PC<p>Just had an interesting (but a bit unsettling) conversation with <a href="https://mas.to/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> about what it thought its future abilities would be. It’s a long response but worth the read.</p><p>My question: What new <a href="https://mas.to/tags/emergent" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>emergent</span></a> <a href="https://mas.to/tags/abilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>abilities</span></a> will occur when <a href="https://mas.to/tags/parameter" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>parameter</span></a> <a href="https://mas.to/tags/numbers" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>numbers</span></a> can be increased by orders of <a href="https://mas.to/tags/magnitude" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>magnitude</span></a>?</p><p>AI’s response: This is a fascinating and actively researched question in AI! As language models like GPT grow in parameter count by orders of magnitude (e.g., from billions to tens or hundreds of trillions),</p><p>1/8</p>
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Preciso trabalhar no doutorado, mas acabei de achar um lance massa que preciso estudar para mostrar 28/11 no Sesc na atividade de simulações biológicas #Boids #Flocking #Python #pygame #Emergent #Simulation #PyNBoids https://github.com/Nikorasu/PyNBoids
GitHubGitHub - Nikorasu/PyNBoids: This is a Boids Simulation, written in Python with Pygame.This is a Boids Simulation, written in Python with Pygame. - GitHub - Nikorasu/PyNBoids: This is a Boids Simulation, written in Python with Pygame.
Take advantage of guided notes for engaging emerging language learners! Get access to digital tools that make guided note creation easier. #teaching #bilingual #ESL #emergent #education #edtech #AI #notes #tcea #tceajmg https://tcea.us18.list-manage.com/track/click?u=d1eb67a479b4605f6015ca3b7&id=ab36c3b17f&e=470f1b78b9
This reportage on #OpenSource is a few years old, but still on the point. The greatest #Emergent process human society ever engaged.
By failing to fundamentally understand what #Software is in the XXI century, #European institutions are set to spectacularly blow up this experiment with the ill advised Cyber Resilience Act (#CRA).
Replied to Jim Donegan 
#Panpsychism is the #fallacy of equating mere physical #structures that abide inexorable, universal, and incorporeal natural #laws with #systems such as an observer that is also enforcing its particular, arbitrary, and local #rules that are the #emergent result caused by the interaction of its parts.
Any #measurement is an #experiment that entails #intention or selection of what is measured, and its comparison against some predefined #expectation depending on the current #knowledge state of the system.
There is no #information outside of a system. Information must be created by the observer system from external #data structures. Two observers will always create different information from the same set of data depending on the state they are in.
J&J’s COVID vaccine is dead in the US; FDA revokes authorization - Enlarge / Boxes of Johnson & Johnson's Janssen COVID-19 vaccine at ... - https://arstechnica.com/?p=1945919 #johnson&johnson #pfizer/biontech #adenovirus #covid-19 #emergent #science #moderna #novavax #vaccine #health #pfizer #mrna #cdc #fda #jj
Ars TechnicaJ&J’s COVID vaccine is dead in the US; FDA revokes authorizationThe withdrawal leaves the two mRNA vaccines and the Novavax protein subunit vaccine.
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@ravenonthill
Point-staking hyperbole aside (& speaking VERY hypothetically), AGI could already be here in a distributed, emergent form, analogous to corporations (or hosted by one or more or etc) & we would not know it. & I'm not sure we *could* know it.
To me, it would be really interesting to figure out how we would recognize #emergent cognition when we saw it. What would we even look for? Nobody getting press hyping #AI right now has the slightest clue.
#emergentAI
#RaymondTallis - Setting #Time Aright
What is the true #NatureOfTime? Is time #Real? #Fundamental or #Emergent?
https://www.youtube.com/watch?v=51NdbR52dN4&ab_channel=CloserToTruth
(1/2)
I know, you do. 
Alas, that is not quite true anymore. There is Emergent In-Context Learning, that is--wait for it--poorly understood:
"Scaling large language models (#LLMs) leads to an #emergent capacity to learn in-context from example demonstrations. Despite progress, theoretical understanding of this phenomenon remains limited. "*
And: you might've missed my latest thread on the subject, which I...
arXiv.orgA Theory of Emergent In-Context Learning as Implicit Structure InductionScaling large language models (LLMs) leads to an emergent capacity to learn
in-context from example demonstrations. Despite progress, theoretical
understanding of this phenomenon remains limited. We argue that in-context
learning relies on recombination of compositional operations found in natural
language data. We derive an information-theoretic bound showing how in-context
learning abilities arise from generic next-token prediction when the
pretraining distribution has sufficient amounts of compositional structure,
under linguistically motivated assumptions. A second bound provides a
theoretical justification for the empirical success of prompting LLMs to output
intermediate steps towards an answer. To validate theoretical predictions, we
introduce a controlled setup for inducing in-context learning; unlike previous
approaches, it accounts for the compositional nature of language. Trained
transformers can perform in-context learning for a range of tasks, in a manner
consistent with the theoretical results. Mirroring real-world LLMs in a
miniature setup, in-context learning emerges when scaling parameters and data,
and models perform better when prompted to output intermediate steps. Probing
shows that in-context learning is supported by a representation of the input's
compositional structure. Taken together, these results provide a step towards
theoretical understanding of emergent behavior in large language models.