The world is under threat from artificial intelligence and needs to do more to keep people safe, experts have urged. A new report compiled by 26 of the world's leading experts paints a terrifying picture of the world in the next 10 years. Physical attacks as well as those on our digital worlds and political system could drastically undermine the safety of humanity, it warns, and people must work together now if they want to keep the world safe. The use of artificial intelligence is likely to empower all kinds of people – including rogue states, criminals, and terrorists, the report warns. Boston Dynamics describes itself as'building dynamic robots and software for human simulation'.

We've co-authored a paper that forecasts how malicious actors could misuse AI technology, and potential ways we can prevent and mitigate these threats. This paper is the outcome of almost a year of sustained work with our colleagues at the Future of Humanity Institute, the Centre for the Study of Existential Risk, the Center for a New American Security, the Electronic Frontier Foundation, and others. AI challenges global security because it lowers the cost of conducting many existing attacks, creates new threats and vulnerabilities, and further complicates the attribution of specific attacks. Like our work on concrete problems in AI safety, we've grounded some of the problems motivated by the malicious use of AI in concrete scenarios, such as: persuasive ads generated by AI systems being used to target the administrator of a security systems; cybercriminals using neural networks and "fuzzing" techniques to create computer viruses with automatic exploit generation capabilities; malicious actors hacking a cleaning robot so that it delivers an explosives payload to a VIP; and rogue states using omniprescent AI-augmented surveillance systems to pre-emptively arrest people who fit a predictive risk profile. We're excited to start having this discussion with our peers, policymakers, and the general public; we've spent the last two years researching and solidifying our internal policies at OpenAI and are going to begin engaging a wider audience on these issues.

All is open source, though not "published" yet so excuse me if the repository is a bit hard to navigate / unclear. I've been building a Unity-esque 2D engine with pygame, which should be easy to plug in with OpenAI's gym. Goal here isn't to build the most optimal environments per se, but a way to implement games that are human and AI playable. Hopefully I'll get to release a slightly more convenient setup soon, with each (sub)project separated into their own repo:)

There has been a discussion recently about using RL to land a SpaceX booster. Coincidentally I've been working on exactly this in OpenAI. It was as much fun as it was frustrating at times. It's trained with a PPO implementation from Unity that I've changed to work with OpenAI (GitHub). The official OpenAI implementation is convoluted and impossible to work with in my opinion. This particular agent took 200'000 tries over the course of 12 hours and 20 million frames (with a frame skip value of 5, so 100 million total frames).

We've designed a method that encourages AIs to teach each other with examples that also make sense to humans. Our approach automatically selects the most informative examples to teach a concept -- for instance, the best images to describe the concept of dogs -- and experimentally we found our approach to be effective at teaching both AIs and humans. Some of the most transformative applications of powerful AI will come from computers and humans collaborating, but getting them to speak a common language is hard. Think about trying to guess the shape of a rectangle when you're only shown a collection of random points inside that rectangle: it's much faster to figure out the correct dimensions of the rectangle when you're given points at the corners of the rectangle instead. Our machine teaching approach works as a cooperative game played between two agents, with one functioning as a student and the other as a teacher.

Generative AI models in chemistry are increasingly popular in the research community, mainly, due to their interest for drug discovery applications. They generate virtual molecules with desired chemical and biological properties (more details in this blog post). However, this flourishing literature still lacks a unified benchmark. Such benchmark would provide a common framework to evaluate and compare different generative models. Moreover, it would help to formulate best practices for this emerging industry of'AI molecule generators': how much training data is needed, for how long the model should be trained, and so on.

A non-profit AI research company, OpenAI, basically, is now, to its list is releasing a new batch of seven unsolved problems which have come up in the course of their research at OpenAI. Very similar to their original Requests for Research which resulted in the upbringing of several papers, the company expects these problems for new people to enter the field to be a fun and a meaningful way to do the same, as well as to hone the skills for practitioners. Not to forget that is also is a great way to get a job at OpenAI that aims at enacting and discovering the path to safe general artificial intelligence. Also, If one is not sure where to begin, they also have some solved starter problems. Environment: Start with two snakes, and scale from there and then with multiple snakes have a reasonably large field; snakes grow when eating randomly-appearing fruit; a snake dies when colliding with another snake, itself, or the wall; and the game ends when all snakes die.

Using the top solution from our type system optimization, we can now label data from Wikipedia using labels generated by the type system. Using this data (in our experiments, 400M tokens for each of English and French), we can now train a bidirectional LSTM to independently predict all the type memberships for each word. On the Wikipedia source text, we only have supervision on intra-wiki links, however this is sufficient to train a deep neural network to predict type membership with an F1 of over 0.91. One of our type systems, discovered by beam search, includes types such as Aviation, Clothing, and Games (as well as surprisingly specific ones like 1754 in Canada -- indicating 1754 was an exciting year in the dataset of 1,000 Wikipedia articles it was trained on); you can also view the full type system. Predicting entities in a document usually relies on a "coherence" metric between different entities, e.g.

We present a deep generative model for Zero-Shot Learning (ZSL). Unlike most existing methods for this problem, that represent each class as a point (via a semantic embedding), we represent each seen/unseen class using a class-specific latent-space distribution, conditioned on class attributes. We use these latent-space distributions as a prior for a supervised variational autoencoder (VAE), which also facilitates learning highly discriminative feature representations for the inputs. The entire framework is learned end-to-end using only the seen-class training data. At test time, the label for an unseen-class test input is the class that maximizes the VAE lower bound. We further extend the model to a (i) semi-supervised/transductive setting by leveraging unlabeled unseen-class data via an unsupervised learning module, and (ii) few-shot learning where we also have a small number of labeled inputs from the unseen classes. We compare our model with several state-of-the-art methods through a comprehensive set of experiments on a variety of benchmark data sets.

Although supervised learning requires a labeled dataset, obtaining labels from experts is generally expensive. For this reason, crowdsourcing services are attracting attention in the field of machine learning as a way to collect labels at relatively low cost. However, the labels obtained by crowdsourcing, i.e., from non-expert workers, are often noisy. A number of methods have thus been devised for inferring true labels, and several methods have been proposed for learning classifiers directly from crowdsourced labels, referred to as "learning from crowds." A more practical problem is learning from crowdsourced labeled data and unlabeled data, i.e., "semi-supervised learning from crowds." This paper presents a novel generative model of the labeling process in crowdsourcing. It leverages unlabeled data effectively by introducing latent features and a data distribution. Because the data distribution can be complicated, we use a deep neural network for the data distribution. Therefore, our model can be regarded as a kind of deep generative model. The problems caused by the intractability of latent variable posteriors is solved by introducing an inference model. The experiments show that it outperforms four existing models, including a baseline model, on the MNIST dataset with simulated workers and the Rotten Tomatoes movie review dataset with Amazon Mechanical Turk workers.