Global AI Weekly
Issue number: 7 | Monday, May 15, 2023
Highlights
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ChatGPT Prompt Engineering for Developers
What you’ll learn in this course In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications. Using the OpenAI API, you’ll...
deeplearning.ai
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Mastering AI Prompts: Comprehensive Guide for Better Output
In a world increasingly dominated by artificial intelligence, the ability to effectively use AI prompts has become essential. This… Continue reading on Medium »
medium.comVideo
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Using R with Azure Machine Learning
This week Seth welcomes Marck Vaisman to showcase using R with Azure Machine Learning. They'll talk about what is possible with R today on AzureML using the CLI v2 since the original R SDK was deprecated. Walk through an end-to-end example showing key tasks in the ML Lifecycle with R, including environment configuration, model development, and model deployment.
youtube.comArticles
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Finally, ChatGPT Has A Competition And It’s Google’s Bard
The best thing about Bard is its ability to access the Internet, which ChatGPT cannot. Continue reading on Level Up Coding »
levelup.gitconnected.com
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Training machines to learn more like humans do
Researchers identify a property that helps computer vision models learn to represent the visual world in a more stable, predictable way.
news.mit.edu
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Language models can explain neurons in language models
We use GPT-4 to automatically write explanations for the behavior of neurons in large language models and to score those explanations. We release a dataset of these (imperfect) explanations and scores for every neuron in GPT-2.
openai.comLocalAI
Self-hosted, community-driven, local OpenAI-compatible API". Designed to let you run local models such as those enabled by llama.cpp without rewriting your existing code that calls the OpenAI REST APIs. Reminds me of the various S3-compatible storage APIs that exist today.
simonwillison.netLanguage Identification: Building an End-to-End AI Solution using PyTorch
Language Identification is the process of identifying the primary language from multiple audio input samples. In natural language processing (NLP), language identification is an important problem and a challenging issue. There are many language-related tasks such as entering text on your phone, finding news articles you enjoy, or discovering answers to questions that you may have. All these tasks are powered by NLP models. To decide which model to invoke at a particular point in time, we must perform language identification.
pytorch.orgStructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects
StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects Robots operating in human environments must be able to rearrange objects into semantically-meaningful configurations, even if these objects are previously unseen. We focus on the problem of building physically-valid structures without step-by-step instructions.
research.nvidia.comStudy: AI models fail to reproduce human judgements about rule violations
Models trained using common data-collection techniques judge rule violations more harshly than humans would, researchers report.
news.mit.edu