Research being carried out by a research team around Professor Ohbyung Kwon at Kyung Hee University and Dr Christine (Eunyoung) Sung at Jake Jabs College of Business and Entrepreneurship, Montana State University, involves examining consumers' evaluations of fashion products designed using generative adversarial networks (GANs), an Artificial Intelligence (AI) technology. They analyse consumers' buying behaviour and offer practical advice for businesses that are considering using GANs to develop products for the retail fashion market. Artificial Intelligence (AI) technology is changing the retail landscape. Generative AI is being used to produce creative outputs; tasks that have traditionally been considered exclusive to humans. In particular, generative adversarial networks (GANs), an Artificial Intelligence technology, powerful machine learning models that can generate realistic images, videos, and voice outputs, are successfully performing creative tasks previously considered unique to humans.

A collaboration between Microsoft Research Asia and Duke University has produced a machine learning system capable of generating video solely from a text prompt, without the use of Generative Adversarial Networks (GANs). The project is titled GODIVA (Generating Open-DomaIn Videos from nAtural Descriptions), and builds on some of the approaches used by OpenAI's DALL-E image synthesis system, revealed earlier this year. Early results from GODIVA, with frames from videos created from two prompts. The top two examples were generated from the prompt'Play golf on grass', and the bottom third from the prompt'A baseball game is played'. GODIVA uses the Vector Quantised-Variational AutoEncoder (VQ-VAE) model first introduced by researchers from Google's DeepMind project in 2018, and also an essential component in DALL-E's transformational capabilities. Earlier work: VQ-VAE infers frames from very limited supplied source material.

In December 2019, Utah startup Latitude launched a pioneering online game called AI Dungeon that demonstrated a new form of human-machine collaboration. The company used text-generation technology from artificial intelligence company OpenAI to create a choose-your-own adventure game inspired by Dungeons & Dragons. When a player typed out the action or dialog they wanted their character to perform, algorithms would craft the next phase of their personalized, unpredictable adventure. Last summer, OpenAI gave Latitude early access to a more powerful, commercial version of its technology. In marketing materials, OpenAI touted AI Dungeon as an example of the commercial and creative potential of writing algorithms.\

House Republicans are moving closer to ousting Conference Chair Liz Cheney (R-Wyo.) What we're hearing: Most members recognize Cheney can't be succeeded by a white man, given their top two leaders -- House Minority Leader Kevin McCarthy (R-Calif.)

When GPT-3 was introduced by OpenAI in May 2020 the news spread like wildfire. Not only amongst the AI community but even within the mainstream media there were headlines like "A robot wrote this article" and "Have you read something written by GPT-3?". Before GPT-3, the largest language model was Turing-NLG with 17 billion parameters, released in February 2020. Later that year, OpenAI blew this out the park with 175 billion parameters. Suddenly, there was a language model that could produce content that was often indistinguishable from humans.

Chartbusters is a global challenge in which developers use AWS DeepComposer to create original compositions and compete in monthly challenges to showcase their machine learning (ML) and generative artificial intelligence (AI) skills. Regardless of your background in music or ML, one of the two new challenges will be right for you. You can choose between two different challenges this season. In the basic challenge, Melody-Go-Round, you can use any of the generative AI models available in the AWS DeepComposer Music studio to create new compositions. In the advanced challenge, Melody Harvest, you train a custom generative AI model with your own dataset using Amazon SageMaker.

From cutting costs to improving customer experience, forecasting is the crux of retail supply chain management (SCM) and the key to better supply chain performance. Several retailers are using AI/ML models to gather datasets and provide forecast guidance in applications such as Cognitive Demand Forecasting, Product End-of-Life, Forecasting, and Demand Integrated Product Flow. Early work in these areas looked at classical algorithms to improve on a gamut of challenges such as network flow and graphs. But the recent disruptions have made it critical for supply chains to have the resiliency to handle unexpected events. The biggest challenge lies in matching supply with demand. Reinforcement Learning (RL) with its ability to train systems to respond to unforeseen environments, is being increasingly adopted in SCM to improve forecast accuracy, solve supply chain optimization challenges, and train systems to respond to unforeseen circumstances. Companies like UPS and Amazon have developed RL algorithms to define winning AI strategies and keep up with rising consumer delivery expectations. While there are many ways to build RL algorithms for supply chain use cases, the OpenAI Gym toolkit is becoming the preferred choice because of the robust framework for event-driven simulations. This white paper explores the application of RL in supply chain forecasting and describes how to build suitable RL models and algorithms by using the OpenAI Gym toolkit.

If you want to see a sample of output, grab your FREE BOOK of samples today. You can check out some samples or Register for a Membership to begin using the LitRPG Adventures Workshop tools right away! The LitRPG Adventures Workshop generators are powered by the GPT-3 API from OpenAI, one of the largest language models in the world. Yes, I got access to a supercomputer and decided to teach it D&D. Payment is done through Paypal or Stripe and is completely safe.

In this series around GPT language model, we will focus on the paper "Extract Training Data from Large Language Models" The authors want to show that they can extract verbatim data from a language model such as GPT-2. More interestingly, they explain that they can extract verbatim that have appeared only a few times in the training data from the model itself. Naturally, that can be very dangerous if you own a company and you are using customers' data to train a language model. In their own words, "the paper demonstrates that (…), an adversary can perform a training data extraction attack to recover individual training examples by querying the language model." Who would want to risk leaking private information?

As more data, better algorithms, and higher computing power continue to shape the future of artificial intelligence (AI), reliable machine learning …