The book sounds intriguing, but it's not in my lending library.
Happily, there are lots of Tanner Lecture videos available online:
https://www.youtube.com/watch?v=yI-MCqeCILs Wendy Brown from 2019
https://www.youtube.com/watch?v=NJprCvmrpoY Kim Stanley Robinson from
March 2023
-- rec --
-- rec --
On Sun, Oct 8, 2023 at 11:56 AM David Eric Smith <desm...@santafe.edu>
wrote:
> 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. 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. https://en.wikipedia.org/wiki/Sugata_Mitra.
>
> On Sun, 8 Oct 2023 at 11:20, David Eric Smith <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?
>> [image: 9780674279384-lg.jpg]
>>
>> 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>
>> <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> 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>
>> 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> *On Behalf Of *Tom Johnson
>>> *Sent:* Saturday, October 7, 2023 2:38 PM
>>> *To:* The Friday Morning Applied Complexity Coffee Group <
>>> 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> 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
>>>
>>> -. --- - / ...- .- .-.. .. -.. / -- --- .-. ... . / -.-. --- -.. .
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>>>
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>>
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>> FRIAM Applied Complexity Group listserv
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>> -. --- - / ...- .- .-.. .. -.. / -- --- .-. ... . / -.-. --- -.. .
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>>
> <9780674279384-lg.jpg><9780674279384-lg.jpg>-. --- - / ...- .- .-.. ..
> -.. / -- --- .-. ... . / -.-. --- -.. .
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