Shane Legg

Shane Legg is a person.

• Example(s):
  • Shane Legg, 2014, when DeepMind Technologies was acquired by Google, Inc..
  • Shane Legg, 2010, when he cofounded DeepMind Technologies (along with Demis Hassabis and Mustafa Suleyman).
  • ...
• See: DeepMind, AlphaGo, Superintelligence Influencer, Friendly Artificial Intelligence.

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References

• https://en.wikipedia.org/wiki/Shane_Legg

2023

• (Morris et al., 2023) ⇒ Meredith Ringel Morris, Jascha Sohl-dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clement Farabet, and Shane Legg. (2023). “Levels of AGI: Operationalizing Progress on the Path to AGI.” doi:10.48550/arXiv.2311.02462

2023

• (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Shane_Legg Retrieved:2023-11-10.
  • Shane Legg is a machine learning researcher and entrepreneur. With Demis Hassabis and Mustafa Suleyman, he cofounded DeepMind Technologies (later bought by Google and now called Google DeepMind), and works there as the chief AGI scientist. He is also known for his academic work on artificial general intelligence, including his thesis supervised by Marcus Hutter.

2017

• (Christiano et al., 2017) ⇒ Paul F. Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. (2017). “Deep Reinforcement Learning from Human Preferences.” In: Advances in neural information processing systems 30

2015

• (Mnih et al., 2015) ⇒ Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis. (2015). “Human-level Control through Deep Reinforcement Learning.” In: Nature, 518(7540).

2007

• (Legg & Hutter, 2007) ⇒ Shane Legg, and Marcus Hutter. (2007). “Universal Intelligence: A Definition of Machine Intelligence.” Minds and Machines, 17.
  • ABSTRACT: A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.

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