AI Breakthroughs and Challenges in 2018 with David Krueger and Roman Yampolskiy
Every January, we like to look back over the past 12 months at the progress that’s been made in the world of artificial intelligence. Welcome to our annual “AI breakthroughs” podcast, 2018 edition. Ariel was joined for this retrospective by researchers Roman Yampolskiy and David Krueger. Roman is an AI Safety researcher and professor at the University of Louisville. He also recently published the book Artificial Intelligence Safety & Security. David is a PhD
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Show Notes
Every January, we like to look back over the past 12 months at the progress that’s been made in the world of artificial intelligence. Welcome to our annual “AI breakthroughs” podcast, 2018 edition.
Ariel was joined for this retrospective by researchers Roman Yampolskiy and David Krueger. Roman is an AI Safety researcher and professor at the University of Louisville. He also recently published the book Artificial Intelligence Safety & Security. David is a PhD candidate in the Mila lab at the University of Montreal, where he works on deep learning and AI safety. He's also worked with safety teams at the Future of Humanity Institute and DeepMind and has volunteered with 80,000 hours.
Roman and David shared their lists of 2018’s most promising AI advances, as well as their thoughts on some major ethical questions and safety concerns. They also discussed media coverage of AI research, why talking about “breakthroughs” can be misleading, and why there may have been more progress in the past year than it seems.
Topics discussed in this podcast include:
- DeepMind progress, as seen with AlphaStar and AlphaFold
- Manual dexterity in robots, especially QT Opt and Dactyl
- Advances in creativity, as with Generative Adversarial Networks (GANs)
- Feature-wise transformations
- Continuing concerns about DeepFakes
- Scaling up AI systems
- Neuroevolution
- Google Duplex, the AI assistant that sounds human on the phone
- The General Data Protection Regulation (GDPR) and AI policy more broadly
Publications discussed in this podcast include:
- Supervising strong learners by amplifying weak experts (OpenAI)
- AI safety via debate (OpenAI)
- Scalable agent alignment via reward modeling: a research direction (DeepMind)
- AI and Compute (OpenAI)
- A Style-Based Generator Architecture for Generative Adversarial Networks
- IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
- ImageNet/ResNet-50 Training in 224 Seconds
- Attention Is All You Need