ScaleFold: Reducing AlphaFold Initial Training Time to 10 Hours Zhu et al.: https://
arxiv.org/abs/2404.11068 #ArtificialIntelligence #DeepLearning #MachineLearning
MACHINE LEARNING
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ScaleFold Reduces AlphaFold Training Time to 10 Hours
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Understanding AI Layers: From Broad Concepts to Generative AI
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Peeling the layers of #AI: From broad AI concepts to the niche realm of #GenerativeAI, each layer builds upon the next. Stay updated with @ingliguori for insights into AI's complex structure and master its potential with 'The Digital Edge' https://
bit.ly/3u4pILl #DeepLearning -
Llama 3: Dense Model Efficiency vs Sparse MoE Scaling Strategy
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Thing that most impresses me about Llama 3: how did they pack so much knowledge and reasoning into a dense 8b and a 70b so well, when everyone else has been scaling sparse MoEs. This still doesn’t mean having a lot of GPUs is not important. Probably even more important
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Agent Quality Breakthroughs and Automated AI Testing Loops
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Folks, agent-based models have been a staple of social science for a long time. The idea of using simulated agents is not that big of a deal. The breakthrough is a potential big jump in agent quality and the addition of automated testing loops, including AI hypotheses testing.
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Can AI Achieve Superhuman Intelligence Beyond Training Data?
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I one discussion you see often is whether superhuman intelligence is possible, given that AI is human trained I don’t buy all of these points, but I think this is a well-laid out list of arguments AI may be able to be more “intelligent” than the source material it was trained on
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Kaplan Scaling Laws and Chinchilla Ratio in GPT-3 Sizing
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i think this implication is drawn from the context re: kaplan scaling laws paper. and yes could extrapolate but i think the chinchilla ratio contributed to gpt3 sizing?
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Vector Embeddings Enable Efficient Semantic Similarity Search
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It allows for efficient nearest neighbor search and retrieval based on semantic similarity. The key innovation is the use of vector embeddings, where data like text is converted into a dense vector representation that captures semantic meaning.
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Vector Space Mapping for Semantic Search and Fuzzy Matching
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Similar concepts are mapped to nearby points in the high-dimensional vector space, enabling fuzzy matching and retrieval based on conceptual relevance rather than just keyword matching.
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Vector Databases: Storing High-Dimensional Data Embeddings
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What is a vector databases? A vector database is a database system designed to store and search high-dimensional vector representations of data, such as text embeddings produced by large language models.
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CrowdLab: Python Library for Consensus Labels and Quality Scoring
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This Python library works like magic! With a single line of code & the two matrices you see in the image below, I can find: – a consensus label
– quality score for individual & consensus labels
– overall quality score for each annotator Introducing CrowdLab! A weighted