"For many decades hype outstripped fact in terms of AI and machine learning. "This report looks at the practices that just don't work anymore and suggests broad approaches that might help: for example, how to design software and hardware to make it less hackable - and what type of laws and international regulations might work in tandem with this." The report urges policy makers and researchers to work together to understand and prepare for how the technology could be used maliciously, and calls for developers to be proactive and mindful of how it could be misused. Those who contributed to the study include the Elon Musk-founded non-profit research firm OpenAI and international digital rights group the Electronic Frontier Foundation.

Abstract: The design of small molecules with bespoke properties is of central importance to drug discovery. However significant challenges yet remain for computational methods, despite recent advances such as deep recurrent networks and reinforcement learning strategies for sequence generation, and it can be difficult to compare results across different works. This work proposes 19 benchmarks selected by subject experts, expands smaller datasets previously used to approximately 1.1 million training molecules, and explores how to apply new reinforcement learning techniques effectively for molecular design. The benchmarks here, built as OpenAI Gym environments, will be open-sourced to encourage innovation in molecular design algorithms and to enable usage by those without a background in chemistry. Finally, this work explores recent development in reinforcement-learning methods with excellent sample complexity (the A2C and PPO algorithms) and investigates their behavior in molecular generation, demonstrating significant performance gains compared to standard reinforcement learning techniques.

In a new report, called The Malicious Use of Artificial Intelligence (AI), the authors - who are made up of AI researchers and civil liberties groups - warn that if breakthroughs in AI continue at the current pace then technology will soon become so powerful that it will outmanoeuvre many digital and physical defence systems. Jack Clark, head of policy at OpenAI, San Francisco-based AI group whose backers include Elon Musk and Peter Thiel, said: "What struck a lot of us was the amount that happened in the last five years -- if that continues, you see the chance of creating really dangerous things." The report also warns that drones and driverless cars could be commandeered and used as weapons and that malevolent AI could be used to organise swarms of drones. Also, political systems could be hacked by using tools for online advertising and commerce to manipulate voters.

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.