Beneficial Artificial Intelligence

AI systems are now, by many measures, outperforming humans on tasks ranging from drug development to gaming to visual art. AI researchers and top technology investors expect this progress to continue. A 2022 survey of over 4,000 researchers publishing in NeurIPS and ICML (top AI conferences) concluded that it is 50% likely that unaided machines can accomplish every single task better and more cheaply than human workers by 2059.

Improvements in AI have enormous potential to better our world: increasing the speed and accuracy of medical diagnoses, reducing traffic accidents by making autonomous vehicles possible, facilitating personalised education, accelerating the development of sustainable energy, and more. But the risks of transformative AI are high. On average, respondents to the above mentioned survey of top AI researchers said that when unaided machines can accomplish every task better and more cheaply than human workers, the effect is 14% likely to be “extremely bad (e.g. human extinction)”.

In order to mitigate these risks, we need to determine how to build AI systems that reliably pursue their user’s goals. Moreover, we need to achieve sufficient coordination between companies and governments to ensure that these solutions are broadly implemented, and so that the long-term trajectory of our world is not determined by a malicious or reckless actor.

Are you a major philanthropist seeking to learn more about these areas? Get in touch with our Co-CEOs Natalie Cargill and Simran Dhaliwal at natalie@longview.org and simran@longview.org.

Center for Human-Compatible Artificial Intelligence

Training at the first academic centre dedicated to AI safety.

As the first centre of its kind, the Center for Human-Compatible AI at UC Berkeley is a landmark in AI safety field-building efforts. Led by Stuart Russell, co-author of one of the most widely-used textbooks on AI, the centre has played a key role in establishing AI alignment as a problem worthy of substantial attention. The centre also trains excellent PhD students in work on AI safety. Since its founding in 2016, the centre has had an excellent track record of placing PhD students in the very top academic departments and AI companies.

Ought

Aligning AI through aligned subunits.

We supported Ought’s work to test a key hypothesis underlying other AI alignment research. Iterated amplification is a technique that uses a team of aligned AI systems to build a more powerful AI system. Then a team of these more powerful AI systems trains a yet more powerful AI system, and so forth. If this proposal works, each iteration would produce a more powerful agent which is still aligned with the goals of the AI system at the beginning of the chain. Success would be a major step toward it being possible to align modern machine learning systems. Failure would indict many leading approaches to AI alignment, making this a particularly important hypothesis for testing.

AI Impacts

Empirically assessing the creation of human-level AI.

It is likely that AI systems able to bring greater-than-human intelligence to any task will emerge in the coming decades. The potential impacts of such an event remain radically understudied. AI Impacts is a research and outreach nonprofit which aims to make progress on these questions. They research potential paths to AI development, when general artificial intelligence might be developed, and the likely economic impacts of human-level AI. Their expert surveys (referenced above) are some of the most important evidence about the future of AI.