The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to …

GPT is short for Generative Pre-training Transformer (GPT), a language model written by Alec Radford and published in 2018 by OpenAI, Elon Musks's artificial intelligence research laboratory. It uses a generative model of language (where two neural networks perfect each other by competition) and is able to acquire knowledge of the world and process long-range dependencies by pre-training on diverse sets of written material with long stretches of contiguous text. GPT-2 (Generative Pretrained Transformer 2) was announced in February 2019 and is an unsupervised transformer language model trained on 8 million documents for a total of 40 GB of text from articles shared via Reddit submissions. Elon Musk was famously reluctant to release it as he was concerned it could be used to spam social networks with fake news. In May 2020 OpenAI announced GPT-3 (Generative Pretrained Transformer 3), a model which contains two orders of magnitude more parameters than GPT-2 (175 billion vs 1.5 billion parameters) and which offers a dramatic improvement over GPT-2.

Background and motivation: Combining Deep Reinforcement Learning (Deep RL) and Health Systems Simulations has significant potential, for both research into improving Deep RL performance and safety, and in operational practice. While individual toolkits exist for Deep RL and Health Systems Simulations, no framework to integrate the two has been established. Aim: Provide a framework for integrating Deep RL Networks with Health System Simulations, and to ensure this framework is compatible with Deep RL agents that have been developed and tested using OpenAI Gym. Methods: We developed our framework based on the OpenAI Gym framework, and demonstrate its use on a simple hospital bed capacity model. We built the Deep RL agents using PyTorch, and the Hospital Simulatation using SimPy. Results: We demonstrate example models using a Double Deep Q Network or a Duelling Double Deep Q Network as the Deep RL agent. Conclusion: SimPy may be used to create Health System Simulations that are compatible with agents developed and tested on OpenAI Gym environments. GitHub repository of code: https://github.com/MichaelAllen1966/learninghospital

And a tool like this has many new uses, both good (from powering better chatbots to helping people code) and bad (from powering better misinformation bots to helping kids cheat on their homework). But when a new AI milestone comes along it too often gets buried in hype. Even Sam Altman, who co-founded OpenAI with Elon Musk, tried to tone things down: "The GPT-3 hype is way too much. It's impressive (thanks for the nice compliments!) but it still has serious weaknesses and sometimes makes very silly mistakes. AI is going to change the world, but GPT-3 is just a very early glimpse. We have a lot still to figure out."

Drug discovery aims at designing novel molecules with specific desired properties for clinical trials. Over past decades, drug discovery and development have been a costly and time consuming process. Driven by big chemical data and AI, deep generative models show great potential to accelerate the drug discovery process. Existing works investigate different deep generative frameworks for molecular generation, however, less attention has been paid to the visualization tools to quickly demo and evaluate model's results. Here, we propose a visualization framework which provides interactive visualization tools to visualize molecules generated during the encoding and decoding process of deep graph generative models, and provide real time molecular optimization functionalities. Our work tries to empower black box AI driven drug discovery models with some visual interpretabilities.

In practice, machine learning experts are often confronted with imbalanced data. Without accounting for the imbalance, common classifiers perform poorly and standard evaluation metrics mislead the practitioners on the model's performance. A common method to treat imbalanced datasets is under- and oversampling. In this process, samples are either removed from the majority class or synthetic samples are added to the minority class. In this paper, we follow up on recent developments in deep learning. We take proposals of deep generative models, including our own, and study the ability of these approaches to provide realistic samples that improve performance on imbalanced classification tasks via oversampling. Across 160K+ experiments, we show that all of the new methods tend to perform better than simple baseline methods such as SMOTE, but require different under- and oversampling ratios to do so. Our experiments show that the way the method of sampling does not affect quality, but runtime varies widely. We also observe that the improvements in terms of performance metric, while shown to be significant when ranking the methods, often are minor in absolute terms, especially compared to the required effort. Furthermore, we notice that a large part of the improvement is due to undersampling, not oversampling. We make our code and testing framework available.

Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions – something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting.

OpenAI, a non-profit artificial intelligence research company backed by Peter Thiel, Elon Musk, Reid Hoffman, Marc Benioff, Sam Altman and others, released its third generation of language prediction model (GPT-3) into the open-source wild. Language models allow computers to produce random-ish sentences of approximately the same length and grammatical structure as those in a given body of text. In my early experiments with GPT-3 I found that GPT-3's predicted sentences, when published on the bitcointalk.org I imagine that similar results can be obtained by republishing GPT-3's outputs to other message boards, blogs, and social media. I predict that, unlike its two predecessors (PTB and OpenAI GPT-2), OpenAI GPT-3 will eventually be widely used to pretend the author of a text is a person of interest, with unpredictable and amusing effects on various communities.

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for the graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.

At its core, GPT-2 is a powerful prediction engine. It learned to grasp the structure of the English language by looking at billions of examples of words, sentences, and paragraphs, scraped from the corners of the internet. With that structure, it could then manipulate words into new sentences by statistically predicting the order in which they should appear. So researchers at OpenAI decided to swap the words for pixels and train the same algorithm on images in ImageNet, the most popular image bank for deep learning. Because the algorithm was designed to work with one-dimensional data (i.e., strings of text), they unfurled the images into a single sequence of pixels.