what happens when we realize we were just stochastic parrots all along?
LLMS
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LangChain 0.0.12 Release with AI21Labs and Manifest Integrations
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LangChain version 0.0.12 Two super exciting integrations! @AI21Labs integration (from a friend of @YuvalinTheDeep
) Integration with @HazyResearch
's manifest library (with help from @laurel_orr1
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Build Train Evaluate GPT Models NVIDIA NeMo Megatron
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Learn how to construct, train, and evaluate a GPT model using NVIDIA NeMo with Megatron on Jupyter Notebooks in a new free, hands-on lab from NVIDIA LaunchPad. Get started: https://
nvda.ws/3tm9ABs -
Model Capabilities Emerge with Scale: Sentiment Classification Study
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This is a wonderful study on the emergence of model capabilities with scale. It is remarkable to see sentiment classification working with 1 data point, and fascinating to see the effectiveness of chain of thought prompting growing with scale.
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AGI Parameter Count vs Brain Synapses: Current Models Scale
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A common view is that human level AGI will require a parameter count in the order of magnitude of the brain’s 100 trillion synapses. The large language models and image generators are only about 1/1000 of that, but they already contain more information than a single human
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Multiple prompts and fine-tunes serve different AI purposes
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Depends! We have many prompts and fine-tunes in place, each with a different purpose, some working together.
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Training Transformers at Scale for Scientific Applications
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Transformers and other large language models have shown impressive capabilities as foundation models for domains such as NLP and CV. Join Andy Hock, our VP of Product, at #SC22, as he discusses new algorithms, software, and hardware for training transformers at scale for science
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GPT performance varies significantly by use case
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Like most things with GPT, it’s super case by case
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Prompt Examples: Impact on Model Performance and Variation
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Depends on the prompt! More examples is especially helpful when the examples relate to one another and contain explicit information to inform future generations. But too many examples can lead to decreased performance and variation in responses.
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Writing Effective AI Prompts: Language Skills and Formatting
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For example, my go-to is to provide instructions in plain English, then examples in JSON for easy parsing. I’ve seen others use English for everything. Or markdown. One thing is clear though — great language + reasoning skills are necessary to write good prompts.