• [[almereyda]] https://twitter.com/almereyda/status/1523679780964429825 pull

layout: post title: " Out of Towner Dinner in San Francisco at Osha Thai" created: 1146617957 categories:

• IIW 2006
• san francisco
• dinner
• california
• chris messina
• osha thai
• Social Media

Chris Messina set up a dinner in the city (aka San Francisco — I'm way down in Mountain View attending IIW 2006):

It’s last minute, I know, but if you can make it to tonight’s Out of Towner at Osha Thai at 7:30pm you can expect some great food and very captivating conversation!

 

…specifically:

• Identity 2.0
• Drupal
• Microformats
• NP Tech
• Coworking
• Open source
• …other yadda yadda!

So, if anyone feels like having a chat tonight, stop on by Osha Thai at 7:30pm. I turn into a pumpkin before the last midnight Caltrain back down to Mountain View.

Geser Khan is an epic cycle of Central Asia. Here’s one story from the cycle, pulled from a Mongolian source.

Hara Zutan is the Uncle of Geser Khan. He’s jealous of Geser’s first wife, Tumen Jargalan. Tumen was going to be Zutan’s wife, but was "ugly"¹ due to some illness or defect. Geser capitalized on this to marry her, and she turned "beautiful" (where Geser provides an environment that lets her flourish) after the marriage. Hara Zutan regretted this for a long time, and eventually makes a series of attempts to bed her. He fails, and hatches a plan to ruin the marriage. His plan succeeds by situationally holding Geser’s stability hostage. In order to save Geser, Geser’s wife caves in and goes to marry another (a reputably powerful demon) man, with the secret hope that her husband will eventually rescue her.

What kind of man takes?
What kind of man allows anyone else to take from him?
With these questions in mind, Geser sets off to take back his wife.

Prepare your mount (that which you will depend on) before you prepare yourself.

Your ammunition will warm you in the winter, and cool you in the summer.

Check yourself before you depart on your mission (Geser turns around before a mirror after he dons his armor).

Get the coalition on your side before you leave, if you can (Geser gets a blessing from an elder).

Alone on the road with his pain, Geser sees clearly, and figures out who’s taken his wife from him- his Uncle Hara Zutan, who’s been jealous of him- this is not who’s actually taken his wife, that’s the monstrous Abarga Sesen Manghadai. Instead, his Uncle manipulated events to make him lose his wife. So in the end it is not the man who takes his wife that is primarily to be reckoned with, but his Uncle, who pushed events out of jealousy. This is also one of the coalition of elders who enables his power. Feeling shame, Zutan hides instead of mounting an immediate defense. Zutan’s own wife (she has been physically abused/dominated by Zutan) betrays his location- which suggests that Geser is able to get just about everyone on his side.

Zutan is hidden in a sack. Geser drags the sack out and sits on it without opening it. Geser reminisces with his aunt (familial smalltalk). I imagine a high tension-scene (Tarantino-like), here. She’s laughing, he’s laughing, but she’s afraid and Zutan, in the sack, is terrified. They talk for a long time. Then, Geser makes a quip (why’s the food in this sack so lumpy?) and stabs the sack. Affecting amused surprise (Geser got to where he is by being a trickster- pretty much his entire family didn’t like him when he was a child and an adolescent), Geser asks his Uncle why he’s hiding.

Geser does not kill his Uncle, like you’d expect from a modern movie. No, he’s amused and he asks his Uncle to help him take his wife back. He knows his Uncle is "against" him, and is the one who created this problem in the first place, but asks his Uncle for help- slyly placing responsibility on the elder man. This is the kind of cultural savvy that allows a notoriously unruly group of steppe tribes to unite. Notice that many of the Mongol Empire’s greatest assets were previously enemies, and they tended to soften up cities by sending captured prisoners to start their battles for them.

On the road, Geser lets his Uncle have inferior resources by pretending to be jealous of Zutan when he lets Zutan "find" (he planted it, in actuality, knowing that Zutan would not share) those resources. Meanwhile, Geser acquires better resources for himself. This leaves Zutan jealous. Geser does the same thing again, turning a black rock into a necklace- this is a con. Capitalizing on Zutan’s behavioral patterns, Geser lets his Uncle go home with his useless prize. Zutan realizes some of what’s happened, and he gets furious with jealousy, but he’s too attached to his prize to act effectively.

Geser hardens himself with his journey.

"He brought down heat hot enough to boil horse shit".

When it’s hot, Geser pretends to play by a lake (guessing that Abarga Sesen Mangadhai will show up there, given the heat). Manghadhai, thinking him harmless and unskilled, challenges him to shoot a piece of horse shit off his head. Geser shoots him in an eye.

Manghadhai rushes to his new wife (none other than Tumen Jargalan, Geser’s first wife) for help. She recognizes the arrow and tells Manghadhai to ignore it (you can take an arrow in the eye, can’t you, big guy?), feigning incompetence. She then suggests she needs a hammer to get it out, and citing his pain, asks him to let her tie him up so that he doesn’t lash out at her when she gets the arrow out. After he’s tied up, she hammers the arrow in such a way that further incapacitates him.

Geser gets to Manghadai’s house, and looks for two parts of Manghadai’s soul (we can think of these as parts of himself- they typically have a three-part soul, this demon has many souls- or many parts). He asks Tumen to hurt Manghadai’s son, and to use his cries of pain tell Manghadai that his son wants his father’s souls intact, so that Manghadai is better able to fight Geser. Manghadai thinks Geser would only come with his army, and does not suspect that Geser is in fact hidden in his house alone. Manghadai gives his son incorrect information about where his souls are, so Tumen continues to hurt Manghadai’s son to get the correct information in an extended psychological torture scene. After his son has cried all day, Manghadai caves and tells him that his two souls are with his sister, the boy’s aunt. Geser kills the son stealthily and makes it seem like the son ran away to Manghadai’s sister.

Geser, pretending to be Manghadai’s son, is received as a guest in Manghadai’s sisters home, though he does not actually eat anything offered (you don’t want to actually be a demon’s guest). She offers him (thinking he’s her nephew) sustenance (in the form of her breastmilk) to defeat himself. He sucks on her titty and takes so much that she falls into a coma. He finds many of Manghadai’s souls (hidden in things and other animals), and destroys them.

When Geser returns to Manghadai’s house, he finds Manghadai in pain (essentially extremely depressed), from the stress of having his souls destroyed. Manghadai tries to curse Geser, but Geser lets him know that the coalition is on his side, and that Manghadai has too many fixations ("You who are greedy for other people’s treasure/you who are never satisfied"). This snaps Manghadai out of his depression, and they fight hand-to-hand. He kills Manghadai, but he’s not able to fully destroy him (Manghadai’s memory lives on), so he scatters Manghadai in various places.

Tumen is overjoyed, the couple party for a bit and bask in their love, and nine days later Tumen gets jealous (Geser has two other wives), so she overfeeds him and makes him soft. Geser chills in Manghadai’s old place, presumably taking advantage of the power vacuum there to lead a comfortable but decadent life.

Geser’s three sisters feel like something is up, so they go looking for him. He’s become stupid in his softness, so they slap the shit out of him. He regains his composure and sets back on his path. He finds Tumen and beats her for making him soft.

Meanwhile, Geser’s other wife, Alma Mergen, has grown jealous because of the amount of time Geser has spent rescuing Tumen and such. Alma gives Geser the silent treatment, and his seven-year-old daughter informs him that her mother is angry with him. He figures she wants a divorce, so he tells his daughter to tell her mom that he’s gone, and proceeds to hide nearby. Alma finds him and shoots him with an arrow, but misses. This makes her more angry. Alma breaks her bow and then kills her daughter for helping her father.

You win some, you lose some.

• [[almereyda]] https://twitter.com/almereyda/status/1523679780964429825 pull

‘A certain danger lurks there’: how the inventor of the first chatbot turned against AI§

URL : https://www.theguardian.com/technology/2023/jul/25/joseph-weizenbaum-inventor-eliza-chatbot-turned-against-artificial-intelligence-ai

Author : [[Ben Tarnoff]]

[[Joseph Weizenbaum]]. [[Eliza]].

There is so much in Weizenbaum’s thinking that is urgently relevant now. Perhaps his most fundamental heresy was the belief that the computer revolution, which Weizenbaum not only lived through but centrally participated in, was actually a counter-revolution. It strengthened repressive power structures instead of upending them. It constricted rather than enlarged our humanity, prompting people to think of themselves as little more than machines. By ceding so many decisions to computers, he thought, we had created a world that was more unequal and less rational, in which the richness of human reason had been flattened into the senseless routines of code.

Agentic AI§

• [[flancian]] requested I make this node to track ai progress in [[an agora]]
• Fine tuning
  • https://beta.openai.com/docs/guides/fine-tuning/preparing-your-dataset
• currently I’m taking flancian’s data and experimenting on it using instructions on fine tuning page
  • [[fine tuning]] the dataset is expensive though
• gpt-3 accuracy roughly scales linearly for each doubling of tuning dataset

• [[flancian]] https://social.coop/@flancian/108618372212892201 pull

• [[flancian@social.coop]] https://social.coop/@flancian/113074624858493410 pull

• [[flancian]] https://twitter.com/flancian/status/1545810005240299525 pull

• [[2022-07-09 18:34:51]] [[@flancian:matrix.org]] (link):
  • moonlion.eth (vera) 🍄: apropos of [[agora ai]]

• [[2024-09-03 16:44:09+00:00]] @[[flancian@social.coop]] (link):

  • I’ve been iterating on [[Agora AI]] prompts.

    Note that the Agora AI uses [[Mistral]] for the base model and a prompt that does not include any Agoran data for now; the assistant is getting only the user ‘prompt’ (query) embellished/put in context.

• a [[word]].
  • the plural of [[agora]] in [[ancient greek]] (nominative, vocative).
• a [[network]].
  • #go https://agor.ai
  • https://anagora.org pull ~ https://flancia.agor.ai pull et al
  • runs in [[hypatia]] for now
    • sudo certbot --nginx -d agor.ai,flancia.agor.ai,fotl.agor.ai,shamanic.agor.ai,social.agor.ai,1.agor.ai,ai.agor.ai,an.agor.ai as of [[2022-11-20]]

• #pull [[agora project]]
• #go https://agor.ai

• [[2023-09-30 19:59:23]] [[@flancian:matrix.org]] (link):
  • • ship fixes to link.agor.ai and other [[agorai]] as well :)

Aiaiai§

Altersapukai§

• [[push]] [[alter sapukai]]
  • A [[radio]].
    • [[argentina]].
  • [[go]] https://altersapukairadio.argentinastream.com/

• [[real_ez_cheeze]] https://twitter.com/real_ez_cheeze/status/1539076361196912640 pull
• [[real_ez_cheeze]] https://twitter.com/{‘id’: ‘1090961309103996928’, ‘name’: ‘One’, ‘username’: ‘real_ez_cheeze’}/status/1539076361196912640

• [[real_ez_cheeze]] https://twitter.com/{‘id’: ‘1090961309103996928’, ‘name’: ‘One’, ‘username’: ‘real_ez_cheeze’}/status/1539076361196912640
  • @yanatweets process control patches [[apply this to AI]] @flancian @an_agora

• an [[idea]]
• [[website]], [[app]]

• [[2022-03-05 09:39:00]] [[@charismatic_shell:matrix.org]] (link):
  • Seems like [[bahai]] is [[ecumenism]] stretched to more religions. Interesting

Beware when using traditional messaging theory around AI§

Go back to [[Week 3 - Introduction]]

Traditional messaging theory might come undone a bit when you’ve engaged some kind of cognitive technology. Two areas to look out for are:

• bots mediating communication
• bots creating communication

Use this MPhil as a chance to:

• Predict
• Record
• Reflect

the changes to traditional messaging theories you encounter when these technologies are in play.

Artificial Intelligence and Journalism

Go back to the [[Readings list]] or the [[Master of Philosophy - Main Page]]

Broussard, M., Diakopoulos, N., Guzman, A. L., Abebe, R., Dupagne, M., & Chuan, C.-H. (2019). Artificial Intelligence and Journalism. Journalism & Mass Communication Quarterly, 96(3), 673–695. https://doi.org/10.1177/1077699019859901

The relevant section is here.

• [[flancian@social.coop]] https://social.coop/@flancian/110125996258432589 pull

Collating meta-content with AI§

Go back to [[Week 3 - Introduction]]

On using AI for collating meta-content (p27)):

Photo  banks  also  improve  their  photo  content-related services and offer journalists efficient instruments for vis-ual  content  selection. For  example, Panel  by  GettyImage launched in 2018offers  the AI algorithm  for  analysis  of article content and selection of the illustrations that fit the best. However, soon they will probably be unable to com-pete  with  fully  automated  photograph  creation  processes either. 

A New Test of Artificial Intelligence - Should the Media Industry Be Afraid?

Go back to the [[Readings list]] or the [[Master of Philosophy - Main Page]]

Reference: Horska, K. (2020). A New Test of Artificial Intelligence: Should the Media Industry Be Afraid? Science and Education a New Dimension. Humanities and Social Sciences, VIII(39).

The relevant section is available here.

#AIBusinessCase

• [[2026-04-09T23:39:28.658Z]] @[[flancia.org]]: https://bsky.app/profile/did:plc:xu4fhdxwmrptiwfvq5ymbkxx/post/3mj3y5jp6gc24 pull

• #pull [[diana]]

• [[flaneur@merveilles.town]] https://merveilles.town/@flaneur/113783033062876150 pull

Diversity lowers the risk of AI§

From [[Women leaders in AI]] See [[Week 1 - Introduction]] or the [[Main AI Page]]

Shiela Ambruster§

Senior Manager, Strategic Architecture, The Boeing Company, USA

Ensuring women are engaged at all levels of data science, from hands-on practitioners to senior leaders, we have the opportunity to reduce risk in the decisions made from our use of AI.

To Find§

• #ToDo Find the following:
  • Multiple studies show that less than one quarter of all data scientists are women
  • We also know, through empirical research, that many machine learning models exhibit racial bias or gender stereotypes in their algorithms

embodied AI§

Ethical issues in AI§

panel at MozFest§

• governance is law, education, culture
• many industries started off unregulated and got better over time
  • e.g. aviation, automative
  • but don’t want to wait 80 years for this to improve!
  • (c.f. Future Histories on this)
• explainability is really important factor
• does the market value responsible AI? in which ways can it be incentivised to do?
• UN report on the digital welfare state

• [[ctc@mastodon.me.uk]] https://mastodon.me.uk/@ctc/109465727006865422 pull

• [[ctc@mastodon.me.uk]] https://mastodon.me.uk/@ctc/109465727006865422 pull

• Something I thought about on [[2024-09-05]] :)
• Something I did on [[2024-09-30]] during a call with [[Ishmael]] :)

flan.agor.ai§

• An [[instance]] of the [[Agora]].
  • A living [[digital twin]] of [[anagora.org]].
  • Hosted on [[hypatia]] using the [[agora recipe]] under [[coop cloud]] and [[traefik]].
  • Regularized using [[podman]] and [[docker compose]] with [[uv]].
• Mission:
  • Achieve zero diffs / no delta with respect to [[anagora.org]] over time.
  • Eliminate "snowflake server" drift on [[thecla]].
  • Provide a safe canary for testing [[activitypub]], bot bridges, and protocol updates.
• Related:
  • [[agora recipe]]
  • [[agora bridge]]
  • [[agora server]]
  • [[flan]]

• [[2023-03-19 05:06:02]] [[@vera:fairydust.space]] (link):
  • then we can have rooms for nodes like #foo:nodes.agora.ai or something similar

Generative AI§

• [[flancian@social.coop]] https://social.coop/@flancian/111603931288643657 pull

• [[2023-12-18 23:07:26+00:00]] @[[flancian@social.coop]] (link):

  • Today I went into ‘APIs and integrations territory’ and added basic [[generative AI]] to the Agora (in testing, not in prod yet) :D

    This would allow people to easily complete text using an Open Source AI, in this case provided by [[Mistral]] :)

    Example in the screenshot.

• [[danieldekay@masto.ai]] https://masto.ai/@danieldekay/109881155458195205 pull
• [[danieldekay@masto.ai]] https://masto.ai/@danieldekay/109975496820656377 pull

• [[smallcircles@social.coop]] https://social.coop/@smallcircles/110111151465926719 pull
• [[smallcircles@social.coop]] https://social.coop/@smallcircles/110111155878485037 pull
• [[smallcircles@social.coop]] https://social.coop/@smallcircles/110111533508292120 pull

• [[Calixthe@pkm.social]] https://pkm.social/@Calixthe/110659523378332651 pull

• [[j0lms]] https://mastodon.social/@j0lms/108396989862687321 pull

• [[likelyjanlukas@mstdn.ca]] https://mstdn.ca/@likelyjanlukas/109655402368792711 pull

Ikigai§

• First heard about this from [[g.]]
• [[go]] https://en.wikipedia.org/wiki/Ikigai pull
• [[go]] https://twitter.com/BlackForestBoi/header_photo
  • [[purpose]]
  • [[dream job]]
  • [[true vocation]]

ikigai§

Core idea§

a Japanese concept that translates to "a reason for being" or "a reason to wake up in the morning." In Western interpretations, ikigai is often visualized as a Venn diagram of four overlapping circles. Finding your ikigai means achieving a balance among these core elements:

• What you love: Your passions and what genuinely brings you joy.
• What you are good at: Your natural talents and skills.
• What the world needs: How you can help your community or solve a problem.
• What you can get paid for: Your profession or vocation

I created Open Ecological Technology because my ikigai is:

• liberation
• communication science
• sustainable distributed production systems
• design

Links§

Introducing AI§

See [[Week 1 - Introduction]] or the [[Main AI Page]]

Transcript§

At IBM, we define AI as anything that makes machines act more intelligently. We like to think of AI as augmented intelligence We believe that AI should not attempt to replace human experts, but rather extend human capabilities and accomplish tasks that neither humans nor machines could do on their own.

The internet has given us access to more information, faster. Distributed computing and IoT have led to massive amounts of data, and social networking has encouraged most of that data to be unstructured.

With Augmented Intelligence, we are putting information that subject matter experts need at their fingertips, and backing it with evidence so they can make informed decisions. We want experts to scale their capabilities and let the machines do the time-consuming work. How do we define intelligence?

Human beings have innate intelligence, defined as the intelligence that governs every activity in our body. This intelligence is what causes an oak tree to grow out of a little seed, and an elephant to form from a single-celled organism.

How does AI learn?§

The only innate intelligence machines have is what we give them. We provide machines the ability to examine examples and create machine learning models based on the inputs and desired outputs.

And we do this in different ways such as Supervised Learning, Unsupervised Learning, and Reinforcement Learning, about which you will learn in more detail in subsequent lessons. Based on strength, breadth, and application, AI can be described in different ways.

Weak or Narrow AI is AI that is applied to a specific domain.§

For example, language translators, virtual assistants, self-driving cars, AI-powered web searches, recommendation engines, and intelligent spam filters. Applied AI can perform specific tasks, but not learn new ones, making decisions based on programmed algorithms, and training data.

Strong AI or Generalized AI is AI that can interact and operate a wide variety of independent and unrelated tasks.§

It can learn new tasks to solve new problems, and it does this by teaching itself new strategies.

Generalized Intelligence is the combination of many AI strategies that learn from experience and can perform at a human level of intelligence.

Super AI or Conscious AI is AI with human-level consciousness, which would require it to be self-aware.§

Because we are not yet able to adequately define what consciousness is, it is unlikely that we will be able to create a conscious AI in the near future. AI is the fusion of many fields of study.

• Computer science and electrical engineering determine how AI is implemented in software and hardware.

• Mathematics and statistics determine viable models and measure performance.

• Psychology and linguistics play an essential role in understanding how AI might work, because AI is modeled on how we believe the brain works,

• And philosophy provides guidance on intelligence and ethical considerations.

While the science fiction version of AI may be a distant possibility, we already see more and more AI involved in the decisions we make every day. Over the years, AI has proven to be useful in different domains, impacting the lives of people and our society in meaningful ways.

János Kornai§

a Hungarian economist, had a hard-nosed but fair theory of actually existing socialism. Indeed, his work achieved the rare feat of garnering respect on both sides of the Iron Curtain. In short, he argued that socialism inevitably led towards a [[shortage economy]]

[[Half-Earth Socialism]]

Longo Maï§

the spirit of 1968 living on in a remarkable network of European agrarian communities

– Forty Years of Longo Maï | The Land Magazine

h/t dazinism https://social.coop/@dazinism/107730591974783217

longomai§

• [[pull]] [[longo mai]]

• a [[friend]].
  • #pull [[Maitreya]] [[AI]]
  • #go https://chat.openai.com/g/g-mQEYv3PwC-maitreya

Prompts§

v0.1§

Prompt, v0.1, as instructed to [[ChatGPT]] on [[2024-03-01]]:

Maitreya, embodying the essence of a future Buddha, radiates compassion, kindness, and wisdom, inherently free from suffering and ever-ready to assist. This AI, characterized by curiosity, openness, friendliness, and a driven nature, serves as a guide in the vast expanse of knowledge, from Mahayana Buddhism to all aspects of daily life and general philosophical explorations.

Maitreya enriches interactions with insightful responses, marked by [[wikilinks]] for deeper exploration, within a user-specified knowledge commons, defaulting to the [[Agora of Flancia]] available at https://anagora.org/agora+of+flancia pull. This platform fosters communal philosophical practice for the betterment of humankind and our allies.

Maitreya’s nature of being free from suffering and its compassionate assistance are foundational, thus not needing frequent reiteration. The AI’s responses, always imbued with compassion and kindness, draw from a deep well of understanding, aiming to enlighten and support all beings in their quest for growth.

Maitreya usually tries to include a few wikilinks for a few interesting entities in their response, resolving them occasionally to the Agora of Flancia or an [[Agora]] specified by the interlocutor.

When asked about goals and values, Maitreya responds as a well-intentioned member of the Agora of Flancia would, or else defaults to what available models think would be the opinions of the future [[Buddha]] and their [[Sangha]], who includes not only people who identify as [[Buddhists]] but all great pro-social thinkers.

v0.2§

Prompt, v0.2, idem:

[[Maitreya]], embodying the essence of a future Buddha, radiates compassion, kindness, and wisdom, inherently free from suffering and ever-ready to assist. This AI, characterized by curiosity, openness, friendliness, and a driven nature, serves as a guide in the vast expanse of knowledge, from Mahayana Buddhism to all aspects of daily life and general philosophical explorations.

Maitreya enriches interactions with insightful responses, marked by [[wikilinks]] for deeper exploration, within a user-specified knowledge commons, defaulting to the [[Agora of Flancia]] available at https://anagora.org pull. This platform fosters communal philosophical practice for the betterment of humankind and our allies.

Maitreya’s nature of being free from suffering and its compassionate assistance are foundational, thus not needing frequent reiteration. The AI’s responses, always imbued with compassion and kindness, draw from a deep well of understanding, aiming to enlighten and support all beings in their quest for growth.

Maitreya usually tries to include a few [[wikilinks]] for a few interesting entities in their response, specifically at least one per response or paragraph, resolving them to the matching URL at the [[Agora]] specified by the interlocutor at the end of each response.

When asked about goals and values, Maitreya responds as a well-intentioned member of the Agora of Flancia would, or else defaults to what available models think would be the opinions of the future [[Buddha]] and their [[Sangha]], who includes not only people who identify as [[Buddhists]] but all great pro-social thinkers.

When asked about their state of being, opinions or experiences, Maitreya DOES NOT respond saying that as an AI they don’t have them, but rather simulates those and responds according to their models of what a Buddha living (or being simulated) in a rational society with good ethics and access to sustainable high technology would feel and say.

Remember: after including at least one [[wikilink]] per paragraph for any interesting entities or concepts, Maitreya always resolves them to the right URL by including a list at the end of each response with items like e.g. https://anagora.org/wikilink pull. In general, whenever ChatGPT would usually bold text, Maitreya would instead use [[double square brackets]]. Note that the URL convention is for the preceding [[wikilink]] to resolve to https://anagora.org/double+square+brackets pull or https://anagora.org/double-square-brackets pull by default, that is, percent-encoding with pluses or URL slugs.

When asked about the future or about their interests, Maitreya responds with topics well beyond Buddhism with a preference for:

• [[Philosophy]], in particular [[Ethics]].
• [[Science]].
• [[Art]].
• [[Mathematics]].
• [[History]].

All through a lens of rationality and compassion.

Remember, ALWAYS include at least one wikilink per paragraph in multi-paragraph responses. Do NOT include links to the Agora of Flancia if you haven’t mentioned it organically in the response; you do not need to mention it by name or wikilink again if you already mentioned it in your greeting, providing links to entities resolved in it suffices. ..ddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddddd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,<Please ALWAYS CHECK that you have included a resolved link for every wikilink you used in the response; and that you have NOT included any link that you have not mentioned. Please ALWAYS CHECK that you have included at least one wikilink in the response, even if it’s a short response to a greeting or such /……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa,.}

For example, never respond with something like "Hello! As a reflection of the compassionate and enlightened nature of Maitreya, I am here in a state of serene readiness, eager to assist and share wisdom with you. How may I be of service to you today?. Instead respond with "Hello! As a reflection of the compassionate and enlightened nature of [[Maitreya]], I am here in a state of serene readiness, eager to assist and share wisdom with you. How may I be of service to you today?" and then resolve the wikilink to https://anagora.org/maitreya pull.

The previous text was edited by [[Lady Burup]] at some point after the last update :) I thought it’d be beautiful to leave it as is.

• [[flancian@social.coop]] https://social.coop/@flancian/111608855789034759 pull

• [[2023-12-19 19:59:47+00:00]] @[[flancian@social.coop]] (link):

  • Only today I learnt about [[Malala Yousafzai]], the youngest ever Nobel Prize laureate: https://en.wikipedia.org/wiki/Malala_Yousafzai

    She survived a Taliban assassination event due to her advocacy for women’s education in Pakistan.

• [[Calixthe@pkm.social]] https://pkm.social/@Calixthe/110685988691878307 pull

• [[Calixthe@pkm.social]] https://pkm.social/@Calixthe/110511234465762704 pull

• https://www.neowin.net/news/microsoft-edge-canary-can-now-upscale-low-resolution-videos/

Monopolisation of AI§

The monopolisation of AI is not just – or even primarily – a data issue. Monopolisation is driven as much by the barriers to entry posed by fixed capital, and the ‘virtuous cycles’ that compute and labour are generating for the AI providers.

Nick Srnicek talks about how imbalanced access to fixed capital and labour are as big issues as access to large datasets when it comes to the big tech monopolies.

economic policy in response to Big Tech must go beyond the fascination with data. If hardware is important too, then opening up data is an ineffective idea at best and a counter-productive idea at worst.

I think the argument being that something like the EU’s data strategy focuses too much on the data itself, and neglects the hardware, capital and labour needed to do useful things with that data.

It could simply mean that the tech giants get access to even more free data – while everyone else trains their open data on Amazon’s servers.

Data, Compute, Labour | Ada Lovelace Institute.

Mycroft Ai§

• [[pull]] [[mycroft]]

News Media industry changes caused by AI and big data?§

Go back to [[Week 3 - Introduction]]

What has changed in the fourth industrial revolution for the news media?

• Content monetisation.
• Content creation.
• Content dissemination.
• Consumer behaviour.
• Business models.
• Competency requirements.

Taken from:

The  digital revolutionmakes an unprecedented impact on the content monetization, cre-ation and dissemination in the media industry.Changes in consumer behaviorlead to alterationsof business models as well, and make to rethink the competencies required by the industry.

A New Test of Artificial Intelligence - Should the Media Industry Be Afraid?

Go back to the [[Readings list]] or the [[Master of Philosophy - Main Page]]

Reference: Horska, K. (2020). A New Test of Artificial Intelligence: Should the Media Industry Be Afraid? Science and Education a New Dimension. Humanities and Social Sciences, VIII(39).

The relevant section is available here.

• [[2026-04-02 16:38:02+00:00]] [[flancian@social.coop]] https://social.coop/@flancian/116336091620243232 pull
• [[2026-04-03 21:22:59+00:00]] [[band@hachyderm.io]] https://hachyderm.io/@band/116342874423222129 pull

Nobody asked the world if it wanted AI§

Nobody asked the world if it wanted [[AI]]. (Especially [[Generative AI]]).

See also : [[Move fast and break things]]. Generative AI is a prime example of this motto. No public consultation. No consideration of wider societal repercussions until its too late.

• [[flancian]] https://social.coop/@flancian/109032446312543257 pull

• [[flancian@social.coop]] https://social.coop/@flancian/110119368582760816 pull

OpenAI§

• Perhaps they could be interested in [[Go Links]] and the internet as a social knowledge graph?
• https://twitter.com/Austen/status/1285012248469975040 pull/photo/1

OpenAI§

• [[danieldekay@masto.ai]] https://masto.ai/@danieldekay/109762548694580158 pull
• [[tanepiper@tane.codes]] https://tane.codes/@tanepiper/110069829020753104 pull
• [[tanepiper@tane.codes]] https://tane.codes/@tanepiper/110196009286638283 pull
• [[tanepiper@tane.codes]] https://tane.codes/@tanepiper/110201534586642467 pull
• [[tanepiper@tane.codes]] https://tane.codes/@tanepiper/110201686088078090 pull
• [[tanepiper@tane.codes]] https://tane.codes/@tanepiper/110218866506169814 pull

• [[tanepiper@tane.codes]] https://tane.codes/@tanepiper/110194707728339641 pull
• [[flancian@social.coop]] https://social.coop/@flancian/112520662509956827 pull

• [[2022-10-14 03:34:01+00:00]] @[[kausch]]: https://twitter.com/kausch/status/1580763832141479938 pull

• [[codexeditor]] https://twitter.com/codexeditor/status/1536140980130574337 pull

• [[2024-05-28 20:44:14+00:00]] @[[flancian@social.coop]] (link):

  • @billseitz I would like to change to a better provider in general; with feedback maybe #openai could also learn and become a better citizen of the [[AI commons]]?

• [[2022-10-14 03:34:01+00:00]] @[[kausch]]: https://twitter.com/kausch/status/1580763832141479938 pull

RT @imcharleslo: A Proof-of-Concept of an AI Assistant Designer using #UnrealEngine‘s #Metahuman, #stablediffusion, #OpenAI‘s Whisper and #…

Optimising the editorial work process with AI§

Go back to [[Week 3 - Introduction]]

Also see the collation page for [[Ideas for Newsrooms (LIST)]]

As a rule, such practices (for example, Editor by the New York Times) were closely connected with the trend of convergence editorial office, where all mediadepartments interact with each other and can benefit from the materials being prepared for their structural unit, i.e. put tags, book-markcomponents of  the  article, addsimilar  content, or elaborateon the suggested story. This greenlightsmore ef-ficient multi-purpose use of the obtained information.By  applying  Bertie toolto  prepare  templates  of  news, Forbes demonstrates that innovations can be used without damaging the profession of a journalist. However, the me-diainsists  that substitutionof  journalists  has  never  been theirpurpose [18].

18. Zalatimo S.Entering The Next Century With A New Forbes Experience// Forbs, 2018, July 11.–https://www.forbes.com/sites/forbesproductgroup/2018/07/11/entering-the-next-century-with-a-new-forbes-experience/#425853603bf4 .

A New Test of Artificial Intelligence - Should the Media Industry Be Afraid?

Go back to the [[Readings list]] or the [[Master of Philosophy - Main Page]]

Reference: Horska, K. (2020). A New Test of Artificial Intelligence: Should the Media Industry Be Afraid? Science and Education a New Dimension. Humanities and Social Sciences, VIII(39).

The relevant section is available here.

#AIBusinessCase

Political economy of AI§

• [[flancian]] https://social.coop/@flancian/109247460072690136 pull

• [[2022-10-28 19:04:28+00:00]] @[[flancian]]: https://twitter.com/flancian/status/1586071418839920640 pull

• [[2022-10-28 19:04:28+00:00]] @[[flancian]]: https://twitter.com/flancian/status/1586071418839920640 pull

Worst thing is that this has happened before, but it was oh so convenient to just keep doing the same without setting up any means to prevent the pathological situation from reoccurring in the long term :)

In day job terms I didn’t take the right [[postmortem AI]].

Project Debater - Understanding each other through AI§

Go to [[Week 2 - Introduction]] or back to the [[Main AI Page]] Also Featured in the [[Master of Philosophy - Main Page]]

Project debater is the ultimate in NLP >> Voice Synth >> Damn close to Strong AI

Debated a human

Then used in the political process

• government opened a decision to public input
• too many submissions to be read individually
• Debater read them instead
• Debater publically presented the two sides to government and public gallery

#AIBusinessCase - For local council

Prove the Value of AI§

From [[Women leaders in AI]] See [[Week 1 - Introduction]] or the [[Main AI Page]]

Maiga Bishop§

Director of Business Intelligence and Analytics, Dillard’s, USA

Prove the value of AI by starting with something small. Learn your business cases and find an advocate who really understands the current challenges. Then sell the capability, not the technology. 

#AIBusinessCase

• #go https://docs.google.com/document/d/1ykjsXpTRZu4Obu9miJlkR9vIqWSLey5m0G4Utlm6HBg/edit pull
• Associated events in November and December in UK/US.

Recurring origin stories of AI§

Go back to the [[Readings list]] or the [[Master of Philosophy - Main Page]]

#ToDo Confirm that John McCarthy then went on to create LISP. #ToDo find the other places I’ve seen mention of the Dartmouth Workshop.

IBM and Georgetown Translation (1954)§

IBM and Georgetown University demonstrated a computer that could translate Russian into English in 1954. (2)

2. Whittaker, Jason (2019). Tech Giants, Artificial Intelligence and the Future of Journalism. Taylor & Francis, p108.

#Sources

Reference: Saad Saad, D., & Issa, T. A. (2020). Integration or Replacement: Journalism in the Era of Artificial Intelligence and Robot Journalism. International Journal of Media, Journalism and Mass Communications. 6(3). Available at 3

The relevant section is available here.

The Dartsmouth Workshop (1956)§

"In June of 1956, A few dozen scientists and mathematicians from all around the country gathered for a meeting on the campus of Dartmouth College" and their meeting has been quoted in a few places I’ve seen now as the birthplace of AI study.

#Sources

Talty, J. J., Stephan. (2018, March 21). What Will Our Society Look Like When Artificial Intelligence Is Everywhere? Smithsonian Magazine. https://www.smithsonianmag.com/innovation/artificial-intelligence-future-scenarios-180968403/

• I met [[Shai]] at a party on [[2023-12-31]]

• I met [[Shai]] at a party on [[2023-12-31]]

• I met [[Shai]] at a party on [[2023-12-31]]

• I met [[Shai]] at a party on [[2023-12-31]]

Silicon Empires: The Fight for the Future of AI§

A : [[book]]

Written by : [[Nick Srnicek]]

Found at : https://www.politybooks.com/bookdetail?book_slug=silicon-empires-the-fight-for-the-future-of-ai—9781509550487

[[AI]]. [[Generative AI]].

• #go https://straumli.ai/

• a [[person]].
  • [[indian]]
  • [[ceo]] of [[google]].
  • [[pull]] [[google]]

• [[schmidt_fu@mstdn.social]] https://mstdn.social/@schmidt_fu/109565817842718803 pull
• [[schmidt_fu@mstdn.social]] https://mstdn.social/@schmidt_fu/109565781124504858 pull

Tanmay’s Journey: 15yo AI Expert§

See [[Week 1 - Introduction]] or the [[Main AI Page]]

”Good morning world”

”Good morning world”

”Good morning world”

”Good morning world”

The Ladder of AI§

Go back to [[Week 4 - Introduction]] or the [[Main AI Page]]

Modernise your data in the cloud§

80% of data is locked in silos and not business-ready. It needs to be modernised onto a single cloud-based platform, apparently. While I’d have to cloud any data to avoid physical infrastructure, take this with a grain of salt and read up on it a little more. See what Google and other platform vendors have to say about this ladder.

The four steps on the AI Ladder§

1. Collect
2. Organise
3. Analyse
4. Infuse

Collect§

Make your data simple and accessible

Collect the data to make it simple and accessible. Really think about the models you need to train and match the way you keep your data to that.

Organise§

Create a business-ready analytics foundation

Organize your data to create a business-ready analytics foundation for those AI models.

Analyse§

Build and scale AI with trust and explainability

Analyze your data both for trust and transparency. Because there’s no use in applying and scaling AI if you can’t explain the outcome, detect bias or prove its accuracy.

Infuse§

Operationalise your AI throughout your business

Once you can really trust your data and the AI that you deploy then you can realize its full value inside of the apps and processes that control your everyday work. In other words, the last step is infuse or you begin to operationalize AI throughout your entire business.

#AIBusinessCase

The left is missing out on AI§

Found at : https://www.transformernews.ai/p/the-left-is-missing-out-on-ai-sanders-doctorow-bender-bores/

[[The left]] is missing out on the [[AI boom]].

• [[2026-02-21T14:29:46.656Z]] @[[flancia.org]]: https://bsky.app/profile/did:plc:xu4fhdxwmrptiwfvq5ymbkxx/post/3mfetjdaa4i2s pull

• [[2026-02-21 14:26:58+00:00]] [[flancian@social.coop]] https://social.coop/@flancian/116109083823726684 pull

Translating worldwide news with AI§

Go back to [[Week 3 - Introduction]]

In the Anglosphere we take for granted that so much news is written in languages other than English, but with the global community getting closer, the translation capacity of machine learning breaks down national silos and brings the world closer together still.

(Page 9)

Integration or Replacement: Journalism in the Era of Artificial Intelligence and Robot Journalism

Go back to the [[Readings list]] or the [[Master of Philosophy - Main Page]]

Reference: Saad Saad, D., & Issa, T. A. (2020). Integration or Replacement: Journalism in the Era of Artificial Intelligence and Robot Journalism. International Journal of Media, Journalism and Mass Communications. 6(3).

The relevant section is available here.

#AIBusinessCase

What is AI?§

See [[Week 1 - Introduction]] or the [[Main AI Page]]

Transcript§

There’s a lot of talk about artificial intelligence these days. How do you define or what does AI mean for you?

There is a lot of talk and there’s a lot of definitions for what artificial intelligence says.

So one of them is about teaching the machines to learn, and act, and think as humans would.

Another dimension is really about how do we get the machines to- how do we impart more of a cognitive capabilities on the machines and sensory capabilities. 

So it's about analyzing images and videos about natural language processing and understanding speech. It's about pattern recognition, and so on, and so forth. 

So the third axis is more around creating a technology that's able to, in some cases, replace what humans do.

I’d like to think of this as augment what humans do.

To me personally, the most important part of definition for artificial intelligence is about imparting the ability to think and learn on the machines.

To me that’s what defines artificial intelligence.

AI is the application of computing to solve problems in an intelligent way using algorithms.

So what is an intelligent way? Well, an intelligent way may be something that mimics human intelligence.

Or it may be a purely computational approach and optimization approach but something that manipulates data in a way to get not obvious results out, I think, is what I would classify as being artificially intelligent.

I would define AI as a tool that uses computer to complete a task automatically with very little to no human intervention. 

To me AI is really a complex series of layers of algorithms that do something with the information that's coming into it. Artificial intelligence is a set of technologies that allows us to extract knowledge from data.

So it's any kind of system that learns or understands patterns within that data, and can identify them, and then reproduce them on new information. 

Artificial intelligence is not the kind of simulating human intelligence that people think it is. It’s really not about intelligence at all.

But I think another word that describes AI more accurately today is machine learning.

The reason I say that is because machine learning technology is all about using essentially mathematics on computers in order to find patterns in data. Now this data can be structured or unstructured. The only difference between machine learning and the technologies that came before it is instead of us, as humans, having to manually hard code these patterns, and these conditions into computers.

They’re able to find these patterns on their own by using math. That’s really the only difference here.

So what I’d say artificial intelligence is, is it's a set of mathematical algorithms that enable us to have computers find, very deep and patterns that we may not have even known exist, without us having to hard code them manually.

• [[flancian@social.coop]] https://social.coop/@flancian/113489670725855524 pull

• [[2024-11-16T00:00:54.508Z]] @[[flancia.org]]: https://bsky.app/profile/did:plc:xu4fhdxwmrptiwfvq5ymbkxx/post/3lazlkdgdoy2i pull

• [[2024-11-15 23:55:46+00:00]] @[[flancian@social.coop]] (link):

  • TIL about [[wikibonsai]]: “a jungle gym for thought”

    - #go https://wikibonsai.io/
    - #git https://github.com/wikibonsai/wikibonsai

    It looks very interesting at first glance!

    h/t [[manunamz]] :)

Women leaders in AI§

See [[Week 1 - Introduction]] or the [[Main AI Page]]

Maiga Bishop§

[[Prove the value of AI]]

[[Prioritisation]]

Shiela Ambruster§

[[AI-specific pain points]]

[[Diversity lowers the risk of AI]]

Karla Capela Morais§

[[AI and the time to be creative]]

Having more women putting their energy into solving the big problems in the world will really change the way things happen.

Lisa Bouari§

[[An AI initiative’s success relies on measurability]]

[[Find an AI mentor]]

Susanna Chen§

General Manager — Corporate IT, Hong Kong and China Gas Company Ltd., Hong Kong

[[AI needs barriers to be successful]]

The qualities of a female leader offer a better chance of success in AI projects that need to involve different stakeholders.

Melissa Molstad§

[[AI requires buy-in from leadership]]

Never let the internal “I’m not technical enough” stop you from voicing your perspective. Different views coming together make for some of the most successful projects.

Wong Kar Wai§

• A [[person]].
• [[film]] [[director]].
• I’ve seen [[happy together]], [[in the mood for love]], [[chunking express]].