Un repaso a ChatGPT, una mirada crítica a la hipérbole tecnológica de 2022 y una palabra clave para 2023: #Gobernanza. En @disruptores_inn, celebrando 5 años de #Descartes con 50 artículos https://
elespanol.com/invertia/disru
ptores-innovadores/opinion/20230115/avance-tecnologico-no-ciegue/733556651_13.html
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LLMS
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ChatGPT critical review and technology governance for 2023
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AI says ‘mountain’ frequently, even for ASCII smiley face art
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It says “mountain” a lot though. I gave it ASCII art of a smiley face and it said “mountain” too. I suspect it has some intuition about shapes being vaguely square vs. triangular etc. but mostly I think it’s good at guessing what typical subjects are.
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ChatGPT Invention Bias on Global Warming Truth
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ChatGPT will make anything up, but on global warming it knows Only One Truth.
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What Happens When AI Has Read Everything Online?
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What Happens When #AI Has Read Everything? https://
theatlantic.com/technology/arc
hive/2023/01/artificial-intelligence-ai-chatgpt-dall-e-2-learning/672754/
… @TheAtlantic #MachineLearning #BigData #Analytics @Shi4Tech @CatherineAdenle #100DaysofCode #womenwhocode #serverless #ML @anijov #IoT @FmFrancoise @BIScorecard #DataScience #DeepLearning #DigitalTransformation -
Computers as Syntactic Machines and LLMs’ Semantic Progress
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Computers are syntactic machines. Large language models are a small step toward semantics.
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GPT-3 understands ASCII art but not rasterized images
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It definitely can understand small, cartoon-style ASCII art (e.g. it knows ">" is a fish and "(o_o)" is a face) but it isn't good at rasterized images. E.g. GPT-3 could do this back in August:
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GPT-3 identifies ASCII art MNIST as ‘8’ 57%, ‘4’ absent in top-5
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There's a thread going around claiming ChatGPT / GPT‑3 can recognize subjects (e.g. "a bird", "a person") in images rasterized as ASCII art. I'm skeptical. E.g., GPT‑3 identifies this ASCII-art MNIST digit as "8" 57% of the time and "4" doesn't appear in its top-5 choices:
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Google Research Responds to AI Criticism with 2022 Achievements Overview
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Google PR counterreaction begins, from Jeff Dean, reminding everyone of the extensive contributions from Google Research across LLMs, ViTs, Multimodal, and Generative text/audio/video: https://
ai.googleblog.com/2023/01/google
-research-2022-beyond-language.html?m=1
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Memory-Augmented LLMs Achieve Computational Universality
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Google Brain & Alberta U Paper Confirms the Computational Universality of Memory-Augmented Large Language Models https://
syncedreview.com/2023/01/18/goo
gle-brain-alberta-u-paper-confirms-the-computational-universality-of-memory-augmented-large-language-models/
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Cross-attention in Transformer architecture article recommendation
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nice article on cross-attention as supplementary too https://
vaclavkosar.com/ml/cross-atten
tion-in-transformer-architecture
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