Shanahan recalls that AI was a niche field when he began his PhD in the 1980s. His thesis explored speeding up Prolog queries by reusing previously established relationships. He describes the frame problem as deciding what matters in a situation and avoiding needless reasoning about what stays the same, such as a room’s wall color when equipment moves.
Ex Machina's Scientific Advisor
Speaker
Murray Shanahan, an AI researcher at DeepMind and Imperial College London, discusses AI, consciousness, and scientific advising on Ex Machina with a Y Combinator host.
Shanahan moved from classical, logic-based AI to computational neuroscience and back toward AI and machine learning. He sees the frame problem in systems that must identify useful information: in Space Invaders, for example, avoiding falling bombs matters more than the invaders’ colors or shapes. He also notes that Go players have used AlphaGo to explore strategies that conventional wisdom had rejected, and describes the potential for people and machines to complement each other.
Shanahan says physical embodiment is central to human intelligence because brains support movement and interaction with a complex world. Alex Garland contacted him after reading his book Embodiment and the Inner Life; Shanahan read the script, discussed ideas with Garland, and made a few small corrections. He calls the film’s test a “Garland test”: Caleb sees Ava is a robot and must decide whether to treat her as conscious through their interactions.
Shanahan describes a script direction for Ava’s escape scene that would have shown alien-looking visual effects from her point of view; the effect was cut, preserving ambiguity about her experience. He says Asimov’s laws are fictional devices and that current AI cannot comprehend such rules. He also cautions that reaching brain-scale computing power would not itself produce human-level intelligence, which may require unknown conceptual breakthroughs.
Shanahan is interested in combining ideas from symbolic AI with deep reinforcement learning. He says DQN learned Atari games from pixels and trial and error, sometimes reaching human or superhuman performance, but learning was slow compared with how quickly people infer objects and rules. He also discusses advising a play about memory loss and working with the art collective Random International on Rain Room and a moving light sculpture that evokes a person.
Takeaways
- Design learning systems to focus on information that affects the task, while setting aside irrelevant details.
- Pair trial-and-error learning with representations or rules that help systems learn faster.
- Treat brain-scale compute estimates as measures of hardware capacity, not evidence that human-level intelligence is near.
- When assessing machine consciousness, distinguish observable behavior from certainty about subjective experience.
- Use AI as a partner that can help people explore strategies they might not discover alone.
Mentioned in this talk
Search inside this talk
What was claimed 13
- built
Google DeepMind built DQN
“our very first Atari program, DQN”
3:47inDemis Hassabis: Agents, AGI & The Next Big Scientific BreakthroughYouTube Verified2 sources· Talk
- role atAlex Garland is writer and director at Ex Machina
“Alex Garland, the writer and director”
YouTube Unverified1 source· Talk
- role atMurray Shanahan is scientific adviser at Ex Machina
“I'm not sure if I was ever officially scientific adviser”
YouTube Unverified1 source· Talk
- builtMurray Shanahan built Ex Machinawrote the hidden code Easter egg shown on screen
“He said the window was mine to fill with a hidden message”
YouTube Unverified1 source· Talk
- role atMurray Shanahan is AI researcher at
Google DeepMind
“you work here at DeepMind”
YouTube Unverified1 source· Talk
- featuredeep reinforcement learning: Can learn tasks from trial and error
“has to learn by trial and error how to get a good score”
YouTube Unverified1 source· Talk
- metricDQN: Reached superhuman performance in some Atari games and human-level performance in others
“reaching superhuman performance in some cases, human-level performance in others”
YouTube Unverified1 source· Talk
- featureDQN: Learns Atari games from screen pixels and trial and error
“All the system sees is the screen—the pixels”
YouTube Unverified1 source· Talk
- builtMurray Shanahan built DQNplayed with the system before joining DeepMind
“Before I joined DeepMind, I started playing with their DQN system”
YouTube Unverified1 source· Talk
- builtNick Payne built Elegywrote the play
“The play, written by Nick Payne, was called Elegy.”
YouTube Unverified1 source· Talk
- built
“I've also been involved with an artistic collective called Random International. They do amazingly cool stuff.”
YouTube Unverified1 source· Talk
- builtMurray Shanahan built Rain Roomworked on the installation
“You worked on that, you know?”
YouTube Unverified1 source· Talk
- built
“They constructed this beautiful sculpture”
YouTube Unverified1 source· Talk








