Check out this cool new service that lets users generate visual content with the help of an AI called Artflow. The AI generates portraits based on short, textual input by the user. The machine learning system interprets submitted prompts into images. In short, all you have to do is write a short description of a character you desire to generate and the AI will generate one based on your description. According to the creators, the AI uses Generative Adversarial Networks as well as the CLIP model from OpenAI and does not use any existing images, so the chances of generating an exact copy of an existing image are practically zero.

Recently, Github and OpenAI released one of the most anticipated AI-based tools for developers -- Github Copilot. The Artificial Intelligence (AI) tool is advertised as a pair programming assistant that does much more than usual code autocomplete tools out there. By no means is Copilot a tool intended to substitute developers in any way. Instead, the tool is meant to be used as an assistant that can facilitate many of the "boring" and "repetitive" parts of programming and lets coders worry about parts of the process that require human thinking and reasoning. It is important to note that GitHub Copilot is based on a recent deep learning model published by OpenAI in a paper called "Evaluating Large Language Models Trained on Code". This research paper introduces Codex, a GPT-like language model fine-tuned on publicly available code from GitHub.

OpenAI's GPT-3 is the most powerful AI system I've ever used. Trained on billions of web pages and tens of thousands of books, the system can generate nearly any kind of text, from news articles to computer code to sea shanties.

Since OpenAI released CLIP, trained on internet pictures and their nearby text, people have been using it to generate images. In all these methods - CLIP Dall-E, CLIP BigGAN, CLIP FFT, CLIP VQGAN, CLIP diffusion - you come up with a text prompt, some algorithm presents its images to CLIP, and CLIP's role is to judge how well the images match the prompt. With CLIP's judgements for feedback, the algorithm can self-adjust to make its images match the prompt. But we also do the reverse and set up an app where you give CLIP an image, and then CLIP judges how well text matches the image. One such app is CLIP backpropagation.

One possible definition of reinforcement learning (RL) is a computational approach to learning how to maximize the total sum of rewards when interacting with an environment. While a definition is useful, this tutorial aims to illustrate what reinforcement learning is through images, code, and video examples and along the way introduce reinforcement learning terms like agents and environments. As a previous post noted, machine learning (ML), a sub-field of AI, uses neural networks or other types of mathematical models to learn how to interpret complex patterns. Two areas of ML that have recently become very popular due to their high level of maturity are supervised learning (SL), in which neural networks learn to make predictions based on large amounts of data, and reinforcement learning (RL), where the networks learn to make good action decisions in a trial-and-error fashion, using a simulator. RL is the tech behind mind-boggling successes such as DeepMind's AlphaGo Zero and the StarCraft II AI (AlphaStar) or OpenAI's DOTA 2 AI ("OpenAI Five").

The OpenAI Gym project contains hundreds of control problems whose goal is to provide a testbed for reinforcement learning algorithms. One such problem is Freeway-ram-v0, where the observations presented to the agent are 128 bytes of RAM. While the goals of the project are for non-expert AI agents to solve the control problems with general training, in this work, we seek to learn more about the problem, so that we can better evaluate solutions. In particular, we develop on oracle to play the game, so that we may have baselines for success. We present details of the oracle, plus optimal game-playing situations that can be used for training and testing AI agents.

In recent years, near-term noisy intermediate scale quantum (NISQ) computing devices have become available. One of the most promising application areas to leverage such NISQ quantum computer prototypes is quantum machine learning. While quantum neural networks are widely studied for supervised learning, quantum reinforcement learning is still just an emerging field of this area. To solve a classical continuous control problem, we use a continuous-variable quantum machine learning approach. We introduce proximal policy optimization for photonic variational quantum agents and also study the effect of the data re-uploading. We present performance assessment via empirical study using Strawberry Fields, a photonic simulator Fock backend and a hybrid training framework connected to an OpenAI Gym environment and TensorFlow. For the restricted CartPole problem, the two variations of the photonic policy learning achieve comparable performance levels and a faster convergence than the baseline classical neural network of same number of trainable parameters.

Take some of the most popular language models, for example. OpenAI trained its GPT-3 model on 45 terabytes of data. To train the final version of MegatronLM, a language model similar to but smaller than GPT-3, Nvidia ran 512 V100 GPUs over nine days. A single V100 GPU can consume between 250 and 300 watts. If we assume 250 watts, then 512 V100 GPUS consumes 128,000 watts, or 128 kilowatts (kW).

Codex is a descendant of GPT-3 – its training data contains both natural language and billions of lines of source code from publicly available sources, including code in public GitHub repositories. "We see this as a tool to multiply programmers," OpenAI's CTO and co-founder Greg Brockman told The Verge. "Programming has two parts to it: you have'think hard about a problem and try to understand it,' and'map those small pieces to existing code, whether it's a library, a function, or an API.'" The second part is tedious, he says, but it's what Codex is best at. "It takes people who are already programmers and removes the drudge work."