a joke, half a test (Do they really want us to write down any question that we think of?), she seems a bit surprised to have her query treated seriously.

Thanks for that question Neisha. Let's use it as an example of how to think of keywords for each of your questions. What would be a good one for that question?

Chicken nuggets.

Not really. That’s too specific. What's a more general word.

“Food,” somebody yells.

Right, write that down Neisha. What kind of food are we talking about?

Junk food. Fast food. Fried food.

Right. Right. Where do you get chicken nuggets?

Down on Nostrand Avenue where all the fast food places are.

And who…

Neisha catches the drift, interrupts: It’s in my neighborhood and not in White people's neighborhoods. They get healthy food, which is hard to find where I live.

So could we add “health” to your keywords?

Yeah.

And what else is in your description? What about “inequality?“

And “racism.”

What else?

They’re good, Mister.

So, what about “delicious? “

Do we have to write five keywords for every question?

Yup.

Ahhhh.

But what a gift this question was! Do you see how a question can start with something personal, something real for you, even if you aren't sure how important it is? Keep putting the personal pronoun, I, in your questions, then ask your friends and your teachers to help you find the social justice behind them. That's what to look for in your keywords.

Twitter bots, recombinatory poetry, generative fiction is infinitely fascinating because it can be so random and unexpected. It's like experimenting with how we ascribe meaning, try to find purpose in the otherwise incomprehensible.