It’s a good point/counterpinet, Pieter. I don’t want to stand against using new tools to relieve a scarcity constraint, and surely education has been a severe one.
I have what appears to be an enormous suspicion of a bait and switch, in which suddenly we find ourselves in a world where simultaneously: 1. Sal Khan and Daphne Koller own everything, and nobody else owns anything; and 2. People have no skills (say I, as I use the scary-good google translate to take enormous pressure, though less shame, off me for not learning a language I should have learned by now). Or, as a popular bumper sticker in Santa Fe used to say post-2016: Where are we going? And what am I doing in this handbasket? I should mention, on this point, that part of my cast of mind was shaped by an article I read some time ago in some education chronicle, about how the command-and-control interface to the computer was taking up more and more space in classrooms, and kids were starting to show severe systematic deficits in particular areas. They could command anything they wanted into existence on the computer, and failed to learn that the rest of the world of people doesn’t work that way. Negotiating skills were mentioned in the article. My description of a dystopian non-education was meant to suggest that for several students to have to hear exactly the same thing would require them to come to terms with the fact that it will fit them differently, and they should become used to handling that too. It is interesting that I could never use a chatbot as a first stage of writing something, as the optimistic chatbots suggest everybody will do, and which I can believe for some people will be a good fit. For me, I can’t even take looking at a page of my own writing when I am in a tangle, as a start to getting out of it. All that pre-formed text is in the way of whatever part of my brain tries to bring into focus what I should do next. I know that chatbots and writing is not your point about education, and don’t mean to derail those points; the chatbots-and-journalism theme was one of the things from the original thread that put me onto the education sidetrack. What (at least in my imagination) they have in common is that there are certain mental operations that I only do when I have “space”, and many of these tools seem designed to make sure nobody ever has to leave any “space” unfilled by some external stimulus, ever again. I worry about it as a development-weakening prosthetic, though I understand that the argument for that would need to be made case by case, versus the likely (and I believe you, in many cases demonstrated) economies and advantages of personalization. Fortunately, I am neither designing anything nor voting on anything just now, so there is no danger I will hold anybody else back who is trying to make something work. Eric > On Oct 8, 2023, at 7:45 AM, Pieter Steenekamp <piet...@randcontrols.co.za> > wrote: > > I'd like to reflect how LLM's and AI could disrupt education. > > My paradigm is not to think in terms of the current educational model, but to > throw out all preconceived ideas and to design it from scratch. There are > obviously zillions of ways to do it and a good model will need to evolve, but > the following are merely two examples to trigger new and novel approaches, > one from Sal Khan and one from my own experience. > > 1 Sal Khan's approach to use AI to personalize education is based on the idea > that every student can benefit from a customized learning path that adapts to > their needs and preferences. He argues that AI can help scale the benefits of > one-to-one tutoring, which has been shown to improve students' performance > significantly. He also believes that AI can enhance the role of teachers by > providing them with useful feedback, insights, and assistance. He envisions a > future where every student has access to an AI-powered personal tutor and > every teacher has an AI teaching assistant. > > Sal Khan is the founder and CEO of Khan Academy, a nonprofit education > platform that offers free online courses, videos, and exercises for learners > of all ages. He has been a pioneer in using technology to democratize > education and make it accessible to anyone, anywhere. He has also been > exploring the potential of AI to transform education for the better. In his > 2023 TED talk¹, he demonstrated Khanmigo, an AI-powered guide that can help > students with various subjects, skills, and tasks. Khanmigo can detect > students' mistakes and misconceptions, provide effective feedback and > explanations, encourage Socratic dialogue and debate, and assist with writing > and storytelling. Khanmigo also acts as a teaching assistant for teachers, > helping them with lesson planning, grading, and progress reports. > > Sal Khan's approach to use AI to personalize education is based on his vision > of creating a more humanistic and learner-centered education system. He > believes that AI can empower students to learn at their own pace, explore > their interests, and develop their creativity. He also believes that AI can > support teachers to focus more on the social and emotional aspects of > teaching, such as building relationships, fostering curiosity, and inspiring > passion. He hopes that by using AI to personalize education, we can create a > more equitable and inclusive learning environment for everyone.. > > Source: Conversation with Bing, 2023/10/08 > (1) . https://bing.com/search?q=Sal+Khan+AI+education. > (2) Sal Khan’s 2023 TED Talk: AI in the classroom can transform education. > https://blog.khanacademy.org/sal-khans-2023-ted-talk-ai-in-the-classroom-can-transform-education/. > (3) Sal Khan: How AI could save (not destroy) education | TED Talk. > https://www.ted.com/talks/sal_khan_how_ai_could_save_not_destroy_education/c. > (4) The Future Of AI And Education: Insights From Sal Khan. > https://www.openbusinesscouncil.org/the-future-of-ai-and-education-insights-from-sal-khan-in-conversation-with-bill-gates > > <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fwww.openbusinesscouncil.org%2fthe-future-of-ai-and-education-insights-from-sal-khan-in-conversation-with-bill-gates&c=E,1,2zh-o7p-ZRG5J2pDpv8lLYBDKVFUgyvlCSvOCnrGzy0OEqHE_M-1ND6g52xVjiUgyBncevhmKdnOnnuuTkeZ66oyJXl1nY6Pan2Zr01pkGQWF2U,&typo=1>. > (5) undefined. > https://www.ted.com/podcasts/rethinking-with-adam-grant/khan-academy-founder-sal-khan-on-ai-and-the-future-of-education-transcript. > (6) undefined. > https://podcasts.apple.com/us/podcast/how-ai-could-save-not-destroy-education-sal-khan/id160904630?i=1000611325934. > (7) undefined. https://www.ted.com/contact%29. > (8) en.wikipedia.org <http://en.wikipedia.org/>. > https://en.wikipedia.org/wiki/Sal_Khan. > > 2 In South Africa, we are currently facing a significant education crisis. > While there are some excellent private schools, they come with a high price > tag. On the other hand, the state of public schools is a complete disaster. > > During the COVID-19 lockdowns, I had the opportunity to work with children > ranging from 6 to 16 years old, and I decided to adopt a radically different > educational approach. This approach was inspired by Sugata Mittra's belief > that teachers may not be necessary if the learning environment is > well-managed (as elaborated below). > > As part of our informal schooling setup, we incorporated a daily "speech" > session at 11 am. This session was not limited to just speaking; the children > were also required to submit handwritten copies of their speeches and present > them to the group. Following each child's presentation, we engaged in a Q&A > and discussion session. > > Even though we didn't have access to ChatGPT at that time, if I were to > recreate this experience today, I would encourage the children to utilize > resources like ChatGPT and other freely available AI tools on the internet. > This approach aligns with preparing them for the demands of today's workplace. > > Children possess an innate desire to learn, seek challenges, and take pride > in their accomplishments. Providing them with maximum freedom within defined > boundaries is an excellent approach to help them flourish. > > Reference, info on Sugata Mitra from the internet > The School in the Cloud is a concept developed by Sugata Mitra, an > educational researcher and TED Prize winner. He proposes that children can > learn anything by themselves, with the help of the Internet and their peers, > without the need for formal teaching or supervision. He calls this method > Self-Organized Learning Environments (SOLE)¹. > > Mitra conducted several experiments to test his idea, such as the "Hole in > the Wall" project, where he installed computers in slums and villages in > India and observed how children learned to use them without any guidance¹. He > also created learning labs in different locations, where children could > access online resources and mentors, called the "Granny Cloud", who supported > their curiosity and exploration². > > Mitra's vision is to transform the way children learn, by giving them more > autonomy, creativity and collaboration. He believes that this approach can > prepare them for the future, where memorization of facts is less important > than critical thinking and problem-solving. He also invites anyone, anywhere, > to join his global experiment in self-organized learning, by creating their > own SOLEs and sharing their discoveries². > > The School in the Cloud is also the subject of a documentary by Jerry > Rothwell, which follows Mitra's three-year experiment and the impact of his > work on children and educators around the world³. You can also read more > about Mitra's ideas and experiences in his book "The School in the Cloud: The > Emerging Future of Learning"⁴.. > > Source: Conversation with Bing, 2023/10/08 > (1) Sugata Mitra: Build a School in the Cloud | TED Talk. > https://www.ted.com/talks/sugata_mitra_build_a_school_in_the_cloud. > (2) Sugata Mitra creates a School in the Cloud | Prize-winning wishes | TED > .... > https://www.ted.com/participate/ted-prize/prize-winning-wishes/school-in-the-cloud. > (3) The School in the Cloud - United Nations Western Europe. > https://unric.org/en/the-school-in-the-cloud/ > <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2funric.org%2fen%2fthe-school-in-the-cloud%2f&c=E,1,P04gf7AkTCBw5Pu_pIZ15Sa4DD1-gfP_8k-B2baFWOI2jWFHYyPbbaBsn0OsnINICl6TI9QThLvR2A5qOglBMS7zz_Tmtt90LZBHRFp-&typo=1>. > (4) The School in the Cloud by Sugata Mitra (ebook) - eBooks.com > <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2feBooks.com&c=E,1,wsixh_9R4QKLkD8DtVqVcLh8kGp_7BfjsaQ5St9_onGfLCa4Ou6JZjv0T2UFtSbIYPqSp0o5p5t2ZJssBHfXMnLUgHb4gPP2_oEKNle93g,,&typo=1&ancr_add=1> > > https://www.ebooks.com/en-us/book/209765777/the-school-in-the-cloud/sugata-mitra/ > > <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fwww.ebooks.com%2fen-us%2fbook%2f209765777%2fthe-school-in-the-cloud%2fsugata-mitra%2f&c=E,1,ALPLtj1Pd8Kx3SVvle5l5lE2km7dGj6PWIWblhALEROHD-5OllWa3vIAV_dpaEw1Tt9GJyLnLfyIb5iufGiV8vo3uAWeoE-zwANhJFQ5BQ,,&typo=1>. > (5) undefined. https://www.ted.com/contact%29. > (6) en.wikipedia.org <http://en.wikipedia.org/>. > https://en.wikipedia.org/wiki/Sugata_Mitra. > > On Sun, 8 Oct 2023 at 11:20, David Eric Smith <desm...@santafe.edu > <mailto:desm...@santafe.edu>> wrote: >> In reading about the role of LLMs in personalization, I just realized how to >> further destroy education. As in many of these things, one starts with an >> insight that is basically good, and then hands it to a SV company to be >> turned into something profoundly nihilistic. >> >> The lecturer sits at “the front” of wherever this happens. The body of >> students sit in something that makes them manifestly a group. But what the >> lecturer says isn’t what they hear. The lecturer speaks into a computer, >> and the students all wear headsets (probably full VR immersions). An >> intervening LLM, which has been trained on exchanges with each student and >> with the lecturer, replaces whatever the lecturer says with a “personalized” >> version of the “content” which is then fed to each student. >> >> No two people will have heard quite the same version of anything. I guess, >> if they ever wanted to discuss the material with each other, another LLM >> could be recruited to stand between them to convert what either of them said >> into a different stream for the other to hear. That can be an additional >> service for being “in the class”, which one can purchase for a fee, like the >> fee to get on an airplane earlier in the line than some other passenger who >> paid a lower fee. >> >> The only thing I find weird is that I haven’t already head this suggested >> 1000 times even in the popular press. It seems like the first idea that >> would occur to any SV engineer, or (though probably requiring a cognitive >> exoskeleton) any university administrator. >> >> Eric >> >> Oh yeah, p.s.: The insight that wasn’t evil is that this is the difference >> between lectures and books or other broadcast media. In a real lecture, in >> the old world where people spent time with each other, the teacher gets to >> know the students enough to learn what they can use, and renders the >> material with empathy for that. I’ll leave the reader to put together which >> parts of the resulting exchange get left out of my LLM implementation. >> >> p.p.s. I should mention, has any of you read this book? >> >> Nihilistic Times — Wendy Brown >> hup.harvard.edu >> <https://www.hup.harvard.edu/catalog.php?isbn=9780674279384>Nihilistic >> Times — Wendy Brown >> <https://www.hup.harvard.edu/catalog.php?isbn=9780674279384> >> hup.harvard.edu <https://www.hup.harvard.edu/catalog.php?isbn=9780674279384> >> >> I got to spend a little time in the orbit of this woman last year (didn’t >> really “meet”, since I was just a quiet sitter-in in a group’s weekly >> discussion), and she seems to me like just a fine mind. The book is a >> chautauqua of sorts, not aspiring to a destination per se. But it is a good >> combination of empathy, analytic clarity, and erudition, nicely combined. >> Bears on how many things make up the machinery of nihilism, extending well >> beyond relations I recognized as part of an integration, though mentioning >> many things I rail against in daily life. >> >> >> >> >>> On Oct 7, 2023, at 8:36 PM, Steve Smith <sasm...@swcp.com >>> <mailto:sasm...@swcp.com>> wrote: >>> >>> Tom - >>> >>> This is probably a little less relevant to the business of journalism than >>> you deserve but this is a pretty good (IMO) fleshing out of some of the >>> technical details of (L)LMs which might fill in some blanks: >>> >>> https://developers.google.com/machine-learning/resources/intro-llms >>> >>> There must be (by now) some (yet) more relevant resources for Journalists >>> (for example), not only to de-Jargonize a little but also to make it (yet >>> more) relevant to your domain. >>> >>> Here, straight from the Horses' Mouth (GPT 3.5) itself ( I leave it to you >>> to tell us how well these answers work for you?). It is probably worth >>> engaging with chatGPT yourself and asking more and more focused >>> questions... a good self-study in "prompt engineering"! >>> >>> http://chat.openai.com >>> <https://linkprotect.cudasvc.com/url?a=http%3a%2f%2fchat.openai.com&c=E,1,5AQR_YXXM2e5NRiZ2jwiMxV8_p-R8Bm41a-VJIvezvBxlpk5Dl9j1kvUr04X8zxM96dWITUcudcORIY8fwPup8paM-YALfjNmU7xfsuVh_3ViFZl3ak,&typo=1> >>> *** >>> >>> Introduction to Modern Language Models for Veteran Journalists >>> >>> In today's digital age, the field of journalism is undergoing a significant >>> transformation, largely driven by advancements in technology. One key >>> innovation that has had a profound impact on the industry is the >>> development of modern language models, often abbreviated as LLMs. While you >>> may not be a tech expert, understanding the basics of LLMs can be >>> invaluable for journalists. So, let's break it down in simple terms. >>> >>> **What Are Language Models?** >>> >>> At its core, a language model is like a super-smart digital writer. It's a >>> computer program trained to understand and generate human language. Think >>> of it as a tool that helps computers understand and use words, sentences, >>> and paragraphs just like humans do. >>> >>> **The Power of Large Language Models (LLMs)** >>> >>> Now, let's talk about Large Language Models (LLMs). These are the big >>> players in the world of language models. They're like language >>> supercomputers because they're trained on massive amounts of text from the >>> internet. This means they've learned a lot about how people write and talk. >>> >>> **How LLMs Help Journalists** >>> >>> 1. **Content Generation**: LLMs can generate written content, from news >>> articles to summaries and even creative pieces. Journalists can use them to >>> draft stories faster and get ideas flowing. >>> >>> 2. **Fact-Checking**: They're handy for fact-checking, helping you quickly >>> verify information by scanning a vast database of knowledge. >>> >>> 3. **Summarization**: LLMs can take long, complex documents and create >>> concise summaries, saving you time on research. >>> >>> 4. **Translation**: They can also translate text between languages, which >>> can be incredibly useful for international reporting. >>> >>> 5. **Personalization**: Some news websites use LLMs to tailor content to >>> individual readers, providing a more personalized news experience. >>> >>> 6. **Social Media Analysis**: LLMs can sift through social media data, >>> helping journalists understand public sentiment and trends. >>> >>> **Potential Concerns** >>> >>> While LLMs are incredibly powerful, there are some concerns to keep in >>> mind. They might unintentionally spread misinformation if not used >>> carefully, and they can't replace human judgment or critical thinking. >>> >>> **Conclusion** >>> >>> In a nutshell, modern language models (LLMs) are like digital writing >>> assistants that can help journalists write better, research faster, and >>> adapt to the changing landscape of digital journalism. While they're >>> powerful tools, it's important to use them responsibly, keeping in mind >>> their strengths and limitations, as well as the ethical considerations of >>> automated content creation in journalism. >>> Can you elaborate on some of the technical jargon? >>> >>> Certainly! Here are some common technical jargon related to Large Language >>> Models (LLMs) explained in journalist-friendly terms: >>> >>> 1. **Pretraining**: This is the initial phase where the LLM learns from a >>> massive amount of text data on the internet. Think of it as the model >>> absorbing a vast library of information to understand language better. >>> >>> 2. **Fine-tuning**: After pretraining, LLMs undergo fine-tuning to >>> specialize them for specific tasks. It's like giving the model extra >>> training in a particular subject, like finance or healthcare, to make it >>> more useful for specialized reporting. >>> >>> 3. **Token**: In LLMs, text is broken down into smaller units called >>> tokens. A token can be as short as one character or as long as one word. >>> Knowing the number of tokens is important because it affects how much text >>> the model can handle at once. >>> >>> 4. **Inference**: This term refers to the process of using a trained LLM to >>> generate text or make predictions. Journalists use inference to create >>> content, answer questions, or analyze data. >>> >>> 5. **Prompt**: A prompt is the input you provide to an LLM to get a >>> response. It's like giving the model a question or a starting point for it >>> to generate text. Journalists often craft prompts to get specific >>> information or insights. >>> >>> 6. **Bias**: LLMs can sometimes produce biased or unfair content because >>> they've learned from biased internet data. Journalists should be aware of >>> this and critically evaluate the model's outputs for any biases. >>> >>> 7. **Algorithm**: This is a fancy word for a set of rules or instructions >>> that an LLM follows to perform tasks. Algorithms guide how the model >>> generates text and makes decisions. >>> >>> 8. **NLP (Natural Language Processing)**: NLP is the field of study that >>> focuses on teaching computers to understand and generate human language. >>> LLMs are a product of NLP research. >>> >>> 9. **ML (Machine Learning)**: LLMs are a type of machine learning model. >>> Machine learning is a way to make computers learn from data and improve >>> their performance over time. >>> >>> 10. **Training Data**: This is the text data used to teach an LLM. It's >>> like the model's textbooks, and the quality and diversity of this data >>> influence how well the LLM performs. >>> >>> 11. **Bias Mitigation**: This is the process of reducing bias in LLMs. It's >>> important in journalism to ensure that the information provided by the >>> model is fair and unbiased. >>> >>> 12. **Ethical AI**: This refers to the practice of using LLMs responsibly >>> and ethically. Journalists should consider the ethical implications of >>> using these tools, especially when it comes to issues like privacy and >>> misinformation. >>> >>> 13. **API (Application Programming Interface)**: Some LLMs offer APIs that >>> allow journalists to interact with the model through code, enabling custom >>> applications and integrations. >>> >>> 14. **Fine-Tuned Models**: These are LLMs that have been trained for >>> specific tasks, like summarization, translation, or question-answering. >>> They are more specialized and can be particularly useful for journalism. >>> >>> Understanding these terms can help journalists effectively leverage LLMs in >>> their work while also being mindful of their limitations and ethical >>> considerations. >>> >>> >>> >>> - Steve >>> >>> On 10/7/23 5:13 PM, Tom Johnson wrote: >>> >>>> Thanks. Those are impressive numbers. >>>> Tom >>>> >>>> On Sat, Oct 7, 2023 at 4:11 PM Marcus Daniels <mar...@snoutfarm.com >>>> <mailto:mar...@snoutfarm.com>> wrote: >>>>> The “large” refers to the number of parameters used. A smaller large >>>>> language model – a deep neural net -- start about 3 billion parameters, >>>>> but larger ones like Claude 2 (the latest large language model of the >>>>> company that wrote the paper Steve mentioned) have more than 130 billion >>>>> parameters. Amazingly, it is possible using (rooms of) GPUs and other >>>>> accelerators to optimize in this a space of this size. The billions of >>>>> parameters come from the vocabulary size – the number of tokens that need >>>>> to be discriminated, the many layers of transformers that are needed to >>>>> capture the complexity of human and non-human languages (like DNA), and >>>>> the context window size – how many paragraphs or pages the model is >>>>> trained on at a time. A small language model might be suitable for >>>>> understanding the geometries of chemicals, say. >>>>> >>>>> >>>>> From: Friam <friam-boun...@redfish.com >>>>> <mailto:friam-boun...@redfish.com>> On Behalf Of Tom Johnson >>>>> Sent: Saturday, October 7, 2023 2:38 PM >>>>> To: The Friday Morning Applied Complexity Coffee Group <friam@redfish.com >>>>> <mailto:friam@redfish.com>> >>>>> Subject: Re: [FRIAM] Language Model Understanding >>>>> >>>>> >>>>> Thanks for passing this along, Steve. I wish, however, the authors of >>>>> this short piece would have included a definition of, in their usage, >>>>> "Large Language Models" and "Small Language Models." Perhaps I can find >>>>> those in the larger paper. >>>>> >>>>> Tom >>>>> >>>>> >>>>> On Sat, Oct 7, 2023 at 12:34 PM Steve Smith <sasm...@swcp.com >>>>> <mailto:sasm...@swcp.com>> wrote: >>>>> >>>>> This popular-press article came through my Google News feed recently >>>>> which I thought might be useful to the Journalists/English-Majors on the >>>>> list to help understand how LLMs work, etc. When I read it in detail >>>>> (forwarded from my TS (TinyScreenPhone) on my LS (Large Screen Laptop)) I >>>>> found it a bit more detailed and technical than I'd expected, but >>>>> nevertheless rewarding and possibly offering some traction to >>>>> Journalism/English majors as well as those with a larger investment in >>>>> the CS/Math implied. >>>>> >>>>> Decomposing Language Models into Understandable Components >>>>> >>>>> <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fwww.anthropic.com%2findex%2fdecomposing-language-models-into-understandable-components&c=E,1,m6E2NyQiQqZTmLRighr8jjGwfjDVwi00TYuM_4iskHnmAw78zWd55VlGTXwTdzWWkVGzInmkQvZFTQYuycBPX1Ii3Lxo1Bb5DZUFcSW0rUdv4Ma-WSM,&typo=1> >>>>> >>>>> and the (more) technical paper behind the article >>>>> >>>>> https://transformer-circuits.pub/2023/monosemantic-features/index.html >>>>> >>>>> <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2ftransformer-circuits.pub%2f2023%2fmonosemantic-features%2findex.html&c=E,1,PIXQOjp_g2tr-D-_MpOt_B6uVdfil6N3FViwvJ78HkMFtSu7LpKOXlQSpqz_GQuJyD31X5eqiYtu2d2_VgXFqA4pekVbchFYvvf9GwiQ1zIgLfEt&typo=1> >>>>> Despite having sent a few dogs into vaguely similar scuffles in my >>>>> careen(r): >>>>> >>>>> Faceted Ontologies for Pre Incident Indicator Analysis >>>>> <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fapps.dtic.mil%2fsti%2ftr%2fpdf%2fADA588086.pdf&c=E,1,nPyDgDuU0nMwlV3W24mUTteOTIfbrs9SipIKre1b1_XIxOIWJ1ok_IKz181AKiB6geR5Mb1z6-DrUslqOaBkYUxiG5O8Zhc2mG3ALmDJQjCULUXzR9v8IUhidw,,&typo=1> >>>>> SpindleViz >>>>> <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fwww.ehu.eus%2fccwintco%2fuploads%2fc%2fc6%2fHAIS2010_925.pdf&c=E,1,YwucoE_ffMQO_zO0A6WWAg8Y0J7wyHAGiJfcntyFBphchxgl4Tx6INQAtF7qA9XB07R52Jwnqfs4YO9s2MHP3Lnh-nUkpbULJfatlSIig_XB5ixy7yxF&typo=1> >>>>> ... >>>>> >>>>> ... I admit to finding this both intriguing and well over my head on >>>>> casual inspection... the (metaphorical?) keywords that drew me in most >>>>> strongly included Superposition and Thought Vectors, though they are (nod >>>>> to Glen) probably riddled (heaped, overflowing, bursting, bloated ... ) >>>>> with excess meaning. >>>>> >>>>> https://gabgoh.github.io/ThoughtVectors/ >>>>> <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fgabgoh.github.io%2fThoughtVectors%2f&c=E,1,WnnKNla0iVte02BV2Imnk1J5revdzixjz7Z3w4XFowr2Ti3fkKSk8kKj8BQdnL11w3MtGPIKl2FP5Y-6zSsJ5O9A0luCjDPRrFpM_kDhF0jacJbhFA,,&typo=1> >>>>> This leads me (surprise!) to an open ended discursive series of thoughts >>>>> probably better left for a separate posting (probably rendered in a >>>>> semasiographic language like Heptapod B >>>>> <https://en.wikipedia.org/wiki/Heptapod_languages#Orthography>). >>>>> >>>>> <must... stop... now... > >>>>> >>>>> - Steve >>>>> >>>>> -. --- - / ...- .- .-.. .. -.. / -- --- .-. ... . / -.-. --- -.. . >>>>> FRIAM Applied Complexity Group listserv >>>>> Fridays 9a-12p Friday St. Johns Cafe / Thursdays 9a-12p Zoom >>>>> https://bit.ly/virtualfriam >>>>> <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fbit.ly%2fvirtualfriam&c=E,1,7Se1kU5K0qRIvWJRD4XXT6OVdefOkjmd8iBwB4_ScBm8nmwT9MUlDa2OlXu7gsTvtdy7EwO5VnLshsDYL-lYhTu-S1P_ak0_11QPeoudf_Pg&typo=1> >>>>> to (un)subscribe http://redfish.com/mailman/listinfo/friam_redfish.com >>>>> <https://linkprotect.cudasvc.com/url?a=http%3a%2f%2fredfish.com%2fmailman%2flistinfo%2ffriam_redfish.com&c=E,1,XgC24CHCqH-MiGAYACD7z6PtLS8L3PA6Ntnnm8-GFS-8K9DlJz1wbcz4YhFucq-Ucbr9oeSbxnsUcDSZ0XfT5aalKerL34Pq0R57j_mDeX4RrFvxbg,,&typo=1> >>>>> FRIAM-COMIC http://friam-comic.blogspot.com/ >>>>> <https://linkprotect.cudasvc.com/url?a=http%3a%2f%2ffriam-comic.blogspot.com%2f&c=E,1,1GkLbYQODhOcaZjv5c91-iHu4986bkpXTFuaNhmX9fFK-EzJOVs6x1AKJrFBa7SMS9GHwxtjJZQ_CrzTmsDpTJ-KHGBzYStX4zSsywCdFT2GG8q4MO_gHwI,&typo=1> >>>>> archives: 5/2017 thru present >>>>> https://redfish.com/pipermail/friam_redfish.com/ >>>>> <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fredfish.com%2fpipermail%2ffriam_redfish.com%2f&c=E,1,pPB6Gx8uMJadpxScEftegu18poh3jvMC3dByqfb7-kH5q9a1uN-Hx18Hpf2mWKYR1aATb5b9IUgqwfoulMiH2oh8K03lnbespQNIBfFm-D_etq1aSira&typo=1> >>>>> 1/2003 thru 6/2021 http://friam.383.s1.nabble.com/ >>>>> >>>>> -. --- - 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