How do you learn and find out things? Usually by getting feedback.
Read more in my article 
Medium · Feeding Back and Forward | by Aki Kärkkäinen | Medium
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#FeedForward
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#Zoomposium with Dr. #Patrick #Krauß: Building instructions for #artificial #consciousness
Transferring the various stages of #Damasio's #theory of consciousness 1:1 into concrete #schematics for #deeplearning. To this end, strategies such as #feedforward connections, #recurrent #connections in the form of #reinforcement learning and #unsupervised #learning are used to simulate the #biological #processes of the #neuronal #networks.
More at: https://philosophies.de/index.php/2023/10/24/bauanleitung-kuenstliches-bewusstsein/
#Zoomposium with Dr. #Patrick #Krauß: Building instructions for #artificial #consciousness
Transferring the various stages of #Damasio's #theory of consciousness 1:1 into concrete #schematics for #deeplearning. To this end, strategies such as #feedforward connections, #recurrent #connections in the form of #reinforcement learning and #unsupervised #learning are used to simulate the #biological #processes of the #neuronal #networks.
More at: https://philosophies.de/index.php/2023/10/24/bauanleitung-kuenstliches-bewusstsein/
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#FWakeLyricLinks (previous post refs)
#FWakeSilent #TrumpSilent ++ #SoundOfSilence
forward refs: #FWakeFeedForward #FeedForward
#HelloDarknessOpera "Hello Darkness Opera" ++ #OperasByRoundSparrow
#FWakeCancer #FWakeInMyBrain "FWake In my Brain"
#FWakeRestlessDreams #FWakeNarrowStreets /\
Replied in thread
Replied in thread
Escribí este brevísimo resumen del artículo de Hattie & Timperley (2007) para mi módulo de Evaluación en la Maestría en Didáctica de la Educación Superior (UCLAEH):
El poder de la retroalimentación y devolución formativa
Demoliendo hoteles · El poder de la retroalimentación y devolución formativaEn este posteo(1) presento un resumen sucinto del trabajo de revisión de Hattie(2) y Timperley (2007), titulado “El poder de la retroalimentación”. En dicho artículo, la retroalimentaci…
In a Striking Discovery, AI Shows Human-Like Memory Formation
https://scitechdaily.com/in-a-striking-discovery-ai-shows-human-like-memory-formation/ #AI #human #brain #memory #hippocampus #modeling #FeedForward #Transformer
SciTechDaily · In a Striking Discovery, AI Shows Human-Like Memory FormationResearchers have discovered that AI memory consolidation processes resemble those in the human brain, specifically in the hippocampus, offering potential for advancements in AI and a deeper understanding of human memory mechanisms. An interdisciplinary team consisting of researchers from the Cent
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Addendum 10
1 Wide Feedforward All You Need
https://arxiv.org/abs/2309.01826
* 2 non-embedding components in transformer architecture: attention; feed forward network
* attention captures interdependencies betw. words regardless of position
* FFN non-linearly transforms ea. input token independently
* FFN (sig. fract. parameters) highly redundant
* modest drop in accuracy removing FFN on decoder layers & sharing single FFN across encoder
A jargon-free explanation of how AI large language models work
Word vectors - Humans represent words with letters. Language models use a long list of numbers
Each layer of an LLM is a transformer - Each layer takes a sequence of inputs—each word—and adds information
Feed-forward layers predict the next word
#LLM #neuralnetwork #artificialintelligence #ai #generativeAI #WordVectors #transformer #FeedForward #data #bigdata #tech #innovation
Ars Technica · A jargon-free explanation of how AI large language models work
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A new type of #neuralnetworks and #AI 2/3
... Recent successes at one shot tasks where states changes become learning also point in this direction.
Free neural networks from being #feedforward. Genetic algorithms come to mind for training, but we'd need a suitable base structure that supports gene exchanges & some kind of recursion for repeated structures.
Another idea is to use backpropagation to develop a new learning method, just like when a #programming language or manufacturing...
Absorbing Phase Transitions in Artificial Deep Neural Networks
https://arxiv.org/abs/2307.02284
To summarize, we believe that the this work places the order-to-chaos transition in the initialized artificial deep neural networks in the broader context of absorbing phase transitions, & serves as the first step toward the systematic comparison between natural/biological & artificial neural networks.
...
arXiv.orgAbsorbing Phase Transitions in Artificial Deep Neural NetworksTheoretical understanding of the behavior of infinitely-wide neural networks
has been rapidly developed for various architectures due to the celebrated
mean-field theory. However, there is a lack of a clear, intuitive framework for
extending our understanding to finite networks that are of more practical and
realistic importance. In the present contribution, we demonstrate that the
behavior of properly initialized neural networks can be understood in terms of
universal critical phenomena in absorbing phase transitions. More specifically,
we study the order-to-chaos transition in the fully-connected feedforward
neural networks and the convolutional ones to show that (i) there is a
well-defined transition from the ordered state to the chaotics state even for
the finite networks, and (ii) difference in architecture is reflected in that
of the universality class of the transition. Remarkably, the finite-size
scaling can also be successfully applied, indicating that intuitive
phenomenological argument could lead us to semi-quantitative description of the
signal propagation dynamics.
Colleagues get surprised when I say I enjoy “marking” and I always think that is because their focus is wrong; I see it as “giving feedback and feedforward” rather than just marking or grading. This blog has many excellent tips on providing effective #feedback and #feedforward - despite the clunky name. I think seeing the process for what it really is could help lecturers get more out of it. #SoTL
https://www.facultyfocus.com/articles/educational-assessment/ten-tips-efficient-effective-grading/
Faculty Focus | Higher Ed Teaching & LearningTen Tips for More Efficient and Effective GradingEffective grading does not have to take inordinate amounts of time, nor do faculty need to sacrifice quality for timely grading.
Distinct early and late neural mechanisms regulate feature-specific sensory adaptation in the human visual system https://www.pnas.org/doi/10.1073/pnas.2216192120 fatigue and sharpening "at different points in the sensory processing cascade, likely reflecting distinct #feedforward and #feedback interactions"; #neuroscience
#FediLZ #Feedback #FeedForward #matheedu
Ich habe Mal wieder mein Selbsteinschätzungsraster überarbeitet. Danke @noelte030 für die Inspiration durch seine Bücher.
Was haltet ihr davon? Verbesserungsvorschläge?
