Humans are considered to have general intelligence. Endowing machines with artificial general intelligence (AGI) would allow them to adapt to a variety of situations, maximising their potential. Looking ahead, there are likely to be areas of scientific and intellectual progress that will require the types of planning, abstract reasoning, and meaningful understanding of the world that we associate with general intelligence.
The big question is whether the potential given by further generality also implies higher risks and unknowns in comparison with the more specialised, constrained, systems we are already used to. Different issues are raised by a machine translation system, specialised for a task, compared to a versatile personal assistant at home, aimed at covering more and more daily tasks. If this association between risk and generality exists, can we find trade-offs or limitations that ensure flexibility and robustness at the same time?
This project investigates this and other related questions for several scenarios, including the profiles of generalised automation that are both safe and efficient, and the understanding of social dominance through general mind modelling, according to several AGI paradigms. The result is a systematic cartography of the risks of AGI from the perspective of AGI’s definitional concept: generality.
TECHNICAL DESCRIPTION AND GOALS
Many paradigms exist, and more will be created, for developing and understanding AI. Under these paradigms, the key benefits and risks materialise very differently. One dimension pervading all these paradigms is the notion of generality, which plays a central role, and provides the middle letter, in AGI, artificial general intelligence. This project explores the safety issues of present and future AGI paradigms from the perspective of measures of generality, as a complementary dimension to performance. We investigate the following research questions:
- Should we define generality in terms of tasks, goals or dominance? How does generality relate to capability, to computational resources, and ultimately to risks?
- What are the safe trade-offs between general systems with limited capability or less general systems with higher capability? How is this related to the efficiency and risks of automation?
- Can we replace the monolithic notion of performance explosion with breadth growth? How can this help develop safe pathways for more powerful AGI systems?
These questions are analysed for paradigms such as reinforcement learning, inverse reinforcement learning, adversarial settings (Turing learning), oracles, cognition as a service, learning by demonstration, control or traces, teaching scenarios, curriculum and transfer learning, naturalised induction, cognitive architectures, brain-inspired AI, among others.
A Researcher (Post-Doc) will be incorporated full-time for year 2 and year 3 of the project. We’ll post the description of the position in early 2019 aiming to recruit a person by the summer of 2019. If you’re interested, feel free contact the project coordinators.
ASSOCIATES AND ADVISORY BOARD:
The Associates strengthen the expertise and outreach in several areas of the project:
- Rob Alexander, University of York, UK.
- Jan Feyereisl, AI Roadmap Institute, CZ.
- Cèsar Ferri, Universitat Politècnica de València, ES.
- Adrià Garriga-Alonso, University of Cambridge, UK.
- Judy Goldsmith, University of Kentucky, KY.
- Emilia Gómez, Centre for Advanced Studies, Joint Research Centre, European Commission, EU.
- Henry Shevlin, University of Cambridge, UK.
- Kristinn Thórisson, Reykjavik University, IS.
The International Advisory Board (IAB) of the project is composed of the following people:
- Allan Dafoe, Yale and FHI, Oxford, UK.
- Virginia Dignum, Delft University of Technology, NL.
- Kenji Doya, Okinawa Institute of Science and Technology, JP.
- Tomas Mikolov, Facebook AI Research, US.
- Vincent Müller, University of Leeds, Anatolia College, UK, GR.
- Ute Schmid, University of Bamberg, DE.
- Murray Shanahan, DeepMind, Imperial College, UK.
- Michael Witbrock, DRSM, Thomas J. Watson Research Center, IBM, US.
- Yi Zeng, Chinese Academy of Sciences, CN.
EVENTS AND NEWS:
The project is co-organising the following events:
- Safe AI 2019. The AAAI's Workshop on Artificial Intelligence Safety at AAAI 2019 to take place in Honolulu, Hawaii, 27 January 2019.
- Generality and Intelligence: from Biology to AI, 5th October, 2018, as part of the MIT-IBM AI Week and the Cambridge^2 initiative, a series of more extensive workshops taking place in Cambridge, UK, and Cambridge, MA, co-organised by the MIT-IBM Watson AI Lab and the Leverhulme Centre for the Future of Intelligence.