OpenAI, the non-profit founded by Elon Musk in 2015 – he's no longer part of it – has released the biggest and final version of the GPT-2 text-generating language model, which it has admitted could be dangerous in the wrong hands. However, it says the newly released full model's output is only slightly more convincing to humans than the previous version. The organization released the first portion of the model in February as part of a staged process, beginning with just 124 million parameters. It held back the full model with 1.5 billion parameters because scientists believed it was too dangerous and could be used by malicious actors, such as terrorists and state-sponsored hackers. Among the malicious purposes for which OpenAI admitted GPT-2 might be used are generating misleading news articles, impersonating others online, automating the production of abusive or fake content for social media, and automating the creation of spam and phishing content.

We've seen no strong evidence of misuse so far They are going against their own word, but nevertheless, it's nice to see that they are releasing everything. EDIT: The unicorn example added below from https://talktotransformer.com/, which has already been updated with the newest 1.5B parameters model. Input: In a shocking finding, scientist discovered a herd of unicorns living in a remote, previously unexplored valley, in the Andes Mountains. Even more surprising to the researchers was the fact that the unicorns spoke perfect English. Output: While there are only a few documented instances of unicorns in the wild, the researchers said the finding proves that there are still large numbers of wild unicorns that remain to be studied.

OpenAI today published the final model in its staged release for GPT-2, the spooky text generator the AI community's been talking about all year. GPT-2 uses machine learning to generate novel text based on a limited input. Basically, you can type a few sentences about anything you like and the AI will spit out some'related' text. GPT-2 makes up text that didn't previously exist– at least according to OpenAI's research paper. If death, in some obscure and distant hour, Strikes me still as I slept, if I yet dream: Is that my peace with an eternity spent?

Language doesn't exist in a vacuum. It's based on having experiences in and knowledge of the real world. Douglas Hofstadter would either laugh in your faces or succumb to a deep depression if he only knew what you small-minded pretenders were working on.

As the final model release of GPT-2's staged release, we're releasing the largest version (1.5B parameters) of GPT-2 along with code and model weights to facilitate detection of outputs of GPT-2 models. While there have been larger language models released since August, we've continued with our original staged release plan in order to provide the community with a test case of a full staged release process. We hope that this test case will be useful to developers of future powerful models, and we're actively continuing the conversation with the AI community on responsible publication. Our partners at Cornell University surveyed people to assign GPT-2 text a credibility score across model sizes. People gave the 1.5B model a "credibility score" of 6.91 out of 10.

Last February, OpenAI, an artificial intelligence research group based in San Francisco, announced that it has been training an AI language model called GPT-2, and that it now "generates coherent paragraphs of text, achieves state-of-the-art performance on many language-modelling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarisation – all without task-specific training". If true, this would be a big deal. But, said OpenAI, "due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper." Given that OpenAI describes itself as a research institute dedicated to "discovering and enacting the path to safe artificial general intelligence", this cautious approach to releasing a potentially powerful and disruptive tool into the wild seemed appropriate.

This project challenges the car racing problem from OpenAI gym environment. The problem is very challenging since it requires computer to finish the continuous control task by learning from pixels. To tackle this challenging problem, we explored two approaches including evolutionary algorithm based genetic multi-layer perceptron and double deep Q-learning network. The result shows that the genetic multi-layer perceptron can converge fast but when training many episodes, double deep Q-learning can get better score. We analyze the result and draw a conclusion that for limited hardware resources, using genetic multi-layer perceptron sometimes can be more efficient.

Musk is the face behind numerous tech companies, including SpaceX, Tesla, Neuralink, The Boring Company and OpenAI. During an event at Stanford University in 2015, the entrepreneur was asked by DFJ General Partner Steve Jurvetson what he thought the year 2035 would look like. The 48-year-old entrepreneur did not disappoint.

DD-PPO is distributed (uses multiple machines), decentralized (lacks a centralized server), and synchronous (no computation is ever'stale'), making it conceptually simple and easy to implement. In our experiments on training virtual robots to navigate in Habitat-Sim (Savva et al., 2019), DD-PPO exhibits near-linear scaling - achieving a speedup of 107x on 128 GPUs over a serial implementation. We leverage this scaling to train an agent for 2.5 Billion steps of experience (the equivalent of 80 years of human experience) - over 6 months of GPU-time training in under 3 days of wall-clock time with 64 GPUs. This massive-scale training not only sets the state of art on Habitat Autonomous Navigation Challenge 2019, but essentially'solves' the task - near-perfect autonomous navigation in an unseen environment without access to a map, directly from an RGB-D camera and a GPS Compass sensor. Fortuitously, error vs computation exhibits a power-law-like distribution; thus, 90% of peak performance is obtained relatively early (at 100 million steps) and relatively cheaply (under 1 day with 8 GPUs). Finally, we show that the scene understanding and navigation policies learned can be transferred to other navigation tasks - the analog of'ImageNet pre-training task-specific fine-tuning' for embodied AI. Our model outperforms ImageNet pre-trained CNNs on these transfer tasks and can serve as a universal resource (all models code will be publicly available). 1 I NTRODUCTION Recent advances in deep reinforcement learning (RL) have given rise to systems that can outperform human experts at variety of games (Silver et al., 2017; Tian et al., 2019; OpenAI, 2018). These advances, even more-so than those from supervised learning, rely on significant numbers of training samples, making them impractical without large-scale, distributed parallelization. Thus, scaling RL via multi-node distribution is of importance to AI - that is the focus of this work. Several works have proposed systems for distributed RL (Heess et al., 2017; Liang et al., 2018; Tian et al., 2019; Silver et al., 2016; OpenAI, 2018; Espeholt et al., 2018). These works utilize two core components: 1) workers that collect experience ('rollout workers'), and 2) a parameter server that optimizes the model. The rollout workers are then distributed across, potentially, thousands of CPUs 1 .

Deep generative models for graphs have shown great promise in the area of drug design, but have so far found little application beyond generating graph-structured molecules. In this work, we demonstrate a proof of concept for the challenging task of road network extraction from image data. This task can be framed as image-conditioned graph generation, for which we develop the Generative Graph Transformer (GGT), a deep autoregressive model that makes use of attention mechanisms for image conditioning and the recurrent generation of graphs. We benchmark GGT on the application of road network extraction from semantic segmentation data. For this, we introduce the Toulouse Road Network dataset, based on real-world publicly-available data. We further propose the StreetMover distance: a metric based on the Sinkhorn distance for effectively evaluating the quality of road network generation. The code and dataset are publicly available.