That element of randomness is partially why GPT-3--or its less powerful predecessor, GPT-2--is taking time to gain widespread commercial traction as a tool to power chatbots or auto-generate ads. After nearly a year of experimentation, however, how such a technology might be tamed for marketing purposes is beginning to take shape. Working through OpenAI's closely guarded API program, startups and agency technologists have reined in GPT-3's more eccentric tendencies, which can range from nonsensical prose to inappropriate or explicit content, in order to put it to use for rote performance marketing tasks like A/B testing endless variations of a digital ad, generating product descriptions or assigning email subject lines. Meanwhile, other companies are capitalizing on GPT-3's stranger side for creative tools. While still nascent, projects like these offer a glimpse into a future where humans might work hand in hand with generative AI on creative copywriting and the give-and-take forces that might define such a relationship.

We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. More specifically, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure. Estimating the distribution supported on this low-dimensional structure such as a low-dimensional manifold is challenging due to its singularity with respect to the Lebesgue measure in the ambient space. In the considered model, a usual likelihood approach can fail to estimate the target distribution consistently due to the singularity. We prove that a novel and effective solution exists by perturbing the data with an instance noise which leads to consistent estimation of the underlying distribution with desirable convergence rates. We also characterize the class of distributions that can be efficiently estimated via deep generative models. This class is sufficiently general to contain various structured distributions such as product distributions, classically smooth distributions and distributions supported on a low-dimensional manifold. Our analysis provides some insights on how deep generative models can avoid the curse of dimensionality for nonparametric distribution estimation. We conduct thorough simulation study and real data analysis to empirically demonstrate that the proposed data perturbation technique improves the estimation performance significantly.

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.