2019-06-24 · Recent developments in artificial intelligence and machine learning have spurred interest in the growing field of AI safety, which studies how to prevent human-harming accidents when deploying AI systems. This paper thus explores the intersection of AI safety with evolutionary computation, to show how safety issues arise in evolutionary computation and how understanding from evolutionary

Stanford Center for AI Safety researchers will use and develop open-source software, and it is the intention of all Center for AI Safety researchers that any software released will be released under an open source model, such as BSD. For more information, please view our Corporate Membership Document.

I think there are many questions whose answers would be useful for technical AGI safety research, but which will probably require expertise outside AI to answer. In this post I list 30 of them, divided into four categories. Feel free to get in touch if you’d like to discuss these questions and why I think they’re important in more detail. I personally think that making progress on the ones 2019-06-24 · Recent developments in artificial intelligence and machine learning have spurred interest in the growing field of AI safety, which studies how to prevent human-harming accidents when deploying AI systems. This paper thus explores the intersection of AI safety with evolutionary computation, to show how safety issues arise in evolutionary computation and how understanding from evolutionary This approach is called worst-case AI safety. This post elaborates on possible focus areas for research on worst-case AI safety to support the (so far mostly theoretical) concept with more concrete ideas. Many, if not all, of the suggestions may turn out to be infeasible.

NIOSH has been at the forefront of workplace safety and robotics, creating the Center for Occupational Robotics Research (CORR) and posting blogs such as A Robot May Not Injure a Worker: Working safely with robots. Life 3.0 outlines the current state of AI safety research and the questions we’ll need to answer as a society if we want the technology to be used for good. Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. In this paper we discuss one such potential impact: the problem of accidents in machine learning systems, defined as unintended and harmful behavior that may emerge from poor design of real-world AI systems. We present a list of five The AISafety workshop seeks to explore new ideas on safety engineering, as well as broader strategic, ethical and policy aspects of safety-critical AI-based systems.

16 Oct 2020 Is Artificial Intelligence ready to take on workplace health and safety? In fact, a study performed by independent research firm Verdantix

Developing a superintelligent AI might be very dangerous if it turns AM Session 9.30-12.00: Artificial Intelligence projects at Lund University: the view from development of AI concern democracy, AI development, and AI safety. Multidisciplinary AI research in the European Framework Programme [VIDEO]. Science, Research and University jobs in Europe.

This is a science and engineering based forum created to discuss the various aspects of AI and AGI safety. Topics may include research, design,

Why AI Safety? MIRI is a nonprofit research group based in Berkeley, California. We do technical research aimed at ensuring that smarter-than-human AI systems have a positive impact on the world. This page outlines in broad strokes why we view this as a critically important goal to work toward today.

AI safety is a relatively new field of research focused on techniques Abstract. In this position paper, we propose that the community consider encouraging researchers to include two riders, a “Lay Summary” and an “AI Safety PAIRSI is a nonprofit research organization located in Berkeley, CA, USA. AI Safety Camp connects you with interesting collaborators worldwide to discuss and decide on a concrete research proposal, gear up online as a team, and try AI Safety is a new and fast growing research field.
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It thus seems particularly important to have different research teams tackle the problems from different perspectives and under different assumptions. Perhaps the tens of millions currently funding AI safety research could be spent more effectively by involving more people who do not claim such ignorance.
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The AISafety workshop seeks to explore new ideas on safety engineering, as well as broader strategic, ethical and policy aspects of safety-critical AI-based systems.

We believe this work is valuable because the the development of AGI (artificial general intelligence) creates existential risks for humanity, and AGI systems are likely to exhibit mental phenomena, so AI AI safety research is a broad, interdisciplinary field – covering technical aspects of how to actually create safe AI systems, as well as broader strategic, ethical and policy issues ( examples ). See below for more on the different types of AI safety research. To date, the majority of technical AI safety research has focused on developing a theoretical understanding about the nature and causes of unsafe behaviour.

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This paper is the first installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. In it, the authors introduce three categories of AI safety issues: problems of robustness, assurance, and specification. Other papers in this

AI safety researchers can tap into a growing pool of grants that fund innovative approaches for addressing the problem. Some of the research monies are coming from the same foundations that are addressing many types of existential threats, including global warming, nuclear weapons, and biotechnology. I think in general it’s that we’ve shown through our initial research that there are ways to make progress on the problem of AI safety and alignment. CHAI’s creation was a bit of an experiment - when it was founded in 2016 it wasn’t necessarily clear that this would be possible, for some of the reasons we’ve already discussed.