An optimistic look at the creative potential of generative AI.

Extension for Visual Studio Code - Use browser or official API integration for OpenAI ChatGPT, GPT3.5, GPT3 and Codex. Create new files & projects with one click. Copilot to learn code, add tests via GPT models. Google LaMDA Bard integration is work-in-progress.

Installs OpenAI Gym on MacOS - . GitHub Gist: instantly share code, notes, and snippets.

OpenAI Gym is a toolkit for testing reinforcement learning algorithms. Gym is fun and powerful, but installation can be a challenge. This…

This is not so much an issue as opposed to a 'How To' if you'd like to install this version of Tensorflow in Conda. Prerequisites: You must be on macOS Big Sur If you have a...

I have been able to follow the workarounds to install Apple's special TensorFlow distribution on the M1 Mac Mini. I have also been able to successfully install Open AI's gym on the device. ...

240 votes and 30 comments so far on Reddit

CLIP is a bridge between computer vision and natural language processing. I'm here to break CLIP down for you in an accessible and fun read! In this post, I'll cover what CLIP is, how CLIP works, and why CLIP is cool.

Tutorials for advanced natural language processing methods designed for computational social science research.

Tutorial description: This workshop provides an introduction to information extraction for social science–techniques for identifying specific words, phrases...

Semantic search is a compelling technology allowing us to search using abstract concepts and meaning rather than relying on specific words. However, sometimes a simple keyword search can be just as valuable — especially if we know the exact wording of what we're searching for. Pinecone allows you to pair semantic search with a basic keyword filter. If you know that the document you're looking for contains a specific word or set of words, you simply tell Pinecone to restrict the search to only include documents with those keywords. We even support functionality for keyword search using sets of words with AND, OR, NOT logic. In this video, we will explore these features through a start-to-finish example of basic keyword search in Pinecone. 🌲 Pinecone Docs Page: https://www.pinecone.io/docs/examples/metadata-filtered-search/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 NER Powered Semantic Search 01:19 Dependencies and Hugging Face Datasets Prep 04:18 Creating NER Entities with Transformers 07:00 Creating Embeddings with Sentence Transformers 07:48 Using Pinecone Vector Database 11:33 Indexing the Full Medium Articles Dataset 15:09 Making Queries to Pinecone 17:01 Final Thoughts

A tutorial on simple implementation of CLIP model in PyTorch.

We’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized, similar to the “zero-shot” capabilities of GPT-2 and GPT-3.

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image - openai/CLIP: CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text s...

OpenAI's CLIP explained simply and intuitively with visuals and code. Language models (LMs) can not rely on language alone. That is the idea behind the "Experience Grounds Language" paper, that proposes a framework to measure LMs' current and future progress. A key idea is that, beyond a certain threshold LMs need other forms of data, such as visual input. The next step beyond well-known language models; BERT, GPT-3, and T5 is "World Scope 3". In World Scope 3, we move from large text-only datasets to large multi-modal datasets. That is, datasets containing information from multiple forms of media, like *both* images and text. The world, both digital and real, is multi-modal. We perceive the world as an orchestra of language, imagery, video, smell, touch, and more. This chaotic ensemble produces an inner state, our "model" of the outside world. AI must move in the same direction. Even specialist models that focus on language or vision must, at some point, have input from the other modalities. How can a model fully understand the concept of the word "person" without *seeing* a person? OpenAI's Contrastive Learning In Pretraining (CLIP) is a world scope three model. It can comprehend concepts in both text and image and even connect concepts between the two modalities. In this video we will learn about multi-modality, how CLIP works, and how to use CLIP for different use cases like encoding, classification, and object detection. 🌲 Pinecone article: https://pinecone.io/learn/clip/ 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP

archived 8 Mar 2023 15:29:24 UTC

Skeptics and fans alike wrestle with the practical and ethical questions such tools present.

ChatFred: Access ChatGPT, DALL·E 2, and other OpenAI models.

I’ve written a long-ish post attempting to answer that question over at P&G’s Signal360 publication, please head there (and sign up for their newsletter!) if you’d like to rea…

A great resource for all scientists.

Discover amazing ML apps made by the community

Hi I am Fatih Kadir Akın,The curator of the popular "Awesome ChatGPT Prompts" repository on GitHub, and prompts.chat.In this comprehensive guide, you'll discover expert strategies for crafting compelling ChatGPT prompts that drive engaging and informative conversations. From understanding the principles of effective prompting to mastering the art of constructing clear and concise prompts, this e-book will provide you with the tools you need to take your ChatGPT conversations to the next level.Some of the highlighted contents:Overview of ChatGPT and its capabilitiesHow to write clear and concise promptsTips for avoiding jargon and ambiguitySteps for crafting effective ChatGPT promptsCommon mistakes to avoid when crafting ChatGPT promptsCommon issues that may arise when using ChatGPTCase studies and best practicesThe book is going to be updated by time.View Awesome ChatGPT Prompts RepositoryHow to Make Money with ChatGPT: Strategies, Tips, and Tactics

The Biggest Rivalry: ChatGPT vs Google Bard! Here's a comparison of the technical differences between the two AI engines.

Large language models excel at a wide range of complex tasks. However, enabling general inference in the real world, e.g., for robotics problems, raises the challenge of grounding. We propose embodied language models to directly incorporate real-world continuous sensor modalities into language models and thereby establish the link between words and percepts. Input to our embodied language model are multi-modal sentences that interleave visual, continuous state estimation, and textual input encodings. We train these encodings end-to-end, in conjunction with a pre-trained large language model, for multiple embodied tasks including sequential robotic manipulation planning, visual question answering, and captioning. Our evaluations show that PaLM-E, a single large embodied multimodal model, can address a variety of embodied reasoning tasks, from a variety of observation modalities, on multiple embodiments, and further, exhibits positive transfer: the model benefits from diverse joint training across internet-scale language, vision, and visual-language domains. Our largest model, PaLM-E-562B with 562B parameters, in addition to being trained on robotics tasks, is a visual-language generalist with state-of-the-art performance on OK-VQA, and retains generalist language capabilities with increasing scale.

Project page for PaLM-E: An Embodied Multimodal Language Model.

ChatGPT-style AI model adds vision to guide a robot without special training.

Convolutional Neural Networks (CNNs) have been the undisputed champions of Computer Vision (CV) for almost a decade. Their widespread adoption kickstarted the world of deep learning; without them, the field of AI would look very different today. Rather than manual feature extraction, deep learning CNNs are capable of doing image classification, object detection, and much more automatically for a vast number of datasets and use cases. All they need is training data. Deep CNNs are the de-facto standard in computer vision. New models using vision transformers (ViT) and multi-modality may change this in the future, but for now, CNNs still dominate state-of-the-art benchmarks in vision. In this hands-on video, we will learn why this is, how to implement deep learning CNNs for computer vision tasks like image classification using Python and PyTorch, and everything you could need to know about well known CNNs like LeNet, AlexNet, VGGNet, and ResNet. 🌲 Pinecone article: https://pinecone.io/learn/cnn 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/nlp-transformers 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Intro 01:59 What Makes a Convolutional Neural Network 03:24 Image preprocessing for CNNs 09:15 Common components of a CNN 11:01 Components: pooling layers 12:31 Building the CNN with PyTorch 14:14 Notable CNNs 17:52 Implementation of CNNs 18:52 Image Preprocessing for CNNs 22:46 How to normalize images for CNN input 23:53 Image preprocessing pipeline with pytorch 24:59 Pytorch data loading pipeline for CNNs 25:32 Building the CNN with PyTorch 28:08 CNN training parameters 28:49 CNN training loop 30:27 Using PyTorch CNN for inference