A new general language machine learning model is pushing the boundaries of what AI can do. Why it matters: OpenAI's GPT-3 system can reasonably make sense of and write human language. It's still a long way from genuine artificial intelligence, but it may be looked back on as the iPhone of AI, opening the door to countless commercial applications -- both benign and potentially dangerous. Driving the news: After announcing GPT-3 in a paper in May, OpenAI recently began offering a select group of people access to the system's API to help the nonprofit explore the AI's full capabilities. How it works: GPT-3 works the same way as predecessors like OpenAI's GPT-2 and Google's BERT -- analyzing huge swathes of the written internet and using that information to predict which words tend to follow after each other.

"I share my early experiments with OpenAI's new language prediction model (GPT-3) beta. I explain why I think GPT-3 has disruptive potential comparable to that of blockchain technology." Araoz put GPT-3 to the test in several other ways, using it to make complex texts more understandable, to write poetry in the style of Borges in the Spanish language and write music in ABC notation.

Developer Manuel Araoz has played a practical joke online to demonstrate the potential of artificial intelligence bots -- by having a bot write an article about itself. According to a July 18 post on Araoz's blog, AI development company OpenAI released GPT-3, the third generation of its language prediction model capable of creating "random-ish sentences of approximately the same length and grammatical structure as those in a given body of text." The blog entry provides practical information regarding how the technology could be used to impersonate well-known figures by simulating their writing styles -- for example, Araoz used it to create a fake interview with Albert Einstein. He predicted that the GPT-3 could potentially replace journalists, political speech writers, and advertising copywriters. The bot's predicted sentences were used for posts on the bitcointalk.org "There are lots of posts for GPT-3 to study and learn from.

All said, I'm extremely excited to see which new technologies are built on GPT-3 and how OpenAI continues to improve on its model. Increased attention and funding in NLP and GPT-3 might be enough to ward off fears from many critics that an AI winter might be coming (myself included). Despite the shortfalls of the model, I am hoping that everyone can be optimistic about a future where humans and machines will communicate with each other in a unified language and the ability to create tools using technology will be accessible to billions of more people.

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.