OpenAI's ChatGPT was launched with significant hype by the artificial intelligence (AI) research laboratory on 30 November 2022 and surpassed the 1 million user mark in just six days.

As research into artificial intelligence (AI) continues its march forward, computers are becoming more and more human-like all the time. Making headlines of late has been the new ChatGPT, developed by OpenAI - an artificial intelligence research and deployment company that says its mission is "to ensure that artificial general intelligence benefits all of humanity." OpenAI already took the world by storm with its DALL-E project, which, using AI, created new images based on human input, such as: "show me an astronaut riding a horse." But now, ChatGPT is moving into the text-based world of AI, allowing users to carry on human-like conversations but with a (mostly) know-it-all computer that is ever-learning. Of course, we're all weather geeks here at FOX Weather, so I had to test its meteorological chops.

Venture capitalists are rushing to invest in artificial intelligence start-ups as growing hype around "generative AI" fills the void left by failing cryptocurrency and blockchain ventures. The recent leap in developments of sophisticated computer programs that can write scripts and create art in seconds has driven a surge of investor interest, creating a rare bright spot in a start-up landscape dominated by tumbling valuations and job cuts. OpenAI, a San Francisco-based company in which Microsoft is the largest funder, released the newest form of its GPT-3.5 software to the public last week, which can converse with users through text: answer follow-up questions, admit mistakes and reject inappropriate requests. In five days, ChatGPT surpassed 1mn users and was praised by billionaire Elon Musk, a co-founder of OpenAI who left the board in 2018, who tweeted: "ChatGPT is scary good. We are not far from dangerously strong AI."

The introduction of transformers has made a huge impact on Artificial Intelligence, especially in the Natural Language Processing domain. Transformers paved way for the most awaited success of transfer learning in Natural Language Processing. As a result, many large language models came into existence, and now we are able to build beneficial applications on top of these cutting-edge models. A transformer is, in simpler language, an encoder-decoder architecture with a self-attention mechanism on both sides. The encoder block takes input and converts it into numerical form, and the decoder block takes that numerical form and converts it to text.

Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data \& models or focused on uni-modal language or vision learning. To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning. We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes. We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible. Source code and instructions to reproduce this study will be available at https://github.com/LAION-AI/scaling-laws-openclip

This study introduces and examines the potential of an AI system to generate health awareness messages. The topic of folic acid, a vitamin that is critical during pregnancy, served as a test case. Using prompt engineering, we generated messages that could be used to raise awareness and compared them to retweeted human-generated messages via computational and human evaluation methods. The system was easy to use and prolific, and computational analyses revealed that the AI-generated messages were on par with human-generated ones in terms of sentiment, reading ease, and semantic content. Also, the human evaluation study showed that AI-generated messages ranked higher in message quality and clarity. We discuss the theoretical, practical, and ethical implications of these results.

For robots to be generally useful, they must be able to find arbitrary objects described by people (i.e., be language-driven) even without expensive navigation training on in-domain data (i.e., perform zero-shot inference). We explore these capabilities in a unified setting: language-driven zero-shot object navigation (L-ZSON). Inspired by the recent success of open-vocabulary models for image classification, we investigate a straightforward framework, CLIP on Wheels (CoW), to adapt open-vocabulary models to this task without fine-tuning. To better evaluate L-ZSON, we introduce the Pasture benchmark, which considers finding uncommon objects, objects described by spatial and appearance attributes, and hidden objects described relative to visible objects. We conduct an in-depth empirical study by directly deploying 21 CoW baselines across Habitat, RoboTHOR, and Pasture. In total, we evaluate over 90k navigation episodes and find that (1) CoW baselines often struggle to leverage language descriptions, but are proficient at finding uncommon objects. (2) A simple CoW, with CLIP-based object localization and classical exploration -- and no additional training -- matches the navigation efficiency of a state-of-the-art ZSON method trained for 500M steps on Habitat MP3D data. This same CoW provides a 15.6 percentage point improvement in success over a state-of-the-art RoboTHOR ZSON model.

It is an artificial intelligence (AI)-based app that enables you to interact with your data via natural language. After uploading a dataset, users ask questions about or request analyses in English. The app generates and runs R code to answer that question with plots and numeric results. The requests are structured and sent to OpenAI's advanced AI system, which returns R code. The R code is cleaned up and executed in a Shiny environment, showing results and error messages. Multiple requests are logged to produce an R Markdown file, which can be knitted into an HTML report.

The graph represents a network of 2,186 Twitter users whose tweets in the requested range contained "datascientist", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 12 December 2022 at 20:58 UTC. The requested start date was Monday, 12 December 2022 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 4-day, 1-hour, 36-minute period from Wednesday, 07 December 2022 at 23:23 UTC to Monday, 12 December 2022 at 00:59 UTC.

We are partnering with Learn Prompting in order to help build and spread how to do prompting and become better prompt engineers, which we believe will become more and more popular, and people will even be hired for this role in the near future (do you think prompt engineering will be a real job? We plan on covering the A to Z of prompting, including great applied and comprehensive tutorials for all the large and *hot* models, as well as a fun competition coming up towards the end of the year. Learn more about this new course and stay tuned for the fun competition with prizes -- including money -- in our new #learn-prompting channel on Discord! We will use this new channel to share announcements and answer questions related to Learn Prompting. You can also reach out to @Trigaten and me at any time.