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@refikanadol
's upcoming exhibit at @MuseumModernArt demonstrates the power of the latest #AI technologies to transform art. Learn how Anadol is "bending data" into art with NVIDIA's StyleGAN software and GPUs. Come and join the exhibit opening on Nov. 19. https://
nvda.ws/3TMEIVp
GENERATIVE AI
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AI Art Exhibit Opens at MoMA with NVIDIA StyleGAN Technology
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Whisper Fine-tuning Sprint Guide Now Available for All
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Can't wait for the Whisper fine-tuning sprint to start!? Come check out the official whisper fine-tuning guide from @sanchitgandhi99
, all you need is a colab account to get the ball rolling! https://
huggingface.co/blog/fine-tune
-whisper
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AI Magical Dragon NFT Collection Reaches Near 10 ETH Floor
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One of One Edition "AI MAGICAL DRAGON NFT" Only TWO (2) art pieces away from a 10 Eth Floor !! 4,000 art pieces 0.1 % listed!! https://
opensea.io/collection/aim
agical
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MONTREAL.AI #AIDragon #NFT #MontrealAI #NFTartists #NFTCommuntiy -
Addressing AI-Generated Content Challenges in Education
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OpenAI can write college papers and pass a plagiarism check What will be some ways for educators to combat this?
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Unlocking Reasoning Capabilities in Smaller Language Models
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Unlocking reasoning in smaller language models is a great direction
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General-Purpose Neural Networks Through Simple Scalable Objectives
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when the core unlock was achieving a kind of general-purpose computer neural net via simple scalable objectives that have strong training signal (many bits of contraints per training example). Like language modeling, and not like reinforcement learning.
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Scaling Language Models: Neural Networks as General-Purpose Text Computers
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TLDR: LMs have been around forever. Not obvious finding: turns out that if you scale up the training set and use a powerful enough neural net (Transformer), the network becomes a kind of general-purpose computer over text.
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A Decade of AI Progress: Language Models and Neural Networks
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I wrote this thread because I spent the last ~decade, obsessing over directions that would make fastest progress in AI, and was very interested in language models (e.g. my semi-famous 2015 post "The Unreasonable Effectiveness of Recurrent Neural Networks" https://
karpathy.github.io/2015/05/21/rnn
-effectiveness/
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Transformer Training Objective Difficulty Forces Weight Space Optimization
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The second critical ingredient is that while a Transformer seems ~able to act as a general-purpose computer in principle, the training objective has to be hard enough to actually force the optimization to discover and converge onto it in the "weights space" of the network.
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Language Modeling as Universal Learning Objective Through Text Compression
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Turns out language modeling (i.e. ~next word prediction; equivalent to compression) of internet text is this excellent objective – v simple to define and collect data for at scale. It forces the neural net to learn a lot about the world, "multi-tasking" across many domains.