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
LLMS
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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.
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Emergent Capabilities in Large-Scale Language Models Explained
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As language models increase in scale they sometimes exhibit various useful emergent capabilities. In today’s post, we explore this phenomenon to better understand its dependence on model properties and its potential impact in applications. Read more at https://
goo.gle/3hvJMAb -
Larger AI Models Show Increased Bias Demonstration
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It looks like the bigger the model, the harder it gets for it not to demonstrate biases. Very cool stuff from @mathemakitten
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huggingface.co/blog/zero-shot
-eval-on-the-hub
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LangChain 0.0.11 Release: New Embeddings and Text Splitting Features
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LangChain version 0.0.11: – @CohereAI embedding support from @abdrahman_issam – @NLTK_org and @spacy_io support for text splitting from @deliprao "Optimized Prompts" from @sjwhitmore misc cleanup from @deliprao and @nlarusstone https://
github.com/hwchase17/lang
chain
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Cohere’s LLM Applications Talk at PyData NYC
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Attending @PyData NYC? Join @CohereAI's @hemanham on Nov 10th, 11:45am EDT, where he'll give a "gentle intro" to real-world applications of large language models. No prior knowledge needed! Read more about the talk: https://
nyc2022.pydata.org/cfp/talk/AEFDA
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