Reinforcement learning (RL) is a popular machine learning paradigm for game playing, robotics control, and other sequential decision tasks. However, RL agents often have long learning times with high data requirements because they begin by acting randomly. In order to better learn in complex tasks, this article argues that an external teacher can often significantly help the RL agent learn. OpenAI Gym is a common framework for RL research, including a large number of standard environments and agents, making RL research significantly more accessible. This article introduces our new open-source RL framework, the Human Input Parsing Platform for Openai Gym (HIPPO Gym), and the design decisions that went into its creation. The goal of this platform is to facilitate human-RL research, again lowering the bar so that more researchers can quickly investigate different ways that human teachers could assist RL agents, including learning from demonstrations, learning from feedback, or curriculum learning.

You've probably never wondered what a knight made of spaghetti would look like, but here's the answer anyway--courtesy of a clever new artificial intelligence program from OpenAI, a company in San Francisco. The program, DALL-E, released earlier this month, can concoct images of all sorts of weird things that don't exist, like avocado armchairs, robot giraffes, or radishes wearing tutus. OpenAI generated several images, including the spaghetti knight, at WIRED's request. DALL-E is a version of GPT-3, an AI model trained on text scraped from the web that's capable of producing surprisingly coherent text. DALL-E was fed images and accompanying descriptions; in response, it can generate a decent mashup image.

When prompted to generate "a mural of a blue pumpkin on the side of a building," OpenAI's new deep ... [ ] learning model DALL-E produces this series of original images. OpenAI has done it again. Earlier this month, OpenAI--the research organization behind last summer's much-hyped language model GPT-3--released a new AI model named DALL-E. While it has generated less buzz than GPT-3 did, DALL-E has even more profound implications for the future of AI. In a nutshell, DALL-E takes text captions as input and produces original images as output. For instance, when fed phrases as diverse as "a pentagonal green clock," "a sphere made of fire" or "a mural of a blue pumpkin on the side of a building," DALL-E is able to generate shockingly accurate visual renderings.

A virtual robot arm has learned to solve a wide range of different puzzles--stacking blocks, setting the table, arranging chess pieces--without having to be retrained for each task. It did this by playing against a second robot arm that was trained to give it harder and harder challenges. Self play: Developed by researchers at OpenAI, the identical robot arms--Alice and Bob--learn by playing a game against each other in a simulation, without human input. The robots use reinforcement learning, a technique in which AIs are trained by trial and error what actions to take in different situations to achieve certain goals. The game involves moving objects around on a virtual tabletop.

An artist can draw a baby daikon radish wearing a tutu and walking a dog, even if they've never seen one before. But this kind of visual mashup has long been a trickier task for computers. Now, a new artificial-intelligence model can create such images with clarity -- and cuteness. This week nonprofit research company OpenAI released DALL-E, which can generate a slew of impressive-looking, often surrealistic images from written prompts such as "an armchair in the shape of an avocado" or "a painting of a capybara sitting in a field at sunrise." (And yes, the name DALL-E is a portmanteau referencing surrealist artist Salvador Dalí and animated sci-fi film "WALL-E.") A new AI model from OpenAI, DALL-E, can create pictures from the text prompt "an illustration of a baby daikon radish in a tutu walking a dog".

OpenAI, the company co-founded by Elon Musk and backed by Microsoft, has already mastered Dota 2 and the art of writing fake news. Now, it has reached another milestone with DALL-E (a portmanteau of "Wall-E" and "Dali"), an AI app that can create an image out of nearly any description. For example, if you ask for "a cat made of sushi" or a "high quality illustration of a giraffe turtle chimera," it will deliver those things, often with startlingly good quality (and sometimes not). DALL-E can create images based on a description of its attributes, like "a pentagonal green clock," or "a collection of glasses is sitting on a table." In the latter example, it places both drinking and eye glasses on a table with varying degrees of success.

A neural network uses text captions to create outlandish images – such as armchairs in the shape of avocados – demonstrating it understands how language shapes visual culture. OpenAI, an artificial intelligence company that recently partnered with Microsoft, developed the neural network, which it calls DALL-E. It is a version of the company's GPT-3 language model that can create expansive written works based on short text prompts, but DALL-E produces images instead. "The world isn't just text," says Ilya Sutskever, co-founder of OpenAI. "Humans don't just talk: we also see. A lot of important context comes from looking."

For all GPT-3's flair, its output can feel untethered from reality, as if it doesn't know what it's talking about. By grounding text in images, researchers at OpenAI and elsewhere are trying to give language models a better grasp of the everyday concepts that humans use to make sense of things. DALL·E and CLIP come at this problem from different directions. At first glance, CLIP (Contrastive Language-Image Pre-training) is yet another image recognition system. Except that it has learned to recognize images not from labeled examples in curated data sets, as most existing models do, but from images and their captions taken from the internet.

In this paper we present an approach for training deep generative models solely based on solving determined systems of linear equations. A network that uses this approach, called a StarNet, has the following desirable properties: 1) training requires no gradient as solution to the system of linear equations is not stochastic, 2) is highly scalable when solving the system of linear equations w.r.t the latent codes, and similarly for the parameters of the model, and 3) it gives desirable least-square bounds for the estimation of latent codes and network parameters within each layer.

Ever wonder how close has AI gotten to impersonating human beings? The latest GPT-3 can code computer programs, compose tweets, summarize emails, write news, answer questions, translate languages, and write fiction and poetry too. It can take up almost any virtual English language task. In the latest recent milestone on YouTube, it created an app that functions similar to Instagram. Dubbed as one of the most important advancements in AI in recent years, GPT-3 or Generative Pre-Trained Transformer 3 has raised the AI goal posts many notches toward the stratosphere.