"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

Material

Energie

Wasser / Kühlmittel

Produktion

Betrieb

Abfallbewirtschaftung

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)

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

न :: NA :: naive

ल :: LA :: lazy

न :: NA :: narrative

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!)

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

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%.

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

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