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Five Predictions for AI in 2023 - by Rak Garg - Rak's Facts

rakgarg.substack.com · 1,457 words · saved by 1 readers

Special thanks to my colleagues Sam Crowder and Dawit Heck for their work on this post, which originally appeared on BCV’s website. We’re living in a special time; we have the compute, funding, and expertise to augment wide swaths of knowledge work using foundation models (also referred to as large language models or LLMs in NLP use cases). But despite their near-magical characteristics, foundation models are held back from practical usage due to a variety of technical constraints. Thanks for reading Rak's Facts! Subscribe for free to receive new posts and support my work. Before we get into the weeds, let’s cover what makes foundation models, well, foundational. Emergent behavior. As parameter count and training data volume scale, LLMs exhibit new behaviors implicitly. In-context learning, which enables LLMs to perform lightly specialized tasks based on user input rather than a full fine-tuning process, is an example. Previous generations of NLP models were tightly coupled to tasks. L

Special thanks to my colleagues Sam Crowder and Dawit Heck for their work on this post, which originally appeared on BCV’s website. We’re living in a special time; we have the compute, funding, and expertise to augment wide swaths of knowledge work using foundation models (also referred to as large language models or LLMs in NLP use cases). But despite their near-magical characteristics, foundation models are held back from practical usage due to a variety of technical constraints. Before we get into the weeds, let’s cover what makes foundation models, well, foundational. Emergent…

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