This paper addresses the dire need for a platform that efficiently provides a framework for running reinforcement learning (RL) experiments. We propose the CaiRL Environment Toolkit as an efficient, compatible, and more sustainable alternative for training learning agents and propose methods to develop more efficient environment simulations. There is an increasing focus on developing sustainable artificial intelligence. However, little effort has been made to improve the efficiency of running environment simulations. The most popular development toolkit for reinforcement learning, OpenAI Gym, is built using Python, a powerful but slow programming language. We propose a toolkit written in C++ with the same flexibility level but works orders of magnitude faster to make up for Python's inefficiency. This would drastically cut climate emissions. CaiRL also presents the first reinforcement learning toolkit with a built-in JVM and Flash support for running legacy flash games for reinforcement learning research. We demonstrate the effectiveness of CaiRL in the classic control benchmark, comparing the execution speed to OpenAI Gym. Furthermore, we illustrate that CaiRL can act as a drop-in replacement for OpenAI Gym to leverage significantly faster training speeds because of the reduced environment computation time.

If you've been itching to try OpenAI's image synthesis tool but have been stymied by the lack of an invitation, now's your chance. Today, OpenAI announced that it removed the waitlist for its DALL-E AI image generator service. That means anyone can sign up and use it. DALL-E is a deep learning image synthesis model that has been trained on hundreds of millions of images pulled from the Internet. It uses a technique called latent diffusion to learn associations between words and images.

Artificial intelligence (AI) is a revolutionary technology disrupting virtually every sector of the economy, from manufacturing and retail to sports and entertainment. The average consumer may not know that AI is all around them. Companies can use AI to curate users' social media feeds, make new drug discoveries, power digital voice assistants, and allow smartphone owners to unlock their phones with facial recognition. One specific type of AI – generative AI – is an advancement in AI that holds great promise. Take a deeper look at generative AI, examples of its common applications, and how it can drive growth for businesses of all types and sizes.

Speech recognition remains a challenge in artificial intelligence, but OpenAI's latest move takes us one step closer to solving it. The software is an automatic speech recognition (ASR) system trained on 680.000 hours of multilingual and multitask supervised data from the web. Other organizations like Google, Meta and Amazon have all tried to design ASR-systems that lie at the core of many products. OpenAI now could outperform every one of those ASR-systems. What makes this new software different is the robustness against background noises, accents and technical terminology.

AI art tools are changing the idea of creativity and getting whackier every week. In a span of just a few years, AI art generators have gone from creating incomprehensible pictures to realistic content. Researchers at Meta AI just took a leap into generating art through prompts. The company on Thursday announced Make-A-Video, a new AI system that turns text prompts into brief, soundless video clips. We're pleased to introduce Make-A-Video, our latest in #GenerativeAI research! With just a few words, this state-of-the-art AI system generates high-quality videos from text prompts.

DALL-E leverages text prompts for image creation. I've been experimenting with prompt design for game art generation, looking for a specific aesthetic of "cute, but horrifying". As generating imagery with AI becomes more widely available, prompt crafting will become more important. Here's some examples that use lots of "nudges" in the prompt to get a final style. I've tried referencing several different artists known for disturbing styles to create the aesthetic that I'm looking for.

I've seen a lot of comments on Twitter that seem to completely misunderstand the process of getting a decent result with AI generators like Stable Diffusion and DALL-E 2. People seem to assume that it's just "push button, recieve bacon" without any real creativity in the equation. As someone who has done a lot of this experimentation in the past few months, I'd like to challenge that assertion and show you what the process for getting a decent result actually involves. First, you need to start off with a vision for what you want. I'm going to pull my fictional world Malto, specifically an area named Kanar. It is a very green area, lots of bamboo and the local architecture takes advantage of it.

OpenAI says it's already testing DALL-E with several potential customers with the aim of turning it into a paid service. While it's free to try for individuals, you only get 50 credits per month, and each set of four text-prompted images eats up a credit. You can purchase an additional 115 credits for $15, which works out to 13 cents per action. But be aware, each frame you add to an image in Outpainting also costs you a credit. It could get spendy if you get really into tinkering with the AI.

Pokémon are unique little monsters, but often inspired by real-life animals or even some objects. And thinking about this freedom of diversity, a new model of Artificial Intelligence manages to transform any known character into a unique and unique Pokémon version. A website reveals a new Artificial Intelligence software that is capable of transforming a phrase or a character into a Pokémon in a matter of seconds, with beautiful and well-made images, which have been catching the public's attention. The application in question was developed by programmer Justin Pinkney, and called text-to-Pokemon. It allows you to put the name of any celebrity or character on the site to have access to a version of him in the world of skillful little monsters.

Artificial intelligence is getting better and better at generating an image in response to a handful of words, with publicly available AI image generators such as DALL-E 2 and Stable Diffusion. Now, Meta researchers are taking AI a step further: they're using it to concoct videos from a text prompt. Meta CEO Mark Zuckerberg posted on Facebook on Thursday about the research, called Make-A-Video, with a 20-second clip that compiled several text prompts that Meta researchers used and the resulting (very short) videos. The prompts include "A teddy bear painting a self portrait," "A spaceship landing on Mars," "A baby sloth with a knitted hat trying to figure out a laptop," and "A robot surfing a wave in the ocean." The videos for each prompt are just a few seconds long, and they generally show what the prompt suggests (with the exception of the baby sloth, which doesn't look much like the actual creature), in a fairly low-resolution and somewhat jerky style.