``A ten, kdo nás dokáže krmit pestrými a mihotavými obrázky, nás bude schopen dostat kamkoli bude chtít.''

``And the one able to feed us with colorful blinking images shall be able to get us there, where (s)he wants.''

``Und (der|die|das)jenige die uns mit den bunten bewegenden Bildern futtern kann, wird uns genau dort bringen, wo (er|sie|es) will.''
  
          Jan Sokol, Kleine Philosophie des Menschens (1994)

UDK.AI = Stable Diffusion + matrix protocol + Kastalia KMS + ChatGPT conversation archive

something's going on...

art is EXACTLY the domain where use of ARTificial intelligence is meaningful

curation, replicability, accountability in AI gen art should not be neglected

UdK Berlin (currently) still has a slight "decisive strategic advantage" (DSA)

public sector (currently) has structural difficulties to fund or provide long-term perspective for truly innovative projects

ethical (moratorium?) and ecological (KKN) aspects must not be neglected

Primer is a post-smartphone, book-like, do-it-Yourself educational instrument (Bildunginstrument).

It is generally believed that acquisition of reading skill(s) can be fostered (resp. inhibited) by  learner’s exposure to appropriate (resp. inappropriate) social, pedagogic and instrumental context. It is also believed that well-designed digital tools may also help children learn how to read.

reading is essentially a process of translation of textual sequences into their phonetic representations

spoken word thus play a fundamental role in reading acquisition

highly accurate automatic speech recognition (ASR ) systems exist for many languages but they are still strongly biased towards accurate processing of adult voices

HOWEVER: in reading acquisition or reading fostering scenarios one deals with speakers whoseutterances of sequences-to-be-read exhibit peculiar characteristics

 

Learner 1 (L1) - is a 5-year old – pre-school bilingual (90% German, 10% Slovak) daughter of the main author of this article

three HMPL-C2 exercise 1 (E1) sessions were executed on days 1, 3 and 5 of the study

each HMPL-C2-E1 session consisted of human-testing phase followed by a mutual human-machine learning phase

in each phase, sequences consisted of 5 repetitions of syllables started with occlusive labial consonant M or B and followed by the vowel A, E, I, O or U, thus yielding sequences from “MA MA MA MA MA” to “BU BU BU BU BU"

speech recordings collected during the learning phase subsequently provided input for the acoustic-model fine-tuning process

As of 2023, there exists no publicly available ASR model which could accurately and reliably process child speech.

In our IHIET 2023 article, we introduce two innovations with which the problem can be partially bypasssed in context of digitally supported reading acquisition app:

1. Transformation of a generic ASR problem into a sort of extended multi-class classification problem by means of extending a generic acoustic model with a domain-specific, minimalist language model (“scorer”).
2. Human-machine peer learning (HMPL) whereby the artificial utterence-processing tutor U incrementally and gradually adapts its parameters to a particular learner, a human individual I.

In concrete terms, we have shown that after three sessions focusing on acquisition of grapheme-vowel and CV-bigrapheme correspondences had lead, in case of one particular learner, to decrease of WER from 96% to 48%.

न :: NA :: naive

ल :: LA :: lazy

न :: NA :: narrative

After sorting CSV by date, one observes:

poppy chewers culture emerged in Frica, their site Farwallow being the main hub, after mastery of boat-building technology around 7200 BP expansion both to north (Rabbithole) as well to the east (Bourville) ensues

oldest rabbit skinners settlements were discovered in Youresia (Tertexo-Astumanca-Zava cluster), somewhat newer ones in Orient

rabbit skinner settlement dates cover short time spans (one, max two radio-carbon dated samples for HG), some poppy-chewer settlements cover much longer time spans (oh that splendid longevity of Farwallow, Rabbithole, Bearcall!)

Uploaded the CSV files and started the conversation with the prompt:

"The CSV file contains five columns which will allow us to correctly solve the riddle in computational archaelogy. The first column is name of the archeological site, is kind of arbitrary but will allow us to provide the narrative which will be the solution to the riddle. Second column encodes the longitude data, third column contains the lattitude information. It is the fourth column which is crucial: it contains radiocarbon dates (BP=before present) sampled by the archeologist at the site. Note that one site can refer to multiple samples associated to different times (different samples are separated by the pipe symbol separator "|" in the fourth csv column). Finally, the fifth column contains information whether the site was the farmer (F) or hunter-gatherer (HG) site. Execute reasoning based on the data provide and try to answer the question: were farmers and hunter-gatherers in conflict, or did the F culture assimilate the HG peacefully ?"

...and thanks for all the fish

daniel@udk-berlin.de

https://baumhaus.digital/911

Slava Ukrajine

P.S. hopefully see You at next Archaeoriddle workshop

popis toho ako sme sa dostali od Adama k Terminatorovi

prehĺbenie základných pojmov - umelá inteligencia a jej hlavné pod-druhy (diskriminatívna UI a generatívna UI), strojové učenie, neurónová sieť, (ro)bot, Turingov test, singularita

pár konkrétnych tipov a rád ako vám UI môže uľahčiť resp. skrášliť Život (GPT4, DALL-e 3, Stable Diffusion, Merlin, PlantNet atd.)

3 obrazy buducnosti: dystopický, utopický, realistický

Umelá inteligencia (UI, alebo v angličtine "Artificial Intelligence", AI) odkazuje na simuláciu ľudskej inteligencie v strojoch, ktoré sú programované navrhnuté tak, aby mysleli a učili sa podobne ako ľudia. UI zahŕňa viaceré aspekty, ako sú učenie sa, riešenie problémov, vnímanie, porozumenie jazyku a obrazom atď. Základom je to, že systémy s umelou inteligenciou sú schopné vykonávať úlohy, ktoré by, keby ich vykonával človek, vyžadovali použitie inteligencie.

GPT4 PROMPT: "I will have a 10 minute impulse talk at Weizenbaum institute about digital sustainability (DS). It will be fairly critical, starting with the idea that concept of DS is an oxymoron, given that digitalization as we currently practice it, is definitely not sustainable. Greenwashing will also be mentioned. Then I will move to more optimistic perspectives: solarpunk and  permacomputing in order to end with introduction of the concept of digital ascesis. Provide five alternatives for a fairly short title..."

Nachhaltigkeit von was ? Von Umwelt? Von biologischen Vielfalt ? Von etablierten Institutionen ? Von "status quo" ? Von nicht-nachhaltigen Strukturen und Praktiken (z.B. unbegrenzte Wachstum im begrenzten Raum ?)

... einen fehlerhaften Schluss, der dadurch zustandekommt, dass der Ausdruck für den Mittelbegiff in zwei verschiedenen Bedeutungen im Ober- und Untersatz des Schlusses gebraucht wird...

Mann kann, in Theorie, über Nachhaltigkeit von Digitalisierung sprechen wenn alle Ressourcen wie

Material

Energie

Wasser / Kühlmittel

und alle Aspekte, Nebenwirkungen und "collaterals" der

Produktion

Betrieb

Abfallbewirtschaftung

berücksichtigt sind.

"There are a handful of firms with 20k+ A/H100 GPUs, and individual researchers can access 100s or 1,000s of GPUs for pet projects. The chief among these are researchers at OpenAI, Google, Anthropic, Inflection, X, and Meta, who will have the highest ratios of compute resources to researchers. A few of the firms above as well as multiple Chinese firms will 100k+ by the end of next year...."

(Patel & Nishball, 2023, Google Gemini Eats The World)

An exercised, disciplined poor one practically always gets better "last laugh" than all those pesky rich ones driven by ὕβρις and vanity.

"Permacomputing is both a concept and a community of practice oriented around issues of resilience and regenerativity in computer and network technology inspired by permaculture.

In a time where computing epitomizes industrial waste, permacomputing encourages the maximizing of hardware lifespans, minimizing energy use and focussing on the use of already available computational resources. We do this we want to find out how we can practice good relations with the Earth by learning from ecological systems to leverage and re-center existing technologies and practices..."

permacomputing.net

"Solarpunk beschreibt eine Zukunft in der Klimakatastrophe und Umweltverschmutzung zumindest teilweise überwunden sind und Inklusion und Diversität das Zusammenleben prägen. Solarpunk wird von Gruppen weltweit in diversen Sprachen über eine Vielzahl von Medien wie Literatur, Kunst, Architektur, Mode, Musik und Computerspielen transportiert. Es wird oft eine ökologische Zukunft beschrieben, die tatsächlich durch vorhandene Technologien und politische Bewegungen erreichbar scheint und eine positive Zukunft für die Menschheit darstellt."

https://de.wikipedia.org/wiki/Solarpunk

Elemente naturwissenschaftlichen Wissens aus Biologie, Ökologie und Botanik sind kritisch mit Elementen der Informationswissenschaften wie Informatik, Kybernetik und KI gegengelesen.

Unterricht im Freien (outdoor on-line learning)

"eco-artificial education" mithilfe von Apps wie Merlin, Naturblick, SvampeAtlas

eigene Artefakte (digitale Fibel, Magic Wand 0)

eigenes Software (teacher.js, Kastalia Knowledge Management System)

Das "Bildung Biodigitale" Konzept wurde im Rahmen des Projektes teacher.solar / Stifterverbands Senior Fellowship für Innovationen in der Hochschulbildung entwickelt.

https://udk.ai/day0

Human-Machine Peer Learning (HMPL): What and Why?:::Make Your Own Digital Primer

Can machines teach ?

This How To aims to demonstrate how anyone, including teachers, parents, and students, can create their own Personal Primer - a book-like educational instrument that combines digital education objectives.

The session will cover existing Primer prototypes and off-the-shelf components, such as Raspberry Pi Zero and e-ink display, needed to build the Primer. Participants will learn how to design and deploy personalized educational instruments that differ from smartphones and tablets.

"obsessed" by human-robot relations from early childhood onwards (hello Johny 5!)

Čapek, Asimov, Turing, Cyberpunk ...

since beginning of my academic life I try to transpose main elements of ontogeny of human intelligence into computational domain (Master: Facial expression recognition; PhD: Computational models of language acquisition)

2011 Paper "Central Problem of Roboethics: from defintion towards solution" provides a potential solution to what AI folks label these days as "Alignment problem"

the solution is: To raise machines as we raise our (own) children.

one of the last pieces of puzzles is "peer learning"

Beschreibung, wie wir von Adam zu Terminator kamen

Vertiefung grundlegender Begriffe - Künstliche Intelligenz und ihre Hauptunterarten (diskriminative KI und generative KI), maschinelles Lernen, neuronale Netze, (Ro)bot, Turing-Test, Singularität

Einige konkrete Tipps und Ratschläge, wie KI Ihnen das Leben erleichtern oder verschönern kann (GPT4, DALL-E 3, Stable Diffusion, Merlin, PlantNet usw.)

3 Bilder der Zukunft: dystopisch, utopisch, realistisch

Künstliche Intelligenz (KI, oder auf Englisch "Artificial Intelligence", AI) bezieht sich auf die Simulation menschlicher Intelligenz in Maschinen, die so programmiert entworfen sind, dass sie denken und lernen können wie Menschen. KI umfasst mehrere Aspekte wie Lernen, Problemlösung, Wahrnehmung, Sprach- und Bildverständnis usw. Das Wesentliche ist, dass Systeme mit künstlicher Intelligenz Aufgaben ausführen können, die, wenn sie von einem Menschen ausgeführt würden, den Einsatz von Intelligenz erfordern würden.

Maschinelles Lernen (Machine Learning) gibt Computern die Fähigkeit, "zu lernen" ohne explizit für eine spezifische Aufgabe programmiert zu sein. Maschinelles Lernen ermöglicht es Computern, Muster und allgemeine Gesetzmäßigkeiten in Daten zu erkennen, die es ihnen später erlauben, unbekannte Informationen vorherzusagen oder zu klassifizieren. Zum Beispiel kann ein maschinelles Lernsystem lernen, Katzen auf Fotos zu erkennen, indem es "studiert", was Tausende von Katzenbildern gemeinsam haben und lernt, welche Merkmale oder Eigenschaften für Katzen typisch sind. Solch ein System kann dann eine Katze auf einem neuen, bisher ungesehenen Bild identifizieren.

Questio: Können Maschinen Gut und Böse unterscheiden?